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Abstract Background Endocrine therapy is the most important treatment modality of breast cancer patients whose tumors express the estrogen receptor α (ERα). The androgen receptor (AR) is also expressed in the vast majority (80–90%) of ERα-positive tumors. AR-targeting drugs are not used in clinical practice, but have been evaluated in multiple trials and preclinical studies. Methods We performed a genome-wide study to identify hormone/drug-induced single nucleotide polymorphism (SNP) genotype - dependent gene-expression, known as PGx-eQTL, mediated by either an AR agonist (dihydrotestosterone) or a partial antagonist (enzalutamide), utilizing a previously well characterized lymphoblastic cell line panel. The association of the identified SNPs-gene pairs with breast cancer phenotypes were then examined using three genome-wide association (GWAS) studies that we have published and other studies from the GWAS catalog. Results We identified 13 DHT-mediated PGx-eQTL loci and 23 Enz-mediated PGx-eQTL loci that were associated with breast cancer outcomes post ER antagonist or aromatase inhibitors (AI) treatment, or with pharmacodynamic (PD) effects of AIs. An additional 30 loci were found to be associated with cancer risk and sex-hormone binding globulin levels. The top loci involved the genes IDH2 and TMEM9, the expression of which were suppressed by DHT in a PGx-eQTL SNP genotype-dependent manner. Both of these genes were overexpressed in breast cancer and were associated with a poorer prognosis. Therefore, suppression of these genes by AR agonists may benefit patients with minor allele genotypes for these SNPs. Conclusions We identified AR-related PGx-eQTL SNP-gene pairs that were associated with risks, outcomes and PD effects of endocrine therapy that may provide potential biomarkers for individualized treatment of breast cancer.

Abstract Background Tumor immune infiltration and peripheral blood immune signatures have prognostic and predictive value in breast cancer. Whether distinct peripheral blood immune phenotypes are associated with response to neoadjuvant chemotherapy (NAC) remains understudied. Methods Peripheral blood mononuclear cells from 126 breast cancer patients enrolled in a prospective clinical trial (NCT02022202) were analyzed using Cytometry by time-of-flight with a panel of 29 immune cell surface protein markers. Kruskal–Wallis tests or Wilcoxon rank-sum tests were used to evaluate differences in immune cell subpopulations according to breast cancer subtype and response to NAC. Results There were 122 evaluable samples: 47 (38.5%) from patients with hormone receptor-positive, 39 (32%) triple-negative (TNBC), and 36 (29.5%) HER2-positive breast cancer. The relative abundances of pre-treatment peripheral blood T, B, myeloid, NK, and unclassified cells did not differ according to breast cancer subtype. In TNBC, higher pre-treatment myeloid cells were associated with lower pathologic complete response (pCR) rates. In hormone receptor-positive breast cancer, lower pre-treatment CD8 + naïve and CD4 + effector memory cells re-expressing CD45RA (TEMRA) T cells were associated with more extensive residual disease after NAC. In HER2 + breast cancer, the peripheral blood immune phenotype did not differ according to NAC response. Conclusions Pre-treatment peripheral blood immune cell populations (myeloid in TNBC; CD8 + naïve T cells and CD4 + TEMRA cells in luminal breast cancer) were associated with response to NAC in early-stage TNBC and hormone receptor-positive breast cancers, but not in HER2 + breast cancer. Trial registration NCT02022202 . Registered 20 December 2013.

Abstract Background Dysregulated Notch signalling contributes to breast cancer development and progression, but validated tools to measure the level of Notch signalling in breast cancer subtypes and in response to systemic therapy are largely lacking. A transcriptomic signature of Notch signalling would be warranted, for example to monitor the effects of future Notch-targeting therapies and to learn whether altered Notch signalling is an off-target effect of current breast cancer therapies. In this report, we have established such a classifier. Methods To generate the signature, we first identified Notch-regulated genes from six basal-like breast cancer cell lines subjected to elevated or reduced Notch signalling by culturing on immobilized Notch ligand Jagged1 or blockade of Notch by γ-secretase inhibitors, respectively. From this cadre of Notch-regulated genes, we developed candidate transcriptomic signatures that were trained on a breast cancer patient dataset (the TCGA-BRCA cohort) and a broader breast cancer cell line cohort and sought to validate in independent datasets. Results An optimal 20-gene transcriptomic signature was selected. We validated the signature on two independent patient datasets (METABRIC and Oslo2), and it showed an improved coherence score and tumour specificity compared with previously published signatures. Furthermore, the signature score was particularly high for basal-like breast cancer, indicating an enhanced level of Notch signalling in this subtype. The signature score was increased after neoadjuvant treatment in the PROMIX and BEAUTY patient cohorts, and a lower signature score generally correlated with better clinical outcome. Conclusions The 20-gene transcriptional signature will be a valuable tool to evaluate the response of future Notch-targeting therapies for breast cancer, to learn about potential effects on Notch signalling from conventional breast cancer therapies and to better stratify patients for therapy considerations.

Abstract Triple negative breast cancer (TNBC) is a heterogeneous group of tumors which lack estrogen receptor, progesterone receptor, and HER2 expression. Targeted therapies have limited success in treating TNBC, thus a strategy enabling effective targeted combinations is an unmet need. To tackle these challenges and discover individualized targeted combination therapies for TNBC, we integrated phosphoproteomic analysis of altered signaling networks with patient-specific signaling signature (PaSSS) analysis using an information-theoretic, thermodynamic-based approach. Using this method on a large number of TNBC patient-derived tumors (PDX), we were able to thoroughly characterize each PDX by computing a patient-specific set of unbalanced signaling processes and assigning a personalized therapy based on them. We discovered that each tumor has an average of two separate processes, and that, consistent with prior research, EGFR is a major core target in at least one of them in half of the tumors analyzed. However, anti-EGFR monotherapies were predicted to be ineffective, thus we developed personalized combination treatments based on PaSSS. These were predicted to induce anti-EGFR responses or to be used to develop an alternative therapy if EGFR was not present. In-vivo experimental validation of the predicted therapy showed that PaSSS predictions were more accurate than other therapies. Thus, we suggest that a detailed identification of molecular imbalances is necessary to tailor therapy for each TNBC. In summary, we propose a new strategy to design personalized therapy for TNBC using pY proteomics and PaSSS analysis. This method can be applied to different cancer types to improve response to the biomarker-based treatment.

Most patients with metastatic prostate cancer eventually develop resistance to primary androgen deprivation therapy. To identify predictive biomarker for Abiraterone acetate/prednisone resistance, we screened alternative splice variants between responders and non-responders from the PROMOTE clinical study and pinned down the most significant variant, CENPK–delta8. Through preclinical patient-derived mouse xenograft (PDX) and 3D organoids obtained from responders and non-responders, as well as in vitro models, aberrant CENPK–delta8 expression was determined to link to drug resistance via enhanced migration and proliferation. The FLNA and FLOT1 were observed to specifically bind to CENK–delta8 rather than wild-type CENPK, underscoring the role of CENPK–delta8 in cytoskeleton organization and cell migration. Our study, leveraging data from the PROMOTE study, TCGA, and TCGA SpliceReq databases, highlights the important function of alternative splice variants in drug response and their potential to be prognostic biomarkers for improving individual therapeutic outcomes in precision medicine.

Cancer is typically treated with combinatorial therapy, and such combinations may be synergistic. However, discovery of these combinations has proven difficult as brute force combinatorial screening approaches are both logistically complex and resource-intensive. Therefore, computational approaches to augment synergistic drug discovery are of interest, but current approaches are limited by their dependencies on combinatorial drug screening training data or molecular profiling data. These dataset dependencies can limit the number and diversity of drugs for which these approaches can make inferences. Herein, we describe a novel computational framework, ReCorDE (Recurrent Correlation of Drugs with Enrichment), that uses publicly-available cell line-derived monotherapy cytotoxicity datasets to identify drug classes targeting shared vulnerabilities across multiple cancer lineages; and we show how these inferences can be used to augment synergistic drug combination discovery. Additionally, we demonstrate in preclinical models that a drug class combination predicted by ReCorDE to target shared vulnerabilities (PARP inhibitors and Aurora kinase inhibitors) exhibits class-class synergy across lineages. ReCorDE functions independently of combinatorial drug screening and molecular profiling data, using only extensive monotherapy cytotoxicity datasets as its input. This allows ReCorDE to make robust inferences for a large, diverse array of drugs. In conclusion, we have described a novel framework for the identification of drug classes targeting shared vulnerabilities using monotherapy cytotoxicity datasets, and we showed how these inferences can be used to aid discovery of novel synergistic drug combinations.

Triple negative breast cancer (TNBC) accounts for 15–20% of all breast cancers and mainly affects pre-menopausal and minority women. Because of the lack of ER, PR or HER2 expression in TNBC, there are limited options for tailored therapies. While TNBCs respond initially to standard of care chemotherapy, tumor recurrence commonly occurs within 1 to 3 years post-chemotherapy and is associated with early organ metastasis and a high incidence of mortality. One of the major mechanisms responsible for drug resistance and emergence of organ metastasis is activation of epithelial to mesenchymal transition (EMT) reprogramming. EMT-mediated cancer cell plasticity also promotes the enrichment of cancer cells with a CD44high/CD24low and/or ALDHhigh cancer stem-like phenotype [cancer stem cells (CSCs)], characterized by an increased capacity for tumor self-renewal, intrinsic drug resistance, immune evasion and metastasis. In this study we demonstrate for the first time a positive feedback loop between AURKA and intra-tumoral PD-L1 oncogenic pathways in TNBC. Genetic targeting of intra-tumoral PD-L1 expression impairs the enrichment of ALDHhigh CSCs and enhances the therapeutic efficacy of AURKA-targeted therapy. Moreover, dual AURKA and PD-L1 pharmacological blockade resulted in the strongest inhibition of tumor growth and organ metastatic burden. Taken together, our findings provide a compelling preclinical rationale for the development of novel combinatorial therapeutic strategies aimed to inhibit cancer cell plasticity, immune evasion capacity and organ metastasis in patients with advanced TNBC.

Prediction of the response of cancer patients to different treatments and identification of biomarkers of drug response are two major goals of individualized medicine. Here, we developed a deep learning framework called TINDL, completely trained on preclinical cancer cell lines (CCLs), to predict the response of cancer patients to different treatments. TINDL utilizes a tissue-informed normalization to account for the tissue type and cancer type of the tumors and to reduce the statistical discrepancies between CCLs and patient tumors. Moreover, by making the deep learning black box interpretable, this model identifies a small set of genes whose expression levels are predictive of drug response in the trained model, enabling identification of biomarkers of drug response. Using data from two large databases of CCLs and cancer tumors, we showed that this model can distinguish between sensitive and resistant tumors for 10 (out of 14) drugs, outperforming various other machine learning models. In addition, our small interfering RNA (siRNA) knockdown experiments on 10 genes identified by this model for one of the drugs (tamoxifen) confirmed that tamoxifen sensitivity is substantially influenced by all of these genes in MCF7 cells, and seven of these genes in T47D cells. Furthermore, genes implicated for multiple drugs pointed to shared mechanism of action among drugs and suggested several important signaling pathways. In summary, this study provides a powerful deep learning framework for prediction of drug response and identification of biomarkers of drug response in cancer. The code can be accessed at https://github.com/ddhostallero/tindl.

Abstract Background Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype. Patients with TNBC are primarily treated with neoadjuvant chemotherapy (NAC). The response to NAC is prognostic, with reductions in overall survival and disease-free survival rates in those patients who do not achieve a pathological complete response (pCR). Based on this premise, we hypothesized that paired analysis of primary and residual TNBC tumors following NAC could identify unique biomarkers associated with post-NAC recurrence. Methods and results We investigated 24 samples from 12 non-LAR TNBC patients with paired pre- and post-NAC data, including four patients with recurrence shortly after surgery ( 48 months). These tumors were collected from a prospective NAC breast cancer study (BEAUTY) conducted at the Mayo Clinic. Differential expression analysis of pre-NAC biopsies showed minimal gene expression differences between early recurrent and nonrecurrent TNBC tumors; however, post-NAC samples demonstrated significant alterations in expression patterns in response to intervention. Topological-level differences associated with early recurrence were implicated in 251 gene sets, and an independent assessment of microarray gene expression data from the 9 paired non-LAR samples available in the NAC I-SPY1 trial confirmed 56 gene sets. Within these 56 gene sets, 113 genes were observed to be differentially expressed in the I-SPY1 and BEAUTY post-NAC studies. An independent (n = 392) breast cancer dataset with relapse-free survival (RFS) data was used to refine our gene list to a 17-gene signature. A threefold cross-validation analysis of the gene signature with the combined BEAUTY and I-SPY1 data yielded an average AUC of 0.88 for six machine-learning models. Due to the limited number of studies with pre- and post-NAC TNBC tumor data, further validation of the signature is needed. Conclusion Analysis of multiomics data from post-NAC TNBC chemoresistant tumors showed down regulation of mismatch repair and tubulin pathways. Additionally, we identified a 17-gene signature in TNBC associated with post-NAC recurrence enriched with down-regulated immune genes.

Abstract Poly (ADP-ribose) polymerase (PARP) inhibitors are one of the most exciting classes of targeted therapy agents for cancers with homologous recombination (HR) deficiency. However, many patients without apparent HR defects also respond well to PARP inhibitors/cisplatin. The biomarker responsible for this mechanism remains unclear. Here, we identified a set of ribosomal genes that predict response to PARP inhibitors/cisplatin in HR-proficient patients. PARP inhibitor/cisplatin selectively eliminates cells with high expression of the eight genes in the identified panel via DNA damage (ATM) signaling-induced pro-apoptotic ribosomal stress, which along with ATM signaling-induced pro-survival HR repair constitutes a new model to balance the cell fate in response to DNA damage. Therefore, the combined examination of the gene panel along with HR status would allow for more precise predictions of clinical response to PARP inhibitor/cisplatin. The gene panel as an independent biomarker was validated by multiple published clinical datasets, as well as by an ovarian cancer organoids library we established. More importantly, its predictive value was further verified in a cohort of PARP inhibitor-treated ovarian cancer patients with both RNA-seq and WGS data. Furthermore, we identified several marketed drugs capable of upregulating the expression of the genes in the panel without causing HR deficiency in PARP inhibitor/cisplatin-resistant cell lines. These drugs enhance PARP inhibitor/cisplatin sensitivity in both intrinsically resistant organoids and cell lines with acquired resistance. Together, our study identifies a marker gene panel for HR-proficient patients and reveals a broader application of PARP inhibitor/cisplatin in cancer therapy.

Abstract Background The AMP‐activated protein kinase (AMPK) is a central regulator of energy homeostasis, with deregulation leading to cancer and other diseases. However, how this pathway is dysregulated in cancer has not been well clarified. Methods Using a tandem affinity purification/mass‐spec technique and biochemical analyses, we identified tumor protein D52 (TPD52) as an AMPKα‐interacting molecule. To explore the biological effects of TPD52 in cancers, we conducted biochemical and metabolic assays in vitro and in vivo with cancer cells and TPD52 transgenic mice. Finally, we assessed the clinical significance of TPD52 expression in breast cancer patients using bioinformatics techniques. Results TPD52, initially identified to be overexpressed in many human cancers, was found to form a stable complex with AMPK in cancer cells. TPD52 directly interacts with AMPKα and inhibits AMPKα kinase activity in vitro and in vivo. In TPD52 transgenic mice, overexpression of TPD52 leads to AMPK inhibition and multiple metabolic defects. Clinically, high TPD52 expression predicts poor survival of breast cancer patients. Conclusion The findings revealed that TPD52 is a novel regulator of energy stress‐induced AMPK activation and cell metabolism. These results shed new light on AMPK regulation and understanding of the etiology of cancers with TPD52 overexpression.

Historically, the clinical utility of oncolytic virotherapy as a treatment for a wide range of cancer types was first demonstrated by three pilot human clinical trials conducted in Japan in the 1970s and 1980s using a wild-type Urabe mumps virus (MuV) clinical isolate. Using a sample of the actual original oncolytic Urabe MuV clinical trial virus stock (MuV-U-Japan) used in these Japanese clinical trials, we found that MuV-U-Japan consisted of a wide variety of very closely related Urabe MuVs that differed by an average of only three amino acids. Two MuV-U-Japan isolates, MuV-UA and MuV-UC, potently killed a panel of established human breast cancer cell lines in vitro, significantly extended survival of nude mice with human triple-negative breast cancer (TNBC) MDA-MB-231 tumor xenografts in vivo, and demonstrated significant killing activity against breast cancer patient-derived xenograft (PDX) cell lines grown as 3D organoids, including PDXs from patients resistant to anthracycline- and taxane-based chemotherapy. We also report success in developing a large-scale MuV-U production and purification process suitable for supporting Investigational New Drug applications for clinical trials. This study demonstrates the suitability of the MuV-UC virus for translation to modern clinical trials for treating patients with TNBC.

Abstract Alternative polyadenylation (APA) is a common genetic regulatory mechanism that generates distinct 3′ ends for RNA transcripts. Changes in APA have been associated with multiple biological processes and disease phenotypes. However, the role of hormones and their drug analogs in APA remains largely unknown. In this study, we investigated transcriptome‐wide the impact of glucocorticoids on APA in 30 human B‐lymphoblastoid cell lines. We found that glucocorticoids could regulate APA for a subset of genes, possibly by changing the expression of 142 RNA‐binding proteins, some with known APA‐regulating properties. Interestingly, genes with glucocorticoid‐mediated APA were enriched in viral translation‐related pathways, while genes with glucocorticoid‐mediated expression were enriched in interferon and interleukin pathways, suggesting that glucocorticoid‐mediated APA might result in functional consequences distinct from gene expression. For example, glucocorticoids, a pharmacotherapy for severe COVID‐19, were found to change the APA but not the expression of LY6E, an important antiviral inhibitor in coronavirus diseases. Glucocorticoid‐mediated APA was also cell‐type‐specific, suggesting an action of glucocorticoids that may be unique to immune regulation. We also observed evidence for genotype‐dependent glucocorticoid‐mediated APA (referred to as pharmacogenomic‐alterative polyadenylation quantitative trait loci), providing potential functional mechanisms for a series of common genetic variants that had previously been associated with immune disorders, but without a clear mechanism. In summary, this study reports a series of observations regarding the impact of glucocorticoids on APA, raising the possibility that this mechanism might have implications for both disease pathophysiology and drug therapy.

Abstract Background Methotrexate is the preferred initial disease-modifying antirheumatic drug (DMARD) for rheumatoid arthritis (RA). However, clinically useful tools for individualized prediction of response to methotrexate treatment in patients with RA are lacking. We aimed to identify clinical predictors of response to methotrexate in patients with rheumatoid arthritis (RA) using machine learning methods. Methods Randomized clinical trials (RCT) of patients with RA who were DMARD-naïve and randomized to placebo plus methotrexate were identified and accessed through the Clinical Study Data Request Consortium and Vivli Center for Global Clinical Research Data. Studies with available Disease Activity Score with 28-joint count and erythrocyte sedimentation rate (DAS28-ESR) at baseline and 12 and 24 weeks were included. Latent class modeling of methotrexate response was performed. The least absolute shrinkage and selection operator (LASSO) and random forests methods were used to identify predictors of response. Results A total of 775 patients from 4 RCTs were included (mean age 50 years, 80% female). Two distinct classes of patients were identified based on DAS28-ESR change over 24 weeks: “good responders” and “poor responders.” Baseline DAS28-ESR, anti-citrullinated protein antibody (ACPA), and Health Assessment Questionnaire (HAQ) score were the top predictors of good response using LASSO (area under the curve [AUC] 0.79) and random forests (AUC 0.68) in the external validation set. DAS28-ESR ≤ 7.4, ACPA positive, and HAQ ≤ 2 provided the highest likelihood of response. Among patients with 12-week DAS28-ESR > 3.2, ≥ 1 point improvement in DAS28-ESR baseline-to-12-week was predictive of achieving DAS28-ESR ≤ 3.2 at 24 weeks. Conclusions We have developed and externally validated a prediction model for response to methotrexate within 24 weeks in DMARD-naïve patients with RA, providing variably weighted clinical features and defined cutoffs for clinical decision-making.

Abstract Combination antidepressant pharmacotherapies are frequently used to treat major depressive disorder (MDD). However, there is no evidence that machine learning approaches combining multi-omics measures (e.g., genomics and plasma metabolomics) can achieve clinically meaningful predictions of outcomes to combination pharmacotherapy. This study examined data from 264 MDD outpatients treated with citalopram or escitalopram in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) and 111 MDD outpatients treated with combination pharmacotherapies in the Combined Medications to Enhance Outcomes of Antidepressant Therapy (CO-MED) study to predict response to combination antidepressant therapies. To assess whether metabolomics with functionally validated single-nucleotide polymorphisms (SNPs) improves predictability over metabolomics alone, models were trained/tested with and without SNPs. Models trained with PGRN-AMPS’ and CO-MED’s escitalopram/citalopram patients predicted response in CO-MED’s combination pharmacotherapy patients with accuracies of 76.6% (p

Poly(ADP-ribose) (PAR) polymerase inhibitors (PARPi) either have been approved or being tested in the clinic for the treatment of a variety of cancers with homologous recombination deficiency (HRD). However, cancer cells can develop resistance to PARPi drugs through various mechanisms, and new biomarkers and combination therapeutic strategies need to be developed to support personalized treatment. In this study, a genome-wide CRISPR screen was performed in a prostate cancer cell line with 3D culture condition which identified novel signals involved in DNA repair pathways. One of these genes, TBL1XR1, regulates sensitivity to PARPi in prostate cancer cells. Mechanistically, we show that TBL1XR1 interacts with and stabilizes SMC3 on chromatin and promotes γH2AX spreading along the chromatin of the cells under DNA replication stress. TBL1XR1-SMC3 double knockdown (knockout) cells have comparable sensitivity to PARPi compared to SMC3 knockdown or TBL1XR1 knockout cells, and more sensitivity than WT cells. Our findings provide new insights into mechanisms underlying response to PARPi or platin compounds in the treatment of malignancies.

The cytochromes P450 (CYPs) represent a large gene superfamily that plays an important role in the metabolism of both exogenous and endogenous compounds. We have reported that the testis-specific Y-encoded-like proteins (TSPYLs) are novel CYP gene transcriptional regulators. However, little is known of mechanism(s) by which TSPYLs regulate CYP expression or the functional consequences of that regulation. The TSPYL gene family includes six members, TSPYL1 to TSPYL6. However, TSPYL3 is a pseudogene, TSPYL5 is only known to regulates the expression of CYP19A1, and TSPYL6 is expressed exclusively in the testis. Therefore, TSPYL 1, 2 and 4 were included in the present study. To better understand how TSPYL1, 2, and 4 might influence CYP expression, we performed a series of pull-downs and mass spectrometric analyses. Panther pathway analysis of the 2272 pulled down proteins for all 3 TSPYL isoforms showed that the top five pathways were the Wnt signaling pathway, the Integrin signaling pathway, the Gonadotropin releasing hormone receptor pathway, the Angiogenesis pathway and Inflammation mediated by chemokines and cytokines. Specifically, we observed that 177 Wnt signaling pathway proteins were pulled down with the TSPYLs. Subsequent luciferase assays showed that TSPYL1 knockdown had a greater effect on the activation of Wnt signaling than did TSPYL2 or TSPYL4 knockdown. Therefore, in subsequent experiments, we focused our attention on TSPYL1. HepaRG cell qRT-PCR showed that TSPYL1 regulated the expression of CYPs involved in cholesterol-metabolism such as CYP1B1 and CYP7A1. Furthermore, TSPYL1 and β-catenin regulated CYP1B1 expression in opposite directions and TSPYL1 appeared to regulate CYP1B1 expression by blocking β-catenin binding to the TCF7L2 transcription factor on the CYP1B1 promoter. In β-catenin and TSPYL1 double knockdown cells, CYP1B1 expression and the generation of CYP1B1 downstream metabolites such as 20-HETE could be restored. Finally, we observed that TSPYL1 expression was associated with plasma cholesterol levels and BMI during previous clinical studies of obesity. In conclusion, this series of experiments has revealed a novel mechanism for regulation of the expression of cholesterol-metabolizing CYPs, particularly CYP1B1, by TSPYL1 via Wnt/β-catenin signaling, raising the possibility that TSPYL1 might represent a molecular target for influencing cholesterol homeostasis.

Background: Individuals with major depressive disorder (MDD) and a lifetime history of attempted suicide demonstrate lower antidepressant response rates than those without a prior suicide attempt. Identifying biomarkers of antidepressant response and lifetime history of attempted suicide may help augment pharmacotherapy selection and improve the objectivity of suicide risk assessments. Towards this goal, this study sought to use network science approaches to establish a multi-omics (genomic and metabolomic) signature of antidepressant response and lifetime history of attempted suicide in adults with MDD.Methods: Single nucleotide variants (SNVs) which associated with suicide attempt(s) in the literature were identified and then integrated with a) p180-assayed metabolites collected prior to antidepressant pharmacotherapy and b) a binary measure of antidepressant response at 8 weeks of treatment using penalized regression-based networks in 245 ‘Pharmacogenomics Research Network Antidepressant Medication Study (PGRN-AMPS)’ and 103 ‘Combining Medications to Enhance Depression Outcomes (CO-MED)’ patients with major depressive disorder. This approach enabled characterization and comparison of biological profiles and associated antidepressant treatment outcomes of those with (N = 46) and without (N = 302) a self-reported lifetime history of suicide attempt.Results: 351 SNVs were associated with suicide attempt(s) in the literature. Intronic SNVs in the circadian genes CLOCK and ARNTL (encoding the CLOCK:BMAL1 heterodimer) were amongst the top network analysis features to differentiate patients with and without a prior suicide attempt. CLOCK and ARNTL differed in their correlations with plasma phosphatidylcholines, kynurenine, amino acids, and carnitines between groups. CLOCK and ARNTL-associated phosphatidylcholines showed a positive correlation with antidepressant response in individuals without a prior suicide attempt which was not observed in the group with a prior suicide attempt.Conclusion: Results provide evidence for a disturbance between CLOCK:BMAL1 circadian processes and circulating phosphatidylcholines, kynurenine, amino acids, and carnitines in individuals with MDD who have attempted suicide. This disturbance may provide mechanistic insights for differential antidepressant pharmacotherapy outcomes between patients with MDD with versus without a lifetime history of attempted suicide. Future investigations of CLOCK:BMAL1 metabolic regulation in the context of suicide attempts may help move towards biologically-augmented pharmacotherapy selection and stratification of suicide risk for subgroups of patients with MDD and a lifetime history of attempted suicide.

Abstract The application of patient-derived xenografts (PDX) in drug screening and testing is a costly and time-consuming endeavor. While cell lines permit extensive mechanistic studies, many human breast cancer cell lines lack patient characteristics and clinical treatment information. Establishing cell lines that retain patient’s genetic and drug response information would enable greater drug screening and mechanistic studies. Therefore, we utilized breast cancer PDX from the Mayo Breast Cancer Genome Guided Therapy Study (BEAUTY) to establish two immortalized, genomically unique breast cancer cell lines. Through extensive genetic and therapeutic testing, the cell lines were found to retain the same clinical subtype, major somatic alterations, and drug response phenotypes as their corresponding PDX and patient tumor. Our findings demonstrate PDX can be utilized to develop immortalized breast cancer cell lines and provide a valuable tool for understanding the molecular mechanism of drug resistance and exploring novel treatment strategies.

Resistance to second generation anti-androgen therapies such as enzalutamide (ENZ) can emerge in prostate cancer patients. Here, the authors identify an ENZ-resistant mechanism driven by AR-dependent transcription of noncanonical targets that make resistant cells susceptible to dual inhibition of BET and CBP/p300 signaling.

Objective: To assess the potential impact of Pharmacogenomic (PGx) variation in cytochrome P450 2D6 (CYP2D6) enzyme function, using loss in quality-adjusted life years (QALYs) associated with treatment problems, and the willingness to pay to avoid treatment problems from patients’ and payers’ perspectives. Patients and Methods: The study included patients prescribed tramadol or codeine, or both, between January 1, 2005, and December 31, 2017. Demographic information and adverse drug events, including adverse drug events and poor pain control, were collected from the electronic health records using natural language processing techniques and review by trained abstractors. Patients’ willingness to pay and QALY estimates were based on comprehensive literature review. The CYP2D6 phenotypes were divided into 4 groups: ultra-rapid metabolizers, normal metabolizers, intermediate metabolizers, and poor metabolizers. Results: Among the 2860 identified patients, 63 (2%) were ultrarapid metabolizers, 1449 (50%) were normal metabolizers, 1155 (40%) were intermediate metabolizers, and 193 (7%) were poor metabolizers. The patients’ average estimated willingness-to-pay value to avoid treatment problems was $23 per month; poor metabolizers developed problems with the highest estimated willingness-to-pay value ($32 per month). The mean QALY loss among all patients was 0.024 QALYs (8.8 healthy days); poor metabolizers had the highest loss (0.027 QALYs, 9.9 healthy days). Conclusion: Patients with various phenotypes developed different treatment problem profiles. Poor CYP2D6 metabolizers developed problems with highest willingness to pay, and they might potentially benefit most from PGx-guided treatment and problem prevention.

Single nucleotide variants in the open reading frames (ORFs) of pharmacogenes are important causes of interindividual variability in drug response. The functional characterization of variants of unknown significance within ORFs remains a major challenge for pharmacogenomics. Deep mutational scanning (DMS) is a high‐throughput technique that makes it possible to analyze the functional effect of hundreds of variants in a parallel and scalable fashion. We adapted a “landing pad” DMS system to study the function of missense variants in the ORFs of cytochrome P450 family 2 subfamily C member 9 (CYP2C9) and cytochrome P450 family 2 subfamily C member 19 (CYP2C19). We studied 230 observed missense variants in the CYP2C9 and CYP2C19 ORFs and found that 19 of 109 CYP2C9 and 36 of 121 CYP2C19 variants displayed less than ~ 25% of the wild‐type protein expression, a level that may have clinical relevance. Our results support DMS as an efficient method for the identification of damaging ORF variants that might have potential clinical pharmacogenomic application.

Background: Trastuzumab (Herceptin) is the key systemic therapy for HER2-positive breast cancer. However, the initial response rate is limited to approximately 50% in patients. Moreover, most patients, especially at an advanced stage, eventually develop acquired resistance. Understanding the mechanisms of trastuzumab resistance is crucial for achieving better treatment outcome in this group of patients. Methods: A trastuzumab-resistant (TR) cell line was developed using the BT474 HER2-positive breast cancer cell line. Whole-transcriptome expression array was performed and the TR-related gene NDUFA4L2 was identified by differential expression analysis between BT474 and BT474-TR. Mitochondrial localization of NDUFA4L2 was confirmed by immunofluorescence and western blotting using mitochondrial fractionation. Mitochondrial function and energy metabolism were evaluated using Seahorse, ATP production, and lactate production assays, and cellular reactive oxygen species (ROS) levels were determined using DCFDA. NDUFA4L2 expression in patients was evaluated by immunohistochemistry, and relapse-free survival was analyzed using the Kaplan–Meier method. Results: NDUFA4L2 was highly expressed in the TR HER2-positive breast cancer cell line. High expression level of NDUFA4L2 was associated with shorter relapse-free intervals in trastuzumab-treated HER2-positive breast cancer patients. Overexpression of NDUFA4L2 enhanced Warburg effects, enhanced aerobic glycolysis, reduced oxygen consumption, and lowered ROS production. Mechanistically, overexpression of NDUFA4L2 facilitated mitochondrial relocalization of HER2 and suppressed ROS production, thus rendering cancer cells more resistant to trastuzumab treatment. Conclusions: We identified NDUFA4L2 as a new biomarker and potential therapeutic target for TR HER2-positive breast cancer.

Abstract Background Our previous genome-wide association study using the MA.27 aromatase inhibitors adjuvant trial identified SNPs in the long noncoding RNA MIR2052HG associated with breast cancer-free interval. MIR2052HG maintained ERα both by promoting AKT/FOXO3-mediated ESR1 transcription and by limiting ubiquitin-mediated ERα degradation. Our goal was to further elucidate MIR2052HG’s mechanism of action. Methods RNA-binding protein immunoprecipitation assays were performed to demonstrate that the transcription factor, early growth response protein 1 (EGR1), worked together with MIR2052HG to regulate that lemur tyrosine kinase-3 (LMTK3) transcription in MCF7/AC1 and CAMA-1 cells. The location of EGR1 on the LMTK3 gene locus was mapped using chromatin immunoprecipitation assays. The co-localization of MIR2052HG RNA and the LMTK3 gene locus was determined using RNA-DNA dual fluorescent in situ hybridization. Single-nucleotide polymorphisms (SNP) effects were evaluated using a panel of human lymphoblastoid cell lines. Results MIR2052HG depletion in breast cancer cells results in a decrease in LMTK3 expression and cell growth. Mechanistically, MIR2052HG interacts with EGR1 and facilitates its recruitment to the LMTK3 promoter. LMTK3 sustains ERα levels by reducing protein kinase C (PKC) activity, resulting in increased ESR1 transcription mediated through AKT/FOXO3 and reduced ERα degradation mediated by the PKC/MEK/ERK/RSK1 pathway. MIR2052HG regulated LMTK3 in a SNP- and aromatase inhibitor-dependent fashion: the variant SNP increased EGR1 binding to LMTK3 promoter in response to androstenedione, relative to wild-type genotype, a pattern that can be reversed by aromatase inhibitor treatment. Finally, LMTK3 overexpression abolished the effect of MIR2052HG on PKC activity and ERα levels. Conclusions Our findings support a model in which the MIR2052HG regulates LMTK3 via EGR1, and LMTK3 regulates ERα stability via the PKC/MEK/ERK/RSK1 axis. These results reveal a direct role of MIR2052HG in LMTK3 regulation and raise the possibilities of targeting MIR2052HG or LMTK3 in ERα-positive breast cancer.

Ufmylation is a ubiquitylation-like protein modification but only a few ufmylation substrates and functions have been discovered so far. Here, the authors demonstrate ufmylation of histone H4 upon DNA damage and show that this modification is involved in the amplification of ATM activation.

Objective: To determine histopathologic, exome, and transcriptome nucleic acid material yield from prospectively collected metastatic tissue biopsy specimens in patients with metastatic castration-resistant prostate cancer (mCRPC). Patients and Methods: Patients with mCRPC initiating abiraterone acetate therapy underwent 2 serial metastatic site core needle biopsies after study activation on May 17, 2013. Multiple cores were obtained, and from each core, 1- to 2-mm segments were separated and formalin fixed for histopathologic examination. Tumor purity was determined for DNA and RNA from the rest of the biopsy specimen. RNA quality was assessed by calculation of an RNA integrity number and a DV200 score. Results: A total of 89 patients underwent 172 uniformly processed core needle biopsies (89 on visit 1 and 83 on visit 2) between May 30, 2013, and September 10, 2015. Metastatic sites biopsied included bone (131), lymph nodes (31), liver (5), lung (3), and pelvic soft tissues (2). Of the 172 biopsy specimens, 85 (49%) had at least one of the multiple cores positive for tumor on histopathologic examination (53 of 88 [60%] from visit 1 and 32 of 83 [39%] from visit 2; P=.006). Metastatic carcinoma was observed in 50 of 130 bone lesion specimens (38%), compared to 35 of 41 nonbone specimens (85%) (P

Abstract Background The role of tumor protein D54 in breast cancer has not been studied and its function in breast cancer remains unclear. In our previous pharmacogenomic studies using lymphoblastoid cell line (LCL), this protein has been identified to affect metformin response. Although metformin has been widely studied as a prophylactic and chemotherapeutic drug, there is still a lack of biomarkers predicting the response to metformin in breast cancer. In this study, we revealed the novel function of TPD54 in breast cancer through understanding how TPD54 altered the cancer cell sensitivity to metformin. Methods The role of TPD54 in altering cellular sensitivity to metformin treatment was carried out by either knockdown or overexpression of TPD54, followed by measuring cell viability and reactive oxygen species (ROS) production in MCF7 breast cancer cell line and breast cancer patient-derived xenografts. Functional analysis of TPD54 in breast cancer cells was demonstrated by studying TPD54 protein localization and identification of potential binding partners of TPD54 through immunoprecipitation followed by mass spectrometry. The effect of TPD54 on pyruvate dehydrogenase (PDH) protein regulation was demonstrated by western blot, immunoprecipitation, and site-directed mutagenesis. Results TPD54 inhibited colony formation and enhanced cellular sensitivity to metformin treatment in MCF7 cells and breast cancer patient-derived xenografts. Mechanistic study indicated that TPD54 had mitochondrial localization, bound to and stabilized pyruvate dehydrogenase E1α by blocking pyruvate dehydrogenase kinase 1 (PDK1)-mediated serine 232 phosphorylation. TPD54 knockdown increased PDH E1α protein degradation and led to decreased PDH enzyme activity, which reduced mitochondrial oxygen consumption and reactive oxygen species (ROS) production, thus contributing to the resistance of breast cancer cells to metformin treatment. Conclusion We have discovered a novel mechanism by which TPD54 regulates pyruvate dehydrogenase and affects the sensitivity of breast cancer to metformin treatment. Our findings highlight the important post-translational regulation of PDK1 on PDH E1α and the potential application of TPD54 as a biomarker for selecting tumors that may be sensitive to metformin therapy. These provide new insights into understanding the regulation of PDH complexes and the resistance mechanisms of cancer cells to metformin treatment.

Aromatase inhibitors (AIs) reduce breast cancer recurrence and prolong survival, but up to 30% of patients exhibit recurrence. Using a genome-wide association study of patients entered on MA.27, a phase III randomized trial of anastrozole versus exemestane, we identified a single nucleotide polymorphism (SNP) in CUB And Sushi multiple domains 1 (CSMD1) associated with breast cancer–free interval, with the variant allele associated with fewer distant recurrences. Mechanistically, CSMD1 regulates CYP19 expression in an SNP- and drug-dependent fashion, and this regulation is different among 3 AIs: anastrozole, exemestane, and letrozole. Overexpression of CSMD1 sensitized AI-resistant cells to anastrozole but not to the other 2 AIs. The SNP in CSMD1 that was associated with increased CSMD1 and CYP19 expression levels increased anastrozole sensitivity, but not letrozole or exemestane sensitivity. Anastrozole degrades estrogen receptor α (ERα), especially in the presence of estradiol (E2). ER+ breast cancer organoids and AI- or fulvestrant-resistant breast cancer cells were more sensitive to anastrozole plus E2 than to AI alone. Our findings suggest that the CSMD1 SNP might help to predict AI response, and anastrozole plus E2 serves as a potential new therapeutic strategy for patients with AI- or fulvestrant-resistant breast cancers.

We present Knowledge Engine for Genomics (KnowEnG), a free-to-use computational system for analysis of genomics data sets, designed to accelerate biomedical discovery. It includes tools for popular bioinformatics tasks such as gene prioritization, sample clustering, gene set analysis, and expression signature analysis. The system specializes in "knowledge-guided" data mining and machine learning algorithms, in which user-provided data are analyzed in light of prior information about genes, aggregated from numerous knowledge bases and encoded in a massive "Knowledge Network." KnowEnG adheres to "FAIR" principles (findable, accessible, interoperable, and reuseable): its tools are easily portable to diverse computing environments, run on the cloud for scalable and cost-effective execution, and are interoperable with other computing platforms. The analysis tools are made available through multiple access modes, including a web portal with specialized visualization modules. We demonstrate the KnowEnG system's potential value in democratization of advanced tools for the modern genomics era through several case studies that use its tools to recreate and expand upon the published analysis of cancer data sets.

Oral anticoagulant (OAC) therapy has been the main treatment approach for stroke prevention for decades. Warfarin is the most widely prescribed OAC in the United States, but is difficult to manage due to variability in dose requirements across individuals. Pharmacogenomics may mitigate risk concerns related to warfarin use by fostering the opportunity to facilitate individualized medicine approaches to warfarin treatment (e.g., genome-guided dosing). While various economic evaluations exist examining the cost-effectiveness of pharmacogenomics testing for warfarin, few observational studies exist to support these studies, with even fewer using genotype as the main exposure of interest. We examined a cohort of individuals initiating warfarin therapy between 2004 and 2017 and examined bleeding and cost outcomes for the year following initiation using Mayo Clinic's billing and administrative data, as well the Mayo Clinic Rochester Cost Data Warehouse. Analyses included descriptive summaries, comparison of characteristics across exposure groups, reporting of crude outcomes, and multivariate analyses. We included N = 1,143 patients for analyses. Just over a third of our study population (34.9%) carried a warfarin-sensitive phenotype. Sensitive individuals differed in their baseline characteristics by being of older age and having a higher number of comorbid conditions; myocardial infarction, diabetes, and cancer in particular. The occurrence of bleeding events was not significantly different across exposure groups. No significant differences across exposure groups existed in either the likelihood of incurring all-cause healthcare costs or in the magnitude of those costs. Warfarin-sensitive individuals were no more likely to utilize cardiovascular-related healthcare services; however, they had lower total and inpatient cardiovascular-related costs compared to warfarin-insensitive patients. No significant differences existed in any other categories of costs. We found limited evidence that warfarin-sensitive individuals have different healthcare spending than warfarin-insensitive individuals. Additional real-world studies are needed to support the traditional economic evaluations currently existing in the literature.

Abstract Background Development of distant metastases involves a complex multistep biological process termed the invasion-metastasis cascade, which includes dissemination of cancer cells from the primary tumor to secondary organs. NOTCH developmental signaling plays a critical role in promoting epithelial-to-mesenchymal transition, tumor stemness, and metastasis. Although all four NOTCH receptors show oncogenic properties, the unique role of each of these receptors in the sequential stepwise events that typify the invasion-metastasis cascade remains elusive. Methods We have established metastatic xenografts expressing high endogenous levels of NOTCH3 using estrogen receptor alpha-positive (ERα+) MCF-7 breast cancer cells with constitutive active Raf-1/mitogen-associated protein kinase (MAPK) signaling (vMCF-7Raf-1) and MDA-MB-231 triple-negative breast cancer (TNBC) cells. The critical role of NOTCH3 in inducing an invasive phenotype and poor outcome was corroborated in unique TNBC cells resulting from a patient-derived brain metastasis (TNBC-M25) and in publicly available claudin-low breast tumor specimens collected from participants in the Molecular Taxonomy of Breast Cancer International Consortium database. Results In this study, we identified an association between NOTCH3 expression and development of metastases in ERα+ and TNBC models. ERα+ breast tumor xenografts with a constitutive active Raf-1/MAPK signaling developed spontaneous lung metastases through the clonal expansion of cancer cells expressing a NOTCH3 reprogramming network. Abrogation of NOTCH3 expression significantly reduced the self-renewal and invasive capacity of ex vivo breast cancer cells, restoring a luminal CD44low/CD24high/ERαhigh phenotype. Forced expression of the mitotic Aurora kinase A (AURKA), which promotes breast cancer metastases, failed to restore the invasive capacity of NOTCH3-null cells, demonstrating that NOTCH3 expression is required for an invasive phenotype. Likewise, pharmacologic inhibition of NOTCH signaling also impaired TNBC cell seeding and metastatic growth. Significantly, the role of aberrant NOTCH3 expression in promoting tumor self-renewal, invasiveness, and poor outcome was corroborated in unique TNBC cells from a patient-derived brain metastasis and in publicly available claudin-low breast tumor specimens. Conclusions These findings demonstrate the key role of NOTCH3 oncogenic signaling in the genesis of breast cancer metastasis and provide a compelling preclinical rationale for the design of novel therapeutic strategies that will selectively target NOTCH3 to halt metastatic seeding and to improve the clinical outcomes of patients with breast cancer.

Abstract Background Transfer of genetic material from microbes or viruses into the host genome is known as horizontal gene transfer (HGT). The integration of viruses into the human genome is associated with multiple cancers, and these can now be detected using next-generation sequencing methods such as whole genome sequencing and RNA-sequencing. Results We designed a novel computational workflow, HGT-ID, to identify the integration of viruses into the human genome using the sequencing data. The HGT-ID workflow primarily follows a four-step procedure: i) pre-processing of unaligned reads, ii) virus detection using subtraction approach, iii) identification of virus integration site using discordant and soft-clipped reads and iv) HGT candidates prioritization through a scoring function. Annotation and visualization of the events, as well as primer design for experimental validation, are also provided in the final report. We evaluated the tool performance with the well-understood cervical cancer samples. The HGT-ID workflow accurately detected known human papillomavirus (HPV) integration sites with high sensitivity and specificity compared to previous HGT methods. We applied HGT-ID to The Cancer Genome Atlas (TCGA) whole-genome sequencing data (WGS) from liver tumor-normal pairs. Multiple hepatitis B virus (HBV) integration sites were identified in TCGA liver samples and confirmed by HGT-ID using the RNA-Seq data from the matched liver pairs. This shows the applicability of the method in both the data types and cross-validation of the HGT events in liver samples. We also processed 220 breast tumor WGS data through the workflow; however, there were no HGT events detected in those samples. Conclusions HGT-ID is a novel computational workflow to detect the integration of viruses in the human genome using the sequencing data. It is fast and accurate with functions such as prioritization, annotation, visualization and primer design for future validation of HGTs. The HGT-ID workflow is released under the MIT License and available at http://kalarikrlab.org/Software/HGT-ID.html.

Abstract Background Patient-derived xenografts (PDXs) are increasingly used in cancer research as a tool to inform cancer biology and drug response. Most available breast cancer PDXs have been generated in the metastatic setting. However, in the setting of operable breast cancer, PDX models both sensitive and resistant to chemotherapy are needed for drug development and prospective data are lacking regarding the clinical and molecular characteristics associated with PDX take rate in this setting. Methods The Breast Cancer Genome Guided Therapy Study (BEAUTY) is a prospective neoadjuvant chemotherapy (NAC) trial of stage I-III breast cancer patients treated with neoadjuvant weekly taxane+/-trastuzumab followed by anthracycline-based chemotherapy. Using percutaneous tumor biopsies (PTB), we established and characterized PDXs from both primary (untreated) and residual (treated) tumors. Tumor take rate was defined as percent of patients with the development of at least one stably transplantable (passed at least for four generations) xenograft that was pathologically confirmed as breast cancer. Results Baseline PTB samples from 113 women were implanted with an overall take rate of 27.4% (31/113). By clinical subtype, the take rate was 51.3% (20/39) in triple negative (TN) breast cancer, 26.5% (9/34) in HER2+, 5.0% (2/40) in luminal B and 0% (0/3) in luminal A. The take rate for those with pCR did not differ from those with residual disease in TN (p = 0.999) and HER2+ (p = 0.2401) tumors. The xenografts from 28 of these 31 patients were such that at least one of the xenografts generated had the same molecular subtype as the patient. Among the 35 patients with residual tumor after NAC adequate for implantation, the take rate was 17.1%. PDX response to paclitaxel mirrored the patients’ clinical response in all eight PDX tested. Conclusions The generation of PDX models both sensitive and resistant to standard NAC is feasible and these models exhibit similar biological and drug response characteristics as the patients’ primary tumors. Taken together, these models may be useful for biomarker discovery and future drug development.

An amendment to this paper has been published and can be accessed via the original article.

Abstract Background Breast cancer is the most common invasive cancer among women. Currently, there are only a few models used for therapy selection, and they are often poor predictors of therapeutic response or take months to set up and assay. In this report, we introduce a microfluidic OrganoPlate® platform for extracellular matrix (ECM) embedded tumor culture under perfusion as an initial study designed to investigate the feasibility of adapting this technology for therapy selection. Methods The triple negative breast cancer cell lines MDA-MB-453, MDA-MB-231 and HCC1937 were selected based on their different BRCA1 and P53 status, and were seeded in the platform. We evaluate seeding densities, ECM composition (Matrigel®, BME2rgf, collagen I) and biomechanical (perfusion vs static) conditions. We then exposed the cells to a series of anti-cancer drugs (paclitaxel, olaparib, cisplatin) and compared their responses to those in 2D cultures. Finally, we generated cisplatin dose responses in 3D cultures of breast cancer cells derived from 2 PDX models. Results The microfluidic platform allows the simultaneous culture of 96 perfused micro tissues, using limited amounts of material, enabling drug screening of patient-derived material. 3D cell culture viability is improved by constant perfusion of the medium. Furthermore, the drug response of these triple negative breast cancer cells was attenuated by culture in 3D and differed from that observed in 2D substrates. Conclusions We have investigated the use of a high-throughput organ-on-a-chip platform to select therapies. Our results have raised the possibility to use this technology in personalized medicine to support selection of appropriate drugs and to predict response to therapy in a real time fashion.

Abstract Background We previously performed a case–control genome-wide association study in women treated with selective estrogen receptor modulators (SERMs) for breast cancer prevention and identified single nucleotide polymorphisms (SNPs) in ZNF423 as potential biomarkers for response to SERM therapy. The ZNF423rs9940645 SNP, which is approximately 200 bp away from the estrogen response elements, resulted in the SNP, estrogen, and SERM-dependent regulation of ZNF423 expression and, “downstream”, that of BRCA1. Methods Electrophoretic mobility shift assay–mass spectrometry was performed to identify proteins binding to the ZNF423 SNP and coordinating with estrogen receptor alpha (ERα). Clustered, regularly interspaced short palindromic repeats (CRISPR)/Cas9 genome editing was applied to generate ZR75-1 breast cancer cells with different ZNF423 SNP genotypes. Both cultured cells and mouse xenograft models with different ZNF423 SNP genotypes were used to study the cellular responses to SERMs and poly(ADP-ribose) polymerase (PARP) inhibitors. Results We identified calmodulin-like protein 3 (CALML3) as a key sensor of this SNP and a coregulator of ERα, which contributes to differential gene transcription regulation in an estrogen and SERM-dependent fashion. Furthermore, using CRISPR/Cas9-engineered ZR75-1 breast cancer cells with different ZNF423 SNP genotypes, striking differences in cellular responses to SERMs and PARP inhibitors, alone or in combination, were observed not only in cells but also in a mouse xenograft model. Conclusions Our results have demonstrated the mechanism by which the ZNF423 rs9940645 SNP might regulate gene expression and drug response as well as its potential role in achieving more highly individualized breast cancer therapy.

Pharmacogenetics: Sequencing finds DNA variants affecting chemoprevention response DNA sequencing has revealed genetic variants that explain inherited variation in responses to preventative drug therapy for breast cancer. Richard Weinshilboum from the Mayo Clinic in Rochester, Minnesota, USA, and colleagues previously showed that genetic variations in or near two genes—ZNF423 and CTSO—affected how well the drugs tamoxifen and raloxifene reduced the rate of breast cancer occurrence. Building on that finding, Weinshilboum’s team has now sequenced these two genes in 199 patients who developed breast cancer during chemopreventative drug therapy and 201 patients who did not. They identified around 4000 single-nucleotide polymorphisms across each gene, 21 of which were close to estrogen response element (ERE) motifs to which ER α binds. Functional studies pointed to molecular ways in which some of these gene variants alter drug activity.

Abstract Background Identification of genes whose basal mRNA expression predicts the sensitivity of tumor cells to cytotoxic treatments can play an important role in individualized cancer medicine. It enables detailed characterization of the mechanism of action of drugs. Furthermore, screening the expression of these genes in the tumor tissue may suggest the best course of chemotherapy or a combination of drugs to overcome drug resistance. Results We developed a computational method called ProGENI to identify genes most associated with the variation of drug response across different individuals, based on gene expression data. In contrast to existing methods, ProGENI also utilizes prior knowledge of protein–protein and genetic interactions, using random walk techniques. Analysis of two relatively new and large datasets including gene expression data on hundreds of cell lines and their cytotoxic responses to a large compendium of drugs reveals a significant improvement in prediction of drug sensitivity using genes identified by ProGENI compared to other methods. Our siRNA knockdown experiments on ProGENI-identified genes confirmed the role of many new genes in sensitivity to three chemotherapy drugs: cisplatin, docetaxel, and doxorubicin. Based on such experiments and extensive literature survey, we demonstrate that about 73% of our top predicted genes modulate drug response in selected cancer cell lines. In addition, global analysis of genes associated with groups of drugs uncovered pathways of cytotoxic response shared by each group. Conclusions Our results suggest that knowledge-guided prioritization of genes using ProGENI gives new insight into mechanisms of drug resistance and identifies genes that may be targeted to overcome this phenomenon.

Overexpression of SNAIL confers tumour cells with cancer stem-like characteristics associated with tumour progression. Here the authors show that inhibition of CDK4/6 blocks tumour metastasis in triple negative breast cancer by targeting DUB3 which in turns deubiquitinates and stabilises SNAIL1.

The Akt pathway is a central regulator that promotes cell survival in response to extracellular signals. Depletion of SIRT7, an NAD+-dependent deacetylase that is the least-studied sirtuin, is known to significantly increase Akt activity in mice through unknown mechanisms. In this study, we demonstrate that SIRT7 depletion in breast cancer cells results in Akt hyper-phosphorylation and increases cell survival following genotoxic stress. Mechanistically, SIRT7 specifically interacts with and deacetylates FKBP51 at residue lysines 28 and 155 (K28 and K155), resulting in enhanced interactions among FKBP51, Akt, and PHLPP, as well as Akt dephosphorylation. Mutating both lysines to arginines abolishes the effect of SIRT7 on Akt activity through FKBP51 deacetylation. Finally, energy stress strengthens SIRT7-mediated effects on Akt dephosphorylation through FKBP51 and thus sensitizes cancer cells to cytotoxic agents. These results reveal a direct role of SIRT7 in Akt regulation and raise the possibility of using the glucose analog 2-deoxy-D-glucose (2DG) as a chemo-sensitizing agent.

Basal gene expression levels have been shown to be predictive of cellular response to cytotoxic treatments. However, such analyses do not fully reveal complex genotype- phenotype relationships, which are partly encoded in highly interconnected molecular networks. Biological pathways provide a complementary way of understanding drug response variation among individuals. In this study, we integrate chemosensitivity data from a large-scale pharmacogenomics study with basal gene expression data from the CCLE project and prior knowledge of molecular networks to identify specific pathways mediating chemical response. We first develop a computational method called PACER, which ranks pathways for enrichment in a given set of genes using a novel network embedding method. It examines a molecular network that encodes known gene-gene as well as gene-pathway relationships, and determines a vector representation of each gene and pathway in the same low-dimensional vector space. The relevance of a pathway to the given gene set is then captured by the similarity between the pathway vector and gene vectors. To apply this approach to chemosensitivity data, we identify genes whose basal expression levels in a panel of cell lines are correlated with cytotoxic response to a compound, and then rank pathways for relevance to these response-correlated genes using PACER. Extensive evaluation of this approach on benchmarks constructed from databases of compound target genes and large collections of drug response signatures demonstrates its advantages in identifying compound-pathway associations compared to existing statistical methods of pathway enrichment analysis. The associations identified by PACER can serve as testable hypotheses on chemosensitivity pathways and help further study the mechanisms of action of specific cytotoxic drugs. More broadly, PACER represents a novel technique of identifying enriched properties of any gene set of interest while also taking into account networks of known gene-gene relationships and interactions.

Development of resistance to chemotherapy is a major obstacle in extending the survival of patients with cancer. Although originally defined as an immune checkpoint molecule, B7-H1 (also named as PD-L1 or CD274) was found to play a role in cancer chemoresistance; however, the underlying mechanism of action of B7-H1 in regulation of chemotherapy sensitivity remains unclear in cancer cells. Here we show that development of chemoresistance depends on an increased activation of ERK in cancer cells overexpressing B7-H1. Conversely, B7-H1 knockout (KO) by CRISPR/Cas9 renders human cancer cells susceptible to chemotherapy in a cell-context dependent manner through a reduced activation of p38 MAPK. B7-H1 was found to associate with the catalytic subunit of DNA-dependent protein kinase (DNA-PKcs) and this association promoted or maintained the activation of ERK or p38 MAPK in cancer cells. Importantly, we found that targeting B7-H1 by anti-B7-H1 monoclonal antibody (H1A) increased the sensitivity of human triple negative breast cancer cells to cisplatin therapy in vivo. Our results suggest that targeting B7-H1 by an antibody capable of disrupting B7-H1 signals may be a new approach to sensitize cancer cells to chemotherapy.

BRCA1 is a key regulator of DNA double-strand break repair, functioning to promote homologous recombination and repress non-homologous end-joining. Here the authors show that the ubiquitin ligase UHRF1 is recruited to breaks by BRCA1, where it targets RIF1 and thereby facilitates recombination.

Studies reported a strong genetic correlation between the Big Five personality traits and major depressive disorder (MDD). Moreover, personality traits are thought to be associated with response to antidepressants treatment that might partly be mediated by genetic factors. In this study, we examined whether polygenic scores (PGSs) derived from the Big Five personality traits predict treatment response and remission in patients with MDD who were prescribed selective serotonin reuptake inhibitors (SSRIs). In addition, we performed meta-analyses of genome-wide association studies (GWASs) on these traits to identify genetic variants underpinning the cross-trait polygenic association. The PGS analysis was performed using data from two cohorts: the Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS, n = 529) and the International SSRI Pharmacogenomics Consortium (ISPC, n = 865). The cross-trait GWAS meta-analyses were conducted by combining GWAS summary statistics on SSRIs treatment outcome and on the personality traits. The results showed that the PGS for openness and neuroticism were associated with SSRIs treatment outcomes at p

Neutropenia secondary to chemotherapy in breast cancer patients can be life-threatening and there are no biomarkers available to predict the risk of drug-induced neutropenia in those patients. We previously performed a genome-wide association study (GWAS) for neutropenia events in women with breast cancer who were treated with 5-fluorouracil, epirubicin and cyclophosphamide and recruited to the SUCCESS-A trial. A genome-wide significant single-nucleotide polymorphism (SNP) signal in the tumor necrosis factor superfamily member 13B (TNFSF13B) gene, encoding the cytokine B-cell activating factor (BAFF), was identified in that GWAS. Taking advantage of these existing GWAS data, in the present study we utilized a pathway-based analysis approach by leveraging knowledge of the pharmacokinetics and pharmacodynamics of drugs and breast cancer pathophysiology to identify additional SNPs/genes associated with the underlying etiology of chemotherapy-induced neutropenia. We identified three SNPs in the hyaluronan mediated motility receptor (HMMR) gene that were significantly associated with neutropenia (p < 1.0E-04). Those three SNPs were trans-expression quantitative trait loci for the expression of TNFSF13B (p < 1.0E-04). The minor allele of these HMMR SNPs was associated with a decreased TNFSF13B mRNA level. Additional functional studies performed with lymphoblastoid cell lines (LCLs) demonstrated that LCLs possessing the minor allele for the HMMR SNPs were more sensitive to drug treatment. Knock-down of TNFSF13B in LCLs and HL-60 promyelocytic cells and treatment of those cells with BAFF modulated the cell sensitivity to chemotherapy treatment. These results demonstrate that HMMR SNP-dependent cytotoxicity of these chemotherapeutic agents might be related to TNFSF13B expression level. In summary, utilizing a pathway-based approach for the analysis of GWAS data, we identified additional SNPs in the HMMR gene that were associated with neutropenia and also were correlated with TNFSF13B expression.

DBC1 (deleted in breast cancer 1), also known as CCAR2 or KIAA1967, is an important negative regulator of SIRT1 and cellular stress response. Although the Dbc1 gene localizes at a region that is homozygously deleted in breast cancer, its role in tumorigenesis remains unclear. It has been suggested to be either a tumor suppressor or an oncogene. Therefore, the function of DBC1 in cancer needs to be further explored. Here, we report that Dbc1 knockout mice are tumor prone, suggesting that DBC1 functions as a tumor suppressor in vivo. Our data suggest that the increased tumor incidence in Dbc1 knockout mice is independent of Sirt1. Instead, we found that DBC1 loss results in less p53 protein in vitro and in vivo. DBC1 directly binds p53 and stabilizes it through competition with MDM2. These studies reveal that DBC1 plays an important role in tumor suppression through p53 regulation.

Tamoxifen is one of the most commonly employed endocrine therapies for patients with estrogen receptor α (ERα)-positive breast cancer. Unfortunately the clinical benefit is limited due to intrinsic and acquired drug resistance. We previously reported a genome-wide association study that identified common SNPs near the CTSO gene and in ZNF423 associated with development of breast cancer during tamoxifen therapy in the NSABP P-1 and P-2 breast cancer prevention trials. Here, we have investigated their roles in ERα-positive breast cancer growth and tamoxifen response, focusing on the mechanism of CTSO. We performed in vitro studies including luciferase assays, cell proliferation, and mass spectrometry-based assays using ERα-positive breast cancer cells and a panel of genomic data-rich lymphoblastoid cell lines. We report that CTSO reduces the protein levels of BRCA1 and ZNF423 through cysteine proteinase-mediated degradation. We also have identified a series of transcription factors of BRCA1 that are regulated by CTSO at the protein level. Importantly, the variant CTSO SNP genotypes are associated with increased CTSO and decreased BRCA1 protein levels that confer resistance to tamoxifen. Characterization of the effect of both CTSO SNPs and ZNF423 SNPs on tamoxifen response revealed that cells with different combinations of CTSO and ZNF423 genotypes respond differently to Tamoxifen, PARP inhibitors or the combination of the two drugs due to SNP dependent differential regulation of BRCA1 levels. Therefore, these genotypes might be biomarkers for selection of individual drug to achieve the best efficacy.

The inflammatory tumoral-immune response alters the physiology of the tumor microenvironment, which may attenuate genomic instability. In addition to inducing inflammatory immune responses, several pathogenic bacteria produce genotoxins. However the extent of microbial contribution to the tumor microenvironment biology remains unknown. We utilized The Cancer Genome Atlas, (TCGA) breast cancer data to perform a novel experiment utilizing unmapped and mapped RNA sequencing read evidence to minimize laboratory costs and effort. Our objective was to characterize the microbiota and associate the microbiota with the tumor expression profiles, for 668 breast tumor tissues and 72 non-cancerous adjacent tissues. The prominent presence of Proteobacteria was increased in the tumor tissues and conversely Actinobacteria abundance increase in non-cancerous adjacent tissues. Further, geneset enrichment suggests Listeria spp to be associated with the expression profiles of genes involved with epithelial to mesenchymal transitions. Moreover, evidence suggests H. influenza may reside in the surrounding stromal material and was significantly associated with the proliferative pathways: G2M checkpoint, E2F transcription factors, and mitotic spindle assembly. In summary, further unraveling this complicated interplay should enable us to better diagnose and treat breast cancer patients.

Objectives To determine the frequency of pathogenic inherited mutations in 157 select genes from patients with metastatic castrate-resistant prostate cancer (mCRPC).Design Observational.Setting Multisite US-based cohort.Participants Seventy-one adult male patients with histological confirmation of prostate cancer, and had progressive disease while on androgen deprivation therapy.Results Twelve patients (17.4%) showed evidence of carrying pathogenic or likely pathogenic germline variants in the ATM, ATR, BRCA2, FANCL, MSR1, MUTYH, RB1, TSHR and WRN genes. All but one patient opted in to receive clinically actionable results at the time of study initiation. We also found that pathogenic germline BRCA2 variants appear to be enriched in mCRPC compared to familial prostate cancers.Conclusions Pathogenic variants in cancer-susceptibility genes are frequently observed in patients with mCRPC. A substantial proportion of patients with mCRPC or their family members would derive clinical utility from mutation screening.Trial registration number NCT01953640; Results.