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Purpose:Basal-like breast cancer (BLBC) is a particularly aggressive intrinsic molecular subtype of breast cancer that lacks targeted therapies. There is also no clinically useful test to risk stratify patients with BLBC. We hypothesized that a transcriptome-based phenotypic characterization of BLBC tumors and their microenvironments may overcome these challenges.Experimental Design:We conducted a retrospective correlative genomic sequencing study using a matched pairs design with validation in five independent cohorts. The study was conducted on a large population-based prospective cohort of the major molecular subtypes of breast cancer conducted in the greater Seattle-Puget Sound metropolitan area. Cases consisted of women 20–69 years of age first diagnosed with invasive breast cancer identified through the population-based Surveillance Epidemiology and End Results program. Patients for this analysis (n = 949) were identified from the 1,408 patients with stage I–III triple-negative breast cancer [estrogen receptor–negative (ER−), progesterone receptor–negative (PR−), HER2−]. Of the 949 women, 248 developed a recurrence after their initial diagnosis. A matched set of 67 recurrent and nonrecurrent BLBC tumors was subjected to transcriptome sequencing. Through RNA sequencing of the matched sets of recurrent and nonrecurrent BLBC tumors, we aimed to identify prognostic phenotypes.To identify nonredundant and uncorrelated prognostic genes, we used an ensemble of variable selection algorithms, which resulted in a ranking of genes on the basis of their expected utility in classification. Using leave-one-out cross-validation, we trained a random forest classifier on the basis of the top 21 genes (BRAVO-DX). Validations were performed in five independent triple-negative or BLBC cohorts, and biomarker robustness and transferability were demonstrated by employing real-time PCR.Results:We found that cancer cell intrinsic and immunologic phenotypes are independent predictors of recurrence. By simultaneously interrogating the tumor and its microenvironment, we developed a compound risk model that stratified patients into low-, medium-, and high-risk groups, with a 14%/56%/74% chance of recurrence, respectively. Biologically, the primary tumors of patients who developed a recurrence had increased growth factor signaling and stem-like features, while nonrecurrent tumors showed high lymphocyte infiltration with clonal expansion of T and B cells, as well as antitumor polarization of macrophages. We validated our model in five independent cohorts, including three large cohorts, where BRAVO-DX was highly informative in identifying patients with disease recurrence [HR, 6.79 (95% confidence interval (CI), 1.89–24.37); HR, 3.45 (95% CI, 2.41–4.93); and HR, 1.69 (95% CI, 1.17–2.46)]. A smaller gene set focused on the tumor immunophenotype, BRAVO-IMMUNE, was highly prognostic in all five cohorts.Conclusions:Together, these results indicate that phenotypic characteristics of BLBCs and their microenvironment are associated with recurrence-free survival and demonstrate the utility of intrinsic and extrinsic phenotypes as independent prognostic biomarkers in BLBC. Pending further evaluation and validation, our prognostic model has the potential to inform clinical decision-making for patients with BLBC as it identifies those at high risk of rapidly progressing on standard chemotherapy, as well as those who may benefit from alternative first-line therapies.

Supplementary Figures S1 through S10

Purpose: Basal-like breast cancer (BLBC) is a particularly aggressive intrinsic molecular subtype of breast cancer that lacks targeted therapies. There is also no clinically useful test to risk stratify patients with BLBC. We hypothesized that a transcriptome-based phenotypic characterization of BLBC tumors and their microenvironments may overcome these challenges. Experimental Design: We conducted a retrospective correlative genomic sequencing study using a matched pairs design with validation in five independent cohorts. The study was conducted on a large population-based prospective cohort of the major molecular subtypes of breast cancer conducted in the greater Seattle-Puget Sound metropolitan area. Cases consisted of women 20–69 years of age first diagnosed with invasive breast cancer identified through the population-based Surveillance Epidemiology and End Results program. Patients for this analysis (n = 949) were identified from the 1,408 patients with stage I–III triple-negative breast cancer [estrogen receptor–negative (ER−), progesterone receptor–negative (PR−), HER2−]. Of the 949 women, 248 developed a recurrence after their initial diagnosis. A matched set of 67 recurrent and nonrecurrent BLBC tumors was subjected to transcriptome sequencing. Through RNA sequencing of the matched sets of recurrent and nonrecurrent BLBC tumors, we aimed to identify prognostic phenotypes. To identify nonredundant and uncorrelated prognostic genes, we used an ensemble of variable selection algorithms, which resulted in a ranking of genes on the basis of their expected utility in classification. Using leave-one-out cross-validation, we trained a random forest classifier on the basis of the top 21 genes (BRAVO-DX). Validations were performed in five independent triple-negative or BLBC cohorts, and biomarker robustness and transferability were demonstrated by employing real-time PCR. Results: We found that cancer cell intrinsic and immunologic phenotypes are independent predictors of recurrence. By simultaneously interrogating the tumor and its microenvironment, we developed a compound risk model that stratified patients into low-, medium-, and high-risk groups, with a 14%/56%/74% chance of recurrence, respectively. Biologically, the primary tumors of patients who developed a recurrence had increased growth factor signaling and stem-like features, while nonrecurrent tumors showed high lymphocyte infiltration with clonal expansion of T and B cells, as well as antitumor polarization of macrophages. We validated our model in five independent cohorts, including three large cohorts, where BRAVO-DX was highly informative in identifying patients with disease recurrence [HR, 6.79 (95% confidence interval (CI), 1.89–24.37); HR, 3.45 (95% CI, 2.41–4.93); and HR, 1.69 (95% CI, 1.17–2.46)]. A smaller gene set focused on the tumor immunophenotype, BRAVO-IMMUNE, was highly prognostic in all five cohorts. Conclusions: Together, these results indicate that phenotypic characteristics of BLBCs and their microenvironment are associated with recurrence-free survival and demonstrate the utility of intrinsic and extrinsic phenotypes as independent prognostic biomarkers in BLBC. Pending further evaluation and validation, our prognostic model has the potential to inform clinical decision-making for patients with BLBC as it identifies those at high risk of rapidly progressing on standard chemotherapy, as well as those who may benefit from alternative first-line therapies.

Glioblastoma (GBM) is the most aggressive nervous system cancer. Understanding its molecular pathogenesis is crucial to improving diagnosis and treatment. Integrated analysis of genomic, proteomic, post-translational modification and metabolomic data on 99 treatment-naive GBMs provides insights to GBM biology. We identify key phosphorylation events (e.g., phosphorylated PTPN11 and PLCG1) as potential switches mediating oncogenic pathway activation, as well as potential targets for EGFR-, TP53-, and RB1-altered tumors. Immune subtypes with distinct immune cell types are discovered using bulk omics methodologies, validated by snRNA-seq, and correlated with specific expression and histone acetylation patterns. Histone H2B acetylation in classical-like and immune-low GBM is driven largely by BRDs, CREBBP, and EP300. Integrated metabolomic and proteomic data identify specific lipid distributions across subtypes and distinct global metabolic changes in IDH-mutated tumors. This work highlights biological relationships that could contribute to stratification of GBM patients for more effective treatment. Wang et al. perform integrated proteogenomic analysis of adult glioblastoma (GBM), including metabolomics, lipidomics, and single nuclei RNA-Seq, revealing insights into the immune landscape of GBM, cell-specific nature of EMT signatures, histone acetylation in classical GBM, and the existence of signaling hubs which could provide therapeutic vulnerabilities.

Basal-like breast cancer (BLBC) is a particularly aggressive intrinsic molecular subtype of breast cancer that lacks targeted therapies. There is also no clinically useful test to risk stratify patients with BLBC. We hypothesized that a transcriptome-based phenotypic characterization of BLBC tumors and their microenvironments may overcome these challenges.We conducted a retrospective correlative genomic sequencing study using a matched pairs design with validation in five independent cohorts. The study was conducted on a large population-based prospective cohort of the major molecular subtypes of breast cancer conducted in the greater Seattle-Puget Sound metropolitan area. Cases consisted of women 20-69 years of age first diagnosed with invasive breast cancer identified through the population-based Surveillance Epidemiology and End Results program. Patients for this analysis (We found that cancer cell intrinsic and immunologic phenotypes are independent predictors of recurrence. By simultaneously interrogating the tumor and its microenvironment, we developed a compound risk model that stratified patients into low-, medium-, and high-risk groups, with a 14%/56%/74% chance of recurrence, respectively. Biologically, the primary tumors of patients who developed a recurrence had increased growth factor signaling and stem-like features, while nonrecurrent tumors showed high lymphocyte infiltration with clonal expansion of T and B cells, as well as antitumor polarization of macrophages. We validated our model in five independent cohorts, including three large cohorts, where BRAVO-DX was highly informative in identifying patients with disease recurrence [HR, 6.79 (95% confidence interval (CI), 1.89-24.37); HR, 3.45 (95% CI, 2.41-4.93); and HR, 1.69 (95% CI, 1.17-2.46)]. A smaller gene set focused on the tumor immunophenotype, BRAVO-IMMUNE, was highly prognostic in all five cohorts.Together, these results indicate that phenotypic characteristics of BLBCs and their microenvironment are associated with recurrence-free survival and demonstrate the utility of intrinsic and extrinsic phenotypes as independent prognostic biomarkers in BLBC. Pending further evaluation and validation, our prognostic model has the potential to inform clinical decision-making for patients with BLBC as it identifies those at high risk of rapidly progressing on standard chemotherapy, as well as those who may benefit from alternative first-line therapies.

Whether mutations in cancer driver genes directly affect cancer immune phenotype and T cell immunity remains a standing question. ARID1A is a core member of the polymorphic BRG/BRM-associated factor chromatin remodeling complex. ARID1A mutations occur in human cancers and drive cancer development. Here, we studied the molecular, cellular, and clinical impact of ARID1A aberrations on cancer immunity. We demonstrated that ARID1A aberrations resulted in limited chromatin accessibility to IFN-responsive genes, impaired IFN gene expression, anemic T cell tumor infiltration, poor tumor immunity, and shortened host survival in many human cancer histologies and in murine cancer models. Impaired IFN signaling was associated with poor immunotherapy response. Mechanistically, ARID1A interacted with EZH2 via its carboxyl terminal and antagonized EZH2-mediated IFN responsiveness. Thus, the interaction between ARID1A and EZH2 defines cancer IFN responsiveness and immune evasion. Our work indicates that cancer epigenetic driver mutations can shape cancer immune phenotype and immunotherapy.

Background Renal cell carcinomas (RCCs) are a heterogeneous group of neoplasms. Recent sequencing studies revealed various molecular features associated with histologic RCC subtypes, including chromophobe renal cell carcinoma (ChRCC). Objective To characterize the gene expression and biomarker signatures associated with ChRCC. Design, setting, and participants We performed integrative analysis on RNA sequencing data available from 1049 RCC specimens from The Cancer Genome Atlas and in-house studies. Our workflow identified genes relatively enriched in ChRCC, including Forkhead box I1 (FOXI1), Rh family C glycoprotein (RHCG), and LINC01187. We assessed the expression pattern of FOXI1 and RHCG protein by immunohistochemistry (IHC) and LINC01187 mRNA by RNA in situ hybridization (RNA-ISH) in whole tissue sections representing a cohort of 197 RCC cases, including both primary and metastatic tumors. Outcome measurements and statistical analysis The FOXI1 and RHCG IHC staining, as well as the LINC01187 RNA-ISH staining, was evaluated in each case for intensity, pattern, and localization of expression. Results and limitations All primary and metastatic classic ChRCCs demonstrated homogeneous positive labeling for FOXI1, RHCG proteins, and LINC01187 transcript. Unclassified RCC with oncocytic features, oncocytoma, and hybrid oncocytic tumor, as well as all but two cases of eosinophilic ChRCC also stained positive. Importantly, metastatic and primary RCC of all other subtypes did not demonstrate any unequivocal staining for FOXI1, RHCG, or LINC01187. In normal kidney, FOXI1, RHCG, and LINC01187 were detected in the distal nephron segment, specifically in intercalated cells. Two cases of eosinophilic ChRCC with focal expression of FOXI1 and LINC01187, and Golgi-like RHCG staining were found to contain MTOR gene mutations upon DNA sequencing. Conclusions We demonstrate a pipeline for the identification and validation of RCC subtype–specific biomarkers that can aid in the confirmation of cell of origin and may facilitate accurate classification and diagnosis of renal tumors. Patient summary FOXI1, RHCG, and LINC01187 are lineage-specific signature genes for chromophobe renal cell carcinoma.

The androgen receptor (AR) plays a critical role in the development of the normal prostate as well as prostate cancer. Using an integrative transcriptomic analysis of prostate cancer cell lines and tissues, we identified ARLNC1 (AR-regulated long noncoding RNA 1) as an important long noncoding RNA that is strongly associated with AR signaling in prostate cancer progression. Not only was ARLNC1 induced by the AR protein, but ARLNC1 stabilized the AR transcript via RNA-RNA interaction. ARLNC1 knockdown suppressed AR expression, global AR signaling and prostate cancer growth in vitro and in vivo. Taken together, these data support a role for ARLNC1 in maintaining a positive feedback loop that potentiates AR signaling during prostate cancer progression and identify ARLNC1 as a novel therapeutic target.

Methodological breakthroughs over the past four decades have repeatedly revolutionized transcriptome profiling. Using RNA sequencing (RNA-seq), it has now become possible to sequence and quantify the transcriptional outputs of individual cells or thousands of samples. These transcriptomes provide a link between cellular phenotypes and their molecular underpinnings, such as mutations. In the context of cancer, this link represents an opportunity to dissect the complexity and heterogeneity of tumours and to discover new biomarkers or therapeutic strategies. Here, we review the rationale, methodology and translational impact of transcriptome profiling in cancer.

SUMMARY Metastasis is the primary cause of cancer-related deaths. While The Cancer Genome Atlas (TCGA) has sequenced primary tumor types obtained from surgical resections, much less comprehensive molecular analysis is available from clinically acquired metastatic cancers. Here, we perform whole exome and transcriptome sequencing of 500 adult patients with metastatic solid tumors of diverse lineage and biopsy site. The most prevalent genes somatically altered in metastatic cancer included TP53, CDKN2A, PTEN, PIK3CA, and RB1. Putative pathogenic germline variants were present in 12.2% of cases of which 75% were related to defects in DNA repair. RNA sequencing complemented DNA sequencing for the identification of gene fusions, pathway activation, and immune profiling. Integrative sequence analysis provides a clinically relevant, multi-dimensional view of the complex molecular landscape and microenvironment of metastatic cancers.

Transcription factors play a key role in the development of diverse cancers, and therapeutically targeting them has remained a challenge. In prostate cancer, the gene encoding the transcription factor ERG is recurrently rearranged and plays a critical role in prostate oncogenesis. Here, we identified a series of peptides that interact specifically with the DNA binding domain of ERG. ERG inhibitory peptides (EIPs) and derived peptidomimetics bound ERG with high affinity and specificity, leading to proteolytic degradation of the ERG protein. The EIPs attenuated ERG-mediated transcription, chromatin recruitment, protein-protein interactions, cell invasion and proliferation, and tumor growth. Thus, peptidomimetic targeting of transcription factor fusion products may provide a promising therapeutic strategy for prostate cancer as well as other malignancies.

Our recent study revealed recurrent chromosomal losses and somatic mutations of genes in the Hippo pathway in mucinous tubular and spindle cell carcinoma (MTSCC). Here, we performed an integrative analysis of 907 renal cell carcinoma (RCC) samples (combined from The Cancer Genome Atlas and in-house studies) and the Knepper dataset of microdissected rat nephrons. We identified VSTM2A and IRX5 as novel cancer- and lineage-specific biomarkers in MTSCC. We then assessed their expression by RNA in situ hybridization (ISH) in 113 tumors, including 33 MTSCC, 40 type 1 papillary RCC (PRCC), 8 type 2 PRCC, 2 unclassified RCC, 15 clear cell RCC, and 15 chromophobe RCC. Sensitivity and specificity were calculated as the area under the receiver operating characteristics curve (AUC). All MTSCC tumors demonstrated moderate to high expression of VSTM2A (mean ISH score = 255). VSTM2A gene expression assessed by RNA sequencing (RNA-seq) strongly correlated with VSTM2A ISH score (r(2) = 0.81, P = 0.00016). The majority of non-MTSCC tumors demonstrated negative or low expression of VSTM2A. IRX5, nominated as a lineage-specific biomarker, showed moderate to high expression in MTSCC tumors (mean ISH score = 140). IRX5 gene expression assessed by RNA-seq strongly correlated with IRX5 ISH score (r(2) = 0.69, P = 0.00291). VSTM2A (AUC: 99.2%) demonstrated better diagnostic efficacy than IRX5 (AUC: 87.5%), and may thus serve as a potential diagnostic marker to distinguish tumors with overlapping histology. Furthermore, our results suggest MTSCC displays an overlapping phenotypic expression pattern with the loop of Henle region of normal nephrons.

We present a proteogenomic study of 108 human papilloma virus (HPV)-negative head and neck squamous cell carcinomas (HNSCCs). Proteomic analysis systematically catalogs HNSCC-associated proteins and phosphosites, prioritizes copy number drivers, and highlights an oncogenic role for RNA processing genes. Proteomic investigation of mutual exclusivity between FAT1 truncating mutations and 11q13.3 amplifications reveals dysregulated actin dynamics as a common functional consequence. Phosphoproteomics characterizes two modes of EGFR activation, suggesting a new strategy to stratify HNSCCs based on EGFR ligand abundance for effective treatment with inhibitory EGFR monoclonal antibodies. Widespread deletion of immune modulatory genes accounts for low immune infiltration in immune-cold tumors, whereas concordant upregulation of multiple immune checkpoint proteins may underlie resistance to anti-programmed cell death protein 1 monotherapy in immune-hot tumors. Multi-omic analysis identifies three molecular subtypes with high potential for treatment with CDK inhibitors, anti-EGFR antibody therapy, and immunotherapy, respectively. Altogether, proteogenomics provides a systematic framework to inform HNSCC biology and treatment.

We report a comprehensive proteogenomics analysis, including whole-genome sequencing, RNA sequencing, and proteomics and phosphoproteomics profiling, of 218 tumors across 7 histological types of childhood brain cancer: low-grade glioma (n = 93), ependymoma (32), high-grade glioma (25), medulloblastoma (22), ganglioglioma (18), craniopharyngioma (16), and atypical teratoid rhabdoid tumor (12). Proteomics data identify common biological themes that span histological boundaries, suggesting that treatments used for one histological type may be applied effectively to other tumors sharing similar proteomics features. Immune landscape characterization reveals diverse tumor microenvironments across and within diagnoses. Proteomics data further reveal functional effects of somatic mutations and copy number variations (CNVs) not evident in transcriptomics data. Kinase-substrate association and co-expression network analysis identify important biological mechanisms of tumorigenesis. This is the first large-scale proteogenomics analysis across traditional histological boundaries to uncover foundational pediatric brain tumor biology and inform rational treatment selection.

Rapid advances in the discovery of long noncoding RNAs (lncRNAs) have identified lineage- and cancer-specific biomarkers that may be relevant in the clinical management of prostate cancer (PCa). Here we assembled and analyzed a large RNA-seq dataset, from 585 patient samples, including benign prostate tissue and both localized and metastatic PCa to discover and validate differentially expressed genes associated with disease aggressiveness. We performed Sample Set Enrichment Analysis (SSEA) and identified genes associated with low versus high Gleason score in the RNA-seq database. Comparing Gleason 6 versus 9+ PCa samples, we identified 99 differentially expressed genes with variable association to Gleason grade as well as robust expression in prostate cancer. The top-ranked novel lncRNA PCAT14, exhibits both cancer and lineage specificity. On multivariate analysis, low PCAT14 expression independently predicts for BPFS ( P =.00126), PSS ( P =.0385), and MFS ( P =.000609), with trends for OS as well ( P =.056). An RNA in-situ hybridization (ISH) assay for PCAT14 distinguished benign vs malignant cases, as well as high vs low Gleason disease. PCAT14 is transcriptionally regulated by AR, and endogenous PCAT14 overexpression suppresses cell invasion. Thus, Using RNA-sequencing data we identify PCAT14, a novel prostate cancer and lineage-specific lncRNA. PCAT14 is highly expressed in low grade disease and loss of PCAT14 predicts for disease aggressiveness and recurrence.

Studies on the efficacy of small molecule inhibitors in Merkel cell carcinoma (MCC) have been limited and largely inconclusive. In this study, we investigated the therapeutic potential of a potent BET degrader, BETd-246, in the treatment of MCC. We found that MCC cell lines were significantly more sensitive to BETd-246 than to BET inhibitor treatment. Therapeutic targeting of BET proteins resulted in a loss of “MCC signature” genes but not MYC expression as previously described irrespective of Merkel cell polyomavirus (MCPyV) status. In MCPyV+ MCC cells, BETd-246 alone suppressed downstream targets in the MCPyV-LT Ag axis. We also found enrichment of HOX and cell cycle genes in MCPyV− MCC cell lines that were intrinsically resistant to BETd-246. Our findings uncover a requirement for BET proteins in maintaining MCC lineage identity and point to the potential utility of BET degraders for treating MCC.

Noncoding mutations in cancer genomes are frequent but challenging to interpret. PVT1 encodes an oncogenic lncRNA, but recurrent translocations and deletions in human cancers suggest alternative mechanisms. Here, we show that the PVT1 promoter has a tumor-suppressor function that is independent of PVT1 lncRNA. CRISPR interference of PVT1 promoter enhances breast cancer cell competition and growth in vivo. The promoters of the PVT1 and the MYC oncogenes, located 55 kb apart on chromosome 8q24, compete for engagement with four intragenic enhancers in the PVT1 locus, thereby allowing the PVT1 promoter to regulate pause release of MYC transcription. PVT1 undergoes developmentally regulated monoallelic expression, and the PVT1 promoter inhibits MYC expression only from the same chromosome via promoter competition. Cancer genome sequencing identifies recurrent mutations encompassing the human PVT1 promoter, and genome editing verified that PVT1 promoter mutation promotes cancer cell growth. These results highlight regulatory sequences of lncRNA genes as potential disease-associated DNA elements.

Resistance to androgen deprivation therapies and increased androgen receptor (AR) activity are major drivers of castration-resistant prostate cancer (CRPC). Although prior work has focused on targeting AR directly, co-activators of AR signaling, which may represent new therapeutic targets, are relatively underexplored. Here we demonstrate that the mixed-lineage leukemia protein (MLL) complex, a well-known driver of MLL fusion-positive leukemia, acts as a co-activator of AR signaling. AR directly interacts with the MLL complex via the menin-MLL subunit. Menin expression is higher in CRPC than in both hormone-naive prostate cancer and benign prostate tissue, and high menin expression correlates with poor overall survival of individuals diagnosed with prostate cancer. Treatment with a small-molecule inhibitor of menin-MLL interaction blocks AR signaling and inhibits the growth of castration-resistant tumors in vivo in mice. Taken together, this work identifies the MLL complex as a crucial co-activator of AR and a potential therapeutic target in advanced prostate cancer.

Despite the promise of checkpoint inhibitor therapy, prostate cancer has remained resistant to this treatment. Innate immune agonists, however, have been shown to have anti-tumor effects in various cancer types, such as melanoma and colon cancer, and have been nominated to be used in combination with approved anti-PD1/PD-L1 drugs. For instance, STING agonist + anti-PD-1 combination therapy has been demonstrated in a syngeneic mouse model of melanoma. Other innate immune agonists, such as TLR7/8 and TLR9 have also been shown to have potential anti-tumor effects. We hypothesized that innate immune agonists may be effective in the MycCaP syngeneic mouse model of prostate cancer. To compare the efficacy of three innate agonists targeting the STING (3’3’ cGAMP), TLR7/8 (CL097), and TLR9 (ODN2395) pathways, FVB mice were first injected bilaterally with MycCaP subcutaneous tumors. Upon establishment of tumors, mice were administered 3 doses of a single-sided intratumoral injection of each drug. The injected tumors had responses of 50% (6/12), 23% (3/13), and 8% (1/13) for the STING, TLR7/8 and, TLR8 agonists, respectively. Contralateral tumors showed no significant regression. Published data have reported inherent resistance of MycCaP tumors to anti-PD-1 treatment in vivo. Given our results, we hypothesize that the addition of a STING agonist could enhance efficacy of anti-PD-1 therapy in this model. Mice with MycCaP tumors were administered anti-PD1 alone (n=10) or in combination with bilateral injections of the STING agonist (n=14). Combined therapy resulted in significant reduction in tumor size (71%) compared to anti-PD1 alone (9%). Additionally, there was a significant increase in the Ifnb1 (p=.04), Ifng (p=.008), Tnf-α (p=.006), IRF3 (p=.003) and II6 (p Citation Format: Jonathan Gurkan, Jae Eun Choi, Jean Tien, Marcin Cieślik, Luigi Franchi, Arul M. Chinnaiyan. Evaluating the efficacy of a STING agonist in a murine model of prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4091.

We have devised a method for isolating virtually pure and comprehensive libraries of restriction fragments that contained replication initiation sites (bubbles) in vivo. We have now sequenced and mapped the bubble-containing fragments from GM06990, a near-normal EBV-transformed lymphoblastoid cell line, and have compared origin distributions with a comprehensive replication timing study recently published for this cell line. We find that early-firing origins, which represent ∼32% of all origins, overwhelmingly represent zones, associate only marginally with active transcription units, are localized within large domains of open chromatin, and are significantly associated with DNase I hypersensitivity. Origin “density” falls from early- to mid-S-phase, but rises again in late S-phase to levels only 17% lower than in early S-phase. Unexpectedly, late origin density calculated on the 1-Mb scale increases as a function of increasing chromatin compaction. Furthermore, the median efficiency of origins in late-replicating, heterochromatic domains is only 25% lower than in early-replicating euchromatic loci. Thus, the activation of early- and late-firing origins must be regulated by quintessentially different mechanisms. The aggregate data can be unified into a model in which initiation site selection is driven almost entirely by epigenetic factors that fashion both the long-range and local chromatin environments, with underlying DNA sequence and local transcriptional activity playing only minor roles. Importantly, the comprehensive origin map we have prepared for GM06990 overlaps moderately well with origin maps recently reported for the genomes of four different human cell lines based on the distributions of small nascent strands.

MicroRNA-101, a tumor suppressor microRNA (miR), is often downregulated in cancer and is known to target multiple oncogenes. Some of the genes that are negatively regulated by miR-101 expression include histone methyltransferase EZH2 (enhancer of zeste homolog 2), COX2 (cyclooxygenase-2), POMP (proteasome maturation protein), CERS6, STMN1, MCL-1 and ROCK2, among others. In the present study, we show that miR-101 targets transcriptional coactivator SUB1 homolog (Saccharomyces cerevisiae)/PC4 (positive cofactor 4) and regulates its expression. SUB1 is known to have diverse role in vital cell processes such as DNA replication, repair and heterochromatinization. SUB1 is known to modulate transcription and acts as a mediator between the upstream activators and general transcription machinery. Expression profiling in several cancers revealed SUB1 overexpression, suggesting a potential role in tumorigenesis. However, detailed regulation and function of SUB1 has not been elucidated. In this study, we show elevated expression of SUB1 in aggressive prostate cancer. Knockdown of SUB1 in prostate cancer cells resulted in reduced cell proliferation, invasion and migration in vitro, and tumor growth and metastasis in vivo. Gene expression analyses coupled with chromatin immunoprecipitation revealed that SUB1 binds to the promoter regions of several oncogenes such as PLK1 (Polo-like kinase 1), C-MYC, serine-threonine kinase BUB1B and regulates their expression. Additionally, we observed SUB1 downregulated CDKN1B expression. PLK1 knockdown or use of PLK1 inhibitor can mitigate oncogenic function of SUB1 in benign prostate cancer cells. Thus, our study suggests that miR-101 loss results in increased SUB1 expression and subsequent activation of known oncogenes driving prostate cancer progression and metastasis. This study therefore demonstrates functional role of SUB1 in prostate cancer, and identifies its regulation and potential downstream therapeutic targets of SUB1 in prostate cancer.

Background Inducible transcription factors (TFs) mediate transcriptional responses to environmental cues. In response to multiple inflammatory signals active NF-κB dimers enter the nucleus and trigger cell-type-, and stimulus-specific transcriptional programs. Although much is known about NF-κB inducing pathways and about locus-specific mechanisms of transcriptional control, it is poorly understood how the pre-existing chromatin landscape determines NF-κB target selection and activation. Specifically, it is not known which epigenetic marks and pre-bound TFs serve genome-wide as positive (negative) cues for active NF-κB. Results We applied multivariate and combinatorial data mining techniques on a comprehensive dataset of DNA methylation, DNase I hypersensitivity, eight epigenetic marks, and 34 TFs to arrive at genome-wide patterns that predict NF-κB binding. Strikingly, we observed NF-κB recruitment to accessible and nucleosome-bound sites. Within nucleosomal DNA NF-κB binding was primed by H3K4me1 and H2A.Z, but also hyper-methylated DNA outside of promoters and CpG-islands. Many of these predictors showed combinatorial cooperativity and statistically significant interactions. Recruitment to pre-accessible sites was more frequent and influenced by chromatin-associated TFs. We observed that specific TF-combinations are greatly enriched for (or depleted of) NF-κB binding events. Conclusions We provide evidence of NF-κB binding within genomic regions that lack classical marks of activity. These pioneer binding events are relatively often associated with transcriptional regulation. Further, our predictive models indicate that specific combinations of epigenetic marks and transcription factors predetermine the NF-κB cistrome, supporting the feasibility of using statistical approaches to identify “histone codes”. Electronic supplementary material The online version of this article (doi:10.1186/s13040-015-0071-3) contains supplementary material, which is available to authorized users.

The androgen receptor (AR) signaling plays a key role in the development of the normal prostate as well as prostate cancer. While substantial efforts have been undertaken to study AR-regulated protein-coding genes in primary prostate cancer and castration-resistant prostate cancer, few studies have investigated the role of long noncoding RNAs. In this study, we employed transcriptome sequencing to delineate long noncoding RNAs (lncRNAs) associated with AR signaling in prostate cancer progression. ARlnc1 (AR-regulated lncRNA 1) was identified as being the top AR-induced, cancer-associated lncRNA in an integrative analysis of prostate cancer cell lines and tissues. Not only was ARlnc1 induced by AR, but ARlnc1 also was shown to sustain AR signaling by stabilizing the AR transcript via interaction with the AR 3' UTR. Knockdown of ARlnc1 suppressed AR expression, global AR signaling, and prostate cancer growth in vitro and in vivo. Taken together, these data support a role for ARlnc1 in maintaining a positive feedback loop that potentiates AR signaling during prostate cancer progression, and identifies ARlnc1 as a novel therapeutic target. Citation Format: Yajia Zhang, Sethuramasundaram Pitchiaya, Marcin Cieślik, Yashar S. Niknafs, Jean C. Tien, Yasuyuki Hosono, Lisha Wang, Yuanyuan Qiao, Xuhong Cao, Mats Ljungman, Hui Jiang, Rohit Mehra, Shuling Guo, Rohit Malik, Arul M. Chinnaiyan. The androgen receptor-regulated lncRNA landscape reveals a role for ARlnc1 in prostate cancer progression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2458.

Using integrative genomic analysis of 360 metastatic castration-resistant prostate cancer (mCRPC) samples, we identified a novel subtype of prostate cancer typified by biallelic loss of CDK12 that is mutually exclusive with tumors driven by DNA repair deficiency, ETS fusions, and SPOP mutations. CDK12 loss is enriched in mCRPC relative to clinically localized disease and characterized by focal tandem duplications (FTDs) that lead to increased gene fusions and marked differential gene expression. FTDs associated with CDK12 loss result in highly recurrent gains at loci of genes involved in the cell cycle and DNA replication. CDK12 mutant cases are baseline diploid and do not exhibit DNA mutational signatures linked to defects in homologous recombination. CDK12 mutant cases are associated with elevated neoantigen burden ensuing from fusion-induced chimeric open reading frames and increased tumor T cell infiltration/clonal expansion. CDK12 inactivation thereby defines a distinct class of mCRPC that may benefit from immune checkpoint immunotherapy.

The integrity and X-ray diffraction quality of protein crystals depend on the three-dimensional order of relatively weak but reproducible intermolecular contacts. Despite their importance, relatively little attention has been paid to the chemical and physical nature of these contacts, which are often regarded as stochastic and thus not different from randomly selected protein surface patches. Here, logistic regression was used to analyze crystal contacts in a database of 821 unambiguously monomeric proteins with structures determined to 2.5 A resolution or better. It is shown that the propensity of a surface residue for incorporation into a crystal contact is not a linear function of its solvent-accessible surface area and that amino acids with low exposed surfaces, which are typically small and hydrophobic, have been underestimated with respect to their contact-forming potential by earlier area-based calculations. For any given solvent-exposed surface, small and hydrophobic residues are more likely to be involved in crystal contacts than large and charged amino acids. Side-chain entropy is the single physicochemical property that is most negatively correlated with the involvement of amino acids in crystal contacts. It is also shown that crystal contacts with larger buried surfaces containing eight or more amino acids have cores that are depleted of polar amino acids.

To facilitate flexible and efficient structural bioinformatics analyses, new functionality for three-dimensional structure processing and analysis has been introduced into PyCogent -- a popular feature-rich framework for sequence-based bioinformatics, but one which has lacked equally powerful tools for handling stuctural/coordinate-based data. Extensible Python modules have been developed, which provide object-oriented abstractions (based on a hierarchical representation of macromolecules), efficient data structures (e.g. kD-trees), fast implementations of common algorithms (e.g. surface-area calculations), read/write support for Protein Data Bank-related file formats and wrappers for external command-line applications (e.g. Stride). Integration of this code into PyCogent is symbiotic, allowing sequence-based work to benefit from structure-derived data and, reciprocally, enabling structural studies to leverage PyCogent's versatile tools for phylogenetic and evolutionary analyses., Comment: 36 pages, 4 figures (including supplemental information)

The epithelial-to-mesenchymal transition (EMT) is a cellular dedifferentiation process that is critical to development, wound healing and metastasis. Like other cell state transitions, such as differentiation, EMT is accompanied by genome-wide epigenetic reprogramming. However, the relationship between reprogramming and functional changes in the cell is poorly understood. In an A549 non-small cell lung cancer EMT model system we observed changes in chromatin state between epithelial and mesenchymal states. Multivariate analyses were applied to paired (epithelial and mesenchymal) ChIP-seq data for 18 histone modifications/variants and expression microarray data. We observed epigenetic co-regulation of genes associated with EMT, as well as their proximal enhancers. We also observed epigenetic activation or repression of functionally distinct sets of enhancers. These genes and enhancers are regulated and bound by a small set of transcription factors, specifically AP-1, NF-κB and c-Myc. These transcription factors themselves also a show an epigenetic profile similar to the EMT-related genes. Together, these observations suggest a chromatin-mediated transcriptional feedback mechanism that establishes and maintains the phenotypic switch.

Resistance to androgen deprivation therapies and increased androgen receptor (AR) activity are major drivers of castration resistant prostate cancer (CRPC). Substantial prior work has focused on targeting AR directly; however, the identification and therapeutic targeting of co-activators of AR signaling remains an underexplored area of potential clinical significance. Here we demonstrate that the MLL (mixed-lineage leukemia) complex, a well-known contributor in MLL-fusion-positive leukemia, acts as a co-activator of AR signaling. AR directly interacts with the MLL complex via its critical subunit, menin. Small molecule inhibition of the menin-MLL interaction blocks AR signaling and inhibits the growth of castration resistant tumors in vivo. Furthermore, we find that menin is up-regulated in castration resistant prostate cancer and high expression correlates with poor overall survival. Taken together, our study identifies the MLL complex as a critical co-activator of AR that can be targeted in advanced prostate cancer. Citation Format: Rohit Malik, Amjad P. Khan, Irfan A. Asangani, Marcin Cieślik, John R. Prensner, Xiaoju Wang, Matthew K. Iyer, Xia Jiang, Dmitry Borkin, June Escara-Wilke, Rachell Stender, Yi-Mi Wu, Xuhong Cao, Felix Y. Feng, Jolanta Grembecka, Tomasz Cierpicki, Arul M. Chinnaiyan. Targeting the MLL complex in castration resistant prostate cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3636. doi:10.1158/1538-7445.AM2015-3636

Chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) is the most widely used method for characterizing the epigenetic states of chromatin on a genomic scale. With the recent availability of large genome-wide data sets, often comprising several epigenetic marks, novel approaches are required to explore functionally relevant interactions between histone modifications. Computational discovery of "chromatin states" defined by such combinatorial interactions enabled descriptive annotations of genomes, but more quantitative approaches are needed to progress towards predictive models. We propose non-negative matrix factorization (NMF) as a new unsupervised method to discover combinatorial patterns of epigenetic marks that frequently co-occur in subsets of genomic regions. We show that this small set of combinatorial "codes" can be effectively displayed and interpreted. NMF codes enable dimensionality reduction and have desirable statistical properties for regression and classification tasks. We demonstrate the utility of codes in the quantitative prediction of Pol2-binding and the discrimination between Pol2-bound promoters and enhancers. Finally, we show that specific codes can be linked to molecular pathways and targets of pluripotency genes during differentiation. We have introduced and evaluated a new computational approach to represent combinatorial patterns of epigenetic marks as quantitative variables suitable for predictive modeling and supervised machine learning. To foster widespread adoption of this method we make it available as an open-source software-package – epicode at https://github.com/mcieslik-mctp/epicode .

Background Bioinformatic analyses typically proceed as chains of data-processing tasks. A pipeline, or 'workflow', is a well-defined protocol, with a specific structure defined by the topology of data-flow interdependencies, and a particular functionality arising from the data transformations applied at each step. In computer science, the dataflow programming (DFP) paradigm defines software systems constructed in this manner, as networks of message-passing components. Thus, bioinformatic workflows can be naturally mapped onto DFP concepts. Results To enable the flexible creation and execution of bioinformatics dataflows, we have written a modular framework for parallel pipelines in Python ('PaPy'). A PaPy workflow is created from re-usable components connected by data-pipes into a directed acyclic graph, which together define nested higher-order map functions. The successive functional transformations of input data are evaluated on flexibly pooled compute resources, either local or remote. Input items are processed in batches of adjustable size, all flowing one to tune the trade-off between parallelism and lazy-evaluation (memory consumption). An add-on module ('NuBio') facilitates the creation of bioinformatics workflows by providing domain specific data-containers (e.g., for biomolecular sequences, alignments, structures) and functionality (e.g., to parse/write standard file formats). Conclusions PaPy offers a modular framework for the creation and deployment of parallel and distributed data-processing workflows. Pipelines derive their functionality from user-written, data-coupled components, so PaPy also can be viewed as a lightweight toolkit for extensible, flow-based bioinformatics data-processing. The simplicity and flexibility of distributed PaPy pipelines may help users bridge the gap between traditional desktop/workstation and grid computing. PaPy is freely distributed as open-source Python code at http://muralab.org/PaPy, and includes extensive documentation and annotated usage examples.

Background The epithelial-mesenchymal transition (EMT) is a de-differentiation process required for wound healing and development. In tumors of epithelial origin aberrant induction of EMT contributes to cancer progression and metastasis. Studies have begun to implicate epigenetic reprogramming in EMT; however, the relationship between reprogramming and the coordination of cellular processes is largely unexplored. We have previously developed a system to study EMT in a canonical non-small cell lung cancer (NSCLC) model. In this system we have shown that the induction of EMT results in constitutive NF-κB activity. We hypothesized a role for chromatin remodeling in the sustained deregulation of cellular signaling pathways. Results We mapped sixteen histone modifications and two variants for epithelial and mesenchymal states. Combinatorial patterns of epigenetic changes were quantified at gene and enhancer loci. We found a distinct chromatin signature among genes in well-established EMT pathways. Strikingly, these genes are only a small minority of those that are differentially expressed. At putative enhancers of genes with the ‘EMT-signature’ we observed highly coordinated epigenetic activation or repression. Furthermore, enhancers that are activated are bound by a set of transcription factors that is distinct from those that bind repressed enhancers. Upregulated genes with the ‘EMT-signature’ are upstream regulators of NF-κB, but are also bound by NF-κB at their promoters and enhancers. These results suggest a chromatin-mediated positive feedback as a likely mechanism for sustained NF-κB activation. Conclusions There is highly specific epigenetic regulation at genes and enhancers across several pathways critical to EMT. The sites of these changes in chromatin state implicate several inducible transcription factors with critical roles in EMT (NF-κB, AP-1 and MYC) as targets of this reprogramming. Furthermore, we find evidence that suggests that these transcription factors are in chromatin-mediated transcriptional feedback loops that regulate critical EMT genes. In sum, we establish an important link between chromatin remodeling and shifts in cellular reprogramming.