Incorporating network diffusion and peak location information for better single-cell ATAC-seq data analysis.

Single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) data provided new insights into the understanding of epigenetic heterogeneity and transcriptional regulation. With the increasing abundance of dataset resources, there is an urgent need to extract more useful information through high-quality data analysis methods specifically designed for scATAC-seq. However, analyzing scATAC-seq data poses challenges due to its near binarization, high sparsity and ultra-high dimensionality properties. Here, we proposed a novel network diffusion-based computational method to comprehensively analyze scATAC-seq data, named Single-Cell ATAC-seq Analysis via Network Refinement with Peaks Location Information (SCARP). SCARP formulates the Network Refinement diffusion method under the graph theory framework to aggregate information from different network orders, effectively compensating for missing signals in the scATAC-seq data. By incorporating distance information between adjacent peaks on the genome, SCARP also contributes to depicting the co-accessibility of peaks. These two innovations empower SCARP to obtain lower-dimensional representations for both cells and peaks more effectively. We have demonstrated through sufficient experiments that SCARP facilitated superior analyses of scATAC-seq data. Specifically, SCARP exhibited outstanding cell clustering performance, enabling better elucidation of cell heterogeneity and the discovery of new biologically significant cell subpopulations. Additionally, SCARP was also instrumental in portraying co-accessibility relationships of accessible regions and providing new insight into transcriptional regulation. Consequently, SCARP identified genes that were involved in key Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to diseases and predicted reliable cis-regulatory interactions. To sum up, our studies suggested that SCARP is a promising tool to comprehensively analyze the scATAC-seq data.

Reverse network diffusion to remove indirect noise for better inference of gene regulatory networks.

MOTIVATION: Gene regulatory networks (GRNs) are vital tools for delineating regulatory relationships between transcription factors and their target genes. The boom in computational biology and various biotechnologies has made inferring GRNs from multi-omics data a hot topic. However, when networks are constructed from gene expression data, they often suffer from false-positive problem due to the transitive effects of correlation. The presence of spurious noise edges obscures the real gene interactions, which makes downstream analyses, such as detecting gene function modules and predicting disease-related genes, difficult and inefficient. Therefore, there is an urgent and compelling need to develop network denoising methods to improve the accuracy of GRN inference. RESULTS: In this study, we proposed a novel network denoising method named REverse Network Diffusion On Random walks (RENDOR). RENDOR is designed to enhance the accuracy of GRNs afflicted by indirect effects. RENDOR takes noisy networks as input, models higher-order indirect interactions between genes by transitive closure, eliminates false-positive effects using the inverse network diffusion method, and produces refined networks as output. We conducted a comparative assessment of GRN inference accuracy before and after denoising on simulated networks and real GRNs. Our results emphasized that the network derived from RENDOR more accurately and effectively captures gene interactions. This study demonstrates the significance of removing network indirect noise and highlights the effectiveness of the proposed method in enhancing the signal-to-noise ratio (SNR) of noisy networks. AVAILABILITY AND IMPLEMENTATION: The R package RENDOR is provided at https://github.com/Wu-Lab/RENDOR and other source code and data are available at https://github.com/Wu-Lab/RENDOR-reproduce. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

ActivePPI: quantifying protein-protein interaction network activity with Markov random fields.

MOTIVATION: Protein-protein interactions (PPI) are crucial components of the biomolecular networks that enable cells to function. Biological experiments have identified a large number of PPI, and these interactions are stored in knowledge bases. However, these interactions are often restricted to specific cellular environments and conditions. Network activity can be characterized as the extent of agreement between a PPI network (PPIN) and a distinct cellular environment measured by protein mass spectrometry, and it can also be quantified as a statistical significance score. Without knowing the activity of these PPI in the cellular environments or specific phenotypes, it is impossible to reveal how these PPI perform and affect cellular functioning. RESULTS: To calculate the activity of PPIN in different cellular conditions, we proposed a PPIN activity evaluation framework named ActivePPI to measure the consistency between network architecture and protein measurement data. ActivePPI estimates the probability density of protein mass spectrometry abundance and models PPIN using a Markov-random-field-based method. Furthermore, empirical P-value is derived based on a nonparametric permutation test to quantify the likelihood significance of the match between PPIN structure and protein abundance data. Extensive numerical experiments demonstrate the superior performance of ActivePPI and result in network activity evaluation, pathway activity assessment, and optimal network architecture tuning tasks. To summarize it succinctly, ActivePPI is a versatile tool for evaluating PPI network that can uncover the functional significance of protein interactions in crucial cellular biological processes and offer further insights into physiological phenomena. AVAILABILITY AND IMPLEMENTATION: All source code and data are freely available at https://github.com/zpliulab/ActivePPI.

CDK9 inhibition induces epigenetic reprogramming revealing strategies to circumvent resistance in lymphoma.

Diffuse large B-cell lymphoma (DLBCL) exhibits significant genetic heterogeneity which contributes to drug resistance, necessitating development of novel therapeutic approaches. Pharmacological inhibitors of cyclin-dependent kinases (CDK) demonstrated pre-clinical activity in DLBCL, however many stalled in clinical development. Here we show that AZD4573, a selective inhibitor of CDK9, restricted growth of DLBCL cells. CDK9 inhibition (CDK9i) resulted in rapid changes in the transcriptome and proteome, with downmodulation of multiple oncoproteins (eg, MYC, Mcl-1, JunB, PIM3) and deregulation of phosphoinotiside-3 kinase (PI3K) and senescence pathways. Following initial transcriptional repression due to RNAPII pausing, we observed transcriptional recovery of several oncogenes, including MYC and PIM3. ATAC-Seq and ChIP-Seq experiments revealed that CDK9i induced epigenetic remodeling with bi-directional changes in chromatin accessibility, suppressed promoter activation and led to sustained reprograming of the super-enhancer landscape. A CRISPR library screen suggested that SE-associated genes in the Mediator complex, as well as AKT1, confer resistance to CDK9i. Consistent with this, sgRNA-mediated knockout of MED12 sensitized cells to CDK9i. Informed by our mechanistic findings, we combined AZD4573 with either PIM kinase or PI3K inhibitors. Both combinations decreased proliferation and induced apoptosis in DLBCL and primary lymphoma cells in vitro as well as resulted in delayed tumor progression and extended survival of mice xenografted with DLBCL in vivo. Thus, CDK9i induces reprogramming of the epigenetic landscape, and super-enhancer driven recovery of select oncogenes may contribute to resistance to CDK9i. PIM and PI3K represent potential targets to circumvent resistance to CDK9i in the heterogeneous landscape of DLBCL.

Transcriptional profiling identifies an androgen receptor activity-low, stemness program associated with enzalutamide resistance.

The androgen receptor (AR) antagonist enzalutamide is one of the principal treatments for men with castration-resistant prostate cancer (CRPC). However, not all patients respond, and resistance mechanisms are largely unknown. We hypothesized that genomic and transcriptional features from metastatic CRPC biopsies prior to treatment would be predictive of de novo treatment resistance. To this end, we conducted a phase II trial of enzalutamide treatment (160 mg/d) in 36 men with metastatic CRPC. Thirty-four patients were evaluable for the primary end point of a prostate-specific antigen (PSA)50 response (PSA decline ≥50% at 12 wk vs. baseline). Nine patients were classified as nonresponders (PSA decline <50%), and 25 patients were classified as responders (PSA decline ≥50%). Failure to achieve a PSA50 was associated with shorter progression-free survival, time on treatment, and overall survival, demonstrating PSA50's utility. Targeted DNA-sequencing was performed on 26 of 36 biopsies, and RNA-sequencing was performed on 25 of 36 biopsies that contained sufficient material. Using computational methods, we measured AR transcriptional function and performed gene set enrichment analysis (GSEA) to identify pathways whose activity state correlated with de novo resistance. TP53 gene alterations were more common in nonresponders, although this did not reach statistical significance (P = 0.055). AR gene alterations and AR expression were similar between groups. Importantly, however, transcriptional measurements demonstrated that specific gene sets-including those linked to low AR transcriptional activity and a stemness program-were activated in nonresponders. Our results suggest that patients whose tumors harbor this program should be considered for clinical trials testing rational agents to overcome de novo enzalutamide resistance.

Discovering cooperative biomarkers for heterogeneous complex disease diagnoses.

Biomarkers with high reproducibility and accurate prediction performance can contribute to comprehending the underlying pathogenesis of related complex diseases and further facilitate disease diagnosis and therapy. Techniques integrating gene expression profiles and biological networks for the identification of network-based disease biomarkers are receiving increasing interest. The biomarkers for heterogeneous diseases often exhibit strong cooperative effects, which implies that a set of genes may achieve more accurate outcome prediction than any single gene. In this study, we evaluated various biomarker identification methods that consider gene cooperative effects implicitly or explicitly, and proposed the gene cooperation network to explicitly model the cooperative effects of gene combinations. The gene cooperation network-enhanced method, named as MarkRank, achieves superior performance compared with traditional biomarker identification methods in both simulation studies and real data sets. The biomarkers identified by MarkRank not only have a better prediction accuracy but also have stronger topological relationships in the biological network and exhibit high specificity associated with the related diseases. Furthermore, the top genes identified by MarkRank involve crucial biological processes of related diseases and give a good prioritization for known disease genes. In conclusion, MarkRank suggests that explicit modeling of gene cooperative effects can greatly improve biomarker identification for complex diseases, especially for diseases with high heterogeneity.

Identification of DNA motif pairs on paired sequences based on composite heterogeneous graph.

Motivation: The interaction between DNA motifs (DNA motif pairs) influences gene expression through partnership or competition in the process of gene regulation. Potential chromatin interactions between different DNA motifs have been implicated in various diseases. However, current methods for identifying DNA motif pairs rely on the recognition of single DNA motifs or probabilities, which may result in local optimal solutions and can be sensitive to the choice of initial values. A method for precisely identifying DNA motif pairs is still lacking. Results: Here, we propose a novel computational method for predicting DNA Motif Pairs based on Composite Heterogeneous Graph (MPCHG). This approach leverages a composite heterogeneous graph model to identify DNA motif pairs on paired sequences. Compared with the existing methods, MPCHG has greatly improved the accuracy of motifs prediction. Furthermore, the predicted DNA motifs demonstrate heightened DNase accessibility than the background sequences. Notably, the two DNA motifs forming a pair exhibit functional consistency. Importantly, the interacting TF pairs obtained by predicted DNA motif pairs were significantly enriched with known interacting TF pairs, suggesting their potential contribution to chromatin interactions. Collectively, we believe that these identified DNA motif pairs held substantial implications for revealing gene transcriptional regulation under long-range chromatin interactions.

Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data.

Single-cell RNA sequencing (scRNA-seq) distinguishes cell types, states and lineages within the context of heterogeneous tissues. However, current single-cell data cannot directly link cell clusters with specific phenotypes. Here we present Scissor, a method that identifies cell subpopulations from single-cell data that are associated with a given phenotype. Scissor integrates phenotype-associated bulk expression data and single-cell data by first quantifying the similarity between each single cell and each bulk sample. It then optimizes a regression model on the correlation matrix with the sample phenotype to identify relevant subpopulations. Applied to a lung cancer scRNA-seq dataset, Scissor identified subsets of cells associated with worse survival and with TP53 mutations. In melanoma, Scissor discerned a T cell subpopulation with low PDCD1/CTLA4 and high TCF7 expression associated with an immunotherapy response. Beyond cancer, Scissor was effective in interpreting facioscapulohumeral muscular dystrophy and Alzheimer's disease datasets. Scissor identifies biologically and clinically relevant cell subpopulations from single-cell assays by leveraging phenotype and bulk-omics datasets.

Mammary collagen is under reproductive control with implications for breast cancer.

Mammographically-detected breast density impacts breast cancer risk and progression, and fibrillar collagen is a key component of breast density. However, physiologic factors influencing collagen production in the breast are poorly understood. In female rats, we analyzed gene expression of the most abundantly expressed mammary collagens and collagen-associated proteins across a pregnancy, lactation, and weaning cycle. We identified a triphasic pattern of collagen gene regulation and evidence for reproductive state-dependent composition. An initial phase of collagen deposition occurred during pregnancy, followed by an active phase of collagen suppression during lactation. The third phase of collagen regulation occurred during weaning-induced mammary gland involution, which was characterized by increased collagen deposition. Concomitant changes in collagen protein abundance were confirmed by Masson's trichrome staining, second harmonic generation (SHG) imaging, and mass spectrometry. We observed similar reproductive-state dependent collagen patterns in human breast tissue obtained from premenopausal women. SHG analysis also revealed structural variation in collagen across a reproductive cycle, with higher packing density and more collagen fibers arranged perpendicular to the mammary epithelium in the involuting rat mammary gland compared to nulliparous and lactating glands. Involution was also characterized by high expression of the collagen cross-linking enzyme lysyl oxidase, which was associated with increased levels of cross-linked collagen. Breast cancer relevance is suggested, as we found that breast cancer diagnosed in recently postpartum women displayed gene expression signatures consistent with increased collagen deposition and crosslinking compared to breast cancers diagnosed in age-matched nulliparous women. Using publicly available data sets, we found this involution-like, collagen gene signature correlated with poor progression-free survival in breast cancer patients overall and in younger women. In sum, these findings of physiologic collagen regulation in the normal mammary gland may provide insight into normal breast function, the etiology of breast density, and inform breast cancer risk and outcomes.

A Cancer Cell-Intrinsic GOT2-PPARδ Axis Suppresses Antitumor Immunity.

Despite significant recent advances in precision medicine, pancreatic ductal adenocarcinoma (PDAC) remains near uniformly lethal. Although immune-modulatory therapies hold promise to meaningfully improve outcomes for patients with PDAC, the development of such therapies requires an improved understanding of the immune evasion mechanisms that characterize the PDAC microenvironment. Here, we show that cancer cell-intrinsic glutamic-oxaloacetic transaminase 2 (GOT2) shapes the immune microenvironment to suppress antitumor immunity. Mechanistically, we find that GOT2 functions beyond its established role in the malate-aspartate shuttle and promotes the transcriptional activity of nuclear receptor peroxisome proliferator-activated receptor delta (PPARδ), facilitated by direct fatty acid binding. Although GOT2 is dispensable for cancer cell proliferation in vivo, the GOT2-PPARδ axis promotes spatial restriction of both CD4+ and CD8+ T cells from the tumor microenvironment. Our results demonstrate a noncanonical function for an established mitochondrial enzyme in transcriptional regulation of immune evasion, which may be exploitable to promote a productive antitumor immune response. SIGNIFICANCE: Prior studies demonstrate the important moonlighting functions of metabolic enzymes in cancer. We find that the mitochondrial transaminase GOT2 binds directly to fatty acid ligands that regulate the nuclear receptor PPARδ, and this functional interaction critically regulates the immune microenvironment of pancreatic cancer to promote tumor progression. See related commentary by Nwosu and di Magliano, p. 2237.. This article is highlighted in the In This Issue feature, p. 2221.

Mesenchymal Lineage Heterogeneity Underlies Nonredundant Functions of Pancreatic Cancer-Associated Fibroblasts.

Cancer-associated fibroblast (CAF) heterogeneity is increasingly appreciated, but the origins and functions of distinct CAF subtypes remain poorly understood. The abundant and transcriptionally diverse CAF population in pancreatic ductal adenocarcinoma (PDAC) is thought to arise from a common cell of origin, pancreatic stellate cells (PSC), with diversification resulting from cytokine and growth factor gradients within the tumor microenvironment. Here we analyzed the differentiation and function of PSCs during tumor progression in vivo. Contrary to expectations, we found that PSCs give rise to a numerically minor subset of PDAC CAFs. Targeted ablation of PSC-derived CAFs within their host tissue revealed nonredundant functions for this defined CAF population in shaping the PDAC microenvironment, including production of specific extracellular matrix components and tissue stiffness regulation. Together, these findings link stromal evolution from distinct cells of origin to transcriptional heterogeneity among PDAC CAFs and demonstrate unique functions for CAFs of a defined cellular origin. SIGNIFICANCE: By tracking and ablating a specific CAF population, we find that a numerically minor CAF subtype from a defined cell of origin plays unique roles in establishing the pancreatic tumor microenvironment. Together with prior studies, this work suggests that mesenchymal lineage heterogeneity and signaling gradients diversify PDAC CAFs.See related commentary by Cukierman, p. 296.This article is highlighted in the In This Issue feature, p. 275.

Transcriptional profiling of matched patient biopsies clarifies molecular determinants of enzalutamide-induced lineage plasticity.

The androgen receptor (AR) signaling inhibitor enzalutamide (enza) is one of the principal treatments for metastatic castration-resistant prostate cancer (CRPC). Several emergent enza clinical resistance mechanisms have been described, including lineage plasticity in which the tumors manifest reduced dependency on the AR. To improve our understanding of enza resistance, herein we analyze the transcriptomes of matched biopsies from men with metastatic CRPC obtained prior to treatment and at progression (n = 21). RNA-sequencing analysis demonstrates that enza does not induce marked, sustained changes in the tumor transcriptome in most patients. However, three patients' progression biopsies show evidence of lineage plasticity. The transcription factor E2F1 and pathways linked to tumor stemness are highly activated in baseline biopsies from patients whose tumors undergo lineage plasticity. We find a gene signature enriched in these baseline biopsies that is strongly associated with poor survival in independent patient cohorts and with risk of castration-induced lineage plasticity in patient-derived xenograft models, suggesting that tumors harboring this gene expression program may be at particular risk for resistance mediated by lineage plasticity and poor outcomes.

Pharmacologic Targeting of Mcl-1 Induces Mitochondrial Dysfunction and Apoptosis in B-Cell Lymphoma Cells in a TP53- and BAX-Dependent Manner.

PURPOSE: Bcl-2 has been effectively targeted in lymphoid malignancies. However, resistance is inevitable, and novel approaches to target mitochondrial apoptosis are necessary. AZD5991, a selective BH3-mimetic in clinical trials, inhibits Mcl-1 with high potency. EXPERIMENTAL DESIGN: We explored the preclinical activity of AZD5991 in diffuse large B-cell lymphoma (DLBCL) and ibrutinib-resistant mantle cell lymphoma (MCL) cell lines, MCL patient samples, and mice bearing DLBCL and MCL xenografts using flow cytometry, immunoblotting, and Seahorse respirometry assay. Cas9 gene editing and ex vivo functional drug screen assays helped identify mechanisms of resistance to Mcl-1 inhibition. RESULTS: Mcl-1 was expressed in DLBCL and MCL cell lines and primary tumors. Treatment with AZD5991 restricted growth of DLBCL cells independent of cell of origin and overcame ibrutinib resistance in MCL cells. Mcl-1 inhibition led to mitochondrial dysfunction as manifested by mitochondrial membrane depolarization, decreased mitochondrial mass, and induction of mitophagy. This was accompanied by impairment of oxidative phosphorylation. TP53 and BAX were essential for sensitivity to Mcl-1, and oxidative phosphorylation was implicated in resistance to Mcl-1 inhibition. Induction of prosurvival proteins (e.g., Bcl-xL) in stromal conditions that mimic the tumor microenvironment rendered protection of primary MCL cells from Mcl-1 inhibition, while BH3-mimetics targeting Bcl-2/xL sensitized lymphoid cells to AZD5991. Treatment with AZD5991 reduced tumor growth in murine lymphoma models and prolonged survival of MCL PDX mice. CONCLUSIONS: Selective targeting Mcl-1 is a promising therapeutic approach in lymphoid malignancies. TP53 apoptotic network and metabolic reprogramming underlie susceptibility to Mcl-1 inhibition.

Postpartum breast cancer has a distinct molecular profile that predicts poor outcomes.

Young women's breast cancer (YWBC) has poor prognosis and known interactions with parity. Women diagnosed within 5-10 years of childbirth, defined as postpartum breast cancer (PPBC), have poorer prognosis compared to age, stage, and biologic subtype-matched nulliparous patients. Genomic differences that explain this poor prognosis remain unknown. In this study, using RNA expression data from clinically matched estrogen receptor positive (ER+) cases (n = 16), we observe that ER+ YWBC can be differentiated based on a postpartum or nulliparous diagnosis. The gene expression signatures of PPBC are consistent with increased cell cycle, T-cell activation and reduced estrogen receptor and TP53 signaling. When applied to a large YWBC cohort, these signatures for ER+ PPBC associate with significantly reduced 15-year survival rates in high compared to low expressing cases. Cumulatively these results provide evidence that PPBC is a unique entity within YWBC with poor prognostic phenotypes.

BET Bromodomain Inhibition Blocks an AR-Repressed, E2F1-Activated Treatment-Emergent Neuroendocrine Prostate Cancer Lineage Plasticity Program.

PURPOSE: Lineage plasticity in prostate cancer-most commonly exemplified by loss of androgen receptor (AR) signaling and a switch from a luminal to alternate differentiation program-is now recognized as a treatment resistance mechanism. Lineage plasticity is a spectrum, but neuroendocrine prostate cancer (NEPC) is the most virulent example. Currently, there are limited treatments for NEPC. Moreover, the incidence of treatment-emergent NEPC (t-NEPC) is increasing in the era of novel AR inhibitors. In contradistinction to de novo NEPC, t-NEPC tumors often express the AR, but AR's functional role in t-NEPC is unknown. Furthermore, targetable factors that promote t-NEPC lineage plasticity are also unclear. EXPERIMENTAL DESIGN: Using an integrative systems biology approach, we investigated enzalutamide-resistant t-NEPC cell lines and their parental, enzalutamide-sensitive adenocarcinoma cell lines. The AR is still expressed in these t-NEPC cells, enabling us to determine the role of the AR and other key factors in regulating t-NEPC lineage plasticity. RESULTS: AR inhibition accentuates lineage plasticity in t-NEPC cells-an effect not observed in parental, enzalutamide-sensitive adenocarcinoma cells. Induction of an AR-repressed, lineage plasticity program is dependent on activation of the transcription factor E2F1 in concert with the BET bromodomain chromatin reader BRD4. BET inhibition (BETi) blocks this E2F1/BRD4-regulated program and decreases growth of t-NEPC tumor models and a subset of t-NEPC patient tumors with high activity of this program in a BETi clinical trial. CONCLUSIONS: E2F1 and BRD4 are critical for activating an AR-repressed, t-NEPC lineage plasticity program. BETi is a promising approach to block this program.

Alternative splicing of LSD1+8a in neuroendocrine prostate cancer is mediated by SRRM4.

Neuroendocrine prostate cancer (NEPC) is the most virulent form of prostate cancer. Importantly, our recent work examining metastatic biopsy samples demonstrates NEPC is increasing in frequency. In contrast to prostate adenocarcinomas that express a luminal gene expression program, NEPC tumors express a neuronal gene expression program. Despite this distinction, the diagnosis of NEPC is often challenging, demonstrating an urgent need to identify new biomarkers and therapeutic targets. Our prior work demonstrated that the histone demethylase LSD1 (KDM1A) is important for survival of prostate adenocarcinomas, but little was known about LSD1's role in NEPC. Recently, a neural-specific transcript variant of LSD1-LSD1+8a-was discovered and demonstrated to activate neuronal gene expression in neural cells. The splicing factor SRRM4 was previously shown to promote LSD1+8a splicing in neuronal cells, and SRRM4 promotes NEPC differentiation and cell survival. Therefore, we sought to determine if LSD1+8a might play a role in NEPC and whether LSD1+8a splicing was linked to SRRM4. To investigate a potential role for LSD1+8a in NEPC, we examined a panel of prostate adenocarcinoma and NEPC patient-derived xenografts and metastatic biopsies. LSD1+8a was expressed exclusively in NEPC samples and correlated significantly with elevated expression of SRRM4. Using SRRM4-overexpressing cell lines, we determined that SRRM4 mediates alternative splicing of LSD1+8a. Finally, using gain of function studies, we confirmed that LSD1+8a and SRRM4 co-regulate target genes distinct from canonical LSD1. Our findings suggest further study of the interplay between SRRM4 and LSD1+8a and mechanisms by which LSD1+8a regulates gene expression in NEPC is warranted.

Copy Number Loss of 17q22 Is Associated with Enzalutamide Resistance and Poor Prognosis in Metastatic Castration-Resistant Prostate Cancer.

PURPOSE: The purpose of this study was to measure genomic changes that emerge with enzalutamide treatment using analyses of whole-genome sequencing and RNA sequencing. EXPERIMENTAL DESIGN: One hundred and one tumors from men with metastatic castration-resistant prostate cancer (mCRPC) who had not been treated with enzalutamide (n = 64) or who had enzalutamide-resistant mCRPC (n = 37) underwent whole genome sequencing. Ninety-nine of these tumors also underwent RNA sequencing. We analyzed the genomes and transcriptomes of these mCRPC tumors. RESULTS: Copy number loss was more common than gain in enzalutamide-resistant tumors. Specially, we identified 124 protein-coding genes that were more commonly lost in enzalutamide-resistant samples. These 124 genes included eight putative tumor suppressors located at nine distinct genomic regions. We demonstrated that focal deletion of the 17q22 locus that includes RNF43 and SRSF1 was not present in any patient with enzalutamide-naïve mCRPC but was present in 16% (6/37) of patients with enzalutamide-resistant mCRPC. 17q22 loss was associated with lower RNF43 and SRSF1 expression and poor overall survival from time of biopsy [median overall survival of 19.3 months in 17q22 intact vs. 8.9 months in 17q22 loss, HR, 3.44 95% confidence interval (CI), 1.338-8.867, log-rank P = 0.006]. Finally, 17q22 loss was linked with activation of several targetable factors, including CDK1/2, Akt, and PLK1, demonstrating the potential therapeutic relevance of 17q22 loss in mCRPC. CONCLUSIONS: Copy number loss is common in enzalutamide-resistant tumors. Focal deletion of chromosome 17q22 defines a previously unappreciated molecular subset of enzalutamide-resistant mCRPC associated with poor clinical outcome.

RNA-seq from archival FFPE breast cancer samples: molecular pathway fidelity and novel discovery.

BACKGROUND: Formalin-fixed, paraffin-embedded (FFPE) tissues for RNA-seq have advantages over fresh frozen tissue including abundance and availability, connection to rich clinical data, and association with patient outcomes. However, FFPE-derived RNA is highly degraded and chemically modified, which impacts its utility as a faithful source for biological inquiry. METHODS: True archival FFPE breast cancer cases (n = 58), stored at room temperature for 2-23 years, were utilized to identify key steps in tissue selection, RNA isolation, and library choice. Gene expression fidelity was evaluated by comparing FFPE data to public data obtained from fresh tissues, and by employing single-gene, gene set and transcription network-based regulon analyses. RESULTS: We report a single 10 µm section of breast tissue yields sufficient RNA for RNA-seq, and a relationship between RNA quality and block age that was not linear. We find single-gene analysis is limiting with FFPE tissues, while targeted gene set approaches effectively distinguish ER+ from ER- breast cancers. Novel utilization of regulon analysis identified the transcription factor KDM4B to associate with ER+ disease, with KDM4B regulon activity and gene expression having prognostic significance in an independent cohort of ER+ cases. CONCLUSION: Our results, which outline a robust FFPE-RNA-seq pipeline for broad use, support utilizing FFPE tissues to address key questions in the breast cancer field, including the delineation between indolent and life-threatening disease, biological stratification and molecular mechanisms of treatment resistance.

BET bromodomain inhibition blocks the function of a critical AR-independent master regulator network in lethal prostate cancer.

BET bromodomain inhibitors block prostate cancer cell growth at least in part through c-Myc and androgen receptor (AR) suppression. However, little is known about other transcriptional regulators whose suppression contributes to BET bromodomain inhibitor anti-tumor activity. Moreover, the anti-tumor activity of BET bromodomain inhibition in AR-independent castration-resistant prostate cancers (CRPC), whose frequency is increasing, is also unknown. Herein, we demonstrate that BET bromodomain inhibition blocks growth of a diverse set of CRPC cell models, including those that are AR-independent or in which c-Myc is not suppressed. To identify transcriptional regulators whose suppression accounts for these effects, we treated multiple CRPC cell lines with the BET bromodomain inhibitor JQ1 and then performed RNA-sequencing followed by Master Regulator computational analysis. This approach identified several previously unappreciated transcriptional regulators that are highly expressed in CRPC and whose suppression, via both transcriptional or post-translational mechanisms, contributes to the anti-tumor activity of BET bromodomain inhibitors.

Genomic Drivers of Poor Prognosis and Enzalutamide Resistance in Metastatic Castration-resistant Prostate Cancer.

BACKGROUND: Metastatic castration-resistant prostate cancer (mCRPC) is the lethal form of the disease. Several recent studies have identified genomic alterations in mCRPC, but the clinical implications of these genomic alterations have not been fully elucidated. OBJECTIVE: To use whole-genome sequencing (WGS) to assess the association between key driver gene alterations and overall survival (OS), and to use whole-transcriptome RNA sequencing to identify genomic drivers of enzalutamide resistance. DESIGN, SETTING, AND PARTICIPANTS: We performed survival analyses and gene set enrichment analysis (GSEA) on WGS and RNA sequencing results for a cohort of 101 mCRPC patients. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: OS was the clinical endpoint for all univariate and multivariable survival analyses. Candidate drivers of enzalutamide resistance were identified in an unbiased manner, and mutations of the top candidate were further assessed for enrichment among enzalutamide-resistant patients using Fisher's exact test. RESULTS AND LIMITATIONS: Harboring two DNA alterations in RB1 was independently predictive of poor OS (median 14.1 vs 42.0mo; p=0.007) for men with mCRPC. GSEA identified the Wnt/ß-catenin pathway as the top differentially modulated pathway among enzalutamide-resistant patients. Furthermore, ß-catenin mutations were exclusive to enzalutamide-resistant patients (p=0.01) and independently predictive of poor OS (median 13.6 vs 41.7mo; p=0.025). CONCLUSIONS: The presence of two RB1 DNA alterations identified in our WGS analysis was independently associated with poor OS among men with mCRPC. The Wnt/ß-catenin pathway plays an important role in enzalutamide resistance, with differential pathway expression and enrichment of ß-catenin mutations in enzalutamide-resistant patients. Moreover, ß-catenin mutations were predictive of poor OS in our cohort. PATIENT SUMMARY: We observed a correlation between genomic findings for biopsy samples from metastases from men with metastatic castration-resistant prostate cancer (mCRPC) and clinical outcomes. This work sheds new light on clinically relevant genomic alterations in mCRPC and provides a roadmap for the development of new personalized treatment regimens in mCRPC.