Claire Margolis

Genomic correlates of response to immune checkpoint therapies in clear cell renal cell carcinoma

SNFing out antitumor immunity Immune checkpoint inhibitors induce durable tumor regressions in some, but not all, cancer patients. Understanding the mechanisms that determine tumor sensitivity to these drugs could potentially expand the number of patients who benefit (see the Perspective by Ghorani and Quezada). Pan et al. discovered that tumor cells in which a specific SWI/SNF chromatin remodeling complex had been experimentally inactivated were more sensitive to T cell–mediated killing. The cells were more responsive to interferon-γ, leading to increased secretion of cytokines that promote antitumor immunity. Miao et al. examined the genomic features of tumors from patients with metastatic renal cell carcinoma who had been treated with immune checkpoint inhibitors. Tumors harboring inactivating mutations in PBRM1, which encodes a subunit of the same SWI/SNF complex, were more likely to respond to the drugs. Science, this issue p. 770, p. 801; see also p. 745 Renal cell cancers with mutations in a specific chromatin regulator have a better clinical response to immunotherapy. Immune checkpoint inhibitors targeting the programmed cell death 1 receptor (PD-1) improve survival in a subset of patients with clear cell renal cell carcinoma (ccRCC). To identify genomic alterations in ccRCC that correlate with response to anti–PD-1 monotherapy, we performed whole-exome sequencing of metastatic ccRCC from 35 patients. We found that clinical benefit was associated with loss-of-function mutations in the PBRM1 gene (P = 0.012), which encodes a subunit of the PBAF switch-sucrose nonfermentable (SWI/SNF) chromatin remodeling complex. We confirmed this finding in an independent validation cohort of 63 ccRCC patients treated with PD-1 or PD-L1 (PD-1 ligand) blockade therapy alone or in combination with anti–CTLA-4 (cytotoxic T lymphocyte-associated protein 4) therapies (P = 0.0071). Gene-expression analysis of PBAF-deficient ccRCC cell lines and PBRM1-deficient tumors revealed altered transcriptional output in JAK-STAT (Janus kinase–signal transducers and activators of transcription), hypoxia, and immune signaling pathways. PBRM1 loss in ccRCC may alter global tumor-cell expression profiles to influence responsiveness to immune checkpoint therapy.

Genomic correlates of response to immune checkpoint blockade in microsatellite-stable solid tumors

Tumor mutational burden correlates with response to immune checkpoint blockade in multiple solid tumors, although in microsatellite-stable tumors this association is of uncertain clinical utility. Here we uniformly analyzed whole-exome sequencing (WES) of 249 tumors and matched normal tissue from patients with clinically annotated outcomes to immune checkpoint therapy, including radiographic response, across multiple cancer types to examine additional tumor genomic features that contribute to selective response. Our analyses identified genomic correlates of response beyond mutational burden, including somatic events in individual driver genes, certain global mutational signatures, and specific HLA-restricted neoantigens. However, these features were often interrelated, highlighting the complexity of identifying genetic driver events that generate an immunoresponsive tumor environment. This study lays a path forward in analyzing large clinical cohorts in an integrated and multifaceted manner to enhance the ability to discover clinically meaningful predictive features of response to immune checkpoint blockade.Analysis of sequencing data from 249 cancer patients with clinically annotated outcomes to immune checkpoint therapy identifies correlates of treatment response. The results highlight complexity in identifying events that generate an immunoresponsive tumor environment.

Intron retention as a novel source of cancer neoantigens

Personalized cancer vaccine strategies directed at tumor neoantigens derived from somatic mutations in the DNA are currently under prospective evaluation1, 2. Alterations in tumor RNA, rather than DNA, may also represent a previously-unexplored source of neoantigens. Here, we show that intron retention, a widespread feature of cancer transcriptomes3, 4, represents a novel source of tumor neoantigens. We developed an in silico approach to identify retained intron neoantigens from RNA sequencing data and applied this methodology to tumor samples from patients with melanoma treated with immune checkpoint blockade5, 6, discovering that the retained intron neoantigen burden in these samples augments the DNA-derived, somatic neoantigen burden. We validated the existence of retained intron derived neoantigens by implementing this technique on cancer cell lines with mass spectrometry-derived immunopeptidome data7, 8, revealing that retained intron neoantigens were complexed with MHC I experimentally. Unexpectedly, we observed a trend toward lack of clinical benefit from immune checkpoint blockade in high retained intron load-tumors, which harbored transcriptional signatures consistent with cell cycle dysregulation and DNA damage repair. Our results demonstrate the contribution of transcriptional dysregulation to the overall burden of tumor neoantigens, provide a foundation for augmenting personalized cancer vaccine development with a new class of tumor neoantigens, and demonstrate how global transcriptional dysregulation may impact selective response to immune checkpoint blockade. Statement of significance We developed and experimentally validated a computational pipeline to identify a novel class of tumor neoantigens derived from RNA-based intron retention, which is prevalent throughout cancer transcriptomes. The discovery of transcriptionally-derived tumor neoantigens expands the tumor immunopeptidome and contributes potential substrates for personalized cancer vaccine development.

Intron retention is a source of neoepitopes in cancer

We present an in silico approach to identifying neoepitopes derived from intron retention events in tumor transcriptomes. Using mass spectrometry immunopeptidome analysis, we show that retained intron neoepitopes are processed and presented on MHC I on the surface of cancer cell lines. RNA-derived neoepitopes should be considered for prospective personalized cancer vaccine development.

Abstract 571: Meta-analysis of genomic predictors of response to immune checkpoint therapy in metastatic melanoma

Introduction: Immune checkpoint therapies benefit a subset of patients with metastatic melanoma, but ability to predict clinical outcomes is limited. This meta-analysis of genomic predictors of outcomes to aPD1 and aCTLA4 in melanoma combines 220 sequenced tumors from 3 published cohorts, aiming to validate existing hypotheses regarding response to immune checkpoint therapies and discover new relationships with greater power. Methods: Genomic data and clinical annotations from published cohorts were analyzed with standardized pipelines for somatic variant calling, mutational signature deconvolution, and neoantigen prediction. Patients were stratified into clinical benefit (CB) and no clinical benefit (NCB) as described in Van Allen et al. 2015. Analyses were repeated using two other published response metrics (CB=PFS>6 months; CB=CR or PR). Results: Nonsynonymous mutational burden was significantly higher in CB vs. NCB using all 3 response metrics, though significance was less pronounced using PFS alone (p C substitutions (S5), mismatch repair (S6), alkylating agents (S11), UV (S7), and T>G substitutions (S17). The proportion of mutations in S7 or S11 was positively correlated with mutational burden (Spearman’s rho=0.66), while S5 and S1 were anti-correlated (rho=-0.62). In a multivariate logistic model, S7 and S11 activity were independent predictors of CB adjusting for mutational load (p 1/2 of mutations in S7 or S11, compared to only 36/71 of low-mutation NCB (p 500 genes were mutated more frequently in either CB or NCB (p Conclusions: In this meta-analysis of 220 patients, harmonized clinical and whole exome analysis confirmed that mutational burden correlates with CB from aPD1 and aCTLA4 therapy, while mutational signatures and alterations in specific genes potentially provide additional predictive power. Citation Format: Diana Miao, David Liu, Daniel Keliher, Sachet Shukla, Bastian Schilling, Claire Margolis, Alicia Smart, Levi Garraway, Stephen Hodi, Dirk Schadendorf, Eliezer M. Van Allen. Meta-analysis of genomic predictors of response to immune checkpoint therapy in metastatic melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 571. doi:10.1158/1538-7445.AM2017-571

Abstract 5647: Intron retention as a novel source of tumor neoantigens associated with response to checkpoint inhibitor therapy

Background: Development of immune checkpoint inhibitors has substantially improved outcomes in patients diagnosed with metastatic melanoma. However, only a minority of patients treated experience long-term clinical benefit, and clinicians have limited ability to predict which patients will respond. Recent studies have demonstrated that the burden of tumor neoantigens generated by expressed somatic mutations is predictive of response to immunotherapy. Intron retention, which is widespread in cancer transcriptomes, represents a putative source of tumor neoantigens by generating peptides that are available for presentation through the MHC I pathway. Methods: We developed a neoantigen prediction pipeline to identify patient-specific neoantigens from transcriptome sequencing data, which enables identification of retained intron neoantigens from clinical cohorts receiving checkpoint inhibitor therapy. This pipeline incorporates published methods for detecting intron retention events from transcriptome data, detects open reading frames that extend from normal transcripts into intronic sequences, and identifies neoepitopes predicted as strong binders based on the patient’s HLA molecules. We applied this pipeline to a cohort of 41 melanoma patients receiving checkpoint inhibitor therapy and classified patient outcomes as receiving clinical benefit (CB) (n=14), no clinical benefit (NCB) (n=22), or long-term survival without clinical benefit (LS) (n=5). Results: Our initial analysis identified a mean retained intron neoantigen burden of 7709 per sample, without significant difference between response groups. In one patient who derived clinical benefit from checkpoint inhibition, neoantigen load from nonsynonymous mutations was low (407, 0.34 standard deviations (SD) below a mean of 1,015 among CB patients), while retained intron neoantigen load was high (14579, 1.7 SDs above a mean of 7517 among CB patients), suggesting that retained intron neoantigen load may explain response in some patients with low mutational burden. Preliminary analysis of specific neoantigens suggests that a retained intron in ZNF880 identified in patients expressing HLA-A01:01 is present in 6 of 6 patients experiencing clinical benefit, but only 2 of 7 patients not experiencing clinical benefit. The same analysis was performed on two additional cohorts of melanoma tumor samples to assess whether a larger sample size could aid in the identification of recurrent neoepitopes generated by retained introns. Conclusions: Application of this approach to data from patients receiving checkpoint blockade with selective response identifies response-associated neoantigens that may warrant further investigation. Identification of a novel source of neoantigens associated with immunotherapy response will provide valuable prognostic information to patients and inform the development of next generation immunotherapeutics. Citation Format: Alicia C. Smart, Claire Margolis, Diana Miao, David Liu, Jihye Park, Meng Xiao He, Brendan Reardon, Stephanie Mullane, Bastian Schilling, Levi A. Garraway, Dirk Schadendorf, Eliezer M. Van Allen. Intron retention as a novel source of tumor neoantigens associated with response to checkpoint inhibitor therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5647. doi:10.1158/1538-7445.AM2017-5647