Breitenstein, Matthew K.

AbstractPrecision medicine is at the forefront of biomedical research. Cancer registries provide rich perspectives and electronic health records (EHRs) are commonly utilized to gather additional clinical data elements needed for translational research. However, manual annotation is resource‐intense and not readily scalable. Informatics‐based phenotyping presents an ideal solution, but perspectives obtained can be impacted by both data source and algorithm selection. We derived breast cancer (BC) receptor status phenotypes from structured and unstructured EHR data using rule‐based algorithms, including natural language processing (NLP). Overall, the use of NLP increased BC receptor status coverage by 39.2% from 69.1% with structured medication information alone. Using all available EHR data, estrogen receptor‐positive BC cases were ascertained with high precision (P = 0.976) and recall (R = 0.987) compared with gold standard chart‐reviewed patients. However, status negation (R = 0.591) decreased 40.2% when relying on structured medications alone. Using multiple EHR data types (and thorough understanding of the perspectives offered) are necessary to derive robust EHR‐based precision medicine phenotypes.

Abstract Background. DLBCL is the most common non-Hodgkin lymphoma (NHL) subtype in western countries and is known to be clinically heterogeneous. Gene expression profiling has identified two major, biologically distinctive DLBCL subtypes on the basis of their cell-of-origin (COO): the germinal center B-cell (GCB) - characterized by BCL2 rearrangement and C-REL amplification, and activated B-cell (ABC) - characterized by constitutive activation of the NF-kB pathway. Recently, the InterLymph Subtypes Project identified medical history, lifestyle, and family history risk factors for DLBCL using a large pooled analysis of case-control studies. In this study we evaluated these same risk factors for etiologic heterogeneity as defined by DLBCL COO. Methods. For this analysis we used a clinic-based study of newly diagnosed NHL cases and frequency matched controls, enrolled from 2002-2012, with 474 DLBCL cases and 2203 controls. COO was determined clinically using the Hans algorithm, which is based on immunohistochemistry markers using formalin-fixed, paraffin-embedded tumor tissue. Amongst DLBCL cases 107 were ABC, 207 were GCB, and 160 were missing subtype (tissue unavailable). Risk factor data, including body mass index (BMI), smoking, alcohol use, any allergy, asthma, blood transfusion, diabetes, rheumatoid arthritis, sun exposure, lived on a farm and family history of hematologic malignancy, were collected from self-administered questionnaires. Polytomous logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals for ABC and GCB subtypes, adjusted for age and sex. Results. The mean age of cases was 60.4 years with 53% male; the mean age of controls was 61.6 years and 53% male. We identified a strong positive association for BMI with risk of ABC but not GBC DLBCL (p-heterogeneity 0.025). Compared to BMI 18.5-24.9 kg/m2, those with a BMI of 25.0-29.9 (OR = 1.65; 0.93-2.93) or 30+ (OR = 2.61; 1.48-4.62) were at increased risk of ABC DLBCL. There was no association for a BMI of 25.0-29.9 (OR = 0.91; 0.63-1.31) or 30+ (OR = 1.21; 0.83-1.76) with GCB DLBCL. There was no evidence of significant heterogeneity between ABC and GCB DLBCL for the other risk factors that we evaluated. Further adjustment of the BMI association for educational level, smoking, and alcohol only slightly attenuated these findings. Conclusions. We observed a strong positive association of BMI with ABC but not GCB DLBCL. In contrast, other evaluated risk factors showed no heterogeneity by COO. While BMI has not been clearly associated with NHL overall, there has been evidence for an association with DLBCL. The etiologic heterogeneity we observed for ABC DLBCL is notable as BMI is also a strong risk factor for multiple myeloma, another post-germinal center B-cell malignancy. While requiring replication, our findings suggest BMI may influence B-cell neoplasia through effect on post-germinal center pathways. Citation Format: Matthew K. Breitenstein, Megan M. O’Byrne, Andrew L. Feldman, Carrie A. Thompson, William R. Macon, Grzegorz S. Nowakowski, Stephen M. Ansell, Susan L. Slager, Thomas M. Habermann, James R. Cerhan. Body mass index shows etiologic heterogeneity for risk of diffuse large B-cell lymphoma (DLBCL) subtype defined by cell-of-origin. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1762.

Abstract Background: Rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) therapy is standard of care for patients with newly diagnosed DLBCL. A majority of patients respond to R-CHOP, however ∼9% will have primary refractory disease and outcomes in these patients are extremely poor. While there is some knowledge of the pharmacogenomic (PGx) determinants of individual agents within R-CHOP, little is known about the PGx determinants of combination therapy. Our study aims to elucidate germline PGx determinants of drug bioavailability and undesirable side chain reactions in DLBCL patients treated with R-CHOP having potential to influence refractory disease. Methods: Newly diagnosed, DLBCL patients treated with R-CHOP between 2002 and 2012 with germline DNA and genotyping data were selected from the Molecular Epidemiology Resource within the Iowa/Mayo Lymphoma SPORE. Refractory disease was defined as either i) a best response of stable or progressive disease or ii) progressive disease by the end of the planned number of cycles of R-CHOP. PGx candidates within known pharmacokinetics and pharmacodynamics (PD) pathways for 11 genes (1,040 single nucleotide polymorphisms (SNPs)) were selected for study (CPT 2012;92(4):414-7). Odds ratios (ORs) and 95% confidence intervals (CI) were used to estimate the association of SNPs with refractory disease, controlling for age, gender, and age-adjusted International Prognostic Index. Results: The median age at diagnosis of 424 of DLBCL patients treated with R-CHOP was 62 years (range 18-92); 45.7% were female; and 58.4% had Ann Arbor Stage III-IV. By the end of initial therapy 40 patients had primary refractory disease. Of the 1,040 SNPs evaluated, 120 (12%) were significantly associated with refractory disease at the 5% significance threshold suggesting novel associations with drug metabolism. The top SNP from each PGx candidate gene are shown in Table, with ALDH5A1 and CYP2B6 showing the most significant associations. Conclusions: While needing replication, we identified associations within two PGx candidate genes that are potentially functionally related to cyclophosphamide PD: alleles within CYP2B6 may have relevance in R-CHOP outcomes due to potential competition with other drug metabolizing enzymes shared with prednisone and vincristine biotransformation in the liver, and alleles withinALDH5A1 may impact a deactivating side chain reaction in B lymphocytes. Table: R-CHOP PGx candidates with known side chain reactionsPGx candidaten SNPsTop SNPChrOR* (95% CI)P-valueMAFABCB1365rs3480093571.86 (1.14 – 3.04)1.32E-020.29ADH1A32rs122996841.66 (0.60 - 4.53)3.27E-010.038ALDH3A116rs2228100171.26 (0.71 - 2.22)4.28E-010.24ALDH5A1192rs946103466.30 (2.43 - 16.4)1.55E-040.030CBR110rs2835266212.10 (0.90 - 4.89)8.76E-020.052CBR356rs45527331212.12 (0.91 - 4.98)8.29E-020.050CYP2B6107rs1038376192.38 (1.44 - 3.94)7.74E-040.27CYP3A421rs2898858372.32 (0.80 - 6.78)1.23E-010.028CYP3A531rs778032871.69 (0.68 - 4.16)2.57E-010.054SLC22A16193rs11217403561.80 (1.06 - 3.03)2.78E-020.21SOD116rs1041740211.35 (0.83 - 2.19)2.26E-010.32Chr=chromosome, MAF=minor allele frequency, *Adjusted for age, sex, and age-adjusted International Prognostic Index Citation Format: Matthew K. Breitenstein, Susan L. Slager, Anne J. Novak, Matthew J. Maurer, Carrie A. Thompson, Umar Farooq, Brian K. Link, Thomas M. Habermann, Curtis L. Olswold, Andrew L. Feldman, Grzegorz S. Nowakowski, Stephen M. Ansell, Thomas E. Witzig, Richard M. Weinshilboum, Liewei Wang, James R. Cerhan. Pharmacogenomic of refractory response to R-CHOP therapy in diffuse large B-cell lymphoma (DLBCL) patients. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr B143.

AbstractThe recent announcement of the Precision Medicine Initiative by President Obama has brought precision medicine (PM) to the forefront for healthcare providers, researchers, regulators, innovators, and funders alike. As technologies continue to evolve and datasets grow in magnitude, a strong computational infrastructure will be essential to realize PM’s vision of improved healthcare derived from personal data. In addition, informatics research and innovation affords a tremendous opportunity to drive the science underlying PM. The informatics community must lead the development of technologies and methodologies that will increase the discovery and application of biomedical knowledge through close collaboration between researchers, clinicians, and patients. This perspective highlights seven key areas that are in need of further informatics research and innovation to support the realization of PM.

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

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