Driving molecular pharmacology research utilizing EHRs and enhanced analytics:

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1 Driving molecular pharmacology research utilizing EHRs and enhanced analytics: An application of metformin in breast cancer and type II diabetes mellitus. Academic Health Center Informatics Grand Rounds, University of Minnesota 1/5/2015 M.K. Breitenstein, PhD

2 Background Training Fellow Cancer Genetic Epidemiology Training Program, Mayo Clinic (current) PhD Biomedical Health Informatics (2014) MS Health Services Research (2011) BS Biology (2009)

3 Overview How can we drive treatment personalization and molecular discovery in cancer research using integrated biomedical informatics and enhanced analytics? 1. Medication repurposing 2. Treatment personalization 3. Elucidating novel biology Context: Effect of metformin in breast cancer and its molecular mechanism

4 Part 1: Metformin repurposing Hypothesis: Metformin improves breast cancer treatment outcomes Aim: Understand implications for metformin and insulin on breast cancer (survival) outcomes Novelty: Utilized enhanced analytics and EHR data to overcome limitations of traditional approaches

5 Metformin 101 Metformin: Antihyperglycemic medication Monotherapy is the first line treatment for T2DM Important implications for T2DM severity: Use of antidiabetic therapy beyond metformin monotherapy is due to either: 1. T2DM severity or 2. Pharmacogenomic variation

6 Metformin in Breast Cancer & T2DM T2DM * Diabetes Staging: 1. Metabolic syndromes (5) 2. Microvascular complications (4) 3. Macrovascular complications (5) * Staging Implication: 1. T2DM severity or 2. Pharmacogenomic variation Metformin* Breast Cancer: Connected Outcomes: All Cause Mortality Insulin ER+/PR+ ~70% HER2+ Breast Cancer- Specific Mortality

7 Metformin in breast cancer What is known about the effect of metformin in breast cancer treatment? All cause mortality outcomes (8 studies) 50% null or protective association Breast cancer-specific mortality (3 studies) 1 study identified protective association Receptor status / biology (2 studies) Triple negative (1 study) null association HER2+ (1 study) protective Impact of metformin is unclear and subject to active research

8 Metformin in breast cancer How do we study the effect of metformin on breast cancer outcomes? Effect of metformin is contained within T2DM and breast cancer mortality outcomes 1. Considerations for T2DM are necessary when looking at diabetes medications 2. Breast cancer registry, diabetes registry, and death index to necessary to properly execute study Existing studies balance sample size vs. data richness Overcame some limitations using local EHR data to augment a local cancer registry

9 Cohort Phenotype: Female patients with unilateral, nonrecurrent breast cancer diagnosis between 1998 and 2011 and T2DM at breast cancer diagnosis (n=1,188) 6 months antidiabetic therapy after breast cancer treatment initiation Combination of cancer registry and EHR data Unique cases of unilateral breast cancer (n = 35,341) New diagnosis of breast cancer in study period (01/01/ /31/2011) (n = 13,163) Attributable type 2 diabetes mellitus phenotype (n = 1,188) Exclude male patients Exclude patients with known diagnosis of breast cancer (recurrent cases) prior to study period Exclude patients with T1DM or ambiguous diabetes status in EHR Median age at breast cancer dx: 67 (34-95) years Median follow-up time: 83 (6-191) months

10 Traditional approach* Table 2a: Univariate Analysis of Metformin and Insulin All Cause Mortality Breast Cancer-Specific Mortality N HR p-val CI-low CI-high N HR p-val CI-low CI-high Metformin Insulin < < Table 2b: Multivariate model of T2DM medications All Cause Mortality Breast Cancer-Specific Mortality N HR p-val CI-low CI-high HR p-val CI-low CI-high Metformin Insulin < < Thiazolidinedione Sulfonylurea For all cause and breast cancer-specific mortality outcomes: Metformin appears to be protective Insulin appears to be detrimental Confidence intervals overlap, alternative approach is needed * Unpublished finding

11 American Society for Clinical Pharmacology and Therapeutics: Presidential Trainee Award 2015 David Goldstein Trainee Award 2015 Offered alternative approach American Society for Clinical Pharmacology and Therapeutics (ASCPT) 2015 Annual Meeting

12 Methods* Step 1: Stratified Cox proportional hazard regression Model 1: Mortality due to T2DM severity Predictors and all cause mortality Step 2: Control for mortality due to T2DM severity using linear offset of T2DM severity (Step 1) Study association of metformin and insulin in Cox models of breast cancer treatment and biology Aim: Separately model the effects of metformin (n=299) and insulin (n=197) for T2DM severity and breast cancer treatment effects * Unpublished finding

13 Rationale unpublished finding!! =!!!!!!! Linear predictor All Cause Mortality: Unpublished item has been redacted Breast Cancer-Specific Mortality: Unpublished item has been redacted 4 main effects to consider: All cause mortality: 1. Metformin due to T2DM severity 2. Insulin due to T2DM severity Breast cancer specific mortality: 3. Metformin due to breast cancer treatment 4. Insulin due to breast cancer treatment

14 Step 1: Mortality due to T2DM severity* Metformin Models (Two baseline hazard ratios) N HR p-val CI-low CI-high Model 1: All Cause Mortality Metformin Insulin < Thiazolidinedione Sulfonylurea Model 2: Breast Cancer Specific Survival Endpoint Metformin Insulin Strata 1: All cause morality, attributable to T2DM severity Basis for T2DM severity offset3 Aim: Disambiguate mortality associations * Unpublished finding

15 Step 2a: Treatment Perspective* Full Model Breast Cancer Specific Mortality N HR p-val CI-low CI-high Age Metformin Insulin Fulvestrant < Aromatase inhibitors Trastuzumab Selective estrogen receptor modulators Methotrexate Chemotherapy Adjusted for T2DM severity Step 2a: Study association of metformin and insulin in Cox model of breast cancer treatment * Unpublished finding

16 Step 2b: Biology Perspective* Full Model Breast Cancer Specific Mortality N HR p-val CI-low CI-high Age Metformin Insulin Progesterone receptor - positive Estrogen receptor - positive HER2 actionable - negative HER2 actionable - positive Adjusted for T2DM severity Step 2b: Study association of metformin and insulin in Cox model of breast cancer biology * Unpublished finding

17 Summary of Results* Separated impact of metformin: 1. T2DM Severity (all cause): Protective for metformin (HR=0.544,p=0.0017) Detrimental for insulin (HR=2.144,p<0.0001) 2. Breast Cancer Effect (adjusted for T2DM severity): Metformin (protective): Standard treatment (HR=0.388,p=0.0353) Biology (HR=0.339,p=0.0159) Insulin (detrimental): Standard treatment (HR=1.956,p=0.0170) Biology (HR=2.201,p=0.0131) * Unpublished finding

18 Conclusions Linear offsets offer one potential approach to adjust for within-study confounding Potentially identified these effects more appropriately Beyond the implications for T2DM severity: Metformin: appears to be protective in breast cancer treatment Insulin: appears to be detrimental in breast cancer treatment Refinements to T2DM staging component are ongoing T2DM complications, labs

19 Part 2: Metformin personalization Hypothesis: Variation within candidate genes impact glycemic response to metformin Aim: Understand metformin pharmacogenomics using EHR phenotypes of T2DM and candidate gene variation Novelty: Utilized EHR data and biorepositories with traditional analytics/methodological approaches

20 Pharmacogenomic variation may also be responsible for patients prescribed alternative antidiabetic therapies Can EHR data with genomic data be utilized to replicate known pharmacogenomic candidates? Breitenstein et. al. Medical Informatics Europe 2015

21 Metformin Pharmacokinetics (PK) Well understood (intestine ) Bloodstream PK mechanism of movement throughout the body Absorption (liver) (kidney) Excretion No clear intermediary phenotype for metformin PK Mortality outcomes are a definitive phenotype, but inappropriate Wang & Weinshilboum 2014

22 Gong 2012 Metformin Pharmacodynamics (PD) uptake ~ A1c intermediary phenotype PD: the mechanism of impact for the drug on tissue/organ processes ~ master regulatory switch for cellular metabolism Not clearly understood Glycemic response (outcome phenotype) more appropriately captures PD variation

23 Study Design Pre-exposure New metformin exposure Maintenance dose period End of therapy or monotherapy n=258 x Avg. A1c months Avg. A1c x + 6 x = duration of exposure Δ A1c All patients exposed to metformin monotherapy for antidiabetic! therapy Clinical outcome: Glycemic response (ΔA1c) Adjusted for age, gender, and obesity Ideal to adjust for T2DM severity, but underpowered Gene- and SNP-level analysis Table 1: Cohort Demographics n (%) Female 89 (34.5) Male 169 (64.5) BMI < (24.8) BMI >= 30 to < (38.8) BMI >=35 93 (36.1) Median A1c > (39.1) Median Range Change in A1c Age Breitenstein et. al. Medical Informatics Europe 2015

24 *Adjusted for age, gender, and obesity; not significant after adjustment for multiple testing AMPK Candidate Genes (n=17)! Gene-Level Filtering* Table 4: Principal Component (PC) Analysis Gene Name nsnps npcs P-value PRKAB SLC29A PRKAG SLC47A GCKR SLC47A STK PRKAA ATM PRKAA SLC22A PPARG PRKAB PRKAG PRKAG SLC22A SLC22A Bonferroni correction: 0.05/17= Breitenstein et. al. Medical Informatics Europe 2015

25 Plotted SNPs PRKAB2 LD Block PRKAB2 Top SNP in FMO5 log10(p value) r p= p= PRKAB2 FMO5 rs Recombination rate (cm/mb) PRKAB2 PDIA3P FMO5 is a potentially biologically relevant insight Need to reduce chance of being false discovery FMO Position on chr1 (Mb) +/- 50 kilo base pairs (kbp) Breitenstein et. al. Medical Informatics Europe 2015

26 FMO5 LD Block Plotted SNPs FMO5 +/- 20 kbp log10(p value) rs p= p= r Recombination rate (cm/mb) PRKAB2 PDIA3P FMO5 CHD1L Position on chr1 (Mb) Breitenstein et. al Joint Summits on Translational Science

27 FMO1 FMO4 LD Block -20 kbp from FMO3, Plotted SNPs FMO1, FMO2, FMO3, FMO4 +20 kbp from FMO4 3 rs r log10(p value) Recombination rate (cm/mb) MROH9 FMO3 MIR1295A MIR1295B FMO6P FMO2 FMO1 FMO4 TOP1P Position on chr1 (Mb) Breitenstein et. al Joint Summits on Translational Science

28 FMO Gene and SNP Results SNP-Level Analysis Gene Genotyped Minor Major Top SNP Name SNPs (n) Allele Allele MAF BETA CIs P-value FMO5 31 rs A C (-1.28;-0.297) * FMO4 15 rs G A (-0.055;0.467) FMO3 19 rs C T (-0.296;0.081) FMO2 14 rs T C (-0.025;0.476) FMO1 12 rs G A (-0.062;0.461) MAF = minor allele frequency, * marginally significant after correction for multiple testing Gene-Level Analysis Gene Genotyped SNPs (n) npcs P-value FMO FMO FMO FMO FMO FMO5 appears to be marginally significant after bonferroni correction Alternative analysis approaches, replication, and experimental validation are warranted Potential biologically relevant association, but unclear Breitenstein et. al Joint Summits on Translational Science

29 FMO5 implications Results hint at potential mechanism of metformin biotransformation, however this is unlikely. 1. FMO5 enzyme is known to oxidize nitrogencontaining drugs Metformin is a nitrogen rich compound Other FMOs have similar function, but co-located away Did not find association in other FMOs 2. Expressed in liver and kidneys where metformin uptake is known to occur Known alternative splicing of FMO5 that alter function 3. Potentially induced by estrogens Potential ER+ breast cancer implications Experimental validation is planned Interaction with another gene product? Breitenstein et. al Joint Summits on Translational Science

30 EHR data with genomic data did not replicate known pharmacogenomic candidates Identified potentially novel biological mechanism Evidence is weak Experiment validation is underway, but unlikely to be biotransformation of metformin via FMO5 Interaction with other nitrogen rich compound? Breitenstein et. al Joint Summits on Translational Science

31 Part 3: Metformin to elucidate novel biology Question: Can metformin metabolite expression serve as intermediary phenotype? Aim: Utilize metformin cross sections in EHR to develop metabolomics informed pharmacogenomics hypothesis Novelty: Population with integrated EHR data and biorepository samples utilized to develop molecular hypotheses

32 What is metabolomics? Metabolomics Study of metabolism at global level Capture biochemical events in blood Bioinformatics techniques define metabolomic signatures Result in metabolome Profile of small-molecule metabolites Highly variable Metabolomics informed pharmacogenomics Metabolite can serve as intermediary phenotype for either candidate gene or GWAS analysis Proving to be a very successful approach

33 Study Design EHR data Biorepository sample Metabolome Cases: Unpublished item has been redacted Controls: Unpublished item has been redacted Plasma WBCs RBCs Metabolite processing (existing sample, known sample date & time) metabolites Unpublished findings have been redacted * Unpublished finding

34 Preliminary Analysis* Unpublished findings have been redacted * Unpublished finding

35 Metabolite of interest* Unpublished findings have been redacted cytosol Implicates urea cycle Other downstream pathways Subject to ongoing work * Unpublished finding mitochondrion

36 Part 4: Vision moving forward

37 Summary How can we drive treatment personalization and molecular discovery in cancer research using integrated biomedical informatics and enhanced analytics? Demonstrated by 3 studies 1. Metformin pharmacoepidemiology 2. Metformin pharmacogenomics 3. Metformin metabolomics

38 Treatment personalization and molecular discovery Biomedical informatics Clinical pharmacology Cancer Research Molecular epidemiology Goal is to research at the intersection of 4 fields

39 Treatment personalization and molecular discovery What we covered today. Study 2: Metformin pharmacogenomics Study 1: Metformin pharmacoepidemiology Biomedical informatics Clinical pharmacology Cancer Research Molecular epidemiology Study 3: Metformin metabolomics Goal is to research at the intersection of 4 fields

40 Translational Framework for Multilevel Phenotyping In progress: T2DM severity from larger population Risk Adjustment Systemtatic Phenotyping Population Population Health Socioecological Contex Clinical Context Study 1: Metformin pharmacoepidemiology Systematic Phenotyping Clinical Phenotype Sub-Phenotypes Longitudinal Cohorts Medicine (e.g., drug response, receptor status) M.K. Breitenstein 2014 Study 2: Metformin pharmacogenomics Study 3: Metformin metabolomics Biology Biology Biological Phenotype Genomics!Transcriptomics!Proteomics!Metabolomics M.K. Breitenstein 2014 Cite as: A Framework for the Multilevel Integration of Molecular, Clinical, and Population Data in the Context of Breast Cancer: Challenges and Considerations of Socioecological Conditions and Pharmacogenomics.

41 Acknowledgements Research support: Mayo Clinic Pharmacogenomics Research Network through NIH grant U19-GM , PGRN Network Resource, Pharmacogenomics of Phase II Drug Metabolizing Enzymes Mayo Clinic Center for Individualize Medicine Mayo Clinic Metabolomics Resource Core U24DK from the National Institute of Diabetes and Digestive and Kidney Diseases Mayo Clinic CTSA grant UL1TR from the National Center for Advancing Translational Sciences(NCATs) National Cancer Institute funded Genetic Epidemiology Training Program at Mayo Clinic R25 CA092049

42 Acknowledgements Mentorship: Richard M. Weinshilboum, MD Liewei Wang, MD, PhD Jyotishman Pathak, PhD James Cerhan, MD, PhD Gyorgy Simon, PhD

43 Thank you! Matthew K. Breitenstein, PhD Research Fellow Division of Epidemiology Department of Health Sciences Research Mayo Clinic