Big Data to Knowledge: Application of Machine Learning to Predictive Modeling of Therapeutic Response in Cancer.

Background: In recent years, the availability of high throughput technologies, establishment of large molecular patient data repositories, and advancement in computing power and storage have allowed elucidation of complex mechanisms implicated in therapeutic response in cancer patients. The breadth and depth of such data, alongside experimental noise and missing values, requires a sophisticated human-machine interaction that would allow effective learning from complex data and accurate forecasting of future outcomes, ideally embedded in the core of machine learning design. Objective: In this review, we will discuss machine learning techniques utilized for modeling of treatment response in cancer, including Random Forests, support vector machines, neural networks, and linear and logistic regression. We will overview their mathematical foundations and discuss their limitations and alternative approaches in light of their application to therapeutic response modeling in cancer. Conclusion: We hypothesize that the increase in the number of patient profiles and potential temporal monitoring of patient data will define even more complex techniques, such as deep learning and causal analysis, as central players in therapeutic response modeling.

Azithromycin in the Treatment of Preterm Prelabor Rupture of Membranes Demonstrates a Lower Risk of Chorioamnionitis and Postpartum Endometritis with an Equivalent Latency Period Compared with Erythromycin Antibiotic Regimens.

Objective: To determine if antibiotic regimens including azithromycin versus erythromycin has an impact on pregnancy latency and development of clinical chorioamnionitis in the context of preterm prelabor rupture of membranes. Study Design. We conducted a prospective observational cohort study and followed all women receiving antibiotic regimens including either azithromycin or erythromycin in the context of preterm prelabor rupture of membranes. Primary outcomes were the duration of pregnancy latency period and development of chorioamnionitis. Secondary outcomes included neonatal sepsis with positive blood culture, cesarean delivery, postpartum endometritis, and meconium-stained amniotic fluid. Results: This study included 310 patients, with 142 receiving the azithromycin regimen and 168 receiving the erythromycin regimen. Patients receiving the azithromycin regimen had a statistically significant advantage in overall rates of clinical chorioamnionitis (13.4% versus 25%, p = 0.010), neonatal sepsis (4.9% versus 14.9%, p = 0.004), and postpartum endometritis (14.8% versus 31%, p = 0.001). In crude and adjusted models, when comparing the azithromycin group with the erythromycin group, a decreased risk was noted for the development of clinical chorioamnionitis, neonatal sepsis, and postpartum endometritis. Pregnancy latency by regimen was not significantly different in crude and adjusted models. Conclusion: Our study suggests that latency antibiotic regimens substituting azithromycin for erythromycin have lower rates and decreased risk of clinical chorioamnionitis, neonatal sepsis, and postpartum endometritis with no difference in pregnancy latency.

Targeted RNA-sequencing for the quantification of measurable residual disease in acute myeloid leukemia.

Great effort is spent on developing therapies to improve the dire outcomes of those diagnosed with acute myeloid leukemia. The methods for quantifying response to therapeutic intervention have however lacked sensitivity. Patients achieving a complete remission as defined by conventional cytomorphological methods therefore remain at risk of subsequent relapse due to disease persistence. Improved risk stratification is possible based on tests designed to detect this residual leukemic burden (measurable residual disease). However, acute myeloid leukemia is a genetically diverse set of diseases, which has made it difficult to develop a single, highly reproducible, and sensitive assay for measurable residual disease. Here we present the development of a digital targeted RNA-sequencing-based approach designed to overcome these limitations by detecting all newly approved European LeukemiaNet molecular targets for measurable residual disease in acute myeloid leukemia in a single standardized assay. Iterative modifications and novel bioinformatics approaches resulted in a greater than 100-fold increase in performance compared with commercially available targeted RNA-sequencing approaches and a limit of detection as low as one leukemic cell in 100,000 cells measured, which is comparable to quantitative polymerase chain reaction analysis, the current gold standard for the detection of measurable residual disease. This assay, which can be customized and expanded, is the first demonstrated use of high-sensitivity RNA-sequencing for measurable residual disease detection in acute myeloid leukemia and could serve as a broadly applicable standardized tool.

Physician Review of a Celiac Disease Risk Estimation and Decision-Making Expert System.

Objective: Celiac disease is a genetic disease affecting people of all ages, resulting in small intestine enteropathy. It is considered to be a clinical chameleon. Average prevalence of celiac disease is 1 out of 100 people with data indicating the risk may be as high as 22% for those with first-degree relatives with the disease. Eighty-three percent of people with celiac disease may be undiagnosed. Average duration to diagnosis is 10 years. Data indicate that there is a lack of consensus regarding diagnostics and symptomatology.Method: A clinical decision support system (CDSS) was developed using Exsys Corvid for expert analysis (CD-CDSS). The CD-CDSS was divided into symptoms and manifestations with 80 points of navigation, and a serology section, and was validated by 13 experts in the field of celiac disease using a 10-statement 5-point Likert scale.Results: This scale was analyzed using Cronbach's alpha reliability coefficient, which was calculated using SPSS and revealed good internal consistency and reliability with a result of 0.813. One hundred percent of experts agreed that the CD-CDSS is capable of guiding a health care professional through the diagnostic process, contains an accurate list of symptoms based on the clinical literature, and can foster improved awareness and education about celiac disease and that there is a need for this system.Conclusions: A celiac disease risk estimation and decision-making expert system was successfully developed and evaluated by medical professionals, with 100% agreeing that this CD-CDSS is medically accurate and can guide health care professionals through the diagnostic process.

ETV4 promotes metastasis in response to activation of PI3-kinase and Ras signaling in a mouse model of advanced prostate cancer.

Combinatorial activation of PI3-kinase and RAS signaling occurs frequently in advanced prostate cancer and is associated with adverse patient outcome. We now report that the oncogenic Ets variant 4 (Etv4) promotes prostate cancer metastasis in response to coactivation of PI3-kinase and Ras signaling pathways in a genetically engineered mouse model of highly penetrant, metastatic prostate cancer. Using an inducible Cre driver to simultaneously inactivate Pten while activating oncogenic Kras and a fluorescent reporter allele in the prostate epithelium, we performed lineage tracing in vivo to define the temporal and spatial occurrence of prostate tumors, disseminated tumor cells, and metastases. These analyses revealed that though disseminated tumors cells arise early following the initial occurrence of prostate tumors, there is a significant temporal lag in metastasis, which is temporally coincident with the up-regulation of Etv4 expression in primary tumors. Functional studies showed that knockdown of Etv4 in a metastatic cell line derived from the mouse model abrogates the metastatic phenotype but does not affect tumor growth. Notably, expression and activation of ETV4, but not other oncogenic ETS genes, is correlated with activation of both PI3-kinase and Ras signaling in human prostate tumors and metastases. Our findings indicate that ETV4 promotes metastasis in prostate tumors that have activation of PI3-kinase and Ras signaling, and therefore, ETV4 represents a potential target of therapeutic intervention for metastatic prostate cancer.

Mechanism-centric regulatory network identifies NME2 and MYC programs as markers of Enzalutamide resistance in CRPC.

Heterogeneous response to Enzalutamide, a second-generation androgen receptor signaling inhibitor, is a central problem in castration-resistant prostate cancer (CRPC) management. Genome-wide systems investigation of mechanisms that govern Enzalutamide resistance promise to elucidate markers of heterogeneous treatment response and salvage therapies for CRPC patients. Focusing on the de novo role of MYC as a marker of Enzalutamide resistance, here we reconstruct a CRPC-specific mechanism-centric regulatory network, connecting molecular pathways with their upstream transcriptional regulatory programs. Mining this network with signatures of Enzalutamide response identifies NME2 as an upstream regulatory partner of MYC in CRPC and demonstrates that NME2-MYC increased activities can predict patients at risk of resistance to Enzalutamide, independent of co-variates. Furthermore, our experimental investigations demonstrate that targeting MYC and its partner NME2 is beneficial in Enzalutamide-resistant conditions and could provide an effective strategy for patients at risk of Enzalutamide resistance and/or for patients who failed Enzalutamide treatment.

High intratumoral plasma cells content in primary prostate cancer defines a subset of tumors with potential susceptibility to immune-based treatments.

BACKGROUND: Data on advanced prostate cancer (PCa) suggest more prior systemic therapies might reduce tumor immune responsiveness. In treatment-naïve primary PCa, recent work correlated intratumoral plasma cell content with enhanced tumor immune-responsiveness. We sought to identify features of localized PCa at a high risk of recurrence following local treatment with high plasma cell content to help focus future immune-based neoadjuvant trials. METHODS: We performed retrospective analyses of molecular profiles from three independent cohorts of over 1300 prostate tumors. We used Wilcoxon Rank Sum to compare molecular pathways between tumors with high and low intratumoral plasma cell content and multivariable Cox proportional hazards regression analyses to assess metastasis-free survival. RESULTS: We validated an expression-based signature for intratumoral plasma cell content in 113 primary prostate tumors with both RNA-expression data and digital image quantification of CD138+ cells (plasma cell marker) based on immunohistochemisty. The signature showed castration-resistant tumors (n = 101) with more prior systemic therapies contained lower plasma cell content. In high-grade primary PCa, tumors with high plasma cell content were associated with increased predicted response to immunotherapy and decreased response to androgen-deprivation therapy. Master regulator analyses identified upregulated transcription factors implicated in immune (e.g. SKAP1, IL-16, and HCLS1), and B-cell activity (e.g. VAV1, SP140, and FLI-1) in plasma cell-high tumors. Master regulators overactivated in tumors with low plasma cell content were associated with shorter metastasis-free survival following radical prostatectomy. CONCLUSIONS: Markers of plasma cell activity might be leveraged to augment clinical trial targeting and selection and better understand the potential for immune-based treatments in patients with PCa at a high risk of recurrence following local treatment.

Tissue-specific biological aging predicts progression in prostate cancer and acute myeloid leukemia.

Introduction: Chronological aging is a well-recognized diagnostic and prognostic factor in multiple cancer types, yet the role of biological aging in manifesting cancer progression has not been fully explored yet. Methods: Given the central role of chronological aging in prostate cancer and AML incidence, here we investigate a tissue-specific role of biological aging in prostate cancer and AML progression. We have employed Cox proportional hazards modeling to associate biological aging genes with cancer progression for patients from specific chronological aging groups and for patients with differences in initial cancer aggressiveness. Results: Our prostate cancer-specific investigations nominated four biological aging genes (CD44, GADD45B, STAT3, GFAP) significantly associated with time to disease progression in prostate cancer in Taylor et al. patient cohort. Stratified survival analysis on Taylor dataset and validation on an independent TCGA and DKFZ PRAD patient cohorts demonstrated ability of these genes to predict prostate cancer progression, especially for patients with higher Gleason score and for patients younger than 60 years of age. We have further tested the generalizability of our approach and applied it to acute myeloid leukemia (AML). Our analysis nominated three AML-specific biological aging genes (CDC42EP2, CDC42, ALOX15B) significantly associated with time to AML overall survival, especially for patients with favorable cytogenetic risk score and for patients older than 56 years of age. Discussion: Comparison of the identified PC and AML markers to genes selected at random and to known markers of progression demonstrated robustness of our results and nominated the identified biological aging genes as valuable markers of prostate cancer and AML progression, opening new avenues for personalized therapeutic management and potential novel treatment investigations.

OncoLoop: A Network-Based Precision Cancer Medicine Framework.

Prioritizing treatments for individual patients with cancer remains challenging, and performing coclinical studies using patient-derived models in real time is often unfeasible. To circumvent these challenges, we introduce OncoLoop, a precision medicine framework that predicts drug sensitivity in human tumors and their preexisting high-fidelity (cognate) model(s) by leveraging drug perturbation profiles. As a proof of concept, we applied OncoLoop to prostate cancer using genetically engineered mouse models (GEMM) that recapitulate a broad spectrum of disease states, including castration-resistant, metastatic, and neuroendocrine prostate cancer. Interrogation of human prostate cancer cohorts by Master Regulator (MR) conservation analysis revealed that most patients with advanced prostate cancer were represented by at least one cognate GEMM-derived tumor (GEMM-DT). Drugs predicted to invert MR activity in patients and their cognate GEMM-DTs were successfully validated in allograft, syngeneic, and patient-derived xenograft (PDX) models of tumors and metastasis. Furthermore, OncoLoop-predicted drugs enhanced the efficacy of clinically relevant drugs, namely, the PD-1 inhibitor nivolumab and the AR inhibitor enzalutamide. SIGNIFICANCE: OncoLoop is a transcriptomic-based experimental and computational framework that can support rapid-turnaround coclinical studies to identify and validate drugs for individual patients, which can then be readily adapted to clinical practice. This framework should be applicable in many cancer contexts for which appropriate models and drug perturbation data are available. This article is highlighted in the In This Issue feature, p. 247.

Random survival forest model identifies novel biomarkers of event-free survival in high-risk pediatric acute lymphoblastic leukemia.

High-risk pediatric B-ALL patients experience 5-year negative event rates up to 25%. Although some biomarkers of relapse are utilized in the clinic, their ability to predict outcomes in high-risk patients is limited. Here, we propose a random survival forest (RSF) machine learning model utilizing interpretable genomic inputs to predict relapse/death in high-risk pediatric B-ALL patients. We utilized whole exome sequencing profiles from 156 patients in the TARGET-ALL study (with samples collected at presentation) further stratified into training and test cohorts (109 and 47 patients, respectively). To avoid overfitting and facilitate the interpretation of machine learning results, input genomic variables were engineered using a stepwise approach involving univariable Cox models to select variables directly associated with outcomes, genomic coordinate-based analysis to select mutational hotspots, and correlation analysis to eliminate feature co-linearity. Model training identified 7 genomic regions most predictive of relapse/death-free survival. The test cohort error rate was 12.47%, and a polygenic score based on the sum of the top 7 variables effectively stratified patients into two groups, with significant differences in time to relapse/death (log-rank P = 0.001, hazard ratio = 5.41). Our model outperformed other EFS modeling approaches including an RSF using gold-standard prognostic variables (error rate = 24.35%). Validation in 174 standard-risk patients and 3 patients who failed to respond to induction therapy confirmed that our RSF model and polygenic score were specific to high-risk disease. We propose that our feature selection/engineering approach can increase the clinical interpretability of RSF, and our polygenic score could be utilized for enhance clinical decision-making in high-risk B-ALL.

PACER lncRNA regulates COX-2 expression in lung cancer cells.

Long noncoding RNAs (lncRNAs) are known to regulate gene expression; however, in many cases, the mechanism of this regulation is unknown. One novel lncRNA relevant to inflammation and arachidonic acid (AA) metabolism is the p50-associated COX-2 extragenic RNA (PACER). We focused our research on the regulation of PACER in lung cancer. While the function of PACER is not entirely understood, PACER is known to play a role in inflammation-associated conditions. Our data suggest that PACER is critically involved in COX-2 transcription and dysregulation in lung cancer cells. Our analysis of The Cancer Genome Atlas (TCGA) expression data revealed that PACER expression is significantly higher in lung adenocarcinomas than normal lung tissues. Additionally, we discovered that elevated PACER expression strongly correlates with COX-2 expression in lung adenocarcinoma patients. Specific siRNA-mediated knockdown of PACER decreases COX-2 expression indicating a direct relationship. Additionally, we show that PACER expression is induced upon treatment with proinflammatory cytokines to mimic inflammation. Treatment with prostaglandin E2 (PGE2) induces both PACER and COX-2 expression, suggesting a PGE2-mediated feedback loop. Inhibition of COX-2 with celecoxib decreased PACER expression, confirming this self-regulatory process. Significant overlap between the COX-2 promotor and the PACER promotor led us to investigate their transcriptional regulatory mechanisms. Treatment with pharmacologic inhibitors of NF-κB or AP-1 showed a modest effect on both PACER and COX-2 expression but did not eliminate expression. These data suggest that the regulation of expression of both PACER and COX-2 is complex and intricately linked.

Bone Progenitors Pull the Strings on the Early Metabolic Rewiring Occurring in Prostate Cancer Cells.

Metastatic prostate cancer (PCa) cells soiling in the bone require a metabolic adaptation. Here, we identified the metabolic genes fueling the seeding of PCa in the bone niche. Using a transwell co-culture system of PCa (PC3) and bone progenitor cells (MC3T3 or Raw264.7), we assessed the transcriptome of PC3 cells modulated by soluble factors released from bone precursors. In a Principal Component Analysis using transcriptomic data from human PCa samples (GSE74685), the altered metabolic genes found in vitro were able to stratify PCa patients in two defined groups: primary PCa and bone metastasis, confirmed by an unsupervised clustering analysis. Thus, the early transcriptional metabolic profile triggered in the in vitro model has a clinical correlate in human bone metastatic samples. Further, the expression levels of five metabolic genes (VDR, PPARA, SLC16A1, GPX1 and PAPSS2) were independent risk-predictors of death in the SU2C-PCF dataset and a risk score model built using this lipid-associated signature was able to discriminate a subgroup of bone metastatic PCa patients with a 23-fold higher risk of death. This signature was validated in a PDX pre-clinical model when comparing MDA-PCa-183 growing intrafemorally vs. subcutaneously, and appears to be under the regulatory control of the Protein Kinase A (PKA) signaling pathway. Secretome analyses of conditioned media showcased fibronectin and type-1 collagen as critical bone-secreted factors that could regulate tumoral PKA. Overall, we identified a novel lipid gene signature, driving PCa aggressive metastatic disease pointing to PKA as a potential hub to halt progression.

Genomic analysis and long-term outcomes of a phase 1 clinical trial on cytoreductive radical prostatectomy.

Purpose: Approximately 7% of patients with newly diagnosed prostate cancer (PCa) in the US will have have metastatic disease. The dogma that there is no role for surgery in this population has been questioned recently. Here we report long-term outcomes of a phase 1 clinical trial on cytoreductive radical prostatectomy. Materials and methods: This is a multicenter phase 1 trial. The major inclusion criterion was biopsy proven N1M0 or NxM1a/b PCa. Primary end point was the Clavien-Dindo-based major complication rate. Secondary outcomes were biochemical progression and overall survival. RNA-seq correlative study was conducted in nine select cases as a pilot study. Results: Final accrual was 32 patients of which 25 and 7 were cNxM1 and cN1M0, respectively. With the median follow-up of 46 months (interquartile range 31.7 - 52.7 months), 25 out of the 32 patients (75%) were alive at the time of last contact. There were three disparate groups based on the oncologic outcome: favorable, intermediate, and poor. In seven men with favorable response, androgen deprivation therapy was switched to intermittent approach and five remain free of any evidence of disease after more than two years off all systemic therapy with the normalization of serum testosterone. Of these five patients, three had M1 disease. Long-term use of one pad or less per day was 80%. RNA-seq analysis revealed an enriched downregulation of tumor necrosis factor (TNF)-α signature in the favorable group. Conclusion: Overall long-term oncologic outcome of cytoreductive radical prostatectomy was significantly higher than historical results. Importantly, the combination of surgery with systemic therapy may result in a long durable response in a minority of men who present with metastatic PCa.

Mechanism-Centric Approaches for Biomarker Detection and Precision Therapeutics in Cancer.

Biomarker discovery is at the heart of personalized treatment planning and cancer precision therapeutics, encompassing disease classification and prognosis, prediction of treatment response, and therapeutic targeting. However, many biomarkers represent passenger rather than driver alterations, limiting their utilization as functional units for therapeutic targeting. We suggest that identification of driver biomarkers through mechanism-centric approaches, which take into account upstream and downstream regulatory mechanisms, is fundamental to the discovery of functionally meaningful markers. Here, we examine computational approaches that identify mechanism-centric biomarkers elucidated from gene co-expression networks, regulatory networks (e.g., transcriptional regulation), protein-protein interaction (PPI) networks, and molecular pathways. We discuss their objectives, advantages over gene-centric approaches, and known limitations. Future directions highlight the importance of input and model interpretability, method and data integration, and the role of recently introduced technological advantages, such as single-cell sequencing, which are central for effective biomarker discovery and time-cautious precision therapeutics.

The expression of YWHAZ and NDRG1 predicts aggressive outcome in human prostate cancer.

Some prostate cancers (PCas) are histo-pathologically grouped within the same Gleason Grade (GG), but can differ significantly in outcome. Herein, we aimed at identifying molecular biomarkers that could improve risk prediction in PCa. LC ESI-MS/MS was performed on human PCa and benign prostatic hyperplasia (BPH) tissues and peptide data was integrated with omic analyses. We identified high YWHAZ and NDRG1 expression to be associated with poor PCa prognosis considering all Gleason scores (GS). YWHAZ and NDRG1 defined two subpopulations of PCa patients with high and intermediate risk of death. Multivariable analyses confirmed their independence from GS. ROC analysis unveiled that YWHAZ outperformed GS beyond 60 months post-diagnosis. The genomic analysis of PCa patients with YWHAZ amplification, or increased mRNA or protein levels, revealed significant alterations in key DNA repair genes. We hereby state the relevance of YWHAZ in PCa, showcasing its role as an independent strong predictor of aggressiveness.

NKX3.1 Localization to Mitochondria Suppresses Prostate Cancer Initiation.

Mitochondria provide the first line of defense against the tumor-promoting effects of oxidative stress. Here we show that the prostate-specific homeoprotein NKX3.1 suppresses prostate cancer initiation by protecting mitochondria from oxidative stress. Integrating analyses of genetically engineered mouse models, human prostate cancer cells, and human prostate cancer organotypic cultures, we find that, in response to oxidative stress, NKX3.1 is imported to mitochondria via the chaperone protein HSPA9, where it regulates transcription of mitochondrial-encoded electron transport chain (ETC) genes, thereby restoring oxidative phosphorylation and preventing cancer initiation. Germline polymorphisms of NKX3.1 associated with increased cancer risk fail to protect from oxidative stress or suppress tumorigenicity. Low expression levels of NKX3.1 combined with low expression of mitochondrial ETC genes are associated with adverse clinical outcome, whereas high levels of mitochondrial NKX3.1 protein are associated with favorable outcome. This work reveals an extranuclear role for NKX3.1 in suppression of prostate cancer by protecting mitochondrial function. SIGNIFICANCE: Our findings uncover a nonnuclear function for NKX3.1 that is a key mechanism for suppression of prostate cancer. Analyses of the expression levels and subcellular localization of NKX3.1 in patients at risk of cancer progression may improve risk assessment in a precision prevention paradigm, particularly for men undergoing active surveillance.See related commentary by Finch and Baena, p. 2132.This article is highlighted in the In This Issue feature, p. 2113.

EZH2 inhibition activates a dsRNA-STING-interferon stress axis that potentiates response to PD-1 checkpoint blockade in prostate cancer.

Prostate cancers are considered to be immunologically 'cold' tumors given the very few patients who respond to checkpoint inhibitor (CPI) therapy. Recently, enrichment of interferon-stimulated genes (ISGs) predicted a favorable response to CPI across various disease sites. The enhancer of zeste homolog-2 (EZH2) is overexpressed in prostate cancer and known to negatively regulate ISGs. In the present study, we demonstrate that EZH2 inhibition in prostate cancer models activates a double-stranded RNA-STING-ISG stress response upregulating genes involved in antigen presentation, Th1 chemokine signaling and interferon response, including programmed cell death protein 1 (PD-L1) that is dependent on STING activation. EZH2 inhibition substantially increased intratumoral trafficking of activated CD8+ T cells and increased M1 tumor-associated macrophages, overall reversing resistance to PD-1 CPI. Our study identifies EZH2 as a potent inhibitor of antitumor immunity and responsiveness to CPI. These data suggest EZH2 inhibition as a therapeutic direction to enhance prostate cancer response to PD-1 CPI.

Clarithromycin use for adjunct surgical prophylaxis before non-elective cesarean deliveries to adapt to azithromycin shortages in COVID-19 pandemic.

OBJECTIVE: This study aimed to evaluate safety and effectiveness of clarithromycin as adjunctive antibiotic prophylaxis for patients undergoing non-elective cesarean delivery in comparison with no macrolides, to adapt to azithromycin shortages in COVID-19 pandemic. STUDY DESIGN: We conducted a multi-center, prospective observational cohort study from March 23, 2020 through June 1, 2020. We followed all women receiving either clarithromycin or no macrolide antibiotic for adjunct surgical prophylaxis for non-elective cesarean deliveries. The primary outcome was development of postpartum endometritis. Secondary outcomes included meconium-stained amniotic fluid at time of cesarean delivery, neonatal sepsis, neonatal intensive care unit admission, and neonatal acute respiratory distress syndrome. All patients in this study were tested for SARS-CoV-2 infection and resulted negative. RESULTS: This study included 240 patients, with 133 patients receiving clarithromycin and 107 patients receiving no adjunct macrolide prophylaxis. Patients receiving clarithromycin were noted to have significantly lower rates of postpartum endometritis as compared to those who did not receive adjunct prophylaxis (4.5% versus 11.2%, p = 0.025). In crude (unadjusted) analysis, a significantly lower risk of developing endometritis was noted in the clarithromycin group as compared to the control group (66% decreased risk, 95% CI 0.12 to 0.95, p = 0.040). When adjusted for perceived confounders, a significant difference was again noted (67% decreased risk, 95% CI 0.11 to 0.97, p = 0.034). Stratified analysis of significantly different demographic factors including Black race, BMI, and age was performed. A significantly decreased risk of development of endometritis when taking clarithromycin versus no adjunct macrolide was noted for Black race women in crude and adjusted models (crude: 87% decreased risk, 95% CI 0.08 to 0.83, p = 0.032; adjusted: 91% decreased risk, 95% CI 0.06 to 0.79, p = 0.026). This was also noted for women aged 18-29 years in crude and adjusted models (crude: model, 79% decreased risk, 95% CI 0.06 to 0.80, p = 0.014; adjusted model: 75% decreased risk, 95% CI 0.06 to 0.94, p = 0.028). All other stratified analyses did not yield significant differences in endometritis risk. CONCLUSION: Our study suggests that administration of clarithromycin for adjunctive surgical prophylaxis for non-elective cesarean deliveries may be a safe option that may provide suitable endometritis prophylaxis in cases where azithromycin is unavailable, as was the case during the start of COVID-19 pandemic, most especially for Black race women and women ages 18-29 years.

Bipotent Progenitors Do Not Require Androgen Receptor for Luminal Specification during Prostate Organogenesis.

Androgen receptor (AR) plays a fundamental role in most aspects of adult prostate homeostasis, and anti-androgen therapy represents the cornerstone of prostate cancer treatment. However, early prostate organogenesis takes place during pre-pubertal stages when androgen levels are low, raising the possibility that AR function is more limited during prostate development. Here, we use inducible AR deletion and lineage tracing in genetically engineered mice to show that basal and luminal epithelial progenitors do not require cell-autonomous AR activity during prostate development. We also demonstrate the existence of a transient bipotent luminal progenitor that can generate luminal and basal progeny, yet is also independent of AR function. Furthermore, molecular analyses of AR-deleted luminal cells isolated from developing prostates indicate their similarity to wild-type cells. Our findings suggest that low androgen levels correlate with luminal plasticity in prostate development and may have implications for understanding how AR inhibition promotes lineage plasticity in prostate cancer.

Genome-wide analysis of therapeutic response uncovers molecular pathways governing tamoxifen resistance in ER+ breast cancer.

BACKGROUND: Prioritization of breast cancer patients based on the risk of resistance to tamoxifen plays a significant role in personalized therapeutic planning and improving disease course and outcomes. METHODS: In this work, we demonstrate that a genome-wide pathway-centric computational framework elucidates molecular pathways as markers of tamoxifen resistance in ER+ breast cancer patients. In particular, we associated activity levels of molecular pathways with a wide spectrum of response to tamoxifen, which defined markers of tamoxifen resistance in patients with ER+ breast cancer. FINDINGS: We identified five biological pathways as markers of tamoxifen failure and demonstrated their ability to predict the risk of tamoxifen resistance in two independent patient cohorts (Test cohort1: log-rank p-value = 0.02, adjusted HR = 3.11; Test cohort2: log-rank p-value = 0.01, adjusted HR = 4.24). We have shown that these pathways are not markers of aggressiveness and outperform known markers of tamoxifen response. Furthermore, for adoption into clinic, we derived a list of pathway read-out genes and their associated scoring system, which assigns a risk of tamoxifen resistance for new incoming patients. INTERPRETATION: We propose that the identified pathways and their read-out genes can be utilized to prioritize patients who would benefit from tamoxifen treatment and patients at risk of tamoxifen resistance that should be offered alternative regimens. FUNDING: This work was supported by the Rutgers SHP Dean's research grant, Rutgers start-up funds, Libyan Ministry of Higher Education and Scientific Research, and Katrina Kehlet Graduate Award from The NJ Chapter of the Healthcare Information Management Systems Society.