Cancer treatment by immune checkpoint blockade (ICB) can bring long-lasting clinical benefits, but only a fraction of patients respond to treatment. To predict ICB response, we developed TIDE, a computational method to model two primary mechanisms of tumor immune evasion: the induction of T cell dysfunction in tumors with high infiltration of cytotoxic T lymphocytes (CTL) and the prevention of T cell infiltration in tumors with low CTL level. We identified signatures of T cell dysfunction from large tumor cohorts by testing how the expression of each gene in tumors interacts with the CTL infiltration level to influence patient survival. We also modeled factors that exclude T cell infiltration into tumors using expression signatures from immunosuppressive cells. Using this framework and pre-treatment RNA-Seq or NanoString tumor expression profiles, TIDE predicted the outcome of melanoma patients treated with first-line anti-PD1 or anti-CTLA4 more accurately than other biomarkers such as PD-L1 level and mutation load. TIDE also revealed new candidate ICB resistance regulators, such as SERPINB9, demonstrating utility for immunotherapy research.

BackgroundUnderstanding the interactions between tumor and the host immune system is critical to finding prognostic biomarkers, reducing drug resistance, and developing new therapies. Novel computational methods are needed to estimate tumor-infiltrating immune cells and understand tumor–immune interactions in cancers.ResultsWe analyze tumor-infiltrating immune cells in over 10,000 RNA-seq samples across 23 cancer types from The Cancer Genome Atlas (TCGA). Our computationally inferred immune infiltrates associate much more strongly with patient clinical features, viral infection status, and cancer genetic alterations than other computational approaches. Analysis of cancer/testis antigen expression and CD8 T-cell abundance suggests that MAGEA3 is a potential immune target in melanoma, but not in non-small cell lung cancer, and implicates SPAG5 as an alternative cancer vaccine target in multiple cancers. We find that melanomas expressing high levels of CTLA4 separate into two distinct groups with respect to CD8 T-cell infiltration, which might influence clinical responses to anti-CTLA4 agents. We observe similar dichotomy of TIM3 expression with respect to CD8 T cells in kidney cancer and validate it experimentally. The abundance of immune infiltration, together with our downstream analyses and findings, are accessible through TIMER, a public resource at http://cistrome.org/TIMER.ConclusionsWe develop a computational approach to study tumor-infiltrating immune cells and their interactions with cancer cells. Our resource of immune-infiltrate levels, clinical associations, as well as predicted therapeutic markers may inform effective cancer vaccine and checkpoint blockade therapies.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-1028-7) contains supplementary material, which is available to authorized users.

Many human cancers are resistant to immunotherapy, for reasons that are poorly understood. We used a genome-scale CRISPR-Cas9 screen to identify mechanisms of tumor cell resistance to killing by cytotoxic T cells, the central effectors of antitumor immunity. Inactivation of >100 genes—including Pbrm1, Arid2, and Brd7, which encode components of the PBAF form of the SWI/SNF chromatin remodeling complex—sensitized mouse B16F10 melanoma cells to killing by T cells. Loss of PBAF function increased tumor cell sensitivity to interferon-γ, resulting in enhanced secretion of chemokines that recruit effector T cells. Treatment-resistant tumors became responsive to immunotherapy when Pbrm1 was inactivated. In many human cancers, expression of PBRM1 and ARID2 inversely correlated with expression of T cell cytotoxicity genes, and Pbrm1-deficient murine melanomas were more strongly infiltrated by cytotoxic T cells.

Despite growing numbers of immune checkpoint blockade (ICB) trials with available omics data, it remains challenging to evaluate the robustness of ICB response and immune evasion mechanisms comprehensively. To address these challenges, we integrated large-scale omics data and biomarkers on published ICB trials, nonimmunotherapy tumor profiles, and CRISPR screens on a web platform TIDE (http://tide.dfci.harvard.edu). We processed the omics data for over 33K samples in 188 tumor cohorts from public databases, 998 tumors from 12 ICB clinical studies, and eight CRISPR screens that identified gene modulators of the anticancer immune response. Integrating these data on the TIDE web platform with three interactive analysis modules, we demonstrate the utility of public data reuse in hypothesis generation, biomarker optimization, and patient stratification.

Motivation: Clustering algorithms play an important role in the analysis of biological networks, and can be used to uncover functional modules and obtain hints about cellular organization. While most available clustering algorithms work well on biological networks of moderate size, such as the yeast protein physical interaction network, they either fail or are too slow in practice for larger networks, such as functional networks for higher eukaryotes. Since an increasing number of larger biological networks are being determined, the limitations of current clustering approaches curtail the types of biological network analyses that can be performed.Results: We present a fast local network clustering algorithm SPICi. SPICi runs in time O(V log V+E) and space O(E), where V and E are the number of vertices and edges in the network, respectively. We evaluate SPICi's performance on several existing protein interaction networks of varying size, and compare SPICi to nine previous approaches for clustering biological networks. We show that SPICi is typically several orders of magnitude faster than previous approaches and is the only one that can successfully cluster all test networks within very short time. We demonstrate that SPICi has state-of-the-art performance with respect to the quality of the clusters it uncovers, as judged by its ability to recapitulate protein complexes and functional modules. Finally, we demonstrate the power of our fast network clustering algorithm by applying SPICi across hundreds of large context-specific human networks, and identifying modules specific for single conditions.Availability: Source code is available under the GNU Public License at http://compbio.cs.princeton.edu/spiciContact: mona@cs.princeton.eduSupplementary information: Supplementary data are available at Bioinformatics online.

The Cutoff protein regulates piRNA cluster expression and piRNA production in the Drosophila germlineThe identity and function of many factors involved in the piRNA pathway remain unknown. Here, in Drosophila, cutoff plays a role in regulating piRNA cluster transcript levels and biogenesis together with the heterochromatin protein Rhino.

Tumor-infiltrating B cells are an important component in the microenvironment with unclear anti-tumor impacts. We enhanced our previous computational algorithm TRUST to extract the B cell immunoglobulin (Ig) hypervariable regions from bulk tumor RNA-seq data. TRUST assembled over 30 million complementarity-determining region 3 (CDR3s) of the B cell heavy chain (IgH) from The Cancer Genome Atlas (TCGA). Widespread B cell clonal expansions and Ig subclass switch events were observed in diverse human cancers. Prevalent somatic copy number alterations in MICA and MICB genes related to antibody-dependent cell mediated cytotoxicity (ADCC) were identified in tumors with elevated B cell activity. IgG3-1 subclass switch interacts with the B cell receptor affinity maturation and defects in the ADCC pathway. Comprehensive pan-cancer analyses of tumor-infiltrating B cell receptor repertoires identified novel tumor immune evasion mechanisms through genetic alterations. The IgH sequences identified here are potentially useful resources for future development of immunotherapies.

Cancer results from a breakdown of normal gene expression control, so the study of gene regulation is critical to cancer research. To gain insight into the transcriptional and epigenetic factors regulating abnormal gene expression patterns in cancers, we developed the Cistrome Cancer web resource (http://cistrome.org/CistromeCancer/). We conducted the systematic integration and modeling of over ten thousand tumor molecular profiles from The Cancer Genome Atlas (TCGA) with over twenty-three thousand ChIP-seq and chromatin accessibility profiles from our Cistrome collection. The results include reconstruction of functional enhancer profiles, “super-enhancer” target genes, as well as predictions of active transcription factors (TF) and their target genes for each TCGA cancer type. Cistrome Cancer reveals novel insights from integrative analyses combining chromatin profiles with tumor molecular profiles, and will be a useful resource to the cancer gene regulation community.