Genomic and transcriptomic analysis of checkpoint blockade response in advanced non-small cell lung cancer.

Anti-PD-1/PD-L1 agents have transformed the treatment landscape of advanced non-small cell lung cancer (NSCLC). To expand our understanding of the molecular features underlying response to checkpoint inhibitors in NSCLC, we describe here the first joint analysis of the Stand Up To Cancer-Mark Foundation cohort, a resource of whole exome and/or RNA sequencing from 393 patients with NSCLC treated with anti-PD-(L)1 therapy, along with matched clinical response annotation. We identify a number of associations between molecular features and outcome, including (1) favorable (for example, ATM altered) and unfavorable (for example, TERT amplified) genomic subgroups, (2) a prominent association between expression of inducible components of the immunoproteasome and response and (3) a dedifferentiated tumor-intrinsic subtype with enhanced response to checkpoint blockade. Taken together, results from this cohort demonstrate the complexity of biological determinants underlying immunotherapy outcomes and reinforce the discovery potential of integrative analysis within large, well-curated, cancer-specific cohorts.

Tumor-induced double positive T cells display distinct lineage commitment mechanisms and functions.

Transcription factors ThPOK and Runx3 regulate the differentiation of "helper" CD4+ and "cytotoxic" CD8+ T cell lineages respectively, inducing single positive (SP) T cells that enter the periphery with the expression of either the CD4 or CD8 co-receptor. Despite the expectation that these cell fates are mutually exclusive and that mature CD4+CD8+ double positive (DP) T cells are present in healthy individuals and augmented in the context of disease, yet their molecular features and pathophysiologic role are disputed. Here, we show DP T cells in murine and human tumors as a heterogenous population originating from SP T cells which re-express the opposite co-receptor and acquire features of the opposite cell type's phenotype and function following TCR stimulation. We identified distinct clonally expanded DP T cells in human melanoma and lung cancer by scRNA sequencing and demonstrated their tumor reactivity in cytotoxicity assays. Our findings indicate that antigen stimulation induces SP T cells to differentiate into DP T cell subsets gaining in polyfunctional characteristics.

Combined tumor and immune signals from genomes or transcriptomes predict outcomes of checkpoint inhibition in melanoma.

Immune checkpoint blockade (CPB) improves melanoma outcomes, but many patients still do not respond. Tumor mutational burden (TMB) and tumor-infiltrating T cells are associated with response, and integrative models improve survival prediction. However, integrating immune/tumor-intrinsic features using data from a single assay (DNA/RNA) remains underexplored. Here, we analyze whole-exome and bulk RNA sequencing of tumors from new and published cohorts of 189 and 178 patients with melanoma receiving CPB, respectively. Using DNA, we calculate T cell and B cell burdens (TCB/BCB) from rearranged TCR/Ig sequences and find that patients with TMBhigh and TCBhigh or BCBhigh have improved outcomes compared to other patients. By combining pairs of immune- and tumor-expressed genes, we identify three gene pairs associated with response and survival, which validate in independent cohorts. The top model includes lymphocyte-expressed MAP4K1 and tumor-expressed TBX3. Overall, RNA or DNA-based models combining immune and tumor measures improve predictions of melanoma CPB outcomes.

SMART Cancer Navigator: A Framework for Implementing ASCO Workshop Recommendations to Enable Precision Cancer Medicine.

PURPOSE: Data standards and interoperability are critical for improving care for patients with cancer. Recent efforts by ASCO include the Data Standards and Interoperability Summit in 2016, which led to the Omics and Precision Oncology and Advancing Interoperability workshops. To facilitate improved patient care, several recommendations for data sharing and standardization were made to the community. METHODS: To address these recommendations, we developed SMART Cancer Navigator, a Web application that uses application programming interfaces to gather clinical and genomic data from 11 public knowledge bases ranging from basic to clinical content coverage; three (CIViC, ClinVar, and OncoKB) explicitly linked genomic variants to clinical factors such as prognosis and treatment selection. We illustrated the utility of this application by selecting one of the monthly case studies presented by the ASCO University Molecular Oncology Tumor Board: Ovarian Cancer (BRCA Mutation). We also performed analyses on information from the three clinico-genomic knowledge bases to corroborate previous work and illustrate the state of data sharing among publicly available resources. RESULTS: SMART Cancer Navigator aggregates and contextualizes data from 11 different knowledge bases and stores user queries in a lightweight Web application that can link into Fast Healthcare Interoperability Resources-enabled electronic health records. Potentially relevant clinical trials and/or approved treatments were identified for three mutations found in a hypothetical patient with advanced ovarian cancer. A comparison of the three clinico-genomic knowledge bases indicated substantial differences in coverage at the gene and variant levels. CONCLUSION: SMART Cancer Navigator has immediate relevance to practicing oncologists and others. Additional knowledge bases can be added without undue effort. As a first step toward utility, we generalized and disseminated the resulting implementation (https://smart-cancer-navigator.github.io) and data sets.