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As SARS-CoV-2 variants continue to emerge capable of evading neutralizing antibodies, it has become increasingly important to fully understand the breadth and functional profile of T cell responses to determine their impact on the immune surveillance of variant strains. Here, sampling healthy individuals, we profiled the kinetics and polyfunctionality of T cell immunity elicited by mRNA vaccination. Modeling of anti-spike T cell responses against ancestral and variant strains of SARS-CoV-2 suggested that epitope immunodominance and cross-reactivity are major predictive determinants of T cell immunity. To identify immunodominant epitopes across the viral proteome, we generated a comprehensive map of CD4+ and CD8+ T cell epitopes within non-spike proteins that induced polyfunctional T cell responses in convalescent patients. We found that immunodominant epitopes mainly resided within regions that were minimally disrupted by mutations in emerging variants. Conservation analysis across historical human coronaviruses combined with in silico alanine scanning mutagenesis of non-spike proteins underscored the functional importance of mutationally-constrained immunodominant regions. Collectively, these findings identify immunodominant T cell epitopes across the mutationally-constrained SARS-CoV-2 proteome, potentially providing immune surveillance against emerging variants, and inform the design of next-generation vaccines targeting antigens throughout SARS-CoV-2 proteome for broader and more durable protection.

Neoantigen immunoediting drives immune checkpoint blockade efficacy, yet the molecular features of neoantigens and how neoantigen immunogenicity shapes treatment response remain poorly understood. To address these questions, 80 patients with non-small cell lung cancer were enrolled in the biomarker cohort of CheckMate 153 (CA209-153), which collected radiographic guided biopsy samples before treatment and during treatment with nivolumab. Early loss of mutations and neoantigens during therapy are both associated with clinical benefit. We examined 1,453 candidate neoantigens, including many of which that had reduced cancer cell fraction after treatment with nivolumab, and identified 196 neopeptides that were recognized by T cells. Mapping these neoantigens to clonal dynamics, evolutionary trajectories and clinical response revealed a strong selection against immunogenic neoantigen-harboring clones. We identified position-specific amino acid and physiochemical features related to immunogenicity and developed an immunogenicity score. Nivolumab-induced microenvironmental evolution in non-small cell lung cancer shared some similarities with melanoma, yet critical differences were apparent. This study provides unprecedented molecular portraits of neoantigen landscapes underlying nivolumab's mechanism of action., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Experts discuss the challenges and opportunities of using artificial intelligence (AI) to study the evolution of cancer cells and their microenvironment, improve diagnosis, predict treatment response, and ensure responsible implementation in the clinic., Competing Interests: Declaration of interests M.G. received research funding from the NSF, Google, Quanta Computing, Janssen, Moore Foundation, Volkswagen Foundation, and Takeda. These funds have been paid to MIT, and not to M.G. personally. The affiliations of M.G. are: MIT in EECS & IMES; LIDS; CSAIL & JClinic Faculty Member; and CIFAR Azrieli Global Scholar at Vector Institute. D.C. is a co-inventor on a patent (US11230599/EP4226944A3) filed by MSKCC on using tumor mutational burden to predict immunotherapy response, which has been licensed to Personal Genome Diagnostics (PGDx). J.T. is a shareholder and co-founder of Ellogon.AI. J.T. is a collaborator of Kaiko.ai, obtaining research funding. F.M. is the co-founder of ModellaAI. Patents around generative AI for medicine and corresponding work conducted in the lab of F.M. have been licensed to ModellaAI. J.N.K. declares consulting services for Owkin, France; DoMore Diagnostics, Norway; Panakeia, UK; Scailyte, Switzerland; Mindpeak, Germany; and MultiplexDx, Slovakia. Furthermore, J.N.K. holds shares in StratifAI GmbH, Germany, has received a research grant by GSK, and has received honoraria by AstraZeneca, Bayer, Eisai, Janssen, MSD, BMS, Roche, Pfizer, and Fresenius. The other authors declare no competing interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Head and neck squamous-cell carcinoma (HNSCC) is a disease with a generally poor prognosis; half of treated patients eventually develop recurrent and/or metastatic (R/M) disease. Patients with R/M HNSCC generally have incurable disease with a median survival of 10 to 15 months. Although immune-checkpoint blockade (ICB) has improved outcomes in patients with R/M HNSCC, identifying patients who are likely to benefit from ICB remains a challenge. Biomarkers in current clinical use include tumor mutational burden and immunohistochemistry for programmed death-ligand 1, both of which have only modest predictive power. Machine learning (ML) has the potential to aid in clinical decision-making as an approach to estimate a tumor's likelihood of response or a patient's likelihood of experiencing clinical benefit from therapies such as ICB. Previously, we described a random forest ML model that had value in predicting ICB response using 11 or 16 clinical, laboratory, and genomic features in a pan-cancer development cohort. However, its applicability to certain cancer types, such as HNSCC, has been unknown, due to a lack of cancer-type-specific validation. Here, we present the first validation of a random forest ML tool to predict the likelihood of ICB response in patients with R/M HNSCC. The tool had adequate predictive power for tumor response (area under the receiver operating characteristic curve = 0.65) and was able to stratify patients by overall (HR = 0.53 [95% CI 0.29-0.99], p = 0.045) and progression-free (HR = 0.49 [95% CI 0.27-0.87], p = 0.016) survival. The overall accuracy was 0.72. Our study validates an ML predictor in HNSCC, demonstrating promising performance in a novel cohort of patients. Further studies are needed to validate the generalizability of this algorithm in larger patient samples from additional multi-institutional contexts.

Immunologic recognition of peptide antigens bound to class I major histocompatibility complex (MHC) molecules is essential to both novel immunotherapeutic development and human health at large. Current methods for predicting antigen peptide immunogenicity rely primarily on simple sequence representations, which allow for some understanding of immunogenic features but provide inadequate consideration of the full scale of molecular mechanisms tied to peptide recognition. We here characterize contributions that unsupervised and supervised artificial intelligence (AI) methods can make toward understanding and predicting MHC(HLA-A2)-peptide complex immunogenicity when applied to large ensembles of molecular dynamics simulations. We first show that an unsupervised AI method allows us to identify subtle features that drive immunogenicity differences between a cancer neoantigen and its wild-type peptide counterpart. Next, we demonstrate that a supervised AI method for class I MHC(HLA-A2)-peptide complex classification significantly outperforms a sequence model on small datasets corrected for trivial sequence correlations. Furthermore, we show that both unsupervised and supervised approaches reveal determinants of immunogenicity based on time-dependent molecular fluctuations and anchor position dynamics outside the MHC binding groove. We discuss implications of these structural and dynamic immunogenicity correlates for the induction of T cell responses and therapeutic T cell receptor design., (© The Author(s) 2024. Published by Oxford University Press.)

BACKGROUNDRecurrent and/or metastatic (R/M) head and neck squamous cell carcinoma (HNSCC) is generally an incurable disease, with patients experiencing median survival of under 10 months and significant morbidity. While immune checkpoint blockade (ICB) drugs are effective in approximately 20% of patients, the remaining experience limited clinical benefit and are exposed to potential adverse effects and financial costs. Clinically approved biomarkers, such as tumor mutational burden (TMB), have a modest predictive value in HNSCC.METHODSWe analyzed clinical and genomic features, generated using whole-exome sequencing, in 133 ICB-treated patients with R/M HNSCC, of whom 69 had virus-associated and 64 had non-virus-associated tumors.RESULTSHierarchical clustering of genomic data revealed 6 molecular subtypes characterized by a wide range of objective response rates and survival after ICB therapy. The prognostic importance of these 6 subtypes was validated in an external cohort. A random forest-based predictive model, using several clinical and genomic features, predicted progression-free survival (PFS), overall survival (OS), and response with greater accuracy than did a model based on TMB alone. Recursive partitioning analysis identified 3 features (systemic inflammatory response index, TMB, and smoking signature) that classified patients into risk groups with accurate discrimination of PFS and OS.CONCLUSIONThese findings shed light on the immunogenomic characteristics of HNSCC tumors that drive differential responses to ICB and identify a clinical-genomic classifier that outperformed the current clinically approved biomarker of TMB. This validated predictive tool may help with clinical risk stratification in patients with R/M HNSCC for whom ICB is being considered.FUNDINGFundación Alfonso Martín Escudero, NIH R01 DE027738, US Department of Defense CA210784, The Geoffrey Beene Cancer Research Center, The MSKCC Population Science Research Program, the Jayme Flowers Fund, the Sebastian Nativo Fund, and the NIH/NCI Cancer Center Support Grant P30 CA008748.

Programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1)-blockade immunotherapies have limited efficacy in the treatment of bladder cancer. Here, we show that NKG2A associates with improved survival and responsiveness to PD-L1 blockade immunotherapy in bladder tumors that have high abundance of CD8 + T cells. In bladder tumors, NKG2A is acquired on CD8 + T cells later than PD-1 as well as other well-established immune checkpoints. NKG2A + PD-1 + CD8 + T cells diverge from classically defined exhausted T cells through their ability to react to human leukocyte antigen (HLA) class I-deficient tumors using T cell receptor (TCR)-independent innate-like mechanisms. HLA-ABC expression by bladder tumors is progressively diminished as disease progresses, framing the importance of targeting TCR-independent anti-tumor functions. Notably, NKG2A + CD8 + T cells are inhibited when HLA-E is expressed by tumors and partly restored upon NKG2A blockade in an HLA-E-dependent manner. Overall, our study provides a framework for subsequent clinical trials combining NKG2A blockade with other T cell-targeted immunotherapies, where tumors express higher levels of HLA-E., Competing Interests: Declaration of interests L.W., R.P.S., and J.Z. are employees of Sema4. A.H. receives research funds from Zumutor Biologics and is on the advisory boards of HTG Molecular Diagnostics, Immunorizon, UroGen, and Takeda. N.B. is an extramural member of the Parker Institute for Cancer Immunotherapy; receives research funds from Regeneron, Harbor Biomedical, DC Prime, and Dragonfly Therapeutics; and is on the advisory boards of Neon Therapeutics, Novartis, Avidea, Boehringer Ingelheim, Rome Therapeutics, Rubius Therapeutics, Roswell Park Comprehensive Cancer Center, BreakBio, Carisma Therapeutics, CureVac, Genotwin, BioNTech, Gilead Therapeutics, Tempest Therapeutics, and the Cancer Research Institute. A patent related to this work was filed to the United States Patent and Trademark Office (63/313,823)., (Copyright © 2022 Elsevier Inc. All rights reserved.)

The effects of cytokine and protein stabilizing carriers, such as serum albumin, on tumor response to immune checkpoint blockade (ICB) is not well understood. By examining 1714 patients across 16 cancer types, we found that high pretreatment serum albumin level predicts favorable tumor radiographic response following ICB treatment in a dose-dependent fashion. Serum albumin is a candidate biomarker that can be combined with tumor mutational burden (TMB) for additional predictive capacity, and the tumor response rate to ICB was ~49% in the albumin-high/TMB-high group., (© 2022. The Author(s).)

Only a fraction of patients with cancer respond to immune checkpoint blockade (ICB) treatment, but current decision-making procedures have limited accuracy. In this study, we developed a machine learning model to predict ICB response by integrating genomic, molecular, demographic and clinical data from a comprehensively curated cohort (MSK-IMPACT) with 1,479 patients treated with ICB across 16 different cancer types. In a retrospective analysis, the model achieved high sensitivity and specificity in predicting clinical response to immunotherapy and predicted both overall survival and progression-free survival in the test data across different cancer types. Our model significantly outperformed predictions based on tumor mutational burden, which was recently approved by the U.S. Food and Drug Administration for this purpose 1 . Additionally, the model provides quantitative assessments of the model features that are most salient for the predictions. We anticipate that this approach will substantially improve clinical decision-making in immunotherapy and inform future interventions., (© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Immune checkpoint blockade (ICB) therapy has substantially improved the outcomes of patients with many types of cancers, including renal cell carcinoma (RCC). Initially studied as monotherapy, immunotherapy-based combination regimens have improved the clinical benefit achieved by ICB monotherapy and have revolutionized RCC treatment. While biomarkers like PD-L1 and tumor mutational burden (TMB) are FDA approved as biomarkers for ICB monotherapy, there are no known biomarkers for combination immunotherapies. Here, we describe the clinical outcomes and genomic determinants of response from a phase Ib/II clinical trial on patients with advanced RCC evaluating the efficacy of lenvatinib, a multi-kinase inhibitor mainly targeting VEGFR and FGFR plus pembrolizumab, an anti-PD1 immunotherapy. Concurrent treatment with lenvatinib and pembrolizumab resulted in an objective response rate of 79% (19/24) and tumor shrinkage in 96% (23/24) of patients. While tumor mutational burden (TMB) did not predict for clinical benefit, germline HLA-I diversity strongly impacted treatment efficacy. Specifically, HLA-I evolutionary divergence (HED), which measures the breadth of a patient's immunopeptidome, was associated with both improved clinical benefit and durability of response. Our results identify lenvatinib plus pembrolizumab as a highly active treatment strategy in RCC and reveal HLA-I diversity as a critical determinant of efficacy for this combination. HED also predicted better survival in a separate cohort of patients with RCC following therapy with anti-PD-1-based combination therapy. IMPLICATIONS: These findings have substantial implications for RCC therapy and for understanding immunogenetic mechanisms of efficacy and warrants further investigation., (©2021 American Association for Cancer Research.)

Although mutations in DNA are the best-studied source of neoantigens that determine response to immune checkpoint blockade, alterations in RNA splicing within cancer cells could similarly result in neoepitope production. However, the endogenous antigenicity and clinical potential of such splicing-derived epitopes have not been tested. Here, we demonstrate that pharmacologic modulation of splicing via specific drug classes generates bona fide neoantigens and elicits anti-tumor immunity, augmenting checkpoint immunotherapy. Splicing modulation inhibited tumor growth and enhanced checkpoint blockade in a manner dependent on host T cells and peptides presented on tumor MHC class I. Splicing modulation induced stereotyped splicing changes across tumor types, altering the MHC I-bound immunopeptidome to yield splicing-derived neoepitopes that trigger an anti-tumor T cell response in vivo. These data definitively identify splicing modulation as an untapped source of immunogenic peptides and provide a means to enhance response to checkpoint blockade that is readily translatable to the clinic., Competing Interests: Declaration of interests O.A.-W. has served as a consultant for H3B Biomedicine, Foundation Medicine, Merck, Prelude Therapeutics, and Janssen, and is on the scientific advisory board of Envisagenics, Pfizer Boulder, and AIChemy. O.A.-W. has received prior research funding from Loxo Oncology and H3 Biomedicine unrelated to this work. S.X.L. has served as a consultant (uncompensated) for PTC Therapeutics. B.R. has served as a consultant for Bayer and Roche. D.Z. reports clinical research support to his institution from Astra Zeneca, Plexxikon, and Genentech and personal/consultancy fees from Merck, Synlogic Therapeutics, Tesaro, Bristol Myers Squibb (BMS), Genentech, Xencor, Memgen, Calidi Biotherapeutics, and Agenus. L.A.D. is a member of the board of directors of Personal Genome Diagnostics (PGDx) and Jounce Therapeutics. L.A.D. is a compensated consultant to PGDx, 4Paws (PetDx), Innovatus CP, Se’er, Delfi, Kinnate, and Neophore. L.A.D. is an uncompensated consultant for, but has received clinical trial support from, Merck. L.A.D. holds equity in PGDx, Jounce, Se’er, Delfi, Kinnate, and Neophore and divested equity in Thrive Earlier Detection in 2021. His spouse holds equity in Amgen. J.D.W. is a consultant for Amgen, Apricity, Arsenal IO, Ascentage Pharma, AstraZeneca, Astellas, Boehringer Ingelheim, BMS, Chugai, Dragonfly, F Star, Eli Lilly, Georgiamune, IMVAQ, Merck, Polynoma, Psioxus, Recepta, Trieza, Truvax, Sellas, and Werewolf Therapeutics. J.D.W. has grant/research support from BMS and Sephora. J.D.W. reports equity in Tizona Pharmaceuticals, Imvaq, Beigene, Linneaus, Apricity, Arsenal IO, and Georgiamune. T.M. is an inventor on patents involving the use of anti-PD-1 antibodies. T.M. is a consultant for Immunos Therapeutics and Pfizer. T.M. is a cofounder of and equity holder in IMVAQ. T.M. receives research funding from BMS, Surface Oncology, Kyn Therapeutics, Infinity Pharmaceuticals, Peregrine Pharmaceuticals, Adaptive Biotechnologies, Leap Therapeutics, and Aprea Therapeutics. S.X.L., O.A.-W., and R.K.B. are inventors on a patent application submitted by FHCRC related to this work., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Clear cell renal cell carcinomas (ccRCCs) are highly immune infiltrated, but the effect of immune heterogeneity on clinical outcome in ccRCC has not been fully characterized. Here we perform paired single-cell RNA (scRNA) and T cell receptor (TCR) sequencing of 167,283 cells from multiple tumor regions, lymph node, normal kidney, and peripheral blood of two immune checkpoint blockade (ICB)-naïve and four ICB-treated patients to map the ccRCC immune landscape. We detect extensive heterogeneity within and between patients, with enrichment of CD8A + tissue-resident T cells in a patient responsive to ICB and tumor-associated macrophages (TAMs) in a resistant patient. A TCR trajectory framework suggests distinct T cell differentiation pathways between patients responding and resistant to ICB. Finally, scRNA-derived signatures of tissue-resident T cells and TAMs are associated with response to ICB and targeted therapies across multiple independent cohorts. Our study establishes a multimodal interrogation of the cellular programs underlying therapeutic efficacy in ccRCC., Competing Interests: Declaration of interests T.A.C. is a co-founder of Gritstone Oncology and holds equity. T.A.C. holds equity in An2H. T.A.C. acknowledges grant funding from Bristol-Myers Squibb, AstraZeneca, Illumina, Pfizer, An2H, and Eisai. T.A.C. has served as an advisor for Bristol-Myers Squibb, Illumina, Eisai, and An2H. M.S.K. has licensed the use of TMB for the identification of patients who benefit from immune checkpoint therapy to PGDx. S.W. holds equity in Illumina., (Copyright © 2021 Elsevier Inc. All rights reserved.)

T cell discrimination of self and non-self is the foundation of the adaptive immune response, and is orchestrated by the interaction between T cell receptors (TCRs) and their cognate ligands presented by major histocompatibility (MHC) molecules. However, the impact of host immunogenetic variation on the diversity of the TCR repertoire remains unclear. Here, we analyzed a cohort of 666 individuals with TCR repertoire sequencing. We show that TCR repertoire diversity is positively associated with polymorphism at the human leukocyte antigen class I (HLA-I) loci, and diminishes with age and cytomegalovirus (CMV) infection. Moreover, our analysis revealed that HLA-I polymorphism and age independently shape the repertoire in healthy individuals. Our data elucidate key determinants of human TCR repertoire diversity, and suggest a mechanism underlying the evolutionary fitness advantage of HLA-I heterozygosity., Competing Interests: Competing interestsNot applicable., (© The Author(s) 2020.)

Purpose: Salivary gland carcinomas (SGC) are rare, aggressive cancers with high rates of recurrence and distant metastasis. These factors, and a lack of active systemic therapies, contribute to poor clinical outcome. Response rates with immune checkpoint blockade have been low, although clinical data remain sparse. To improve the efficacy of therapies, a more comprehensive understanding of relevant molecular alterations and immunologic processes is needed., Experimental Design: To characterize the immune microenvironment and neoantigen landscape of SGCs, we performed RNA sequencing (RNA-seq) in 76 tumors representing the three most lethal histologies: adenoid cystic carcinoma (ACC), myoepithelial carcinoma (MECA), and salivary duct carcinoma (SDC). We analyzed transcriptomic profiles, tumor-infiltrating immune cell populations, and measures of T-cell activation/dysfunction. In 37 cases also undergoing exome sequencing, we analyzed somatic mutations and neoantigens., Results: SDCs exhibited high levels of immune infiltration, with corresponding higher levels of T-cell dysfunction, and higher mutational load. In contrast, ACCs were characterized by an immune-excluded microenvironment, the presence of M2-polarized macrophages and myeloid-derived suppressor cells, and very low mutational load. MECAs were more heterogeneous, with both immune-low and immune-high phenotypes represented. Across all SGCs, levels of immune infiltration were associated with mutation- and fusion-derived neoantigens, and with aggressive clinical behavior., Conclusions: These findings provide new insights into the immune microenvironment and neoantigen landscape of SGCs, showing that mechanisms of immune escape appear to differ by histology. These data nominate potential immunologic vulnerabilities and may help guide the next steps of investigation in precision immunotherapy for these difficult-to-treat cancers., (©2020 American Association for Cancer Research.)

Functional diversity of the highly polymorphic human leukocyte antigen class I (HLA-I) genes underlies successful immunologic control of both infectious disease and cancer. The divergent allele advantage hypothesis dictates that an HLA-I genotype with two alleles with sequences that are more divergent enables presentation of more diverse immunopeptidomes 1-3 . However, the effect of sequence divergence between HLA-I alleles-a quantifiable measure of HLA-I evolution-on the efficacy of immune checkpoint inhibitor (ICI) treatment for cancer remains unknown. In the present study the germline HLA-I evolutionary divergence (HED) of patients with cancer treated with ICIs was determined by quantifying the physiochemical sequence divergence between HLA-I alleles of each patient's genotype. HED was a strong determinant of survival after treatment with ICIs. Even among patients fully heterozygous at HLA-I, patients with an HED in the upper quartile respond better to ICIs than patients with a low HED. Furthermore, HED strongly impacts the diversity of tumor, viral and self-immunopeptidomes and intratumoral T cell receptor clonality. Similar to tumor mutation burden, HED is a fundamental metric of diversity at the major histocompatibility complex-peptide complex, which dictates ICI efficacy. The data link divergent HLA allele advantage to immunotherapy efficacy and unveil how ICI response relies on the evolved efficiency of HLA-mediated immunity.

Tumor immunology has changed the landscape of cancer treatment. Yet, not all patients benefit as cancer immune responsiveness (CIR) remains a limitation in a considerable proportion of cases. The multifactorial determinants of CIR include the genetic makeup of the patient, the genomic instability central to cancer development, the evolutionary emergence of cancer phenotypes under the influence of immune editing, and external modifiers such as demographics, environment, treatment potency, co-morbidities and cancer-independent alterations including immune homeostasis and polymorphisms in the major and minor histocompatibility molecules, cytokines, and chemokines. Based on the premise that cancer is fundamentally a disorder of the genes arising within a cell biologic process, whose deviations from normality determine the rules of engagement with the host's response, the Society for Immunotherapy of Cancer (SITC) convened a task force of experts from various disciplines including, immunology, oncology, biophysics, structural biology, molecular and cellular biology, genetics, and bioinformatics to address the complexity of CIR from a holistic view. The task force was launched by a workshop held in San Francisco on May 14-15, 2018 aimed at two preeminent goals: 1) to identify the fundamental questions related to CIR and 2) to create an interactive community of experts that could guide scientific and research priorities by forming a logical progression supported by multiple perspectives to uncover mechanisms of CIR. This workshop was a first step toward a second meeting where the focus would be to address the actionability of some of the questions identified by working groups. In this event, five working groups aimed at defining a path to test hypotheses according to their relevance to human cancer and identifying experimental models closest to human biology, which include: 1) Germline-Genetic, 2) Somatic-Genetic and 3) Genomic-Transcriptional contributions to CIR, 4) Determinant(s) of Immunogenic Cell Death that modulate CIR, and 5) Experimental Models that best represent CIR and its conversion to an immune responsive state. This manuscript summarizes the contributions from each group and should be considered as a first milestone in the path toward a more contemporary understanding of CIR. We appreciate that this effort is far from comprehensive and that other relevant aspects related to CIR such as the microbiome, the individual's recombined T cell and B cell receptors, and the metabolic status of cancer and immune cells were not fully included. These and other important factors will be included in future activities of the taskforce. The taskforce will focus on prioritization and specific actionable approach to answer the identified questions and implementing the collaborations in the follow-up workshop, which will be held in Houston on September 4-5, 2019.

Anti-tumor immunity is driven by self versus non-self discrimination. Many immunotherapeutic approaches to cancer have taken advantage of tumor neoantigens derived from somatic mutations. Here, we demonstrate that gene fusions are a source of immunogenic neoantigens that can mediate responses to immunotherapy. We identified an exceptional responder with metastatic head and neck cancer who experienced a complete response to immune checkpoint inhibitor therapy, despite a low mutational load and minimal pre-treatment immune infiltration in the tumor. Using whole-genome sequencing and RNA sequencing, we identified a novel gene fusion and demonstrated that it produces a neoantigen that can specifically elicit a host cytotoxic T cell response. In a cohort of head and neck tumors with low mutation burden, minimal immune infiltration and prevalent gene fusions, we also identified gene fusion-derived neoantigens that generate cytotoxic T cell responses. Finally, analyzing additional datasets of fusion-positive cancers, including checkpoint-inhibitor-treated tumors, we found evidence of immune surveillance resulting in negative selective pressure against gene fusion-derived neoantigens. These findings highlight an important class of tumor-specific antigens and have implications for targeting gene fusion events in cancers that would otherwise be less poised for response to immunotherapy, including cancers with low mutational load and minimal immune infiltration.

Background: Tobacco smoking creates DNA damage, inducing mutations and potentially altering the tumor immune microenvironment. These types of genetic and immune microenvironment alterations are critical factors known to affect tumor response to immunotherapy. Here we analyze the association between the mutational signature of tobacco smoking, tumor mutational load, and metrics of immune activity in squamous cell carcinomas arising in the head and neck and lung., Methods: Using RNA and DNA sequencing data from The Cancer Genome Atlas head and neck (HNSC; n = 287) and lung (LUSC; n = 130) squamous cell carcinoma data sets and two independent gene expression data sets (HNSC, n = 136; LUSC, n = 75), we examined associations between the mutational smoking signature, mutation count, immune cell infiltration, cytolytic activity, and interferon-γ signaling., Results: An increasing mutational smoking signature was associated with statistically significantly increased overall mutational load in both HNSC (ρ = .33, P = 1.01 × 10-7) and LUSC (ρ = .49, P = 2.80 × 10-9). In HNSC, a higher mutational smoking signature was associated with lower levels of immune infiltration (ρ = -.37, P = 1.29 × 10-10), cytolytic activity (ρ = -.28, P = 4.07 × 10-6), and interferon-γ pathway signaling (ρ = .39, P = 3.20 × 10-11). In LUSC, these associations were reversed (ρ = .19, P = .03; ρ = .20, P = .02; and ρ = .18, P = .047, respectively). Differentially expressed genes between smoking-high and smoking-low tumors revealed broad tobacco-induced immunosuppression in HNSC, in contrast to a tumor-inflamed microenvironment in smokers with LUSC., Conclusions: In squamous cell carcinomas, the genetic smoking signature is associated with higher mutational load, but variable effects on tumor immunity can occur, depending on anatomic site. In HNSC, smoking is predominantly immunosuppressive; in LUSC, more pro-inflammatory. Both tumor mutation load and immune microenvironment affect clinical response to immunotherapy. Thus, the mutational smoking signature is likely to have relevance for immunotherapeutic investigation in smoking-associated cancers.

Increasing evidence shows that tumor clonal architectures are often the consequence of a complex branching process, yet little is known about the expected dynamics and extent to which these divergent subclonal expansions occur. Here, we develop and implement more than 88,000 instances of a stochastic evolutionary model simulating genetic drift and neoplastic progression. Under different combinations of population genetic parameter values, including those estimated for colorectal cancer and glioblastoma multiforme, the distribution of sizes of subclones carrying driver mutations had a heavy right tail at the time of tumor detection, with only 1 to 4 dominant clones present at ≥10% frequency. In contrast, the vast majority of subclones were present at <10% frequency, many of which had higher fitness than currently dominant clones. The number of dominant clones (≥10% frequency) in a tumor correlated strongly with the number of subclones (<10% of the tumor). Overall, these subclones were frequently below current standard detection thresholds, frequently harbored treatment-resistant mutations, and were more common in slow-growing tumors. Significance: The model presented in this paper addresses tumor heterogeneity by framing expectations for the number of resistant subclones in a tumor, with implications for future studies of the evolution of therapeutic resistance. Cancer Res; 78(3); 830-9. ©2017 AACR ., (©2017 American Association for Cancer Research.)

The mechanisms by which immune checkpoint blockade modulates tumor evolution during therapy are unclear. We assessed genomic changes in tumors from 68 patients with advanced melanoma, who progressed on ipilimumab or were ipilimumab-naive, before and after nivolumab initiation (CA209-038 study). Tumors were analyzed by whole-exome, transcriptome, and/or T cell receptor (TCR) sequencing. In responding patients, mutation and neoantigen load were reduced from baseline, and analysis of intratumoral heterogeneity during therapy demonstrated differential clonal evolution within tumors and putative selection against neoantigenic mutations on-therapy. Transcriptome analyses before and during nivolumab therapy revealed increases in distinct immune cell subsets, activation of specific transcriptional networks, and upregulation of immune checkpoint genes that were more pronounced in patients with response. Temporal changes in intratumoral TCR repertoire revealed expansion of T cell clones in the setting of neoantigen loss. Comprehensive genomic profiling data in this study provide insight into nivolumab's mechanism of action., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Whole-genome analyses of human medulloblastomas show that the dominant clone at relapse is present as a rare subclone at primary diagnosis.

Purpose: Oropharyngeal carcinoma positive for human papillomavirus type 16 (HPV16) has a significantly better prognosis than oropharyngeal carcinoma unrelated to HPV. Within HPV16-positive oropharyngeal carcinoma, biomarkers of prognosis are urgently needed to individualize care. We hypothesized that serum antibodies specific to HPV16, the major HPV type causing oropharyngeal carcinoma, have biologic relevance and are potential biomarkers for improved prognosis among patients with HPV16-positive oropharyngeal carcinoma., Experimental Design: IgG antibodies to the HPV16 antigens E1, E4-E7, L1, L2, and the N-terminal and C-terminal fragments of E2 (NE2, CE2) were quantified using a custom programmable enzyme-linked immunosorbent assay. Sera were obtained at diagnosis from 209 oropharyngeal carcinoma patients (96 HPV16-positive). The ratios of median fluorescent intensity (MFI) for each antigen to MFI for control GST protein were determined. Kaplan-Meier survival curves and Cox proportional hazards regression were used to determine survival differences between groups. ROC curves were used to determine the best combination of E antibodies to predict disease recurrence., Results: E1, NE2, and E6 antibody positivity were all strongly associated with improved overall and progression-free survival in the entire cohort and in patients with known HPV16-positive tumors (P < 0.05). For both overall and progression-free survival among HPV-positive patients, hazard ratios were 0.2 for NE2, 0.3 for E1, and 0.3 for E6 antibody positivity., Conclusions: We identified three HPV16-specific antibodies that are associated with improved overall and progression-free survival in patients with HPV-related oropharyngeal carcinoma. These results suggest that differential serologic responses in patients may reflect differential biologic processes within the host and tumor and may have prognostic value., (©2015 American Association for Cancer Research.)

Despite the availability of major histocompatibility complex (MHC)-binding peptide prediction algorithms, the development of T-cell vaccines against pathogen and tumor antigens remains challenged by inefficient identification of immunogenic epitopes. CD8(+) T cells must distinguish immunogenic epitopes from nonimmunogenic self peptides to respond effectively against an antigen without endangering the viability of the host. Because this discrimination is fundamental to our understanding of immune recognition and critical for rational vaccine design, we interrogated the biochemical properties of 9,888 MHC class I peptides. We identified a strong bias toward hydrophobic amino acids at T-cell receptor contact residues within immunogenic epitopes of MHC allomorphs, which permitted us to develop and train a hydrophobicity-based artificial neural network (ANN-Hydro) to predict immunogenic epitopes. The immunogenicity model was validated in a blinded in vivo overlapping epitope discovery study of 364 peptides from three HIV-1 Gag protein variants. Applying the ANN-Hydro model on existing peptide-MHC algorithms consistently reduced the number of candidate peptides across multiple antigens and may provide a correlate with immunodominance. Hydrophobicity of TCR contact residues is a hallmark of immunogenic epitopes and marks a step toward eliminating the need for empirical epitope testing for vaccine development.