Abstract
Purpose:
Understanding the mutational landscape of recurrent/metastatic head and neck squamous cell carcinoma (R/M HNSCC) is important in identifying biomarkers to determine which patients may benefit from immune checkpoint inhibitors (ICI).
Experimental Design:
The HAWK (NCT02207530), CONDOR (NCT02319044), and EAGLE (NCT02369874) studies evaluated R/M HNSCC treatment with durvalumab or durvalumab–tremelimumab. Tumor tissue samples pooled from HAWK/CONDOR (n = 153) and plasma cell–free DNA samples from EAGLE (n = 285) were analyzed to identify somatic alterations and association with survival.
Results:
The mutational landscape was similar in tissue and plasma. Compared with the wild type, TP53 mutations were associated with significantly shorter overall survival (OS; HR; 95% confidence interval) with standard of care (SoC; EAGLE: 2.12; 1.20–3.78) and ICIs (HAWK/CONDOR: 1.49; 1.05–2.12 and EAGLE: 1.44; 0.99–2.10). In EAGLE, patients with TP53 mutations had significantly longer OS with durvalumab–tremelimumab versus SoC (P = 0.045). KMT2D mutations were associated with a trend toward longer OS (HR; 95% confidence interval) versus the wild type in HAWK/CONDOR (0.81; 0.56–1.19) and a trend toward longer OS with ICIs versus SoC in EAGLE. For both mutations, a European Cooperative Oncology Group performance status of 1 was associated with worsened OS, and PD-L1 positivity was associated with improved OS.
Conclusions:
This is the first large-scale study to show the mutational landscape of R/M HNSCC and its association with clinical outcomes in patients treated with ICIs or SoC. The TP53 mutation was a negative prognostic marker; however, treatment with durvalumab–tremelimumab significantly improved survival over SoC. Further investigation of KMT2D as a predictive biomarker for immunotherapy in R/M HNSCC is warranted.
Translational Relevance.
In many patients with recurrent/metastatic head and neck squamous cell carcinoma (R/M HNSCC), the efficacy of immune checkpoint inhibitors (ICI) is limited. This retrospective molecular analysis of data from the HAWK, CONDOR, and EAGLE studies, which investigated durvalumab with/without tremelimumab, seeks to further understand the mutational landscape of R/M HNSCC and identify biomarkers that can help ascertain which patients with R/M HNSCC with disease progression on/after platinum-based chemotherapy may benefit from treatment with ICIs. The TP53 mutation was found to be a negative prognostic biomarker in R/M HNSCC, yet durvalumab plus tremelimumab performed significantly better than standard-of-care chemotherapy for patients with the TP53 mutation. KMT2D mutations were also associated with sensitivity to ICIs, and further investigation of KMT2D as a predictive biomarker for immunotherapy is warranted.
Introduction
Recurrent or metastatic head and neck squamous cell carcinoma (R/M HNSCC) is associated with poor prognosis and survival, with objective response rates of 4% to 14% and median overall survival (OS) of 4.3 to 6.7 months, following second-line treatment with chemotherapeutics (methotrexate, cetuximab, or paclitaxel; ref. 1).
Immune checkpoint inhibitors (ICI) targeting PD-1 and PD-L1 have demonstrated significant survival benefit for various tumor types when compared with standard therapies in prospective randomized clinical trials (2–4). Pembrolizumab (anti–PD-1) is approved by the FDA and European Medicines Agency (EMA) for the first-line treatment of metastatic or unresectable, recurrent HNSCC in combination with platinum and 5-fluorouracil [by the EMA for patients whose tumors express PD-L1 with a combined positive score (CPS) ≥1] or as monotherapy for patients whose tumors express PD-L1 with a CPS ≥1 (5, 6). Pembrolizumab and nivolumab (anti–PD-1) are approved by the FDA and EMA as monotherapy for R/M HNSCC with disease progression on/after platinum-based chemotherapy (pembrolizumab is approved by the EMA for patients whose tumors express PD-L1 with a tumor proportion score ≥50%; refs. 5–8), and in this setting, durvalumab (anti–PD-L1) with/without tremelimumab (anti–cytotoxic T-lymphocyte–associated antigen 4) has demonstrated variable antitumor activity (9–11).
Although treatment with ICIs can result in significant clinical benefit, many patients with R/M HNSCC experience limited efficacy; thus, biomarkers to identify patients most likely to benefit from ICI treatment are of increasing interest (9–13). Genomic complexity, intratumoral genetic heterogeneity, and high instability of HNSCC genomes may explain this treatment resistance and the tendency for locoregional recurrence (14, 15). PD-L1 has shown potential as a biomarker (13), with PD-L1 status used to guide treatment selection for R/M HNSCC [pembrolizumab monotherapy is recommended for untreated HNSCC with PD-L1 CPS ≥1 (FDA and EMA) and in patients with disease progression on/after chemotherapy with PD-L1 and a tumor proportion score ≥50% (EMA); refs. 5, 6]. A recent study demonstrated that the tumor mutational burden (TMB) is predictive of survival with second-line ICI treatment versus chemotherapy in platinum-resistant R/M HNSCC (16). Analysis of plasma-derived ctDNA is a noninvasive method that has shown potential for guiding treatment selection in some tumor types, including HNSCC (16–18).
Furthermore, clinical differences exist between HNSCCs: oropharyngeal cancers and human papillomavirus (HPV)–positive HNSCCs are associated with better clinical outcomes compared with non–oropharyngeal cancers and HPV-negative HNSCCs, respectively, with oropharyngeal cancers also being more frequently HPV-positive (64%) compared with non–oropharyngeal cancers (6%), although regional variations in HNSCC etiology should be taken into account (15, 19, 20). Understanding the molecular landscape of HNSCCs may illuminate the mechanisms underlying differential clinical outcomes and allow targeted treatment selection.
In this retrospective study, we conducted a comprehensive molecular analysis of data from the HAWK (9), CONDOR (10), and EAGLE (11) studies investigating durvalumab with/without tremelimumab in R/M HNSCC with disease progression on/after platinum-based chemotherapy and investigated the associations of identified mutations with survival after ICI treatment. These data further define the mutational landscape in R/M HNSCC and provide preliminary evidence for biomarkers that may be predictive of clinical benefit from ICI therapy.
Materials and Methods
Patients
Patients aged ≥18 years with histologically or cytologically (EAGLE only) confirmed R/M HNSCC of the oral cavity, oropharynx, larynx, or hypopharynx were included (9–11). In HAWK, only patients with PD-L1–high expression [tumor cells (TC) ≥25%] were included, whereas CONDOR only included those with PD-L1–low or –negative expression (TCs < 25%). Patients were not amenable to curative therapy and had tumor progression or recurrence during/after systemic treatment with one platinum-based regimen for R/M disease (HAWK/CONDOR/EAGLE) or progression within 6 months of the last dose of platinum therapy given as part of multimodality therapy with curative intent (EAGLE; refs. 9–11). All studies were conducted in accordance with ethical principles originating from the Declaration of Helsinki and consistent with International Conference on Harmonization and Good Clinical Practice guidelines, applicable regulatory requirements, and sponsor's policy on Bioethics and Human Biological Samples. All patients provided written informed consent before performing any protocol-related procedures (9–11).
Study designs
Study designs from HAWK, CONDOR, and EAGLE have been previously published (9–11). The phase II HAWK (NCT02207530; single-arm) and CONDOR (NCT02319044; randomized) and the phase III EAGLE (NCT02369874; randomized) studies were international, multicenter studies investigating second-line treatment with durvalumab monotherapy [10 mg/kg i.v. every 2 weeks (HAWK, CONDOR, and EAGLE)] and/or tremelimumab monotherapy [10 mg/kg i.v. every 4 weeks for seven doses, then every 12 weeks for two doses for up to 12 months (CONDOR)] and/or their combination [durvalumab 20 mg/kg every 4 weeks plus tremelimumab 1 mg/kg every 4 weeks for four cycles followed by durvalumab 10 mg/kg every 2 weeks (CONDOR and EAGLE)] in patients with R/M HNSCC (9–11). In CONDOR, the randomization ratio was 2:1:1 for combination therapy, durvalumab monotherapy, and tremelimumab monotherapy arms, respectively. In EAGLE, patients were randomized 1:1:1 to durvalumab monotherapy, durvalumab plus tremelimumab in combination, or investigator’s choice of single-agent standard of care (SoC), administered according to local regulations.
Sample collection for biomarker analysis was a prespecified exploratory objective of the HAWK, CONDOR, and EAGLE studies. In HAWK and CONDOR, either newly acquired (preferred) or archival tumor tissue (<3 years old) was collected (9, 10). In EAGLE, plasma samples were collected at baseline. Tumor samples from the phase II HAWK/CONDOR studies were pooled and used for signal finding and proof of concept. ctDNA analysis of plasma samples from the randomized phase III EAGLE study was used to assess whether mutations were predictive biomarkers of immunotherapy.
Mutational profiling of tumor tissue and plasma-derived ctDNA
In the HAWK/CONDOR studies, paired formalin-fixed, paraffin-embedded tumor (recent or archival tissue <3 years old) and peripheral blood mononuclear cell samples (germline controls) were evaluated by whole-exome sequencing, with 100 million reads at 200× coverage, using the HiSeq 4000 system (Illumina) for 2 × 100 paired-end reads. Somatic alterations were identified using VarDict (AstraZeneca; ref. 21). Only single-nucleotide variants (SNV) and small insertions and deletions (indels; <20 bp) with allele frequencies <80% and ≥5%, respectively, were retained in the mutation-calling pipeline.
In EAGLE, baseline plasma samples containing ctDNA were profiled to identify somatic alterations, including SNVs, indels, and copy-number amplifications, using the GuardantOMNI platform (Guardant Health), a targeted next-generation sequencing platform using a 500-gene panel with a 2 Mb DNA footprint (1 Mb coding regions only). Copy-number losses are typically hard to assess from ctDNA; hence, only amplifications derived from plasma are reported here.
The number of somatic mutations per patient sample was used to assess the mutational burden of tumors. Genes for investigation were selected based on historical evidence around HNSCC-associated mutations (15). Oncogenic and likely oncogenic mutations were annotated using information from ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/), Catalogue of Somatic Mutations in Cancer (https://cancer.sanger.ac.uk/cosmic), and OncoKB (https://www.oncokb.org/; ref. 22).
Evaluation of other biomarkers
PD-L1 expression status was determined using the VENTANA PD-L1 (SP263) Assay (Ventana Medical Systems), with high PD-L1 expression defined as patients whose tumors express PD-L1 in ≥25% of TCs (23). HPV was assessed locally using any method (HAWK, CONDOR, and EAGLE), or centrally using p16 IHC (HAWK and CONDOR).
Statistical analysis
The Wilcoxon rank-sum test was used to compare continuous variables, and the Fisher exact test was used to compare binary data. All P values were two-sided. Analyses were performed using SAS (version 9.4, SAS Institute) and R (version 4.1.2, R Foundation). Survival analyses were run using the R “survival” package (24, 25). The Kaplan–Meier method was used to calculate univariate survival estimates for OS and progression-free survival (PFS). A Cox proportional hazard model was used to define the association between the mutational status of genes with HRs for OS and PFS. P values were assessed using the log-rank test. Multivariate Cox proportional hazard model analyses were used to compute HRs and 95% confidence intervals for TP53 and KMT2D mutations in the EAGLE dataset, using risk factors that included mutation presence, treatment arm, HPV status, PD-L1 status, smoking status, TMB category, European Cooperative Oncology Group (ECOG) performance status, and mutation × treatment arm interaction. Schoenfeld residuals by time were checked for proportional hazard (26). Statistical measures of biomarker data and association with clinical outcomes are descriptive only and considered exploratory.
Data availability
Data underlying the findings described in this article may be obtained in accordance with AstraZeneca’s data sharing policy described at: https://astrazenecagrouptrials.pharmacm.com/ST/Submission/Disclosure. Data for studies directly listed on Vivli can be requested through Vivli at www.vivli.org. Data for studies not listed on Vivli could be requested through Vivli at https://vivli.org/members/enquiries-about-studies-not-listed-on-the-vivli-platform/. The AstraZeneca Vivli member page is also available outlining further details: https://vivli.org/ourmember/astrazeneca/.
Results
Patient demographics and clinical characteristics
Tissue samples were available for analysis from 153 of 378 patients from the HAWK (n = 50/111) and CONDOR (n = 103/267) studies, and plasma samples were obtained from 285 of 736 patients in the EAGLE study. Between HAWK/CONDOR and EAGLE, patients’ baseline clinical characteristics were generally similar, except for ECOG performance status and HPV status (Table 1). Objective response rates in the HAWK/CONDOR and EAGLE studies were also summarized (Table 1).
Table 1.
Patient demographics and clinical characteristics in the HAWK/CONDOR tissue and EAGLE plasma cohorts.
| HAWK/CONDOR (N = 153a) | EAGLE (N = 285) | |
|---|---|---|
| Median (range) age, years | 60 (23–84) | 60 (23–84) |
| Age <65 years | 106 (69.3) | 128 (44.9) |
| Sex, male | 114 (74.5) | 241 (84.6) |
| Race, White | 132 (86.3) | 211 (74.0) |
| ECOG performance status = 1 | 116 (75.8) | 194 (68.1) |
| Smoking history | ||
| Yes | 115 (75.2) | 219 (76.8) |
| No | 38 (24.8) | 66 (23.2) |
| Primary tumor site | ||
| Oral cavity | 44 (28.8) | 70 (24.6) |
| Oropharynx | 66 (43.1) | 113 (39.6) |
| Hypopharynx | 18 (11.8) | 53 (18.6) |
| Larynx | 24 (15.7) | 40 (14.0) |
| Other | 1 (0.7) | 9 (3.2) |
| PD-L1 high | 50 (32.7) | 94 (33.0) |
| HPV positive | 46 (30.1) | 57 (20.0) |
| Oral cavity | 7 (4.6) | 6 (2.1) |
| Oropharynx | 30 (19.6) | 38 (13.3) |
| Hypopharynx | 3 (2.0) | 5 (1.8) |
| Larynx | 6 (3.9) | 4 (1.4) |
| Other | 0 (0) | 4 (1.4) |
| Objective response | ||
| CR | 0 (0) | 7 (2.5) |
| PR | 17 (11.1) | 51 (17.9) |
| SD | 34 (22.2) | 75 (26.3) |
| PD | 96 (62.7) | 143 (50.2) |
| NE | 6 (3.9) | 9 (3.2) |
Data are n (%) unless otherwise specified.
Abbreviations: CR, complete response; NE, not evaluated; PD, progressive disease; PR, partial response; SD, stable disease.
50 tissue samples were obtained from patients enrolled in HAWK, and 103 tissue samples were obtained from patients enrolled in CONDOR.
Somatic mutational patterns in second-line R/M HNSCC tumor tissue and plasma-derived ctDNA
In both datasets, significantly more somatic mutation counts (number of somatic SNVs/indels) were detected in samples from patients with versus without smoking history (Fig. 1A and B). No association was observed between somatic mutation counts and PD-L1 or HPV status in tissue or plasma samples (Fig. 1C–F). There was no statistical difference in the number of somatic mutation counts between HPV-positive or HPV-negative patients with oropharyngeal cancer and a history of smoking, although a numerically higher number of mutations were seen in patients with smoking history, regardless of HPV status (Supplementary Fig. S1).
Figure 1.
Somatic SNVs/indels and somatic mutational patterns derived from tumor tissue and plasma samples from patients with R/M HNSCC. Somatic SNV/indel counts detected in the HAWK/CONDOR tissue and EAGLE plasma samples by smoking history (A and B, respectively), PD-L1 status (C and D, respectively), and HPV status (E and F, respectively). The somatic mutational patterns across demographics and tumor sites in the HAWK/CONDOR tissue samples (G) and the EAGLE plasma samples (H). NA, not applicable; Neg, negative; Pos, positive; RTK, receptor tyrosine kinase.
Mutational landscape similarities between tumor tissue and plasma-derived ctDNA
A similar mutational landscape was observed in the HAWK/CONDOR tissue and EAGLE plasma samples across demographics and tumor locations (Fig. 1G and H). Mutational prevalence was significantly correlated between tissue and plasma samples (Supplementary Fig. S2; Spearman ρ = 0.456, P = 0.001). Six genes had mutational prevalence of ≥20% in EAGLE [TP53, FAT1 (promotor mutations found in EAGLE plasma samples only), NOTCH1, PIK3CA, KMT2D, and TERT; Fig. 1H and Table 2]. TP53 was the most frequently mutated gene in both tissue (64%) and plasma (78%) samples; other tumor-suppressor gene mutations had moderate prevalence (NF1, 16% and 9%; IGF2R, 13% and 6%; PTCH1, 13% and 7%, respectively). Frequent mutations were identified in several epigenetic regulation and DNA damage response and repair (DDR) genes, with comparable prevalence found between tissue and plasma samples (Fig. 1G and H; Table 2).
Table 2.
Mutational prevalence across demographics and tumor locations of select genes in the HAWK/CONDOR tissue samples and EAGLE plasma samples.a
| HAWK/CONDOR (tissue) | |||||||
|---|---|---|---|---|---|---|---|
| All pts(N = 153c) | HPV−b(N = 100) | HPV+b(N = 46) | White(N = 132) | Others(N = 21) | OPC(N = 66) | Non-OPC(N = 87) | |
| TP53 | 98 (64%) | 78 (78%) | 13 (28%) | 88 (67%) | 10 (48%) | 31 (47%) | 67 (77%) |
| FAT1 b | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| NOTCH1 | 42 (27%) | 27 (27%) | 13 (28%) | 36 (27%) | 6 (29%) | 19 (29%) | 23 (26%) |
| PIK3CA | 26 (17%) | 14 (14%) | 11 (24%) | 21 (16%) | 5 (24%) | 10 (15%) | 16 (18%) |
| KMT2D | 47 (31%) | 33 (33%) | 13 (28%) | 41 (31%) | 6 (29%) | 19 (29%) | 28 (32%) |
| TERT d | 9 (6%) | 7 (7%) | 1 (2%) | 8 (6%) | 1 (5%) | 3 (5%) | 6 (7%) |
| EAGLE (plasma) | |||||||
|---|---|---|---|---|---|---|---|
| All pts(N = 285) | HPV−b(N = 216) | HPV+(N = 57)b | White(N = 211) | Others(N = 74) | OPC(N = 113) | Non-OPC(N = 172) | |
| TP53 | 221 (78%) | 183 (85%) | 29 (51%) | 164 (78%) | 57 (77%) | 78 (69%) | 143 (83%) |
| FAT1 b | 66 (23%) | 45 (21%) | 14 (25%) | 45 (21%) | 21 (28%) | 25 (22%) | 41 (24%) |
| NOTCH1 | 57 (20%) | 48 (22%) | 7 (12%) | 44 (21%) | 13 (18%) | 18 (16%) | 39 (23%) |
| PIK3CA | 61 (21%) | 48 (22%) | 9 (16%) | 47 (22%) | 14 (19%) | 24 (21%) | 37 (22%) |
| KMT2D | 85 (30%) | 66 (31%) | 13 (23%) | 63 (30%) | 22 (30%) | 27 (24%) | 58 (34%) |
| TERT d | 63 (22%) | 55 (25%) | 5 (9%) | 36 (17%) | 27 (37%) | 17 (15%) | 46 (27%) |
Data are n (%) unless otherwise specified.
Abbreviations: OPC, oropharyngeal cancer; pts, patients; WES, whole exome sequencing.
Table contains the genes in which more than 20% prevalence was reported.
HPV testing was carried out on all HNSCC locations.
50 tissue samples were obtained from patients enrolled in HAWK, and 103 tissue samples were obtained from patients enrolled in CONDOR.
FAT1 and TERT promoter mutations are included in the GuardantOMNI panel used for plasma sequencing of EAGLE samples; however, promotor mutations are not available from WES data, resulting in lower reported frequencies of FAT1 and TERT1 mutations in HAWK/CONDOR.
A significantly higher prevalence of TP53 mutations was observed in the HPV-negative versus HPV-positive subgroup in both tissue (78% vs. 28%, P = 1.4e−08) and plasma (85% vs. 51%, P = 3.9e−07) samples (Table 2). The prevalence of PIK3CA mutations was numerically greater in the HPV-positive versus HPV-negative subgroup for tissue samples (24% vs. 14%, P = 0.16); however, for plasma samples, the prevalence of PIK3CA mutations was numerally lower in the HPV-positive versus HPV-negative subgroup (16% vs. 22%, P = 0.36). NOTCH1 mutations had similar prevalence between HPV subgroups for tissue samples but were more prevalent in the HPV-negative subgroup for plasma samples. In plasma samples, TERT promoter mutations were significantly more prevalent in the HPV-negative versus HPV-positive subgroup (25% vs. 9%, P = 0.007). Among a total of 70 observed TERT mutations, 59 mutations occurring in 57 patients were in the promoter and were likely oncogenic [based on OncoKB (22)] recurrent mutations, −124 C>T, n = 34 and −146 C>T, n = 11.
Copy-number alteration landscape in R/M HNSCC
Amplifications were identified in 98 genes from 145 of 285 (51%) patients in EAGLE plasma samples; for patients with amplifications, a median of 3 was found (Fig. 2). CCND1 on 11q13 was the most frequently amplified gene, amplified more frequently in HPV-negative than HPV-positive tumors (29% vs. 11%, P = 0.005). FGF3, FGF19, PIK3CA, and PIK3CB had recurrent amplifications in >10% of plasma samples (Fig. 2). Notably, CCND1, FGF3, and FGF19 on 11q13 were co-amplified in samples from most patients, as were amplifications in PIK3CA and PIK3CB. Recurrent amplifications in several receptor tyrosine kinases, such as EGFR and ERBB2, predominated in HPV-negative tumors.
Figure 2.
CNA mutational landscape across demographics and tumor locations in plasma samples from patients with R/M HNSCC from the EAGLE study. Mutations with prevalence ≥4% are shown. CNA, copy-number alteration; NA, not applicable; Neg, negative; Pos, positive.
TP53 mutations and resistance to immunotherapy and SoC chemotherapy in R/M HNSCC
TP53 mutations were identified in 64% of patients in HAWK/CONDOR and 78% of patients in EAGLE (Fig. 1G and H; Table 2). Compared with the wild type (WT), TP53 mutations were associated with shorter OS in HAWK/CONDOR (ICI, P = 0.024; Fig. 3A) and in EAGLE (SoC, P = 0.009; Fig. 3B and ICI, P = 0.055; Fig. 3C). Similar results were observed for PFS (Supplementary Fig. S3A–S3C), supporting TP53 mutation as a negative prognostic marker.
Figure 3.
TP53 mutations and resistance to immunotherapy and SoC chemotherapy in R/M HNSCC assessed by OS. Kaplan–Meier plots of OS in the TP53 WT and mutant subgroups for patients treated with ICIs in the HAWK/CONDOR study (A), SoC in the EAGLE study (B), or ICIs in the EAGLE study (C). Kaplan–Meier plots of OS in the SoC, durvalumab, or durvalumab plus tremelimumab treatment subgroups for patients with TP53 mutations in the EAGLE study (D). Multivariate Cox proportional hazard model analysis showing OS HRs according to individual risk factors in the EAGLE study (E). Kaplan–Meier plots of OS in the SoC, durvalumab, or durvalumab plus tremelimumab treatment subgroups for patients with TP53 mutations and R/M HNSCC non–OPC (F) or OPC (G) in the EAGLE study. CI, confidence interval; Neg, negative; OPC, oropharyngeal cancer; Pos, positive; PS, performance status.
In EAGLE, the 18-month OS rate of ICI-treated patients whose tumors harbored TP53 mutations was roughly four times that of patients treated with SoC (23–25% vs. 6%, respectively; Fig. 3D). In patients with TP53 mutations, OS HRs were <1, favoring ICIs over SoC (Fig. 3D). Based on this observation, a multivariate Cox proportional hazard model analysis of the EAGLE dataset was undertaken which showed significantly longer OS for patients with TP53 mutations who were treated with durvalumab plus tremelimumab (P = 0.045) and a trend toward improved OS for patients treated with durvalumab versus SoC (Fig. 3E). OS HRs were not confounded by TMB category [even though TP53 mutations were significantly correlated with increased somatic mutation numbers, a surrogate of the TMB (Supplementary Fig. S3D)], HPV status, or smoking history; however, ECOG performance status and PD-L1 status were strongly associated with OS (Fig. 3E).
In EAGLE, in patients whose tumors harbored TP53 mutations, durvalumab significantly improved OS versus SoC in patients with non–oropharyngeal cancer (P = 0.012), and durvalumab and durvalumab plus tremelimumab both numerically improved 18-month OS rates versus SoC in patients with non–oropharyngeal cancer and oropharyngeal cancer (Fig. 3F and G).
Predictability of mutations for immunotherapy outcomes
The TMB was greater in tissue and plasma samples with KMT2D mutations compared with WT KMT2D (Fig. 4A and B). Compared with the WT, a trend of longer OS was found in patients with KMT2D mutations in HAWK/CONDOR (Fig. 4C), whereas shorter OS was found in the EAGLE SoC arm (Fig. 4D). In EAGLE, patients whose tumors harbored KMT2D mutations showed significantly longer OS with ICIs versus SoC (Fig. 4E). A multivariate Cox proportional hazard model analysis of the EAGLE dataset showed a trend toward longer OS for patients with KMT2D mutations treated with durvalumab compared with patients treated with SoC (Fig. 4F). OS HRs were not associated with TMB category, HPV status, or smoking history; however, ECOG performance status and PD-L1 status were strongly associated with OS, similar to results for TP53 mutations (Fig. 4F). In patients with non–oropharyngeal cancer, OS was significantly longer for durvalumab plus tremelimumab, and a trend for longer OS for durvalumab, versus SoC. In patients with oropharyngeal cancer, OS versus SoC trended longer in both ICI arms (Fig. 4G and H; limited statistical power due to small sample size).
Figure 4.
KMT2D mutations and response to immunotherapy and SoC chemotherapy in R/M HNSCC. The TMB by KMT2D mutation status detected in the HAWK/CONDOR study tissue samples (A) and EAGLE study plasma samples (B). Kaplan–Meier plots of OS in the KMT2D WT and mutant subgroups for patients treated with ICIs in the HAWK/CONDOR study (C) and SoC in the EAGLE study (D). Kaplan–Meier plots of OS in the SoC, durvalumab, or durvalumab plus tremelimumab treatment subgroups for patients with KMT2D mutations in the EAGLE study (E). Multivariate Cox proportional hazard model analysis showing OS HRs according to individual risk factors in the EAGLE study (F). Kaplan–Meier plots of OS in the SoC, durvalumab, or durvalumab plus tremelimumab treatment subgroups for patients with KMT2D mutations and R/M HNSCC non-OPC (G) or OPC (H) in the EAGLE study. CI, confidence interval; Neg, negative; OPC, oropharyngeal cancer; Pos, positive; PS, performance status.
In EAGLE, TERT promoter mutations correlated with significantly worse OS and PFS, compared with WT TERT, for patients treated with durvalumab plus tremelimumab (HR, 3.32; log-rank P < 0.001 and HR, 2.19, P = 0.003, respectively) or SoC (HR, 1.91, P = 0.010 and HR, 2.45, P = 9.5e−04, respectively; Supplementary Fig. S4A–S4D); in the durvalumab arm, there was a trend for poorer OS but no significant difference in either OS or PFS (Supplementary Fig. S4E and S4F). CCND1 amplifications correlated with a significantly worse outcome in the EAGLE durvalumab arm only (OS HR, 2.12, P = 0.002; Supplementary Fig. S5A–S5C). PIK3CA and PIK3CB amplifications were associated with significantly shorter OS in both the durvalumab and SoC arms (Supplementary Fig. S5D–S5F).
Discussion
These results suggest that for R/M HNSCC, the mutational landscape derived from sequencing plasma or tumor samples is comparable. Consistent with the understanding that carcinogens in tobacco could cause DNA damage and gene mutations (27), samples in this analysis from patients with smoking history showed numerically higher somatic mutation counts than those without smoking history. No association was observed between somatic mutation counts and PD-L1 or HPV status, similar to previous reports in treatment-naïve patients (15, 28).
TP53 was the most frequently mutated gene, in concordance with reports from primary tumors (15) and other studies in R/M HNSCC (29). Consistent with other studies, and with the known pathogenesis of HPV-associated HNSCC, TP53 was more frequently mutated in the HPV-negative subgroup (15, 29–31). Recurrent mutations in the KMT2D epigenetic regulation gene in R/M HNSCC are prevalent (32). Notably, KMT2D showed numerically increased mutation frequency compared with HNSCC tumors from the treatment-naïve The Cancer Genome Atlas (TCGA) cohort (31% in tissue/32% in plasma vs. 18% in TCGA), as did other genes involved in epigenetic regulation or DDR, and TERT (15, 33). LRP1B and NOTCH1 had higher mutation frequency, and PIK3CA had lower mutation frequency, in HAWK/CONDOR tissue samples compared with TCGA cohort. However, these genes and FAT1 showed similar mutation frequency between EAGLE plasma samples and TCGA cohort (15, 33). This suggests that the somatic mutational landscape in R/M HNSCC may be modified in comparison with treatment-naïve HNSCC. Alternatively, the differences in mutation prevalence may be driven by differences in patient population (HAWK/CONDOR and EAGLE examined R/M HNSCC only, whereas TCGA HNSCC cohort included all stages) or sample type (sequencing of plasma ctDNA in EAGLE captured primary and metastatic sites, whereas sequencing of tissue in HAWK/CONDOR and TCGA cohort captured only a single tumor location) or differences in the proportion of HPV-related versus nonrelated tumors between cohorts. Additional studies are required to explore the impact of first-line treatment on these genes. Tumor-suppressor p53 is critical for maintaining genomic stability and preventing oncogenesis; functional mutations of TP53 confer resistance to chemotherapies in multiple tumor types (34). In the EAGLE SoC arm, TP53 mutations were associated with significantly shorter OS compared with WT TP53, with a similar trend observed for PFS, suggesting TP53 mutations in R/M HNSCC confer resistance to chemotherapies, consistent with results in other tumor types (34). However, patients whose tumors harbored TP53 mutations showed significantly longer OS for durvalumab plus tremelimumab, and a trend for longer OS for durvalumab, versus SoC. Although HPV-negative tumors more frequently harbored TP53 mutations, OS HRs were not significantly different in HPV-positive and HPV-negative patients. In contrast to previous studies which showed longer OS for patients with low-risk or WT TP53 mutations versus high-risk TP53 mutations in patients with HNSCC or HPV-negative HNSCC (35, 36), application of the same classification system to the analysis of OS and PFS in HAWK, CONDOR, and EAGLE did not differentiate high-risk versus low-risk TP53 mutations in patients or the HPV-negative subgroup. In patients whose tumors harbored TP53 mutations, the 18-month survival rate was approximately four times higher with ICIs versus SoC, highlighting the predictive value of TP53 mutations for durvalumab therapies. In order to evaluate the influence of TP53 mutational status on disease-free survival of patients with locally advanced HNSCC, the phase II ECOG-ACRIN 3132 trial utilizes TP53 mutational status as an integral biomarker for stratification of patients before randomization to adjuvant radiotherapy with or without cisplatin (37). Future studies of ICIs may be similarly designed to determine whether TP53 mutational status could be used as a prognostic or predictive biomarker. TP53 mutations were significantly correlated with somatic mutation numbers, suggesting TP53 mutations may result in accumulation of mutations leading to better outcomes with ICI treatment; however, TP53 mutations were predictive of outcomes independent of the TMB.
Similar to results in esophageal squamous cell carcinoma (ref. 38), R/M HNSCC tumors harboring KMT2D mutations were associated with a higher mutational burden compared with the KMT2D WT. In tumor models, KMT2D mutations have been shown to sensitize tumors to ICIs (32) and have been significantly correlated with longer OS in esophageal squamous cell carcinoma (38). Consistent with these observations, a trend for longer OS was found in patients whose tumors harbored KMT2D mutations versus the KMT2D WT in HAWK/CONDOR. In EAGLE, patients whose tumors harbored KMT2D mutations showed a trend for longer OS when receiving durvalumab compared with SoC, suggesting KMT2D mutations may be a predictive biomarker for durvalumab. Further validation in R/M HNSCC with a larger sample size is warranted.
Consistent with previous reports, TERT promoter mutations were significantly correlated with worse PFS and OS with both ICIs and SoC, suggesting a potentially prognostic effect (39). Amplification of CCND1 was correlated with worse outcomes in the EAGLE durvalumab arm, consistent with reports from a meta-analysis across multiple tumor types (40). As oncogenic drivers, PIK3CA and PIK3CB amplifications were associated with shorter OS with durvalumab or SoC, suggesting their amplification may be prognostic for poor outcomes.
Limitations of our study include that the HAWK/CONDOR (9, 10) studies did not include a SoC arm; therefore, these findings need further validation. The HAWK (9), CONDOR (10), and EAGLE (11) studies investigated ICI treatment in patients with R/M HNSCC with progression on/after platinum-based chemotherapy; thus, these findings may not be applicable to first-line ICI treatment. The fact that patients were undergoing second-line treatment for HNSCC may also account for the high frequency of TP53 mutations observed across HPV-positive samples. It is also conceivable that a plasma-based analysis based on p53 mutations may be confounded by the detection of ctDNA and mutations attributable to clonal hematopoiesis (41). However, it is unlikely that these are major confounders in the present analysis, as mutation frequency is broadly similar in both tissue and plasma samples, and the prognostic impact is observed in tissue-only analyses in the HAWK and CONDOR studies. Furthermore, the same analytical assays were used for both p16/HPV-positive and p16/HPV-negative populations. Although p16 status is used as a surrogate marker for HPV, a proportion of p16-positive patients have been shown to be HPV negative; therefore, it is possible that some HPV-negative patients were classified as HPV positive in the present study (42). Additionally, given that durvalumab and tremelimumab are not used in clinical practice in HNSCC, it is important to establish the clinical relevance of these potential biomarkers in patients receiving current ICI SoC. Some tissue samples analyzed from HAWK/CONDOR (9, 10) were archival (up to 3 years old, potentially including diagnostic samples from treatment-naïve patients) and therefore may not have been representative of the disease at the time of treatment. Plasma samples were taken at baseline in EAGLE (11). Different assays were also used for each sample type (whole-exome sequencing in tissue vs. next-generation sequencing with a targeted 500-gene panel in plasma), though similar results were observed. Finally, evaluable samples were not available for all patients, potentially due to ascertainment bias. Overall, we consider the study population to be representative of patients with second-line HNSCC; cohorts were equivalent in terms of patient and tumor characteristics, with the exception of PD-L1 status. However, it is important to note the inherent difficulty of accurately profiling the genotype of a heterogeneous tumor using limited tissue samples (43).
To the best of our knowledge, this is the first large-scale study to show the mutational landscape of R/M HNSCC with progression on/after platinum-based chemotherapy and its association with ICI efficacy. Tumor tissue samples and liquid biopsies demonstrated similar mutational landscapes, supporting liquid biopsy as an option for mutation profiling of R/M HNSCC. There were frequent mutations in DDR and epigenetic regulation genes, possibly induced by previous treatment. Mutations in TP53 were a negative prognostic biomarker, but patients with TP53 mutations performed better with immunotherapy than SoC chemotherapy. KMT2D mutations are also associated with sensitivity to immunotherapies; further investigation and prospective validation of KMT2D as a predictive biomarker for immunotherapy in R/M HNSCC is warranted. Overall, future studies involving immune checkpoint blockade may benefit through further investigation of these findings.
Supplementary Material
Supplementary Figure S1. Somatic SNVs/indels derived from tumor tissue and plasma samples in patients with OPC. Somatic SNVs/indels counts detected in the HAWK/CONDOR tissue and EAGLE plasma samples by HPV status and according to smoking history.
Supplementary Figure S2. Scatterplot with Spearman’s rho correlation of 47 genes (tissue prevalence >3% and plasma prevalence >5%) between HAWK/CONDOR tissue and EAGLE plasma data.
Supplementary Figure S3. TP53 mutations and resistance to immunotherapy and SoC chemotherapy in R/M HNSCC assessed by PFS. Kaplan-Meier plot of PFS in the TP53 wild type and mutant subgroups for patients treated with ICI in the HAWK/CONDOR study (A), SoC in the EAGLE study (B), or ICI in the EAGLE study (C). Somatic mutation counts by TP53 mutation status detected in the EAGLE study plasma samples (D).
Supplementary Figure S4. TERT mutations and response to immunotherapy and SoC chemotherapy in the EAGLE study. Kaplan-Meier plots of OS and PFS for patients treated with durvalumab plus tremelimumab (A and B), SoC (C and D), and durvalumab (E and F) in the EAGLE study.
Supplementary Figure S5. CCND1 and PIK3CA/PIK3CB amplification and response to immunotherapy and SoC chemotherapy in the EAGLE study. Kaplan-Meier plot of OS in the CCND1 wild-type and amplification subgroups for patients treated with durvalumab plus tremelimumab (A), durvalumab (B), or SoC (C) in the EAGLE study. Kaplan-Meier plot of OS in the PIK3CA/PIK3CB wild-type and amplification subgroups for patients treated with durvalumab plus tremelimumab (D), durvalumab (E), or SoC (F) in the EAGLE study.
Acknowledgments
The CONDOR, HAWK, and EAGLE studies were sponsored by AstraZeneca. The authors would like to thank the patients, their families and caregivers, and all investigators involved in this study. We would like to thank Jiabu Ye, PhD, and Philip He, PhD, for contributing to the study. Medical writing support, under the direction of authors, was provided by Elaine Groat, PhD, and Claire Tinderholm, PhD, CMC Connect, a division of IPG Health Medical Communications, and was funded by AstraZeneca, in accordance with Good Publication Practice (GPP 2022). Results published here are in part based upon data generated by TCGA Research Network: https://www.cancer.gov/tcga.
Footnotes
Note: Supplementary data for this article are available at Clinical Cancer Research Online (http://clincancerres.aacrjournals.org/).
Authors’ Disclosures
P. McCoon reports other support from AstraZeneca outside the submitted work. Z. Lai reports other support from AstraZeneca outside the submitted work. S. Wildsmith reports a patent for WO/2021/228988 pending, as well as employment with AstraZeneca. N. Morsli reports other support from AstraZeneca during the conduct of the study, as well as other support from AstraZeneca outside the submitted work. R. Raja reports other support from AstraZeneca outside the submitted work, as well as a patent for Blood-based Tumor Mutation Burden Predicts Overall Survival in NSCLC issued to AstraZeneca. N. Holoweckyj reports personal fees from AstraZeneca outside the submitted work. J. Walker reports personal fees from AstraZeneca during the conduct of the study. M. de los Reyes reports other support from AstraZeneca outside the submitted work. R. Mesía reports personal fees from MSD, Merck Healthcare KGaA, Roche, PDS Biotechnology, Genmab, and Regeneron outside the submitted work. L. Licitra reports personal fees from MSD IT, Merck Serono SpA Healthcare Professional, Merck Healthcare KGaA, GlaxoSmithKline, F. Hoffman La Roche Ltd, EMD Serono Research & Development Institute, Inc., Boehringer Ingelheim International GmbH, Simon-Kucher & Partners, Rgenta Therapeutics, Inc., Alentis Therapeutics AG, MedImmune Limited, Simon-Kucher & Partners Italia Srl, Bristol Myers Squibb, ALTIS Omnia Pharma Service Srl, AbbVie Srl, Purple Biotech, AVEO Pharmaceuticals, Inc., ALX Oncology Inc., and Bicara Therapeutics Inc., as well as other support from Adlai Nortye, AstraZeneca, Debiopharm International SA, Eli Lilly and Company, Exelixis, ISA Therapeutics, Merck Sharp & Dohme Corp., Nektar Therapeutics, Regeneron, Sun Pharmaceuticals, Incyte Biosciences International Srl, Gilead Sciences, Inc., Genmab, Merck Healthcare KGaA, and TAE Life Sciences outside the submitted work. R.L. Ferris reports other support from AstraZeneca/MedImmune, Coherus BioSciences, CureVac, CytoAgents, Merck, Merus N.V., Mirror Biologics, Inc., Rakuten Medical Inc., Regeneron, and SIRPant Immunotherapeutics, personal fees from Bicara Therapeutics, EMD Serono, Everest Clinical Research Corporation, and Nanobiotix, and personal fees and other support from Novasenta during the conduct of the study. J. Fayette reports personal fees and nonfinancial support from AstraZeneca during the conduct of the study, as well as personal fees from BMS, Seagen, Takeda, and Meru Pharmaceuticals and personal fees and nonfinancial support from MSD and Merck Serono outside the submitted work. D.P. Zandberg reports personal fees from Blueprint Medicines, MacroGenics, Merck, Inhibrx, Seagen, Bicara Therapeutics, and Coherus BioSciences and grants from Merck and Bristol Myers Squibb outside the submitted work. L.L. Siu reports grants and personal fees from AstraZeneca/MedImmune during the conduct of the study, as well as grants from AbbVie, Amgen, Astellas Pharma Inc., Avid Bioservices, Bayer, Boehringer Ingelheim, Bristol Myers Squibb, Celgene, GlaxoSmithKline, Intensity Therapeutics, Karyopharm Therapeutics, Merck, Mirati Therapeutics, Inc., Novartis, Pfizer, Roche/Genentech, Shattuck Labs, and Symphogen and personal fees from Treadwell Therapeutics, Agios, Amgen, Arvinas, Coherus BioSciences, Daiichi Sankyo, GlaxoSmithKline, Janpix, Marengo Therapeutics, Merck, Navire Pharma, Pfizer, Relay Therapeutics, Roche, Rubius Therapeutics, Symphogen, Tessa Therapeutics, and Voronoi outside the submitted work. R. Haddad reports grants from AstraZeneca during the conduct of the study, as well as personal fees and other support from AstraZeneca, GlaxoSmithKline, Merck, EMD Serono, Genmab, Genzyme, Genentech, Pfizer, RAPT Therapeutics, PDS Biotechnology, Boehringer Ingelheim, Nanobiotix, Hookipa Pharma, ISA Therapeutics BV, DSMB, and JNJ outside the submitted work. No disclosures were reported by the other authors.
Authors’ Contributions
P. McCoon: Conceptualization, formal analysis, supervision, writing–original draft, project administration, writing–review and editing. Y. Wang: Formal analysis, writing–original draft, writing–review and editing. Z. Lai: Formal analysis, writing–original draft, writing–review and editing. Q. Zhang: Conceptualization, formal analysis, writing–original draft, writing–review and editing. W. Li: Conceptualization, data curation, formal analysis, supervision, writing–original draft, project administration, writing–review and editing. S. Wildsmith: Conceptualization, data curation, supervision, writing–original draft, project administration, writing–review and editing. N. Morsli: Conceptualization, formal analysis, writing–original draft, writing–review and editing. R. Raja: Data curation, formal analysis, writing–original draft, writing–review and editing. N. Holoweckyj: Data curation, writing–original draft, project administration, writing–review and editing. J. Walker: Conceptualization, data curation, supervision, writing–original draft, writing–review and editing. M. de los Reyes: Conceptualization, formal analysis, writing–original draft, project administration, writing–review and editing. R. Mesía: Data curation, formal analysis, writing–original draft, writing–review and editing. L. Licitra: Data curation, formal analysis, writing–original draft, writing–review and editing. R.L. Ferris: Data curation, formal analysis, writing–original draft, writing–review and editing. J. Fayette: Data curation, formal analysis, writing–original draft, writing–review and editing. D.P. Zandberg: Data curation, formal analysis, supervision, writing–original draft, writing–review and editing. L.L. Siu: Data curation, formal analysis, supervision, writing–original draft, writing–review and editing. R. Haddad: Conceptualization, data curation, formal analysis, supervision, writing–original draft, writing–review and editing.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary Figure S1. Somatic SNVs/indels derived from tumor tissue and plasma samples in patients with OPC. Somatic SNVs/indels counts detected in the HAWK/CONDOR tissue and EAGLE plasma samples by HPV status and according to smoking history.
Supplementary Figure S2. Scatterplot with Spearman’s rho correlation of 47 genes (tissue prevalence >3% and plasma prevalence >5%) between HAWK/CONDOR tissue and EAGLE plasma data.
Supplementary Figure S3. TP53 mutations and resistance to immunotherapy and SoC chemotherapy in R/M HNSCC assessed by PFS. Kaplan-Meier plot of PFS in the TP53 wild type and mutant subgroups for patients treated with ICI in the HAWK/CONDOR study (A), SoC in the EAGLE study (B), or ICI in the EAGLE study (C). Somatic mutation counts by TP53 mutation status detected in the EAGLE study plasma samples (D).
Supplementary Figure S4. TERT mutations and response to immunotherapy and SoC chemotherapy in the EAGLE study. Kaplan-Meier plots of OS and PFS for patients treated with durvalumab plus tremelimumab (A and B), SoC (C and D), and durvalumab (E and F) in the EAGLE study.
Supplementary Figure S5. CCND1 and PIK3CA/PIK3CB amplification and response to immunotherapy and SoC chemotherapy in the EAGLE study. Kaplan-Meier plot of OS in the CCND1 wild-type and amplification subgroups for patients treated with durvalumab plus tremelimumab (A), durvalumab (B), or SoC (C) in the EAGLE study. Kaplan-Meier plot of OS in the PIK3CA/PIK3CB wild-type and amplification subgroups for patients treated with durvalumab plus tremelimumab (D), durvalumab (E), or SoC (F) in the EAGLE study.
Data Availability Statement
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