Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction.

Many approaches to identify therapeutically relevant neoantigens couple tumor sequencing with bioinformatic algorithms and inferred rules of tumor epitope immunogenicity. However, there are no reference data to compare these approaches, and the parameters governing tumor epitope immunogenicity remain unclear. Here, we assembled a global consortium wherein each participant predicted immunogenic epitopes from shared tumor sequencing data. 608 epitopes were subsequently assessed for T cell binding in patient-matched samples. By integrating peptide features associated with presentation and recognition, we developed a model of tumor epitope immunogenicity that filtered out 98% of non-immunogenic peptides with a precision above 0.70. Pipelines prioritizing model features had superior performance, and pipeline alterations leveraging them improved prediction performance. These findings were validated in an independent cohort of 310 epitopes prioritized from tumor sequencing data and assessed for T cell binding. This data resource enables identification of parameters underlying effective anti-tumor immunity and is available to the research community.

Impaired mitochondrial oxidative phosphorylation limits the self-renewal of T cells exposed to persistent antigen.

The majority of tumor-infiltrating T cells exhibit a terminally exhausted phenotype, marked by a loss of self-renewal capacity. How repetitive antigenic stimulation impairs T cell self-renewal remains poorly defined. Here, we show that persistent antigenic stimulation impaired ADP-coupled oxidative phosphorylation. The resultant bioenergetic compromise blocked proliferation by limiting nucleotide triphosphate synthesis. Inhibition of mitochondrial oxidative phosphorylation in activated T cells was sufficient to suppress proliferation and upregulate genes linked to T cell exhaustion. Conversely, prevention of mitochondrial oxidative stress during chronic T cell stimulation allowed sustained T cell proliferation and induced genes associated with stem-like progenitor T cells. As a result, antioxidant treatment enhanced the anti-tumor efficacy of chronically stimulated T cells. These data reveal that loss of ATP production through oxidative phosphorylation limits T cell proliferation and effector function during chronic antigenic stimulation. Furthermore, treatments that maintain redox balance promote T cell self-renewal and enhance anti-tumor immunity.

Identification of unique neoantigen qualities in long-term survivors of pancreatic cancer.

Pancreatic ductal adenocarcinoma is a lethal cancer with fewer than 7% of patients surviving past 5 years. T-cell immunity has been linked to the exceptional outcome of the few long-term survivors, yet the relevant antigens remain unknown. Here we use genetic, immunohistochemical and transcriptional immunoprofiling, computational biophysics, and functional assays to identify T-cell antigens in long-term survivors of pancreatic cancer. Using whole-exome sequencing and in silico neoantigen prediction, we found that tumours with both the highest neoantigen number and the most abundant CD8+ T-cell infiltrates, but neither alone, stratified patients with the longest survival. Investigating the specific neoantigen qualities promoting T-cell activation in long-term survivors, we discovered that these individuals were enriched in neoantigen qualities defined by a fitness model, and neoantigens in the tumour antigen MUC16 (also known as CA125). A neoantigen quality fitness model conferring greater immunogenicity to neoantigens with differential presentation and homology to infectious disease-derived peptides identified long-term survivors in two independent datasets, whereas a neoantigen quantity model ascribing greater immunogenicity to increasing neoantigen number alone did not. We detected intratumoural and lasting circulating T-cell reactivity to both high-quality and MUC16 neoantigens in long-term survivors of pancreatic cancer, including clones with specificity to both high-quality neoantigens and predicted cross-reactive microbial epitopes, consistent with neoantigen molecular mimicry. Notably, we observed selective loss of high-quality and MUC16 neoantigenic clones on metastatic progression, suggesting neoantigen immunoediting. Our results identify neoantigens with unique qualities as T-cell targets in pancreatic ductal adenocarcinoma. More broadly, we identify neoantigen quality as a biomarker for immunogenic tumours that may guide the application of immunotherapies.

Spatial and functional heterogeneities shape collective behavior of tumor-immune networks.

Tumor growth involves a dynamic interplay between cancer cells and host cells, which collectively form a tumor microenvironmental network that either suppresses or promotes tumor growth under different conditions. The transition from tumor suppression to tumor promotion is mediated by a tumor-induced shift in the local immune state, and despite the clinical challenge this shift poses, little is known about how such dysfunctional immune states are initiated. Clinical and experimental observations have indicated that differences in both the composition and spatial distribution of different cell types and/or signaling molecules within the tumor microenvironment can strongly impact tumor pathogenesis and ultimately patient prognosis. How such "functional" and "spatial" heterogeneities confer such effects, however, is not known. To investigate these phenomena at a level currently inaccessible by direct observation, we developed a computational model of a nascent metastatic tumor capturing salient features of known tumor-immune interactions that faithfully recapitulates key features of existing experimental observations. Surprisingly, over a wide range of model formulations, we observed that heterogeneity in both spatial organization and cell phenotype drove the emergence of immunosuppressive network states. We determined that this observation is general and robust to parameter choice by developing a systems-level sensitivity analysis technique, and we extended this analysis to generate other parameter-independent, experimentally testable hypotheses. Lastly, we leveraged this model as an in silico test bed to evaluate potential strategies for engineering cell-based therapies to overcome tumor associated immune dysfunction and thereby identified modes of immune modulation predicted to be most effective. Collectively, this work establishes a new integrated framework for investigating and modulating tumor-immune networks and provides insights into how such interactions may shape early stages of tumor formation.

Prior anti-CTLA-4 therapy impacts molecular characteristics associated with anti-PD-1 response in advanced melanoma.

Immune checkpoint inhibitors (ICIs), including CTLA-4- and PD-1-blocking antibodies, can have profound effects on tumor immune cell infiltration that have not been consistent in biopsy series reported to date. Here, we analyze seven molecular datasets of samples from patients with advanced melanoma (N = 514) treated with ICI agents to investigate clinical, genomic, and transcriptomic features of anti-PD-1 response in cutaneous melanoma. We find that prior anti-CTLA-4 therapy is associated with differences in genomic, individual gene, and gene signatures in anti-PD-1 responders. Anti-CTLA-4-experienced melanoma tumors that respond to PD-1 blockade exhibit increased tumor mutational burden, inflammatory signatures, and altered cell cycle processes compared with anti-CTLA-4-naive tumors or anti-CTLA-4-experienced, anti-PD-1-nonresponsive melanoma tumors. We report a harmonized, aggregate resource and suggest that prior CTLA-4 blockade therapy is associated with marked differences in the tumor microenvironment that impact the predictive features of PD-1 blockade therapy response.

Anti-GD2 CAR-NKT cells in relapsed or refractory neuroblastoma: updated phase 1 trial interim results.

Vα24-invariant natural killer T cells (NKTs) have anti-tumor properties that can be enhanced by chimeric antigen receptors (CARs). Here we report updated interim results from the first-in-human phase 1 evaluation of autologous NKTs co-expressing a GD2-specific CAR with interleukin 15 (IL15) (GD2-CAR.15) in 12 children with neuroblastoma (NB). The primary objectives were safety and determination of maximum tolerated dose (MTD). The anti-tumor activity of GD2-CAR.15 NKTs was assessed as a secondary objective. Immune response evaluation was an additional objective. No dose-limiting toxicities occurred; one patient experienced grade 2 cytokine release syndrome that was resolved by tocilizumab. The MTD was not reached. The objective response rate was 25% (3/12), including two partial responses and one complete response. The frequency of CD62L+NKTs in products correlated with CAR-NKT expansion in patients and was higher in responders (n = 5; objective response or stable disease with reduction in tumor burden) than non-responders (n = 7). BTG1 (BTG anti-proliferation factor 1) expression was upregulated in peripheral GD2-CAR.15 NKTs and is a key driver of hyporesponsiveness in exhausted NKT and T cells. GD2-CAR.15 NKTs with BTG1 knockdown eliminated metastatic NB in a mouse model. We conclude that GD2-CAR.15 NKTs are safe and can mediate objective responses in patients with NB. Additionally, their anti-tumor activity may be enhanced by targeting BTG1. ClinicalTrials.gov registration: NCT03294954 .

Lineage tracing reveals clonal progenitors and long-term persistence of tumor-specific T cells during immune checkpoint blockade.

Paired single-cell RNA and T cell receptor sequencing (scRNA/TCR-seq) has allowed for enhanced resolution of clonal T cell dynamics in cancer. Here, we report a scRNA/TCR-seq analysis of 187,650 T cells from 31 tissue regions, including tumor, adjacent normal tissues, and lymph nodes (LN), from three patients with non-small cell lung cancer after immune checkpoint blockade (ICB). Regions with viable cancer cells are enriched for exhausted CD8+ T cells, regulatory CD4+ T cells (Treg), and follicular helper CD4+ T cells (TFH). Tracking T cell clonotypes across tissues, combined with neoantigen specificity assays, reveals that TFH and tumor-specific exhausted CD8+ T cells are clonally linked to TCF7+SELL+ progenitors in tumor draining LNs, and progressive exhaustion trajectories of CD8+ T, Treg, and TFH cells with proximity to the tumor microenvironment. Finally, longitudinal tracking of tumor-specific CD8+ and CD4+ T cell clones reveals persistence in the peripheral blood for years after ICB therapy.

Bystander T cells in cancer immunology and therapy.

Cancer-specific T cells are required for effective anti-cancer immunity and have a central role in cancer immunotherapy. However, emerging evidence suggests that only a small fraction of tumor-infiltrating T cells are cancer specific, and T cells that recognize cancer-unrelated antigens (so-called 'bystanders') are abundant. Although the role of cancer-specific T cells in anti-cancer immunity has been well established, the implications of bystander T cells in tumors are only beginning to be understood. It is becoming increasingly clear that bystander T cells are not a homogeneous group of cells but, instead, they differ in their specificities, their activation states and effector functions. In this Perspective, we discuss recent studies of bystander T cells in tumors, including experimental and computational approaches that enable their identification and functional analysis and viewpoints on how these insights could be used to develop new therapeutic approaches for cancer immunotherapy.

Ipilimumab alone or in combination with nivolumab in patients with advanced melanoma who have progressed or relapsed on PD-1 blockade: clinical outcomes and translational biomarker analyses.

BACKGROUND: There are no validated biomarkers that can aid clinicians in selecting who would best benefit from anticytotoxic T lymphocyte-associated antigen 4 monotherapy versus combination checkpoint blockade in patients with advanced melanoma who have progressive disease after programmed death 1 (PD-1) blockade. METHODS: We conducted a randomized multicenter phase II trial in patients with advanced melanoma. Patients were randomly assigned to receive either 1 mg/kg of nivolumab plus 3 mg/kg of ipilimumab or 3 mg/kg of ipilimumab every 3 weeks for up to four doses. Patients were stratified by histological subtype and prior response to PD-1 therapy. The primary clinical objective was overall response rate by week 18. Translational biomarker analyses were conducted in patients with blood and tissue samples. RESULTS: Objective responses were seen in 5 of 9 patients in the ipilimumab arm and 2 of 10 patients in the ipilimumab+nivolumab arm; disease control rates (DCRs) (66.7% vs 60.0%) and rates of grade 3-4 adverse events (56% vs 50%) were comparable between arms. In a pooled analysis, patients with clinical benefit (CB), defined as Response Evaluation Criteria in Solid Tumors response or progression-free for 6 months, showed increased circulating CD4+ T cells with higher polyfunctionality and interferon gamma production following treatment. Tumor profiling revealed enrichment of NRAS mutations and activation of transcriptional programs associated with innate and adaptive immunity in patients with CB. CONCLUSIONS: In patients with advanced melanoma that previously progressed on PD-1 blockade, objective responses were seen in both arms, with comparable DCRs. Findings from biomarker analyses provided hypothesis-generating signals for validation in future studies of larger patient cohorts. TRIAL REGISTRATION NUMBER: NCT02731729.

Hallmarks of Resistance to Immune-Checkpoint Inhibitors.

Immune-checkpoint inhibitors (ICI), although revolutionary in improving long-term survival outcomes, are mostly effective in patients with immune-responsive tumors. Most patients with cancer either do not respond to ICIs at all or experience disease progression after an initial period of response. Treatment resistance to ICIs remains a major challenge and defines the biggest unmet medical need in oncology worldwide. In a collaborative workshop, thought leaders from academic, biopharma, and nonprofit sectors convened to outline a resistance framework to support and guide future immune-resistance research. Here, we explore the initial part of our effort by collating seminal discoveries through the lens of known biological processes. We highlight eight biological processes and refer to them as immune resistance nodes. We examine the seminal discoveries that define each immune resistance node and pose critical questions, which, if answered, would greatly expand our notion of immune resistance. Ultimately, the expansion and application of this work calls for the integration of multiomic high-dimensional analyses from patient-level data to produce a map of resistance phenotypes that can be utilized to guide effective drug development and improved patient outcomes.

TCR signal strength defines distinct mechanisms of T cell dysfunction and cancer evasion.

T cell receptor (TCR) signal strength is a key determinant of T cell responses. We developed a cancer mouse model in which tumor-specific CD8 T cells (TST cells) encounter tumor antigens with varying TCR signal strength. High-signal-strength interactions caused TST cells to up-regulate inhibitory receptors (IRs), lose effector function, and establish a dysfunction-associated molecular program. TST cells undergoing low-signal-strength interactions also up-regulated IRs, including PD1, but retained a cell-intrinsic functional state. Surprisingly, neither high- nor low-signal-strength interactions led to tumor control in vivo, revealing two distinct mechanisms by which PD1hi TST cells permit tumor escape; high signal strength drives dysfunction, while low signal strength results in functional inertness, where the signal strength is too low to mediate effective cancer cell killing by functional TST cells. CRISPR-Cas9-mediated fine-tuning of signal strength to an intermediate range improved anti-tumor activity in vivo. Our study defines the role of TCR signal strength in TST cell function, with important implications for T cell-based cancer immunotherapies.

Cell-Free DNA Detection of Tumor Mutations in Heterogeneous, Localized Prostate Cancer Via Targeted, Multiregion Sequencing.

Cell-free DNA (cfDNA) may allow for minimally invasive identification of biologically relevant genomic alterations and genetically distinct tumor subclones. Although existing biomarkers may detect localized prostate cancer, additional strategies interrogating genomic heterogeneity are necessary for identifying and monitoring aggressive disease. In this study, we aimed to evaluate whether circulating tumor DNA can detect genomic alterations present in multiple regions of localized prostate tumor tissue. METHODS: Low-pass whole-genome and targeted sequencing with a machine-learning guided 2.5-Mb targeted panel were used to identify single nucleotide variants, small insertions and deletions (indels), and copy-number alterations in cfDNA. The majority of this study focuses on the subset of 21 patients with localized disease, although 45 total individuals were evaluated, including 15 healthy controls and nine men with metastatic castration-resistant prostate cancer. Plasma cfDNA was barcoded with duplex unique molecular identifiers. For localized cases, matched tumor tissue was collected from multiple regions (one to nine samples per patient) for comparison. RESULTS: Somatic tumor variants present in heterogeneous tumor foci from patients with localized disease were detected in cfDNA, and cfDNA mutational burden was found to track with disease severity. Somatic tissue alterations were identified in cfDNA, including nonsynonymous variants in FOXA1, PTEN, MED12, and ATM. Detection of these overlapping variants was associated with seminal vesicle invasion (P = .019) and with the number of variants initially found in the matched tumor tissue samples (P = .0005). CONCLUSION: Our findings demonstrate the potential of targeted cfDNA sequencing to detect somatic tissue alterations in heterogeneous, localized prostate cancer, especially in a setting where matched tumor tissue may be unavailable (ie, active surveillance or treatment monitoring).

Prioritization of SARS-CoV-2 epitopes using a pan-HLA and global population inference approach.

SARS-CoV-2 T cell response assessment and vaccine development may benefit from an approach that considers the global landscape of the human leukocyte antigen (HLA) proteins. We predicted the binding affinity between 9-mer and 15-mer peptides from the SARS-CoV-2 peptidome for 9,360 class I and 8,445 class II HLA alleles, respectively. We identified 368,145 unique combinations of peptide-HLA complexes (pMHCs) with a predicted binding affinity less than 500nM, and observed significant overlap between class I and II predicted pMHCs. Using simulated populations derived from worldwide HLA frequency data, we identified sets of epitopes predicted in at least 90% of the population in 57 countries. We also developed a method to prioritize pMHCs for specific populations. Collectively, this public dataset and accessible user interface (Shiny app: https://rstudio-connect.parkerici.org/content/13/) can be used to explore the SARS-CoV-2 epitope landscape in the context of diverse HLA types across global populations.

Society for Immunotherapy of Cancer clinical and biomarkers data sharing resource document: Volume II-practical challenges.

The development of strongly predictive validated biomarkers is essential for the field of immuno-oncology (IO) to advance. The highly complex, multifactorial data sets required to develop these biomarkers necessitate effective, responsible data-sharing efforts in order to maximize the scientific knowledge and utility gained from their collection. While the sharing of clinical- and safety-related trial data has already been streamlined to a large extent, the sharing of biomarker-aimed clinical trial derived data and data sets has been met with a number of hurdles that have impaired the progression of biomarkers from hypothesis to clinical use. These hurdles include technical challenges associated with the infrastructure, technology, workforce, and sustainability required for clinical biomarker data sharing. To provide guidance and assist in the navigation of these challenges, the Society for Immunotherapy of Cancer (SITC) Biomarkers Committee convened to outline the challenges that researchers currently face, both at the conceptual level (Volume I) and at the technical level (Volume II). The committee also suggests possible solutions to these problems in the form of professional standards and harmonized requirements for data sharing, assisting in continued progress toward effective, clinically relevant biomarkers in the IO setting.

Society for Immunotherapy of Cancer clinical and biomarkers data sharing resource document: Volume I-conceptual challenges.

The sharing of clinical trial data and biomarker data sets among the scientific community, whether the data originates from pharmaceutical companies or academic institutions, is of critical importance to enable the development of new and improved cancer immunotherapy modalities. Through data sharing, a better understanding of current therapies in terms of their efficacy, safety and biomarker data profiles can be achieved. However, the sharing of these data sets involves a number of stakeholder groups including patients, researchers, private industry, scientific journals and professional societies. Each of these stakeholder groups has differing interests in the use and sharing of clinical trial and biomarker data, and the conflicts caused by these differing interests represent significant obstacles to effective, widespread sharing of data. Thus, the Society for Immunotherapy of Cancer (SITC) Biomarkers Committee convened to identify the current barriers to biomarker data sharing in immuno-oncology (IO) and to help in establishing professional standards for the responsible sharing of clinical trial data. The conclusions of the committee are described in two position papers: Volume I-conceptual challenges and Volume II-practical challenges, the first of which is presented in this manuscript. Additionally, the committee suggests actions by key stakeholders in the field (including organizations and professional societies) as the best path forward, encouraging the cultural shift needed to ensure responsible data sharing in the IO research setting.

Circulating Tumor DNA Analysis to Assess Risk of Progression after Long-term Response to PD-(L)1 Blockade in NSCLC.

PURPOSE: Treatment with PD-(L)1 blockade can produce remarkably durable responses in patients with non-small cell lung cancer (NSCLC). However, a significant fraction of long-term responders ultimately progress and predictors of late progression are unknown. We hypothesized that circulating tumor DNA (ctDNA) analysis of long-term responders to PD-(L)1 blockade may differentiate those who will achieve ongoing benefit from those at risk of eventual progression. EXPERIMENTAL DESIGN: In patients with advanced NSCLC achieving long-term benefit from PD-(L)1 blockade (progression-free survival ≥ 12 months), plasma was collected at a surveillance timepoint late during/after treatment to interrogate ctDNA by Cancer Personalized Profiling by Deep Sequencing. Tumor tissue was available for 24 patients and was profiled by whole-exome sequencing (n = 18) or by targeted sequencing (n = 6). RESULTS: Thirty-one patients with NSCLC with long-term benefit to PD-(L)1 blockade were identified, and ctDNA was analyzed in surveillance blood samples collected at a median of 26.7 months after initiation of therapy. Nine patients also had baseline plasma samples available, and all had detectable ctDNA prior to therapy initiation. At the surveillance timepoint, 27 patients had undetectable ctDNA and 25 (93%) have remained progression-free; in contrast, all 4 patients with detectable ctDNA eventually progressed [Fisher P < 0.0001; positive predictive value = 1, 95% confidence interval (CI), 0.51-1; negative predictive value = 0.93 (95% CI, 0.80-0.99)]. CONCLUSIONS: ctDNA analysis can noninvasively identify minimal residual disease in patients with long-term responses to PD-(L)1 blockade and predict the risk of eventual progression. If validated, ctDNA surveillance may facilitate personalization of the duration of immune checkpoint blockade and enable early intervention in patients at high risk for progression.

It Is a Capital Mistake to Theorize Who to Treat with Checkpoint Inhibitors before One Has Data.

Immunotherapy results in remarkable clinical benefit in a subset of cancer patients by activating the patient's own immune system. The factors determining which cancer patients will benefit are diverse. Success in realizing precision immunotherapy needs collaboration to bring together multiple diverse data sets. Defining multi-factorial biomarker algorithms for immunotherapy requires new approaches and methodologies that use deep molecular and cellular profiling of the tumor microenvironment, systemic immunity with clinical metadata from clinical trials, and other databases.

Clonal replacement of tumor-specific T cells following PD-1 blockade.

Immunotherapies that block inhibitory checkpoint receptors on T cells have transformed the clinical care of patients with cancer1. However, whether the T cell response to checkpoint blockade relies on reinvigoration of pre-existing tumor-infiltrating lymphocytes or on recruitment of novel T cells remains unclear2-4. Here we performed paired single-cell RNA and T cell receptor sequencing on 79,046 cells from site-matched tumors from patients with basal or squamous cell carcinoma before and after anti-PD-1 therapy. Tracking T cell receptor clones and transcriptional phenotypes revealed coupling of tumor recognition, clonal expansion and T cell dysfunction marked by clonal expansion of CD8+CD39+ T cells, which co-expressed markers of chronic T cell activation and exhaustion. However, the expansion of T cell clones did not derive from pre-existing tumor-infiltrating T lymphocytes; instead, the expanded clones consisted of novel clonotypes that had not previously been observed in the same tumor. Clonal replacement of T cells was preferentially observed in exhausted CD8+ T cells and evident in patients with basal or squamous cell carcinoma. These results demonstrate that pre-existing tumor-specific T cells may have limited reinvigoration capacity, and that the T cell response to checkpoint blockade derives from a distinct repertoire of T cell clones that may have just recently entered the tumor.

Tumor Characteristics Associated with Benefit from Pembrolizumab in Advanced Non-Small Cell Lung Cancer.

PURPOSE: Several biomarkers have been individually associated with response to PD-1 blockade, including PD-L1 and tumor mutational burden (TMB) in non-small cell lung cancer (NSCLC), and CD8 cells in melanoma. We sought to examine the relationship between these distinct variables with response to PD-1 blockade and long-term benefit. EXPERIMENTAL DESIGN: We assessed the association between baseline tumor characteristics (TMB, PD-L1, CD4, and CD8) and clinical features and outcome in 38 patients with advanced NSCLC treated with pembrolizumab (median follow-up of 4.5 years, range 3.8-5.5 years). RESULTS: PD-L1 expression and CD8 infiltration correlated with each other and each significantly associated with objective response rate (ORR) and progression-free survival (PFS). TMB was independent of PD-L1 and CD8 expression, and trended towards association with ORR and PFS. There was no association between CD4 infiltration and outcomes. Only PD-L1 expression was correlated with overall survival (OS). Among 5 patients with long-term survival >3 years with no additional systemic therapy, PD-L1 expression was the only discriminating feature. The increased predictive value for PFS and OS of composite biomarker inclusive of PD-L1, CD8, CD4, and TMB was limited. CONCLUSIONS: In patients with NSCLC treated with PD-1 blockade with long-term follow up, TMB, PD-L1, and CD8 were each associated with benefit from PD-1 blockade. Pretreatment PD-L1 expression was correlated with T lymphocyte infiltration and OS, whereas models incorporating TMB and infiltrating CD4 and CD8 lymphocytes did not substantially add to the predictive value of PD-L1 alone for OS.