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Background: Cyclin dependent 4/6 kinase inhibitors (CDK4/6i) and endocrine therapy (ET) have improved progression-free survival (PFS) and overall survival in hormone-receptor (HR)-positive metastatic breast cancer (MBC), but progression of disease is inevitable. Serum thymidine kinase-1 (TK1) is a secreted marker of proliferation that is prognostic in patients (pts) with HR-positive, HER2-negative MBC and may be predictive of ET and CDK 4/6i response. PROMISE (NCT0281902) is a prospective study enrolling women with HR-positive MBC starting palbociclib (Pb) + letrozole (L) (1st line) or Pb + fulvestrant (2nd line). We undertook a comprehensive “omic” assessment of blood, tumor, urine and the fecal microbiome in order to identify novel genomic variants and pathways associated with an early decline in TK1 (measured after 2 months) and PFS. Additionally, patient derived xenografts/organoids were generated at baseline and progression to test new therapeutic approaches to overcome resistance to CDK4/6i and ET. We report the initial association between the baseline genomic landscape and baseline TK1 levels. Methods: FFPE tumor biopsies were obtained for DNA/RNA sequencing (TempusTM) and blood samples for TK1 (Divitum® assay, Biovica) were collected pretreatment (pre-Pb) and after 2 cycles of Pb + ET (post-Pb2). Both whole-exome (exome capture) sequencing (WES) and RNA-Seq used the Integrated DNA Technologies xGen Exome Research Panel v1.0 capture kit. TK1+ disease was defined as > 200 Du/L and TK1- disease as below limit of detection up to 200 Du/L. We tested the association between genomic and transcriptomic characteristics with baseline TK1 data in pretreatment samples where both WES and RNA-seq and TK1 was available. The data were analyzed using bioinformatics pipelines for somatic and germline mutations and copy number alterations. The current analysis focuses on baseline 1st-line pre-Pb omics data in conjunction with baseline TK1 levels. The database was locked for analysis on 5/29/2020. Results: Thirty-three pts (median age: 59 yrs.) were evaluable, with paired samples for TK1 in 32. Six pts had TK1+ disease pre-Pb and post-Pb2. Twenty-two pts had TK1- disease pre-Pb and post-Pb2. Four pts had a decrease in TK1 after 2 cycles of treatment that altered the classification from TK1+ to TK1-. Both baseline RNA seq and serum TK1 (n=16) were available for 4 TK1+ and 12 TK1- pts. In this group, 476 genes were differentially regulated (398 upregulated; 78 downregulated). Pathway analysis demonstrated enrichment in complement and coagulation cascade pathway, PPAR signaling pathway, and metabolism-related pathways related to up-regulation of CYP and UGT gene families. Further testing for the association of WES data with baseline TK1+ (n=8) and TK1- (n=16) disease demonstrated somatic copy number variations on chromosomes 6, 11, 12 and 15. CDK4 copy number gains were observed in 3/8 TK1+ pts and 0/16 TK1- pts. We also observed that somatic mutations (LOH, copy number and/or SNV/INDELs) were more prevalent in the TK1+ compared to the TK1- pre-Pb group in several cancer-associated genes (FAS [p=0.06] PTEN, PIK3CB, NAB2, SOX9 and FAT1 [p=0.08], TP53, and MAP2K4 [p=0.22]). Conversely, we also noted that 6/7 pts with GATA3 mutations had TK1- disease (p=0.23). Conclusions: Using a comprehensive “omics” approach, our data suggest that a secreted biomarker of proliferation (TK1) obtained prior to initiating CDK4/6i and ET for the first line treatment of HR+ MBC is associated with established and novel genes and pathways associated with prognosis of pts receiving ET and CDK 4/6i. Analysis of on-treatment (after 2 cycles) tumor RNA seq and its association with change in TK1 as well as data from the 2nd-line cohort will be presented at the meeting. Citation Format: Ciara C O Sullivan, Krishna R Kalari, Vera J Suman, Peter T Vedell, Ann Moyer, Abraham D Eyman Casey, Jason Sinnwell, Xiaojia Tang, Kevin Thompson, Alvaro Moreno-Aspitia, Donald W Northfelt, Minetta C Liu, Tufia C Haddad, Saranya Chumsri, Prema Peethambaram, Kathryn J Ruddy, Karthik V Giridhar, Roberto A Leon-Ferre, Adrian Nordmark, Mattias Bergqvist, Brendan P McMenomy, Richard M Weinshilboum, Liewei Wang, Matthew P Goetz. Novel genomic variants and pathways associated with baseline serum thymidine kinase 1 levels in HR-positive HER2-negative MBC patients commencing palbociclib and letrozole [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS5-24.

Background: Androgen receptor (AR) is expressed in 60%-90% of breast cancers. The role of AR in breast cancer largely depends on estrogen receptor (ER) status and remains controversial. In ER+ cancers, AR expression is associated with improved prognosis. Enzalutamide (ENZ), an AR antagonist, impairs AR signaling, inhibits ER+/AR+ breast cancer cell proliferation and has been shown to mitigate resistance to anti-estrogen therapies. A recent study identified that RAD140, an AR agonist, suppressed the growth of ER+/AR+ breast cancer via stimulating AR signaling, resulting in down regulation of ERα. Both AR agonists and antagonists are effective in treating ER+ breast cancers. Identifying the subgroup of patients who most likely will benefit from an individual drug is important in clinical practice. Here, we sought to characterize the relative AR to ER levels and their relationships to drug response in ER+ breast cancers.Methods: We evaluated AR and ER expression levels among 68 samples with ER+ breast cancers. On each tumor, IHC staining for AR and ER were performed and results were scored by multiplying the percentage of positive cells by the intensity. TCGA-RPPA (reverse phase protein array) dataset was used to verify AR protein distribution in ER+ cancers. We created MCF7 and T47D cells with doxycycline-inducible AR and ER expression system to manipulate the relative AR to ER ratios and determined the antitumor activity of ENZ and RAD140 by cell proliferation assays. The levels of AR and ER in cells were measured by western blot. Linear regression was used to test the association between dose-response area under curve (AUC) and AR, ER levels. Drug preference was modeled against AR/ER expression levels (AR/ER ratios) using logistic regression.Results: Among 68 ER+ breast cancers, 69.12% were AR-positive. More than 2/3 cases (48 of 68) had more ER than AR expressed. The AR to ER ratios varied from 0 to 6. In the TCGA cohort, 84.15% of 347 ER+ patients had AR/ER ratios less than 1.The range of AR to ER ratios in TCGA dataset were comparable with our data (0.004 to 4.210). In our cell line models, the AR/ER ratios were controlled between 0.19 to 4.05. We found that the AUC of RAD140 was negatively associated with AR protein levels (P=0.008) and AR/ER ratios (P=0.013), and not significantly associated with ER expression levels. On the other hand, the AUC of ENZ was significantly negatively associated with ER protein (P=0.0016) but postiviely associated with AR/ER ratio (p=0.037). Preferred treatment comparing efficacy of the two drug can be best determined at extremes of AR/ER ratios. Using clinically relevant dosages, our model predicted that ENZ (10µM) would be a preferred treatment choice and have a better treatment efficacy compared with RAD140 when the AR/ER ratio was ≤ 0.42, whereas RAD140 (1µM) would be the preferred choice with a better treatment efficacy compared with ENZ when AR/ER ratios were ≥ 3.1 The efficacy preference of the two drugs are equivocal for AR/ER ratios between 0.42 and 3.10. Conclusion: We developed preclinical models using AR and ER expression levels to predict AR-targeting drug response. The results support the use of RAD140 in AR high patients and those with an AR/ER ratio >3.10, and enzalutamide in AR low patients and those with an AR/ER ratio Citation Format: Lixuan Wei, Huanyao Gao, Jia Yu, Duan Liu, Huan Zhang, Thanh Nguyen, Marie R Passow, Jodi M Carter, Richard M Weinshilboum, James N Ingle, Liewei Wang. Computational modeling of androgen receptor (AR) and estrogen receptor as predictive biomarkers of response to AR agonists and antagonists [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS5-36.

Heterogeneity in the clinical presentation of major depressive disorder and response to antidepressants limits clinicians’ ability to accurately predict a specific patient’s eventual response to therapy. Validated depressive symptom profiles may be an important tool for identifying poor outcomes early in the course of treatment. To derive these symptom profiles, we first examined data from 947 depressed subjects treated with selective serotonin reuptake inhibitors (SSRIs) to delineate the heterogeneity of antidepressant response using probabilistic graphical models (PGMs). We then used unsupervised machine learning to identify specific depressive symptoms and thresholds of improvement that were predictive of antidepressant response by 4 weeks for a patient to achieve remission, response, or nonresponse by 8 weeks. Four depressive symptoms (depressed mood, guilt feelings and delusion, work and activities and psychic anxiety) and specific thresholds of change in each at 4 weeks predicted eventual outcome at 8 weeks to SSRI therapy with an average accuracy of 77% (p = 5.5E-08). The same four symptoms and prognostic thresholds derived from patients treated with SSRIs correctly predicted outcomes in 72% (p = 1.25E-05) of 1996 patients treated with other antidepressants in both inpatient and outpatient settings in independent publicly-available datasets. These predictive accuracies were higher than the accuracy of 53% for predicting SSRI response achieved using approaches that (i) incorporated only baseline clinical and sociodemographic factors, or (ii) used 4-week nonresponse status to predict likely outcomes at 8 weeks. The present findings suggest that PGMs providing interpretable predictions have the potential to enhance clinical treatment of depression and reduce the time burden associated with trials of ineffective antidepressants. Prospective trials examining this approach are forthcoming.

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.

Selective serotonin reuptake inhibitors (SSRIs) are standard of care for major depressive disorder (MDD) pharmacotherapy, but only approximately half of these patients remit on SSRI therapy. Our previous genome-wide association study identified a single-nucleotide polymorphism (SNP) signal across the glutamate-rich 3 (ERICH3) gene that was nearly genome-wide significantly associated with plasma serotonin (5-HT) concentrations, which were themselves associated with SSRI response for MDD patients enrolled in the Mayo Clinic PGRN-AMPS SSRI trial. In this study, we performed a meta-analysis which demonstrated that those SNPs were significantly associated with SSRI treatment outcomes in four independent MDD trials. However, the function of ERICH3 and molecular mechanism(s) by which it might be associated with plasma 5-HT concentrations and SSRI clinical response remained unclear. Therefore, we characterized the human ERICH3 gene functionally and identified ERICH3 mRNA transcripts and protein isoforms that are highly expressed in central nervous system cells. Coimmunoprecipitation identified a series of ERICH3 interacting proteins including clathrin heavy chain which are known to play a role in vesicular function. Immunofluorescence showed ERICH3 colocalization with 5-HT in vesicle-like structures, and ERICH3 knock-out dramatically decreased 5-HT staining in SK-N-SH cells as well as 5-HT concentrations in the culture media and cell lysates without changing the expression of 5-HT synthesizing or metabolizing enzymes. Finally, immunofluorescence also showed ERICH3 colocalization with dopamine in human iPSC-derived neurons. These results suggest that ERICH3 may play a significant role in vesicular function in serotonergic and other neuronal cell types, which might help explain its association with antidepressant treatment response.

Purpose Pharmacogenomics (PGx) studies how inherited genetic variations in individuals affect drug absorption, distribution, and metabolism. PGx panel testing can potentially help improve efficiency and accuracy in individualizing therapy. This study compared the cost-effectiveness between preemptive PGx panel testing, reactive PGx panel testing and usual care (no testing) in cardiovascular disease management. Methods We developed a decision analytic model from the US payer's perspective for a hypothetical cohort of 10,000 patients ≥45 years old, using a short-term decision tree and long-term Markov model. The testing panel included the following gene-drug pairs: CYP2C19-clopidogrel, CYP2C9/VKORC1-warfarin, and SLCO1B1-statins with 30 test-return days. Costs were reported in 2019 US dollars and effectiveness was measured in quality-adjusted life years (QALYs). The primary outcome was incremental cost-effectiveness ratio (ICER = ΔCost/ΔQALY), assuming 3% discount rate for costs and QALYs. Scenario and probabilistic sensitivity analyses were performed to assess the impact of demographics, risk level, and follow-up timeframe. Results Preemptive testing was found to be cost-effective compared with usual care (ICER $86,227/QALY) at the willingness-to-pay threshold of $100,000/QALY while reactive testing was not (ICER $148,726/QALY). Sensitivity analyses suggested that our cost-effectiveness results were sensitive to longer follow-up, and the age group 45-64 years. Conclusion Compared with usual care, preemptive PGx panel testing was cost-effective in cardiovascular disease management.

Objectives Based on our previous findings that postmenopausal women with estrone (E1) and estradiol (E2) concentrations at or above 1.3 pg/ml and 0.5 pg/ml, respectively, after 6 months of adjuvant anastrozole therapy had a three-fold risk of recurrence, we aimed to identify a single-nucleotide polymorphism (SNP)-based model that would predict elevated E1 and E2 and then validate it in an independent dataset. Patients and methods The test set consisted of 322 women from the M3 study and the validation set consisted of 152 patients from MA.27. All patients were treated with adjuvant anastrozole, had on-anastrozole E1 and E2 concentrations and genome-wide genotyping. Results SNPs were identified from the M3 genome-wide association study. The best model to predict the E1-E2 phenotype with high balanced accuracy was a support vector machine model using clinical factors plus 46 SNPs. We did not have an independent cohort that is similar to the M3 study with clinical, E1-E2 phenotypes and genotype data to test our model. Hence, we chose a nested matched case-control cohort (MA.27 study) for testing. Our E1-E2 model was not validated but we found the MA.27 validation cohort was both clinically and genomically different. Conclusions We identified a SNP-based model that had excellent performance characteristics for predicting the phenotype of elevated E1 and E2 in women treated with anastrozole. This model was not validated in an independent dataset but that dataset was clinically and genomically substantially different. The model will need validation in a prospective study.

Purpose:To determine if the degree of estrogen suppression with aromatase inhibitors (AI: anastrozole, exemestane, letrozole) is associated with efficacy in early-stage breast cancer, and to examine for differences in the mechanism of action between the three AIs.Experimental Design:Matched case–control studies [247 matched sets from MA.27 (anastrozole vs. exemestane) and PreFace (letrozole) trials] were undertaken to assess whether estrone (E1) or estradiol (E2) concentrations after 6 months of adjuvant therapy were associated with risk of an early breast cancer event (EBCE). Preclinical laboratory studies included luciferase activity, cell proliferation, radio-labeled ligand estrogen receptor binding, surface plasmon resonance ligand receptor binding, and nuclear magnetic resonance assays.Results:Women with E1 ≥1.3 pg/mL and E2 ≥0.5 pg/mL after 6 months of AI treatment had a 2.2-fold increase in risk (P = 0.0005) of an EBCE, and in the anastrozole subgroup, the increase in risk of an EBCE was 3.0-fold (P = 0.001). Preclinical laboratory studies examined mechanisms of action in addition to aromatase inhibition and showed that only anastrozole could directly bind to estrogen receptor α (ERα), activate estrogen response element-dependent transcription, and stimulate growth of an aromatase-deficient CYP19A1−/− T47D breast cancer cell line.Conclusions:This matched case–control clinical study revealed that levels of estrone and estradiol above identified thresholds after 6 months of adjuvant anastrozole treatment were associated with increased risk of an EBCE. Preclinical laboratory studies revealed that anastrozole, but not exemestane or letrozole, is a ligand for ERα. These findings represent potential steps towards individualized anastrozole therapy.

Background Sex differences between psychotic depression (PD) and non-psychotic depression (NPD) have received little systematic study. This study was conducted to investigate sex difference in patients with psychotic and non-psychotic major depressive disorder in a Chinese Han population. Methods In this cross-sectional study, a total of 1718 first-episode and drug-naive outpatients with major depressive disorder were recruited. Demographic and clinical characteristics were collected. All subjects were rated on the Hamilton Depression Rating Scale (HAMD), Hamilton Anxiety Rating Scale (HAMA) and the Positive and Negative Syndrome Scale (PANSS). Results The prevalence of PD in female patients (10.97%) was higher than that in male patients (7.99%). Analysis of variance (ANOVA) showed that female patients were older compared with male patients in NPD group, but there were no significant differences in demographic and clinical variables between female and male PD patients. Further, there were no sex differences in the scores of HAMD, HAMA and positive symptom subscale of PANSS in both PD and NPD groups. Two-way ANOVA showed that PD patients had significantly higher scores on the HAMD, HAMA and positive symptom subscale of PANSS than non-PD patients. However, there were no significant effects of sex and sex* subtypes. Limitations The main limitations are cross-sectional design and inability to control selection bias. Conclusions Our findings show significant differences in clinical profiles between PD and NPD patients; however, no sex difference has been observed in the either PD or NPD patients.

Purpose To examine the evidence on the cost-effectiveness of implementing pharmacogenomics (PGx) in cardiovascular disease (CVD) care. Methods We conducted a systematic review using multiple databases from inception to 2018. The titles and abstracts of cost-effectiveness studies on PGx-guided treatment in CVD care were screened, and full texts were extracted. Results We screened 909 studies and included 46 to synthesize. Acute coronary syndrome and atrial fibrillation were the predominantly studied conditions (59%). Most studies (78%) examined warfarin–CYP2C9/VKORC1 or clopidogrel–CYP2C19. A payer’s perspective was commonly used (39%) for cost calculations, and most studies (46%) were US-based. The majority (67%) of the studies found PGx testing to be cost-effective in CVD care, but cost-effectiveness varied across drugs and conditions. Two studies examined PGx panel testing, of which one examined pre-emptive testing strategies. Conclusion We found mixed evidence on the cost-effectiveness of PGx in CVD care. Supportive evidence exists for clopidogrel–CYP2C19 and warfarin–CYP2C9/VKORC1, but evidence is limited in other drug–gene combinations. Gaps persist, including unclear explanation of perspective and cost inputs, underreporting of study design elements critical to economic evaluations, and limited examination of PGx panel and pre-emptive testing for their cost-effectiveness. This review identifies the need for further research on economic evaluations of PGx implementation.

Background: The combination of abiraterone acetate and prednisone (AA/P) is used to treat metastatic prostate cancer, but molecular predictors of treatment response are not well elucidated. We evaluated plasma circulating tumor DNA– (ctDNA-) based copy number alterations (CNAs) to determine treatment-related predictive and prognostic biomarkers for metastatic castration-resistant prostate cancer (mCRPC). Methods: Serial plasma specimens were prospectively collected from 88 chemotherapy-naive mCRPC patients before and after 12 weeks of AA/P treatment. Sequencing-based CNA analyses were performed on 174 specimens. We evaluated CNA-associated 12-week responses for primary resistance, time to treatment change (TTTC) for secondary resistance, and overall survival for prognosis (P < .05). Associations with primary resistance were analyzed using the Fisher exact test. Kaplan–Meier survival curves and Cox regression analyses were used to determine the associations of CNAs with acquired resistance and overall survival. Results: ctDNA reduced by 3.89% in responders and increased by 0.94% in nonresponders (P = .0043). Thirty-one prostate cancer–related genes from whole genome CNAs were tested. AR and AR enhancer amplification were associated with primary resistance (P = .0039) and shorter TTTC (P = .0003). ZFHX3 deletion and PIK3CA amplification were associated with primary resistance (P = .026 and P = .017, respectively), shorter TTTC (P = .0008 and P= .0016, respectively), and poor survival (P = .0025 and P = .0022, respectively). CNA-based risk scores combining selected significant associations (AR, NKX3.1, and PIK3CA) at the univariate level with TTTC were predictive of secondary resistance (P = .0002). and established prognoses for survival based on CNAs in ZFHX3, RB1, PIK3CA, and OPHN1 (P = .002). Multigene risk scores were more predictive than individual genes or clinical risk factors (P < .05). Conclusion: Plasma ctDNA CNAs and risk scores can predict mCRPC-state treatment and survival outcomes.

Background: The combination of cyclin dependent 4/6 kinase inhibitors (CDK4/6i) with endocrine therapy (ET) has resulted in clinically significant improvements in progression-free survival (PFS) and overall survival (OS) in hormone-receptor (HR)-positive metastatic breast cancer (MBC). However, most patients’ disease ultimately progresses on CDK4/6i and ET. Therefore, further research is necessary to understand the mechanisms driving primary and secondary resistance. PROMISE is a multicenter prospective cohort study enrolling women with HR-positive MBC commencing treatment with palbociclib + letrozole (1st line) or palbociclib + fulvestrant (2nd Line). The study provides a comprehensive “omic” assessment of blood, tumor, urine and the fecal microbiome to identify molecular or cellular features associated with primary endocrine resistance (e.g. disease progression ≤ 12 months on treatment) and acquired resistance to CDK 4/6i. Additionally, patient derived xenografts and organoids are created to test new drug strategies designed to overcome resistance to CDK 4/6i and ET. Here, we present initial sequencing results from pretreatment biospecimens collected from PROMISE study participants. Methods: On-study tumor biopsies and blood samples were collected for DNA/RNA sequencing (TempusTM). The analyzed biospecimens were all obtained prior to initiation of palbociclib and ET. We correlated patient clinical characteristics (phenotypes) with molecular data and responses to protocol treatment. The data were analyzed using a series of cutting-edge bioinformatics pipelines for somatic and germline mutations in addition to copy number alterations (CNAs). The study database was locked for analysis on 06/20/2019. Results: We analyzed the somatic single nucleotide variants/INDELs (sSNV/INDEL) profiles across the tumor samples to determine the genes that were least likely to occur as a result of background mutation processes. Twenty-six patients had somatic copy number alterations (sCNA) and/or sSNV/INDEL in at least one of 18 genes with the most significant sSNV/INDEL profiles (p < 0.03) which included clinically and biologically relevant genes. The genes with the most statistically significant sSNV/INDEL mutation profiles were GATA3, PIK3CA, CDH1, and ESR1 (p < 0.0009). We observed a high percentage of tumors with somatic alterations in GATA3 (23% sSNV/INDEL, 15% sCNA), PIK3CA (38%, 12%), CDH1 (19%, 50%) and ESR1 (19%, 58%). ESR1 mutations were more frequent in patients receiving 2nd line treatment. Other frequently altered genes included TP53 (15%, 46%), MAP2K4 (8%, 50%), DNAAF1 (8%, 50%), and CDKN1B (8%, 35%). Further, ZNF317 and F3 were altered in 9 and 7 patients, respectively. Twenty-four samples had alterations in at least one of the CDK4/6 pathway genes (RB1, CCNE2, CCND1, CDK6, ESR1, CDKN2A, CCND3, CDK4, CDK2 and CCNE1). Four patients progressed on therapy; three of the four patients had mutations in PIK3CA, and one had a mutation in ESR1. Results of the RNA sequencing data (N=26) will be presented at the SABCS meeting. Conclusions: This is the first report of a prospective study designed to characterize the genomic landscape of ER+/HER2- MBC prior to palbociclib treatment. We observed high frequencies of known targetable alterations in PIK3CA and ESR1, including in patients that progress, which is consistent with previous reports. RNA sequencing data will be presented at the meeting. Citation Format: Ciara C O Sullivan, Krishna R Kalari, Vera J Suman, Peter T Vedell, Ann Moyer, Erin Carlson, Jason Sinnwell, Tejaswi Alaparthi, Xiaojia Tang, Kevin Thompson, Jaeyun Sung, Alvaro Moreno-Aspitia, Donald Northfelt, Minetta C Liu, Tufia C Haddad, Prema Peethambaram, Saranya Chumsri, Kathryn J Ruddy, Karthik V Giridhar, Roberto A Leon-Ferre, Paula Gill, Mohammad Ranginwala, Asad Javed, Sameer Batoo, Brendan P. McMenomy, Richard Weinshilboum, Liewei Wang, Matthew P Goetz. Comprehensive tumor sequencing to identify biomarkers of palbociclib response: First report of the PROMISE study [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P2-11-07.

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 p p p

Androgen receptor (AR)-positive prostate cancers (PCa) and estrogen receptor (ER)-positive luminal breast cancers (BCa) are generally less responsive to immunotherapy compared to certain tumor types such as melanoma. However, the underlying mechanisms are not fully elucidated. Here we found that FOXA1 overexpression inversely correlated with interferon (IFN) signature and antigen presentation gene expression in PCa and BCa patients. FOXA1 bound STAT2 DNA binding domain and suppressed STAT2 DNA binding activity, IFN signaling gene expression and cancer immune response independently of the transactivation activity of FOXA1 and its mutations detected in prostate and breast cancers. Increased FOXA1 expression promoted cancer immuno- and chemotherapy resistance in mice and PCa and BCa patients. These findings were also validated in bladder cancer expressing high level FOXA1. FOXA1 overexpression could be a prognostic factor to predict therapy resistance and a viable target to sensitize luminal prostate, breast and bladder cancer to immuno- and chemotherapy.

BackgroundPrediction of the response of cancer patients to different treatments and identification of biomarkers of drug sensitivity are two major goals of individualized medicine. In this study, we developed a deep learning framework called TINDL, completely trained on preclinical cancer cell lines, to predict the response of cancer patients to different treatments. TINDL utilizes a tissue-informed normalization to account for the tissue and cancer type of the tumours and to reduce the statistical discrepancies between cell lines and patient tumours. In addition, this model identifies a small set of genes whose mRNA expression are predictive of drug response in the trained model, enabling identification of biomarkers of drug sensitivity.ResultsUsing data from two large databases of cancer cell lines and cancer tumours, we showed that this model can distinguish between sensitive and resistant tumours for 10 (out of 14) drugs, outperforming various other machine learning models. In addition, our siRNA knockdown experiments on 10 genes identified by this model for one of the drugs (tamoxifen) confirmed that all of these genes significantly influence the drug sensitivity of the MCF7 cell line to this drug. In addition, genes implicated for multiple drugs pointed to shared mechanism of action among drugs and suggested several important signaling pathways.ConclusionsIn summary, this study provides a powerful deep learning framework for prediction of drug response and for identification of biomarkers of drug sensitivity in cancer.

We previously reported that testis-specific Y-encoded-like protein (TSPYLs) are transcription regulators for CYP3A4, CYP2C9, and CYP2C19. Here, we observed dual roles for TSPYLs in mediating serotonin transport and the metabolism of selective serotonin reuptake inhibitors (SSRIs) in patients with major depressive disorder (MDD). The widely prescribed SSRIs, citalopram, and escitalopram are metabolized mainly by CYP2C19. The TSPYL1 rs3828743 single nucleotide polymorphism (SNP), which decreases its suppression of CYP2C19 expression, was associated with rapid escitalopram metabolism and worse treatment response in the Mayo PGRN-AMPS clinical trial. We also found that TSPYLs can regulate expression of the serotonin transporter protein, SLC6A4, and, in turn, serotonin transport into cells. The SNPs in tight linkage disequilibrium with the TSPYL1 rs10223646 SNP were significantly correlated with baseline severity of depression in patients with MDD in the Sequenced Treatment Alternatives to Relieve Depression and International SSRI Pharmacogenomics Consortium clinical trials. Our findings suggest that genetic variation in TSPYL genes may be novel indicators for baseline severity of depression and SSRI poor response.

Triple-negative breast cancer (TNBC) accounts for 15–20% of all invasive breast cancers and tends to have aggressive histological features and poor clinical outcomes. Unlike, estrogen receptor- or HER2-positive diseases, TNBC patients currently lack the US FDA-approved targeted therapies. DNA methylation is a critical mechanism of epigenetic modification. It is well known that aberrant DNA methylation contributes to the malignant transformation of cells by silencing critical tumor suppressor genes. DNA methyltransferase inhibitors reactivate silenced tumor suppressor genes and result in tumor growth arrest, with therapeutic effects observed in patients with hematologic malignancies. The antitumor effect of these DNA methyltransferase inhibitors has also been explored in solid tumors, especially in TNBC that currently lacks targeted therapies.

OBJECTIVE. The purpose of this study is to prospectively compare the size of invasive breast cancer before and after neoadjuvant chemotherapy (NAC) at breast MRI and molecular breast imaging (MBI) and to assess the accuracy of post-NAC MBI and MRI relative to pathologic analysis. SUBJECTS AND METHODS. Women with invasive breast cancer greater than or equal to 1.5 cm were enrolled to compare the longest dimension before and after NAC at MRI and MBI. MBI was performed on a dual-detector cadmium zinc telluride system after administration of 6.5 mCi (240 MBq) 99mTc-sestamibi. The accuracy of MRI and MBI in assessing residual disease (invasive disease or ductal carcinoma in situ) was determined relative to pathologic examination. RESULTS. The longest dimension at MRI was within 1.0 cm of that at MBI in 72.3% of cases before NAC and 70.1% of cases after NAC. The difference between the longest dimension at imaging after NAC and pathologic tumor size was within 1 cm for 58.7% of breast MRI cases and 59.6% of MBI cases. Ninety patients underwent both MRI and MBI after NAC. In the 56 patients with invasive residual disease, 10 (17.9%) cases were negative at MRI and 23 (41.1%) cases were negative at MBI. In the 34 patients with breast pathologic complete response, there was enhancement in 10 cases (29.4%) at MRI and uptake in six cases (17.6%) at MBI. Sensitivity, specificity, positive predictive value, and negative predictive value after NAC were 82.8%, 69.4%, 81.4%, and 71.4%, respectively, for MRI and 58.9%, 82.4%, 84.6%, and 54.9%, respectively, for MBI. CONCLUSION. Breast MRI and MBI showed similar disease extent before NAC. MBI may be an alternative to breast MRI in patients with a contraindication to breast MRI. Neither modality showed sufficient accuracy after NAC in predicting breast pathologic complete response to obviate tissue diagnosis to assess for residual invasive disease. Defining the extent of residual disease compared with pathologic evaluation was also limited after NAC for both breast MRI and MBI.

Metabolomics provides valuable tools for the study of drug effects, unraveling the mechanism of action and variation in response due to treatment. In this study we used electrochemistry-based targeted metabolomics to gain insights into the mechanisms of action of escitalopram/citalopram focusing on a set of 31 metabolites from neurotransmitter-related pathways. Overall, 290 unipolar patients with major depressive disorder were profiled at baseline, after 4 and 8 weeks of drug treatment. The 17-item Hamilton Depression Rating Scale (HRSD17) scores gauged depressive symptom severity. More significant metabolic changes were found after 8 weeks than 4 weeks post baseline. Within the tryptophan pathway, we noted significant reductions in serotonin (5HT) and increases in indoles that are known to be influenced by human gut microbial cometabolism. 5HT, 5-hydroxyindoleacetate (5HIAA), and the ratio of 5HIAA/5HT showed significant correlations to temporal changes in HRSD17 scores. In the tyrosine pathway, changes were observed in the end products of the catecholamines, 3-methoxy-4-hydroxyphenylethyleneglycol and vinylmandelic acid. Furthermore, two phenolic acids, 4-hydroxyphenylacetic acid and 4-hydroxybenzoic acid, produced through noncanconical pathways, were increased with drug exposure. In the purine pathway, significant reductions in hypoxanthine and xanthine levels were observed. Examination of metabolite interactions through differential partial correlation networks revealed changes in guanosine–homogentisic acid and methionine–tyrosine interactions associated with HRSD17. Genetic association studies using the ratios of these interacting pairs of metabolites highlighted two genetic loci harboring genes previously linked to depression, neurotransmission, or neurodegeneration. Overall, exposure to escitalopram/citalopram results in shifts in metabolism through noncanonical pathways, which suggest possible roles for the gut microbiome, oxidative stress, and inflammation-related mechanisms.

We set out to determine whether machine learning–based algorithms that included functionally validated pharmacogenomic biomarkers joined with clinical measures could predict selective serotonin reuptake inhibitor (SSRI) remission/response in patients with major depressive disorder (MDD). We studied 1,030 white outpatients with MDD treated with citalopram/escitalopram in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN‐AMPS; n = 398), Sequenced Treatment Alternatives to Relieve Depression (STAR*D; n = 467), and International SSRI Pharmacogenomics Consortium (ISPC; n = 165) trials. A genomewide association study for PGRN‐AMPS plasma metabolites associated with SSRI response (serotonin) and baseline MDD severity (kynurenine) identified single nucleotide polymorphisms (SNPs) in DEFB1,ERICH3,AHR, and TSPAN5 that we tested as predictors. Supervised machine‐learning methods trained using SNPs and total baseline depression scores predicted remission and response at 8 weeks with area under the receiver operating curve (AUC) > 0.7 (P 69% (P ≤ 0.07) in STAR*D and ISPC. These results demonstrate that machine learning can achieve accurate and, importantly, replicable prediction of SSRI therapy response using total baseline depression severity combined with pharmacogenomic biomarkers.

Patient-derived xenografts (PDX) are generated in immune deficient mice and demonstrate histologic and molecular features similar to their corresponding human tumors. However, murine tumors (non-human) spontaneously occur in these models. 120 consecutive patients with high-risk primary breast cancer enrolled in the prospective neoadjuvant BEAUTY study had tumor tissue obtained at the time of diagnosis. These tumor cells, including initial tissue and subsequent generations, were injected into either NSG (n = 365) or NOD-SCID (n = 396) female mice. Mice with initial tumor growth sufficient for transfer to the 2nd generation underwent histologic review by pathologists, including Ki67 staining. After passaging the tumors for up to 4 generations, at least one primary mouse tumor was detected from 24 of the 54 PDX-lines, for a frequency of 3.2% (24 mice out of 761 mice), including murine lymphomas (n = 13), mammary tumors (n = 7), osteosarcomas (n = 2), and hemangiosarcomas (n = 2). While true PDX showed scattered strong staining with Ki67, murine tumors were Ki67 negative. No significant differences (p = 0.062) were observed comparing development of murine tumors in NOD-SCID (n = 8) vs NSG mice (n = 16). While PDX are a useful tool in cancer research, there is a potential for spontaneous murine tumors to arise, which could alter results of studies utilizing PDX. Morphologic review by a pathologist, potentially along with Ki67 staining, is necessary to ensure that tumor growth represents the desired PDX prior to use in downstream studies. This study is the first prospective study evaluating the frequency, type, and time frame for development of non-human tumors.

Selective serotonin reuptake inhibitors (SSRIs) are first-line antidepressants for the treatment of major depressive disorder (MDD). However, treatment response during an initial therapeutic trial is often poor and is difficult to predict. Heterogeneity of response to SSRIs in depressed patients is partly driven by co-occurring somatic disorders such as coronary artery disease (CAD) and obesity. CAD and obesity may also be associated with metabolic side effects of SSRIs. In this study, we assessed the association of CAD and obesity with treatment response to SSRIs in patients with MDD using a polygenic score (PGS) approach. Additionally, we performed cross-trait meta-analyses to pinpoint genetic variants underpinnings the relationship of CAD and obesity with SSRIs treatment response. First, PGSs were calculated at different p value thresholds (PT) for obesity and CAD. Next, binary logistic regression was applied to evaluate the association of the PGSs to SSRIs treatment response in a discovery sample (ISPC, N = 865), and in a replication cohort (STAR*D, N = 1,878). Finally, a cross-trait GWAS meta-analysis was performed by combining summary statistics. We show that the PGSs for CAD and obesity were inversely associated with SSRIs treatment response. At the most significant thresholds, the PGS for CAD and body mass index accounted 1.3%, and 0.8% of the observed variability in treatment response to SSRIs, respectively. In the cross-trait meta-analyses, we identified (1) 14 genetic loci (including NEGR1, CADM2, PMAIP1, PARK2) that are associated with both obesity and SSRIs treatment response; (2) five genetic loci (LINC01412, PHACTR1, CDKN2B, ATXN2, KCNE2) with effects on CAD and SSRIs treatment response. Our findings implicate that the genetic variants of CAD and obesity are linked to SSRIs treatment response in MDD. A better SSRIs treatment response might be achieved through a stratified allocation of treatment for MDD patients with a genetic risk for obesity or CAD.

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

Aromatase inhibitors (AIs) are the treatment of choice for hormone receptor-positive early breast cancer in postmenopausal women. None of the third-generation AIs are superior to the others in terms of efficacy. We attempted to identify genetic factors that could differentiate between the effectiveness of adjuvant anastrozole and exemestane by examining single-nucleotide polymorphism (SNP)-treatment interaction in 4,465 patients. A group of SNPs were found to be differentially associated between anastrozole and exemestane regarding outcomes. However, they showed no association with outcome in the combined analysis. We followed up common SNPs near LY75 and GPR160 that could differentiate anastrozole from exemestane efficacy. LY75 and GPR160 participate in epithelial-to-mesenchymal transition and growth pathways, in both cases with SNP-dependent variation in regulation. Collectively, these studies identified SNPs that differentiate the efficacy of anastrozole and exemestane and they suggest additional genetic biomarkers for possible use in selecting an AI for a given patient.

Purpose: Patient-derived xenografts (PDX) are a research tool for studying cancer biology and drug response phenotypes. While engraftment rates are higher for tumors with more aggressive characteristics, it is uncertain whether engraftment is prognostic for cancer recurrence. Patients and Methods: In a prospective study of patients with breast cancer treated with neoadjuvant chemotherapy (NAC) with taxane ± trastuzumab followed by anthracycline-based chemotherapy, we report the association between breast cancer events and PDX engraftment using tumors derived from treatment naïve (pre-NAC biopsies from 113 patients) and treatment resistant (post-NAC at surgery from 34 patients). Gray test was used to assess whether the cumulative incidence of a breast cancer event differs with respect to either pre-NAC PDX engraftment or post-NAC PDX engraftment. Results: With a median follow-up of 5.7 years, the cumulative incidence of breast cancer relapse did not differ significantly according to pre-NAC PDX engraftment (5-year rate: 13.6% vs. 13.4%; P = 0.89). However, the incidence of a breast event was greater for patients with post-NAC PDX engraftment (5-year rate: 50.0% vs. 19.6%), but this did not achieve significance (P = 0.11). Conclusions: In treatment-naïve breast cancer receiving standard NAC, PDX engraftment was not prognostic for breast cancer recurrence. Further study is needed to establish whether PDX engraftment in the treatment-resistant setting is prognostic for cancer recurrence.

Background: With few targeted therapies available for triple-negative breast cancer (TNBC), there is a critical need to identify biomarkers that can predict chemotherapy-response. Copy number amplifications (CNAmps) have been implicated in tumorigenesis, progression, and treatment response. Previously, we performed a prospective neoadjuvant chemotherapy study (BEAUTY) and identified a 5 Mb CNAmp in TNBC patients associated with pathological complete response rates to taxane- and anthracycline-based chemotherapy. Here we further interrogated this amplicon and its role in chemotherapy response. Methods: Using siRNA screening of genes identified in the 5 Mb CNAmp, followed by cytotoxicity assays, we identified RDH10 significantly associated with chemo-sensitivity in TNBC cell lines. Functional studies were performed using RDH10 knockdown or overexpression followed by cytotoxicity, cell proliferation, luciferase reporter assays, HPLC or western blot.Results: RDH10 is a rate-limiting step involved in the synthesis of all-trans retinoic acid (ATRA) - the active metabolite of retinoid metabolism that is frequently aberrant in cancer. Previous studies demonstrated ATRA to be a potent inhibitor of PIN1, a key regulator of several oncogenic signaling pathways often amplified in TNBC. We discovered that genetic manipulation of RDH10 affected ATRA intracellular concentrations, contributing to PIN1 degradation and taxane-response. Furthermore, co-treatment of TNBC cells with ATRA significantly increased taxane-sensitivity.Conclusion: RDH10 amplification increases endogenous levels of ATRA and degrades PIN1 in TNBC. ATRA-mediated degradation of PIN1 sensitizes TNBC to chemotherapy, suggesting that RDH10 may be a novel biomarker of taxane response and the addition of ATRA to standard chemotherapy may improve chemo-sensitivity.

Importance After percutaneous coronary intervention (PCI), patients withCYP2C19*2or*3loss-of-function (LOF) variants treated with clopidogrel have increased risk of ischemic events. Whether genotype-guided selection of oral P2Y12 inhibitor therapy improves ischemic outcomes is unknown. Objective To determine the effect of a genotype-guided oral P2Y12 inhibitor strategy on ischemic outcomes inCYP2C19LOF carriers after PCI. Design, Setting, and Participants Open-label randomized clinical trial of 5302 patients undergoing PCI for acute coronary syndromes (ACS) or stable coronary artery disease (CAD). Patients were enrolled at 40 centers in the US, Canada, South Korea, and Mexico from May 2013 through October 2018; final date of follow-up was October 2019. Interventions Patients randomized to the genotype-guided group (n = 2652) underwent point-of-care genotyping.CYP2C19LOF carriers were prescribed ticagrelor and noncarriers clopidogrel. Patients randomized to the conventional group (n = 2650) were prescribed clopidogrel and underwent genotyping after 12 months. Main Outcomes and Measures The primary end point was a composite of cardiovascular death, myocardial infarction, stroke, stent thrombosis, and severe recurrent ischemia at 12 months. A secondary end point was major or minor bleeding at 12 months. The primary analysis was in patients withCYP2C19LOF variants, and secondary analysis included all randomized patients. The trial had 85% power to detect a minimum hazard ratio of 0.50. Results Among 5302 patients randomized (median age, 62 years; 25% women), 82% had ACS and 18% had stable CAD; 94% completed the trial. Of 1849 withCYP2C19LOF variants, 764 of 903 (85%) assigned to genotype-guided therapy received ticagrelor, and 932 of 946 (99%) assigned to conventional therapy received clopidogrel. The primary end point occurred in 35 of 903CYP2C19LOF carriers (4.0%) in the genotype-guided therapy group and 54 of 946 (5.9%) in the conventional therapy group at 12 months (hazard ratio [HR], 0.66 [95% CI, 0.43-1.02];P = .06). None of the 11 prespecified secondary end points showed significant differences, including major or minor bleeding inCYP2C19LOF carriers in the genotype-guided group (1.9%) vs the conventional therapy group (1.6%) at 12 months (HR, 1.22 [95% CI, 0.60-2.51];P = .58). Among all randomized patients, the primary end point occurred in 113 of 2641 (4.4%) in the genotype-guided group and 135 of 2635 (5.3%) in the conventional group (HR, 0.84 [95% CI, 0.65-1.07];P = .16). Conclusions and Relevance AmongCYP2C19LOF carriers with ACS and stable CAD undergoing PCI, genotype-guided selection of an oral P2Y12 inhibitor, compared with conventional clopidogrel therapy without point-of-care genotyping, resulted in no statistically significant difference in a composite end point of cardiovascular death, myocardial infarction, stroke, stent thrombosis, and severe recurrent ischemia based on the prespecified analysis plan and the treatment effect that the study was powered to detect at 12 months. Trial Registration ClinicalTrials.gov Identifier:NCT01742117

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., A germline variation within the CSMD1 gene predicts aromatase inhibitor response in breast cancer.

PURPOSE: Describe CYP2C19 sequencing results in the largest series of clopidogrel-treated cases with stent thrombosis (ST), the closest clinical phenotype to clopidogrel resistance. Evaluate the impact of CYP2C19 genetic variation detected by next-generation sequencing (NGS) with comprehensive annotation and functional studies. METHODS: Seventy ST cases on clopidogrel identified from the PLATO trial (n = 58) and Mayo Clinic biorepository (n = 12) were matched 1:1 with controls for age, race, sex, diabetes mellitus, presentation, and stent type. NGS was performed to cover the entire CYP2C19 gene. Assessment of exonic variants involved measuring in vitro protein expression levels. Intronic variants were evaluated for potential splicing motif variations. RESULTS: Poor metabolizers (n = 4) and rare CYP2C19*8, CYP2C19*15, and CYP2C19*11 alleles were identified only in ST cases. CYP2C19*17 heterozygote carriers were observed more frequently in cases (n = 29) than controls (n = 18). Functional studies of CYP2C19 exonic variants (n = 11) revealed 3 cases and only 1 control carrying a deleterious variant as determined by in vitro protein expression studies. Greater intronic variation unique to ST cases (n = 169) compared with controls (n = 84) was observed with predictions revealing 13 allele candidates that may lead to a potential disruption of splicing and a loss-of-function effect of CYP2C19 in ST cases. CONCLUSION: NGS detected CYP2C19 poor metabolizers and paradoxically greater number of so-called rapid metabolizers in ST cases. Rare deleterious exonic variation occurs in 4%, and potentially disruptive intronic alleles occur in 16% of ST cases. Additional studies are required to evaluate the role of these variants in platelet aggregation and clopidogrel metabolism.

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.

Breast cancer is the most common invasive cancer in women worldwide. Functional follow-up of breast cancer genome-wide association studies has led to the discovery of genes that regulate endocrine therapy response in a SNP- and drug-dependent manner. Here, we will present four examples in which functional genomic studies from breast cancer clinical trials led to novel pharmacogenomic insights and molecular mechanisms of selective estrogen receptor modulators and aromatase inhibitors. The approach utilized for studying genetic variability described in this review offers substantial potential for meaningful discoveries that move the field toward precision medicine for patients.

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. Electronic supplementary material The online version of this article (10.1186/s13058-018-1020-0) contains supplementary material, which is available to authorized users.

The prognostic significance of plasma cell-free DNA (cfDNA) androgen receptor amplification (ARamp) in metastatic castration-resistant prostate cancer (mCRPC) compared with circulating tumor cell (CTC) counts is not known. As part of correlative aims of a prospective study in mCRPC, concurrent and serial collections of plasma and CTCs were performed. Specimen collections were performed at baseline after progression on androgen deprivation therapy and then 12 weeks later. QuantStudio3D digital PCR system was used to determine plasma cfDNA AR copy number variations and Cell search assay for enumerating CTC counts. Association of baseline cfDNA ARamp status/CTC counts with overall survival (OS) (primary goal) was evaluated using Kaplan–Meier method and log-rank test (p ≤ 0.05 for significance) and receiver operator curves (ROCs) for ARamp status and CTC counts ≥5. A multivariate analysis was performed using Cox regression models that included ARamp, CTC counts, and other clinical factors. ARamp was detected in 19/70 patients at baseline. At the time of analysis, 28/70 patients had died (median follow-up 806 days; interquartile range: 535–966). ARamp was associated with poor OS (2-year OS of 35% in ARamp vs. 71% in non-ARamp; log-rank p value ≤0.0001). Baseline CTC counts ≥5 (vs.

Neoadjuvant chemotherapy (NAC) for breast cancer is widely utilized, and we performed genome-wide association studies (GWAS) to determine whether germ-line genetic variability was associated with benefit in terms of pathological complete response (pCR), disease-free survival, and overall survival in patients entered on the NSABP B-40 NAC trial, wherein patients were randomized to receive, or not, bevacizumab in addition to chemotherapy. Patient DNA samples were genotyped with the Illumina OmniExpress BeadChip. Replication was attempted with genotyping data from 1398 HER2-negative patients entered on the GeparQuinto NAC study in which patients were also randomized to receive, or not, bevacizumab in addition to chemotherapy. A total of 920 women from B-40 were analyzed, and 237 patients achieved a pCR. GWAS with three phenotypes (pCR, disease-free survival, overall survival) revealed no single nucleotide polymorphisms (SNPs) that were genome-wide significant (i.e. P≤5E-08) signals; P values for top SNPs were 2.04E-07, 5.61E-08, and 5.63E-08, respectively, and these SNPs were not significant in the GeparQuinto data. An ad-hoc GWAS was performed in the patients randomized to bevacizumab (457 patients with 128 pCR) who showed signals on chromosome 6, located within a gene, CDKAL1, that approached, but did not reach, genome-wide significance (top SNP rs7453577, P=2.97E-07). However, this finding was significant when tested in the GeparQuinto data set (P=0.04). In conclusion, we identified no SNPs significantly associated with NAC. The observation, in a hypothesis-generating GWAS, of an SNP in CDKAL1 associated with pCR in the bevacizumab arm of both B-40 and GeparQuinto requires further validation and study.

Triple-negative breast cancer (TNBC) is a heterogeneous disease with poor prognosis that lacks targeted therapies, especially in patients with chemotherapy-resistant disease. Since DNA methylation-induced silencing of tumor suppressors is common in cancer, reversal of promoter DNA hypermethylation by 5-aza-2'-deoxycytidine (decitabine), an FDA-approved DNA methyltransferase (DNMT) inhibitor, has proven effective in treating hematological neoplasms. However, its antitumor effect varies in solid tumors, stressing the importance of identifying biomarkers predictive of therapeutic response. Here, we focused on the identification of biomarkers to select decitabine-sensitive TNBC through increasing our understanding of the mechanism of decitabine action. We showed that protein levels of DNMTs correlated with response to decitabine in patient-derived xenograft (PDX) organoids originating from chemotherapy-sensitive and -resistant TNBCs, suggesting DNMT levels as potential biomarkers of response. Furthermore, all 3 methytransferases, DNMT1, DNMT3A, and DNMT3B, were degraded following low-concentration, long-term decitabine treatment both in vitro and in vivo. The DNMT proteins could be ubiquitinated by the E3 ligase, TNF receptor-associated factor 6 (TRAF6), leading to lysosome-dependent protein degradation. Depletion of TRAF6 blocked decitabine-induced DNMT degradation, conferring resistance to decitabine. Our study suggests a potential mechanism of regulating DNMT protein degradation and DNMT levels as response biomarkers for DNMT inhibitors in TNBCs.

Objective Children of parents with mental illness (COPMI) are vulnerable during the COVID-19 pandemic. The study aimed to assess the psychosocial impacts of the pandemic and identify potential factors influencing their mental health. Method 665 COPMI from six sites including Wuhan in China were enrolled. COPMI's mental health and the impacts of COVID-19 were assessed by an online survey. Univariate and multivariate analyses were performed to examine the association between impact factors and participants’ mental health. Results 16.1 % of participants were in abnormal range of mental health, with interpersonal relationship being the most common problem. 48.6 % of participants reported quite worried about the epidemic. All aspects of adverse effects of COVID-19 were more prevalent among COPMI in Wuhan than in other sites. Concerns about COVID-19 (OR = 1.7, p = 0.02), decreased family income (OR = 2.0, p = 0.02), being physically abused (OR = 2.1, p = 0.04), witnessing family members being physically abused (OR = 2.0, p = 0.03), and needs for promoting family members' mental health (OR = 2.2, p

Numerous pharmacogenetic clinical guidelines and recommendations have been published, but barriers have hindered the clinical implementation of pharmacogenetics. The Translational Pharmacogenetics Program (TPP) of the National Institutes of Health (NIH) Pharmacogenomics Research Network was established in 2011 to catalog and contribute to the development of pharmacogenetic implementations at eight US healthcare systems, with the goal to disseminate real-world solutions for the barriers to clinical pharmacogenetic implementation. The TPP collected and normalized pharmacogenetic implementation metrics through June 2015, including gene-drug pairs implemented, interpretations of alleles and diplotypes, numbers of tests performed and actionable results, and workflow diagrams. TPP participant institutions developed diverse solutions to overcome many barriers, but the use of Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines provided some consistency among the institutions. The TPP also collected some pharmacogenetic implementation outcomes (scientific, educational, financial, and informatics), which may inform healthcare systems seeking to implement their own pharmacogenetic testing programs.

Estrone (E1), the major circulating estrogen in postmenopausal women, promotes estrogen-receptor positive (ER+) breast tumor growth and proliferation. Two major reactions contribute to E1 plasma concentrations, aromatase (CYP19A1) catalyzed E1 synthesis from androstenedione and steroid sulfatase (STS) catalyzed hydrolysis of estrone conjugates (E1Cs). E1Cs have been associated with breast cancer risk and may contribute to tumor progression since STS is expressed in breast cancer where its activity exceeds that of aromatase. We performed genome-wide association studies (GWAS) to identify SNPs associated with variation in plasma concentrations of E1Cs, E1, and androstenedione in 774 postmenopausal women with resected early-stage ER+ breast cancer. Hormone concentrations were measured prior to aromatase inhibitor therapy. Multiple SNPs in SLCO1B1, a gene encoding a hepatic influx transporter, displayed genome-wide significant associations with E1C plasma concentrations and with the E1C/E1 ratio. The top SNP for E1C concentrations, rs4149056 (p = 3.74E−11), was a missense variant that results in reduced transporter activity. Patients homozygous for the variant allele had significantly higher average E1C plasma concentrations than did other patients. Furthermore, three other SLCO1B1 SNPs, not in LD with rs4149056, were associated with both E1C concentrations and the E1C/E1 ratio and were cis-eQTLs for SLCO1B3. GWAS signals of suggestive significance were also observed for E1, androstenedione, and the E1/androstenedione ratio. These results suggest a mechanism for genetic variation in E1C plasma concentrations as well as possible SNP biomarkers to identify ER+ breast cancer patients for whom STS inhibitors might be of clinical value.

// Arjun P. Athreya 1, * , Krishna R. Kalari 2, * , Junmei Cairns 3, * , Alan J. Gaglio 4 , Quin F. Wills 5, 7 , Nifang Niu 6 , Richard Weinshilboum 3 , Ravishankar K. Iyer 1 , Liewei Wang 3 1 Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Champaign, IL, USA 2 Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA 3 Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA 4 Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Champaign, IL, USA 5 Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK 6 Department of Pathology, University of Chicago, Chicago, IL, USA 7 Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK * These authors contributed equally to this work Correspondence to: Liewei Wang, email: wang.liewei@mayo.edu. Ravishankar K. Iyer, email: rkiyer@illinois.edu Keywords: single cell, RNA-seq, breast cancer, metformin, unsupervised learning Received: November 10, 2016 Accepted: February 18, 2017 Published: March 10, 2017 ABSTRACT We demonstrate that model-based unsupervised learning can uniquely discriminate single-cell subpopulations by their gene expression distributions, which in turn allow us to identify specific genes for focused functional studies. This method was applied to MDA-MB-231 breast cancer cells treated with the antidiabetic drug metformin, which is being repurposed for treatment of triple-negative breast cancer. Unsupervised learning identified a cluster of metformin-treated cells characterized by a significant suppression of 230 genes ( p-value < 2E-16). This analysis corroborates known studies of metformin action: a) pathway analysis indicated known mechanisms related to metformin action, including the citric acid (TCA) cycle, oxidative phosphorylation, and mitochondrial dysfunction ( p -value < 1E-9); b) 70% of these 230 genes were functionally implicated in metformin response; c) among remaining lesser functionally-studied genes for metformin-response was CDC42 , down-regulated in breast cancer treated with metformin. However, CDC42’s mechanisms in metformin response remained unclear. Our functional studies showed that CDC42 was involved in metformin-induced inhibition of cell proliferation and cell migration mediated through an AMPK-independent mechanism. Our results points to 230 genes that might serve as metformin response signatures, which needs to be tested in patients treated with metformin and, further investigation of CDC42 and AMPK-independence’s role in metformin's anticancer mechanisms.

Tumour metastasis, the spread of cancer cells from the original tumour site followed by growth of secondary tumours at distant organs, is the primary cause of cancer-related deaths and remains poorly understood. Here we demonstrate that inhibition of CDK4/6 blocks breast tumour metastasis in the triple-negative breast cancer model, without affecting tumour growth. Mechanistically, we identify a deubiquitinase, DUB3, as a target of CDK4/6; CDK4/6-mediated activation of DUB3 is essential to deubiquitinate and stabilize SNAIL1, a key factor promoting epithelial–mesenchymal transition and breast cancer metastasis. Overall, our study establishes the CDK4/6–DUB3 axis as an important regulatory mechanism of breast cancer metastasis and provides a rationale for potential therapeutic interventions in the treatment of breast cancer metastasis., 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.

Sirolimus used in transplantation is often associated with hypercholesterolemia. We measured serum lipid and PCSK9 levels in 51 heart transplant recipients who had their immunosuppressive therapy switched from calcineurin inhibitors to sirolimus. The switch resulted in a 23% increase in LDL cholesterol, and 46% increase in triglycerides and PCSK9 levels increased from 316 ± 105 ng/mL to 343 ± 107 ng/mL (p = 0.04), however the change in PCSK9 levels did not correlate with an increase in lipid levels (p = 0.2). To investigate the mechanism for the variability in the change in PCSK9 levels, lymphoblastoid cell lines were incubated with both sirolimus and everolimus, resulting in a 2-3 fold increase in PCSK9 expression and protein levels in mTOR inhibitor sensitive but not in mTOR inhibitor resistant cell lines. This first in human study demonstrates that sirolimus therapy is associated with elevation in PCSK9 levels which is not associated with sirolimus-induced hypercholesterolemia.

OBJECTIVE: To identify additional genetic variants beyond those observed in a previous genome-wide association study (GWAS) in women treated on the MA.27 clinical trial in which women were randomized to 5 years of adjuvant therapy with anastrozole or exemestane. PATIENTS AND METHODS: We performed a matched case–control study in 234 women who had a recurrence of breast cancer (cases) and 649 women who had not (controls). The analysis was restricted to White women with an estrogen receptor-positive breast cancer. Multiplex PCR-based targeted deep sequencing was performed of the MIR2052HG region on chromosome 8 between positions 75.4 and 75.7, a span of 300 kb, in an attempt to identify additional functional single nucleotide polymorphisms (SNPs). RESULTS: A total of 4677 unique variants were identified that had not been identified in the previous GWAS. Clinical Annotation of Variants analysis revealed 10 variants, including eight SNPs and two insertion–deletion mutations with moderate or high impact. However, none of the common and variant regions was significant after adjustment for the most significant SNP (rs13260300) identified in our previous GWAS. We performed haplotype analysis that revealed two regions in which the haplotypes lost significance when adjusted for this prior GWAS SNP and one region with two significant haplotypes (P=0.046 and 0.031) after adjusting for the GWAS SNP. CONCLUSION: We were unable to identify common or rare variant regions that added value to the findings from our previous GWAS. We did find two haplotypes that were significant after adjusting for our top GWAS SNP but these were considered to be of marginal value.

BackgroundAcylcarnitines have important functions in mitochondrial energetics and β-oxidation, and have been implicated to play a significant role in metabolic functions of the brain. This retrospective study examined whether plasma acylcarnitine profiles can help biochemically distinguish the three phenotypic subtypes of major depressive disorder (MDD)—(core depression (CD+), anxious depression (ANX+), and neurovegetative symptoms of melancholia (NVSM+))—following treatment with a selective serotonin reuptake inhibitor (SSRI).MethodsDepressed outpatients (n=240) from the Mayo Clinic Pharmacogenomics Research Network were treated with citalopram or escitalopram for eight weeks. Plasma samples collected at baseline and eight weeks post-treatment were profiled for multiple-, short-, medium- and long-chain acylcarnitine levels using AbsoluteIDQ®p180-Kit and LC-MS. Linear mixed effects models were used to examine whether acylcarnitine levels discriminate the clinical phenotypes at baseline or eight weeks post-treatment, and whether temporal changes in acylcarnitine profiles differ between groups.ResultsAt baseline, significantly lower concentrations of short- and long-chain acylcarnitines were found in CD+ and NVSM+ compared to ANX+, and the short-chain acylcarnitines remained lower after eight weeks. At eight weeks, the medium- and long-chain acylcarnitines were significantly lower in NVSM+ compared to ANX+. Regarding changes baseline to week eight, short-chain acylcarnitine levels significantly increased in CD+ and ANX+, and medium- and long-chain acylcarnitines significantly decreased in NVSM+ and CD+.ConclusionsIn depressed patients treated with SSRIs, β-oxidation and mitochondrial energetics as evaluated by levels and changes in acylcarnitines may provide the biochemical basis of the clinical heterogeneity of MDD, especially when combined with clinical characteristics.

Common genetic variation in CYP2C19 (cytochrome P450, family 2, subfamily C, polypeptide 19) *2 and *3 alleles leads to a loss of functional protein, and carriers of these loss-of-function alleles when treated with clopidogrel have significantly reduced clopidogrel active metabolite levels and high on-treatment platelet reactivity resulting in increased risk of major adverse cardiovascular events, especially after percutaneous coronary intervention. The Food and Drug Administration has issued a black box warning advising practitioners to consider alternative treatment in CYP2C19 poor metabolizers who might receive clopidogrel and to identify such patients by genotyping. However, routine clinical use of genotyping for CYP2C19 loss-of-function alleles in patients undergoing percutaneous coronary intervention is not recommended by clinical guidelines because of lack of prospective evidence. To address this critical gap, TAILOR-PCI (Tailored Antiplatelet Initiation to Lessen Outcomes due to Decreased Clopidogrel Response After Percutaneous Coronary Intervention) is a large, pragmatic, randomized trial comparing point-of-care genotype-guided antiplatelet therapy with routine care to determine whether identifying CYP2C19 loss-of-function allele patients prospectively and prescribing alternative antiplatelet therapy is beneficial.

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.

PurposeThe majority of cancer patients receive treatments that are minimally informed by omics data. We propose a precision medicine computational framework (PANOPLY: Precision cancer genomic report: single sample inventory) to identify and prioritize drug targets and cancer therapy regimens.MethodsThe PANOPLY approach integrates clinical data with germline and somatic features obtained from multi-omics platforms, and apply machine learning, and network analysis approaches in the context of the individual patient and matched controls. The PANOPLY workflow employs four steps (i) selection of matched controls to the case of interest (ii) identification of case-specific genomic events (iii) identification of suitable drugs using the driver-gene network and random forest analyses and (iv) provide an integrated multi-omics case report of the patient with prioritization of anti-cancer drugs.ResultsThe PANOPLY workflow can be executed on a stand-alone virtual machine and is also available for download as an R package. We applied the method to an institutional breast cancer neoadjuvant chemotherapy study which collected clinical and genomic data as well as patient-derived xenografts (PDXs) to investigate the prioritization offered by PANOPLY. In a chemotherapy-resistant PDX model, we found that that the prioritized drug, olaparib, was more effective than placebo in treating the tumor (P < 0.05). We also applied PANOPLY to in-house and publicly accessible multi-omics tumor datasets with therapeutic response or survival data available.ConclusionPANOPLY shows promise as a means to prioritize drugs based on clinical and multi-omics data for an individual cancer patient. Additional studies are needed to confirm this approach.