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.

Many cofactors bind the hormone-activated estrogen receptor (ER), yet the specific regulators of endogenous ER-mediated gene transcription are unknown. Using chromatin immunoprecipitation (ChIP), we find that ER and a number of coactivators rapidly associate with estrogen responsive promoters following estrogen treatment in a cyclic fashion that is not predicted by current models of hormone activation. Cycles of ER complex assembly are followed by transcription. In contrast, the anti-estrogen tamoxifen (TAM) recruits corepressors but not coactivators. Using a genetic approach, we show that recruitment of the p160 class of coactivators is sufficient for gene activation and for the growth stimulatory actions of estrogen in breast cancer supporting a model in which ER cofactors play unique roles in estrogen signaling.

We propose the Model-based Analysis of Genome-wide CRISPR/Cas9 Knockout (MAGeCK) method for prioritizing single-guide RNAs, genes and pathways in genome-scale CRISPR/Cas9 knockout screens. MAGeCK demonstrates better performance compared with existing methods, identifies both positively and negatively selected genes simultaneously, and reports robust results across different experimental conditions. Using public datasets, MAGeCK identified novel essential genes and pathways, including EGFR in vemurafenib-treated A375 cells harboring a BRAF mutation. MAGeCK also detected cell type-specific essential genes, including BCR and ABL1, in KBM7 cells bearing a BCR-ABL fusion, and IGF1R in HL-60 cells, which depends on the insulin signaling pathway for proliferation.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-014-0554-4) contains supplementary material, which is available to authorized users.

Prostate cancer relapsing from antiandrogen therapies can exhibit variant histology with altered lineage marker expression, suggesting that lineage plasticity facilitates therapeutic resistance. The mechanisms underlying prostate cancer lineage plasticity are incompletely understood. Studying mouse models, we demonstrate that Rb1 loss facilitates lineage plasticity and metastasis of prostate adenocarcinoma initiated by Pten mutation. Additional loss of Trp53 causes resistance to antiandrogen therapy. Gene expression profiling indicates that mouse tumors resemble human prostate cancer neuroendocrine variants; both mouse and human tumors exhibit increased expression of epigenetic reprogramming factors such as Ezh2 and Sox2. Clinically relevant Ezh2 inhibitors restore androgen receptor expression and sensitivity to antiandrogen therapy. These findings uncover genetic mutations that enable prostate cancer progression; identify mouse models for studying prostate cancer lineage plasticity; and suggest an epigenetic approach for extending clinical responses to antiandrogen therapy.

We propose the Model-based Analysis of Genome-wide CRISPR/Cas9 Knockout (MAGeCK) method for prioritizing single-guide RNAs, genes and pathways in genome-scale CRISPR/Cas9 knockout screens. MAGeCK demonstrates better performance compared with existing methods, identifies both positively and negatively selected genes simultaneously, and reports robust results across different experimental conditions. Using public datasets, MAGeCK identified novel essential genes and pathways, including EGFR in vemurafenib-treated A375 cells harboring a BRAF mutation. MAGeCK also detected cell type-specific essential genes, including BCR and ABL1, in KBM7 cells bearing a BCR-ABL fusion, and IGF1R in HL-60 cells, which depends on the insulin signaling pathway for proliferation.

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.

Peroxisome proliferator-activated receptors (PPARs) are nuclear hormone receptors that regulate gene transcription in response to peroxisome proliferators and fatty acids. PPARs also play an important role in the regulation of adipocyte differentiation. It is unclear, however, what naturally occurring compounds activate each of the PPAR subtypes. To address this issue, a screening assay was established using heterologous fusions of the bacterial tetracycline repressor to several members of the peroxisome proliferator-activated receptor (PPAR) family. This assay was employed to compare the activation of PPAR family members by known PPAR activators including peroxisome proliferators and fatty acids. Interestingly, the activation of PPARs by fatty acids was partially inhibited by the cyclooxygenase inhibitor indomethacin, which prevents prostaglandin synthesis. Indeed, prostaglandins PGA1 and 2, PGD1 and 2, and PGJ2-activated PPARs, while a number of other prostaglandins had no effect. We also screened a variety of hydroxyeicosatetraenoic acids (HETEs) for the ability to activate PPARs. 8(S)-HETE, but not other (S)-HETEs, was a strong activator of PPAR␣. Remarkably, PPAR activation by 8(S)-HETE was stereoselective. In addition, 8(S)-HETE was able to induce differentiation of 3T3-L1 preadipocytes. These results indicate that PPARs are differentially activated by naturally occurring eicosanoids and related molecules.The cloning and characterization of nuclear receptors has greatly enhanced our understanding of gene regulation by lipophilic hormones such as steroids, vitamin D, thyroxine, and retinoids. These receptors comprise a superfamily of transcription factors containing highly related DNA-binding domains (1, 2). This family includes multiple subtypes of receptors for thyroxine and retinoids, encoded by distinct genes which are regulated quite differently during development and in the adult.

Chromatin immunoprecipitation, DNase I hypersensitivity and transposase-accessibility assays combined with high-throughput sequencing enable the genome-wide study of chromatin dynamics, transcription factor binding and gene regulation. Although rapidly accumulating publicly available ChIP-seq, DNase-seq and ATAC-seq data are a valuable resource for the systematic investigation of gene regulation processes, a lack of standardized curation, quality control and analysis procedures have hindered extensive reuse of these data. To overcome this challenge, we built the Cistrome database, a collection of ChIP-seq and chromatin accessibility data (DNase-seq and ATAC-seq) published before January 1, 2016, including 13 366 human and 9953 mouse samples. All the data have been carefully curated and processed with a streamlined analysis pipeline and evaluated with comprehensive quality control metrics. We have also created a user-friendly web server for data query, exploration and visualization. The resulting Cistrome DB (Cistrome Data Browser), available online at http://cistrome.org/db, is expected to become a valuable resource for transcriptional and epigenetic regulation studies.