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. 2025 Mar 13;19(4):994–1001. doi: 10.1002/1878-0261.70008

Revisiting CDKN2A dysregulation in Ewing sarcoma

Anjali Paragji 1, Vivek Shastri 1, Elham Nasri 2, John A Ligon 3, Leighton A Elliott 3, Paul Castillo‐Caro 3, Jatinder K Lamba 1, Elias J Sayour 4, Nathan D Seligson 1,5,✉
PMCID: PMC11977643  PMID: 40080912

Abstract

Ewing sarcoma (EwS) is a rare and aggressive malignancy, which frequently affects children. One of the few recurrent genomic variants in EwS is genomic copy number deletion of CDKN2A; however, the clinical consequences of dysregulation of CDKN2A in EwS are unclear. In this study, we revisit CDKN2A to investigate its role as a potential prognostic biomarker in EwS using data from EwS pre‐clinical models as well as clinical samples from patients with EwS. We demonstrate the potential essentiality of CDKN2A dysregulation and sustained downstream CDK4/CCND1 activity. Finally, we present evidence that high expression of CDKN2A is a negative prognostic biomarker at diagnosis in EwS in three independent datasets. Our data may suggest that the role of CDKN2A may change across the clinical context of EwS, however, further study is necessary to validate the function of CDKN2A expression in EwS.

Keywords: biomarker, next‐generation sequencing, prognosis

We revisit CDKN2A to investigate its role as a potential prognostic biomarker in Ewing sarcoma using data from pre‐clinical models as well as clinical samples.

graphic file with name MOL2-19-994-g003.jpg

Abbreviations

95% CI

95% confidence interval

ANOVA

analysis of variance

DepMap

Dependency Map

EwS

Ewing sarcoma

GDSC

Genomics of Drug Sensitivity in Cancer

GEO

Gene Expression Omnibus

HR

hazard ratio

IQR

interquartile range

IRB

Institutional Review Board

LYM

lymphoma

NB

neuroblastoma

OSt

osteosarcoma

RMS

rhabdomyosarcoma

SD

standard deviation

1. Introduction

Ewing sarcoma (EwS) is a rare and aggressive malignancy, which frequently affects children [1]. One of the few recurrent genomic variants in EwS is genomic copy number deletion of CDKN2A [2]. The CDK4/CCND1‐RB1 pathway, regulated by CDKN2A, has emerged as a key molecular dependency in EwS [3, 4, 5]. Initial clinical reports suggested an association between CDKN2A homozygous copy number deletion and poor survival outcomes [2, 6, 7, 8, 9, 10, 11]; however, landmark studies published in 2014 found no significant survival difference based on genomic CDKN2A status [12, 13]. We recently reported that secondary genomic variants are likely not random in fusion driven sarcomas, including EwS, and may hold key biological meaning [14, 15, 16]. In this in silico study, we revisit CDKN2A as a prognostic biomarker in EwS using data from in vitro EwS models as well as clinical samples from patients with EwS. We present evidence of the biologic importance of CDKN2A downstream signaling in EwS and present evidence that suggests that CDKN2A may have a prognostic role in EwS.

2. Materials and methods

2.1. Dependency Map and project Achilles

Data from the Dependency Map (DepMap) and Project Achilles project, a CRISPR‐Cas9 and RNAi knockout database, were downloaded directly from the depmap portal (https://depmap.org/portal/download/custom/) [17]. Gene dependency effects for inactivation of genes downstream of CDKN2A including CDK4, CDK6, CCND1, RB1, MDM2, and TP53 were included for analysis. The gene effect scores evaluate the effect size of knocking out/down a gene normalized against pan‐essential and nonessential genes. Negative scores represent genes essential for proliferation or survival.

2.2. Genomics of Drug Sensitivity in Cancer

Data, including DNA variants, mRNA expression, and palbociclib sensitivity were collected from the Genomics of Drug Sensitivity in Cancer (GDSC) database as previously described (http://cancerrxgene.org, downloaded August 22nd, 2019) [15, 18, 19].

2.3. DNA profiling of EwS tumors

Genomic profiling data of clinical Ewing sarcoma specimens were collected from publicly available datasets from Dermawan et al. [20], Nguyen et al. [21], and Gounder et al. [22]. Data from Dermawan et al. and Nguyen et al. were collected using cBioPortal [23, 24, 25]. Data from Gounder et al were downloaded directly from the source data published alongside their original report [22]. The aggregate frequency of CDKN2A genomic variants were collected from the available literature [2, 6, 7, 8, 9, 10, 12, 13, 26, 27, 28, 29, 30].

2.4. RNA profiling of EwS tumors

Data from independent datasets of EwS samples including GSE17618 (n = 24) [31], GSE63155 (n = 46) [32], and GSE63156 (n = 39) [32], were obtained from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo, downloaded March 3rd, 2024). GSE17618 used the Affymetrix Human Genome U133 Plus 2.0 microarray. GSE63155 and GSE63156 used the Affymetrix Human Exon 1.0 ST microarray. Only primary EwS samples were included. Overlapping probe IDs were aggregated by maximum probe expression.

2.5. Data analysis

This study was approved by the University of Florida Institutional Review Board (#IRB202101136). Data was analyzed in r v.4.1.1 or graphpad prism v.9.2.0. Descriptive statistics, Pearson correlation coefficient, Kruskal–Wallis tests, one‐sample t‐tests, Welch's t‐tests, analysis of variance (ANOVA), uncorrected Dunn's tests, and Mann–Whitney tests were used as appropriate. Survival analysis was tested using both log‐rank and Cox proportional hazard methods. Survival graphs were created using the Kaplan–Meier estimator. Unless otherwise stated, P values ≤ 0.05 were considered statistically significant.

3. Results

3.1. EwS are dependent on CDK4/CCND1 signaling

To validate the dependence of EwS on the CDKN2A pathway, we collected data for 1086 cell lines (24 EwS lines) from DepMap and 710 cell lines (10 EwS lines) from Project Achilles directly from the DepMap portal [17]. For each dataset, we selected gene dependency effects for inactivation of genes downstream of CDKN2A (Fig. 1A). Within EwS cell lines, CCND1 and CDK4 were the only genes selected which resulted in consistent reduction in viability when inactivated in both the DepMap and Project Achilles datasets (Fig. 1B). This data suggests that a bottleneck for EwS proliferation or survival is dependent on active signaling at the CDK4/Cyclin D1 complex, immediately downstream of and regulated by CDKN2A. In comparison with other cancer cell lines, EwS lines demonstrated increased dependency for CCND1 and CDK4 in both datasets (Fig. 1C). Specifically, in comparison to the most common pediatric sarcomas and small round blue cell tumors, EwS lines demonstrated significant dependence on CCND1 and CDK4 (Fig. 1D) [33]. Dependance on CDK4 and CCND1 was not associated with the genomic CDKN2A status in EwS cell lines (Fig. S1).

Fig. 1.

Fig. 1

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Dependency and sensitivity of CDKN2A pathway in EwS. (A) Data from the Dependency Map (DepMap) and Project Achilles project, a CRISPR‐Cas9 and RNAi knockout database. Gene effect scores evaluate the effect size of knocking out/down a gene normalized against pan‐essential and nonessential genes. Negative scores represent genes essential for proliferation. (B) Dependency of genes downstream of CDKN2A in EwS in DepMap (CRISPR, n = 24) and Project Achilles (RNAi, n = 10) datasets. Only P values < 0.05 by one‐sample t‐test with a discrepancy greater than 0.5 were included. Error bars indicate SD. (C) Ranking of dependency scores for CDK4 and CCND1 across cancer cell lines (DepMap, n = 1086; Project Achilles, n = 710). P values calculated by Kolmogorov–Smirnov test. (D) In comparison to other similar cancers, EwS cell lines demonstrated significantly greater dependence on CCND1 and CDK4 (DepMap: EwS n = 24, OSt n = 13, RMS n = 13, NB n = 36, LYM n = 23; Project Achilles: EwS n = 10, OSt n = 4, RMS n = 9, NB n = 9, LYM n = 10). P values generated using the Kruskal–Wallis and uncorrected Dunn's test and are comparison with the dependency effect of EwS. Error bars indicate 95% CI. LYM, lymphoma; NB, neuroblastoma; OSt, osteosarcoma; RMS, rhabdomyosarcoma.

3.2. EwS cell lines are highly sensitive to CDK4/CDK6 inhibition

The potential importance of CCND1 and CDK4 activity can further be demonstrated through the in vitro sensitivity of cell lines to CDK4/6 inhibitors. Using data from the Genomics of Drug Sensitivity in Cancer (GDSC) database, we demonstrate that EwS cell lines are sensitive to treatment with the CDK4/6 inhibitor palbociclib (Fig. 2A). Across 968 cell lines, representing 56 tissue types, EwS lines demonstrated increased sensitivity to palbociclib (Fig. 2B). Specifically, in comparison to osteosarcoma, EwS lines were significantly more sensitive to CDK4/6inhibition with palbociclib (Fig. 2C). Sensitivity to palbociclib in EwS cell lines was not dependent on genomic CDKN2A status (Fig. S2). Taken together, data from DepMap (3.1), Project Achilles (3.1), and GDSC clearly demonstrate a vital signaling bottleneck immediately downstream of CDKN2A at CDK4/CCND1.

Fig. 2.

Fig. 2

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Dependency and sensitivity of CDKN2A pathway in EwS. (A) Association between genomic markers and drug sensitivity were collected directly from the Genomics of Drug Sensitivity in Cancer (GDSC) database (n = 968). (B) Ranking of cell line IC50s for palbociclib. P values calculated by Kolmogorov–Smirnov test. (C) In comparison to osteosarcoma (n = 10) or all other cell lines (n = 937), EwS cell lines (n = 21) demonstrated greater sensitivity to palbociclib. P value generated using the Mann–Whitney test. Error bars indicate IQR. OSt, osteosarcoma.

3.3. The clinical significance of CDKN2A homozygous copy number deletion is unclear

In patient samples, homozygous copy number deletion of CDKN2A is one of the most prevalent genomic variants identified in EwS. To estimate the prevalence of homozygous copy number deletion of CDKN2A in EwS we collected data from 15 clinical studies. Deletion of CDKN2A was identified in 6–32% of EwS, with a median prevalence of approximately 16.7% (Fig. 3A) [2, 6, 7, 8, 9, 10, 12, 13, 20, 22, 26, 27, 28, 29, 30]. To further assess the correlation between CDKN2A homozygous copy number deletion and overall survival in EwS, we collected publicly available genomic sequencing data generated from 178 EwS patients [20]. CDKN2A homozygous copy number deletion was identified in 12 tumors (6.7%) and was associated with reduced overall survival (hazard ratio [HR] 3.7, 95% confidence interval 1.26–10.65, P = 0.02, Fig. 3B). In a multivariate analysis with STAG2 and TP53 mutations, which have also been associated with poor overall survival in EwS [12], CDKN2A homozygous copy number deletion remains significantly associated with overall survival (Fig. 3C).

Fig. 3.

Fig. 3

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Genomic loss of CDKN2A is associated with poor overall survival in EwS. (A) Homozygous copy number deletion of CDKN2A has been reported in 6–32% of EwS, with a median prevalence in selected studies of 16.7%. (B, C) Clinico‐genomic data from Dermawan et al. Kaplan–Meier plot of overall survival for patients with EwS stratified by CDKN2A status (B). Multivariable model for overall survival with molecular covariates TP53 and STAG2 (C). Error bars indicate 95% CI. 95% CI, 95% confidence interval; HR, hazard ratio.

3.4. Elevated expression of CDKN2A is associated with poor overall survival

While significant efforts have been spent to understand the role of homozygous copy number deletion of CDKN2A in EwS, little published data testing the role of intact CDKN2A expression, which is present in 68–94% of EwS, exists. We therefore investigated the clinical association between CDKN2A RNA expression and overall survival using three publicly available datasets for a total of 109 patients with EwS [31, 32]. In each dataset, we identified an association between high expression of CDKN2A and reduced overall survival. As a continuous predictor of overall survival, increases of CDKN2A by a z‐score of 1 represented an increased HR between 1.9 and 2.4 across all three datasets (all P < 0.05, Fig. 4A). We then categorized samples by z‐score, with a z‐score > 1 as CDKN2A high expression and a z‐score < −1 as CDKN2A low expression. For each dataset, a CDKN2A high expression was associated with poor overall survival (Fig. 4B).

Fig. 4.

Fig. 4

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Elevated CDKN2A expression is associated with poor overall survival in EwS. Data from independent datasets of primary EwS samples including GSE17618 (n = 24), GSE63155 (n = 46), and GSE63156 (n = 39), were obtained from the Gene Expression Omnibus (GEO). (A) Expression of CDKN2A is associated with poor overall survival by Cox proportional hazard regression with CDKN2A as a continuous variable (HR is increased risk for each z‐score increase in CDKN2A expression) for three independent clinical datasets. Error bars indicate 95% CI. (B) For each clinical dataset, Kaplan–Meier curves were generated by categorizing samples by z‐score, with a z‐score > 1 as CDKN2A high expression and a z‐score < −1 as CDKN2A low expression. 95% CI, 95% confidence interval; HR, hazard ratio.

4. Discussion

In this study, we present the potential oncogenic essentiality of CDKN2A dysregulation and sustained downstream CDK4/CCND1 activity and complex clinical associations of CDKN2A dysregulation in EwS, confirming prior reports. Furthermore, we demonstrate that treatment with synthetic CDKN2A, in the form of CDK4/6 inhibitors, significantly reduces cell viability in EwS compared to other cancers. It is important to consider the difference between a molecular dependency which is and is not clinically targetable. Despite a dependence on CDK4 and Cyclin D1 activity and the efficacy of CDK4/6 inhibitors in pre‐clinical models of EwS, [3, 4, 34, 35, 36] clinical benefit from these agents has not been reported in patients with EwS [37]. Further studies of CDK4/6 inhibitors in EwS are currently being conducted (Table S1). There is still a need to further dissect the differential response to this therapeutic strategy between pre‐clinical models and actual patient tumors in the relapsed or refractory setting where they are being studied. In a recent pre‐print, Funk et al. [36] reported that chromosome 8 gain led to increased expression of translation initiation factor 4E‐BP1, increased proliferative signaling, and ultimately sensitivity to CDK4/6 inhibitors in EwS. Additionally, sensitivity to CDK2 inhibition has been demonstrated by Musa et al. [38] to limit the proliferation of EwS. EwS tumors are highly proliferative, with the driving EWSR1::FLI1/ERG translocation directly altering multiple proliferative pathways. Further study to dissect individual drivers of proliferation in EwS is necessary.

We also assessed the clinical consequences of CDKN2A in EwS. The evidence for the prognostic value of CDKN2A homozygous copy number deletion in the literature is varied with multiple retrospective studies [2, 6, 7, 8, 9, 10] and one meta‐analysis [11] suggesting a negative prognostic effect (tumor samples coming from multiple treatment time points) and two prospective studies suggesting no significant clinical association (primarily taken from tumors at diagnosis) [12, 13]. In a large retrospective dataset, we found evidence that CDKN2A homozygous copy number deletion was a negative prognostic marker. One possible driver of these inconsistent results is the clinical heterogeneity of patients included in retrospective studies. A second possibility is that patients receiving clinical genomic sequencing of their tumor, as is the case for many of the datasets cited here, are more likely to include refractory, recurrent, or metastatic tumors compared to tumors which are never sent for genomic sequencing. Therefore, with the data available, it is not possible to know when CDKN2A homozygous copy number deletion occurs in the oncogenic course of EwS, or if the effect on overall survival is due to therapy response, metastatic potential, or other mechanisms of aggressive disease.

Inversely, we found that high expression of CDKN2A was a negative prognostic biomarker at diagnosis in EwS in three independent datasets. This finding has previously been reported in an independent set of 33 patients with EwS which associated elevated expression CDKN2A with a reduced event‐free survival [39]. This finding is somewhat limited due to the use of RNA microarrays to generate RNA expression in these cohorts (Fig. S3). These tools are limited in their dynamic range, resulting in an inability to identify complete lack of expression or to differentiate between low and moderately expressed genes. Further research using methods with greater dynamic range, such as RT‐PCR or RNA‐seq, is necessary. Our initial hypothesis for the mechanism of high expression of CDKN2A as a negative prognostic biomarker at diagnosis in EwS was that elevated CDKN2A expression may lead to decreased tumor cell growth rate, decreased sensitivity to chemotherapeutics, and therefore poor clinical outcomes. An exploratory analysis of the GDSC data demonstrates that in EwS cell lines CDKN2A expression may decrease growth rate and chemotherapy sensitivity (Fig. S4).

Taken together, this data may suggest that in the early stage of EwS, and during frontline therapy, elevated expression of CDKN2A may be beneficial to EwS survival. Additionally, in the early (i.e. prior to diagnosis) and later (i.e. prior to relapse) stages of clinical care, CDKN2A homozygous copy number deletion may be beneficial to EwS tumors due to the proliferative advantage conferred. Further study is necessary to validate the function of CDKN2A expression in this context. The data here should be considered as hypothesis generating until further studies can validate the findings functionally and in clinical samples.

There are several limitations to this study, which should be considered when interpreting our results. First, this study is limited by the inherent nature of retrospective studies. Small datasets of this rare sarcoma further reduce the potential external validity of the findings of this study. Like many cancers, particularly true of rare cancers, pre‐clinical EwS models are not perfect surrogates of EwS in the clinic and this limitation has stymied drug development in an era of immunotherapy advances [10, 26, 29, 40]. Finally, it is clear that CDKN2A alone does not explain the total biological or clinical heterogeneity present in EwS. Further research is necessary to understand the role of the CDKN2A pathway in the greater molecular context of EwS.

Taken together, the data presented here suggests that CDKN2A may play a key molecular role in EwS. While it remains unclear what the consequences of CDKN2A suppression are clinically, the role is likely dependent on the clinical context of the disease. Future studies of CDKN2A in EwS should focus on clinical contexts, specific to diagnosis, initial treatment response, and development of relapse and metastasis as well as the multi‐omic dysregulation of CDKN2A identified in EwS, in order to provide more clarity to the role of this complex biomarker.

5. Conclusions

Our findings suggest that CDKN2A dysregulation is a key molecular characteristic in EwS. Furthermore, the prognostic value of CDKN2A in EwS may be context dependent. Future studies should consider CDKN2A as a biomarker in EwS in relation to the clinical treatment course of the patient.

Conflict of interest

The authors declare no Conflict of interest.

Author contributions

NDS designed the research. AP, VS, JKL, EJS, and NDS analyzed the data. AP, VS, EN, JAL, LAE, PC‐C, JKL, EJS, and NDS wrote the paper.

Peer review

The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer‐review/10.1002/1878‐0261.70008.

Code availability

Data was analyzed in r v.4.1.1 or graphpad prism v.9.2.0. No custom code was used for this analysis.

Supporting information

Fig. S1. Dependency for CDK4 and CCND1.

Fig. S2. Palbociclib IC50.

Fig. S3. Distribution in expression of CDKN2A.

Fig. S4. Exploratory analysis of the GDSC.

Table S1. Clinical trials with CDK4/6 inhibitors accepting patients with Ewing sarcoma.

MOL2-19-994-s001.docx (435.2KB, docx)

Acknowledgements

This study was supported by a startup research grant from The University of Florida College of Pharmacy and by the University of Florida Health Jacksonville Center for Research Training.

Data accessibility

Data from the DepMap and Project Achilles project are available from the depmap portal (https://depmap.org/portal/download/custom/) [17]. The gene expression data used in this study are available from the Gene Expression Omnibus repository under accession numbers GSE17618 [31], GSE63155 [32], and GSE63156 [32]. The drug sensitivity data are accessible from the Genomics of Drug Sensitivity in Cancer Database (https://www.cancerrxgene.org/) [15, 18, 19]. Genomic profiling data of clinical Ewing sarcoma specimens were collected from publicly available datasets from Dermawan et al. [20], Nguyen et al. [21], and Gounder et al. [22]. Data from Dermawan et al. and Nguyen et al. were collected using cBioPortal (https://www.cbioportal.org/) [23, 24, 25]. Data from Gounder et al. were downloaded directly from the source data published alongside their original report (https://www.nature.com/articles/s41467‐022‐30496‐0#data‐availability) [22].

References

  • 1. Grünewald TGP, Cidre‐Aranaz F, Surdez D, Tomazou EM, de Álava E, Kovar H, et al. Ewing sarcoma. Nat Rev Dis Primers. 2018;4:5. [DOI] [PubMed] [Google Scholar]
  • 2. Huang HY, Illei PB, Zhao Z, Mazumdar M, Huvos AG, Healey JH, et al. Ewing sarcomas with p53 mutation or p16/p14ARF homozygous deletion: a highly lethal subset associated with poor chemoresponse. J Clin Oncol. 2005;23:548–558. [DOI] [PubMed] [Google Scholar]
  • 3. Kennedy AL, Vallurupalli M, Chen L, Crompton B, Cowley G, Vazquez F, et al. Functional, chemical genomic, and super‐enhancer screening identify sensitivity to cyclin D1/CDK4 pathway inhibition in Ewing sarcoma. Oncotarget. 2015;6:30178–30193. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4. Guenther LM, Dharia NV, Ross L, Conway A, Robichaud AL, Catlett JL II, et al. A combination CDK4/6 and IGF1R inhibitor strategy for Ewing sarcoma. Clin Cancer Res. 2019;25:1343–1357. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5. Miller HE, Gorthi A, Bassani N, Lawrence LA, Iskra BS, Bishop AJR. Reconstruction of Ewing sarcoma developmental context from mass‐scale transcriptomics reveals characteristics of EWSR1‐FLI1 permissibility. Cancer. 2020;12(4):948. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6. López‐Guerrero JA, Pellín A, Noguera R, Carda C, Llombart‐Bosch A. Molecular analysis of the 9p21 locus and p53 genes in Ewing family tumors. Lab Invest. 2001;81:803–814. [DOI] [PubMed] [Google Scholar]
  • 7. Maitra A, Roberts H, Weinberg AG, Geradts J. Aberrant expression of tumor suppressor proteins in the Ewing family of tumors. Arch Pathol Lab Med. 2001;125:1207–1212. [DOI] [PubMed] [Google Scholar]
  • 8. Wei G, Antonescu CR, de Alava E, Leung D, Huvos AG, Meyers PA, et al. Prognostic impact of ink4a deletion in Ewing sarcoma. Cancer. 2000;89:793–799. [DOI] [PubMed] [Google Scholar]
  • 9. Tsuchiya T, Sekine K, Hinohara S, Namiki T, Nobori T, Kaneko Y. Analysis of the p16ink4, p14arf, p15, tp53, and MDM2 genes and their prognostic implications in osteosarcoma and Ewing sarcoma. Cancer Genet Cytogenet. 2000;120:91–98. [DOI] [PubMed] [Google Scholar]
  • 10. Kovar H, Jug G, Aryee DN, Zoubek A, Ambros P, Gruber B, et al. Among genes involved in the rb dependent cell cycle regulatory cascade, the p16 tumor suppressor gene is frequently lost in the Ewing family of tumors. Oncogene. 1997;15:2225–2232. [DOI] [PubMed] [Google Scholar]
  • 11. Honoki K, Stojanovski E, McEvoy M, Fujii H, Tsujiuchi T, Kido A, et al. Prognostic significance of p16 INK4a alteration for Ewing sarcoma: a meta‐analysis. Cancer. 2007;110:1351–1360. [DOI] [PubMed] [Google Scholar]
  • 12. Tirode F, Surdez D, Ma X, Parker M, le Deley MC, Bahrami A, et al. Genomic landscape of Ewing sarcoma defines an aggressive subtype with co‐association of STAG2 and TP53 mutations. Cancer Discov. 2014;4:1342–1353. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Lerman DM, Monument MJ, McIlvaine E, Liu XQ, Huang D, Monovich L, et al. Tumoral TP53 and/or CDKN2A alterations are not reliable prognostic biomarkers in patients with localized Ewing sarcoma: a report from the children's oncology group. Pediatr Blood Cancer. 2015;62:759–765. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14. Walker V, Jin DX, Millis SZ, Nasri E, Corao‐Uribe DA, Tan AC, et al. Gene partners of the EWSR1 fusion may represent molecularly distinct entities. Transl Oncol. 2023;38:101795. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15. Seligson ND, Maradiaga RD, Stets CM, Katzenstein HM, Millis SZ, Rogers A, et al. Multiscale‐omic assessment of EWSR1‐NFATc2 fusion positive sarcomas identifies the mtor pathway as a potential therapeutic target. NPJ Precis Oncol. 2021;5:43. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16. Seligson ND, Awasthi A, Millis SZ, Turpin BK, Meyer CF, Grand'Maison A, et al. Common secondary genomic variants associated with advanced epithelioid hemangioendothelioma. JAMA Netw Open. 2019;2:e1912416. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17. Pacini C, Duncan E, Gonçalves E, Gilbert J, Bhosle S, Horswell S, et al. A comprehensive clinically informed map of dependencies in cancer cells and framework for target prioritization. Cancer Cell. 2024;42:301–316.e9. [DOI] [PubMed] [Google Scholar]
  • 18. Yang W, Soares J, Greninger P, Edelman EJ, Lightfoot H, Forbes S, et al. Genomics of drug sensitivity in cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 2013;41:D955–D961. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19. Ghandi M, Huang FW, Jané‐Valbuena J, Kryukov GV, Lo CC, McDonald ER III, et al. Next‐generation characterization of the cancer cell line encyclopedia. Nature. 2019;569:503–508. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. Dermawan JK, Slotkin E, Tap WD, Meyers P, Wexler L, Healey J, et al. Chromoplexy is a frequent early clonal event in ewsr1‐rearranged round cell sarcomas that can be detected using clinically validated targeted sequencing panels. Cancer Res. 2024;84:1504–1516. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21. Nguyen B, Fong C, Luthra A, Smith SA, RG DN, Nandakumar S, et al. Genomic characterization of metastatic patterns from prospective clinical sequencing of 25,000 patients. Cell. 2022;185:563–575.e11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. Gounder MM, Agaram NP, Trabucco SE, Robinson V, Ferraro RA, Millis SZ, et al. Clinical genomic profiling in the management of patients with soft tissue and bone sarcoma. Nat Commun. 2022;13:3406. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23. Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2:401–404. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cbioportal. Sci Signal. 2013;6:pl1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25. de Bruijn I, Kundra R, Mastrogiacomo B, Tran TN, Sikina L, Mazor T, et al. Analysis and visualization of longitudinal genomic and clinical data from the AACR project genie biopharma collaborative in cBioPortal. Cancer Res. 2023;83:3861–3867. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26. Crompton BD, Stewart C, Taylor‐Weiner A, Alexe G, Kurek KC, Calicchio ML, et al. The genomic landscape of pediatric Ewing sarcoma. Cancer Discov. 2014;4:1326–1341. [DOI] [PubMed] [Google Scholar]
  • 27. Worst BC, van Tilburg CM, Balasubramanian GP, Fiesel P, Witt R, Freitag A, et al. Next‐generation personalised medicine for high‐risk paediatric cancer patients – the inform pilot study. Eur J Cancer. 2016;65:91–101. [DOI] [PubMed] [Google Scholar]
  • 28. Wong M, Mayoh C, Lau LMS, Khuong‐Quang DA, Pinese M, Kumar A, et al. Whole genome, transcriptome and methylome profiling enhances actionable target discovery in high‐risk pediatric cancer. Nat Med. 2020;26:1742–1753. [DOI] [PubMed] [Google Scholar]
  • 29. Brohl AS, Solomon DA, Chang W, Wang J, Song Y, Sindiri S, et al. The genomic landscape of the Ewing sarcoma family of tumors reveals recurrent STAG2 mutation. PLoS Genet. 2014;10:e1004475. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30. Wu L, Yao H, Chen H, Wang A, Guo K, Gou W, et al. Landscape of somatic alterations in large‐scale solid tumors from an Asian population. Nat Commun. 2022;13:4264. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31. Savola S, Klami A, Myllykangas S, Manara C, Scotlandi K, Picci P, et al. High expression of complement component 5 (c5) at tumor site associates with superior survival in Ewing's sarcoma family of tumour patients. ISRN Oncol. 2011;2011:168712. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Volchenboum SL, Andrade J, Huang L, Barkauskas DA, Krailo M, Womer RB, et al. Gene expression profiling of Ewing sarcoma tumors reveals the prognostic importance of tumor‐stromal interactions: a report from the children's oncology group. J Pathol Clin Res. 2015;1:83–94. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33. Gregorio A, Corrias MV, Castriconi R, Dondero A, Mosconi M, Gambini C, et al. Small round blue cell tumours: diagnostic and prognostic usefulness of the expression of b7‐h3 surface molecule. Histopathology. 2008;53:73–80. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34. Dowless M, Lowery CD, Shackleford T, Renschler M, Stephens J, Flack R, et al. Abemaciclib is active in preclinical models of Ewing sarcoma via multipronged regulation of cell cycle, dna methylation, and interferon pathway signaling. Clin Cancer Res. 2018;24:6028–6039. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35. Murakami T, Singh AS, Kiyuna T, Dry SM, Li Y, James AW, et al. Effective molecular targeting of CDK4/6 and IGF‐1R in a rare FUS‐ERG fusion CDKN2a‐deletion doxorubicin‐resistant Ewing's sarcoma patient‐derived orthotopic xenograft (PDOX) nude‐mouse model. Oncotarget. 2016;7:47556–47564. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36. Funk CM, Ehlers AC, Orth MF, Aljakouch K, Li J, Hölting TLB, et al. Chromosome 8 gain drives cancer progression by hijacking the translation factor 4E‐BP1 sensitizing for targeted CDK4/6 inhibition. bioRxiv. 2022. [Google Scholar]
  • 37. Shulman DS, Merriam P, Choy E, Guenther LM, Cavanaugh KL, Kao PC, et al. Phase 2 trial of palbociclib and ganitumab in patients with relapsed Ewing sarcoma. Cancer Med. 2023;12:15207–15216. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38. Musa J, Cidre‐Aranaz F, Aynaud MM, Orth MF, Knott MML, Mirabeau O, et al. Cooperation of cancer drivers with regulatory germline variants shapes clinical outcomes. Nat Commun. 2019;10:4128. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39. Brownhill SC, Taylor C, Burchill SA. Chromosome 9p21 gene copy number and prognostic significance of p16 in esft. Br J Cancer. 2007;96:1914–1923. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40. Morales E, Olson M, Iglesias F, Dahiya S, Luetkens T, Atanackovic D. Role of immunotherapy in Ewing sarcoma. J Immunother Cancer. 2020;8(2):e000653. [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

Fig. S1. Dependency for CDK4 and CCND1.

Fig. S2. Palbociclib IC50.

Fig. S3. Distribution in expression of CDKN2A.

Fig. S4. Exploratory analysis of the GDSC.

Table S1. Clinical trials with CDK4/6 inhibitors accepting patients with Ewing sarcoma.

MOL2-19-994-s001.docx (435.2KB, docx)

Data Availability Statement

Data from the DepMap and Project Achilles project are available from the depmap portal (https://depmap.org/portal/download/custom/) [17]. The gene expression data used in this study are available from the Gene Expression Omnibus repository under accession numbers GSE17618 [31], GSE63155 [32], and GSE63156 [32]. The drug sensitivity data are accessible from the Genomics of Drug Sensitivity in Cancer Database (https://www.cancerrxgene.org/) [15, 18, 19]. Genomic profiling data of clinical Ewing sarcoma specimens were collected from publicly available datasets from Dermawan et al. [20], Nguyen et al. [21], and Gounder et al. [22]. Data from Dermawan et al. and Nguyen et al. were collected using cBioPortal (https://www.cbioportal.org/) [23, 24, 25]. Data from Gounder et al. were downloaded directly from the source data published alongside their original report (https://www.nature.com/articles/s41467‐022‐30496‐0#data‐availability) [22].

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