Cancer immunotherapy targeting co-inhibitory pathways by checkpoint blockade shows remarkable efficacy in a variety of cancer types. However, only a minority of patients respond to treatment due to the stochastic heterogeneity of tumor microenvironment (TME). Recent advances in single-cell RNA-seq technologies enabled comprehensive characterization of the immune system heterogeneity in tumors but posed computational challenges on integrating and utilizing the massive published datasets to inform immunotherapy. Here, we present Tumor Immune Single Cell Hub (TISCH, http://tisch.comp-genomics.org), a large-scale curated database that integrates single-cell transcriptomic profiles of nearly 2 million cells from 76 high-quality tumor datasets across 27 cancer types. All the data were uniformly processed with a standardized workflow, including quality control, batch effect removal, clustering, cell-type annotation, malignant cell classification, differential expression analysis and functional enrichment analysis. TISCH provides interactive gene expression visualization across multiple datasets at the single-cell level or cluster level, allowing systematic comparison between different cell-types, patients, tissue origins, treatment and response groups, and even different cancer-types. In summary, TISCH provides a user-friendly interface for systematically visualizing, searching and downloading gene expression atlas in the TME from multiple cancer types, enabling fast, flexible and comprehensive exploration of the TME.

Standardized and reproducible preclinical models that recapitulate the dynamics of prostate cancer are urgently needed. We established a bank of transplantable patient-derived prostate cancer xenografts that capture the biologic and molecular heterogeneity currently confounding prognostication and therapy development. Xenografts preserved the histopathology, genome architecture, and global gene expression of donor tumors. Moreover, their aggressiveness matched patient observations, and their response to androgen withdrawal correlated with tumor subtype. The panel includes the first xenografts generated from needle biopsy tissue obtained at diagnosis. This advance was exploited to generate independent xenografts from different sites of a primary site, enabling functional dissection of tumor heterogeneity. Prolonged exposure of adenocarcinoma xenografts to androgen withdrawal led to castration-resistant prostate cancer, including the first-in-field model of complete transdifferentiation into lethal neuroendocrine prostate cancer. Further analysis of this model supports the hypothesis that neuroendocrine prostate cancer can evolve directly from adenocarcinoma via an adaptive response and yielded a set of genes potentially involved in neuroendocrine transdifferentiation. We predict that these next-generation models will be transformative for advancing mechanistic understanding of disease progression, response to therapy, and personalized oncology. Cancer Res; 74(4); 1272-83. Ó2013 AACR.

More potent targeting of the androgen receptor (AR) in advanced prostate cancer is driving an increased incidence of neuroendocrine prostate cancer (NEPC), an aggressive and treatment-resistant AR-negative variant. Its molecular pathogenesis remains poorly understood but appears to require TP53 and RB1 aberration. We modeled the development of NEPC from conventional prostatic adenocarcinoma using a patient-derived xenograft and found that the placental gene PEG10 is de-repressed during the adaptive response to AR interference and subsequently highly upregulated in clinical NEPC. We found that the AR and the E2F/RB pathway dynamically regulate distinct post-transcriptional and post-translational isoforms of PEG10 at distinct stages of NEPC development. In vitro, PEG10 promoted cell-cycle progression from G0/G1 in the context of TP53 loss and regulated Snail expression via TGF-β signaling to promote invasion. Taken together, these findings show the mechanistic relevance of RB1 and TP53 loss in NEPC and suggest PEG10 as a NEPC-specific target.