Jonathan J. Havel

Mutational landscape determines sensitivity to PD-1 blockade in non–small cell lung cancer

Immune checkpoint inhibitors, which unleash a patient’s own T cells to kill tumors, are revolutionizing cancer treatment. To unravel the genomic determinants of response to this therapy, we used whole-exome sequencing of non–small cell lung cancers treated with pembrolizumab, an antibody targeting programmed cell death-1 (PD-1). In two independent cohorts, higher nonsynonymous mutation burden in tumors was associated with improved objective response, durable clinical benefit, and progression-free survival. Efficacy also correlated with the molecular smoking signature, higher neoantigen burden, and DNA repair pathway mutations; each factor was also associated with mutation burden. In one responder, neoantigen-specific CD8+ T cell responses paralleled tumor regression, suggesting that anti–PD-1 therapy enhances neoantigen-specific T cell reactivity. Our results suggest that the genomic landscape of lung cancers shapes response to anti–PD-1 therapy. An anticancer drug is more effective against tumors that carry more mutations. More mutations predict better efficacy Despite the remarkable success of cancer immunotherapies, many patients do not respond to treatment. Rizvi et al. studied the tumors of patients with non–small-cell lung cancer undergoing immunotherapy. In two independent cohorts, treatment efficacy was associated with a higher number of mutations in the tumors. In one patient, a tumor-specific T cell response paralleled tumor regression. Science, this issue p. 124

The head and neck cancer immune landscape and its immunotherapeutic implications

Recent clinical trials have demonstrated a clear survival advantage in advanced head and neck squamous cell carcinoma (HNSCC) patients treated with immune checkpoint blockade. These emerging results reveal that HNSCC is one of the most promising frontiers for immunotherapy research. However, further progress in head and neck immuno-oncology will require a detailed understanding of the immune infiltrative landscape found in these tumors. We leveraged transcriptome data from 280 tumors profiled by The Cancer Genome Atlas (TCGA) to comprehensively characterize the immune landscape of HNSCC in order to develop a rationale for immunotherapeutic strategies in HNSCC and guide clinical investigation. We find that both HPV+ and HPV- HNSCC tumors are among the most highly immune-infiltrated cancer types. Strikingly, HNSCC had the highest median Treg/CD8+ T cell ratio and the highest levels of CD56dim NK cell infiltration, in our pan-cancer analysis of the most immune-infiltrated tumors. CD8+ T cell infiltration and CD56dim NK cell infiltration each correlated with superior survival in HNSCC. Tumors harboring genetic smoking signatures had lower immune infiltration and were associated with poorer survival, suggesting these patients may benefit from immune agonist therapy. These findings illuminate the immune landscape of HPV+ and HPV- HNSCC. Additionally, this landscape provides a potentially novel rationale for investigation of agents targeting modulators of Tregs (e.g., CTLA-4, GITR, ICOS, IDO, and VEGFA) and NK cells (e.g., KIR, TIGIT, and 4-1BB) as adjuncts to anti-PD-1 in the treatment of advanced HNSCC.

The role of neoantigens in response to immune checkpoint blockade.

Immune checkpoint blockade has demonstrated substantial promise for the treatment of several advanced malignancies. These agents activate the immune system to attack tumor cells. For example, agents targeting CTLA4 and programmed cell death 1 (PD-1) have resulted in impressive response rates and, in some cases, durable remissions. Neoantigens are mutations that encode immunologically active proteins that can cause the immune system to recognize the affected cell as foreign. Recent data have made it clear that these mutations are, in large part, the functional targets of immune checkpoint blockade. This review summarizes the key discoveries leading up to this important conclusion and discusses possible applications of neoantigens in cancer therapy.

Recurrent SERPINB3 and SERPINB4 mutations in patients who respond to anti-CTLA4 immunotherapy

Immune checkpoint blockade has shown significant promise as an anticancer treatment, yet the determinants of response are not completely understood. Here we show that somatic mutations in SERPINB3 and SERPINB4 are associated with survival after anti-CTLA4 immunotherapy in two independent cohorts of patients with melanoma (n = 174). Interestingly, serpins are homologs of the well-known ovalbumin antigen and are associated with autoimmunity. Our findings have implications for the personalization of immunotherapy.

ImmunoMap: A Bioinformatics Tool for T-cell Repertoire Analysis

TCR sequencing provides insight into antigen-specific immune responses, but biologically meaningful conclusions are difficult to infer. ImmunoMap is a bioinformatics tool that reconciles TCR repertoire function and structure by quantifying the “relatedness” of the response by sequence homology. Despite a dramatic increase in T-cell receptor (TCR) sequencing, few approaches biologically parse the data in a fashion that both helps yield new information about immune responses and may guide immunotherapeutic interventions. To address this issue, we developed a method, ImmunoMap, that utilizes a sequence analysis approach inspired by phylogenetics to examine TCR repertoire relatedness. ImmunoMap analysis of the CD8 T-cell response to self-antigen (Kb-TRP2) or to a model foreign antigen (Kb-SIY) in naïve and tumor-bearing B6 mice showed differences in the T-cell repertoire of self- versus foreign antigen-specific responses, potentially reflecting immune pressure by the tumor, and also detected lymphoid organ–specific differences in TCR repertoires. When ImmunoMap was used to analyze clinical trial data of tumor-infiltrating lymphocytes from patients being treated with anti–PD-1, ImmunoMap, but not standard TCR sequence analyses, revealed a clinically predicative signature in pre- and posttherapy samples. Cancer Immunol Res; 6(2); 151–62. ©2017 AACR.

Multi-dimensional genomic analysis of myoepithelial carcinoma identifies prevalent oncogenic gene fusions

Myoepithelial carcinoma (MECA) is an aggressive salivary gland cancer with largely unknown genetic features. Here we comprehensively analyze molecular alterations in 40 MECAs using integrated genomic analyses. We identify a low mutational load, and high prevalence (70%) of oncogenic gene fusions. Most fusions involve the PLAG1 oncogene, which is associated with PLAG1 overexpression. We find FGFR1-PLAG1 in seven (18%) cases, and the novel TGFBR3-PLAG1 fusion in six (15%) cases. TGFBR3-PLAG1 promotes a tumorigenic phenotype in vitro, and is absent in 723 other salivary gland tumors. Other novel PLAG1 fusions include ND4-PLAG1; a fusion between mitochondrial and nuclear DNA. We also identify higher number of copy number alterations as a risk factor for recurrence, independent of tumor stage at diagnosis. Our findings indicate that MECA is a fusion-driven disease, nominate TGFBR3-PLAG1 as a hallmark of MECA, and provide a framework for future diagnostic and therapeutic research in this lethal cancer.Myoepithelial carcinoma (MECA) is a rare aggressive salivary gland cancer. Here, the authors analyze the genomic landscape of MECA and identify a high prevalence of oncogenic gene fusions, primarily PLAG1 fusions, highlighting TGFBR3-PLAG1 as a potential hallmark of MECA.

High-resolution genomic analysis: the tumor-immune interface comes into focus

A genomic analysis of heterogeneous colorectal tumor samples has uncovered interactions between immunophenotype and various aspects of tumor biology, with implications for informing the choice of immunotherapies for specific patients and guiding the design of personalized neoantigen-based vaccines.Please see related article: http://dx.doi.org/10.1186/s13059-015-0620-6

Abstract 632: Genome-scale neoantigen screening using ATLAS™ prioritizes candidate antigens for immunotherapy in a non-small cell lung cancer patient

Despite the unprecedented efficacy of checkpoint blockade (CPB) therapy in treating some cancers, the majority of patients fail to respond. Several lines of evidence support that the combination of CPB and neoantigen vaccine prolongs survival curves in cancer patients. Capitalizing on neoantigens derived from non-synonymous somatic mutations is a good strategy for therapeutic immunization. Current approaches to neoantigen prioritization involve mutanome sequencing, in silico epitope prediction algorithms, and experimental validation of cancer neoepitopes. Even the best in class in silico epitope prediction algorithms lack the accuracy necessary for efficacious personalized cancer vaccines. We sought to circumvent some of the limitations of currently available prediction algorithms by prioritizing neoantigens empirically using ATLAS™, a technology developed to screen T cell responses from any subject against their entire complement of potential neoantigens. Exome sequences were obtained from peripheral blood mononuclear cells (PBMC) and tumor biopsies from a non-small cell lung cancer patient who had been successfully treated with pembrolizumab. The tumor exome was sequenced and somatic mutations were identified. Individual DNA sequences (399 nucleotides) spanning each mutation site were built, cloned and expressed in E. coli co-expressing listeriolysin O. Polypeptide expression was validated using a surrogate T cell assay or by Western Blotting. Frozen PBMCs, collected pre- and post-therapy, were used to derive dendritic cells (MDDC). Both CD4+ and CD8+ T cells were enriched and expanded using microbeads. The E. coli clones were pulsed onto MDDC in an ordered array, then co-cultured either with CD8+ or with CD4+ T cells overnight. T cell activation was detected by analyzing cytokines in supernatants. Antigens were identified as clones that induced a cytokine response that exceeded three standard deviations of the mean of all negative control wells, then their identities compared with T cell epitopes predicted using previously described algorithms. We found biological evidence for neoantigens that were specifically responsive to peripheral CD8+ and CD4+ T cells, derived from the patient’s tumor, pre- and post-CPB intervention. Some of these neoantigens were identified as a T cell target both pre- and post-CPB therapy. We identified neoantigens for which no epitopes were predicted by in silico methods. These data represent evidence that multiple patient-specific neoantigens can be identified through functional evidence of T cell response from peripheral blood without epitope prediction. By profiling natural and CPB-enhanced immunity to neoantigens, a broad catalog of T cell targets can be identified for development of immunotherapies that engage T cells against cancer to improve outcomes for patients for whom current therapies are ineffective. Citation Format: Lila Ghamsari, Emilio Flano, Judy Jacques, Biao Liu, Zheng Yan, Aula Alami, Christine Kelley, Theresa Zhang, Jonathan Havel, Vladimir Makarov, Taha Merghoub, Jedd D. Wolchok, Matthew Hellman, Pamela Carroll, Timothy Chan, Jessica B. Flechtner. Genome-scale neoantigen screening using ATLAS™ prioritizes candidate antigens for immunotherapy in a non-small cell lung cancer patient [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 632. doi:10.1158/1538-7445.AM2017-632

Abstract 976: ImmunoMap: a novel bioinformatics tool for immune cell repertoire analysis

Background: There has been a dramatic increase in T-cell Receptor (TCR) sequencing spurred, in part, by the clinical demand in Immuno-oncology and technological advances in TCR sequencing. However, there has been little in the way of approaches to parse the data in a biologically meaningful fashion. The ability to parse this data to understand the T-cell repertoire in a structurally relevant manner has the potential to open new discoveries about how the immune system responds to insults such as cancer and infectious diseases. Methods: Here we describe a novel method to visualize and quantify TCR repertoire sequence diversity. This method includes metrics such as visualization of repertoire with: 1) weighted phylogenetic trees that display relatedness and frequency of the sequences; 2) dominant motif analyses identifying clusters of highly homologous sequences that contribute significantly to response and; 3) TCR diversity score measuring the average relatedness (by sequence homology) of all TCR’s in a sample. To demonstrate the power of the approach, we have applied it to understanding the CD8 T Cell response to model self (TRP2) and foreign (SIY) antigens in naive and tumor-bearing (B16 melanoma) B6 mice. Additionally, this method was applied to tumor infiltrating lymphocytes, TIL, taken pre- and on-therapy, from patients undergoing Nivolumab (α-PD1) therapy for metastatic melanoma. Results: Analysis of the naive CD8 response demonstrated a highly conserved (measured by the TCR diversity score) and less clonal response to SIY whereas the response to TRP2 was less conserved and highly clonal. Dominant motif analysis demonstrated highly rich motifs consisting of many homologous sequences in the SIY response but few sequences per motif in the TRP2 response. This may reflect the outcome of tolerance mechanisms to self-antigens. Presence of tumor demonstrated differential immune pressure on the TRP2 vs SIY response. Tumor primed novel SIY motifs but constricted the number of dominant motifs in the TRP2 response while additionally altering the sequence of the motifs. In patients undergoing α-PD1 therapy, we identified signatures in pre- and post-therapy TCR repertoires that correlated with clinical outcome response. Prior to therapy, patients whose dominant motifs were rich with many sequences responded favorably to checkpoint inhibition over those with less rich motifs. After four week on therapy, patients whose TCR repertoires became more conserved responded more favorably to PD1 treatment while those who did not respond had no change in their TCR diversity score. Conclusions: In summary, we have developed and demonstrated a novel method to meaningfully parse and interpret TCR repertoire data and have applied it to yield a novel insight of CD8 T Cell responses to different types of antigens in model systems as well as key characteristics of TIL repertoires from patients who respond clinically to α-PD1 therapy. Citation Format: John-William Sidhom, Catherine A. Bessell, Jonathan J. Havel, Timothy A. Chan, Jonathan P. Schneck. ImmunoMap: a novel bioinformatics tool for immune cell repertoire analysis [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 976. doi:10.1158/1538-7445.AM2017-976

Abstract 2988: Immunogenomic analyses of tumor cells and microenvironment in patients with advanced melanoma before and after treatment with nivolumab

Background: Response to checkpoint blockade may be dependent on tumor mutational load and the presence of antigen-specific effector T cells in the tumor microenvironment; however, how blockade modulates these features during therapy is unclear. We assessed genomic changes in tumors from patients (pts) with advanced melanoma receiving nivolumab (nivo) who progressed on ipilimumab (ipi-P) or were ipi-naive (ipi-N). Methods: Tumor biopsies were collected pretreatment and 4 weeks post first nivo dose from ipi-N or ipi-P pts treated with nivo 3 mg/kg Q2W in the phase 1 open-label CA209-038 study (NCT01621490). Biopsies from 68 pts were analyzed by whole exome, transcriptome, and/or TCR sequencing (paired biopsies from 41, 42, and 34 pts, respectively). Results: Objective response rate (ORR) in the overall cohort (n=85) was 27% with similar ORR in ipi-N and ipi-P cohorts. In the genomic cohort (n=68), ORR was 23% with a similar number of complete or partial responses (CR/PR) in ipi-N and ipi-P pts (n=7 and n=8, respectively). Prior to treatment, mutational and neoantigen load were comparable, regardless of previous treatment. Following nivo treatment, both mutational and neoantigen load were reduced 5-fold in pts who responded (CR/PR; n=9) and 1.2-fold in pts with stable disease (SD, n=13) compared with a 1.1-fold increase in pts with progressive disease (PD, n=19). Intratumoral heterogeneity analysis before and after nivo demonstrated that CR/PR pts generally lost tumor mutation clones/subclones. Novel tumor mutation clones were observed in on-treatment samples from 2 CR/PR pts and all pts who progressed on nivo. Transcriptome analyses revealed significant increases in distinct tumor immune cell subsets (CD8+ T cells and NK cells) and immune checkpoint gene expression (LAG3, CTLA4, PCDC1, and CD274 [PD-L1]) following nivo, which were more pronounced in pts with CR/PR vs PD (log2 fold-changes of 1.24, 1.07, 1.71, and 0.74, respectively). Consistent with the transcriptome analyses, tumor-infiltrating lymphocytes, as assessed by immunohistochemistry, generally increased following nivo in pts who responded: 2.8 vs 1.9-fold change in CR/PR/SD vs PD in the ipi-P cohort; 4.8 vs 1.8-fold change in CR/PR/SD vs PD in the ipi-N cohort. Differences in treatment-related TCR repertoire diversity changes were apparent between pts who responded within the ipi-N and ipi-P cohorts: a decrease in the evenness of T-cell clonotype distribution was observed among pts with CR/PR/SD relative to pts with PD in the ipi-N cohort (P=0.036), but not in the ipi-P cohort. Conclusion: Nivo and ipi modulate T-cell repertoire and tumor mutational heterogeneity in pts with advanced melanoma, presenting potential mechanisms of action underlying successful nivo therapy. These data also show that prior ipi treatment may influence biological response to nivo, but further investigation is warranted. Citation Format: Timothy A. Chan, Nadeem Riaz, Jonathan J. Havel, Vladimir Makarov, Alexis Desrichard, Jennifer S. Sims, F. Stephen Hodi, Salvador Martin-Algarra, William H. Sharfman, Shailender Bhatia, Wen-Jen Hwu, Thomas F. Gajewski, Craig L. Slingluff, Sviatoslav M. Kendall, Han Chang, John-William Sidhom, Jonathan P. Schneck, Nils Weinhold, Christine E. Horak, Walter J. Urba. Immunogenomic analyses of tumor cells and microenvironment in patients with advanced melanoma before and after treatment with nivolumab [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 2988. doi:10.1158/1538-7445.AM2017-2988