SUMMARY Treatment of cancer has been revolutionized by immune checkpoint blockade therapies. Despite the high rate of response in advanced melanoma, the majority of patients succumb to disease. To identify factors associated with success or failure of checkpoint therapy, we profiled transcriptomes of 16,291 individual immune cells from 48 tumor samples of melanoma patients treated with checkpoint inhibitors. Two distinct states of CD8+ T cells were defined by clustering, and associated with patient tumor regression or progression. A single transcription factor, TCF7, was visualized within CD8+ T cells in fixed tumor samples and predicted positive clinical outcome in an independent cohort of checkpoint-treated patients. We delineated the epigenetic landscape and clonality of these T cell states, and demonstrated enhanced anti-tumor immunity by targeting novel combinations of factors in exhausted cells. Our study of immune cell transcriptomes from tumors demonstrates a strategy for identifying predictors, mechanisms and targets for enhancing checkpoint immunotherapy.

Immune checkpoint blockade, exemplified by antibodies targeting the PD-1 receptor, can induce durable tumor regressions in some patients. To enhance the efficacy of existing immunotherapies, we screened for small molecules capable of increasing the activity of T cells suppressed by PD-1. Here, we show that short-term exposure to small-molecule inhibitors of cyclin-dependent kinases 4 and 6 (CDK4/6) significantly enhances T-cell activation, contributing to antitumor effects , due in part to the derepression of NFAT family proteins and their target genes, critical regulators of T-cell function. Although CDK4/6 inhibitors decrease T-cell proliferation, they increase tumor infiltration and activation of effector T cells. Moreover, CDK4/6 inhibition augments the response to PD-1 blockade in a novel organotypic tumor spheroid culture system and in multiple murine syngeneic models, thereby providing a rationale for combining CDK4/6 inhibitors and immunotherapies. Our results define previously unrecognized immunomodulatory functions of CDK4/6 and suggest that combining CDK4/6 inhibitors with immune checkpoint blockade may increase treatment efficacy in patients. Furthermore, our study highlights the critical importance of identifying complementary strategies to improve the efficacy of immunotherapy for patients with cancer. .

Nanotechnology has tremendous potential to contribute to cancer immunotherapy. The “in situ vaccination” immunotherapy strategy directly manipulates identified tumours to overcome local tumour-mediated immunosuppression and subsequently stimulates systemic anti-tumour immunity to treat metastases. We show that inhalation of self-assembling virus-like nanoparticles from Cowpea Mosaic Virus (CPMV) reduces established B16F10 lung melanoma and simultaneously generates potent systemic anti-tumour immunity against poorly immunogenic B16F10 in the skin. Full efficacy required Il-12, Ifn-γ, adaptive immunity, and neutrophils. Inhaled CPMV nanoparticles were rapidly taken up by and activated neutrophils in the tumour microenvironment as an important part of the anti-tumour immune response. CPMV also exhibited clear treatment efficacy and systemic anti-tumour immunity in ovarian, colon, and breast tumour models in multiple anatomic locations. CPMV nanoparticles are stable, nontoxic, modifiable with drugs and antigens, and their nanomanufacture is highly scalable. These properties, combined with their inherent immunogenicity and demonstrated efficacy against a poorly immunogenic tumour, make CPMV an attractive and novel immunotherapy against metastatic cancer.

A comprehensive understanding of the molecular vulnerabilities of every type of cancer will provide a powerful roadmap to guide therapeutic approaches. Efforts such as The Cancer Genome Atlas Project will identify genes with aberrant copy number, sequence, or expression in various cancer types, providing a survey of the genes that may have a causal role in cancer. A complementary approach is to perform systematic loss-of-function studies to identify essential genes in particular cancer cell types. We have begun a systematic effort, termed Project Achilles, aimed at identifying genetic vulnerabilities across large numbers of cancer cell lines. Here, we report the assessment of the essentiality of 11,194 genes in 102 human cancer cell lines. We show that the integration of these functional data with information derived from surveying cancer genomes pinpoints known and previously undescribed lineage-specific dependencies across a wide spectrum of cancers. In particular, we found 54 genes that are specifically essential for the proliferation and viability of ovarian cancer cells and also amplified in primary tumors or differentially overexpressed in ovarian cancer cell lines. One such gene, PAX8 , is focally amplified in 16% of high-grade serous ovarian cancers and expressed at higher levels in ovarian tumors. Suppression of PAX8 selectively induces apoptotic cell death of ovarian cancer cells. These results identify PAX8 as an ovarian lineage-specific dependency. More generally, these observations demonstrate that the integration of genome-scale functional and structural studies provides an efficient path to identify dependencies of specific cancer types on particular genes and pathways.

Using a genome-scale, lentivirally delivered shRNA library, we performed massively parallel pooled shRNA screens in 216 cancer cell lines to identify genes that are required for cell proliferation and/or viability. Cell line dependencies on 11,000 genes were interrogated by 5 shRNAs per gene. The proliferation effect of each shRNA in each cell line was assessed by transducing a population of 11M cells with one shRNA-virus per cell and determining the relative enrichment or depletion of each of the 54,000 shRNAs after 16 population doublings using Next Generation Sequencing. All the cell lines were screened using standardized conditions to best assess differential genetic dependencies across cell lines. When combined with genomic characterization of these cell lines, this dataset facilitates the linkage of genetic dependencies with specific cellular contexts (e.g., gene mutations or cell lineage). To enable such comparisons, we developed and provided a bioinformatics tool to identify linear and nonlinear correlations between these features.