Reversal of viral and epigenetic HLA class I repression in Merkel cell carcinoma.

Cancers avoid immune surveillance through an array of mechanisms, including perturbation of HLA class I antigen presentation. Merkel cell carcinoma (MCC) is an aggressive, HLA-I-low, neuroendocrine carcinoma of the skin often caused by the Merkel cell polyomavirus (MCPyV). Through the characterization of 11 newly generated MCC patient-derived cell lines, we identified transcriptional suppression of several class I antigen presentation genes. To systematically identify regulators of HLA-I loss in MCC, we performed parallel, genome-scale, gain- and loss-of-function screens in a patient-derived MCPyV-positive cell line and identified MYCL and the non-canonical Polycomb repressive complex 1.1 (PRC1.1) as HLA-I repressors. We observed physical interaction of MYCL with the MCPyV small T viral antigen, supporting a mechanism of virally mediated HLA-I suppression. We further identify the PRC1.1 component USP7 as a pharmacologic target to restore HLA-I expression in MCC.

Detecting Neuroendocrine Prostate Cancer Through Tissue-Informed Cell-Free DNA Methylation Analysis.

PURPOSE: Neuroendocrine prostate cancer (NEPC) is a resistance phenotype that emerges in men with metastatic castration-resistant prostate adenocarcinoma (CR-PRAD) and has important clinical implications, but is challenging to detect in practice. Herein, we report a novel tissue-informed epigenetic approach to noninvasively detect NEPC. EXPERIMENTAL DESIGN: We first performed methylated immunoprecipitation and high-throughput sequencing (MeDIP-seq) on a training set of tumors, identified differentially methylated regions between NEPC and CR-PRAD, and built a model to predict the presence of NEPC (termed NEPC Risk Score). We then performed MeDIP-seq on cell-free DNA (cfDNA) from two independent cohorts of men with NEPC or CR-PRAD and assessed the accuracy of the model to predict the presence NEPC. RESULTS: The test cohort comprised cfDNA samples from 48 men, 9 with NEPC and 39 with CR-PRAD. NEPC Risk Scores were significantly higher in men with NEPC than CR-PRAD (P = 4.3 × 10-7) and discriminated between NEPC and CR-PRAD with high accuracy (AUROC 0.96). The optimal NEPC Risk Score cutoff demonstrated 100% sensitivity and 90% specificity for detecting NEPC. The independent, multi-institutional validation cohort included cfDNA from 53 men, including 12 with NEPC and 41 with CR-PRAD. NEPC Risk Scores were significantly higher in men with NEPC than CR-PRAD (P = 7.5×10-12) and perfectly discriminated NEPC from CR-PRAD (AUROC 1.0). Applying the predefined NEPC Risk Score cutoff to the validation cohort resulted in 100% sensitivity and 95% specificity for detecting NEPC. CONCLUSIONS: Tissue-informed cfDNA methylation analysis is a promising approach for noninvasive detection of NEPC in men with advanced prostate cancer.

Androgen receptor and MYC equilibration centralizes on developmental super-enhancer.

Androgen receptor (AR) in prostate cancer (PCa) can drive transcriptional repression of multiple genes including MYC, and supraphysiological androgen is effective in some patients. Here, we show that this repression is independent of AR chromatin binding and driven by coactivator redistribution, and through chromatin conformation capture methods show disruption of the interaction between the MYC super-enhancer within the PCAT1 gene and the MYC promoter. Conversely, androgen deprivation in vitro and in vivo increases MYC expression. In parallel, global AR activity is suppressed by MYC overexpression, consistent with coactivator redistribution. These suppressive effects of AR and MYC are mitigated at shared AR/MYC binding sites, which also have markedly higher levels of H3K27 acetylation, indicating enrichment for functional enhancers. These findings demonstrate an intricate balance between AR and MYC, and indicate that increased MYC in response to androgen deprivation contributes to castration-resistant PCa, while decreased MYC may contribute to responses to supraphysiological androgen therapy.

CDK4/6 inhibition reprograms the breast cancer enhancer landscape by stimulating AP-1 transcriptional activity.

Pharmacologic inhibitors of cyclin-dependent kinases 4 and 6 (CDK4/6) were designed to induce cancer cell cycle arrest. Recent studies have suggested that these agents also exert other effects, influencing cancer cell immunogenicity, apoptotic responses, and differentiation. Using cell-based and mouse models of breast cancer together with clinical specimens, we show that CDK4/6 inhibitors induce remodeling of cancer cell chromatin characterized by widespread enhancer activation, and that this explains many of these effects. The newly activated enhancers include classical super-enhancers that drive luminal differentiation and apoptotic evasion, as well as a set of enhancers overlying endogenous retroviral elements that is enriched for proximity to interferon-driven genes. Mechanistically, CDK4/6 inhibition increases the level of several Activator Protein-1 (AP-1) transcription factor proteins, which are in turn implicated in the activity of many of the new enhancers. Our findings offer insights into CDK4/6 pathway biology and should inform the future development of CDK4/6 inhibitors.

Differential substrate use in EGF- and oncogenic KRAS-stimulated human mammary epithelial cells.

Many metabolic phenotypes in cancer cells are also characteristic of proliferating nontransformed mammalian cells, and attempts to distinguish between phenotypes resulting from oncogenic perturbation from those associated with increased proliferation are limited. Here, we examined the extent to which metabolic changes corresponding to oncogenic KRAS expression differed from those corresponding to epidermal growth factor (EGF)-driven proliferation in human mammary epithelial cells (HMECs). Removal of EGF from culture medium reduced growth rates and glucose/glutamine consumption in control HMECs despite limited changes in respiration and fatty acid synthesis, while the relative contribution of branched-chain amino acids to the TCA cycle and lipogenesis increased in the near-quiescent conditions. Most metabolic phenotypes measured in HMECs expressing mutant KRAS were similar to those observed in EGF-stimulated control HMECs that were growing at comparable rates. However, glucose and glutamine consumption as well as lactate and glutamate production were lower in KRAS-expressing cells cultured in media without added EGF, and these changes correlated with reduced sensitivity to GLUT1 inhibitor and phenformin treatment. Our results demonstrate the strong dependence of metabolic behavior on growth rate and provide a model to distinguish the metabolic influences of oncogenic mutations and nononcogenic growth.

Reprogramming of the FOXA1 cistrome in treatment-emergent neuroendocrine prostate cancer.

Lineage plasticity, the ability of a cell to alter its identity, is an increasingly common mechanism of adaptive resistance to targeted therapy in cancer. An archetypal example is the development of neuroendocrine prostate cancer (NEPC) after treatment of prostate adenocarcinoma (PRAD) with inhibitors of androgen signaling. NEPC is an aggressive variant of prostate cancer that aberrantly expresses genes characteristic of neuroendocrine (NE) tissues and no longer depends on androgens. Here, we investigate the epigenomic basis of this resistance mechanism by profiling histone modifications in NEPC and PRAD patient-derived xenografts (PDXs) using chromatin immunoprecipitation and sequencing (ChIP-seq). We identify a vast network of cis-regulatory elements (N~15,000) that are recurrently activated in NEPC. The FOXA1 transcription factor (TF), which pioneers androgen receptor (AR) chromatin binding in the prostate epithelium, is reprogrammed to NE-specific regulatory elements in NEPC. Despite loss of dependence upon AR, NEPC maintains FOXA1 expression and requires FOXA1 for proliferation and expression of NE lineage-defining genes. Ectopic expression of the NE lineage TFs ASCL1 and NKX2-1 in PRAD cells reprograms FOXA1 to bind to NE regulatory elements and induces enhancer activity as evidenced by histone modifications at these sites. Our data establish the importance of FOXA1 in NEPC and provide a principled approach to identifying cancer dependencies through epigenomic profiling.

Author Correction: Detection of renal cell carcinoma using plasma and urine cell-free DNA methylomes.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Prostate cancer reactivates developmental epigenomic programs during metastatic progression.

Epigenetic processes govern prostate cancer (PCa) biology, as evidenced by the dependency of PCa cells on the androgen receptor (AR), a prostate master transcription factor. We generated 268 epigenomic datasets spanning two state transitions-from normal prostate epithelium to localized PCa to metastases-in specimens derived from human tissue. We discovered that reprogrammed AR sites in metastatic PCa are not created de novo; rather, they are prepopulated by the transcription factors FOXA1 and HOXB13 in normal prostate epithelium. Reprogrammed regulatory elements commissioned in metastatic disease hijack latent developmental programs, accessing sites that are implicated in prostate organogenesis. Analysis of reactivated regulatory elements enabled the identification and functional validation of previously unknown metastasis-specific enhancers at HOXB13, FOXA1 and NKX3-1. Finally, we observed that prostate lineage-specific regulatory elements were strongly associated with PCa risk heritability and somatic mutation density. Examining prostate biology through an epigenomic lens is fundamental for understanding the mechanisms underlying tumor progression.

Detection of renal cell carcinoma using plasma and urine cell-free DNA methylomes.

Improving early cancer detection has the potential to substantially reduce cancer-related mortality. Cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq) is a highly sensitive assay capable of detecting early-stage tumors. We report accurate classification of patients across all stages of renal cell carcinoma (RCC) in plasma (area under the receiver operating characteristic (AUROC) curve of 0.99) and demonstrate the validity of this assay to identify patients with RCC using urine cell-free DNA (cfDNA; AUROC of 0.86).

Plasma cell-free DNA variant analysis compared with methylated DNA analysis in renal cell carcinoma.

PURPOSE: Plasma cell-free DNA (cfDNA) variant analysis is commonly used in many cancer subtypes. Cell-free methylated DNA immunoprecipitation sequencing (cfMeDIP-seq) has shown high sensitivity for cancer detection. To date, studies have not compared the sensitivity of both methods in a single cancer subtype. METHODS: cfDNA from 40 metastatic RCC (mRCC) patients was subjected to targeted panel variant analysis. For 34 of 40, cfMeDIP-seq was also performed. A separate cohort of 38 mRCC patients were used in cfMeDIP-seq analysis to train an RCC classifier. RESULTS: cfDNA variant analysis detected 21 candidate variants in 11 of 40 mRCC patients (28%), after exclusion of 2 germline variants and 6 variants reflecting clonal hematopoiesis. Among 23 patients with parallel tumor sequencing, cfDNA analysis alone identified variants in 9 patients (39%), while cfDNA analysis focused on tumor sequencing variant findings improved the sensitivity to 52%. In 34 mRCC patients undergoing cfMeDIP-seq, cfDNA variant analysis identified variants in 7 (21%), while cfMeDIP-seq detected all mRCC cases (100% sensitivity) with 88% specificity in 34 control subjects. In 5 patients with cfDNA variants and serial samples, variant frequency correlated with response to therapy. CONCLUSION: cfMeDIP-seq is significantly more sensitive for mRCC detection than cfDNA variant analysis. However, cfDNA variant analysis may be useful for monitoring response to therapy.

A Somatically Acquired Enhancer of the Androgen Receptor Is a Noncoding Driver in Advanced Prostate Cancer.

Increased androgen receptor (AR) activity drives therapeutic resistance in advanced prostate cancer. The most common resistance mechanism is amplification of this locus presumably targeting the AR gene. Here, we identify and characterize a somatically acquired AR enhancer located 650 kb centromeric to the AR. Systematic perturbation of this enhancer using genome editing decreased proliferation by suppressing AR levels. Insertion of an additional copy of this region sufficed to increase proliferation under low androgen conditions and to decrease sensitivity to enzalutamide. Epigenetic data generated in localized prostate tumors and benign specimens support the notion that this region is a developmental enhancer. Collectively, these observations underscore the importance of epigenomic profiling in primary specimens and the value of deploying genome editing to functionally characterize noncoding elements. More broadly, this work identifies a therapeutic vulnerability for targeting the AR and emphasizes the importance of regulatory elements as highly recurrent oncogenic drivers.

MADGiC: a model-based approach for identifying driver genes in cancer.

MOTIVATION: Identifying and prioritizing somatic mutations is an important and challenging area of cancer research that can provide new insights into gene function as well as new targets for drug development. Most methods for prioritizing mutations rely primarily on frequency-based criteria, where a gene is identified as having a driver mutation if it is altered in significantly more samples than expected according to a background model. Although useful, frequency-based methods are limited in that all mutations are treated equally. It is well known, however, that some mutations have no functional consequence, while others may have a major deleterious impact. The spatial pattern of mutations within a gene provides further insight into their functional consequence. Properly accounting for these factors improves both the power and accuracy of inference. Also important is an accurate background model. RESULTS: Here, we develop a Model-based Approach for identifying Driver Genes in Cancer (termed MADGiC) that incorporates both frequency and functional impact criteria and accommodates a number of factors to improve the background model. Simulation studies demonstrate advantages of the approach, including a substantial increase in power over competing methods. Further advantages are illustrated in an analysis of ovarian and lung cancer data from The Cancer Genome Atlas (TCGA) project.