ProstaMine: a bioinformatics tool for identifying subtype-specific co-alterations associated with aggressiveness in prostate cancer.

Background: Prostate cancer is a leading cause of cancer-related deaths among men, marked by heterogeneous clinical and molecular characteristics. The complexity of the molecular landscape necessitates tools for identifying multi-gene co-alteration patterns that are associated with aggressive disease. The identification of such gene sets will allow for deeper characterization of the processes underlying prostate cancer progression and potentially lead to novel strategies for treatment. Methods: We developed ProstaMine to systematically identify co-alterations associated with aggressiveness in prostate cancer molecular subtypes defined by high-fidelity alterations in primary prostate cancer. ProstaMine integrates genomic, transcriptomic, and clinical data from five primary and one metastatic prostate cancer cohorts to prioritize co-alterations enriched in metastatic disease and associated with disease progression. Results: Integrated analysis of primary tumors defined a set of 17 prostate cancer alterations associated with aggressive characteristics. We applied ProstaMine to NKX3-1-loss and RB1-loss tumors and identified subtype-specific co-alterations associated with metastasis and biochemical relapse in these molecular subtypes. In NKX3-1-loss prostate cancer, ProstaMine identified novel subtype-specific co-alterations known to regulate prostate cancer signaling pathways including MAPK, NF-kB, p53, PI3K, and Sonic hedgehog. In RB1-loss prostate cancer, ProstaMine identified novel subtype-specific co-alterations involved in p53, STAT6, and MHC class I antigen presentation. Co-alterations impacting autophagy were noted in both molecular subtypes. Conclusion: ProstaMine is a method to systematically identify novel subtype-specific co-alterations associated with aggressive characteristics in prostate cancer. The results from ProstaMine provide insights into potential subtype-specific mechanisms of prostate cancer progression which can be formed into testable experimental hypotheses. ProstaMine is publicly available at: https://bioinformatics.cuanschutz.edu/prostamine.

NPEPPS Is a Druggable Driver of Platinum Resistance.

There is an unmet need to improve the efficacy of platinum-based cancer chemotherapy, which is used in primary and metastatic settings in many cancer types. In bladder cancer, platinum-based chemotherapy leads to better outcomes in a subset of patients when used in the neoadjuvant setting or in combination with immunotherapy for advanced disease. Despite such promising results, extending the benefits of platinum drugs to a greater number of patients is highly desirable. Using the multiomic assessment of cisplatin-responsive and -resistant human bladder cancer cell lines and whole-genome CRISPR screens, we identified puromycin-sensitive aminopeptidase (NPEPPS) as a driver of cisplatin resistance. NPEPPS depletion sensitized resistant bladder cancer cells to cisplatin in vitro and in vivo. Conversely, overexpression of NPEPPS in sensitive cells increased cisplatin resistance. NPEPPS affected treatment response by regulating intracellular cisplatin concentrations. Patient-derived organoids (PDO) generated from bladder cancer samples before and after cisplatin-based treatment, and from patients who did not receive cisplatin, were evaluated for sensitivity to cisplatin, which was concordant with clinical response. In the PDOs, depletion or pharmacologic inhibition of NPEPPS increased cisplatin sensitivity, while NPEPPS overexpression conferred resistance. Our data present NPEPPS as a druggable driver of cisplatin resistance by regulating intracellular cisplatin concentrations. SIGNIFICANCE: Targeting NPEPPS, which induces cisplatin resistance by controlling intracellular drug concentrations, is a potential strategy to improve patient responses to platinum-based therapies and lower treatment-associated toxicities.

Down syndrome is associated with altered frequency and functioning of tracheal multiciliated cells, and response to influenza virus infection.

Individuals with Down syndrome (DS) clinically manifest severe respiratory illnesses; however, there is a paucity of data on how DS influences homeostatic physiology of lung airway, and its reactive responses to pulmonary pathogens. We generated well-differentiated ciliated airway epithelia using tracheas from wild-type and Dp(16)1/Yey mice in vitro, and discovered that Dp(16)1/Yey epithelia have significantly lower abundance of ciliated cells, an altered ciliary beating profile, and reduced mucociliary transport. Interestingly, both sets of differentiated epithelia released similar quantities of viral particles after infection with influenza A virus (IAV). However, RNA-sequencing and proteomic analyses revealed an immune hyperreactive phenotype particularly for monocyte-recruiting chemokines in Dp(16)1/Yey epithelia. Importantly, when we challenged mice in vivo with IAV, we observed immune hyper-responsiveness in Dp(16)1/Yey mice, evidenced by higher quantities of lung airway infiltrated monocytes, and elevated levels of pro-inflammatory cytokines in bronchoalveolar lavage fluid. Our findings illuminate mechanisms underlying DS-mediated pathophysiological changes in airway epithelium.

A harmonized resource of integrated prostate cancer clinical, -omic, and signature features.

Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better characterize the molecular features associated with clinical outcomes and to test hypotheses across multiple, independent patient cohorts. In addition, derived features, such as estimates of cell composition, risk scores, and androgen receptor (AR) scores, can be used to develop novel hypotheses leveraging existing multi-omic datasets. The full potential of such data is yet to be realized as independent datasets exist in different repositories, have been processed using different pipelines, and derived and clinical features are often not provided or  not standardized. Here, we present the curatedPCaData R package, a harmonized data resource representing >2900 primary tumor, >200 normal tissue, and >500 metastatic PCa samples across 19 datasets processed using standardized pipelines with updated gene annotations. We show that meta-analysis across harmonized studies has great potential for robust and clinically meaningful insights. curatedPCaData is an open and accessible community resource with code made available for reproducibility.

SIX1 and EWS/FLI1 co-regulate an anti-metastatic gene network in Ewing Sarcoma.

Ewing sarcoma (ES), which is characterized by the presence of oncogenic fusion proteins such as EWS/FLI1, is an aggressive pediatric malignancy with a high rate of early dissemination and poor outcome after distant spread. Here we demonstrate that the SIX1 homeoprotein, which enhances metastasis in most tumor types, suppresses ES metastasis by co-regulating EWS/FLI1 target genes. Like EWS/FLI1, SIX1 promotes cell growth/transformation, yet dramatically inhibits migration and invasion, as well as metastasis in vivo. We show that EWS/FLI1 promotes SIX1 protein expression, and that the two proteins share genome-wide binding profiles and transcriptional regulatory targets, including many metastasis-associated genes such as integrins, which they co-regulate. We further show that SIX1 downregulation of integrins is critical to its ability to inhibit invasion, a key characteristic of metastatic cells. These data demonstrate an unexpected anti-metastatic function for SIX1, through coordinate gene regulation with the key oncoprotein in ES, EWS/FLI1.

Non-muscle-invasive bladder cancer molecular subtypes predict differential response to intravesical Bacillus Calmette-Guérin.

The recommended treatment for patients with high-risk non-muscle-invasive bladder cancer (HR-NMIBC) is tumor resection followed by adjuvant Bacillus Calmette-Guérin (BCG) bladder instillations. However, only 50% of patients benefit from this therapy. If progression to advanced disease occurs, then patients must undergo a radical cystectomy with risks of substantial morbidity and poor clinical outcome. Identifying tumors unlikely to respond to BCG can translate into alternative treatments, such as early radical cystectomy, targeted therapies, or immunotherapies. Here, we conducted molecular profiling of 132 patients with BCG-naive HR-NMIBC and 44 patients with recurrences after BCG (34 matched), which uncovered three distinct BCG response subtypes (BRS1, 2 and BRS3). Patients with BRS3 tumors had a reduced recurrence-free and progression-free survival compared with BRS1/2. BRS3 tumors expressed high epithelial-to-mesenchymal transition and basal markers and had an immunosuppressive profile, which was confirmed with spatial proteomics. Tumors that recurred after BCG were enriched for BRS3. BRS stratification was validated in a second cohort of 151 BCG-naive patients with HR-NMIBC, and the molecular subtypes outperformed guideline-recommended risk stratification based on clinicopathological variables. For clinical application, we confirmed that a commercially approved assay was able to predict BRS3 tumors with an area under the curve of 0.87. These BCG response subtypes will allow for improved identification of patients with HR-NMIBC at the highest risk of progression and have the potential to be used to select more appropriate treatments for patients unlikely to respond to BCG.

Microbiome Preterm Birth DREAM Challenge: Crowdsourcing Machine Learning Approaches to Advance Preterm Birth Research.

Globally, every year about 11% of infants are born preterm, defined as a birth prior to 37 weeks of gestation, with significant and lingering health consequences. Multiple studies have related the vaginal microbiome to preterm birth. We present a crowdsourcing approach to predict: (a) preterm or (b) early preterm birth from 9 publicly available vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from raw sequences via an open-source tool, MaLiAmPi. We validated the crowdsourced models on novel datasets representing 331 samples from 148 pregnant individuals. From 318 DREAM challenge participants we received 148 and 121 submissions for our two separate prediction sub-challenges with top-ranking submissions achieving bootstrapped AUROC scores of 0.69 and 0.87, respectively. Alpha diversity, VALENCIA community state types, and composition (via phylotype relative abundance) were important features in the top performing models, most of which were tree based methods. This work serves as the foundation for subsequent efforts to translate predictive tests into clinical practice, and to better understand and prevent preterm birth.

Microparticle-Delivered Cxcl9 Prolongs Braf Inhibitor Efficacy in Melanoma.

Patients with BRAF-mutant melanoma show substantial responses to combined BRAF and MEK inhibition, but most relapse within 2 years. A major reservoir for drug resistance is minimal residual disease (MRD), comprised of drug-tolerant tumor cells laying in a dormant state. Towards exploiting potential therapeutic vulnerabilities of MRD, we established a genetically engineered mouse model of BrafV600E-driven melanoma MRD wherein genetic BrafV600E extinction leads to strong but incomplete tumor regression. Transcriptional time-course analysis after BrafV600E extinction revealed that after an initial surge of immune activation, tumors later became immunologically "cold" after MRD establishment. Computational analysis identified candidate T-cell recruiting chemokines as strongly upregulated initially and steeply decreasing as the immune response faded. Therefore, we hypothesized that sustaining chemokine signaling could impair MRD maintenance through increased recruitment of effector T cells. We found that intratumoral administration of recombinant Cxcl9 (rCxcl9), either naked or loaded in microparticles, significantly impaired MRD relapse in BRAF-inhibited tumors, including several complete pathologic responses after microparticle-delivered rCxcl9 combined with BRAF and MEK inhibition. Our experiments constitute proof of concept that chemokine-based microparticle delivery systems are a potential strategy to forestall tumor relapse and thus improve the clinical success of first-line treatment methods.

curatedPCaData: Integration of clinical, genomic, and signature features in a curated and harmonized prostate cancer data resource.

Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better characterize the molecular features associated with clinical outcomes and to test hypotheses across multiple, independent patient cohorts. In addition, derived features, such as estimates of cell composition, risk scores, and androgen receptor (AR) scores, can be used to develop novel hypotheses leveraging existing multi-omic datasets. The full potential of such data is yet to be realized as independent datasets exist in different repositories, have been processed using different pipelines, and derived and clinical features are often not provided or unstandardized. Here, we present the curatedPCaData R package, a harmonized data resource representing >2900 primary tumor, >200 normal tissue, and >500 metastatic PCa samples across 19 datasets processed using standardized pipelines with updated gene annotations. We show that meta-analysis across harmonized studies has great potential for robust and clinically meaningful insights. curatedPCaData is an open and accessible community resource with code made available for reproducibility.

SOPHIE: Generative Neural Networks Separate Common and Specific Transcriptional Responses.

Genome-wide transcriptome profiling identifies genes that are prone to differential expression (DE) across contexts, as well as genes with changes specific to the experimental manipulation. Distinguishing genes that are specifically changed in a context of interest from common differentially expressed genes (DEGs) allows more efficient prediction of which genes are specific to a given biological process under scrutiny. Currently, common DEGs or pathways can only be identified through the laborious manual curation of experiments, an inordinately time-consuming endeavor. Here we pioneer an approach, Specific cOntext Pattern Highlighting In Expression data (SOPHIE), for distinguishing between common and specific transcriptional patterns using a generative neural network to create a background set of experiments from which a null distribution of gene and pathway changes can be generated. We apply SOPHIE to diverse datasets including those from human, human cancer, and bacterial pathogen Pseudomonas aeruginosa. SOPHIE identifies common DEGs in concordance with previously described, manually and systematically determined common DEGs. Further molecular validation indicates that SOPHIE detects highly specific but low-magnitude biologically relevant transcriptional changes. SOPHIE's measure of specificity can complement log2 fold change values generated from traditional DE analyses. For example, by filtering the set of DEGs, one can identify genes that are specifically relevant to the experimental condition of interest. Consequently, these results can inform future research directions. All scripts used in these analyses are available at https://github.com/greenelab/generic-expression-patterns. Users can access https://github.com/greenelab/sophie to run SOPHIE on their own data.

Antigens Expressed by Breast Cancer Cells Undergoing EMT Stimulate Cytotoxic CD8+ T Cell Immunity.

Antigenic differences formed by alterations in gene expression and alternative splicing are predicted in breast cancer cells undergoing epithelial to mesenchymal transition (EMT) and the reverse plasticity known as MET. How these antigenic differences impact immune interactions and the degree to which they can be exploited to enhance immune responses against mesenchymal cells is not fully understood. We utilized a master microRNA regulator of EMT to alter mesenchymal-like EO771 mammary carcinoma cells to a more epithelial phenotype. A computational approach was used to identify neoantigens derived from the resultant differentially expressed somatic variants (SNV) and alternative splicing events (neojunctions). Using whole cell vaccines and peptide-based vaccines, we find superior cytotoxicity against the more-epithelial cells and explore the potential of neojunction-derived antigens to elicit T cell responses through experiments designed to validate the computationally predicted neoantigens. Overall, results identify EMT-associated splicing factors common to both mouse and human breast cancer cells as well as immunogenic SNV- and neojunction-derived neoantigens in mammary carcinoma cells.

High-Content Drug Discovery Targeting Molecular Bladder Cancer Subtypes.

Molecular subtypes of muscle-invasive bladder cancer (MIBC) display differential survival and drug sensitivities in clinical trials. To date, they have not been used as a paradigm for phenotypic drug discovery. This study aimed to discover novel subtype-stratified therapy approaches based on high-content screening (HCS) drug discovery. Transcriptome expression data of CCLE and BLA-40 cell lines were used for molecular subtype assignment in basal, luminal, and mesenchymal-like cell lines. Two independent HCSs, using focused compound libraries, were conducted to identify subtype-specific drug leads. We correlated lead drug sensitivity data with functional genomics, regulon analysis, and in-vitro drug response-based enrichment analysis. The basal MIBC subtype displayed sensitivity to HDAC and CHK inhibitors, while the luminal subtype was sensitive to MDM2 inhibitors. The mesenchymal-like cell lines were exclusively sensitive to the ITGAV inhibitor SB273005. The role of integrins within this mesenchymal-like MIBC subtype was confirmed via its regulon activity and gene essentiality based on CRISPR-Cas9 knock-out data. Patients with high ITGAV expression showed a significant decrease in the median overall survival. Phenotypic high-content drug screens based on bladder cancer cell lines provide rationales for novel stratified therapeutic approaches as a framework for further prospective validation in clinical trials.

A Crowdsourcing Approach to Develop Machine Learning Models to Quantify Radiographic Joint Damage in Rheumatoid Arthritis.

Importance: An automated, accurate method is needed for unbiased assessment quantifying accrual of joint space narrowing and erosions on radiographic images of the hands and wrists, and feet for clinical trials, monitoring of joint damage over time, assisting rheumatologists with treatment decisions. Such a method has the potential to be directly integrated into electronic health records. Objectives: To design and implement an international crowdsourcing competition to catalyze the development of machine learning methods to quantify radiographic damage in rheumatoid arthritis (RA). Design, Setting, and Participants: This diagnostic/prognostic study describes the Rheumatoid Arthritis 2-Dialogue for Reverse Engineering Assessment and Methods (RA2-DREAM Challenge), which used existing radiographic images and expert-curated Sharp-van der Heijde (SvH) scores from 2 clinical studies (674 radiographic sets from 562 patients) for training (367 sets), leaderboard (119 sets), and final evaluation (188 sets). Challenge participants were tasked with developing methods to automatically quantify overall damage (subchallenge 1), joint space narrowing (subchallenge 2), and erosions (subchallenge 3). The challenge was finished on June 30, 2020. Main Outcomes and Measures: Scores derived from submitted algorithms were compared with the expert-curated SvH scores, and a baseline model was created for benchmark comparison. Performances were ranked using weighted root mean square error (RMSE). The performance and reproductivity of each algorithm was assessed using Bayes factor from bootstrapped data, and further evaluated with a postchallenge independent validation data set. Results: The RA2-DREAM Challenge received a total of 173 submissions from 26 participants or teams in 7 countries for the leaderboard round, and 13 submissions were included in the final evaluation. The weighted RMSEs metric showed that the winning algorithms produced scores that were very close to the expert-curated SvH scores. Top teams included Team Shirin for subchallenge 1 (weighted RMSE, 0.44), HYL-YFG (Hongyang Li and Yuanfang Guan) subchallenge 2 (weighted RMSE, 0.38), and Gold Therapy for subchallenge 3 (weighted RMSE, 0.43). Bootstrapping/Bayes factor approach and the postchallenge independent validation confirmed the reproducibility and the estimation concordance indices between final evaluation and postchallenge independent validation data set were 0.71 for subchallenge 1, 0.78 for subchallenge 2, and 0.82 for subchallenge 3. Conclusions and Relevance: The RA2-DREAM Challenge resulted in the development of algorithms that provide feasible, quick, and accurate methods to quantify joint damage in RA. Ultimately, these methods could help research studies on RA joint damage and may be integrated into electronic health records to help clinicians serve patients better by providing timely, reliable, and quantitative information for making treatment decisions to prevent further damage.

SIX1 reprograms myogenic transcription factors to maintain the rhabdomyosarcoma undifferentiated state.

Rhabdomyosarcoma (RMS) is a pediatric muscle sarcoma characterized by expression of the myogenic lineage transcription factors (TFs) MYOD1 and MYOG. Despite high expression of these TFs, RMS cells fail to terminally differentiate, suggesting the presence of factors that alter their functions. Here, we demonstrate that the developmental TF SIX1 is highly expressed in RMS and critical for maintaining a muscle progenitor-like state. SIX1 loss induces differentiation of RMS cells into myotube-like cells and impedes tumor growth in vivo. We show that SIX1 maintains the RMS undifferentiated state by controlling enhancer activity and MYOD1 occupancy at loci more permissive to tumor growth over muscle differentiation. Finally, we demonstrate that a gene signature derived from SIX1 loss correlates with differentiation status and predicts RMS progression in human disease. Our findings demonstrate a master regulatory role of SIX1 in repression of RMS differentiation via genome-wide alterations in MYOD1 and MYOG-mediated transcription.

PRDM paralogs antagonistically balance Wnt/ß-catenin activity during craniofacial chondrocyte differentiation.

Cranial neural crest cell (NCC)-derived chondrocyte precursors undergo a dynamic differentiation and maturation process to establish a scaffold for subsequent bone formation, alterations in which contribute to congenital birth defects. Here, we demonstrate that transcription factor and histone methyltransferase proteins Prdm3 and Prdm16 control the differentiation switch of cranial NCCs to craniofacial cartilage. Loss of either paralog results in hypoplastic and disorganized chondrocytes due to impaired cellular orientation and polarity. We show that these proteins regulate cartilage differentiation by controlling the timing of Wnt/ß-catenin activity in strikingly different ways: Prdm3 represses whereas Prdm16 activates global gene expression, although both act by regulating Wnt enhanceosome activity and chromatin accessibility. Finally, we show that manipulating Wnt/ß-catenin signaling pharmacologically or generating prdm3-/-;prdm16-/- double mutants rescues craniofacial cartilage defects. Our findings reveal upstream regulatory roles for Prdm3 and Prdm16 in cranial NCCs to control Wnt/ß-catenin transcriptional activity during chondrocyte differentiation to ensure proper development of the craniofacial skeleton.

IL13Rα2 Promotes Proliferation and Outgrowth of Breast Cancer Brain Metastases.

PURPOSE: The survival of women with brain metastases (BM) from breast cancer remains very poor, with over 80% dying within a year of their diagnosis. Here, we define the function of IL13Rα2 in outgrowth of breast cancer brain metastases (BCBM) in vitro and in vivo, and postulate IL13Rα2 as a suitable therapeutic target for BM. EXPERIMENTAL DESIGN: We performed IHC staining of IL13Rα2 in BCBM to define its prognostic value. Using inducible shRNAs in TNBC and HER2+ breast-brain metastatic models, we assessed IL13Rα2 function in vitro and in vivo. We performed RNAseq and functional studies to define the molecular mechanisms underlying IL13Rα2 function in BCBM. RESULTS: High IL13Rα2 expression in BCBM predicted worse survival after BM diagnoses. IL13Rα2 was essential for cancer-cell survival, promoting proliferation while repressing invasion. IL13Rα2 KD resulted in FAK downregulation, repression of cell cycle and proliferation mediators, and upregulation of Ephrin B1 signaling. Ephrin-B1 (i) promoted invasion of BC cells in vitro, (ii) marked micrometastasis and invasive fronts in BCBM, and (iii) predicted shorter disease-free survival and BM-free survival (BMFS) in breast primary tumors known to metastasize to the brain. In experimental metastases models, which bypass early tumor invasion, downregulation of IL13Rα2 before or after tumor seeding and brain intravasation decreased BMs, suggesting that IL13Rα2 and the promotion of a proliferative phenotype is critical to BM progression. CONCLUSIONS: Non-genomic phenotypic adaptations at metastatic sites are critical to BM progression and patients' prognosis. This study opens the road to use IL13Rα2 targeting as a therapeutic strategy for BM.

Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth.

Identification of pregnancies at risk of preterm birth (PTB), the leading cause of newborn deaths, remains challenging given the syndromic nature of the disease. We report a longitudinal multi-omics study coupled with a DREAM challenge to develop predictive models of PTB. The findings indicate that whole-blood gene expression predicts ultrasound-based gestational ages in normal and complicated pregnancies (r = 0.83) and, using data collected before 37 weeks of gestation, also predicts the delivery date in both normal pregnancies (r = 0.86) and those with spontaneous preterm birth (r = 0.75). Based on samples collected before 33 weeks in asymptomatic women, our analysis suggests that expression changes preceding preterm prelabor rupture of the membranes are consistent across time points and cohorts and involve leukocyte-mediated immunity. Models built from plasma proteomic data predict spontaneous preterm delivery with intact membranes with higher accuracy and earlier in pregnancy than transcriptomic models (AUROC = 0.76 versus AUROC = 0.6 at 27-33 weeks of gestation).

Androgen Receptor Signaling in Prostate Cancer Genomic Subtypes.

While many prostate cancer (PCa) cases remain indolent and treatable, others are aggressive and progress to the metastatic stage where there are limited curative therapies. Androgen receptor (AR) signaling remains an important pathway for proliferative and survival programs in PCa, making disruption of AR signaling a viable therapy option. However, most patients develop resistance to AR-targeted therapies or inherently never respond. The field has turned to PCa genomics to aid in stratifying high risk patients, and to better understand the mechanisms driving aggressive PCa and therapy resistance. While alterations to the AR gene itself occur at later stages, genomic changes at the primary stage can affect the AR axis and impact response to AR-directed therapies. Here, we review common genomic alterations in primary PCa and their influence on AR function and activity. Through a meta-analysis of multiple independent primary PCa databases, we also identified subtypes of significantly co-occurring alterations and examined their combinatorial effects on the AR axis. Further, we discussed the subsequent implications for response to AR-targeted therapies and other treatments. We identified multiple primary PCa genomic subtypes, and given their differing effects on AR activity, patient tumor genetics may be an important stratifying factor for AR therapy resistance.

Mediator of DNA Damage Checkpoint 1 (MDC1) Is a Novel Estrogen Receptor Coregulator in Invasive Lobular Carcinoma of the Breast.

Invasive lobular carcinoma (ILC) is the most common special histologic subtype of breast cancer, and nearly all ILC tumors express estrogen receptor alpha (ER). However, clinical and laboratory data suggest ILC are strongly estrogen-driven but not equally antiestrogen-sensitive. We hypothesized ILC-specific ER coregulators mediate ER functions and antiestrogen resistance in ILC, and profiled ER-associated proteins by mass spectrometry. Three ER+ ILC cell lines (MDA MB 134VI, SUM44PE, and BCK4) were compared with ER+ invasive ductal carcinoma (IDC) line data, and we examined whether siRNA of identified proteins suppressed ER-driven proliferation in ILC cells. This identified mediator of DNA damage checkpoint 1 (MDC1), a tumor suppressor in DNA damage response (DDR), as a novel ER coregulator in ILC. We confirmed ER:MDC1 interaction was specific to ILC versus IDC cells, and found MDC1 knockdown suppressed ILC cell proliferation and tamoxifen resistance. Using RNA-sequencing, we found in ILC cells MDC1 knockdown broadly dysregulates the ER transcriptome, with ER:MDC1 target genes enriched for promoter hormone response elements. Importantly, our data are inconsistent with MDC1 tumor suppressor functions in DDR, but suggest a novel oncogenic role for MDC1 as an ER coregulator. Supporting this, in breast tumor tissue microarrays, MDC1 protein was frequently low or absent in IDC, but MDC1 loss was rare in ER+ ILC. ER:MDC1 interaction and MDC1 coregulator functions may underlie ER function in ILC and serve as targets to overcome antiestrogen resistance in ILC. IMPLICATIONS: MDC1 has novel ER coregulator activity in ILC, which may underlie ILC-specific ER functions, estrogen response, and antiestrogen resistance.

MAP3K7 Loss Drives Enhanced Androgen Signaling and Independently Confers Risk of Recurrence in Prostate Cancer with Joint Loss of CHD1.

Prostate cancer genomic subtypes that stratify aggressive disease and inform treatment decisions at the primary stage are currently limited. Previously, we functionally validated an aggressive subtype present in 15% of prostate cancer characterized by dual deletion of MAP3K7 and CHD1. Recent studies in the field have focused on deletion of CHD1 and its role in androgen receptor (AR) chromatin distribution and resistance to AR-targeted therapy; however, CHD1 is rarely lost without codeletion of MAP3K7. Here, we show that in the clinically relevant context of co-loss of MAP3K7 and CHD1 there are significant, collective changes to aspects of AR signaling. Although CHD1 loss mainly impacts the expansion of the AR cistrome, loss of MAP3K7 drives increased AR target gene expression. Prostate cancer cell line models engineered to cosuppress MAP3K7 and CHD1 also demonstrated increased AR-v7 expression and resistance to the AR-targeting drug enzalutamide. Furthermore, we determined that low protein expression of both genes is significantly associated with biochemical recurrence (BCR) in a clinical cohort of radical prostatectomy specimens. Low MAP3K7 expression, however, was the strongest independent predictor for risk of BCR over all other tested clinicopathologic factors including CHD1 expression. Collectively, these findings illustrate the importance of MAP3K7 loss in a molecular subtype of prostate cancer that poses challenges to conventional therapeutic approaches. IMPLICATIONS: These findings strongly implicate MAP3K7 loss as a biomarker for aggressive prostate cancer with significant risk for recurrence that poses challenges for conventional androgen receptor-targeted therapies.