Weinshilboum, Richard

Antidepressants have reduced the symptom burden for many Major Depressive Disorder (MDD) patients, but drug‐related side effects and treatment resistance continue to present major challenges. Pharmacogenomics represents one approach to enhance antidepressant efficacy and avoid adverse reactions, but concerns remain with regard to the overall “value equation,” and several barriers must be overcome to achieve the full potential of MDD pharmacogenomics.

Biomedical research is undergoing rapid change, with the development of a series of analytical omics techniques that are capable of generating Biomedical Big Data. These developments provide an unprecedented opportunity to gain novel insight into disease pathophysiology and mechanisms of drug action and response—but they also present significant challenges. Pharmacogenomics is a discipline within Clinical Pharmacology that has been at the forefront in defining, taking advantage of, and dealing with the opportunities and challenges of this aspect of the Post‐Genome Project world. This overview will describe the evolution of germline pharmacogenomic research strategies as we have moved from an era of candidate genes to agnostic genome‐wide association studies (GWAS) coupled with the functional and mechanistic pursuit of GWAS signals. Germline pharmacogenomic studies of breast cancer endocrine therapy will be used to illustrate research strategies that are being applied broadly to omics studies of drug response phenotypes.

AbstractLarge-scale genotyping studies have identified over 70 single nucleotide polymorphisms (SNPs) associated with breast cancer (BC) risk. However, knowledge regarding genetic risk factors associated with the prognosis is limited. The aim of this study was therefore to investigate the prognostic effect of nine known breast cancer risk SNPs. BC patients (n = 1687) randomly sampled in an adjuvant, randomized phase III trial (SUCCESS A study) were genotyped for nine BC risk SNPs: rs17468277(CASP8), rs2981582(FGFR2), rs13281615(8q24), rs3817198(LSP1), rs889312(MAP3K1), rs3803662(TOX3), rs13387042(2q35), rs4973768(SLC4A7), rs6504950(COX11). Cox proportional hazards models were used to test the SNPsʼ association with overall survival (OS) and progression-free survival (PFS). Additional analyses were carried out for molecular subgroups. rs3817198 in LSP1 (lymphocyte-specific protein 1) was the only SNP that significantly influenced OS (p = 0.01) and PFS (p < 0.01) in the likelihood ratio test comparing the genetic survival model with the clinical survival model. In the molecular subgroups, triple-negative patients with two minor alleles in rs3817198 had a much better prognosis relative to OS (adjusted HR 0.03; 95% CI 0.002 – 0.279) and PFS (HR 0.09; 95% CI 0.02 – 0.36) than patients with the common alleles. The same effect on PFS was shown for patients with luminal A tumors (HR 0.19; 95% CI 0.05 – 0.84), whereas patients with luminal B tumors had a poorer PFS with two minor alleles (HR 2.13; 95% CI 1.02 – 4.40). The variant in rs3817198 has a prognostic effect particularly in the subgroup of patients with triple-negative BC, suggesting a possible link with immunomodulation and BC.

AbstractBackground6‐Mercaptopurine (6MP) and methotrexate (MTX) based maintenance therapy is a critical phase of childhood acute lymphoblastic leukemia treatment. Wide interindividual variations in drug disposition warrant frequent doses adjustments, but there is a lack of international consensus on dose adjustment guidelines.ProcedureTo identify relapse predictors, we collected 28,255 data sets on drug doses and blood counts (median: 47/patient) and analyzed erythrocyte (Ery) levels of cytotoxic 6MP/MTX metabolites in 9,182 blood samples (median: 14 samples/patient) from 532 children on MTX/6MP maintenance therapy targeted to a white blood cell count (WBC) of 1.5–3.5 × 109/l.ResultsAfter a median follow‐up of 13.8 years for patients in remission, stepwise Cox regression analysis did not find age, average doses of 6MP and MTX, hemoglobin, absolute lymphocyte counts, thrombocyte counts, or Ery levels of 6‐thioguanine nucleotides or MTX (including its polyglutamates) to be significant relapse predictors. The parameters significantly associated with risk of relapse (N = 83) were male sex (hazard ratio [HR] 2.0 [1.3–3.1], P = 0.003), WBC at diagnosis (HR = 1.04 per 10 × 109/l rise [1.00–1.09], P = 0.048), the absolute neutrophil count (ANC; HR = 1.7 per 109/l rise [1.3–2.4], P = 0.0007), and Ery thiopurine methyltransferase activity (HR = 2.7 per IU/ml rise [1.1–6.7], P = 0.03). WBC was significantly related to ANC (Spearman correlation coefficient, rs = 0.77; P < 0.001), and only a borderline significant risk factor for relapse (HR = 1.28 [95% CI: 1.00–1.64], P = 0.046) when ANC was excluded from the Cox model.ConclusionsThis study indicates that a low neutrophil count is likely to be the best hematological target for dose adjustments of maintenance therapy.

Abstract Background: Tumorigenesis involves both genetic and epigenetic changes. Epigenetic alterations are reversible and are promising cancer therapeutic targets. Decitabine (5-aza-2'-deoxycytidine), a DNA methyltransferase inhibitor, is FDA approved for hematological malignancies. However, the effect of decitabine in breast cancer is not completely understood. Previous reports indicated that one decitabine mechanism involves regulation of protein levels for DNMT1, the major DNA methyltransferase that methylates hemimethylated CpG di-nucleotides in DNA. However, the E3 ligase involved in this process has not been identified. Whether decitabine also regulates DNMT3A and 3B in a similar fashion remains unclear. Therefore, our goals were to 1) understand mechanisms underlying decitabine action, 2) test the antitumor activity of decitabine in breast cancer models and 3) identify biomarkers associated with response to decitabine. Methods and Results: Western blots of breast cancer cell lines showed that DNMT1, DNMT3A, and DNMT3B protein levels decreased following decitabine treatment without a reduction in mRNA levels. Bioinformatic analysis of DNA methyltransferase sequences revealed a potential TRAF6 binding motif, and the interaction with TRAF6 (TNF receptor-associated factor 6) was confirmed by IP. TRAF6 functions as an E3 ligase. To determine whether TRAF6 might be the E3 ligase responsible for the degradation of DNMTs after decitabine treatment, we knocked down TRAF6 by RNA interference or knocked out the TRAF6 gene by CRISPR/Cas9. Down regulation of TRAF6 attenuated DNMT ubiquitination and increased DNMT protein levels, suggesting that TRAF6 might mediate proteasome-dependent degradation of all three DNMTs. This was further confirmed by reconstituting the knockout cells with WT and a TRAF6-C70A mutant, followed by assessing DNMT protein levels. Global DNA methylation was also increased after TRAF6 depletion and was confirmed in TRAF6 knock out cells in which DNMT levels were unaffected by decitabine. Cell cytotoxicity and colony forming assays showed that TRAF6 knockout cells were resistant to decitabine, suggesting that a major decitabine mechanism of action is through the regulation of TRAF6 which, in turn, degrades DNMTs, leading to decreased global methylation. Finally, decitabine significantly induced TRAF6 at both mRNA and protein levels, a process that might create positive feedback leading to increased degradation of DNMT proteins upon decitabine treatment. Based on these results, we further hypothesized that levels of the three DNMTs might influence decitabine response. Using 18 breast cancer patient derived xenograft (PDX) models, we found a wide range of DNMT protein levels regardless of ER/HER2 status. DNMT levels in the PDX models were directly associated with sensitivity to decitabine treatment, confirming our hypothesis. Conclusion: Our data showed that decitabine might be an effective agent for treating breast cancer and revealed a novel mechanism underlying decitabine treatment. Baseline DNMT protein levels may serve as a biomarker for predicting decitabine drug response. Citation Format: Yu J, Qin B, Boughey JC, Moyer AM, Visscher DW, Sinnwell JP, Yin P, Thompson KJ, Docter TJ, Kalari KR, Suman VJ, Wieben ED, Felten SJ, Conners AL, Jones KN, McLaughlin SA, Copland JA III, Moreno Aspitia A, Northfelt DW, Gray RJ, Ingle JN, Lou Z, Weinshilboum R, Goetz MP, Wang L. Regulation of DNA methyltransferases via TRAF6 determines breast cancer response to decitabine. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P3-07-51.

Objective The study aimed to define thresholds of clinically significant change in 17‐item Hamilton Depression Rating Scale (HDRS‐17) scores using the Clinical Global Impression‐Improvement (CGI‐I) Scale as a gold standard. Methods We conducted a secondary analysis of individual patient data from the Pharmacogenomic Research Network Antidepressant Medication Pharmacogenomic Study, an 8‐week, single‐arm clinical trial of citalopram or escitalopram treatment of adults with major depression. We used equipercentile linking to identify levels of absolute and percent change in HDRS‐17 scores that equated with scores on the CGI‐I at 4 and 8 weeks. Additional analyses equated changes in the HDRS‐7 and Bech‐6 scale scores with CGI‐I scores. Results A CGI‐I score of 2 (much improved) corresponded to an absolute decrease (improvement) in HDRS‐17 total score of 11 points and a percent decrease of 50–57%, from baseline values. Similar results were observed for percent change in HDRS‐7 and Bech‐6 scores. Larger absolute (but not percent) decreases in HDRS‐17 scores equated with CGI‐I scores of 2 in persons with higher baseline depression severity. Conclusions Our results support the consensus definition of response based on HDRS‐17 scores ( > 50% decrease from baseline). A similar definition of response may apply to the HDRS‐7 and Bech‐6. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract Breast cancer is the most common invasive cancer in women worldwide and treatment related toxicity has high inter-patient variability. Our goal was to identify additional genetic predictors of toxicity by completing a pathway based analysis taking advantage of the genotyping results obtained through a GWAS of the same patients. For our analysis, we have utilized GWA data from the SUCCESS-A trial in which 3,754 breast cancer patients were treated with adjuvant chemotherapy. We have included SNPs associated with 112 genes involved in drug disposition and 375 genes in selected cancer pathways. We identified 35 SNPs associated with neutropenia (P-value &lt;1×10−4). These patients initially received three cycles consisting of 5-fluorouracil (5-FU), cyclophosphamide, and epirubicin. Currently we are characterizing the function of three SNPs located within an intron of the hyaluronan mediated motility receptor (HMMR) gene that are associated with the neutropenia phenotype (P-value &lt;1×10−4). These SNPs are highly associated with the expression of a gene encoding a B-cell survival factor, TNFSF13B, (P-value &lt;1×10−5). Our in vitro studies using lymphoblastoid cell lines (LCLs) selected based on the genotypes of HMMR gene showed that heterozygous and homozygous variant cells (n = 6) have an increased sensitivity to 5-fluorouracil, but not cyclophosphamide or epirubicin when compared with homozygous wild-type cells (n = 6). LCLs exposed to the previously mentioned chemotherapeutic agents resulted in an upregulation of TNFSF13B gene expression and downregulation of HMMR gene expression. We have utilized a pathway based approach to analyzing GWA data to identify several SNPs in the HMMR gene highly associated with the expression of one of the top signals in the cytopenia GWAS. Our goal is to further characterize the role of HMMR gene variants in chemotherapy response by assessing whether combinations of these agents in vitro (including 5-FU) also have a genotype dependent response to cytotoxicity. Citation Format: Behzad Bidadi, Krishna R. Kalari, Matthias Rubner, Alexander Hein, Matthias W. Beckmann, Peter A. Fasching, Brigitte Rack, Wolfgang Janni, Richard Weinshilboum, Liewei Wang. Utilizing a pathway based analysis of genome wide association data to identify biomarkers of toxicity in breast cancer patients. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 5488. doi:10.1158/1538-7445.AM2015-5488

AbstractWe describe here the vision, motivations, and research plans of the National Institutes of Health Center for Excellence in Big Data Computing at the University of Illinois, Urbana-Champaign. The Center is organized around the construction of “Knowledge Engine for Genomics” (KnowEnG), an E-science framework for genomics where biomedical scientists will have access to powerful methods of data mining, network mining, and machine learning to extract knowledge out of genomics data. The scientist will come to KnowEnG with their own data sets in the form of spreadsheets and ask KnowEnG to analyze those data sets in the light of a massive knowledge base of community data sets called the “Knowledge Network” that will be at the heart of the system. The Center is undertaking discovery projects aimed at testing the utility of KnowEnG for transforming big data to knowledge. These projects span a broad range of biological enquiry, from pharmacogenomics (in collaboration with Mayo Clinic) to transcriptomics of human behavior.