Weinshilboum, Richard M.

Abstract Background: The use of anti-estrogen (AE) therapies (selective estrogen receptor modulators [SERMs], aromatase inhibitors [AIs]) in breast cancer (BC) patients was recently associated with risk of developing rheumatoid arthritis (RA) (J Rheum 2015;42:55-9). We attempted to replicate and extend these results using electronic health record (EHR) enhanced cancer registry data that assessed specific AE therapies and also accounted for potential confounding by age, smoking status, and chemotherapy use. Methods: Using cancer registry and EHR data, we identified a cohort of BC patients who were newly diagnosed and treated at Mayo Clinic Rochester from 1998-2011 and had no previous diagnosis of RA. Smoking status at diagnosis, BC treatments and RA at diagnosis and during follow-up were identified using validated EHR-based algorithms. Hazard ratios (HRs) and 95% confidence intervals (CI) from a multivariate Cox model were used to estimate the association of AE use with risk of RA, controlling for age, smoking status at BC diagnosis, and BC chemotherapy use. Results: The analytic cohort of 9,244 newly diagnosed BC patients treated and followed at Mayo Clinic had a median age at diagnosis of 59 years (range 18-97); 32% were smokers; and 29% were treated with chemotherapy. During a median follow-up of 49 months (range 1-191), 19 patients developed RA. Compared to BC patients not receiving any AE therapy, those receiving AI monotherapy, but not SERM monotherapy or both SERMs and AIs, were at significantly increased risk of developing RA (Table). AE Therapy# of RA Cases / # patientsHR* (95% CI)P-valueNo AE therapy6/4,0711.00 (reference)SERMs only5/2,5371.20 (0.36 - 3.95)0.77AIs only5/7126.37 (1.77 - 22.92)0.005SERMs then AIs3/1,8750.90AI then SERMs0/490.91 (0.21 - 3.86)*Adjusted for age, smoking status and use of chemotherapy Conclusions: Our findings indicate that the use of AIs for BC therapy, but not SERMs or SERMs followed by AIs, is associated with increased risk for development of RA, when controlling for smoking status and use of chemotherapy. While needing replication, these findings suggest a specific role for AIs in RA development and suggest pathways that could be targeted to prevent this potential treatment complication. Citation Format: Matthew K. Breitenstein, Ming-Fen Ho, Richard M. Weinshilboum, James R. Cerhan, Jyotishman Pathak, Tim Bongartz, Liewei Wang, James N. Ingle. Association of anti-estrogen therapy in breast cancer patients and subsequent risk of rheumatoid arthritis: An electronic health record study. [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 LB-191. doi:10.1158/1538-7445.AM2015-LB-191

Studies assessing the effect of bevacizumab (BEV) on breast cancer (BC) outcome have shown different effects on progression‐free and overall survival, suggesting that a subgroup of patients may benefit from this treatment. Unfortunately, no biomarkers exist to identify these patients. Here, we investigate whether single nucleotide polymorphisms (SNPs) in VEGF pathway genes correlate with pathological complete response (pCR) in the neoadjuvant GeparQuinto trial. HER2‐negative patients were randomized into treatment arms receiving either BEV combined with standard chemotherapy or chemotherapy alone. In a pre‐planned biomarker study, DNA was collected from 729 and 724 patients, respectively from both treatment arms, and genotyped for 125 SNPs. Logistic regression assessed interaction between individual SNPs and both treatment arms to predict pCR. Five SNPs may be associated with a better response to BEV, but none of them remained significant after correction for multiple testing. The two SNPs most strongly associated, rs833058 and rs699947, were located upstream of the VEGF‐A promoter. Odds ratios for the homozygous common, heterozygous and homozygous rare rs833058 genotypes were 2.36 (95% CI, 1.49–3.75), 1.20 (95% CI, 0.88–1.64) and 0.61 (95% CI, 0.34–1.12). Notably, some SNPs in VEGF‐A exhibited a more pronounced effect in the triple‐negative subgroup. Several SNPs in VEGF‐A may be associated with improved pCR when receiving BEV in the neoadjuvant setting. Although none of the observed effects survived correction for multiple testing, our observations are consistent with previous studies on BEV efficacy in BC. Further research is warranted to clarify the predictive value of these markers.

Abstract Purpose: Tamoxifen and raloxifene, are primary prevention strategies for women at high risk of breast cancer (Visvanathan, 2013). Recent advances in genetic studies of breast cancer risk have identified common susceptibility loci that explain 14% of familial risk for breast cancer in the general population (Michailidou, 2013). However, it is not known if these loci are risk factors for breast cancer among high-risk women treated with SERMs for breast cancer prevention. We hypothesized that the large risk reduction associated with SERMs, coupled with the fact that several breast cancer loci correlate with family history, may limit the contribution of these common genetic loci to breast cancer in this high risk population. We present the first report to evaluate 75 established breast cancer susceptibility loci, in the context of a polygenic risk score (PRS), as a risk factor for breast cancer among high risk women taking raloxifene and tamoxifen for breast cancer prevention. Methods: We conducted a matched case-control study of 594 cases (i.e., participants who developed breast cancer while on SERM therapy) and 1,171 matched controls selected from the 33,000 participants enrolled in the NSABP P-1 and P-2 breast cancer prevention trials. Genotypes of 75 single nucleotide polymorphisms (SNPs) were available from a genome-wide association study conducted at the RIKEN Center for Genomic Medicine. We formed a quantitative PRS from reported per-SNP odds ratios (OR) for the 75 susceptibility loci. Conditional logistic regression was used to examine the PRS as a risk factor for breast cancer and to assess whether the PRS and breast cancer association differed by treatment type, family history, or other clinical characteristics. Analyses also examined associations of PRS with invasive vs. in situ cancer and ER-positive vs. ER-negative cancer. Results: The PRS ranged from 3.98 to 7.74, and a one unit change in PRS was associated with a 42% increase in breast cancer (OR=1.42; 95% CI: 1.18-1.70; P = 0.0002). There was evidence of a stronger association of PRS with breast cancer among women with no first-degree family history (OR=1.62, 95% CI: 1.18-2.21) compared to those with a positive family history (OR=1.32, 95% CI: 1.06-1.66) (Pintx<0.05). The PRS also appeared a stronger risk factor for ER-positive (OR=1.59, 95% CI: 1.25-2.02, P < 0.0002) vs. ER-negative (OR=1.05, 95% CI: 0.68-1.62, P=0.84) breast cancer, although differences did not reach statistical significance (Pintx=0.10). PRS and breast cancer associations were similar across tamoxifen and raloxifene treatments, age at trial entry, 5-year predicted Gail model risk, hysterectomy status, BMI, presence of atypical hyperplasia and invasive vs. in situ cancer. Conclusion: A polygenic risk score composed of 75 loci was a risk factor for ER-positive breast cancer, especially in the absence of a first-degree family history of breast cancer. Further, the PRS associations with breast cancer were similar for women taking tamoxifen or raloxifene for prevention. These data suggest that common genetic variation adds information on risk of ER-positive breast cancer in a high-risk population receiving SERMs. Citation Format: Celine M Vachon, Daniel J Schaid, James N Ingle, Matthew P Goetz, Donald L Wickerham, Michiaki Kubo, Erin E Carlson, Soonmyung Paik, Norman Wolmark, Yusuke Nakamura, Liewei Wang, Richard M Weinshilboum, Fergus J Couch. The contribution of common genetic variation to breast cancer risk among women receiving tamoxifen or raloxifene within the National Surgical Adjuvant Breast and Bowel Project (NSABP) P-1 and P-2 trials [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P6-10-03.

Abstract Background: Literature addressing metformin exposure post diagnosis of breast cancer hints at a potential protective association on mortality outcomes, although findings have been inconclusive. In this research, we contribute to this body of evidence by using robust clinical indicators to focus on subgroups of breast cancer patients that might be most appropriate for potential metformin treatment. Methods: Robust breast cancer staging, treatment, and medication exposure attributes were compiled from Mayo Clinic Electronic Health Records and Breast Cancer Registry using a combination of structured queries and natural language processing of clinical notes. A cohort of female Breast Cancer Registry patients having type II diabetes mellitus (T2DM) at breast cancer diagnosis with a median follow-up period of 79.0 months after breast cancer diagnosis was identified (N=350). Univariate and multivariate cox proportional hazard regression analysis were used to model characteristics of BMI at diagnosis, age at diagnosis, metformin, insulin, thiazolidinedione, and sulfonylurea exposure after diagnosis, cancer grade, cancer stage, chemotherapy, hormone therapy, radiotherapy, lumpectomy, and mastectomy. Patients presenting with metastatic breast cancer at diagnosis, receiving immunotherapy for breast cancer treatment, and concurrent metformin and insulin exposure for T2DM treatments were excluded from the analysis cohort. Results: Univariate analysis independently identified metformin exposure (HR=0.243,p=0.0016), insulin exposure (HR=3.481,p=0.0210), age at breast cancer diagnosis (HR=1.075,p=0.0001), and targeted radiotherapy (HR=0.337,p=0.0082) as having a significant association with all-cause mortality. The multivariate model of metformin exposure (HR=0.274,p=0.0123), insulin exposure (HR=2.502,p=0.1160), age at breast cancer diagnosis (HR=1.043,p=0.0480), and targeted radiotherapy (HR=0.303,p=0.0044) on all-cause mortality demonstrated significant associations for all variables (Wald Chi-Square=25.2916,p<0.0001). A parallel multivariate model demonstrated metformin exposure (HR=0.101,p=0.0323), insulin exposure (HR=5.241,p=0.0265), and targeted radiotherapy (HR=0.292,p=0.0520) as having significant associations with breast cancer-specific mortality. Discussion: These findings suggest a protective association for metformin and a potential detrimental association for insulin therapy on all-cause and breast cancer-specific mortality amongst non-metastatic breast cancer patients with T2DM. The findings presented include robust clinical indicators for BMI, stage, and grade at diagnosis, post-diagnosis diabetes medication exposure, and endpoints of all cause and breast cancer specific mortality that have not been available in larger population studies. Our findings have potential pharmacogenetic implications for inhibition of localized breast cancer cell growth by targeting downstream components of the adenosine monophosphate-activated protein kinase pathway, the insulin/insulin-like growth factor-1 signaling pathway, and directly and indirectly via additional metabolic pathways with specific drug therapy combinations. Citation Format: Matthew K. Breitenstein, Leiwei Wang, Richard M. Weinshilboum, Jyotishman Pathak. Metformin exposure on survival in localized breast cancer patients with type II diabetes mellitus using electronic health records. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr LB-278. doi:10.1158/1538-7445.AM2014-LB-278

The scaling up of data in clinical pharmacology and the merger of systems biology and pharmacology has led to the emergence of a new discipline of Quantitative and Systems Pharmacology (QSP). This new research direction might significantly advance the discovery, development, and clinical use of therapeutic drugs. Research communities from computational biology, systems biology, and biological engineering—working collaboratively with pharmacologists, geneticists, biochemists, and analytical chemists—are creating and modeling large data on drug effects that is transforming our understanding of how these drugs work at a network level. In this review, we highlight developments in a new and rapidly growing field—pharmacometabolomics—in which large biochemical data‐capturing effects of genome, gut microbiome, and environment exposures is revealing information about metabotypes and treatment outcomes, and creating metabolic signatures as new potential biomarkers. Pharmacometabolomics informs and complements pharmacogenomics and together they provide building blocks for QSP.

Background— The goal of this study was to identify genetic determinants of plasma N-terminal proatrial natriuretic peptide (NT-proANP) in the general community by performing a large-scale genetic association study and to assess its functional significance in in vitro cell studies and on disease susceptibility. Methods and Results— Genotyping was performed across 16 000 genes in 893 randomly selected individuals, with replication in 891 subjects from the community. Plasma NT-proANP 1–98 concentrations were determined using a radioimmunoassay. Thirty-three genome-wide significant single-nucleotide polymorphisms were identified in the MTHFR-CLCN6-NPPA-NPPB locus and were all replicated. To assess the significance, in vitro functional genomic studies and clinical outcomes for carriers of a single-nucleotide polymorphism rs5063 (V32M) located in NPPA that represented the most significant variation in this genetic locus were assessed. The rs5063 variant allozyme in transfected HEK293 cells was decreased to 55±8% of wild-type protein ( P =0.01) as assessed by quantitative western blots. Carriers of rs5063 had lower NT-proANP levels (1427 versus 2291 pmol/L; P <0.001) and higher diastolic blood pressures (75 versus 73 mm Hg; P =0.009) and were at an increased risk of stroke when compared with wild-type subjects independent of age, sex, diabetes mellitus, hypertension, atrial fibrillation, and cholesterol levels (hazard ratio, 1.6; P =0.004). Conclusions— This is the first large-scale genetic association study of circulating NT-proANP levels performed with replication and functional assessment that identified genetic variants in the MTHFR-CLCN6-NPPA-NPPB cluster to be significantly associated with NT-proANP levels. The clinical significance of this variation is related to lower NT-proANP levels, higher blood pressures, and an increased risk of stroke in the general community.

Abstract Rapid development of next generation sequencing technology has enabled the identification of genomic alterations from short sequencing reads. There are a number of software pipelines available for calling single nucleotide variants from genomic DNA but, no comprehensive pipelines to identify, annotate and prioritize expressed SNVs (eSNVs) from non-directional paired-end RNA-Seq data. We have developed the eSNV-Detect, a novel computational system, which utilizes data from multiple aligners to call, even at low read depths, and rank variants from RNA-Seq. Multi-platform comparisons with the eSNV-Detect variant candidates were performed. The method was first applied to RNA-Seq from a lymphoblastoid cell-line, achieving 99.7% precision and 91.0% sensitivity in the expressed SNPs for the matching HumanOmni2.5 BeadChip data. Comparison of RNA-Seq eSNV candidates from 25 ER+ breast tumors from The Cancer Genome Atlas (TCGA) project with whole exome coding data showed 90.6–96.8% precision and 91.6–95.7% sensitivity. Contrasting single-cell mRNA-Seq variants with matching traditional multicellular RNA-Seq data for the MD-MB231 breast cancer cell-line delineated variant heterogeneity among the single-cells. Further, Sanger sequencing validation was performed for an ER+ breast tumor with paired normal adjacent tissue validating 29 out of 31 candidate eSNVs. The source code and user manuals of the eSNV-Detect pipeline for Sun Grid Engine and virtual machine are available at http://bioinformaticstools.mayo.edu/research/esnv-detect/.