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Recent studies suggest that glucocorticoid receptor (GR) activation can cause enzalutamide resistance in advanced prostate cancer (PCa) via functional bypass of androgen receptor (AR) signaling. However, the specific molecular mechanism(s) driving this process remain unknown. We have previously reported that the transcription factor TBX2 is over-expressed in castrate-resistant prostate cancer (CRPC). In this study, using human PCa and CRPC cell line models, we demonstrate that TBX2 downregulates AR and upregulates GR through direct transcriptional regulation. TBX2 also activated the GR via TBX2-GR protein-protein interactions. Together, TBX2-driven repression of AR and activation of GR resulted in enzalutamide resistance. Our laboratory findings are supported by clinical samples, which show a similar and consistent pattern of transcriptional activity among TBX2, AR and GR across patient cohorts. Notably, we report that SP2509, an allosteric inhibitor of the demethylase-independent function of LSD1 (a TBX2-interacting protein in the COREST complex) disrupts both TBX2-LSD1 and TBX2-GR protein-protein interactions, revealing a unique mode of SP2509 action in CRPC. Taken together, our study identifies the TBX2-driven AR- to GR- signaling switch as a molecular mechanism underlying enzalutamide resistance and provides key insights into a potential therapeutic approach for targeting this switch by disrupting TBX2-GR and TBX2-LSD1 protein-protein interactions., Competing Interests: Competing interests: The authors declare no competing interests. Ethics approval and consent to participate: All methods were performed in accordance with the relevant guidelines and regulations. This study did not use any human subjects, vertebrate animals or identifiable images from human research patients., (© 2024. The Author(s).)

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., (© 2024. The Author(s).)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Ramakrishnan, Datta, Panja, Patel, Liu, Craige, Chu, Jean-Marie, Oladoja, Kim and Mitrofanova.)

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., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

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., (©2022 American Association for Cancer Research.)

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., Competing Interests: All authors have no significant conflict of interest., (© 2022 Asian Pacific Prostate Society. Publishing services by Elsevier B.V.)

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.

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 (PGE 2 ) induces both PACER and COX-2 expression, suggesting a PGE 2 -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., Competing Interests: CONFLICTS OF INTEREST Authors have no conflicts of interest to declare., (Copyright: © 2022 Desind et al.)

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., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors.)

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., (© 2021 Bentham Science Publishers.)

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 ., (©2021 American Association for Cancer Research.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Yu and Mitrofanova.)

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., Competing Interests: Competing interests The authors declare no competing interests.

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.

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., Competing Interests: The authors have declared that no competing interests exist.

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., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Understanding the intricacies of lethal prostate cancer poses specific challenges due to difficulties in accurate modeling of metastasis in vivo. Here we show that NPK EYFP mice (for Nkx3.1 CreERT2/+ ; Pten flox/flox ; Kras LSL-G12D/+ ; R26R-CAG - LSL-EYFP/+ ) develop prostate cancer with a high penetrance of metastasis to bone, thereby enabling detection and tracking of bone metastasis in vivo and ex vivo. Transcriptomic and whole-exome analyses of bone metastasis from these mice revealed distinct molecular profiles conserved between human and mouse and specific patterns of subclonal branching from the primary tumor. Integrating bulk and single-cell transcriptomic data from mouse and human datasets with functional studies in vivo unravels a unique MYC/RAS co-activation signature associated with prostate cancer metastasis. Finally, we identify a gene signature with prognostic value for time to metastasis and predictive of treatment response in human patients undergoing androgen receptor therapy across clinical cohorts, thus uncovering conserved mechanisms of metastasis with potential translational significance.

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., (Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)

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., Competing Interests: My coauthors and I have no potential conflicts of interest, real or perceived, and no honorarium, grant, or form of payment was given to anyone to produce the manuscript., (Copyright © 2020 Daniel Martingano et al.)

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.

Despite recent advances in discovering a wide array of novel chemotherapy agents, identification of patients with poor and favorable chemotherapy response prior to treatment administration remains a major challenge in clinical oncology. To tackle this challenge, we present a generalizable genome-wide computational framework pathCHEMO that uncovers interplay between transcriptomic and epigenomic mechanisms altered in biological pathways that govern chemotherapy response in cancer patients. Our approach is tested on patients with lung adenocarcinoma who received adjuvant standard-of-care doublet chemotherapy (i.e., carboplatin-paclitaxel), identifying seven molecular pathway markers of primary treatment response and demonstrating their ability to predict patients at risk of carboplatin-paclitaxel resistance in an independent patient cohort (log-rank p -value = 0.008, HR = 10). Furthermore, we extend our method to additional chemotherapy-regimens and cancer types to demonstrate its accuracy and generalizability. We propose that our model can be utilized to prioritize patients for specific chemotherapy-regimens as a part of treatment planning., Competing Interests: Competing interestsThe authors declare no competing interests.

Deep sequencing genomic analysis is becoming increasingly common in clinical research and practice, enabling accurate identification of diagnostic, prognostic, and predictive determinants. Variant calling, distinguishing between true mutations and experimental errors, is a central task of genomic analysis and often requires sophisticated statistical, computational, and/or heuristic techniques. Although variant callers seek to overcome noise inherent in biological experiments, variant calling can be significantly affected by outside factors including those used to prepare, store, and analyze samples. The goal of this review is to discuss known experimental features, such as sample preparation, library preparation, and sequencing, alongside diverse biological and clinical variables, and evaluate their effect on variant caller selection and optimization.

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., (Copyright © 2019 Ferrata Storti Foundation.)

Deciphering cell-intrinsic mechanisms of metastasis progression in vivo is essential to identify novel therapeutic approaches. Here we elucidate cell-intrinsic drivers of metastatic prostate cancer progression through analyses of genetically engineered mouse models (GEMM) and correlative studies of human prostate cancer. Expression profiling of lineage-marked cells from mouse primary tumors and metastases defines a signature of de novo metastatic progression. Cross-species master regulator analyses comparing this mouse signature with a comparable human signature identifies conserved drivers of metastatic progression with demonstrable clinical and functional relevance. In particular, nuclear receptor binding SET Domain Protein 2 (NSD2) is robustly expressed in lethal prostate cancer in humans, while its silencing inhibits metastasis of mouse allografts in vivo. We propose that cross-species analysis can elucidate mechanisms of metastasis progression, thus providing potential additional therapeutic opportunities for treatment of lethal prostate cancer.

Although it is known that inflammation plays a critical role in prostate tumorigenesis, the underlying processes are not well understood. Based on analysis of genetically engineered mouse models combined with correlative analysis of expression profiling data from human prostate tumors, we demonstrate a reciprocal relationship between inflammation and the status of the NKX3.1 homeobox gene associated with prostate cancer initiation. We find that cancer initiation in aged Nkx3.1 mutant mice correlates with enrichment of specific immune populations and increased expression of immunoregulatory genes. Furthermore, expression of these immunoregulatory genes is similarly increased in human prostate tumors having low levels of NKX3.1 expression. We further show that induction of prostatitis in Nkx3.1 mutant mice accelerates prostate cancer initiation, which is coincident with aberrant cellular plasticity and differentiation. Correspondingly, human prostate tumors having low levels of NKX3.1 have de-regulated expression of genes associated with these cellular processes. We propose that loss of function of NKX3.1 accelerates inflammation-driven prostate cancer initiation potentially via aberrant cellular plasticity and impairment of cellular differentiation.This article has an associated First Person interview with the first author of the paper., Competing Interests: Competing interestsA.C. is founder, equity holder, consultant and director of DarwinHealth Inc., a company that has licensed some of the algorithms used in this manuscript from Columbia University. Columbia University is also an equity holder in DarwinHealth Inc. C.G.D. has ownership interests in Compugen, Harpoon, Kleo, Potenza and Tizona; has served in a consulting or advisory role for Agenus, Dendreon, Janssen, Lilly, Merck, Medimmune, Pierre Fabre and Roche/Genentech; has received research funding from Aduro Biotech, Bristol Myers Squibb and Janssen; and holds patents, receives royalties or has other intellectual property interests with Bristol Myers Squibb, AstraZeneca Medimmune and Janssen. The other authors declare no competing or financial interests, (© 2018. Published by The Company of Biologists Ltd.)

Therapeutic resistance is a central problem in clinical oncology. We have developed a systematic genome-wide computational methodology to allow prioritization of patients with favorable and poor therapeutic response. Our method, which integrates DNA methylation and mRNA expression data, uncovered a panel of 5 differentially methylated sites, which explain expression changes in their site-harboring genes, and demonstrated their ability to predict primary resistance to androgen-deprivation therapy (ADT) in the TCGA prostate cancer patient cohort (hazard ratio = 4.37). Furthermore, this panel was able to accurately predict response to ADT across independent prostate cancer cohorts and demonstrated that it was not affected by Gleason, age, or therapy subtypes. We propose that this panel could be utilized to prioritize patients who would benefit from ADT and patients at risk of resistance that should be offered an alternative regimen. Such approach holds a long-term objective to build an adaptable accurate platform for precision therapeutics., (Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.)

Master regulatory genes of tissue specification play key roles in stem/progenitor cells and are often important in cancer. In the prostate, androgen receptor (AR) is a master regulator essential for development and tumorigenesis, but its specific functions in prostate stem/progenitor cells have not been elucidated. We have investigated AR function in CARNs (CAstration-Resistant Nkx3.1-expressing cells), a luminal stem/progenitor cell that functions in prostate regeneration. Using genetically--engineered mouse models and novel prostate epithelial cell lines, we find that progenitor properties of CARNs are largely unaffected by AR deletion, apart from decreased proliferation in vivo . Furthermore, AR loss suppresses tumor formation after deletion of the Pten tumor suppressor in CARNs; however, combined Pten deletion and activation of oncogenic Kras in AR-deleted CARNs result in tumors with focal neuroendocrine differentiation. Our findings show that AR modulates specific progenitor properties of CARNs, including their ability to serve as a cell of origin for prostate cancer., Competing Interests: CC, NE, EL, SX, ML, BL, SB, HH, AM, MS No competing interests declared, (© 2018, Chua et al.)

Background: Although men on active surveillance for prostate cancer (PCa) may benefit from intervention with 5α-reductase inhibitors (5-ARIs), it has not been resolved whether 5-ARIs are effective for delaying disease progression and, if so, whether specific patients are more likely to benefit., Objective: To identify molecular features predictive of patient response to 5-ARIs., Design, Setting, and Participants: Nkx3.1 mutant mice, a model of early-stage PCa, were treated with the 5-ARI finasteride, and histopathological and molecular analyses were performed. Cross-species computational analyses were used to compare expression profiles for treated mice with those of patients who had received 5-ARIs before prostatectomy., Intervention: Finasteride administered to Nkx3.1 mutant mice. 5-ARI-treated patient specimens obtained retrospectively., Outcome Measurements and Statistical Analysis: Endpoints in mice included histopathology, immunohistochemistry, and molecular profiling. GraphPad Prism software, R-studio, and Matlab were used for statistical and data analyses., Results and Limitations: Finasteride treatment of Nkx3.1 mutant mice resulted in a significant reduction in prostatic intraepithelial neoplasia (PIN), as evident from histopathological and expression profiling analyses. Cross-species computational analysis comparing finasteride-treated mice with two independent 5-ARI-treated patient cohorts showed that reduced NKX3.1 expression is predictive of response to 5-ARI. A limitation of the study is that these retrospective human cohorts have relatively few patients with limited clinical outcome data. Future prospective clinical trials are needed to validate whether stratifying patients on the basis of NKX3.1 expression improves the benefit of 5-ARIs during active surveillance., Conclusions: This co-clinical study implicates NKX3.1 status as a predictor of response to 5-ARIs, and suggests that molecular features, including NKX3.1 expression, may help to identify PCa patients most likely to benefit from 5-ARIs during active surveillance., Patient Summary: The aim of precision cancer prevention is to tailor interventions on the basis of individualized patient characteristics. We propose that patients with low NKX3.1 expression are optimal candidates for intervention with 5α-reductase inhibitors as an adjunct to active surveillance., (Copyright © 2017 European Association of Urology. Published by Elsevier B.V. All rights reserved.)

Current treatments for castration-resistant prostate cancer (CRPC) that target androgen receptor (AR) signaling improve patient survival, yet ultimately fail. Here, we provide novel insights into treatment response for the antiandrogen abiraterone by analyses of a genetically engineered mouse (GEM) model with combined inactivation of Trp53 and Pten , which are frequently comutated in human CRPC. These NPp53 mice fail to respond to abiraterone and display accelerated progression to tumors resembling treatment-related CRPC with neuroendocrine differentiation (CRPC-NE) in humans. Cross-species computational analyses identify master regulators of adverse response that are conserved with human CRPC-NE, including the neural differentiation factor SOX11 , which promotes neuroendocrine differentiation in cells derived from NPp53 tumors. Furthermore, abiraterone-treated NPp53 prostate tumors contain regions of focal and/or overt neuroendocrine differentiation, distinguished by their proliferative potential. Notably, lineage tracing in vivo provides definitive and quantitative evidence that focal and overt neuroendocrine regions arise by transdifferentiation of luminal adenocarcinoma cells. These findings underscore principal roles for TP53 and PTEN inactivation in abiraterone resistance and progression from adenocarcinoma to CRPC-NE by transdifferentiation. Significance: Understanding adverse treatment response and identifying patients likely to fail treatment represent fundamental clinical challenges. By integrating analyses of GEM models and human clinical data, we provide direct genetic evidence for transdifferentiation as a mechanism of drug resistance as well as for stratifying patients for treatment with antiandrogens. Cancer Discov; 7(7); 736-49. ©2017 AACR. See related commentary by Sinha and Nelson, p. 673 This article is highlighted in the In This Issue feature, p. 653 ., (©2017 American Association for Cancer Research.)

To date, reprogramming strategies for generating cell types of interest have been facilitated by detailed understanding of relevant developmental regulatory factors. However, identification of such regulatory drivers often represents a major challenge, as specific gene combinations may be required for reprogramming. Here we show that a computational systems approach can identify cell type specification genes (master regulators) that act synergistically, and demonstrate its application for reprogramming of fibroblasts to prostate tissue. We use three such master regulators (FOXA1, NKX3.1 and androgen receptor, AR) in a primed conversion strategy starting from mouse fibroblasts, resulting in prostate tissue grafts with appropriate histological and molecular properties that respond to androgen-deprivation. Moreover, generation of reprogrammed prostate does not require traversal of a pluripotent state. Thus, we describe a general strategy by which cell types and tissues can be generated even with limited knowledge of the developmental pathways required for their specification in vivo.

The NKX3.1 homeobox gene plays essential roles in prostate differentiation and prostate cancer. We show that loss of function of Nkx3.1 in mouse prostate results in down-regulation of genes that are essential for prostate differentiation, as well as up-regulation of genes that are not normally expressed in prostate. Conversely, gain of function of Nkx3.1 in an otherwise fully differentiated nonprostatic mouse epithelium (seminal vesicle) is sufficient for respecification to prostate in renal grafts in vivo. In human prostate cells, these activities require the interaction of NKX3.1 with the G9a histone methyltransferase via the homeodomain and are mediated by activation of target genes such as UTY (KDM6c), the male-specific paralog of UTX (KDM6a) We propose that an NKX3.1-G9a-UTY transcriptional regulatory network is essential for prostate differentiation, and we speculate that disruption of such a network predisposes to prostate cancer., (Copyright © 2016, American Association for the Advancement of Science.)

Although genetically engineered mouse (GEM) models are often used to evaluate cancer therapies, extrapolation of such preclinical data to human cancer can be challenging. Here, we introduce an approach that uses drug perturbation data from GEM models to predict drug efficacy in human cancer. Network-based analysis of expression profiles from in vivo treatment of GEM models identified drugs and drug combinations that inhibit the activity of FOXM1 and CENPF, which are master regulators of prostate cancer malignancy. Validation of mouse and human prostate cancer models confirmed the specificity and synergy of a predicted drug combination to abrogate FOXM1/CENPF activity and inhibit tumorigenicity. Network-based analysis of treatment signatures from GEM models identified treatment-responsive genes in human prostate cancer that are potential biomarkers of patient response. More generally, this approach allows systematic identification of drugs that inhibit tumor dependencies, thereby improving the utility of GEM models for prioritizing drugs for clinical evaluation., (Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.)

To identify regulatory drivers of prostate cancer malignancy, we have assembled genome-wide regulatory networks (interactomes) for human and mouse prostate cancer from expression profiles of human tumors and of genetically engineered mouse models, respectively. Cross-species computational analysis of these interactomes has identified FOXM1 and CENPF as synergistic master regulators of prostate cancer malignancy. Experimental validation shows that FOXM1 and CENPF function synergistically to promote tumor growth by coordinated regulation of target gene expression and activation of key signaling pathways associated with prostate cancer malignancy. Furthermore, co-expression of FOXM1 and CENPF is a robust prognostic indicator of poor survival and metastasis. Thus, genome-wide cross-species interrogation of regulatory networks represents a valuable strategy to identify causal mechanisms of human cancer., (Copyright © 2014 Elsevier Inc. All rights reserved.)

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.

A key issue in cancer biology is whether oncogenic transformation of different cell types of origin within an adult tissue gives rise to distinct tumour subtypes that differ in their prognosis and/or treatment response. We now show that initiation of prostate tumours in basal or luminal epithelial cells in mouse models results in tumours with distinct molecular signatures that are predictive of human patient outcomes. Furthermore, our analysis of untransformed basal cells reveals an unexpected assay dependence of their stem cell properties in sphere formation and transplantation assays versus genetic lineage tracing during prostate regeneration and adult tissue homeostasis. Although oncogenic transformation of basal cells gives rise to tumours with luminal phenotypes, cross-species bioinformatic analyses indicate that tumours of luminal origin are more aggressive than tumours of basal origin, and identify a molecular signature associated with patient outcome. Our results reveal the inherent plasticity of basal cells, and support a model in which different cells of origin generate distinct molecular subtypes of prostate cancer.

Both the PI3K → Akt → mTOR and mitogen-activated protein kinase (MAPK) signaling pathways are often deregulated in prostate tumors with poor prognosis. Here we describe a new genetically engineered mouse model of prostate cancer in which PI3K-Akt-mTOR signaling is activated by inducible disruption of PTEN, and extracellular signal-regulated kinase 1/2 (ERK1/2) MAPK signaling is activated by inducible expression of a BRAF(V600E) oncogene. These tissue-specific compound mutant mice develop lethal prostate tumors that are inherently resistant to castration. These tumors bypass cellular senescence and disseminate to lymph nodes, bone marrow, and lungs where they form overt metastases in approximately 30% of the cases. Activation of PI3K → Akt → mTOR and MAPK signaling pathways in these prostate tumors cooperate to upregulate c-Myc. Accordingly, therapeutic treatments with rapamycin and PD0325901 to target these pathways, respectively, attenuate c-Myc levels and reduce tumor and metastatic burden. Together, our findings suggest a generalized therapeutic approach to target c-Myc activation in prostate cancer by combinatorial targeting of the PI3K → Akt → mTOR and ERK1/2 MAPK signaling pathways.

Although the prognosis for clinically localized prostate cancer is now favorable, there are still no curative treatments for castration-resistant prostate cancer (CRPC) and, therefore, it remains fatal. In this study, we investigate a new therapeutic approach for treatment of CRPC, which involves dual targeting of a major signaling pathway that is frequently deregulated in the disease. We found that dual targeting of the Akt and mTOR signaling pathways with their respective inhibitors, MK-2206 and ridaforolimus (MK-8669), is highly effective for inhibiting CRPC in preclinical studies in vivo using a refined genetically engineered mouse model of the disease. The efficacy of the combination treatment contrasts with their limited efficacy as single agents, since delivery of MK-2206 or MK-8669 individually had a modest impact in vivo on the overall tumor phenotype. In human prostate cancer cell lines, although not in the mouse model, the synergistic actions of MK-2206 and ridaforolimus (MK-8669) are due in part to limiting the mTORC2 feedback activation of Akt. Moreover, the effects of these drugs are mediated by inhibition of cellular proliferation via the retinoblastoma (Rb) pathway. Our findings suggest that dual targeting of the Akt and mTOR signaling pathways using MK-2206 and ridaforolimus (MK-8669) may be effective for treatment of CRPC, particularly for patients with deregulated Rb pathway activity., (©2012 AACR.)

Accurate computational prediction of protein functions increasingly relies on network-inspired models for the protein function transfer. This task can become challenging for proteins isolated in their own network or those with poor or uncharacterized neighborhoods. Here, we present a novel probabilistic chain-graph-based approach for predicting protein functions that builds on connecting networks of two (or more) different species by links of high interspecies sequence homology. In this way, proteins are able to "exchange" functional information with their neighbors-homologs from a different species. The knowledge of interspecies relationships, such as the sequence homology, can become crucial in cases of limited information from other sources of data, including the protein-protein interactions or cellular locations of proteins. We further enhance our model to account for the Gene Ontology dependencies by linking multiple but related functional ontology categories within and across multiple species. The resulting networks are of significantly higher complexity than most traditional protein network models. We comprehensively benchmark our method by applying it to two largest protein networks, the Yeast and the Fly. The joint Fly-Yeast network provides substantial improvements in precision, accuracy, and false positive rate over networks that consider either of the sources in isolation. At the same time, the new model retains the computational efficiency similar to that of the simpler networks.

Objective: Even though a vaccine for malaria infections has been under intense study for many years, it has resisted several different lines of attack attempted by biologists. More than half of Plasmodium proteins still remain uncharacterized and therefore cannot be used in clinical trials. The task is further complicated by the metamorphic life-cycle of the parasite, which allows for rapid evolutionary changes and diversity among related strains, thus making precise targeting of the appropriate proteins for vaccination a technical challenge. We propose an automated method for predicting functions for the malaria parasite, which capitalizes on the importance of the intraerythrocytic developmental cycle data and expression changes during its five phases, as determined computationally by our segmentation algorithm., Materials and Methods: Our method combines temporal gene expression profiles with protein-protein interaction data, sequence similarity scores, and metabolic pathway information to produce a set of predicted protein functions that can be used as targets for vaccine development. We use a Bayesian approach, which assigns a probability of having (or not having) a particular function to each protein, given the various sources of evidence. In our method, each data source is represented by either a functional linkage graph or a categorical feature vector., Results and Conclusions: The methods are tested on Plasmodium falciparum, the species responsible for the deadliest malaria infections. The algorithm was able to assign meaningful functions to 628 out of 1439 previously unannotated proteins, which are first-choice candidates for experimental vaccine research. We conclude that analyzing time-course gene expression profiles in separate phases leads to much higher prediction accuracy when compared with Pearson correlation coefficients computed across the time course as a whole. Additionally, we demonstrate that temporal expression profiles alone are able to improve the predictive power of the integrated data.

Since we are limited in our knowledge of human demographic history and variations of recombination and mutation rates, large-scale computer simulation is a necessary tool in genetics. Here we propose and computationally simulate a model of evolution for unique and segmentally duplicated regions of human genome. Since such segmentally duplicated regions show a complex behaviour of copy number changes, our model will hopefully lead to a better understanding of the evolutionary developments of CNVs, algorithms for associations studies with CNV markers, and finally, for characterising parameters for stochastic diffusion models, describing asymptotic behaviour of evolutionary processes.