Chromosomal instability degrades developmental phenotypes essential for anti-GD2 immunotherapy outcomes in high-risk neuroblastoma

Ryan James Rebernick; Jae Eun Choi; Calvin Hesse; Noshad Hosseini; Alec Chu; Jin Zhou; Yu Ning; Rui Wang; Xuhong Cao; Meredith Irwin; Yi-Mi Wu; Chandan Kumar; Raja Rabah; Rochelle Bagatell; Arul M Chinnaiyan; Rajen Mody; Marcin Cieslik

doi:10.1016/j.xcrm.2025.102375

. 2025 Sep 26;6(10):102375. doi: 10.1016/j.xcrm.2025.102375

Chromosomal instability degrades developmental phenotypes essential for anti-GD2 immunotherapy outcomes in high-risk neuroblastoma

Ryan James Rebernick ^1,², Jae Eun Choi ^2,^3,⁴, Calvin Hesse ³, Noshad Hosseini ^1,², Alec Chu ^2,⁵, Jin Zhou ^2,⁵, Yu Ning ^2,⁵, Rui Wang ^2,⁵, Xuhong Cao ^2,⁵, Meredith Irwin ⁶, Yi-Mi Wu ^2,⁵, Chandan Kumar ^2,⁵, Raja Rabah ⁵, Rochelle Bagatell ⁷, Arul M Chinnaiyan ^2,^5,^8,^∗, Rajen Mody ^2,^3,^∗∗, Marcin Cieslik ^1,^2,^5,^9,^∗∗∗

PMCID: PMC12629816 PMID: 41015032

Summary

High-risk neuroblastoma (HR-NBL) is a pediatric malignancy that arises during sympathoadrenal development and expresses the surface disialoganglioside GD2. Monoclonal anti-GD2 immunotherapy is a mainstay of HR-NBL treatment; however, it is associated with severe toxicities. Genomic correlates of outcomes following multimodality therapy containing anti-GD2 immunotherapy are limited. We profile 840 tumors and identify actionable ALK gene fusions in HR-NBL. We leverage fetal sympathoadrenal single-cell RNA sequencing to characterize patients with improved post-multimodality outcomes. We demonstrate that patients with improved outcomes have tumors that upregulate noradrenergic and metabolic phenotypes characteristic of mature sympathoblasts—a fetal sympathoadrenal cell population. We show that loss of these developmental phenotypes is mediated by chromosome 11q loss. Targeted analysis of 19 biopsy pairs identifies significant clonal evolution and accumulation of cell-cycle mutations following multimodality treatment. Collectively, we identify chromosomal instability—specifically 11q loss—as key in degrading developmental phenotypes critical for outcomes following multimodality therapy containing anti-GD2 immunotherapy.

Keywords: neuroblastoma, multimodality therapy, anti-GD2, disialoganglioside, immunotherapy, sympathoblast, neuroblast, neuroendocrine, sympathoadrenal, chromosomal instability

Graphical abstract

Highlights

•
Genomic analysis identifies actionable ALK gene fusions in high-risk neuroblastoma
•
Developmental sympathoadrenal phenotypes are associated with post-treatment outcomes
•
Chromosomal instability degrades developmental sympathoadrenal phenotypes
•
Mutations in cell-cycle pathways are increased following multimodality therapy

Rebernick et al. profile 840 tumors and identify actionable ALK gene fusions in high-risk neuroblastoma. Using fetal single-cell RNA sequencing, they demonstrate that developmental phenotypes are associated with outcomes following regimens containing anti-GD2 immunotherapy and that these phenotypes are degraded by chromosomal instability. Tumors progressing after treatment accumulated pro-proliferative cell-cycle mutations.

Introduction

Neuroblastic tumors including neuroblastoma, ganglioneuroblastoma, and ganglioneuroma are pediatric developmental neoplasms that arise during fetal neural crest development.¹ Neuroblastoma arises from a transitory neuroectodermal cell population known as sympathoblasts and has a diverse clinical presentation with the potential for aggressive, high-risk disease.² This is in part facilitated by the ability of neuroblastoma cells to rapidly interconvert between noradrenergic (also described as adrenergic) and mesenchymal differentiation states in a super-enhancer-dependent manner.³^,⁴^,⁵^,⁶

Neuroblastoma patients are commonly stratified into clinically well-defined risk groups including high-risk neuroblastoma (HR-NBL) and low-risk or intermediate-risk neuroblastoma (non-HR-NBL).¹ The 5-year overall survival (OS) for patients with non-HR-NBL exceeds 94%, while HR-NBL patients have 5-year OS rates near 60%.⁷ Genomic features including MYCN amplification status, chromosomal copy number alterations (CNAs), and tumor cell ploidy are used for risk group assignment.¹ MYCN amplification and segmental chromosome losses involving 1p, 3p, 4p, or 11q or gains of 1q, 2p, or 17q are associated with a poor prognosis while CNAs of entire chromosomes are associated with more favorable outcomes.⁸^,⁹^,¹⁰ ALK alterations including activating mutations and focal amplifications are also independent predictors of poor survival in neuroblastoma.¹¹

Treatment of neuroblastoma varies drastically by risk group. While patients with non-HR-NBL are treated with observation, resection, and/or low-dose chemotherapy, patients with HR-NBL routinely receive multimodality therapy in several phases.¹ These phases include induction (intensive multi-agent chemotherapy and primary tumor resection), consolidation (autologous stem cell transplantation, external beam radiotherapy), and post-consolidation (anti-GD2 immunotherapy, differentiating agents). HR-NBL patients whose disease is refractory to multimodality therapy and those that experience disease progression or relapse undergo additional treatment, which may include chemotherapy combined with anti-GD2 immunotherapy or targeted therapies such as ALK inhibitors.¹ Patients with refractory or relapsed disease have a comparably worse prognosis.¹²^,¹³

Monoclonal antibody immunotherapy targeting the surface glycolipid disialoganglioside GD2 was first approved in the United States as part of multimodality therapy for HR-NBL in 2010.¹⁴ Multimodality therapy containing anti-GD2 immunotherapy (MMT-GD2) was highly effective and improved the 5-year OS of patients with HR-NBL to 73% compared to 56% for patients treated with standard therapy alone.¹⁵ Further, chemotherapy given in combination with anti-GD2 immunotherapy (CHEMO-GD2) improved response rates and event-free survival in patients with refractory and relapsed disease.¹⁶^,¹⁷ However, the use of anti-GD2 immunotherapy is associated with significant toxicities including severe pain and infusion reactions.¹⁸ Despite the increasing use of genomic profiling in the management of HR-NBL, no study has comprehensively profiled the genomic correlates of response and/or resistance to treatment regimens containing anti-GD2 immunotherapy.

To address this unmet clinical need, we assembled a large retrospective cohort of 840 neuroblastic tumors, of which 449 are from patients with HR-NBL. This cohort includes 168 previously uncharacterized tumors complete with integrative sequencing (DNA and RNA), long-term patient follow-up, and comprehensive treatment information. We classified HR-NBL patients according to outcomes following treatment with treatment regimens containing anti-GD2 immunotherapy (MMT-GD2 and CHEMO-GD2) and identified genomic, transcriptomic, and developmental features associated with improved outcomes. Additionally, we leveraged 19 paired diagnostic and post-treatment biopsies to define potential innate and acquired resistance mechanisms present in tumors exposed to anti-GD2 immunotherapy.

Results

Clinical and histologic attributes of a retrospective cohort of 168 neuroblastic tumors

A total of 122 patients with neuroblastic tumors diagnosed between May 2012 and June 2023 with sufficient clinical data were included (Figure 1A). We obtained normal and tumor tissue from each patient for a total of 168 unique tumor samples (Figure 1A). We performed clinical exonic DNA and capture RNA sequencing, and each sample was reviewed for sufficient sequencing quality (Table S1). The majority of biopsies (98/168, 58.3%) were from initial diagnosis. Most biopsies (131/168, 78.0%) were from patients with HR-NBL, as defined using the Children’s Oncology Group (COG) risk classification system.⁷ The remainder of biopsies were derived from patients with low-/intermediate-risk neuroblastoma (non-HR-NBL) (29/168, 17.3%) or other neuroblastic histologies including ganglioneuroblastoma and paraganglioma (8/168, 4.7%).

Genomic and transcriptomic profiling of 168 neuroblastic tumors

(A) Profiled samples: non-HR-NBL (low-risk, intermediate-risk, other neuroblastic histologies), NA-HR-NBL (non-amplified high-risk neuroblastoma); and Amp-HR-NBL (MYCN-amplified high-risk neuroblastoma).

(B) Overall survival from diagnosis for MiOncoseq patients.

(C) OncoPrint of tumor samples from MiOncoseq. Includes somatic mutations, copy-number alterations, gene fusions, and germline variants. Also included are telomere maintenance mechanisms (TMM), MYCN and ALK alteration status, and scaled TERT expression. Measures of chromosomal instability (CIN) including the whole genome integrity index (wGII), the frequency of large state transitions (LSTs), and alterations of chromosome arm 1p and 11q shown. Ploidy, age at diagnosis, International Neuroblastoma Staging System (INSS) stage, and available sequencing data shown.

(D) ALK fusions with protein domains shown.

(E) Copy number of chromosome 4 highlighting PHOX2B amplifications.

(F–I) CIN measures including total chromosomes and chromosome arms gained or lost relative to baseline ploidy, LST, and wGII. ANOVA and Wilcoxon rank-sum test p values shown.

(J) Heatmap of co-occurrence (orange) and mutual exclusivity (yellow) for frequently altered features in diagnostic HR-NBL.

The median follow-up time from diagnosis was 43.5 months (IQR: 24.1–66.5 months) for Michigan Medicine patients. For COG patients, median follow-up time was 22.3 months (IQR: 17.0–27.1 months). The median age at diagnosis was 3.6 years in HR-NBL patients and 0.7 years in non-HR-NBL patients (Table S2). Of the 122 total patients, 60 (49%) were female (Table S2). HR-NBL patients had worse overall survival (OS) from time of diagnosis (5-year OS: 62.5%) compared to patients with non-HR-NBL (5-year OS: 100.0%) (Figure 1B). This is similar to other recently published cohorts of HR-NBL that estimate 5-year OS from 62% to 71%.⁷^,¹⁹

We identified MYCN amplification (range: 20–36+ copies) in tumors from 25 of the 94 (26.6%) patients with HR-NBL using DNA sequencing. We compared these amplification calls to the clinically standardized approach of fluorescence in situ hybridization and found strong agreement (Figure S1A). We found no difference in OS from the time of diagnosis between patients with non-amplified HR-NBL (NA-HR-NBL) and patients with MYCN-amplified HR-NBL (Amp-HR-NBL) (Figure 1B).

Identification of ALK gene fusions and PHOX2B amplifications in neuroblastoma

Amp-HR-NBL samples had relatively few driver alterations excluding ALK alterations (Figure 1C). ALK alterations are observed in 6%–10% of HR-NBLs as activating somatic or germline mutations, in 1%–4% as focal amplifications, and 2% as N-terminal truncations.²⁰^,²¹^,²²^,²³ Within the 33 Amp-HR-NBL samples, we detected 5 (15.2%) activating ALK mutations, 1 N-terminal truncation (3.0%), and 2 (6.1%) focal ALK amplification (<5 MB; 16–24 copies) (Figure S1B). Surprisingly, we also identified 2 Amp-HR-NBL samples and 3 NA-HR-NBL samples with ALK gene rearrangements (Figures 1D and S1C; Table S3). The 3 ALK rearrangements present in the NA-HR-NBL samples were direct gene fusions between ALK and 5′ gene partners including EML4, PMEL, and TRMT61B. These fusions were in-frame, preserved the ALK kinase domain, and corresponded to ALK expression above the 75th percentile of all HR-NBL samples. The ALK rearrangements identified in the 2 Amp-HR-NBL were complex fusions, putatively originating from extrachromosomal circular amplicons, involving multiple genes including DDX1/PLBD2 and TRMT61B/SLC5A6.²⁴ Notably, the majority (4/5, 80%) of the HR-NBL tumors with ALK rearrangements had no detectable somatic mutations, germline mutations, N-terminal truncations, or amplifications of ALK, with the exception of PO_3872, which had a concurrent focal ALK amplification (Figure S1B). Thus, these ALK rearrangements are similar to those reported in other cancers and likely therapeutically targetable.²⁵

In addition to ALK, germline mutations of PHOX2B have been implicated in predisposition to neuroblastoma.²⁶^,²⁷ Whether PHOX2B alterations operate via loss or gain of function is not well understood. We detected 4 NA-HR-NBL patients with previously unreported focal somatic amplifications of PHOX2B (<5 MB; 9–12 copies) (Figure 1E). We also identified 2 neuroblastoma-derived cell lines profiled in the Cancer Dependency Map (DepMap) project (CHLA90: ACH-001481, LS:ACH-001548) with copy number values 1.6 times greater than sample ploidy (Median: 0.99, IQR: 0.96–1.06) (Figure S1D). Additionally, we observed the known missense germline PHOX2B mutation (p.Arg100Leu) in one NA-HR-NBL patient and detected a somatic frameshift mutation (p.Gly199ArgfsTer161) in a non-HR-NBL patient. DepMap categorizes PHOX2B as a ”strongly selective” gene, and we found neuroblastoma-derived cell lines to be more sensitive to PHOX2B loss relative to cell lines derived from other cancer types (p = 1.4e−12) (Figure S1E). Collectively, we identified 6 patients with PHOX2B alterations, including focal amplifications, all of which occurred in patients with tumors without a MYCN amplification.

A distinct class of chromosomally stable, MYCN-amplified high-risk neuroblastoma

Chromosomal instability (CIN) is a hallmark of aggressive neuroblastoma.⁸^,²⁸ However, the relationship between CIN, specific CNAs, and clinical risk groups remains underexplored. We identified absolute haplotype-resolved CNAs for all samples and quantified whole chromosome, arm-level, and interstitial CNAs. In addition, we quantified CIN using established genome-wide measures including the whole genome integrity index (wGII), the number of loss of heterozygosity events (nLOH), the number of telomeric allelic imbalances (nTAI), and the quantity of large state transitions (LSTs). We found that Amp-HR-NBL had the fewest whole and arm-level CNAs relative to both NA-HR-NBL and non-HR-NBL (Figures 1F and 1G). Further, all genome-wide measures of CIN were consistently lower in Amp-HR-NBL (Figures 1H, 1I, and S1E). Of these measures, LST and nLOH were significantly different between non-HR-NBL, NA-HR-NBL, and Amp-HR-NBL while wGII and nTAI were not significantly different across all groups (Figures 1H, 1I, and S1F).

Next, we focused on the patterns of CIN by risk group and MYCN amplification status. We found that compared to Amp-HR-NBL samples, NA-HR-NBL samples had more frequent gains of chromosomes 7, 11q, and 12q as well as losses of 3p and 11q (Figure S1G). Loss of 11q and an interstitial gain of 17q spanning chr17q11.2–chr17q21.31 were near ubiquitous in NA-HR-NBL (80% and 93% respectively) but less frequent in Amp-HR-NBL (16%, 75%) (Figure S1G). We also observed frequent focal gains of a section of chr11q13.3 and chr12q24.33 in the NA-HR-NBL samples. Gains of chr11q13.3 have been previously reported in HR-NBL and linked to CCND1—a prominent cell-cycle gene.²¹ The gain of chr12q24.33 contains the histone H3K9 methyltransferase SETDB1, which facilitates alternative lengthening of telomeres (ALT).²⁹ Within the limitations of our sequencing platform, we quantified telomere maintenance mechanisms (TMMs) including ALT and TERT-overexpression (TERT-high). Consistent with previous studies,³⁰ ALT+ HR-NBL samples had minimal TERT expression (median RPKM: 0.098, range: 0.0–1.1 RPKM) (Figure S1H). Interestingly, gain of chr12q24.33 significantly co-occurred (p = 8.6e−3) with the ALT+ phenotype in HR-NBL (Figure 1J).

Designation of high-risk neuroblastoma patients with improved outcomes following treatment regimens containing anti-GD2 immunotherapy

Patients with HR-NBL receive anti-GD2 immunotherapy either as part of multimodality therapy (MMT-GD2) in the post-consolidation setting or in combination with chemotherapy (CHEMO-GD2) in refractory/relapsed (ref/rel) settings (Figure 2A).¹²^,¹⁵^,³¹ Of the 94 HR-NBL patients within our cohort, 81 (86.2%) received anti-GD2 immunotherapy (Figure 2B). Of those 81 patients, 77 (95.1%) had either 18 months of follow-up from the start of anti-GD2 immunotherapy or experienced disease progression (Table S4). Of these 77 patients, 2 (2.6%) were excluded as they had only MMT-GD2 outcomes data but lacked sequencing of a biopsy prior to starting post-consolidation therapy (Figure 2B). Thus, tumors from 75 HR-NBL patients who received treatment regimens containing anti-GD2 immunotherapy were available for analysis.

To identify patients with improved outcomes following regimens containing anti-GD2 immunotherapy, we first divided patients by treatment context (MMT-GD2 and CHEMO-GD2) and then stratified based on disease progression (Figures 2C, S2A, and S2B). Of the 26 patients who received MMT-GD2, those who did not progress, relapse, or die during the entire follow-up period (minimum 18 months from anti-GD2 immunotherapy start) were assigned to the “continued remission” (REM) group. Patients who received MMT-GD2 that failed to meet these criteria were assigned to the “progression” (PRG) group. Of the 26 patients who received MMT-GD2, 10 (38.5%) met the criteria for REM (Figure 2B).

Of the 62 patients who received CHEMO-GD2, those who achieved a best overall response of stable disease or better after the first 6 cycles of CHEMO-GD2 during their first episode of relapsed/refractory disease, those who did not progress or relapse during the 18 months from the start of anti-GD2 immunotherapy, and those who were alive at the end of follow-up were considered to have “continued disease control” (CDC). Patients who received CHEMO-GD2 that failed to meet these criteria were assigned to the “progression” (PRG) group. The revised International Neuroblastoma Response Criteria (INRC) were used to define overall response.³² Of the 62 patients who received CHEMO-GD2, 27 (43.5%) met the criteria for CDC (Figure 2B). There were no differences in age at diagnosis, gender, or the International Neuroblastoma Staging System (INSS) stage by outcome in either the MMT-GD2 or CHEMO-GD2 groups (Table S5). There were marginal differences in biopsy site (p = 0.03) and biopsy time point (p = 0.02) between CHEMO-GD2 groups that were primarily attributable to missing data (Table S5).

Chromosomal stability is associated with improved outcomes following treatment regimens containing anti-GD2 immunotherapy

To identify genomic correlates of outcomes following treatment regimens containing anti-GD2 immunotherapy, we examined somatic mutations, germline mutations, CNAs, and RNA sequencing (Figure 3A). Previous clinical trial data have indicated that patients with high-affinity genotypes of the immunoglobulin Fc receptors 2A and 3A (FCGR2A/FCGR3A) had improved event-free survival in the context of anti-GD2 immunotherapy.¹⁵^,¹⁹^,³³ We noticed no difference in the distribution of either FCGR2A or FCGR3A high-affinity genotypes by outcome following MMT-GD2 or CHEMO-GD2 (Figure 3B).

Genomic and transcriptomic correlates of improved outcomes following treatment regimens containing anti-GD2 immunotherapy

(A) High-risk neuroblastoma (HR-NBL) patients stratified by outcomes following multimodality therapy containing anti-GD2 immunotherapy (MMT-GD2) or chemotherapy given in combination with anti-GD2 immunotherapy (CHEMO-GD2). Somatic mutations, copy-number alterations, gene fusions, and germline variants shown. Also included are telomere maintenance mechanism (TMM), MYCN amplification status, and ALK alteration status. Measures of chromosomal instability (CIN) including the whole genome integrity index (wGII), the frequency of large state transitions (LSTs), and gains or losses of chromosome arms and ploidy shown. REM, continued remission; CDC, continued disease control; PRG, progression.

(B) Recurrent alterations in HR-NBL stratified by MMT-GD2/CHEMO-GD2 and outcomes. FCGR2A/FCGR3A indicate presence of high-affinity SNPs (rs1801274, rs396991). RAS and P53 refer to the presence of detected alterations in these pathways.

(C) TMM stratified by outcomes.

(D–G) CIN measures by outcomes.

(H) Ploidy by outcomes.

(I) MMT-GD2 differentially expressed genes.

(J) Gene ontology (GO) enrichment of genes upregulated in MMT-GD2 REM. Representative pathways shown.

(K) Gene set enrichment analysis (GSEA) results for patients with improved outcomes following MMT-GD2. Representative upregulated pathways with a consistent direction of effect shown. Negative log10 of the adjusted p value shown.

(L and M) GSEA of noradrenergic signature in MMT-GD2 (L) and CHEMO-GD2 (M) groups. Normalized enrichment score (NES) and p value shown.

Categorical significance assessed via chi-squared test. Continuous variable significance assessed via ANOVA with subsequent Wilcoxon rank-sum tests. NS, not significant; ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001 unless otherwise shown.

Dysregulated pathways including telomere maintenance, RAS-MAPK, and p53 along with altered genes including MYCN, ALK, ATRX, and ATM are all associated with poor prognosis.⁷^,³⁴^,³⁵^,³⁶ We found differences in neither the distribution of ALK, ATRX, or ATM alterations nor alterations of RAS-MAPK or p53 pathways as defined by Ackermann et al. across both MMT-GD2/CHEMO-GD2 groups (Figure 3B). TMMs including ALT and TERT-high phenotypes were less frequent in the patients with CDC following CHEMO-GD2 (p = 0.043), with the differences mainly attributable to TERT overexpression (Figure 3C). Further, MYCN amplifications were highly enriched in patients with REM following MMT-GD2 (p = 8.3e−3) (Figure 3B).

As we identified MYCN amplified tumors as having highly stable genomes (Figures 1F–1I), we explored whether measures of CIN varied by outcomes following MMT-GD2 or CHEMO-GD2. We found that patients with PRG following MMT-GD2 were more likely to have tumors with losses of chromosome 11q (p = 5.5e−3) and gains of chromosome 17q (p = 0.039) compared to patients with REM (Figure 3B). In addition, we observed higher measures of whole chromosome CNAs (p = 0.041) and arm-level CNAs (p = 7.5e−3) in patients with PRG following MMT-GD2 compared to patients with REM (Figures 3D and 3E). Patients with PRG following MMT-GD2 also had tumors with higher levels of LST (p = 1.5e−3) and wGII (p = 0.012) compared to patients with REM (Figures 3F and 3G). Both nTAI and nLOH were also nominally higher in patients with PRG following MMT-GD2 compared to patients with REM (Figures S2C and S2D). CIN canonically follows whole genome duplication (WGD).³⁷ However, we found no differences in either tumor ploidy or frequency of WGD by outcome following MMT-GD2 (Figures 3G and S2E).

Noradrenergic and metabolic phenotypes are associated with improved outcomes following treatment regimens containing anti-GD2 immunotherapy

We next sought to identify differences in tumoral gene expression by outcomes following MMT-GD2 or CHEMO-GD2. We identified 217 significantly upregulated and 39 downregulated genes in tumors from patients with REM following MMT-GD2 (Figures 3I; Table S6). Gene ontology (GO) enrichment revealed that the upregulated genes encompassed pathways including the mitochondrial matrix, aerobic metabolism, neuronal synapses, and ribosomal subunits (Figure 3J). We confirmed these findings in the CHEMO-GD2 group using gene set enrichment analysis (GSEA) where we identified 31 upregulated and 1 downregulated significant pathways with the same direction of effect in tumors from patients with CDC (Table S7). The upregulated pathways comprised three primary concepts including cytoplasmic translation (GO:0002181), the mitochondrial respiratory chain (GO:0033108), and oxidative phosphorylation (GO:0006119) (Figure 3K).

Next, we sought to examine outcomes following MMT-GD2 or CHEMO-GD2 in the context of noradrenergic and mesenchymal phenotypes first identified in cell lines.³ An noradrenergic-to-mesenchymal transition (NMT) is hypothesized to mediate anti-GD2 resistance via downregulation of GD3 synthase (ST8SIA1).³⁸ We found that noradrenergic phenotypes were associated with REM following MMT-GD2 (p = 1.6e−15) and CDC following CHEMO-GD2 (p = 1.6e−9) (Figures 3L and 3M). However, we found no relationship between outcomes and either a mesenchymal phenotype (p = 0.67, p = 0.62) or ST8SIA1 expression (p = 0.67, p = 0.62) (Figures 3I and S2F).

Immune populations are prognostic but not associated with improved outcomes following treatment regimens containing anti-GD2 immunotherapy

Anti-GD2 immunotherapy is hypothesized to work primarily through natural killer (NK) cell and phagocyte-mediated antibody-dependent cell-mediated cytotoxicity.³⁹^,⁴⁰ Thus, we hypothesized that immune features would strongly associate with outcomes following MMT-GD2 and CHEMO-GD2. We found that 9 of the 217 upregulated genes (4.1%) and 1 of the 39 downregulated genes (2.6%) in tumors from patients with REM following MMT-GD2 overlapped with the GO Immune Response pathway (GO:0006955) (Figure 4A). One of these genes, NCR3LG1 (B7-H6), is the ligand for the activating NK cell receptor NKp30.⁴¹ NCR3LG1 is regulated by Myc proteins across several cancer types including by MYCN in neuroblastoma.⁴² Consistently, we found that NCR3LG1 is strongly upregulated in Amp-HR-NBL tumors relative to NA-HR-NBL and non-HR-NBL tumors (Figure 4B). Of the other B7 family members, CD276 (B7-H3) was also nominally associated with REM (Figures S3A–S3I). One immune-associated gene, NLRC5, was downregulated in tumors from patients with REM following MMT-GD2. NLRC5 is the master regulator of major histocompatibility complex (MHC) class I genes.⁴³^,⁴⁴ MHC class I loss facilitates NK-cell-mediated killing.⁴⁵^,⁴⁶^,⁴⁷ We found that NLRC5 was strongly correlated with MHC class I gene expression and was downregulated in tumors from patients with Amp-HR-NBL (Figures 4C and S3J–S3O). This is consistent with previous reports that MHC class I molecules are downregulated in Amp-HR-NBL.⁴⁸^,⁴⁹

To determine whether immune infiltration varied by outcomes following MMT-GD2 or CHEMO-GD2, we generated an integrated neuroblastoma single-cell atlas composed of 198,723 cells derived from 27 neuroblastic tumors across three independent studies (Figure S4A).⁵⁰^,⁵¹^,⁵²^,⁵³ We identified known cell types including tumor, mesenchymal, Schwann, endothelial, and immune cells (Figures 4D and 4E). We found that the overall fraction of immune cells was highest in non-HR-NBL and lowest in Amp-HR-NBL (Figure 4F). This is consistent with previous studies relying on immune deconvolution algorithms and bulk RNA-sequencing.⁵⁴^,⁵⁵^,⁵⁶^,⁵⁷ We identified major immune cell populations including T cells, B cells, NK cells, myeloid cells, and a population of proliferating immune cells (Figure 4G). Further, we identified immune subpopulations (Figure 4H). Compared to NA-HR-NBL, Amp-HR-NBL was nominally depleted of total immune cells and lymphoid cells; however, NK and myeloid cells were detected at similar proportions (Figures S4B–S4E).

We leveraged the integrated neuroblastoma single-cell atlas to deconvolute all available bulk-sequenced samples (Figure 4I). We found that samples from Amp-HR-NBL had lower immune cell levels compared to NA-HR-NBL or non-HR-NBL (Figures S4F, S4H, and S4J). Immune populations have previously been associated with prognosis in combined cohorts of HR-NBL and non-HR-NBL.⁵⁸^,⁵⁹ However, we hypothesized this was due to the relative depletion of immune cells in HR-NBL, rather than prognostic utility within HR-NBL itself. Consistently, our analysis showed higher levels of total immune cells, total myeloid cells, total NK cells, and total T cells to be associated with improved prognosis in the complete SEQC cohort of HR-NBL and non-HR-NBL patients (Figure 4J). However, when exclusively HR-NBL patients or non-HR-NBL were examined, no immune population associated with prognosis (Figure 4J). Further, only a single immune population (plasmacytoid dendritic cells) was significantly associated with prognosis in the complete TARGET cohort, which is almost entirely HR-NBL (126/152, 83%) (Figure 4J). Of all the immune subpopulations, type 1 conventional dendritic cells had the strongest prognostic association in the complete SEQC cohort (p = 1.3e−10) (Figures 4J and 4K). However, when only HR-NBL patients were considered, this association was absent (p = 0.24) (Figures 4J and 4L). Finally, we compared immune cell abundance by outcomes following MMT-GD2 and CHEMO-GD2 and found no differences in total or individual immune cell levels (Figure 4J). We confirmed these findings using other single-cell atlases and alternative algorithms (Figures S4L and S4M).⁵⁸^,⁶⁰

A developmental sympathoadrenal single-cell atlas and identification of sympathoadrenal phenotypes of noradrenergic differentiation

Neuroblastoma arises when the neural-crest-derived sympathoadrenal lineage fails to develop and cells arrest in an incompletely differentiated state. Therefore, to better characterize vestigial developmental patterns in neuroblastoma, we created a time-resolved sympathoadrenal single-cell atlas. We generated an integrated sympathoadrenal single-cell atlas composed of 22,012 cells from 36 fetal adrenal gland samples spanning 6–21 weeks post-conception (Figure S5A).⁵⁰^,⁵¹^,⁶¹^,⁶² We identified known cell types including sympathoblasts, Schwann cell precursors (SCPs), bridge cells, connecting progenitor cells (CPCs), and chromaffin cells (Figure 5A). Differentiated populations including sympathoblasts and chromaffin cells were detected at notable frequencies as early as 7 weeks and as late as 21 weeks (Figure 5B). For each annotated cell type, we identified marker gene signatures (Figure S5B; Table S8). Sympathoblasts were identified as ISL1-, STMN2-, and GAP43-expressing cells that lacked SOX10 expression (Figure 5C).² Sympathoblasts also contained a distinct cycling population that had high expression of proliferative markers (Figure 5C). Further, sympathoblasts were enriched for noradrenergic signals and did not express PNMT, the enzyme required for epinephrine synthesis (Figures 5C and 5D). SCPs comparatively expressed mesenchymal signals (Figure 5E).

Neuroblastoma closely resembles sympathoblasts, and the extent of sympathoblast differentiation is associated with improved prognosis.⁵⁰^,⁵¹^,⁶¹^,⁶²^,⁶³ We identified two gene sets that paralleled sympathoblast maturation using the sympathoblasts profiled through single-cell RNA sequencing (Figure 5F). We refer to the consistently upregulated and downregulated genes as the “mature” and “immature” sympathoblast gene sets, respectively (Table S9). The mature gene set peaked in sympathoblasts greater than 13 weeks post-conception, and the immature gene set peaked in sympathoblasts from 6 to 8 weeks (Figure 5G). We examined the mature and immature gene sets in samples profiled using single-nuclei RNA sequencing and found a comparable relationship with developmental time points for the mature but not the immature signature (Figures S5C–S5F). Further, the mature gene set was significantly enriched for pathways related to vesicular transport (Figure S5G; Table S10). We correlated the mature and immature gene sets with over 7,000 other pathways expressed in sympathoblasts and found the mature gene set to be highly correlated with pathways related to neuronal development and migration, while the immature gene set was strongly correlated with pathways related to metabolic processes and the ribosome (Figures S5H and S5I). In humans, sympathoblasts can differentiate into not only sympathetic neurons but also chromaffin cells.²^,⁶² We observed that chromaffin cells had diminished noradrenergic signal (Figure 5D). Recapitulating this, the mature gene set was negatively correlated with the noradrenergic signature and positively correlated with the mesenchymal signature across all fetal cells (Figure 5H).

To refine sympathoblast differentiation in an unbiased manner, we applied weighted gene co-expression network analysis within non-cycling sympathoblasts to identify co-regulated transcriptional modules (Figure 5I). We identified two robust modules (Figure S5J; Table S11). Based on strong GO enrichment, we labeled these sympathoblast-derived co-regulated sets of genes as the “Translation” and “OxPhos” modules (Figure 5J; Table S12). Across sympathoadrenal populations, the Translation module was broadly expressed, while the OxPhos module was restricted to sympathoblasts and peaked in immature sympathoblasts (Figures S5K–S5N). Both modules were strongly associated with a noradrenergic and immature cell state in sympathoblasts and negatively correlated with mature and mesenchymal signals (Figure 5K).

Sympathoadrenal phenotypes are associated with improved outcomes following treatment regimens containing anti-GD2 immunotherapy

Signatures of sympathoadrenal cell types have been previously linked to prognosis in neuroblastoma.⁶² As expected, these cell-type signatures were strongly prognostic in mixed cohorts of HR-NBL and non-HR-NBL (Figure 6A). However, we found no differences in sympathoadrenal cell-type signatures by outcomes following MMT-GD2 or CHEMO-GD2 (Figure 6A). Consistent results were obtained using analogous published sympathoadrenal signatures (Figure S6A).

Next, we examined how our sympathoblast noradrenergic differentiation or maturation gene sets stratified by risk groups. Non-HR-NBL is the most similar to differentiated sympathoblasts while Amp-HR-NBL more closely resembles cycling sympathoblasts.⁶² In agreement, we found the mature sympathoblast gene set to be upregulated in non-HR-NBL (p = 7.5e−11) and the cycling sympathoblast gene set to be upregulated in Amp-HR-NBL (p < 2.2e−16) (Figures 6B and S6B). We also found the immature sympathoblast gene set to be upregulated in Amp-HR-NBL (p < 2.2e−16) (Figure 6B). As expected, both the mature and immature sympathoblast gene sets were highly prognostic in mixed cohorts of HR-NBL and non-HR-NBL, but in opposite directions. The mature gene set was associated with improved prognosis in the mixed SEQC cohort, while the immature gene set corresponded to a poor prognosis (Figures S6C–S6F).

It is also known that MYCN amplification arrests neuroblastoma in an noradrenergic state.³^,⁵^,⁶⁴ We found the noradrenergic signature was upregulated in Amp-HR-NBL (p < 2.2e−16) (Figure S6G). Further, amplification of MYCN is known to metabolically reprogram neuroblastoma in non-canonical ways. Rather than promoting aerobic glycolysis in line with the Warburg effect, Amp-HR-NBL is characterized by mitochondrial oxidative phosphorylation and ribosomal biogenesis.⁶⁵^,⁶⁶^,⁶⁷ Accordingly, both the OxPhos and Translation modules were upregulated in HR-NBL (Figure 6B).

Finally, we asked whether sympathoblast noradrenergic differentiation or maturation gene sets were associated with outcomes following MMT-GD2 or CHEMO-GD2. First, we noted that noradrenergic (p = 5.1e−15, p = 2.1e−9), Translation (p = 5.5e−21, p = 6.5e−17), and OxPhos (p = 2.6e−11, p = 6.7e−22) gene sets were strongly enriched in patients with REM and CDC following MMT-GD2 and CHEMO-GD2, respectively, while the mature gene set was nominally higher (p = 0.07, p = 0.42) (Figures 6C and S6H). Next, we examined these trends at the patient level. We found that the mature (p = 0.0028), Translation (p = 0.049), and OxPhos (p = 0.017) gene sets were higher in patients with REM following MMT-GD2 compared to patients with PRG (Figure 6D). Importantly, all three of these gene sets were increased in both NA-HR-NBL and Amp-HR-NBL patients with REM following MMT-GD2 (Figures S6I–S6K), indicating these gene sets provide added utility beyond MYCN amplification status.

Chromosome 11q loss degrades sympathoadrenal phenotypes in high-risk neuroblastoma

Under the genetic causality model, we asked whether the Symp-GD2 phenotype (mature sympathoblast, Translation, and OxPhos) was modulated by genomic events such as CIN. We found a robust inverse relationship between CIN and the Symp-GD2 phenotype where increased LST and loss of chromosome 11q associated with lower Maturation (p = 8.9e−4), Translation (p = 1.3e−5), and OxPhos (p = 5.5e−4) signals (Figure 6E). To account for co-dependencies between the genetic alterations (Figure 1J), we employed multivariate regression to estimate their independent effects. This identified chromosome 11q loss as the strongest determinant of decreased Symp-GD2 signal, i.e., OxPhos (p = 0.014), Translation (p = 0.021), and Maturation (p = 0.016) (Figure 6F). Chromosome 11q is enriched for genes expressed in sympathoblasts, and transfer of chromosome 11q is sufficient to induce neuronal differentiation of neuroblastoma cells.⁵¹^,⁶⁸^,⁶⁹ We found that loss of 11q had a significant impact on gene expression both in cis and in trans (Figure 6G). This included robust downregulation of metabolic pathways (Figure 6H). Further, in agreement with genetic associations, the transcriptional signature of 11q loss was associated with PRG following MMT-GD2, and only a single REM patient had a greater 11q loss signature score than the median score of the PRG group (Figure 6I).

Patterns of acquired resistance to treatment regimens containing anti-GD2 immunotherapy

No study has examined the impact of regimens containing anti-GD2 immunotherapy on tumor evolution. To accomplish this, we examined paired diagnostic and post-treatment biopsies from 19 HR-NBL patients who stratified into two clinical groups (Figure 7A). The first group of eight patients had a post-treatment biopsy taken following anti-GD2 immunotherapy given as part of MMT-GD2 and/or CHEMO-GD2 (GD2-exposed). The second group of 11 patients did not receive anti-GD2 immunotherapy between biopsies (GD2-naive). All of the GD2-naive post-treatment samples were obtained from surgical resections performed following four to six cycles of induction chemotherapy. Directly comparing the GD2-naive and GD2-exposed post-treatment biopsies allows us to isolate the genomic changes not directly induced by induction therapy.

The mutational burden of neuroblastoma is known to increase with disease progression and treatment.⁷⁰^,⁷¹^,⁷² We found nominally higher levels of mutations in the post-treatment biopsies relative to their diagnostic counterparts (10.5 vs. 8.8; p = 0.57) (Figure S7A). Comparable findings were seen when paired samples were stratified by GD2 exposure (Figure S7B). However, we found profound differences in terms of the mutational composition between the paired biopsies (Figure 7B). Of the 199 mutations detected in the 19 post-treatment samples, only 33 (16.6%) were detected in the corresponding diagnostic biopsy (Figure S7C). This was strikingly low even in the GD2-naive post-treatment group (8/97, 8.2%) which has received comparatively less treatment (Figure 7C). Examining only clonal mutations yielded similar findings (Figures S7D and S7E). On average, only 42% of diagnostic mutations were detected in the post-treatment biopsies.

Relapsed HR-NBL accumulates mutations in ALK as well as the RAS-MAPK, telomere maintenance, and cell cycle pathways.⁷⁰^,⁷¹^,⁷²^,⁷³^,⁷⁴^,⁷⁵ Compared to diagnostic biopsies, both post-treatment groups were enriched for mutations in 33 pathways across three primary concepts: development/differentiation, kinase activity, and calcineurin-NFAT signaling (Figure 7D). Relative to the GD2-naive group, the GD2-exposed group was enriched for mutations in pathways including telomere maintenance, DNA repair, and the cell cycle as well as histone modification and methylation (Figure 7D).

Subsequently, we examined the proportion of samples with a mutated pathway in the diagnostic and post-treatment groups. We found the GD2-exposed group had significantly more samples with mitotic cell-cycle pathway (GO:0007346) mutations (p = 0.008) (Figure 7D). Of the eight GD2-exposed samples, seven (88%) had mutations in genes including MET, AXL, MTOR, ROS1, WNK1, and FGFR1. Notably, FGFR1 and WNK1 represented two of the four genes (including BRCA2 and ARID1B) recurrently mutated in the eight GD2-exposed samples. Although the serine/threonine kinase WNK1 is relatively unexplored in cancer, FGFR1 mutations are known oncogenic drivers in HR-NBL.⁷⁶

In light of the role of CIN in therapeutic resistance, we next set out to understand whether disease progression was associated with clonal selection of CNAs. We first examined the copy-number profiles and found high retention of LOH regions between diagnostic and post-treatment biopsies (Figures 7E and 7F). Whereas few mutations were retained (median: 25%, IQR: 4.5%–77.5%), the majority of LOH events were present in both profiles (median: 95%, IQR: 79.5%–100%) (Figure 7F). In agreement, the GD2-exposed samples had copy-number profiles highly similar to their diagnostic counterparts and the majority (6 of 8, 75%) were diploid (Figure 7E). Only a single TP53-mutant sample underwent WGD and lost chromosome 11q following treatment (Figure 7E). Of the remaining seven GD2-exposed samples, only two had notably increased wGII, LST, or alterations of chromosome 11q or 17q (Figure 7E).

Conversely, the GD2-naive post-treatment samples had profound copy-number shifts. Of the 11, 5 (45%) underwent WGD following treatment and 5 of 11 (45%) gained or lost chromosome arms (Figure 7E). This suggests that genomic shifts induced by therapy either occur early (and are thus detected at induction) or are not selected for in the resistant cells (not detected at time of relapse). Interestingly, one patient in the GD2-naive group presented with diffuse metastatic disease and had a diagnostic biopsy with a MYCN-amplification, which was not observed on subsequent sequencing (Figures S7F and S7G). The two biopsies from this patient shared no somatic mutations but were evolutionarily related as assessed by their LOH profiles (p < 1e−16) (Figure S7H). This further emphasizes the significant heterogeneity present in untreated HR-NBL, and strong clonal selection at relapse.

Surprisingly, we found no significant differences following anti-GD2 exposure in terms of Symp-GD2 phenotypes (Mature, Translation, OxPhos, and Chr11q) or signals implicated from published studies (noradrenergic, mesenchymal, and GD2 synthesis enzymes) (Figure 7G).³^,⁵^,³⁸ The GD2-naive post-treatment group however showed significant downregulation of the cycling sympathoblast signature (Figure 7G). To determine whether cell-cycle mutations were also associated with increased proliferation, we compared cycling sympathoblast scores between diagnostic and post-treatment samples with and without mutations in the mitotic cell-cycle pathway (GO:0007346). Although post-treatment samples had lower proliferative scores in general, those with cell-cycle mutations had significantly higher cycling sympathoblast scores (Figure 7H).

Discussion

In this study, we analyzed 840 neuroblastic tumors and 36 fetal adrenal samples to identify genomic, transcriptomic, and developmental correlates of outcomes following MMT-GD2 and CHEMO-GD2. Notably, we identified that noradrenergic and metabolic phenotypes characteristic of mature sympathoblasts were upregulated in patients with improved outcomes. However, patients with high levels of CIN—specifically loss of chromosome 11q—lost these developmental phenotypes and progressed following MMT-GD2. These findings are notable as they connect CIN with noradrenergic sympathoadrenal phenotypes and establish a model whereby outcomes following treatment regimens containing anti-GD2 immunotherapy are dependent on tumor-intrinsic, developmental features.

Through genomic analysis of 19 patients with paired biopsies, we provided a comprehensive genomic exploration of tumors exposed to anti-GD2 immunotherapy. Previous analyses have characterized the transcriptional states of cells surviving induction chemotherapy and described the accumulation of mutations and mutational heterogeneity of relapsed neuroblastoma.⁷⁰^,⁷¹^,⁷²^,⁷⁴^,⁷⁷ However, no study has explored the genomic alterations accrued in anti-GD2 resistant tumors. We show that while mutationally distinct, paired diagnostic and post-treatment samples often have highly similar LOH profiles. This suggests that in HR-NBL, CIN is an early event followed by mutationally driven clonal evolution. This is consistent with the evolutionary model of HR-NBL established by Korber et al.²⁸ With respect to therapeutic resistance, we found that this clonal evolution appears to favor cell-cycle mutations that restore the proliferative capacity of HR-NBL relative to GD2-naive post-treatment tumors. Induction chemotherapy is known to induce persister cells with non-cycling transcriptional states, and cell-cycle mutations are known to accumulate in relapsed HR-NBL.⁷²^,⁷⁷ Directly comparing cell-cycle mutation rates in relapsed HR-NBL with and without anti-GD2 immunotherapy exposure would clarify whether these mutations are specific to anti-GD2 immunotherapy or a result of other components of multimodality therapy.

ALK alterations are well characterized in neuroblastoma.²⁰^,²¹^,²²^,²³^,⁷⁸ However, ALK gene fusions have not been reported outside of a TENM3-ALK fusion identified in a single NA-HR-NBL patient.⁷⁹^,⁸⁰ Here, we identified five patients who have ALK gene fusions with intact kinase domains, strong read support, and 5′ gene partners including TRMT61B, PMEL, and EML4. These cases have no other somatic or germline ALK alterations, which suggests that these are driving events. ALK gene fusions/rearrangements are commonly detected in inflammatory myelofibrotic tumors (IMTs) and non-small cell lung cancer (NSCLC) where EML4 is the most common 5′ binding partner.⁸¹^,⁸² Several ALK inhibitors are currently FDA-approved for both ALK-aberrant NSCLC and IMTs with approximate response rates of 50%–80%.²⁵ Although ALK inhibitors have recently been evaluated for use in HR-NBL, response rates are lower at approximately 10%–20%.²⁵^,⁸³^,⁸⁴^,⁸⁵^,⁸⁶ Given the higher response rates to ALK inhibition in tumors with frequent ALK gene fusions/rearrangements, it is possible that HR-NBL with ALK gene fusions/rearrangements may have improved responses to ALK inhibition. Further studies are needed to characterize the prevalence of ALK fusions in HR-NBL and experimentally validate these fusions in other cohorts. Detection of these fusions in 15% of Amp-HR-NBL would warrant future clinical trials evaluating the efficacy of FDA-approved ALK inhibitors in ALK-fusion-positive HR-NBL.

In summary, we generated one of the largest clinico-genomic cohorts of neuroblastic tumors to date. We discovered PHOX2B focal amplifications and potentially actionable ALK fusions, identified associations with outcomes following MMT-GD2/CHEMO-GD2, and explored potential mechanisms of therapeutic resistance. As regimens containing anti-GD2 immunotherapy are associated with significant toxicities and highly variable responses, there is an urgent need to identify patients who will benefit from treatment. Additionally, future work in identifying and targeting ALK fusions may improve outcomes for HR-NBL patients.

Limitations of the study

First, this is a retrospective correlative study focused on identifying and describing genomic features associated with outcomes following treatment regimens in HR-NBL. This study was not designed to identify predictive biomarkers or attribute any correlates directly to a specific therapy given as part of the treatment regimen. Second, samples were obtained from a variety of clinical time points, including cases where the tumor specimen may be obtained years prior to the clinical outcome of interest. The variability in biopsy timing may in part explain the lack of associations detected in the CHEMO-GD2 group, with the notable exception of TERT-overexpression enrichment in patients with poor outcomes following CHEMO-GD2. Due to the targeted nature of our sequencing, characterization of ALT and TERT phenotypes was limited and conclusions are conservative given that these regions undergo frequent promoter mutations and structural rearrangements.⁸⁷ Consistently, we were unable to profile KIR/KIR-ligand genotypes that have been previously linked with PFS following anti-GD2 immunotherapy.³³^,⁸⁸^,⁸⁹ Although we identified multiple strong associations with outcomes following regimens containing anti-GD2 immunotherapy, a more comprehensive characterization of ALT, TERT, and KIR is warranted. Third, both single-cell and single-nuclei sequencing from multiple sources was integrated to draw meaningful conclusions about sympathoadrenal and neuroblastoma biology. Methodological steps were taken to minimize subsequent batch effects, mitigate disparate gene profiling across studies, and validate known biology (including previously determined sympathoadrenal markers). However, as a result of these processes, some classical markers of sympathoadrenal cell types may be less prominent. Lastly, due to the limited availability of tissues, this study relied primarily on next-generation sequencing to explore immune phenotypes and expression of enzymes involved in ganglioside synthesis. While our data deemphasize the role of these processes in HR-NBL outcomes, further study using immunohistochemistry or other protein-level assays is warranted.

Resource availability

Lead contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Marcin Cieslik (mcieslik@umich.edu).

Materials availability

All sequencing data generated as part of this study is deposited at GEO and publicly available as identified below. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

Data and code availability

•
Bulk DNA and RNA sequencing data have been deposited at dbGaP phs002431.v1.p1 and are publicly available as of the date of publication. De-identified patient data, analyzed genomic data, and all generated single cell atlases including metadata have been deposited at Zenodo: https://doi.org/10.5281/zenodo.14046017. These data files are publicly available at the date of publication.
•
All original code has been deposited at GitHub (https://github.com/mctp/tpo-public-v2.5) and is publicly available as of the date of publication.
•
Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

Acknowledgments

This work was funded by following grants: Hyundai Hope on Wheel Scholar Grant (R.M.), Children’s Foundation of Michigan (R.M.), and NCI Outstanding Investigator Award R35 CA231996 (A.M.C.). R.J.R. was supported by an NIH F30 fellowship (F30CA275039). Research reported in this publication was also supported by the National Cancer Institute of the National Institutes of Health under the award numbers U10CA180886 (NCTN Operations Center Grant), U10CA180899 (NCTN Statistics & Data Center Grant), and U24CA196173 (COG Biospecimen Bank Grant) and St. Baldrick's Foundation Grant to the Children’s Oncology Group.

Author contributions

Conceptualization, R.J.R., J.C., R.M., and M.C.; methodology, R.J.R., J.C., R.M., and M.C.; investigation, R.J.R., J.C., C.H., N.H., R.M., and M.C.; writing—original draft, R.J.R. and M.C.; writing—review & editing, R.J.R., R.M., R.R., R.B., and M.C.; funding acquisition, M.C., A.C., and R.M.; resources, M.C., A.C., R.R., R.B., and R.M.; data generation, D.R. and X.C.; supervision, M.C., A.C., and R.M.

Declaration of interests

A.M.C. co-founded and is a member of the Scientific Advisory Board for Lynx Dx, Esanik Therapeutics, Medsyn, and Flamingo Therapeutics. A.M.C. serves as a scientific consultant for EdenRoc, Proteovant, Aurigene Oncology, RAPPTA, Belharra, Tempus, and Ascentage Pharmaceuticals. R.M. is a member of the Data Safety Monitoring Committee for Jubilant DraxImage Inc. and Y-mAbs Therapeutics, Inc.

STAR★Methods

Key resources table

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Biological samples

Patient-derived biopsy tissues	University of Michigan Mioncoseq program	https://www.pathology.med.umich.edu/mctp/mi-oncoseq-study
Patient-derived biopsy tissues	Children’s Oncology Group	https://childrensoncologygroup.org/obtainingbiospecimens

Deposited data

Human reference genome NCBI build 38, GRCh38	Genome Reference Consortium	http://www.ncbi.nlm.nih.gov/projects/genome/assembly/grc/human/
Raw data	This paper	phs002431.v1.p1
Analyzed data	This paper	https://doi.org/10.5281/zenodo.14046017
TARGET-Seq data	TARGET project	TARGET-NBL
Fetal adrenal and/or neuroblastoma single cell data	Kildisiute et al.⁵¹	https://www.neuroblastomacellatlas.org/
Fetal adrenal and/or neuroblastoma single cell data	Jansky et al.⁶²	https://adrenal.kitz-heidelberg.de/developmental_programs_NB_viz/
Fetal adrenal and/or neuroblastoma single cell data	Kameneva et al.⁶¹	GEO: GSE147821
Fetal adrenal and/or neuroblastoma single cell data	Dong et al.⁵⁰	GEO: GSE137804
Fetal adrenal and/or neuroblastoma single cell data	Alex’s Lemonade Stand Single-cell Pediatric Cancer Atlas	https://scpca.alexslemonade.org/
Neuroblastoma single cell data – immune annotations	Verhoeven et al.⁵⁸	https://github.com/shenglinmei/NB.immune.atlas/
Sympathoadrenal cell type markers	Jansky et al.⁶²	Table S5
Integrated fetal sympathoadrenal single-cell RNA-sequencing atlas	This paper	https://doi.org/10.5281/zenodo.14046017; fetal_neuroendocrine_cells.rds
Integrated neuroblastoma single-cell RNA-sequencing atlas	This paper	https://doi.org/10.5281/zenodo.14046017; neuroblastoma_sc_all_cells.rds
Integrated neuroblastoma immune single-cell RNA-sequencing atlas	This paper	https://doi.org/10.5281/zenodo.14046017; neuroblastoma_sc_immune_cells.rds
Neuroblastoma cell line copy number and gene expression data	Cancer Dependency Map (DepMap)	https://depmap.org/portal/data_explorer_2/

Software and algorithms

TPO	This paper	https://github.com/mctp/tpo-public-v2.5
CibersortX	https://cibersortx.stanford.edu/	https://cibersortx.stanford.edu/
R version 4.1.1	The R Project for Statistical Computing	https://www.r-project.org/
Limma R package version 3.50.3	Bioconductor	https://bioconductor.org/packages/release/bioc/html/limma.html
Survminer R package version 0.4.9	Comprehensive R Archive Network (CRAN)	https://cran.r-project.org/web/packages/survminer/index.html
Survival R package version 3.3–1	Comprehensive R Archive Network (CRAN)	https://cran.r-project.org/web/packages/survival/index.html
Msigdbr R package version 7.5.1	Comprehensive R Archive Network (CRAN)	https://cran.r-project.org/web/packages/msigdbr/index.html
fGSEA R package version 1.20.0	Bioconductor	https://bioconductor.org/packages/release/bioc/html/fgsea.html
clusterProfiler R package version 4.2.2	Bioconductor	https://www.bioconductor.org/packages/release/bioc/html/clusterProfiler.html
enrichR R package version 1.14.2	Comprehensive R Archive Network (CRAN)	https://cran.r-project.org/web/packages/enrichR/index.html
Singscore R package version 1.14.0	Bioconductor	https://www.bioconductor.org/packages/release/bioc/html/singscore.html
Cooccur R package version 1.3	Comprehensive R Archive Network (CRAN)	https://cran.r-project.org/web/packages/cooccur/index.html
Seurat R package version 4.3.0	https://github.com/satijalab/seurat	https://satijalab.org/seurat/articles/install.html
Ucell R package version 2.0.0	Bioconductor	https://www.bioconductor.org/packages/release/bioc/html/UCell.html
BayesPrism R package version 2.0	Github	https://github.com/Danko-Lab/BayesPrism
hdWGCNA R package version 0.2.24	Github	https://smorabit.github.io/hdWGCNA/
SoupX R package version 1.6.1	Comprehensive R Archive Network (CRAN)	https://cran.r-project.org/web/packages/SoupX/index.html

Open in a new tab

Experimental model and study participant details

Human participants

This study was approved by the Institutional Review Board of the University of Michigan (Michigan Oncology Sequencing Protocol, MI-ONCOSEQ, IRB HUM00056496, HUM00046018). Patients 25 years or younger with a suspected diagnosis of a neuroblastic tumor (neuroblastoma, ganglioneuroblastoma, ganglioneuroma, and paraganglioma) were eligible for the study. All patients or their parents or legal guardians provided informed consent to obtain fresh tumor biopsies and to perform comprehensive molecular profiling of tumor and germline exomes and tumor transcriptomes. From the University of Michigan in Ann Arbor, Michigan, 88 patients with neuroblastic tumors were included between 05/2012 and 06/2023. Additionally 34 patients from the Children’s Oncology Group trial ANBL1221 with neuroblastoma were included. There were 122 patients and 168 unique samples in total. Samples were obtained from patients during diagnostic biopsy pre-treatment (n = 98), local control (n = 32), or on repeat biopsy in refractory/relapsed disease (n = 29). Biopsy time point is unknown for a minority (n = 9) of cases. Demographic (e.g., gender, age, race), clinical (e.g., treatment) and histological information, including tumor staging by INSS was collected (Table S4). Information on socioeconomic status was unavailable as this is not regularly recorded in patient files. Patient gender was examined however did not associate with outcomes of interest. The studies were conducted in patients in accordance with the Declaration of Helsinki. Age-appropriate written informed consent from patients and/or parents was obtained prior to inclusion of each patient in the study.

Method details

Assessment of outcome on treatment regimens containing anti-GD2 immunotherapy

Clinical information from University of Michigan patients’ charts was obtained between 05/2012 and 06/2023. For every patient information on length of follow up, overall survival, mortality, treatment regimens, and time to progression on each treatment regimen was collected. For those that received anti-GD2 immunotherapy, information was collected on the clinical context in which they received the therapy (i.e., post-consolidation, refractory, or relapsed). When patients received anti-GD2 immunotherapy multiple times within a single clinical context only the first administration was considered. Outcome groups were determined as follows.

In the post-consolidation setting (MMT-GD2).

(1)
Patient has to be HR-NBL.
(2)
Patient has a diagnostic or second look biopsy.
(3)
Patient has to have received anti-GD2 immunotherapy as part of post-consolidation multimodality therapy.
(4)
We have at least 18 months of follow-up time after initiation of anti-GD2 immunotherapy in post-consolidation or observed an event within follow-up period.
(5)
Patients were then allocated into.
- a.
  Continued Remission (REM):
  - i.
    No progression AND.
  - ii.
    No relapse AND.
  - iii.
    No death.
- b.
  Progression (PRG):
  - i.
    Patient progressed OR.
  - ii.
    Patient relapsed OR.
  - iii.
    Patient died.

In the Relapsed/Refractory setting (CHEMO-GD2).

(1)
Patient has to be HR-NBL.
(2)
Patient has to have received anti-GD2 + chemotherapy (CHEMO-GD2) as part of ref/rel therapy.
(3)
We have at least 18 months of follow-up time after initiation of anti-GD2 + chemotherapy in ref/rel setting or observed a subsequent event within follow-up period.
(4)
Patients were then allocated into.
- a.
  Continued Disease Control (CDC).
  - i.
    No progression within 18 months AND.
  - ii.
    No relapse within 18 months AND.
  - iii.
    No death AND.
  - iv.
    Achieved best overall response of CR/PR/SD∗ after first 6 cycles chemo-immunotherapy during first episode of refractory/relapsed disease.
- b.
  Progression (PRG).
  - i.
    Patient progressed within 18 months OR.
  - ii.
    Patient relapsed within 18 months OR.
  - iii.
    Patient died OR.
  - iv.
    Patient did not achieve best overall response of CR/PR/SD∗.

∗ The International Neuroblastoma Response Criteria (INRC) were used to define overall response for this study.³² The response criteria integrate response at all sites defined as measurable in this study, including CT/MRI lesions which meet RECIST criteria, MIBG positive lesions, and bone marrow disease.

Integrative clinical sequencing

Histologic sections were evaluated for tumor content prior to sequencing. Nucleic acid preparation and integrative clinical sequencing (comprising exome sequencing and capture RNA-seq) was performed using standard protocols in our sequencing laboratory, which adheres to the Clinical Laboratory Improvement Amendments (CLIA).⁹⁰^,⁹¹^,⁹² In brief, tumor genomic DNA and total RNA were purified from the same sample using the AllPrep DNA/RNA/miRNA kit (Qiagen). Matched normal genomic DNA from blood, buccal swab or saliva was isolated using the DNeasy Blood & Tissue Kit (Qiagen). Targeted Exome-capture libraries of matched pairs of tumor and normal DNA were prepared as previously described,⁹¹^,⁹² using the Agilent SureSelect Human All Exon v4 (Agilent) or OncoSeq (v2-v6a) (Roche) platforms. Transcriptome libraries were prepared from total RNA captured by human all-exon (Agilent SureSelect Human All Exon v4) probes. All samples were sequenced on an Illumina HiSeq 2000, HiSeq 2500, or NovaSeq 6000 (Illumina) in paired-end mode of reads at least 125 bp in length. The primary base call files were converted into FASTQ sequence files using the bcl2fastq converter tool bcl2fastq-1.8.4 or later.

Comprehensive genomics analysis pipeline

All genomic data processing and analysis has been performed using the TPO workflow (https://github.com/mctp/tpo-public-v2.5), which implements standardized pipelines for the analysis of DNA and RNA sequencing data, broadly following community best practices.⁹³ Relevant algorithmic details are explained below, a general overview of its functions has been provided in prior publications.⁹⁴^,⁹⁵^,⁹⁶

Somatic variant calling

Total number of reads, percent of duplicated reads, and percent of chimeric reads were calculated for exome capture data using the same criteria as Picard.⁹⁷ Other quality measures such as GC-bias⁹⁸ and read mappability were factored directly in the CNV and variant calling processes to minimize the effects of data quality on sensitivity of the calls. BBMap’s bbduk⁹⁹ was used to perform trimming of DNA sequencing paired-end reads. Next, the data was aligned to the GRCh38 reference using BWA-mem.¹⁰⁰ Sentieon sort tool¹⁰¹ was used to sort the reads in the BAM files. For somatic variants, all tumor samples were matched with normal tissue. TNscope,¹⁰¹ an improved somatic caller based on GATK Mutect2¹⁰² was used to call the somatic variants and calculate variant allele frequency (VAF) according to its probabilistic model, taking realignment and mapping ambiguity into account. The following settings were used in TNScope.

--max_fisher_pv_active 0.05 --min_tumor_allele_frac 0.0075 --min_init_tumor_lod 2.5 --assemble_mode 4 --trim_soft_clip --normal_contamination_frac 0.25 --prune_factor 4

The called variants were annotated using VEP¹⁰³ and vcfAnno.¹⁰⁴ All detected variants were further stringently filtered based on sequencing evidence (variant allele frequency, coverage, mutation likelihood TLOD and NLOD, strand bias, allele depth, multi-allelic variants), overlap in problematic regions including regions with low-mappability and repetitive sequence, and homopolymer repeats, and in ambiguous cases manually reviewed.

Copy number estimation

Copy-number analysis was performed by using whole-exome sequencing (WES) coverage data and variant calls based on the tumor DNA. CNVEX (https://github.com/mctp/cnvex) was used to estimate CNVs.⁹⁵^,⁹⁶ Briefly, CNVEX estimates coverage within fixed genomic intervals and variant calls to compute B-allele frequencies (BAFs) at variant positions. Coverage values are then normalized for GC-bias using LOESS smoothing across targeted regions within the GC range of 0.3 and 0.7, using the span = 0.5. All the GC normalized coverages and BAFs are then jointly segmented using a custom algorithm based on Circular Binary Segmentation (CBS).¹⁰⁵ The resulting segmented copy-number profiles were then subject to joint inference of tumor purity and ploidy and absolute copy number states, implemented in CNVEX, which is most similar to the mathematical formalism of ABSOLUTE and PureCN.⁹⁸^,¹⁰⁶ Because the copy-number inference problem can have multiple equally likely solutions, further biological insights are necessary to choose the most parsimonious result. The solutions were reviewed by two independent field experts and the most likely solution was selected.

RNA-sequencing expression quantification

For RNA-seq data analysis, including read-trimming, alignment, post-processing, and quantification, we used the same methods as previously described.⁹⁶ Briefly, adapter trimmed paired-end 150bp short reads were aligned using STAR version 2.4.0j to the GRCh38 reference supplemented with human oncogenic viruses.¹⁰⁷ To improve the sensitivity of detecting fusion calls, synthetic single end reads were generated using bbduk from the trimmed paired-end reads. These reads were also aligned using STAR with optimized settings. After alignment, capture RNA-seq data were quantified using Kallisto.¹⁰⁸ The paired-end and single-end RNA-seq alignments were used subsequently as an input to CODAC, a component of TPO designed to call fusions, perform additional QC and realign reads using Minimap and GMAP.¹⁰⁹^,¹¹⁰

Detection of gene fusions from RNA-sequencing

Detection of gene fusions was performed using the CODAC algorithm as previously described.⁹⁵^,⁹⁶ Briefly, CODAC implements detection of genic and intergenic gene fusions based on both split- and discordant-reads as detected through chimeric read alignment using STAR.¹⁰⁷ To maximize sensitivity, STAR is run separately using optimized settings in single-end and paired-end mode for overlapping and non-overlapping read pairs, respectively. The resulting alignments are merged, resulting in candidate fusion junctions identified from the STAR alignments are evaluated based on alignment properties to identify false-positive calls, including incorrect mappings, reference errors/differences, and non-genetic sources, such as circRNAs. The resulting call-set is further filtered against a manually-curated database of recurrent artifacts. STAR settings.

--alignIntronMax 150000 --alignMatesGapMax 150000 --chimSegmentMin 10 --chimJunctionOverhangMin 1 --chimScoreSeparation 0 --chimScoreJunctionNonGTAG 0 \

--chimScoreDropMax 1000 --chimScoreMin 1

Cancer cell fraction and mutation clonality

The cancer cell fraction (CCF) of a variant was calculated as previously described.¹¹¹ Mutations with CCF values > 0.6 were considered clonal.

Differential gene expression analysis in bulk RNA-sequencing

All differential expression analyses were done using the limma R package, with the default settings for the ‘‘voom’’, ‘‘lmFit,’’ ‘‘eBayes,’’ and ‘‘topTable’’ functions.¹¹² All differential expression models included coefficients for tumor purity estimates obtained from DNA sequencing. MMT-GD2 differential expression also included coefficients for biopsy time points. Refractory/relapsed differential expression included coefficients for biopsy time points and anti-GD2 immunotherapy exposure status. Chromosome 11q loss differential expression included coefficients for MYCN amplification status and biopsy site. This allowed us to estimate log fold-changes and adjusted p-values associated while mitigating the confounding effects of tumor content and biopsy site. Select outlier samples were excluded based on multidimensional scaling visualization with the plotMDS() function provided by limma.

Chromosome-level and arm-level copy number alterations

We applied a logic based framework to annotate copy number gains and losses across the genome using data provided from CNVEX including purity, ploidy, and segment-level copy number data. Briefly, the segment-level copy number data was used to calculate the base pair weighted median (WM) copy number and the mode of segment copy number values (M) for each sample. Within a single sample, each segment’s copy number C was labeled as a gain (C > 2 & C > WM & C > M & C>[ploidy+0.5]), a loss (C < 4 & C < WM & C < M & C<[ploidy+0.5]), or if a segment failed to meet either the gain or loss criteria, it was labeled as ‘none’. Following annotation of each individual segment, adjacent segments with the same annotation within a sample were combined. These annotated, combined segments were overlaid onto hg38 chromosome arm coordinates to determine the proportion of each chromosome arm gained or lost. An arm was determined to be gained or lost if more than 50% of the arm was gained or lost respectively. Whole chromosomes were determined to be gained or lost if both arms were gained or lost respectively. This framework allowed for the classification or arm-level events with added flexibility for cases with subsequent focal copy number alterations which followed large alterations.

Whole genome duplication

Whole genome duplication (WGD) was calculated using ploidy estimates and the proportion of the genome with loss of heterozygosity (pLOH). Briefly, for each sample a value was calculated according to the equation: value = 3.2–3∗(pLOH). If the sample had a ploidy both greater than this value and greater than 2.2 it was considered to be whole genome duplicated. A similar process was used to determine samples with 2 instances of WGD. For each sample a value was calculated according to the equation: value = 5.2–3∗(pLOH). If the sample had a ploidy both greater than this value and greater than 2.2 it was considered to have 2 instances of whole genome duplication.

Chromosomal instability measures

The number of whole chromosomes gained or lost for each sample was calculated by summing the number of chromosomes annotated as gained or lost for each sample. The number of chromosome arms gained or lost included only cases in which a single arm within the chromosome was gained or lost or when one arm was gained and the other lost. This prevented whole chromosome gains/losses from being counted as 2 arm gain/losses. Additionally, we quantified several measures of chromosomal instability including the weighted Genome Instability Index (WGII), as well as the number of Large State Transitions (LST), Telomeric Allelic Imbalances (nTAI), and Loss of Heterozygosity (nLOH) events as previously described.⁹⁶ Briefly, wGII was calculated as the proportion of each chromosome that has a different copy number compared to the baseline copy number of the sample. The average of scores for each chromosome were calculated and weighted by the length of the chromosome. For LST, large was defined as 10Mb and a single LST event had a maximum distance of 3Mb between two “large segments” with allelic imbalances. For nTAI, we counted the allelic imbalances with a minimum length of 5Mb that stretched to the telomeric end of each chromosome (10Kb of chromosome start/end for GRCh38). For nLOH, we counted any segment of minimum length of 15Mb with LOH.

Survival analysis

The R packages survival and survminer were used to perform survival analyses.¹¹³^,¹¹⁴ The Kaplan-Meier curve of overall survival was used to compare the prognosis among subtypes (function survfit). Log-rank test was used to test the differential survival outcomes between categorical variables. For analysis of variables in the TARGET/SEQC cohorts of neuroblastoma, continuous variables of interest (deconvoluted cell type proportions, gene signature scores, etc.) were quartiled and comparisons were made between the highest and lowest quartile.

Pathway, gene set enrichment, and overrepresentation analysis

The Molecular Signature Database (MSigDB) was used as a source of gene sets comprising cancer hallmarks, molecular pathways, and oncogenic signatures and accessed using the R package Msigdbr.¹¹⁵^,¹¹⁶ Gene Set Enrichment Analysis (GSEA) was performed in R using the R package fGSEA.¹¹⁷ We used signed -log10(p-values) from limma as input to the algorithm. Enrichment analysis for gene sets of interest was assessed using the ‘enricher’ function from the R package clusterProfiler or in single cell data EnrichR.¹¹⁸^,¹¹⁹ All expressed genes were used as a background list unless otherwise mentioned. All p-values have been adjusted for multiple-hypothesis testing using FDR correction.

Gene signature scoring in bulk RNA-sequencing

Scores in bulk RNA-sequencing samples were generated using the singscore R package.¹²⁰ Briefly, Reads Per Kilobase per Million (RPKM) values are used to rank the expression of each gene within a single sample. The mean ranks are normalized to theoretical maximum values, centered on zero and summed. This allows for the scoring of a signature within a sample independently of other samples.

Mutual exclusivity and co-enrichment analysis

Mutual exclusivity and co-enrichment analysis were performed at the sample level for all diagnostic HR-NBL samples with copy number data available. The R package cooccur was used to determine significance.¹²¹ Briefly, the expected frequencies of co-occurrence between events based on chance are calculated and used to calculate the probability of the observed extent of cooccurrence.

FCγ receptor affinity

Predicted receptor affinities for FCGR2A and FCGR3A were estimated using the genotypes of rs1801274 and rs396991 respectively. The relationship between genotype and affinity was inferred as previously described.¹⁵ For FCGR2A (rs1801274), low affinity: G/G, mixed affinity: A/G, and high affinity: G/G. For FCGR3A (rs396991): low affinity: T/T, mixed affinity: T/G, and high affinity: G/G. Genotypes at these positions were inferred from RNA-sequencing data. A minimum of 5 supporting reads across all samples for a single patient was required to make a homozygous genotype call while 2 reads were accepted for heterozygous genotypes.

Telomere maintenance mechanisms

Classification of telomere maintenance mechanisms (TMMs) was modeled on previously described groupings.³⁵ This classification was adjusted for application with available data types including RNA and exome sequencing. Briefly, samples were first evaluated for Alternative Lengthening of Telomeres (ALT) which was defined as the loss of ATRX, H3F3A, or DAXX through either somatic mutation, homozygous deletion. Additionally, RPKM values for TERT were quartiled across all samples where those in the highest quartile of TERT expression were classified as TERT-high. Samples without ALT or TERT-high phenotypes were classified as TERT-low/non-ALT. This system likely underestimates the presence of ALT as detectable ATRX loss accounts for approximately 60% of all ALT phenotypes.³⁵ Additionally, TERT rearrangements are not detectable using our method of DNA sequencing and would require whole genome sequencing for comprehensive detection.

Single-cell RNA-sequencing data pre-processing

Single cell and single nuclei RNA-sequencing count matrices were obtained from 1 publicly available data repository and 4 previously published studies (see key resources table).⁵⁰^,⁵¹^,⁶¹^,⁶² Metadata including cell annotations was obtained from these studies as available. Cells with fewer than 500 unique molecular identifiers (UMIs) were excluded. Additionally, cells were excluded if they met any of the following criteria: >10% UMIs from mitochondrial genes, >10% UMIs from hemoglobin genes, total UMIs >20,000, <400 total genes measured, or >5000 genes measured.

Integration of fetal adrenal single cell and single nuclei RNA-sequencing

Fetal adrenal single cell and single nuclei RNA-sequencing were integrated using the R-package Seurat.¹²²^,¹²³^,¹²⁴ First, all samples were merged into a single seurat object which was then split into a list of seurat objects by study of origin. Each object was normalized using the SCTransform function with default settings.¹²⁵ We selected 3000 integration features using the SelectIntegrationFeatures function, and the seurat objects were prepped for integration using the PrepSCTIntegration and RunPCA functions with the selected integration features. Anchors for integration were identified using the FindIntegrationAnchors function with k.anchor = 20 and the 4 samples with the most cells from each study used as the reference sample. Finally, the IntegrateData function was used to integrate the data with the normalization method set to ‘SCT’. The integrated object was then processed using the standard seurat analytical method which included the RunPCA, FindNeighbors, FindClusters, and RunUMAP functions. Integration quality was assessed by comparing the distribution of cell types to study of origin on UMAP plots whereby cell types not studies were expected to cluster in the low-dimensional embedding.

Integration of neuroblastoma single cell RNA-sequencing

Neuroblastoma single cell RNA-sequencing was integrated using Seurat. First, all samples were merged into a single seurat object which was then split into a list of seurat objects by sample of origin. Each object was normalized using the SCTransform function with default settings.¹²⁵ We selected 3000 integration features using the SelectIntegrationFeatures function, and the seurat objects were prepped for integration using the PrepSCTIntegration and RunPCA functions with the selected integration features. Anchors for integration were identified using the FindIntegrationAnchors function with k.anchor = 20 and no reference samples selected. Finally, the IntegrateData function was used to integrate the data with the normalization method set to ‘SCT’. The integrated object was then processed using the standard seurat analytical method which included the RunPCA, FindNeighbors, FindClusters, and RunUMAP functions. Integration quality was assessed by comparing the distribution of cell types to sample of origin on UMAP plots whereby cell types not samples were expected to cluster in a low-dimensional embedding.

Fetal adrenal cell annotation

Cell annotations for fetal adrenal cells were available for all cells from the study of origin with the exception of 9,455 cells from Dong et al.⁵⁰ We believe these cells were lacking annotations in the study of origin due to different barcode filtering thresholds. We assigned each unique cell into one of the following broad categories based on its previous annotation: adrenal cortex, endothelium, mesenchyme, erythroid, neuroendocrine, immune, or other. The 9,455 cells lacking a previous cell type, were assigned a broad annotation based on their nearest neighbors and the composition of other cells in their assigned cluster.

Neuroblastoma cell annotation

Cell annotations for the neuroblastoma-derived cells were available for all cells from the study of origin with the exception of data obtained from Alex’s lemonade stand. We assigned each unique cell into one of the following broad categories based on its previous annotation: tumor, immune, mesenchyme, endothelial, and Schwann. The cells lacking a previous cell type, were assigned a broad annotation based on the composition of other cells in their assigned cluster.

Generation of an integrated fetal sympathoadrenal single-cell atlas

Fetal adrenal cells assigned a sympathoadrenal cell label were selected and further filtered for barcodes with <5% of the UMIs from hemoglobin genes. The subsequent Seurat object was split by study of origin, re-integrated, and processed in the same manner as the entire fetal adrenal dataset as outlined above. Integration quality was assessed by comparing the distribution of cell types from the studies of origin within clusters. Cells from similar annotations clustered closely together in a low-dimensional embedding. However, two clusters of fewer than 100 cells were isolated in low-dimensional space and had highly discordant annotations from their studies of origin. These cell clusters were removed and the integration, processing, and integration quality control steps were repeated yielding a satisfactory embedding of fetal sympathoadrenal cells.

Fetal sympathoadrenal cell type annotation

Sympathoadrenal cells in the fetal sympathoadrenal cell atlas were assigned labels through a combination of gene signature scoring and manual curation. First we compiled a list of established markers for sympathoadrenal cell types present in the fetal adrenal gland including Schwann cell Precursors (SCPs), Bridge cells, connecting progenitor cells (CPCs), chromaffin cells, and sympathoblasts (Table S13). We assigned a score for each gene set across all cells using the AddModuleScore_UCell function of the Ucell R package.¹²⁶ Cells were then assigned a preliminary annotation based on the maximum cell type score. Finally we tabulated the preliminary cell type annotation for each cluster and labeled the cluster based on the most represented preliminary annotation in each cluster. This ensured that cells lacking expression of the selected cell type marker genes were classified the same as the cells which they most closely resembled. The final cell type annotations showed close agreement with the cell types obtained from the study of origin.

Selection and annotation of immune cells in the integrated neuroblastoma single-cell atlas

Neuroblastoma-derived cells assigned an immune cell label were selected and filtered for a non-zero expression of the pan-immune cell marker PTPRC (CD45). The subsequent Seurat object was split by sample of origin, and was further filtered to include only samples with more than 50 cells. The remaining samples were re-integrated, and processed in the same manner as the entire neuroblastoma dataset as outlined above. Following integration, some cells were discovered to be residual tumor cells as determined by high expression of the neuroblastoma markers HAND2 and PHOX2B. These cells were removed and the re-integration and processing repeated. Integration quality was assessed by comparing the distribution of cell types from the studies of origin within clusters. Cells from similar annotations clustered closely together in a low-dimensional embedding.

Immune cells were assigned both broad and specific labels through a combination of gene signature scoring and manual curation. First we compiled a list of established markers for broad immune cell types including T cells, B cells, NK cells, and myeloid cells (Table S14). We assigned a score for each immune gene set across all cells using the AddModuleScore_UCell function of the Ucell R package.¹²⁶ Cells were then assigned a broad cell type based on the maximum broad cell type score. Next, for each broad cell type we subsetted all cells with the same assigned broad cell type. Then, within each broad cell type we created a list of established markers for more specific cell type annotations (Table S15). We then scored each specific immune gene set across all cells using the AddModuleScore_UCell function. For example, all cells broadly classified as T cells were selected and scored using signatures for specific T cell populations. After assigning specific immune cell type labels to each cell in all broad cell types, we recombined the object into a single immune cell atlas. Notably, neutrophils were depleted, consistent with previously reported technical artifacts of single cell RNA-sequencing using 10x technologies.¹²⁷

Identification of cell type marker gene signatures

To identify representative markers of cell type populations we leveraged the SoupX R package quickMarkers function.¹²⁸ Briefly, this function uses term frequency–inverse document frequency to identify top marker genes passing a hypergeometric test for each cluster.

Bulk RNA-sequencing sample deconvolution

To deconvolute samples profiled through bulk RNA-sequencing, we utilized the R package BayesPrism.¹²⁹ Additionally, we employed expression profiles from specific immune cell types identified in the immune cell atlas as well as other cells present in the neuroblastoma single cell data (tumor, mesenchyme, endothelial, and Schwann). Each expression profile was composed of a random subsample of 250 cells as recommended in the BayesPrism documentation. The BayesPrism plot.cor.phi function was used to assess the quality of cell type and cell state labels. Genes included in the bulk and single cell expression profiles were filtered as recommended using the cleanup.genes function with the following gene groups specified: "Rb", "Mrp", "other_Rb", "chrM", "MALAT1","chrX", and "chrY". Marker genes for deconvolution were identified using the get.exp.stat function and pruned down to a maximum of 50 genes per cell type. These marker genes and reference expression profiles were used to deconvolute both internal and external bulk sequenced cohorts. Notably, we verified the accuracy of deconvolution using pseudobulk samples constructed from the single cell data. We compared the calculated and actual proportion cell types within each pseudobulk sample. Additionally for internal deconvoluted samples with tumor purity estimates available from DNA-sequencing, we compared the calculated proportion of tumor cells to purity and found a high degree of correlation.

Gene signature scoring in single-cell RNA-sequencing

Gene signature scoring in single cell RNA-sequencing was accomplished using the AddModuleScore_UCell function of the Ucell R package.¹²⁶

Differential gene expression analysis in single cell RNA-sequencing

All differential expression analyses in single cell data were done using the R package limma.¹¹² Limma was applied to pseudobulk samples generated from single cell data using the R package hdWGCNA.¹³⁰ Samples profiled via single nuclei RNA-sequencing were removed to mitigate confounding effects due to sample profiling. Within limma, the default settings for the ‘‘voom’’, ‘‘lmFit,’’ ‘‘eBayes,’’ and ‘‘topTable’’ functions were used.¹¹² To identify genes consistently changing throughout sympathoblast development, we assigned samples into groups based on the weeks post-conception that the sample originated from. Each group had to contain two or more samples. We calculated the p-values and log fold changes between each adjacent time point and between the most and least mature groups. Mature sympathoblast genes had log fold changes greater than 0 across all group comparisons and had an adjusted p-value less than 0.2 in the comparison of the most and least mature groups. Conversely, the immature sympathoblast genes had log fold changes less than 0 across all group comparisons and had an adjusted p-value less than 0.2. This allowed us to characterize genes changing in a consistent fashion throughout sympathoblast development. Both the mature and immature gene sets were scored in sympathoblasts from Jansky et al.⁶² which were profiled by single nuclei RNA-sequencing and not included in derivation of the gene sets. This analysis provided an independent verification of the gene sets across sympathoblasts from 7 to 17 weeks post-conception.

Sympathoblast pathway correlation analysis

To identify pathways that were both expressed in sympathoblasts and correlated with the mature and immature sympathoblast signatures, we selected the raw counts data matrix from the fetal developmental cell atlas. Sympathoblasts profiled from both single cell and single nuclei RNA sequencing were utilized. Genes within the sympathoblast counts matrix were filtered to have a minimum total counts of 196.5 (2% of total number of sympathoblasts). Gene Ontology pathways (MF, BP, CC) were obtained using the R package Msigdbr.¹¹⁵^,¹¹⁶ Pathways with less than 50% of their genes or fewer than 3 total genes expressed in sympathoblasts were removed. We assigned a score for each expressed gene set across all cells using the AddModuleScore_UCell function of the Ucell R package.¹²⁶ A Pearson correlation was then calculated between all pathways and the immature and mature gene set scores.

Fetal transcriptional modules

To identify co-regulated transcriptional modules present in fetal sympathoblasts we applied the hdWGCNA R package.¹³⁰ The hdWGCNA package implements weighted gene co-expression network analysis in single cell datasets. We applied hdWGCNA to sympathoblasts present in the integrated fetal neuroendocrine cell atlas using the consensus method. The atlas was subsetted to include cells in the G1 phase of the cell cycle and only those profiled with single cell RNA-sequencing, not single nuclei RNA-sequencing. Additionally we ran hdWGCNA on sympathoblasts from each constituent dataset individually to ensure the identified modules were consistently co-regulated. The single nuclei data showed the largest discrepancy between all other datasets and was thus not included in the consensus analysis.

Relationship between phenotypic signals and chromosomal instability measures

Univariate and multivariate relationships between phenotypic signals and measures of chromosomal instability measures were determined using the lm() function in R. Multivariate models included the following covariates: chromosome 11q loss, chromosome 17q gain, the quantity of large state transitions (LST), measures of the whole genome integrity index (wGII), MYCN amplification status, and estimated tumor purity. Phenotypic signals were quantified through gene signature scoring as described above. All unique non-outlier RNA libraries were included.

Loss of heterozygosity-based permutation model

The goal of this analysis was to identify the likelihood that 2 samples share a common ancestor using loss of heterozygosity (LOH) profiles. We used a permutation test to quantitatively assess the statistical significance of the observed overlap in loss of heterozygosity (LOH) across a given sample pair. This test measures the probability of observing concurrent LOH of genes in 2 tumors, if the LOHs are happening in random loci of each tumor (i.e., null hypothesis). The first step of the test involves classifying segments according to their LOH status into 2 groups of LOH and noLOH. Next, in each tumor, LOH labels were uniformly shuffled among segments to simulate a randomly labeled genome. To make sure the rate of LOH in the simulated genome is equal or higher than the tumor (to achieve an upper bound of LOH overlaps), we augmented 20% more LOH labels in each tumor and removed any simulation that has less LOH in their genome compared to the tumors. This simulation was repeated for 107,000 iterations, and in each step, the number of overlapping genes with LOH in all 4 simulated samples were measured. The observed number for genes with LOH in both samples was then compared against this simulated distribution, yielding an empirical P-value.

PHOX2B in the cancer dependency map

The Cancer Dependency Map (DepMap) was used for two purposes: 1) to explore PHOX2B copy number profiles in neuroblastoma-derived cell lines, and 2) determine the relative dependency of neuroblastoma-derived cell lines on PHOX2B relative to cell lines derived from other cancer types. Data was obtained from the DepMap Data Explorer and plotted externally in R. The cancer of origin for each cell line was used to identify 50 neuroblastoma-derived cell lines with copy number data. Cell line dependency scores were sourced from DepMap’s CRISPR Public 24Q4+Score, Chronos. Dependency scores were available for 39 neuroblastoma-derived cell lines and 1,139 non-neuroblastoma derived cell lines. Differences in distributions of dependency scores were assessed using a Wilcoxon rank-sum test.

Quantification and statistical analysis

Quantification and statistical analyses were performed using R unless otherwise described. Categorical variables were compared using the chi-square test. Continuous variables were compared using Anova and Wilcoxon rank sum tests unless otherwise noted. Conventional boxplots with sample sizes are used for plotting continuous variables. Correlations between continuous variables were evaluated using the Pearson correlation coefficient.

Published: September 26, 2025

Footnotes

Supplemental information can be found online at https://doi.org/10.1016/j.xcrm.2025.102375.

Contributor Information

Arul M. Chinnaiyan, Email: arul@med.umich.edu.

Rajen Mody, Email: rmody@med.umich.edu.

Marcin Cieslik, Email: mcieslik@med.umich.edu.

Supplemental information

Document S1. Figures S1–S7

mmc1.pdf^{(5.8MB, pdf)}

Table S1. Sequencing quality control metrics

mmc2.xlsx^{(92.9KB, xlsx)}

Table S2. Clinical characteristics of 122 patients with neuroblastic tumors

mmc3.xlsx^{(50.7KB, xlsx)}

Table S3. ALK gene fusions

mmc4.xlsx^{(52KB, xlsx)}

Table S4. Clinical data used to determine outcome groups for treatment regimens containing anti-GD2 immunotherapy

mmc5.xlsx^{(63.2KB, xlsx)}

Table S5. Clinical characteristics of high-risk neuroblastoma patients receiving treatment regimens containing anti-GD2 immunotherapy

mmc6.xlsx^{(51.7KB, xlsx)}

Table S6. Differential gene expression analysis for outcome groups following treatment regimens containing anti-GD2 immunotherapy

mmc7.xlsx^{(2.3MB, xlsx)}

Table S7. Gene set enrichment analysis for MMT-GD2 and CHEMO-GD2 differentially expressed genes

mmc8.xlsx^{(614.7KB, xlsx)}

Table S8. Marker gene signatures for fetal populations

mmc9.xlsx^{(72.1KB, xlsx)}

Table S9. Mature and immature sympathoblast gene signatures

mmc10.xlsx^{(54.6KB, xlsx)}

Table S10. Gene ontology enrichment of mature and immature sympathoblast gene signatures

mmc11.xlsx^{(119.4KB, xlsx)}

Table S11. Sympathoblast-derived transcriptional modules

mmc12.xlsx^{(52.2KB, xlsx)}

Table S12. Gene ontology biological process enrichment of transcriptional modules identified in fetal sympathoblasts

mmc13.xlsx^{(297KB, xlsx)}

Table S13. Fetal cell markers used in single-cell annotation

mmc14.xlsx^{(49.6KB, xlsx)}

Table S14. Broad immune cell markers used in single-cell annotation

mmc15.xlsx^{(49.5KB, xlsx)}

Table S15. Specific immune cell markers used in single-cell annotation

mmc16.xlsx^{(50.5KB, xlsx)}

Document S2. Article plus supplemental information

mmc17.pdf^{(13.9MB, pdf)}

References

1.Matthay K.K., Maris J.M., Schleiermacher G., Nakagawara A., Mackall C.L., Diller L., Weiss W.A. Neuroblastoma. Nat. Rev. Dis. Primers. 2016;2 doi: 10.1038/nrdp.2016.78. [DOI] [PubMed] [Google Scholar]
2.Ponzoni M., Bachetti T., Corrias M.V., Brignole C., Pastorino F., Calarco E., Bensa V., Giusto E., Ceccherini I., Perri P. Recent advances in the developmental origin of neuroblastoma: an overview. J. Exp. Clin. Cancer Res. 2022;41:92. doi: 10.1186/s13046-022-02281-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
3.van Groningen T., Koster J., Valentijn L.J., Zwijnenburg D.A., Akogul N., Hasselt N.E., Broekmans M., Haneveld F., Nowakowska N.E., Bras J., et al. Neuroblastoma is composed of two super-enhancer-associated differentiation states. Nat. Genet. 2017;49:1261–1266. doi: 10.1038/ng.3899. [DOI] [PubMed] [Google Scholar]
4.van Groningen T., Akogul N., Westerhout E.M., Chan A., Hasselt N.E., Zwijnenburg D.A., Broekmans M., Stroeken P., Haneveld F., Hooijer G.K.J., et al. A NOTCH feed-forward loop drives reprogramming from adrenergic to mesenchymal state in neuroblastoma. Nat. Commun. 2019;10:1530. doi: 10.1038/s41467-019-09470-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Boeva V., Louis-Brennetot C., Peltier A., Durand S., Pierre-Eugène C., Raynal V., Etchevers H.C., Thomas S., Lermine A., Daudigeos-Dubus E., et al. Heterogeneity of neuroblastoma cell identity defined by transcriptional circuitries. Nat. Genet. 2017;49:1408–1413. doi: 10.1038/ng.3921. [DOI] [PubMed] [Google Scholar]
6.Thirant C., Peltier A., Durand S., Kramdi A., Louis-Brennetot C., Pierre-Eugène C., Gautier M., Costa A., Grelier A., Zaïdi S., et al. Reversible transitions between noradrenergic and mesenchymal tumor identities define cell plasticity in neuroblastoma. Nat. Commun. 2023;14:2575. doi: 10.1038/s41467-023-38239-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Irwin M.S., Naranjo A., Zhang F.F., Cohn S.L., London W.B., Gastier-Foster J.M., Ramirez N.C., Pfau R., Reshmi S., Wagner E., et al. Revised Neuroblastoma Risk Classification System: A Report From the Children’s Oncology Group. J. Clin. Oncol. 2021;39:3229–3241. doi: 10.1200/JCO.21.00278. [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Janoueix-Lerosey I., Schleiermacher G., Michels E., Mosseri V., Ribeiro A., Lequin D., Vermeulen J., Couturier J., Peuchmaur M., Valent A., et al. Overall genomic pattern is a predictor of outcome in neuroblastoma. J. Clin. Oncol. 2009;27:1026–1033. doi: 10.1200/JCO.2008.16.0630. [DOI] [PubMed] [Google Scholar]
9.Attiyeh E.F., London W.B., Mossé Y.P., Wang Q., Winter C., Khazi D., McGrady P.W., Seeger R.C., Look A.T., Shimada H., et al. Chromosome 1p and 11q deletions and outcome in neuroblastoma. N. Engl. J. Med. 2005;353:2243–2253. doi: 10.1056/NEJMoa052399. [DOI] [PubMed] [Google Scholar]
10.Schleiermacher G., Michon J., Ribeiro A., Pierron G., Mosseri V., Rubie H., Munzer C., Bénard J., Auger N., Combaret V., et al. Segmental chromosomal alterations lead to a higher risk of relapse in infants with MYCN-non-amplified localised unresectable/disseminated neuroblastoma (a SIOPEN collaborative study) Br. J. Cancer. 2011;105:1940–1948. doi: 10.1038/bjc.2011.472. [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Bellini A., Pötschger U., Bernard V., Lapouble E., Baulande S., Ambros P.F., Auger N., Beiske K., Bernkopf M., Betts D.R., et al. Frequency and Prognostic Impact of ALK Amplifications and Mutations in the European Neuroblastoma Study Group (SIOPEN) High-Risk Neuroblastoma Trial (HR-NBL1) J. Clin. Oncol. 2021;39:3377–3390. doi: 10.1200/JCO.21.00086. [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Mody R., Yu A.L., Naranjo A., Zhang F.F., London W.B., Shulkin B.L., Parisi M.T., Servaes S.-E.-N., Diccianni M.B., Hank J.A., et al. Irinotecan, Temozolomide, and Dinutuximab With GM-CSF in Children With Refractory or Relapsed Neuroblastoma: A Report From the Children’s Oncology Group. J. Clin. Oncol. 2020;38:2160–2169. doi: 10.1200/JCO.20.00203. [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Matthay K.K., Yanik G., Messina J., Quach A., Huberty J., Cheng S.-C., Veatch J., Goldsby R., Brophy P., Kersun L.S., et al. Phase II study on the effect of disease sites, age, and prior therapy on response to iodine-131-metaiodobenzylguanidine therapy in refractory neuroblastoma. J. Clin. Oncol. 2007;25:1054–1060. doi: 10.1200/JCO.2006.09.3484. [DOI] [PubMed] [Google Scholar]
14.U.S. Food and Drug Administration (FDA) (2010). Dinutuximab - Orphan Drug Designations and Approvals. FDA.gov. https://www.accessdata.fda.gov/scripts/opdlisting/oopd/detailedIndex.cfm?cfgridkey=324210.
15.Yu A.L., Gilman A.L., Ozkaynak M.F., Naranjo A., Diccianni M.B., Gan J., Hank J.A., Batova A., London W.B., Tenney S.C., et al. Long-Term Follow-up of a Phase III Study of ch14.18 (Dinutuximab) + Cytokine Immunotherapy in Children with High-Risk Neuroblastoma: COG Study ANBL0032. Clin. Cancer Res. 2021;27:2179–2189. doi: 10.1158/1078-0432.CCR-20-3909. [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Furman W.L., Federico S.M., McCarville M.B., Shulkin B.L., Davidoff A.M., Krasin M.J., Sahr N., Sykes A., Wu J., Brennan R.C., et al. A Phase II Trial of Hu14.18K322A in Combination with Induction Chemotherapy in Children with Newly Diagnosed High-Risk Neuroblastoma. Clin. Cancer Res. 2019;25:6320–6328. doi: 10.1158/1078-0432.CCR-19-1452. [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Furman W.L., McCarville B., Shulkin B.L., Davidoff A., Krasin M., Hsu C.-W., Pan H., Wu J., Brennan R., Bishop M.W., et al. Improved Outcome in Children With Newly Diagnosed High-Risk Neuroblastoma Treated With Chemoimmunotherapy: Updated Results of a Phase II Study Using hu14.18K322A. J. Clin. Oncol. 2022;40:335–344. doi: 10.1200/JCO.21.01375. [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Yu A.L., Gilman A.L., Ozkaynak M.F., London W.B., Kreissman S.G., Chen H.X., Smith M., Anderson B., Villablanca J.G., Matthay K.K., et al. Anti-GD2 antibody with GM-CSF, interleukin-2, and isotretinoin for neuroblastoma. N. Engl. J. Med. 2010;363:1324–1334. doi: 10.1056/NEJMoa0911123. [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Desai A.V., Gilman A.L., Ozkaynak M.F., Naranjo A., London W.B., Tenney S.C., Diccianni M., Hank J.A., Parisi M.T., Shulkin B.L., et al. Outcomes Following GD2-Directed Postconsolidation Therapy for Neuroblastoma After Cessation of Random Assignment on ANBL0032: A Report From the Children’s Oncology Group. J. Clin. Oncol. 2022;40:4107–4118. doi: 10.1200/JCO.21.02478. [DOI] [PMC free article] [PubMed] [Google Scholar]
20.De Brouwer S., De Preter K., Kumps C., Zabrocki P., Porcu M., Westerhout E.M., Lakeman A., Vandesompele J., Hoebeeck J., Van Maerken T., et al. Meta-analysis of neuroblastomas reveals a skewed ALK mutation spectrum in tumors with MYCN amplification. Clin. Cancer Res. 2010;16:4353–4362. doi: 10.1158/1078-0432.CCR-09-2660. [DOI] [PubMed] [Google Scholar]
21.Brady S.W., Liu Y., Ma X., Gout A.M., Hagiwara K., Zhou X., Wang J., Macias M., Chen X., Easton J., et al. Pan-neuroblastoma analysis reveals age- and signature-associated driver alterations. Nat. Commun. 2020;11:5183. doi: 10.1038/s41467-020-18987-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Bresler S.C., Weiser D.A., Huwe P.J., Park J.H., Krytska K., Ryles H., Laudenslager M., Rappaport E.F., Wood A.C., McGrady P.W., et al. ALK mutations confer differential oncogenic activation and sensitivity to ALK inhibition therapy in neuroblastoma. Cancer Cell. 2014;26:682–694. doi: 10.1016/j.ccell.2014.09.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Janoueix-Lerosey I., Lequin D., Brugières L., Ribeiro A., de Pontual L., Combaret V., Raynal V., Puisieux A., Schleiermacher G., Pierron G., et al. Somatic and germline activating mutations of the ALK kinase receptor in neuroblastoma. Nature. 2008;455:967–970. doi: 10.1038/nature07398. [DOI] [PubMed] [Google Scholar]
24.Helmsauer K., Valieva M.E., Ali S., Chamorro González R., Schöpflin R., Röefzaad C., Bei Y., Dorado Garcia H., Rodriguez-Fos E., Puiggròs M., et al. Enhancer hijacking determines extrachromosomal circular MYCN amplicon architecture in neuroblastoma. Nat. Commun. 2020;11:5823. doi: 10.1038/s41467-020-19452-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Shreenivas A., Janku F., Gouda M.A., Chen H.-Z., George B., Kato S., Kurzrock R. ALK fusions in the pan-cancer setting: another tumor-agnostic target? npj Precis. Oncol. 2023;7:101. doi: 10.1038/s41698-023-00449-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Trochet D., Bourdeaut F., Janoueix-Lerosey I., Deville A., de Pontual L., Schleiermacher G., Coze C., Philip N., Frébourg T., Munnich A., et al. Germline mutations of the paired-like homeobox 2B (PHOX2B) gene in neuroblastoma. Am. J. Hum. Genet. 2004;74:761–764. doi: 10.1086/383253. [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Raabe E.H., Laudenslager M., Winter C., Wasserman N., Cole K., LaQuaglia M., Maris D.J., Mosse Y.P., Maris J.M. Prevalence and functional consequence of PHOX2B mutations in neuroblastoma. Oncogene. 2008;27:469–476. doi: 10.1038/sj.onc.1210659. [DOI] [PubMed] [Google Scholar]
28.Körber V., Stainczyk S.A., Kurilov R., Henrich K.-O., Hero B., Brors B., Westermann F., Höfer T. Neuroblastoma arises in early fetal development and its evolutionary duration predicts outcome. Nat. Genet. 2023;55:619–630. doi: 10.1038/s41588-023-01332-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Gauchier M., Kan S., Barral A., Sauzet S., Agirre E., Bonnell E., Saksouk N., Barth T.K., Ide S., Urbach S., et al. SETDB1-dependent heterochromatin stimulates alternative lengthening of telomeres. Sci. Adv. 2019;5 doi: 10.1126/sciadv.aav3673. [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Hartlieb S.A., Sieverling L., Nadler-Holly M., Ziehm M., Toprak U.H., Herrmann C., Ishaque N., Okonechnikov K., Gartlgruber M., Park Y.-G., et al. Alternative lengthening of telomeres in childhood neuroblastoma from genome to proteome. Nat. Commun. 2021;12:1269. doi: 10.1038/s41467-021-21247-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Mody R., Naranjo A., Van Ryn C., Yu A.L., London W.B., Shulkin B.L., Parisi M.T., Servaes S.-E.-N., Diccianni M.B., Sondel P.M., et al. Irinotecan-temozolomide with temsirolimus or dinutuximab in children with refractory or relapsed neuroblastoma (COG ANBL1221): an open-label, randomised, phase 2 trial. Lancet Oncol. 2017;18:946–957. doi: 10.1016/S1470-2045(17)30355-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Park J.R., Bagatell R., Cohn S.L., Pearson A.D., Villablanca J.G., Berthold F., Burchill S., Boubaker A., McHugh K., Nuchtern J.G., et al. Revisions to the International Neuroblastoma Response Criteria: A consensus statement from the National Cancer Institute Clinical Trials Planning Meeting. J. Clin. Oncol. 2017;35:2580–2587. doi: 10.1200/JCO.2016.72.0177. [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Siebert N., Jensen C., Troschke-Meurer S., Zumpe M., Jüttner M., Ehlert K., Kietz S., Müller I., Lode H.N. Neuroblastoma patients with high-affinity FCGR2A, -3A and stimulatory KIR 2DS2 treated by long-term infusion of anti-GD2 antibody ch14.18/CHO show higher ADCC levels and improved event-free survival. OncoImmunology. 2016;5 doi: 10.1080/2162402X.2016.1235108. [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Ackermann S., Cartolano M., Hero B., Welte A., Kahlert Y., Roderwieser A., Bartenhagen C., Walter E., Gecht J., Kerschke L., et al. A mechanistic classification of clinical phenotypes in neuroblastoma. Science. 2018;362:1165–1170. doi: 10.1126/science.aat6768. [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Koneru B., Lopez G., Farooqi A., Conkrite K.L., Nguyen T.H., Macha S.J., Modi A., Rokita J.L., Urias E., Hindle A., et al. Telomere Maintenance Mechanisms Define Clinical Outcome in High-Risk Neuroblastoma. Cancer Res. 2020;80:2663–2675. doi: 10.1158/0008-5472.CAN-19-3068. [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Mandriota S.J., Valentijn L.J., Lesne L., Betts D.R., Marino D., Boudal-Khoshbeen M., London W.B., Rougemont A.-L., Attiyeh E.F., Maris J.M., et al. Ataxia-telangiectasia mutated (ATM) silencing promotes neuroblastoma progression through a MYCN independent mechanism. Oncotarget. 2015;6:18558–18576. doi: 10.18632/oncotarget.4061. [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Dewhurst S.M., McGranahan N., Burrell R.A., Rowan A.J., Grönroos E., Endesfelder D., Joshi T., Mouradov D., Gibbs P., Ward R.L., et al. Tolerance of whole-genome doubling propagates chromosomal instability and accelerates cancer genome evolution. Cancer Discov. 2014;4:175–185. doi: 10.1158/2159-8290.CD-13-0285. [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Mabe N.W., Huang M., Dalton G.N., Alexe G., Schaefer D.A., Geraghty A.C., Robichaud A.L., Conway A.S., Khalid D., Mader M.M., et al. Transition to a mesenchymal state in neuroblastoma confers resistance to anti-GD2 antibody via reduced expression of ST8SIA1. Nat. Cancer. 2022;3:976–993. doi: 10.1038/s43018-022-00405-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Sait S., Modak S. Anti-GD2 immunotherapy for neuroblastoma. Expert Rev. Anticancer Ther. 2017;17:889–904. doi: 10.1080/14737140.2017.1364995. [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Modak S., Cheung N.-K.V. Neuroblastoma: Therapeutic strategies for a clinical enigma. Cancer Treat Rev. 2010;36:307–317. doi: 10.1016/j.ctrv.2010.02.006. [DOI] [PubMed] [Google Scholar]
41.Brandt C.S., Baratin M., Yi E.C., Kennedy J., Gao Z., Fox B., Haldeman B., Ostrander C.D., Kaifu T., Chabannon C., et al. The B7 family member B7-H6 is a tumor cell ligand for the activating natural killer cell receptor NKp30 in humans. J. Exp. Med. 2009;206:1495–1503. doi: 10.1084/jem.20090681. [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Textor S., Bossler F., Henrich K.-O., Gartlgruber M., Pollmann J., Fiegler N., Arnold A., Westermann F., Waldburger N., Breuhahn K., et al. The proto-oncogene Myc drives expression of the NK cell-activating NKp30 ligand B7-H6 in tumor cells. OncoImmunology. 2016;5 doi: 10.1080/2162402X.2015.1116674. [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Meissner T.B., Li A., Biswas A., Lee K.-H., Liu Y.-J., Bayir E., Iliopoulos D., van den Elsen P.J., Kobayashi K.S. NLR family member NLRC5 is a transcriptional regulator of MHC class I genes. Proc. Natl. Acad. Sci. USA. 2010;107:13794–13799. doi: 10.1073/pnas.1008684107. [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Meissner T.B., Li A., Kobayashi K.S. NLRC5: a newly discovered MHC class I transactivator (CITA) Microbes Infect. 2012;14:477–484. doi: 10.1016/j.micinf.2011.12.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Ljunggren H.G., Kärre K. In search of the “missing self”: MHC molecules and NK cell recognition. Immunol. Today. 1990;11:237–244. doi: 10.1016/0167-5699(90)90097-s. [DOI] [PubMed] [Google Scholar]
46.Liao N.S., Bix M., Zijlstra M., Jaenisch R., Raulet D. MHC class I deficiency: susceptibility to natural killer (NK) cells and impaired NK activity. Science. 1991;253:199–202. doi: 10.1126/science.1853205. [DOI] [PubMed] [Google Scholar]
47.Wolf N.K., Kissiov D.U., Raulet D.H. Roles of natural killer cells in immunity to cancer, and applications to immunotherapy. Nat. Rev. Immunol. 2023;23:90–105. doi: 10.1038/s41577-022-00732-1. [DOI] [PubMed] [Google Scholar]
48.Bernards R., Dessain S.K., Weinberg R.A. N-myc amplification causes down-modulation of MHC class I antigen expression in neuroblastoma. Cell. 1986;47:667–674. doi: 10.1016/0092-8674(86)90509-x. [DOI] [PubMed] [Google Scholar]
49.Brandetti E., Veneziani I., Melaiu O., Pezzolo A., Castellano A., Boldrini R., Ferretti E., Fruci D., Moretta L., Pistoia V., et al. MYCN is an immunosuppressive oncogene dampening the expression of ligands for NK-cell-activating receptors in human high-risk neuroblastoma. OncoImmunology. 2017;6 doi: 10.1080/2162402X.2017.1316439. [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Dong R., Yang R., Zhan Y., Lai H.-D., Ye C.-J., Yao X.-Y., Luo W.-Q., Cheng X.-M., Miao J.-J., Wang J.-F., et al. Single-Cell Characterization of Malignant Phenotypes and Developmental Trajectories of Adrenal Neuroblastoma. Cancer Cell. 2020;38:716–733.e6. doi: 10.1016/j.ccell.2020.08.014. [DOI] [PubMed] [Google Scholar]
51.Kildisiute G., Kholosy W.M., Young M.D., Roberts K., Elmentaite R., van Hooff S.R., Pacyna C.N., Khabirova E., Piapi A., Thevanesan C., et al. Tumor to normal single-cell mRNA comparisons reveal a pan-neuroblastoma cancer cell. Sci. Adv. 2021;7 doi: 10.1126/sciadv.abd3311. [DOI] [PMC free article] [PubMed] [Google Scholar]
52.Patel A.G., Ashenberg O., Collins N.B., Segerstolpe Å., Jiang S., Slyper M., Huang X., Caraccio C., Jin H., Sheppard H., et al. A spatial cell atlas of neuroblastoma reveals developmental, epigenetic and spatial axis of tumor heterogeneity. bioRxiv. 2024 doi: 10.1101/2024.01.07.574538. Preprint at. [DOI] [Google Scholar]
53.Chapple R.H., Liu X., Natarajan S., Alexander M.I.M., Kim Y., Patel A.G., LaFlamme C.W., Pan M., Wright W.C., Lee H.-M., et al. An integrated single-cell RNA-seq map of human neuroblastoma tumors and preclinical models uncovers divergent mesenchymal-like gene expression programs. Genome Biol. 2024;25:161. doi: 10.1186/s13059-024-03309-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
54.Zhang P., Wu X., Basu M., Dong C., Zheng P., Liu Y., Sandler A.D. MYCN Amplification Is Associated with Repressed Cellular Immunity in Neuroblastoma: An In Silico Immunological Analysis of TARGET Database. Front. Immunol. 2017;8:1473. doi: 10.3389/fimmu.2017.01473. [DOI] [PMC free article] [PubMed] [Google Scholar]
55.Zhong X., Zhang Y., Wang L., Zhang H., Liu H., Liu Y. Cellular components in tumor microenvironment of neuroblastoma and the prognostic value. PeerJ. 2019;7 doi: 10.7717/peerj.8017. [DOI] [PMC free article] [PubMed] [Google Scholar]
56.Wei J.S., Kuznetsov I.B., Zhang S., Song Y.K., Asgharzadeh S., Sindiri S., Wen X., Patidar R., Najaraj S., Walton A., et al. Clinically Relevant Cytotoxic Immune Cell Signatures and Clonal Expansion of T-Cell Receptors in High-Risk MYCN-Not-Amplified Human Neuroblastoma. Clin. Cancer Res. 2018;24:5673–5684. doi: 10.1158/1078-0432.CCR-18-0599. [DOI] [PMC free article] [PubMed] [Google Scholar]
57.Layer J.P., Kronmüller M.T., Quast T., van den Boorn-Konijnenberg D., Effern M., Hinze D., Althoff K., Schramm A., Westermann F., Peifer M., et al. Amplification of N-Myc is associated with a T-cell-poor microenvironment in metastatic neuroblastoma restraining interferon pathway activity and chemokine expression. OncoImmunology. 2017;6 doi: 10.1080/2162402X.2017.1320626. [DOI] [PMC free article] [PubMed] [Google Scholar]
58.Verhoeven B.M., Mei S., Olsen T.K., Gustafsson K., Valind A., Lindström A., Gisselsson D., Fard S.S., Hagerling C., Kharchenko P.V., et al. The immune cell atlas of human neuroblastoma. Cell Rep. Med. 2022;3 doi: 10.1016/j.xcrm.2022.100657. [DOI] [PMC free article] [PubMed] [Google Scholar]
59.Melaiu O., Chierici M., Lucarini V., Jurman G., Conti L.A., De Vito R., Boldrini R., Cifaldi L., Castellano A., Furlanello C., et al. Cellular and gene signatures of tumor-infiltrating dendritic cells and natural-killer cells predict prognosis of neuroblastoma. Nat. Commun. 2020;11:5992. doi: 10.1038/s41467-020-19781-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
60.Newman A.M., Steen C.B., Liu C.L., Gentles A.J., Chaudhuri A.A., Scherer F., Khodadoust M.S., Esfahani M.S., Luca B.A., Steiner D., et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat. Biotechnol. 2019;37:773–782. doi: 10.1038/s41587-019-0114-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
61.Kameneva P., Artemov A.V., Kastriti M.E., Faure L., Olsen T.K., Otte J., Erickson A., Semsch B., Andersson E.R., Ratz M., et al. Single-cell transcriptomics of human embryos identifies multiple sympathoblast lineages with potential implications for neuroblastoma origin. Nat. Genet. 2021;53:694–706. doi: 10.1038/s41588-021-00818-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
62.Jansky S., Sharma A.K., Körber V., Quintero A., Toprak U.H., Wecht E.M., Gartlgruber M., Greco A., Chomsky E., Grünewald T.G.P., et al. Single-cell transcriptomic analyses provide insights into the developmental origins of neuroblastoma. Nat. Genet. 2021;53:683–693. doi: 10.1038/s41588-021-00806-1. [DOI] [PubMed] [Google Scholar]
63.De Preter K., Vandesompele J., Heimann P., Yigit N., Beckman S., Schramm A., Eggert A., Stallings R.L., Benoit Y., Renard M., et al. Human fetal neuroblast and neuroblastoma transcriptome analysis confirms neuroblast origin and highlights neuroblastoma candidate genes. Genome Biol. 2006;7:R84. doi: 10.1186/gb-2006-7-9-r84. [DOI] [PMC free article] [PubMed] [Google Scholar]
64.Gartlgruber M., Sharma A.K., Quintero A., Dreidax D., Jansky S., Park Y.-G., Kreth S., Meder J., Doncevic D., Saary P., et al. Super enhancers define regulatory subtypes and cell identity in neuroblastoma. Nat. Cancer. 2021;2:114–128. doi: 10.1038/s43018-020-00145-w. [DOI] [PubMed] [Google Scholar]
65.Oliynyk G., Ruiz-Pérez M.V., Sainero-Alcolado L., Dzieran J., Zirath H., Gallart-Ayala H., Wheelock C.E., Johansson H.J., Nilsson R., Lehtiö J., Arsenian-Henriksson M. MYCN-enhanced Oxidative and Glycolytic Metabolism Reveals Vulnerabilities for Targeting Neuroblastoma. iScience. 2019;21:188–204. doi: 10.1016/j.isci.2019.10.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
66.van Riggelen J., Yetil A., Felsher D.W. MYC as a regulator of ribosome biogenesis and protein synthesis. Nat. Rev. Cancer. 2010;10:301–309. doi: 10.1038/nrc2819. [DOI] [PubMed] [Google Scholar]
67.Tao L., Mohammad M.A., Milazzo G., Moreno-Smith M., Patel T.D., Zorman B., Badachhape A., Hernandez B.E., Wolf A.B., Zeng Z., et al. MYCN-driven fatty acid uptake is a metabolic vulnerability in neuroblastoma. Nat. Commun. 2022;13:3728. doi: 10.1038/s41467-022-31331-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
68.Guan J., Hallberg B., Palmer R.H. Chromosome Imbalances in Neuroblastoma-Recent Molecular Insight into Chromosome 1p-deletion, 2p-gain, and 11q-deletion Identifies New Friends and Foes for the Future. Cancers. 2021;13 doi: 10.3390/cancers13235897. [DOI] [PMC free article] [PubMed] [Google Scholar]
69.Bader S.A., Fasching C., Brodeur G.M., Stanbridge E.J. Dissociation of suppression of tumorigenicity and differentiation in vitro effected by transfer of single human chromosomes into human neuroblastoma cells. Cell Growth Differ. 1991;2:245–255. [PubMed] [Google Scholar]
70.Gundem G., Levine M.F., Roberts S.S., Cheung I.Y., Medina-Martínez J.S., Feng Y., Arango-Ossa J.E., Chadoutaud L., Rita M., Asimomitis G., et al. Clonal evolution during metastatic spread in high-risk neuroblastoma. Nat. Genet. 2023;55:1022–1033. doi: 10.1038/s41588-023-01395-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
71.Schramm A., Köster J., Assenov Y., Althoff K., Peifer M., Mahlow E., Odersky A., Beisser D., Ernst C., Henssen A.G., et al. Mutational dynamics between primary and relapse neuroblastomas. Nat. Genet. 2015;47:872–877. doi: 10.1038/ng.3349. [DOI] [PubMed] [Google Scholar]
72.Fransson S., Martinez-Monleon A., Johansson M., Sjöberg R.-M., Björklund C., Ljungman G., Ek T., Kogner P., Martinsson T. Whole-genome sequencing of recurrent neuroblastoma reveals somatic mutations that affect key players in cancer progression and telomere maintenance. Sci. Rep. 2020;10 doi: 10.1038/s41598-020-78370-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
73.Schleiermacher G., Javanmardi N., Bernard V., Leroy Q., Cappo J., Rio Frio T., Pierron G., Lapouble E., Combaret V., Speleman F., et al. Emergence of new ALK mutations at relapse of neuroblastoma. J. Clin. Oncol. 2014;32:2727–2734. doi: 10.1200/JCO.2013.54.0674. [DOI] [PubMed] [Google Scholar]
74.Eleveld T.F., Oldridge D.A., Bernard V., Koster J., Colmet Daage L., Diskin S.J., Schild L., Bentahar N.B., Bellini A., Chicard M., et al. Relapsed neuroblastomas show frequent RAS-MAPK pathway mutations. Nat. Genet. 2015;47:864–871. doi: 10.1038/ng.3333. [DOI] [PMC free article] [PubMed] [Google Scholar]
75.Padovan-Merhar O.M., Raman P., Ostrovnaya I., Kalletla K., Rubnitz K.R., Sanford E.M., Ali S.M., Miller V.A., Mossé Y.P., Granger M.P., et al. Enrichment of Targetable Mutations in the Relapsed Neuroblastoma Genome. PLoS Genet. 2016;12 doi: 10.1371/journal.pgen.1006501. [DOI] [PMC free article] [PubMed] [Google Scholar]
76.Cimmino F., Montella A., Tirelli M., Avitabile M., Lasorsa V.A., Visconte F., Cantalupo S., Maiorino T., De Angelis B., Morini M., et al. FGFR1 is a potential therapeutic target in neuroblastoma. Cancer Cell Int. 2022;22:174. doi: 10.1186/s12935-022-02587-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
77.Grossmann L.D., Chen C.-H., Uzun Y., Thadi A., Wolpaw A.J., Louault K., Goldstein Y., Surrey L.F., Martinez D., Calafatti M., et al. Identification and characterization of chemotherapy resistant high-risk neuroblastoma persister cells. Cancer Discov. 2024;14:2387–2406. doi: 10.1158/2159-8290.CD-24-0046. [DOI] [PMC free article] [PubMed] [Google Scholar]
78.George R.E., Sanda T., Hanna M., Fröhling S., Luther W., 2nd, Zhang J., Ahn Y., Zhou W., London W.B., McGrady P., et al. Activating mutations in ALK provide a therapeutic target in neuroblastoma. Nature. 2008;455:975–978. doi: 10.1038/nature07397. [DOI] [PMC free article] [PubMed] [Google Scholar]
79.Matsuno R., Akiyama K., Toyama D., Ikeda H., Yamamoto S. Adolescent pulmonary metastatic neuroblastoma with ALK rearrangement: A case report. Pediatr. Int. 2020;62:507–509. doi: 10.1111/ped.14117. [DOI] [PubMed] [Google Scholar]
80.Hiwatari M., Seki M., Matsuno R., Yoshida K., Nagasawa T., Sato-Otsubo A., Yamamoto S., Kato M., Watanabe K., Sekiguchi M., et al. Novel TENM3-ALK fusion is an alternate mechanism for ALK activation in neuroblastoma. Oncogene. 2022;41:2789–2797. doi: 10.1038/s41388-022-02301-1. [DOI] [PubMed] [Google Scholar]
81.Du X., Shao Y., Qin H.-F., Tai Y.-H., Gao H.-J. ALK-rearrangement in non-small-cell lung cancer (NSCLC). Thorac. Cancer. 2018;9:423–430. doi: 10.1111/1759-7714.12613. [DOI] [PMC free article] [PubMed] [Google Scholar]
82.Lovly C.M., Gupta A., Lipson D., Otto G., Brennan T., Chung C.T., Borinstein S.C., Ross J.S., Stephens P.J., Miller V.A., Coffin C.M. Inflammatory myofibroblastic tumors harbor multiple potentially actionable kinase fusions. Cancer Discov. 2014;4:889–895. doi: 10.1158/2159-8290.CD-14-0377. [DOI] [PMC free article] [PubMed] [Google Scholar]
83.Goldsmith K.C., Park J.R., Kayser K., Malvar J., Chi Y.-Y., Groshen S.G., Villablanca J.G., Krytska K., Lai L.M., Acharya P.T., et al. Lorlatinib with or without chemotherapy in ALK-driven refractory/relapsed neuroblastoma: phase 1 trial results. Nat. Med. 2023;29:1092–1102. doi: 10.1038/s41591-023-02297-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
84.Mossé Y.P., Lim M.S., Voss S.D., Wilner K., Ruffner K., Laliberte J., Rolland D., Balis F.M., Maris J.M., Weigel B.J., et al. Safety and activity of crizotinib for paediatric patients with refractory solid tumours or anaplastic large-cell lymphoma: a Children’s Oncology Group phase 1 consortium study. Lancet Oncol. 2013;14:472–480. doi: 10.1016/S1470-2045(13)70095-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
85.Fischer M., Moreno L., Ziegler D.S., Marshall L.V., Zwaan C.M., Irwin M.S., Casanova M., Sabado C., Wulff B., Stegert M., et al. Ceritinib in paediatric patients with anaplastic lymphoma kinase-positive malignancies: an open-label, multicentre, phase 1, dose-escalation and dose-expansion study. Lancet Oncol. 2021;22:1764–1776. doi: 10.1016/S1470-2045(21)00536-2. [DOI] [PubMed] [Google Scholar]
86.Foster J.H., Voss S.D., Hall D.C., Minard C.G., Balis F.M., Wilner K., Berg S.L., Fox E., Adamson P.C., Blaney S.M., et al. Activity of Crizotinib in Patients with ALK-Aberrant Relapsed/Refractory Neuroblastoma: A Children’s Oncology Group Study (ADVL0912) Clin. Cancer Res. 2021;27:3543–3548. doi: 10.1158/1078-0432.CCR-20-4224. [DOI] [PMC free article] [PubMed] [Google Scholar]
87.Valentijn L.J., Koster J., Zwijnenburg D.A., Hasselt N.E., van Sluis P., Volckmann R., van Noesel M.M., George R.E., Tytgat G.A.M., Molenaar J.J., Versteeg R. TERT rearrangements are frequent in neuroblastoma and identify aggressive tumors. Nat. Genet. 2015;47:1411–1414. doi: 10.1038/ng.3438. [DOI] [PubMed] [Google Scholar]
88.Erbe A.K., Diccianni M.B., Mody R., Naranjo A., Zhang F.F., Birstler J., Kim K., Feils A.S., Hung J.-T., London W.B., et al. KIR/KIR-ligand genotypes and clinical outcomes following chemoimmunotherapy in patients with relapsed or refractory neuroblastoma: a report from the Children’s Oncology Group. J. Immunother. Cancer. 2023;11 doi: 10.1136/jitc-2022-006530. [DOI] [PMC free article] [PubMed] [Google Scholar]
89.Erbe A.K., Wang W., Carmichael L., Kim K., Mendonça E.A., Song Y., Hess D., Reville P.K., London W.B., Naranjo A., et al. Neuroblastoma Patients’ KIR and KIR-Ligand Genotypes Influence Clinical Outcome for Dinutuximab-based Immunotherapy: A Report from the Children's Oncology Group. Clin. Cancer Res. 2018;24:189–196. doi: 10.1158/1078-0432.CCR-17-1767. [DOI] [PMC free article] [PubMed] [Google Scholar]
90.Mody R.J., Wu Y.-M., Lonigro R.J., Cao X., Roychowdhury S., Vats P., Frank K.M., Prensner J.R., Asangani I., Palanisamy N., et al. Integrative Clinical Sequencing in the Management of Refractory or Relapsed Cancer in Youth. JAMA. 2015;314:913–925. doi: 10.1001/jama.2015.10080. [DOI] [PMC free article] [PubMed] [Google Scholar]
91.Robinson D., Van Allen E.M., Wu Y.-M., Schultz N., Lonigro R.J., Mosquera J.-M., Montgomery B., Taplin M.-E., Pritchard C.C., Attard G., et al. Integrative clinical genomics of advanced prostate cancer. Cell. 2015;161:1215–1228. doi: 10.1016/j.cell.2015.05.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
92.Robinson D.R., Wu Y.-M., Lonigro R.J., Vats P., Cobain E., Everett J., Cao X., Rabban E., Kumar-Sinha C., Raymond V., et al. Integrative clinical genomics of metastatic cancer. Nature. 2017;548:297–303. doi: 10.1038/nature23306. [DOI] [PMC free article] [PubMed] [Google Scholar]
93.Regier A.A., Farjoun Y., Larson D.E., Krasheninina O., Kang H.M., Howrigan D.P., Chen B.-J., Kher M., Banks E., Ames D.C., et al. Functional equivalence of genome sequencing analysis pipelines enables harmonized variant calling across human genetics projects. Nat. Commun. 2018;9:4038. doi: 10.1038/s41467-018-06159-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
94.Li G.X., Chen L., Hsiao Y., Mannan R., Zhang Y., Luo J., Petralia F., Cho H., Hosseini N., Leprevost F.d.V., et al. Comprehensive proteogenomic characterization of rare kidney tumors. Cell Rep. Med. 2024;5 doi: 10.1016/j.xcrm.2024.101547. [DOI] [PMC free article] [PubMed] [Google Scholar]
95.Li Y., Lih T.-S.M., Dhanasekaran S.M., Mannan R., Chen L., Cieslik M., Wu Y., Lu R.J.-H., Clark D.J., Kołodziejczak I., et al. Histopathologic and proteogenomic heterogeneity reveals features of clear cell renal cell carcinoma aggressiveness. Cancer Cell. 2023;41:139–163.e17. doi: 10.1016/j.ccell.2022.12.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
96.Chowdhury S., Kennedy J.J., Ivey R.G., Murillo O.D., Hosseini N., Song X., Petralia F., Calinawan A., Savage S.R., Berry A.B., et al. Proteogenomic analysis of chemo-refractory high-grade serous ovarian cancer. Cell. 2023;186:3476–3498.e35. doi: 10.1016/j.cell.2023.07.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
97.GitHub - broadinstitute/picard: A set of command line tools (in Java) for manipulating high-throughput sequencing (HTS) data and formats such as SAM/BAM/CRAM and VCF GitHub. https://github.com/broadinstitute/picard.
98.Riester M., Singh A.P., Brannon A.R., Yu K., Campbell C.D., Chiang D.Y., Morrissey M.P. PureCN: copy number calling and SNV classification using targeted short read sequencing. Source Code Biol. Med. 2016;11:13. doi: 10.1186/s13029-016-0060-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
99.Bushnell B. BBMap: A Fast, Accurate, Splice-Aware Aligner. 2014. https://www.osti.gov/servlets/purl/1241166
100.Li H., Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25:1754–1760. doi: 10.1093/bioinformatics/btp324. [DOI] [PMC free article] [PubMed] [Google Scholar]
101.Freed D., Aldana R., Weber J.A., Edwards J.S. The Sentieon Genomics Tools - A fast and accurate solution to variant calling from next-generation sequence data. bioRxiv. 2017 doi: 10.1101/115717. Preprint at. [DOI] [Google Scholar]
102.Benjamin D., Sato T., Cibulskis K., Getz G., Stewart C., Lichtenstein L. Calling Somatic SNVs and Indels with Mutect2. bioRxiv. 2019 doi: 10.1101/861054. Preprint at. [DOI] [Google Scholar]
103.McLaren W., Gil L., Hunt S.E., Riat H.S., Ritchie G.R.S., Thormann A., Flicek P., Cunningham F. The Ensembl Variant Effect Predictor. Genome Biol. 2016;17:122. doi: 10.1186/s13059-016-0974-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
104.Pedersen B.S., Layer R.M., Quinlan A.R. Vcfanno: fast, flexible annotation of genetic variants. Genome Biol. 2016;17:118. doi: 10.1186/s13059-016-0973-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
105.Olshen A.B., Venkatraman E.S., Lucito R., Wigler M. Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics. 2004;5:557–572. doi: 10.1093/biostatistics/kxh008. [DOI] [PubMed] [Google Scholar]
106.Carter S.L., Cibulskis K., Helman E., McKenna A., Shen H., Zack T., Laird P.W., Onofrio R.C., Winckler W., Weir B.A., et al. Absolute quantification of somatic DNA alterations in human cancer. Nat. Biotechnol. 2012;30:413–421. doi: 10.1038/nbt.2203. [DOI] [PMC free article] [PubMed] [Google Scholar]
107.Dobin A., Davis C.A., Schlesinger F., Drenkow J., Zaleski C., Jha S., Batut P., Chaisson M., Gingeras T.R. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21. doi: 10.1093/bioinformatics/bts635. [DOI] [PMC free article] [PubMed] [Google Scholar]
108.Bray N.L., Pimentel H., Melsted P., Pachter L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 2016;34:525–527. doi: 10.1038/nbt.3519. [DOI] [PubMed] [Google Scholar]
109.Li H. Minimap and miniasm: fast mapping and de novo assembly for noisy long sequences. Bioinformatics. 2016;32:2103–2110. doi: 10.1093/bioinformatics/btw152. [DOI] [PMC free article] [PubMed] [Google Scholar]
110.Wu T.D., Reeder J., Lawrence M., Becker G., Brauer M.J. GMAP and GSNAP for genomic sequence alignment: Enhancements to speed, accuracy, and functionality. Methods Mol. Biol. 2016;1418:283–334. doi: 10.1007/978-1-4939-3578-9_15. [DOI] [PubMed] [Google Scholar]
111.Mumphrey M.B., Hosseini N., Parolia A., Geng J., Zou W., Raghavan M., Chinnaiyan A., Cieslik M. Distinct mutational processes shape selection of MHC class I and class II mutations across primary and metastatic tumors. Cell Rep. 2023;42 doi: 10.1016/j.celrep.2023.112965. [DOI] [PMC free article] [PubMed] [Google Scholar]
112.Ritchie M.E., Phipson B., Wu D., Hu Y., Law C.W., Shi W., Smyth G.K. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47. doi: 10.1093/nar/gkv007. [DOI] [PMC free article] [PubMed] [Google Scholar]
113.Kassambara A., Kosinski M., Biecek P., Scheipl F. Drawing Survival Curves using “ggplot2” [R package survminer version 0.4.9] 2021. https://rpkgs.datanovia.com/survminer/authors.html#citation
114.Therneau, T.M. Survival Analysis [R package survival version 3.3-1]. https://cran.r-project.org/web/packages/survival/index.html.
115.Liberzon A., Birger C., Thorvaldsdóttir H., Ghandi M., Mesirov J.P., Tamayo P. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 2015;1:417–425. doi: 10.1016/j.cels.2015.12.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
116.Dolgalev I. MSigDB Gene Sets for Multiple Organisms in a Tidy Data Format [R package msigdbr version 7.5.1] https://cran.r-project.org/web/packages/msigdbr/index.html
117.Korotkevich G., Sukhov V., Budin N., Shpak B., Artyomov M.N., Sergushichev A. Fast gene set enrichment analysis. bioRxiv. 2021 doi: 10.1101/060012. Preprint at. [DOI] [Google Scholar]
118.Xie Z., Bailey A., Kuleshov M.V., Clarke D.J.B., Evangelista J.E., Jenkins S.L., Lachmann A., Wojciechowicz M.L., Kropiwnicki E., Jagodnik K.M., et al. Gene Set Knowledge Discovery with Enrichr. Curr. Protoc. 2021;1:e90. doi: 10.1002/cpz1.90. [DOI] [PMC free article] [PubMed] [Google Scholar]
119.Wu T., Hu E., Xu S., Chen M., Guo P., Dai Z., Feng T., Zhou L., Tang W., Zhan L., et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation. 2021;2 doi: 10.1016/j.xinn.2021.100141. [DOI] [PMC free article] [PubMed] [Google Scholar]
120.Foroutan M., Bhuva D.D., Lyu R., Horan K., Cursons J., Davis M.J. Single sample scoring of molecular phenotypes. BMC Bioinf. 2018;19:404. doi: 10.1186/s12859-018-2435-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
121.Griffith D.M., Veech J.A., Marsh C.J. cooccur: Probabilistic Species Co-Occurrence Analysis in R. J. Stat. Softw. 2016;69:1–17. doi: 10.18637/jss.v069.c02. [DOI] [Google Scholar]
122.Stuart T., Butler A., Hoffman P., Hafemeister C., Papalexi E., Mauck W.M., 3rd, Hao Y., Stoeckius M., Smibert P., Satija R. Comprehensive Integration of Single-Cell Data. Cell. 2019;177:1888–1902.e21. doi: 10.1016/j.cell.2019.05.031. [DOI] [PMC free article] [PubMed] [Google Scholar]
123.Butler A., Hoffman P., Smibert P., Papalexi E., Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 2018;36:411–420. doi: 10.1038/nbt.4096. [DOI] [PMC free article] [PubMed] [Google Scholar]
124.Satija R., Farrell J.A., Gennert D., Schier A.F., Regev A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 2015;33:495–502. doi: 10.1038/nbt.3192. [DOI] [PMC free article] [PubMed] [Google Scholar]
125.Lause J., Berens P., Kobak D. Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data. Genome Biol. 2021;22:258. doi: 10.1186/s13059-021-02451-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
126.Andreatta M., Carmona S.J. UCell: Robust and scalable single-cell gene signature scoring. Comput. Struct. Biotechnol. J. 2021;19:3796–3798. doi: 10.1016/j.csbj.2021.06.043. [DOI] [PMC free article] [PubMed] [Google Scholar]
127.Salcher S., Sturm G., Horvath L., Untergasser G., Kuempers C., Fotakis G., Panizzolo E., Martowicz A., Trebo M., Pall G., et al. High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer. Cancer Cell. 2022;40:1503–1520.e8. doi: 10.1016/j.ccell.2022.10.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
128.Young M.D., Behjati S. SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data. GigaScience. 2020;9 doi: 10.1093/gigascience/giaa151. [DOI] [PMC free article] [PubMed] [Google Scholar]
129.Chu T., Wang Z., Pe’er D., Danko C.G. Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology. Nat. Cancer. 2022;3:505–517. doi: 10.1038/s43018-022-00356-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
130.Morabito S., Reese F., Rahimzadeh N., Miyoshi E., Swarup V. hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data. Cell Rep. Methods. 2023;3 doi: 10.1016/j.crmeth.2023.100498. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Document S1. Figures S1–S7

mmc1.pdf^{(5.8MB, pdf)}

Table S1. Sequencing quality control metrics

mmc2.xlsx^{(92.9KB, xlsx)}

Table S2. Clinical characteristics of 122 patients with neuroblastic tumors

mmc3.xlsx^{(50.7KB, xlsx)}

Table S3. ALK gene fusions

mmc4.xlsx^{(52KB, xlsx)}

Table S4. Clinical data used to determine outcome groups for treatment regimens containing anti-GD2 immunotherapy

mmc5.xlsx^{(63.2KB, xlsx)}

Table S5. Clinical characteristics of high-risk neuroblastoma patients receiving treatment regimens containing anti-GD2 immunotherapy

mmc6.xlsx^{(51.7KB, xlsx)}

Table S6. Differential gene expression analysis for outcome groups following treatment regimens containing anti-GD2 immunotherapy

mmc7.xlsx^{(2.3MB, xlsx)}

Table S7. Gene set enrichment analysis for MMT-GD2 and CHEMO-GD2 differentially expressed genes

mmc8.xlsx^{(614.7KB, xlsx)}

Table S8. Marker gene signatures for fetal populations

mmc9.xlsx^{(72.1KB, xlsx)}

Table S9. Mature and immature sympathoblast gene signatures

mmc10.xlsx^{(54.6KB, xlsx)}

Table S10. Gene ontology enrichment of mature and immature sympathoblast gene signatures

mmc11.xlsx^{(119.4KB, xlsx)}

Table S11. Sympathoblast-derived transcriptional modules

mmc12.xlsx^{(52.2KB, xlsx)}

Table S12. Gene ontology biological process enrichment of transcriptional modules identified in fetal sympathoblasts

mmc13.xlsx^{(297KB, xlsx)}

Table S13. Fetal cell markers used in single-cell annotation

mmc14.xlsx^{(49.6KB, xlsx)}

Table S14. Broad immune cell markers used in single-cell annotation

mmc15.xlsx^{(49.5KB, xlsx)}

Table S15. Specific immune cell markers used in single-cell annotation

mmc16.xlsx^{(50.5KB, xlsx)}

Document S2. Article plus supplemental information

mmc17.pdf^{(13.9MB, pdf)}

Data Availability Statement

•
Bulk DNA and RNA sequencing data have been deposited at dbGaP phs002431.v1.p1 and are publicly available as of the date of publication. De-identified patient data, analyzed genomic data, and all generated single cell atlases including metadata have been deposited at Zenodo: https://doi.org/10.5281/zenodo.14046017. These data files are publicly available at the date of publication.
•
All original code has been deposited at GitHub (https://github.com/mctp/tpo-public-v2.5) and is publicly available as of the date of publication.
•
Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

[bib1] 1.Matthay K.K., Maris J.M., Schleiermacher G., Nakagawara A., Mackall C.L., Diller L., Weiss W.A. Neuroblastoma. Nat. Rev. Dis. Primers. 2016;2 doi: 10.1038/nrdp.2016.78. [DOI] [PubMed] [Google Scholar]

[bib2] 2.Ponzoni M., Bachetti T., Corrias M.V., Brignole C., Pastorino F., Calarco E., Bensa V., Giusto E., Ceccherini I., Perri P. Recent advances in the developmental origin of neuroblastoma: an overview. J. Exp. Clin. Cancer Res. 2022;41:92. doi: 10.1186/s13046-022-02281-w. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib3] 3.van Groningen T., Koster J., Valentijn L.J., Zwijnenburg D.A., Akogul N., Hasselt N.E., Broekmans M., Haneveld F., Nowakowska N.E., Bras J., et al. Neuroblastoma is composed of two super-enhancer-associated differentiation states. Nat. Genet. 2017;49:1261–1266. doi: 10.1038/ng.3899. [DOI] [PubMed] [Google Scholar]

[bib4] 4.van Groningen T., Akogul N., Westerhout E.M., Chan A., Hasselt N.E., Zwijnenburg D.A., Broekmans M., Stroeken P., Haneveld F., Hooijer G.K.J., et al. A NOTCH feed-forward loop drives reprogramming from adrenergic to mesenchymal state in neuroblastoma. Nat. Commun. 2019;10:1530. doi: 10.1038/s41467-019-09470-w. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib5] 5.Boeva V., Louis-Brennetot C., Peltier A., Durand S., Pierre-Eugène C., Raynal V., Etchevers H.C., Thomas S., Lermine A., Daudigeos-Dubus E., et al. Heterogeneity of neuroblastoma cell identity defined by transcriptional circuitries. Nat. Genet. 2017;49:1408–1413. doi: 10.1038/ng.3921. [DOI] [PubMed] [Google Scholar]

[bib6] 6.Thirant C., Peltier A., Durand S., Kramdi A., Louis-Brennetot C., Pierre-Eugène C., Gautier M., Costa A., Grelier A., Zaïdi S., et al. Reversible transitions between noradrenergic and mesenchymal tumor identities define cell plasticity in neuroblastoma. Nat. Commun. 2023;14:2575. doi: 10.1038/s41467-023-38239-5. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib7] 7.Irwin M.S., Naranjo A., Zhang F.F., Cohn S.L., London W.B., Gastier-Foster J.M., Ramirez N.C., Pfau R., Reshmi S., Wagner E., et al. Revised Neuroblastoma Risk Classification System: A Report From the Children’s Oncology Group. J. Clin. Oncol. 2021;39:3229–3241. doi: 10.1200/JCO.21.00278. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib8] 8.Janoueix-Lerosey I., Schleiermacher G., Michels E., Mosseri V., Ribeiro A., Lequin D., Vermeulen J., Couturier J., Peuchmaur M., Valent A., et al. Overall genomic pattern is a predictor of outcome in neuroblastoma. J. Clin. Oncol. 2009;27:1026–1033. doi: 10.1200/JCO.2008.16.0630. [DOI] [PubMed] [Google Scholar]

[bib9] 9.Attiyeh E.F., London W.B., Mossé Y.P., Wang Q., Winter C., Khazi D., McGrady P.W., Seeger R.C., Look A.T., Shimada H., et al. Chromosome 1p and 11q deletions and outcome in neuroblastoma. N. Engl. J. Med. 2005;353:2243–2253. doi: 10.1056/NEJMoa052399. [DOI] [PubMed] [Google Scholar]

[bib10] 10.Schleiermacher G., Michon J., Ribeiro A., Pierron G., Mosseri V., Rubie H., Munzer C., Bénard J., Auger N., Combaret V., et al. Segmental chromosomal alterations lead to a higher risk of relapse in infants with MYCN-non-amplified localised unresectable/disseminated neuroblastoma (a SIOPEN collaborative study) Br. J. Cancer. 2011;105:1940–1948. doi: 10.1038/bjc.2011.472. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib11] 11.Bellini A., Pötschger U., Bernard V., Lapouble E., Baulande S., Ambros P.F., Auger N., Beiske K., Bernkopf M., Betts D.R., et al. Frequency and Prognostic Impact of ALK Amplifications and Mutations in the European Neuroblastoma Study Group (SIOPEN) High-Risk Neuroblastoma Trial (HR-NBL1) J. Clin. Oncol. 2021;39:3377–3390. doi: 10.1200/JCO.21.00086. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib12] 12.Mody R., Yu A.L., Naranjo A., Zhang F.F., London W.B., Shulkin B.L., Parisi M.T., Servaes S.-E.-N., Diccianni M.B., Hank J.A., et al. Irinotecan, Temozolomide, and Dinutuximab With GM-CSF in Children With Refractory or Relapsed Neuroblastoma: A Report From the Children’s Oncology Group. J. Clin. Oncol. 2020;38:2160–2169. doi: 10.1200/JCO.20.00203. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib13] 13.Matthay K.K., Yanik G., Messina J., Quach A., Huberty J., Cheng S.-C., Veatch J., Goldsby R., Brophy P., Kersun L.S., et al. Phase II study on the effect of disease sites, age, and prior therapy on response to iodine-131-metaiodobenzylguanidine therapy in refractory neuroblastoma. J. Clin. Oncol. 2007;25:1054–1060. doi: 10.1200/JCO.2006.09.3484. [DOI] [PubMed] [Google Scholar]

[bib14] 14.U.S. Food and Drug Administration (FDA) (2010). Dinutuximab - Orphan Drug Designations and Approvals. FDA.gov. https://www.accessdata.fda.gov/scripts/opdlisting/oopd/detailedIndex.cfm?cfgridkey=324210.

[bib15] 15.Yu A.L., Gilman A.L., Ozkaynak M.F., Naranjo A., Diccianni M.B., Gan J., Hank J.A., Batova A., London W.B., Tenney S.C., et al. Long-Term Follow-up of a Phase III Study of ch14.18 (Dinutuximab) + Cytokine Immunotherapy in Children with High-Risk Neuroblastoma: COG Study ANBL0032. Clin. Cancer Res. 2021;27:2179–2189. doi: 10.1158/1078-0432.CCR-20-3909. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib16] 16.Furman W.L., Federico S.M., McCarville M.B., Shulkin B.L., Davidoff A.M., Krasin M.J., Sahr N., Sykes A., Wu J., Brennan R.C., et al. A Phase II Trial of Hu14.18K322A in Combination with Induction Chemotherapy in Children with Newly Diagnosed High-Risk Neuroblastoma. Clin. Cancer Res. 2019;25:6320–6328. doi: 10.1158/1078-0432.CCR-19-1452. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib17] 17.Furman W.L., McCarville B., Shulkin B.L., Davidoff A., Krasin M., Hsu C.-W., Pan H., Wu J., Brennan R., Bishop M.W., et al. Improved Outcome in Children With Newly Diagnosed High-Risk Neuroblastoma Treated With Chemoimmunotherapy: Updated Results of a Phase II Study Using hu14.18K322A. J. Clin. Oncol. 2022;40:335–344. doi: 10.1200/JCO.21.01375. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib18] 18.Yu A.L., Gilman A.L., Ozkaynak M.F., London W.B., Kreissman S.G., Chen H.X., Smith M., Anderson B., Villablanca J.G., Matthay K.K., et al. Anti-GD2 antibody with GM-CSF, interleukin-2, and isotretinoin for neuroblastoma. N. Engl. J. Med. 2010;363:1324–1334. doi: 10.1056/NEJMoa0911123. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib19] 19.Desai A.V., Gilman A.L., Ozkaynak M.F., Naranjo A., London W.B., Tenney S.C., Diccianni M., Hank J.A., Parisi M.T., Shulkin B.L., et al. Outcomes Following GD2-Directed Postconsolidation Therapy for Neuroblastoma After Cessation of Random Assignment on ANBL0032: A Report From the Children’s Oncology Group. J. Clin. Oncol. 2022;40:4107–4118. doi: 10.1200/JCO.21.02478. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib20] 20.De Brouwer S., De Preter K., Kumps C., Zabrocki P., Porcu M., Westerhout E.M., Lakeman A., Vandesompele J., Hoebeeck J., Van Maerken T., et al. Meta-analysis of neuroblastomas reveals a skewed ALK mutation spectrum in tumors with MYCN amplification. Clin. Cancer Res. 2010;16:4353–4362. doi: 10.1158/1078-0432.CCR-09-2660. [DOI] [PubMed] [Google Scholar]

[bib21] 21.Brady S.W., Liu Y., Ma X., Gout A.M., Hagiwara K., Zhou X., Wang J., Macias M., Chen X., Easton J., et al. Pan-neuroblastoma analysis reveals age- and signature-associated driver alterations. Nat. Commun. 2020;11:5183. doi: 10.1038/s41467-020-18987-4. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib22] 22.Bresler S.C., Weiser D.A., Huwe P.J., Park J.H., Krytska K., Ryles H., Laudenslager M., Rappaport E.F., Wood A.C., McGrady P.W., et al. ALK mutations confer differential oncogenic activation and sensitivity to ALK inhibition therapy in neuroblastoma. Cancer Cell. 2014;26:682–694. doi: 10.1016/j.ccell.2014.09.019. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib23] 23.Janoueix-Lerosey I., Lequin D., Brugières L., Ribeiro A., de Pontual L., Combaret V., Raynal V., Puisieux A., Schleiermacher G., Pierron G., et al. Somatic and germline activating mutations of the ALK kinase receptor in neuroblastoma. Nature. 2008;455:967–970. doi: 10.1038/nature07398. [DOI] [PubMed] [Google Scholar]

[bib24] 24.Helmsauer K., Valieva M.E., Ali S., Chamorro González R., Schöpflin R., Röefzaad C., Bei Y., Dorado Garcia H., Rodriguez-Fos E., Puiggròs M., et al. Enhancer hijacking determines extrachromosomal circular MYCN amplicon architecture in neuroblastoma. Nat. Commun. 2020;11:5823. doi: 10.1038/s41467-020-19452-y. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib25] 25.Shreenivas A., Janku F., Gouda M.A., Chen H.-Z., George B., Kato S., Kurzrock R. ALK fusions in the pan-cancer setting: another tumor-agnostic target? npj Precis. Oncol. 2023;7:101. doi: 10.1038/s41698-023-00449-x. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib26] 26.Trochet D., Bourdeaut F., Janoueix-Lerosey I., Deville A., de Pontual L., Schleiermacher G., Coze C., Philip N., Frébourg T., Munnich A., et al. Germline mutations of the paired-like homeobox 2B (PHOX2B) gene in neuroblastoma. Am. J. Hum. Genet. 2004;74:761–764. doi: 10.1086/383253. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib27] 27.Raabe E.H., Laudenslager M., Winter C., Wasserman N., Cole K., LaQuaglia M., Maris D.J., Mosse Y.P., Maris J.M. Prevalence and functional consequence of PHOX2B mutations in neuroblastoma. Oncogene. 2008;27:469–476. doi: 10.1038/sj.onc.1210659. [DOI] [PubMed] [Google Scholar]

[bib28] 28.Körber V., Stainczyk S.A., Kurilov R., Henrich K.-O., Hero B., Brors B., Westermann F., Höfer T. Neuroblastoma arises in early fetal development and its evolutionary duration predicts outcome. Nat. Genet. 2023;55:619–630. doi: 10.1038/s41588-023-01332-y. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib29] 29.Gauchier M., Kan S., Barral A., Sauzet S., Agirre E., Bonnell E., Saksouk N., Barth T.K., Ide S., Urbach S., et al. SETDB1-dependent heterochromatin stimulates alternative lengthening of telomeres. Sci. Adv. 2019;5 doi: 10.1126/sciadv.aav3673. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib30] 30.Hartlieb S.A., Sieverling L., Nadler-Holly M., Ziehm M., Toprak U.H., Herrmann C., Ishaque N., Okonechnikov K., Gartlgruber M., Park Y.-G., et al. Alternative lengthening of telomeres in childhood neuroblastoma from genome to proteome. Nat. Commun. 2021;12:1269. doi: 10.1038/s41467-021-21247-8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib31] 31.Mody R., Naranjo A., Van Ryn C., Yu A.L., London W.B., Shulkin B.L., Parisi M.T., Servaes S.-E.-N., Diccianni M.B., Sondel P.M., et al. Irinotecan-temozolomide with temsirolimus or dinutuximab in children with refractory or relapsed neuroblastoma (COG ANBL1221): an open-label, randomised, phase 2 trial. Lancet Oncol. 2017;18:946–957. doi: 10.1016/S1470-2045(17)30355-8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib32] 32.Park J.R., Bagatell R., Cohn S.L., Pearson A.D., Villablanca J.G., Berthold F., Burchill S., Boubaker A., McHugh K., Nuchtern J.G., et al. Revisions to the International Neuroblastoma Response Criteria: A consensus statement from the National Cancer Institute Clinical Trials Planning Meeting. J. Clin. Oncol. 2017;35:2580–2587. doi: 10.1200/JCO.2016.72.0177. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib33] 33.Siebert N., Jensen C., Troschke-Meurer S., Zumpe M., Jüttner M., Ehlert K., Kietz S., Müller I., Lode H.N. Neuroblastoma patients with high-affinity FCGR2A, -3A and stimulatory KIR 2DS2 treated by long-term infusion of anti-GD2 antibody ch14.18/CHO show higher ADCC levels and improved event-free survival. OncoImmunology. 2016;5 doi: 10.1080/2162402X.2016.1235108. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib34] 34.Ackermann S., Cartolano M., Hero B., Welte A., Kahlert Y., Roderwieser A., Bartenhagen C., Walter E., Gecht J., Kerschke L., et al. A mechanistic classification of clinical phenotypes in neuroblastoma. Science. 2018;362:1165–1170. doi: 10.1126/science.aat6768. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib35] 35.Koneru B., Lopez G., Farooqi A., Conkrite K.L., Nguyen T.H., Macha S.J., Modi A., Rokita J.L., Urias E., Hindle A., et al. Telomere Maintenance Mechanisms Define Clinical Outcome in High-Risk Neuroblastoma. Cancer Res. 2020;80:2663–2675. doi: 10.1158/0008-5472.CAN-19-3068. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib36] 36.Mandriota S.J., Valentijn L.J., Lesne L., Betts D.R., Marino D., Boudal-Khoshbeen M., London W.B., Rougemont A.-L., Attiyeh E.F., Maris J.M., et al. Ataxia-telangiectasia mutated (ATM) silencing promotes neuroblastoma progression through a MYCN independent mechanism. Oncotarget. 2015;6:18558–18576. doi: 10.18632/oncotarget.4061. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib37] 37.Dewhurst S.M., McGranahan N., Burrell R.A., Rowan A.J., Grönroos E., Endesfelder D., Joshi T., Mouradov D., Gibbs P., Ward R.L., et al. Tolerance of whole-genome doubling propagates chromosomal instability and accelerates cancer genome evolution. Cancer Discov. 2014;4:175–185. doi: 10.1158/2159-8290.CD-13-0285. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib38] 38.Mabe N.W., Huang M., Dalton G.N., Alexe G., Schaefer D.A., Geraghty A.C., Robichaud A.L., Conway A.S., Khalid D., Mader M.M., et al. Transition to a mesenchymal state in neuroblastoma confers resistance to anti-GD2 antibody via reduced expression of ST8SIA1. Nat. Cancer. 2022;3:976–993. doi: 10.1038/s43018-022-00405-x. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib39] 39.Sait S., Modak S. Anti-GD2 immunotherapy for neuroblastoma. Expert Rev. Anticancer Ther. 2017;17:889–904. doi: 10.1080/14737140.2017.1364995. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib40] 40.Modak S., Cheung N.-K.V. Neuroblastoma: Therapeutic strategies for a clinical enigma. Cancer Treat Rev. 2010;36:307–317. doi: 10.1016/j.ctrv.2010.02.006. [DOI] [PubMed] [Google Scholar]

[bib41] 41.Brandt C.S., Baratin M., Yi E.C., Kennedy J., Gao Z., Fox B., Haldeman B., Ostrander C.D., Kaifu T., Chabannon C., et al. The B7 family member B7-H6 is a tumor cell ligand for the activating natural killer cell receptor NKp30 in humans. J. Exp. Med. 2009;206:1495–1503. doi: 10.1084/jem.20090681. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib42] 42.Textor S., Bossler F., Henrich K.-O., Gartlgruber M., Pollmann J., Fiegler N., Arnold A., Westermann F., Waldburger N., Breuhahn K., et al. The proto-oncogene Myc drives expression of the NK cell-activating NKp30 ligand B7-H6 in tumor cells. OncoImmunology. 2016;5 doi: 10.1080/2162402X.2015.1116674. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib43] 43.Meissner T.B., Li A., Biswas A., Lee K.-H., Liu Y.-J., Bayir E., Iliopoulos D., van den Elsen P.J., Kobayashi K.S. NLR family member NLRC5 is a transcriptional regulator of MHC class I genes. Proc. Natl. Acad. Sci. USA. 2010;107:13794–13799. doi: 10.1073/pnas.1008684107. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib44] 44.Meissner T.B., Li A., Kobayashi K.S. NLRC5: a newly discovered MHC class I transactivator (CITA) Microbes Infect. 2012;14:477–484. doi: 10.1016/j.micinf.2011.12.007. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib45] 45.Ljunggren H.G., Kärre K. In search of the “missing self”: MHC molecules and NK cell recognition. Immunol. Today. 1990;11:237–244. doi: 10.1016/0167-5699(90)90097-s. [DOI] [PubMed] [Google Scholar]

[bib46] 46.Liao N.S., Bix M., Zijlstra M., Jaenisch R., Raulet D. MHC class I deficiency: susceptibility to natural killer (NK) cells and impaired NK activity. Science. 1991;253:199–202. doi: 10.1126/science.1853205. [DOI] [PubMed] [Google Scholar]

[bib47] 47.Wolf N.K., Kissiov D.U., Raulet D.H. Roles of natural killer cells in immunity to cancer, and applications to immunotherapy. Nat. Rev. Immunol. 2023;23:90–105. doi: 10.1038/s41577-022-00732-1. [DOI] [PubMed] [Google Scholar]

[bib48] 48.Bernards R., Dessain S.K., Weinberg R.A. N-myc amplification causes down-modulation of MHC class I antigen expression in neuroblastoma. Cell. 1986;47:667–674. doi: 10.1016/0092-8674(86)90509-x. [DOI] [PubMed] [Google Scholar]

[bib49] 49.Brandetti E., Veneziani I., Melaiu O., Pezzolo A., Castellano A., Boldrini R., Ferretti E., Fruci D., Moretta L., Pistoia V., et al. MYCN is an immunosuppressive oncogene dampening the expression of ligands for NK-cell-activating receptors in human high-risk neuroblastoma. OncoImmunology. 2017;6 doi: 10.1080/2162402X.2017.1316439. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib50] 50.Dong R., Yang R., Zhan Y., Lai H.-D., Ye C.-J., Yao X.-Y., Luo W.-Q., Cheng X.-M., Miao J.-J., Wang J.-F., et al. Single-Cell Characterization of Malignant Phenotypes and Developmental Trajectories of Adrenal Neuroblastoma. Cancer Cell. 2020;38:716–733.e6. doi: 10.1016/j.ccell.2020.08.014. [DOI] [PubMed] [Google Scholar]

[bib51] 51.Kildisiute G., Kholosy W.M., Young M.D., Roberts K., Elmentaite R., van Hooff S.R., Pacyna C.N., Khabirova E., Piapi A., Thevanesan C., et al. Tumor to normal single-cell mRNA comparisons reveal a pan-neuroblastoma cancer cell. Sci. Adv. 2021;7 doi: 10.1126/sciadv.abd3311. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib52] 52.Patel A.G., Ashenberg O., Collins N.B., Segerstolpe Å., Jiang S., Slyper M., Huang X., Caraccio C., Jin H., Sheppard H., et al. A spatial cell atlas of neuroblastoma reveals developmental, epigenetic and spatial axis of tumor heterogeneity. bioRxiv. 2024 doi: 10.1101/2024.01.07.574538. Preprint at. [DOI] [Google Scholar]

[bib53] 53.Chapple R.H., Liu X., Natarajan S., Alexander M.I.M., Kim Y., Patel A.G., LaFlamme C.W., Pan M., Wright W.C., Lee H.-M., et al. An integrated single-cell RNA-seq map of human neuroblastoma tumors and preclinical models uncovers divergent mesenchymal-like gene expression programs. Genome Biol. 2024;25:161. doi: 10.1186/s13059-024-03309-4. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib54] 54.Zhang P., Wu X., Basu M., Dong C., Zheng P., Liu Y., Sandler A.D. MYCN Amplification Is Associated with Repressed Cellular Immunity in Neuroblastoma: An In Silico Immunological Analysis of TARGET Database. Front. Immunol. 2017;8:1473. doi: 10.3389/fimmu.2017.01473. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib55] 55.Zhong X., Zhang Y., Wang L., Zhang H., Liu H., Liu Y. Cellular components in tumor microenvironment of neuroblastoma and the prognostic value. PeerJ. 2019;7 doi: 10.7717/peerj.8017. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib56] 56.Wei J.S., Kuznetsov I.B., Zhang S., Song Y.K., Asgharzadeh S., Sindiri S., Wen X., Patidar R., Najaraj S., Walton A., et al. Clinically Relevant Cytotoxic Immune Cell Signatures and Clonal Expansion of T-Cell Receptors in High-Risk MYCN-Not-Amplified Human Neuroblastoma. Clin. Cancer Res. 2018;24:5673–5684. doi: 10.1158/1078-0432.CCR-18-0599. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib57] 57.Layer J.P., Kronmüller M.T., Quast T., van den Boorn-Konijnenberg D., Effern M., Hinze D., Althoff K., Schramm A., Westermann F., Peifer M., et al. Amplification of N-Myc is associated with a T-cell-poor microenvironment in metastatic neuroblastoma restraining interferon pathway activity and chemokine expression. OncoImmunology. 2017;6 doi: 10.1080/2162402X.2017.1320626. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib58] 58.Verhoeven B.M., Mei S., Olsen T.K., Gustafsson K., Valind A., Lindström A., Gisselsson D., Fard S.S., Hagerling C., Kharchenko P.V., et al. The immune cell atlas of human neuroblastoma. Cell Rep. Med. 2022;3 doi: 10.1016/j.xcrm.2022.100657. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib59] 59.Melaiu O., Chierici M., Lucarini V., Jurman G., Conti L.A., De Vito R., Boldrini R., Cifaldi L., Castellano A., Furlanello C., et al. Cellular and gene signatures of tumor-infiltrating dendritic cells and natural-killer cells predict prognosis of neuroblastoma. Nat. Commun. 2020;11:5992. doi: 10.1038/s41467-020-19781-y. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib60] 60.Newman A.M., Steen C.B., Liu C.L., Gentles A.J., Chaudhuri A.A., Scherer F., Khodadoust M.S., Esfahani M.S., Luca B.A., Steiner D., et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat. Biotechnol. 2019;37:773–782. doi: 10.1038/s41587-019-0114-2. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib61] 61.Kameneva P., Artemov A.V., Kastriti M.E., Faure L., Olsen T.K., Otte J., Erickson A., Semsch B., Andersson E.R., Ratz M., et al. Single-cell transcriptomics of human embryos identifies multiple sympathoblast lineages with potential implications for neuroblastoma origin. Nat. Genet. 2021;53:694–706. doi: 10.1038/s41588-021-00818-x. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib62] 62.Jansky S., Sharma A.K., Körber V., Quintero A., Toprak U.H., Wecht E.M., Gartlgruber M., Greco A., Chomsky E., Grünewald T.G.P., et al. Single-cell transcriptomic analyses provide insights into the developmental origins of neuroblastoma. Nat. Genet. 2021;53:683–693. doi: 10.1038/s41588-021-00806-1. [DOI] [PubMed] [Google Scholar]

[bib63] 63.De Preter K., Vandesompele J., Heimann P., Yigit N., Beckman S., Schramm A., Eggert A., Stallings R.L., Benoit Y., Renard M., et al. Human fetal neuroblast and neuroblastoma transcriptome analysis confirms neuroblast origin and highlights neuroblastoma candidate genes. Genome Biol. 2006;7:R84. doi: 10.1186/gb-2006-7-9-r84. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib64] 64.Gartlgruber M., Sharma A.K., Quintero A., Dreidax D., Jansky S., Park Y.-G., Kreth S., Meder J., Doncevic D., Saary P., et al. Super enhancers define regulatory subtypes and cell identity in neuroblastoma. Nat. Cancer. 2021;2:114–128. doi: 10.1038/s43018-020-00145-w. [DOI] [PubMed] [Google Scholar]

[bib65] 65.Oliynyk G., Ruiz-Pérez M.V., Sainero-Alcolado L., Dzieran J., Zirath H., Gallart-Ayala H., Wheelock C.E., Johansson H.J., Nilsson R., Lehtiö J., Arsenian-Henriksson M. MYCN-enhanced Oxidative and Glycolytic Metabolism Reveals Vulnerabilities for Targeting Neuroblastoma. iScience. 2019;21:188–204. doi: 10.1016/j.isci.2019.10.020. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib66] 66.van Riggelen J., Yetil A., Felsher D.W. MYC as a regulator of ribosome biogenesis and protein synthesis. Nat. Rev. Cancer. 2010;10:301–309. doi: 10.1038/nrc2819. [DOI] [PubMed] [Google Scholar]

[bib67] 67.Tao L., Mohammad M.A., Milazzo G., Moreno-Smith M., Patel T.D., Zorman B., Badachhape A., Hernandez B.E., Wolf A.B., Zeng Z., et al. MYCN-driven fatty acid uptake is a metabolic vulnerability in neuroblastoma. Nat. Commun. 2022;13:3728. doi: 10.1038/s41467-022-31331-2. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib68] 68.Guan J., Hallberg B., Palmer R.H. Chromosome Imbalances in Neuroblastoma-Recent Molecular Insight into Chromosome 1p-deletion, 2p-gain, and 11q-deletion Identifies New Friends and Foes for the Future. Cancers. 2021;13 doi: 10.3390/cancers13235897. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib69] 69.Bader S.A., Fasching C., Brodeur G.M., Stanbridge E.J. Dissociation of suppression of tumorigenicity and differentiation in vitro effected by transfer of single human chromosomes into human neuroblastoma cells. Cell Growth Differ. 1991;2:245–255. [PubMed] [Google Scholar]

[bib70] 70.Gundem G., Levine M.F., Roberts S.S., Cheung I.Y., Medina-Martínez J.S., Feng Y., Arango-Ossa J.E., Chadoutaud L., Rita M., Asimomitis G., et al. Clonal evolution during metastatic spread in high-risk neuroblastoma. Nat. Genet. 2023;55:1022–1033. doi: 10.1038/s41588-023-01395-x. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib71] 71.Schramm A., Köster J., Assenov Y., Althoff K., Peifer M., Mahlow E., Odersky A., Beisser D., Ernst C., Henssen A.G., et al. Mutational dynamics between primary and relapse neuroblastomas. Nat. Genet. 2015;47:872–877. doi: 10.1038/ng.3349. [DOI] [PubMed] [Google Scholar]

[bib72] 72.Fransson S., Martinez-Monleon A., Johansson M., Sjöberg R.-M., Björklund C., Ljungman G., Ek T., Kogner P., Martinsson T. Whole-genome sequencing of recurrent neuroblastoma reveals somatic mutations that affect key players in cancer progression and telomere maintenance. Sci. Rep. 2020;10 doi: 10.1038/s41598-020-78370-7. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib73] 73.Schleiermacher G., Javanmardi N., Bernard V., Leroy Q., Cappo J., Rio Frio T., Pierron G., Lapouble E., Combaret V., Speleman F., et al. Emergence of new ALK mutations at relapse of neuroblastoma. J. Clin. Oncol. 2014;32:2727–2734. doi: 10.1200/JCO.2013.54.0674. [DOI] [PubMed] [Google Scholar]

[bib74] 74.Eleveld T.F., Oldridge D.A., Bernard V., Koster J., Colmet Daage L., Diskin S.J., Schild L., Bentahar N.B., Bellini A., Chicard M., et al. Relapsed neuroblastomas show frequent RAS-MAPK pathway mutations. Nat. Genet. 2015;47:864–871. doi: 10.1038/ng.3333. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib75] 75.Padovan-Merhar O.M., Raman P., Ostrovnaya I., Kalletla K., Rubnitz K.R., Sanford E.M., Ali S.M., Miller V.A., Mossé Y.P., Granger M.P., et al. Enrichment of Targetable Mutations in the Relapsed Neuroblastoma Genome. PLoS Genet. 2016;12 doi: 10.1371/journal.pgen.1006501. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib76] 76.Cimmino F., Montella A., Tirelli M., Avitabile M., Lasorsa V.A., Visconte F., Cantalupo S., Maiorino T., De Angelis B., Morini M., et al. FGFR1 is a potential therapeutic target in neuroblastoma. Cancer Cell Int. 2022;22:174. doi: 10.1186/s12935-022-02587-x. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib77] 77.Grossmann L.D., Chen C.-H., Uzun Y., Thadi A., Wolpaw A.J., Louault K., Goldstein Y., Surrey L.F., Martinez D., Calafatti M., et al. Identification and characterization of chemotherapy resistant high-risk neuroblastoma persister cells. Cancer Discov. 2024;14:2387–2406. doi: 10.1158/2159-8290.CD-24-0046. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib78] 78.George R.E., Sanda T., Hanna M., Fröhling S., Luther W., 2nd, Zhang J., Ahn Y., Zhou W., London W.B., McGrady P., et al. Activating mutations in ALK provide a therapeutic target in neuroblastoma. Nature. 2008;455:975–978. doi: 10.1038/nature07397. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib79] 79.Matsuno R., Akiyama K., Toyama D., Ikeda H., Yamamoto S. Adolescent pulmonary metastatic neuroblastoma with ALK rearrangement: A case report. Pediatr. Int. 2020;62:507–509. doi: 10.1111/ped.14117. [DOI] [PubMed] [Google Scholar]

[bib80] 80.Hiwatari M., Seki M., Matsuno R., Yoshida K., Nagasawa T., Sato-Otsubo A., Yamamoto S., Kato M., Watanabe K., Sekiguchi M., et al. Novel TENM3-ALK fusion is an alternate mechanism for ALK activation in neuroblastoma. Oncogene. 2022;41:2789–2797. doi: 10.1038/s41388-022-02301-1. [DOI] [PubMed] [Google Scholar]

[bib81] 81.Du X., Shao Y., Qin H.-F., Tai Y.-H., Gao H.-J. ALK-rearrangement in non-small-cell lung cancer (NSCLC). Thorac. Cancer. 2018;9:423–430. doi: 10.1111/1759-7714.12613. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib82] 82.Lovly C.M., Gupta A., Lipson D., Otto G., Brennan T., Chung C.T., Borinstein S.C., Ross J.S., Stephens P.J., Miller V.A., Coffin C.M. Inflammatory myofibroblastic tumors harbor multiple potentially actionable kinase fusions. Cancer Discov. 2014;4:889–895. doi: 10.1158/2159-8290.CD-14-0377. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib83] 83.Goldsmith K.C., Park J.R., Kayser K., Malvar J., Chi Y.-Y., Groshen S.G., Villablanca J.G., Krytska K., Lai L.M., Acharya P.T., et al. Lorlatinib with or without chemotherapy in ALK-driven refractory/relapsed neuroblastoma: phase 1 trial results. Nat. Med. 2023;29:1092–1102. doi: 10.1038/s41591-023-02297-5. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib84] 84.Mossé Y.P., Lim M.S., Voss S.D., Wilner K., Ruffner K., Laliberte J., Rolland D., Balis F.M., Maris J.M., Weigel B.J., et al. Safety and activity of crizotinib for paediatric patients with refractory solid tumours or anaplastic large-cell lymphoma: a Children’s Oncology Group phase 1 consortium study. Lancet Oncol. 2013;14:472–480. doi: 10.1016/S1470-2045(13)70095-0. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib85] 85.Fischer M., Moreno L., Ziegler D.S., Marshall L.V., Zwaan C.M., Irwin M.S., Casanova M., Sabado C., Wulff B., Stegert M., et al. Ceritinib in paediatric patients with anaplastic lymphoma kinase-positive malignancies: an open-label, multicentre, phase 1, dose-escalation and dose-expansion study. Lancet Oncol. 2021;22:1764–1776. doi: 10.1016/S1470-2045(21)00536-2. [DOI] [PubMed] [Google Scholar]

[bib86] 86.Foster J.H., Voss S.D., Hall D.C., Minard C.G., Balis F.M., Wilner K., Berg S.L., Fox E., Adamson P.C., Blaney S.M., et al. Activity of Crizotinib in Patients with ALK-Aberrant Relapsed/Refractory Neuroblastoma: A Children’s Oncology Group Study (ADVL0912) Clin. Cancer Res. 2021;27:3543–3548. doi: 10.1158/1078-0432.CCR-20-4224. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib87] 87.Valentijn L.J., Koster J., Zwijnenburg D.A., Hasselt N.E., van Sluis P., Volckmann R., van Noesel M.M., George R.E., Tytgat G.A.M., Molenaar J.J., Versteeg R. TERT rearrangements are frequent in neuroblastoma and identify aggressive tumors. Nat. Genet. 2015;47:1411–1414. doi: 10.1038/ng.3438. [DOI] [PubMed] [Google Scholar]

[bib88] 88.Erbe A.K., Diccianni M.B., Mody R., Naranjo A., Zhang F.F., Birstler J., Kim K., Feils A.S., Hung J.-T., London W.B., et al. KIR/KIR-ligand genotypes and clinical outcomes following chemoimmunotherapy in patients with relapsed or refractory neuroblastoma: a report from the Children’s Oncology Group. J. Immunother. Cancer. 2023;11 doi: 10.1136/jitc-2022-006530. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib89] 89.Erbe A.K., Wang W., Carmichael L., Kim K., Mendonça E.A., Song Y., Hess D., Reville P.K., London W.B., Naranjo A., et al. Neuroblastoma Patients’ KIR and KIR-Ligand Genotypes Influence Clinical Outcome for Dinutuximab-based Immunotherapy: A Report from the Children's Oncology Group. Clin. Cancer Res. 2018;24:189–196. doi: 10.1158/1078-0432.CCR-17-1767. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib90] 90.Mody R.J., Wu Y.-M., Lonigro R.J., Cao X., Roychowdhury S., Vats P., Frank K.M., Prensner J.R., Asangani I., Palanisamy N., et al. Integrative Clinical Sequencing in the Management of Refractory or Relapsed Cancer in Youth. JAMA. 2015;314:913–925. doi: 10.1001/jama.2015.10080. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib91] 91.Robinson D., Van Allen E.M., Wu Y.-M., Schultz N., Lonigro R.J., Mosquera J.-M., Montgomery B., Taplin M.-E., Pritchard C.C., Attard G., et al. Integrative clinical genomics of advanced prostate cancer. Cell. 2015;161:1215–1228. doi: 10.1016/j.cell.2015.05.001. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib92] 92.Robinson D.R., Wu Y.-M., Lonigro R.J., Vats P., Cobain E., Everett J., Cao X., Rabban E., Kumar-Sinha C., Raymond V., et al. Integrative clinical genomics of metastatic cancer. Nature. 2017;548:297–303. doi: 10.1038/nature23306. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib93] 93.Regier A.A., Farjoun Y., Larson D.E., Krasheninina O., Kang H.M., Howrigan D.P., Chen B.-J., Kher M., Banks E., Ames D.C., et al. Functional equivalence of genome sequencing analysis pipelines enables harmonized variant calling across human genetics projects. Nat. Commun. 2018;9:4038. doi: 10.1038/s41467-018-06159-4. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib94] 94.Li G.X., Chen L., Hsiao Y., Mannan R., Zhang Y., Luo J., Petralia F., Cho H., Hosseini N., Leprevost F.d.V., et al. Comprehensive proteogenomic characterization of rare kidney tumors. Cell Rep. Med. 2024;5 doi: 10.1016/j.xcrm.2024.101547. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib95] 95.Li Y., Lih T.-S.M., Dhanasekaran S.M., Mannan R., Chen L., Cieslik M., Wu Y., Lu R.J.-H., Clark D.J., Kołodziejczak I., et al. Histopathologic and proteogenomic heterogeneity reveals features of clear cell renal cell carcinoma aggressiveness. Cancer Cell. 2023;41:139–163.e17. doi: 10.1016/j.ccell.2022.12.001. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib96] 96.Chowdhury S., Kennedy J.J., Ivey R.G., Murillo O.D., Hosseini N., Song X., Petralia F., Calinawan A., Savage S.R., Berry A.B., et al. Proteogenomic analysis of chemo-refractory high-grade serous ovarian cancer. Cell. 2023;186:3476–3498.e35. doi: 10.1016/j.cell.2023.07.004. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib97] 97.GitHub - broadinstitute/picard: A set of command line tools (in Java) for manipulating high-throughput sequencing (HTS) data and formats such as SAM/BAM/CRAM and VCF GitHub. https://github.com/broadinstitute/picard.

[bib98] 98.Riester M., Singh A.P., Brannon A.R., Yu K., Campbell C.D., Chiang D.Y., Morrissey M.P. PureCN: copy number calling and SNV classification using targeted short read sequencing. Source Code Biol. Med. 2016;11:13. doi: 10.1186/s13029-016-0060-z. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib99] 99.Bushnell B. BBMap: A Fast, Accurate, Splice-Aware Aligner. 2014. https://www.osti.gov/servlets/purl/1241166

[bib100] 100.Li H., Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25:1754–1760. doi: 10.1093/bioinformatics/btp324. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib101] 101.Freed D., Aldana R., Weber J.A., Edwards J.S. The Sentieon Genomics Tools - A fast and accurate solution to variant calling from next-generation sequence data. bioRxiv. 2017 doi: 10.1101/115717. Preprint at. [DOI] [Google Scholar]

[bib102] 102.Benjamin D., Sato T., Cibulskis K., Getz G., Stewart C., Lichtenstein L. Calling Somatic SNVs and Indels with Mutect2. bioRxiv. 2019 doi: 10.1101/861054. Preprint at. [DOI] [Google Scholar]

[bib103] 103.McLaren W., Gil L., Hunt S.E., Riat H.S., Ritchie G.R.S., Thormann A., Flicek P., Cunningham F. The Ensembl Variant Effect Predictor. Genome Biol. 2016;17:122. doi: 10.1186/s13059-016-0974-4. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib104] 104.Pedersen B.S., Layer R.M., Quinlan A.R. Vcfanno: fast, flexible annotation of genetic variants. Genome Biol. 2016;17:118. doi: 10.1186/s13059-016-0973-5. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib105] 105.Olshen A.B., Venkatraman E.S., Lucito R., Wigler M. Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics. 2004;5:557–572. doi: 10.1093/biostatistics/kxh008. [DOI] [PubMed] [Google Scholar]

[bib106] 106.Carter S.L., Cibulskis K., Helman E., McKenna A., Shen H., Zack T., Laird P.W., Onofrio R.C., Winckler W., Weir B.A., et al. Absolute quantification of somatic DNA alterations in human cancer. Nat. Biotechnol. 2012;30:413–421. doi: 10.1038/nbt.2203. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib107] 107.Dobin A., Davis C.A., Schlesinger F., Drenkow J., Zaleski C., Jha S., Batut P., Chaisson M., Gingeras T.R. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21. doi: 10.1093/bioinformatics/bts635. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib108] 108.Bray N.L., Pimentel H., Melsted P., Pachter L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 2016;34:525–527. doi: 10.1038/nbt.3519. [DOI] [PubMed] [Google Scholar]

[bib109] 109.Li H. Minimap and miniasm: fast mapping and de novo assembly for noisy long sequences. Bioinformatics. 2016;32:2103–2110. doi: 10.1093/bioinformatics/btw152. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib110] 110.Wu T.D., Reeder J., Lawrence M., Becker G., Brauer M.J. GMAP and GSNAP for genomic sequence alignment: Enhancements to speed, accuracy, and functionality. Methods Mol. Biol. 2016;1418:283–334. doi: 10.1007/978-1-4939-3578-9_15. [DOI] [PubMed] [Google Scholar]

[bib111] 111.Mumphrey M.B., Hosseini N., Parolia A., Geng J., Zou W., Raghavan M., Chinnaiyan A., Cieslik M. Distinct mutational processes shape selection of MHC class I and class II mutations across primary and metastatic tumors. Cell Rep. 2023;42 doi: 10.1016/j.celrep.2023.112965. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib112] 112.Ritchie M.E., Phipson B., Wu D., Hu Y., Law C.W., Shi W., Smyth G.K. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47. doi: 10.1093/nar/gkv007. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib113] 113.Kassambara A., Kosinski M., Biecek P., Scheipl F. Drawing Survival Curves using “ggplot2” [R package survminer version 0.4.9] 2021. https://rpkgs.datanovia.com/survminer/authors.html#citation

[bib114] 114.Therneau, T.M. Survival Analysis [R package survival version 3.3-1]. https://cran.r-project.org/web/packages/survival/index.html.

[bib115] 115.Liberzon A., Birger C., Thorvaldsdóttir H., Ghandi M., Mesirov J.P., Tamayo P. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 2015;1:417–425. doi: 10.1016/j.cels.2015.12.004. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib116] 116.Dolgalev I. MSigDB Gene Sets for Multiple Organisms in a Tidy Data Format [R package msigdbr version 7.5.1] https://cran.r-project.org/web/packages/msigdbr/index.html

[bib117] 117.Korotkevich G., Sukhov V., Budin N., Shpak B., Artyomov M.N., Sergushichev A. Fast gene set enrichment analysis. bioRxiv. 2021 doi: 10.1101/060012. Preprint at. [DOI] [Google Scholar]

[bib118] 118.Xie Z., Bailey A., Kuleshov M.V., Clarke D.J.B., Evangelista J.E., Jenkins S.L., Lachmann A., Wojciechowicz M.L., Kropiwnicki E., Jagodnik K.M., et al. Gene Set Knowledge Discovery with Enrichr. Curr. Protoc. 2021;1:e90. doi: 10.1002/cpz1.90. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib119] 119.Wu T., Hu E., Xu S., Chen M., Guo P., Dai Z., Feng T., Zhou L., Tang W., Zhan L., et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation. 2021;2 doi: 10.1016/j.xinn.2021.100141. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib120] 120.Foroutan M., Bhuva D.D., Lyu R., Horan K., Cursons J., Davis M.J. Single sample scoring of molecular phenotypes. BMC Bioinf. 2018;19:404. doi: 10.1186/s12859-018-2435-4. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib121] 121.Griffith D.M., Veech J.A., Marsh C.J. cooccur: Probabilistic Species Co-Occurrence Analysis in R. J. Stat. Softw. 2016;69:1–17. doi: 10.18637/jss.v069.c02. [DOI] [Google Scholar]

[bib122] 122.Stuart T., Butler A., Hoffman P., Hafemeister C., Papalexi E., Mauck W.M., 3rd, Hao Y., Stoeckius M., Smibert P., Satija R. Comprehensive Integration of Single-Cell Data. Cell. 2019;177:1888–1902.e21. doi: 10.1016/j.cell.2019.05.031. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib123] 123.Butler A., Hoffman P., Smibert P., Papalexi E., Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 2018;36:411–420. doi: 10.1038/nbt.4096. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib124] 124.Satija R., Farrell J.A., Gennert D., Schier A.F., Regev A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 2015;33:495–502. doi: 10.1038/nbt.3192. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib125] 125.Lause J., Berens P., Kobak D. Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data. Genome Biol. 2021;22:258. doi: 10.1186/s13059-021-02451-7. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib126] 126.Andreatta M., Carmona S.J. UCell: Robust and scalable single-cell gene signature scoring. Comput. Struct. Biotechnol. J. 2021;19:3796–3798. doi: 10.1016/j.csbj.2021.06.043. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib127] 127.Salcher S., Sturm G., Horvath L., Untergasser G., Kuempers C., Fotakis G., Panizzolo E., Martowicz A., Trebo M., Pall G., et al. High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer. Cancer Cell. 2022;40:1503–1520.e8. doi: 10.1016/j.ccell.2022.10.008. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib128] 128.Young M.D., Behjati S. SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data. GigaScience. 2020;9 doi: 10.1093/gigascience/giaa151. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib129] 129.Chu T., Wang Z., Pe’er D., Danko C.G. Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology. Nat. Cancer. 2022;3:505–517. doi: 10.1038/s43018-022-00356-3. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib130] 130.Morabito S., Reese F., Rahimzadeh N., Miyoshi E., Swarup V. hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data. Cell Rep. Methods. 2023;3 doi: 10.1016/j.crmeth.2023.100498. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Chromosomal instability degrades developmental phenotypes essential for anti-GD2 immunotherapy outcomes in high-risk neuroblastoma

Ryan James Rebernick

Jae Eun Choi

Calvin Hesse

Noshad Hosseini

Alec Chu

Jin Zhou

Yu Ning

Rui Wang

Xuhong Cao

Meredith Irwin

Yi-Mi Wu

Chandan Kumar

Raja Rabah

Rochelle Bagatell

Arul M Chinnaiyan

Rajen Mody

Marcin Cieslik

Summary

Graphical abstract

Highlights

Introduction

Results

Clinical and histologic attributes of a retrospective cohort of 168 neuroblastic tumors

Figure 1.

Identification of ALK gene fusions and PHOX2B amplifications in neuroblastoma

A distinct class of chromosomally stable, MYCN-amplified high-risk neuroblastoma

Designation of high-risk neuroblastoma patients with improved outcomes following treatment regimens containing anti-GD2 immunotherapy

Figure 2.

Chromosomal stability is associated with improved outcomes following treatment regimens containing anti-GD2 immunotherapy

Figure 3.

Noradrenergic and metabolic phenotypes are associated with improved outcomes following treatment regimens containing anti-GD2 immunotherapy

Immune populations are prognostic but not associated with improved outcomes following treatment regimens containing anti-GD2 immunotherapy

Figure 4.

A developmental sympathoadrenal single-cell atlas and identification of sympathoadrenal phenotypes of noradrenergic differentiation

Figure 5.

Sympathoadrenal phenotypes are associated with improved outcomes following treatment regimens containing anti-GD2 immunotherapy

Figure 6.

Chromosome 11q loss degrades sympathoadrenal phenotypes in high-risk neuroblastoma

Patterns of acquired resistance to treatment regimens containing anti-GD2 immunotherapy

Figure 7.

Discussion

Limitations of the study

Resource availability

Lead contact

Materials availability

Data and code availability

Acknowledgments

Author contributions

Declaration of interests

STAR★Methods

Key resources table

Experimental model and study participant details

Human participants

Method details

Assessment of outcome on treatment regimens containing anti-GD2 immunotherapy

Integrative clinical sequencing

Comprehensive genomics analysis pipeline

Somatic variant calling

Copy number estimation

RNA-sequencing expression quantification

Detection of gene fusions from RNA-sequencing

Cancer cell fraction and mutation clonality

Differential gene expression analysis in bulk RNA-sequencing

Chromosome-level and arm-level copy number alterations

Whole genome duplication

Chromosomal instability measures

Survival analysis

Pathway, gene set enrichment, and overrepresentation analysis

Gene signature scoring in bulk RNA-sequencing

Mutual exclusivity and co-enrichment analysis

FCγ receptor affinity

Telomere maintenance mechanisms

Single-cell RNA-sequencing data pre-processing

Integration of fetal adrenal single cell and single nuclei RNA-sequencing

Integration of neuroblastoma single cell RNA-sequencing

Fetal adrenal cell annotation

Neuroblastoma cell annotation

Generation of an integrated fetal sympathoadrenal single-cell atlas