POS0514 PREDICTION OF RESPONSE TO METHOTREXATE IN PATIENTS WITH RHEUMATOID ARTHRITIS: A MACHINE LEARNING APPROACH USING CLINICAL TRIAL DATA

Background

Methotrexate (MTX) is the preferred initial disease-modifying drug (DMARD) for rheumatoid arthritis (RA). However, up to 50% of patients respond inadequately to MTX (1). Clinically useful predictors that effectively identify patients with RA who are likely to respond to MTX are lacking. Whether machine learning (ML) can provide robust and clinically useful prediction of response to MTX monotherapy in the first months of treatment in patients with early RA using uniformly collected baseline demographics and clinical data has not been investigated in large patient populations.

Objectives

We aimed to identify clinical predictors of response to MTX as the first DMARD among patients with RA using ML methods.

Methods

Randomized clinical trials (RCT) of patients with RA who were DMARD-naïve and randomized to placebo plus MTX were identified and accessed through the Clinical Study Data Request Consortium and Vivli Center for Global Clinical Research Data. Studies with available Disease Activity Score with 28-joint count and erythrocyte sedimentation rate (DAS28-ESR) at baseline, 12 and 24 weeks were included. Latent class modeling of MTX response was performed. Least absolute shrinkage and selection operator (LASSO) and random forest were used to identify predictors of response.

Results

A total of 775 patients from 4 RCTs were included (mean age 50 years, 80% female). Two distinct classes of patients were identified based on DAS28-ESR change over 24 weeks: “good responders” and “poor responders” to MTX treatment (Figure 1). Baseline DAS28-ESR, anti-citrullinated protein antibody (ACPA) and health assessment questionnaire (HAQ) score were the top predictors of good response to MTX using LASSO (Area Under the Curve [AUC] 0.79) and Random Forest models (AUC 0.68) in the external validation set. DAS28-ESR≤7.4, ACPA positive and HAQ≤2 provided the highest likelihood of response (Table 1). Among patients with 12-week DAS28-ESR>3.2, at least 1 point improvement in DAS28-ESR baseline-to-12-week was predictive of achieving DAS28-ESR≤3.2 at 24 weeks.

Table 1.

Matrix prediction model: Probability of achieving a good response to methotrexate at 24 weeks

DAS28ESR≤7.480.1 (76.4, 83.8)77.3 (70.6, 84)PositiveACPA Status77.1 (68.6, 85.6)74.1 (63.3, 84.9)Negative>7.440.3 (32.1, 48.5)36.5 (29.3, 43.6)Positive36.2 (23.3, 49.1)32.5 (20.9, 44.1)Negative≤2>2HAQ

Footnote: The number in each cell represents the percentage and 95% CI of achieving the outcome, based on the combination of predictors at baseline. DAS28-ESR: Disease Activity Score with 28-joint count with erythrocyte sedimentation rate; HAQ: Health assessment questionnaire score; ACPA: Anti-citrullinated protein antibody.

Figure 1.

Two patient class trajectories identified with latent class modeling of DAS28-ESR (N=775)

Conclusion

We have developed and externally validated a prediction model for response to MTX within 24 weeks in DMARD-naïve patients with RA, providing variably weighted clinical features and defined cut-offs for clinical decision-making. Trajectory of DAS28-ESR change over 24 weeks in patients with moderate-to-high RA disease activity at baseline who are starting MTX can be predicted by baseline DAS28-ESR, ACPA status and HAQ-score. Patients with at least 1 unit decline in DAS28-ESR within the first 12 weeks of treatment who have not achieved low disease activity by week 12, may be more likely to achieve low disease activity at 24 weeks. These parameters should be considered as part of the clinical decision-making process when initiating MTX in DMARD-naïve patients with RA.

References

[1]Aletaha D, Smolen JS. Effectiveness profiles and dose dependent retention of traditional disease modifying antirheumatic drugs for rheumatoid arthritis. An observational study. J Rheumatol. 2002;29(8):1631-8.

Acknowledgements

This abstract is based on research using data from data contributors UCB and Roche that has been made available through Vivli, Inc. Vivli has not contributed to or approved, and is not in any way responsible for, the contents of this publication.

Disclosure of Interests

Stephanie Duong: None declared, Cynthia S. Crowson: None declared, Arjun Athreya: None declared, Elizabeth Atkinson: None declared, John M Davis III Grant/research support from: Pfizer, Kenneth J Warrington Speakers bureau: Chemocentryx, Consultant of: Roche/Genentech, Eric Matteson: None declared, Richard Weinshilboum Shareholder of: OneOme, Liewei Wang Shareholder of: OneOme, Elena Myasoedova: None declared.

FRI0046 PHARMACOGENOMICS-DRIVEN INDIVIDUALIZED PREDICTION OF TREATMENT RESPONSE TO METHOTREXATE IN PATIENTS WITH RHEUMATOID ARTHRITIS: A MACHINE LEARNING APPROACH

Background:

Methotrexate (MTX) is the most common anchor drug for rheumatoid arthritis (RA), but the risk of missing the opportunity for early effective treatment with alternative medications is substantial given the delayed onset of MTX action and 30-40% inadequate response rate. There is a compelling need to accurately predicting MTX response prior to treatment initiation, which allows for effectively identifying patients at RA onset who are likely to respond to MTX.

Objectives:

To test the ability of machine learning approaches with clinical and genomic biomarkers to predict MTX response with replications in independent samples.

Methods:

Age, sex, clinical, serological and genome-wide association study (GWAS) data on patients with early RA of European ancestry from 647 patients (336 recruited in United Kingdom [UK]; 307 recruited across Europe; 70% female; 72% rheumatoid factor [RF] positive; mean age 54 years; mean baseline Disease Activity Score with 28-joint count [DAS28] 5.65) of the PhArmacogenetics of Methotrexate in RA (PAMERA) consortium was used in this study. The genomics data comprised 160 genome-wide significant single nucleotide polymorphisms (SNPs) with p<1×10-5 associated with risk of RA and MTX metabolism. DAS28 score was available at baseline and 3-month follow-up visit. Response to MTX monotherapy at the dose of ≥15 mg/week was defined as good or moderate by the EULAR response criteria at 3 months’ follow up visit. Supervised machine-learning methods were trained with 5-repeats and 10-fold cross-validation using data from PAMERA’s 336 UK patients. Class imbalance (higher % of MTX responders) in training was accounted by using simulated minority oversampling technique. Prediction performance was validated in PAMERA’s 307 European patients (not used in training).

Results:

Age, sex, RF positivity and baseline DAS28 data predicted MTX response with 58% accuracy of UK and European patients (p = 0.7). However, supervised machine-learning methods that combined demographics, RF positivity, baseline DAS28 and genomic SNPs predicted EULAR response at 3 months with area under the receiver operating curve (AUC) of 0.83 (p = 0.051) in UK patients, and achieved prediction accuracies (fraction of correctly predicted outcomes) of 76.2% (p = 0.054) in the European patients, with sensitivity of 72% and specificity of 77%. The addition of genomic data improved the predictive accuracies of MTX response by 19% and achieved cross-site replication. Baseline DAS28 scores and following SNPs rs12446816, rs13385025, rs113798271, and rs2372536 were among the top predictors of MTX response.

Conclusion:

Pharmacogenomic biomarkers combined with DAS28 scores predicted MTX response in patients with early RA more reliably than using demographics and DAS28 scores alone. Using pharmacogenomics biomarkers for identification of MTX responders at early stages of RA may help to guide effective RA treatment choices, including timely escalation of RA therapies. Further studies on personalized prediction of response to MTX and other anti-rheumatic treatments are warranted to optimize control of RA disease and improve outcomes in patients with RA.

Disclosure of Interests:

Elena Myasoedova: None declared, Arjun Athreya: None declared, Cynthia S. Crowson Grant/research support from: Pfizer research grant, Richard Weinshilboum Shareholder of: co-founder and stockholder in OneOme, Liewei Wang: None declared, Eric Matteson Grant/research support from: Pfizer, Consultant of: Boehringer Ingelheim, Gilead, TympoBio, Arena Pharmaceuticals, Speakers bureau: Simply Speaking

A prospective genome-wide study of prostate cancer metastases reveals association of wnt pathway activation and increased cell cycle proliferation with primary resistance to abiraterone acetate-prednisone

Background

Genomic aberrations have been identified in metastatic castration-resistant prostate cancer (mCRPC), but molecular predictors of resistance to abiraterone acetate/prednisone (AA/P) treatment are not known.

Patients and methods

In a prospective clinical trial, mCRPC patients underwent whole-exome sequencing (n = 82) and RNA sequencing (n = 75) of metastatic biopsies before initiating AA/P with the objective of identifying genomic alterations associated with resistance to AA/P. Primary resistance was determined at 12 weeks of treatment using criteria for progression that included serum prostate-specific antigen measurement, bone and computerized tomography imaging and symptom assessments. Acquired resistance was determined using the end point of time to treatment change (TTTC), defined as time from enrollment until change in treatment from progressive disease. Associations of genomic and transcriptomic alterations with primary resistance were determined using logistic regression, Fisher's exact test, single and multivariate analyses. Cox regression models were utilized for determining association of genomic and transcriptomic alterations with TTTC.

Results

At 12 weeks, 32 patients in the cohort had progressed (nonresponders). Median study follow-up was 32.1 months by which time 58 patients had switched treatments due to progression. Median TTTC was 10.1 months (interquartile range: 4.4-24.1). Genes in the Wnt/β-catenin pathway were more frequently mutated and negative regulators of Wnt/β-catenin signaling were more frequently deleted or displayed reduced mRNA expression in nonresponders. Additionally, mRNA expression of cell cycle regulatory genes was increased in nonresponders. In multivariate models, increased cell cycle proliferation scores (≥ 50) were associated with shorter TTTC (hazard ratio = 2.11, 95% confidence interval: 1.17-3.80; P = 0.01).

Conclusions

Wnt/β-catenin pathway activation and increased cell cycle progression scores can serve as molecular markers for predicting resistance to AA/P therapy.

Abstract P1-03-04: Molecular subtyping of androgen receptor-positive patients using gene expression profiles

Breast cancer is a heterogeneous disease, and unsupervised clustering approaches using gene expression data have identified 3-6 distinct subtypes of triple negative breast cancer (TNBC). A genomically and clinically distinct subtype of TNBC is referred to as LAR (Luminal Androgen Receptor). Tumors with this subtype typically express high levels of the AR and exhibit alterations within genes involved in the PI3K pathway (e.g. PIK3CA mutations). Prospective studies are underway using drugs that target the AR alone or in combination with PI3K and CDK 4/6 inhibitors. Given the importance of accurately identifying this subtype, we sought to develop an online tool that uses submitted gene expression data to confidently characterize LAR samples by corroborating the classification with previously published clustering approaches.

Methods: We have investigated TNBC RNA-Seq data from The Cancer Genome Atlas (TCGA) breast cancer study (N=123 samples) by cluster analysis. Analysis of the average silhouette width in both biased and unbiased K-means clustering approaches demonstrated LAR and basal as two distinct and significant clusters. A shrunken centroid model of 426 differentially expressed genes, named as CABAL (Clustering Among BAsal and Luminal androgen receptor), was constructed by comparing LAR and basal subtypes.

Results: We applied the CABAL model to classify the four TNBC microarray datasets that were previously used in clustering experiments as well as an independent RNA-Seq data cohort. Non-negative matrix factorization (NMF) and fuzzy clustering were applied to the samples (N=1046). Clustering similarity among the methods was assessed with the adjusted rand index, and CABAL demonstrated significant similarity with both fuzzy and NMF clustering methods. Similarly, hierarchical clustering analysis performed on the pooled cohort of 1046 samples recapitulated the CABAL classification with an area under the receiver operating curve of 0.91.

Conclusions: Confident and robust identification of samples with the LAR phenotype is paramount in the assessment of clinical associations and therapeutic efficacy. To facilitate LAR identification, we have provided a web-based prediction tool of the CABAL classification, integrated with the NMF and fuzzy clustering results to identify candidate LAR samples. The end user is provided with the pair-wise adjusted rand indexes, thus reinforcing in the clustering characterizations. Further, our online LAR depiction tool provides a set of graphical and tabular summaries, which will be illustrated, while providing additional molecular characterizations of the PAM50 and Metabric classifications. The availability of this tool could advance the genomic research and treatment of TNBC patients.

Citation Format: Thompson KJ, Alaparthi T, Sinnwell JP, Carlson EE, Tang X, Bockol M, Vedell PT, Ingle JN, Suman V, Weinshilboum RM, Wang L, Boughey JC, Kalari KR, Goetz MP. Molecular subtyping of androgen receptor-positive patients using gene expression profiles [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P1-03-04.

Abstract P3-06-10: Multiscale modeling of omics data for precision breast cancer treatment

Introduction: The vast majority of cancer patients continue to receive treatments that are minimally informed by omics data. In the case of breast cancer, only ER and HER2 are routinely used for treatment selection. There is a particular need for personalized treatment in individuals with primary and secondary drug resistance or aggressive breast cancers. Emerging bioinformatics and statistical methods have made a fundamental impact on cancer research. However, challenges remains with regard to patient-centric data analysis and providing genomic data guidance to oncologists. There exists a large number of FDA approved anti-neoplastic drugs used to treat cancers other than breast and the development of innovative informatics methods and algorithms to repurpose those drugs should benefit breast cancer patients.

Methods and Results: We have developed precision care systems (such as PANOPLY and CORPUS) to identify personalized therapies for an individual patient and to deliver genomic reports in a standard, searchable format so that a researcher or an oncologist can quickly navigate through molecular data and obtain prioritized drugs and targets.The PANOPLY (Precision cancer genomics report: single sample inventory) algorithm applies machine learning and topology-based network analysis methods to integrate multi-omics profiles and clinical data; individual-specific molecular alterations are identified and compared with a set of matched-controls having similar clinical data. Since there is a lack of a “gold standard” dataset to test such algorithms, we simulated 500 case-control sets and evaluated drug predictions across multiple simulation scenarios. We applied the PANOPLY algorithm to The Cancer Genome Atlas (TCGA) breast cancer cohort, which consists of multi-omics data and clinical data. In addition, PANOPLY was also applied to an in-house neoadjuvant breast cancer study (BEAUTY) that consists of multi-omics data, clinical data, and patient-derived xenografts (PDXs). In the TCGA breast cancer study we obtained survival data to determine the cases and matched-controls; and in the BEAUTY, we used pathologic complete response (pCR) as an outcome to determine responders and non-responders. Recurrent targetable alterations were not enriched in patients without pCR in the BEAUTY study. We have applied the PANOPLY to non-responder patients to identify individual specific alterations, dysregulated networks, drug targets, and drugs for each patient and stored them as case reports in CORPUS (Computational Oncology Reports and Precision therapeUticS), a web-based repository that allows clinicians to review genomic reports. Using comprehensive “omic” data derived from a triple negative breast cancer patient who had pre and post-neoadjuvant chemotherapy PDXs, PANOPLY prioritized the PARP inhibitors as the top class of drug. Using the PDX models available from this patient, we tested olaparib and confirmed the in vivo antitumor activity (more effective than vehicle with a p-value &lt; 0.05 in the PDXs). Further studies to confirm PANOPLY findings are currently underway.

Conclusions: In summary, the PANOPLY and CORPUS systems incorporate molecular data together with clinical data to provide genomic reports with proposed drug targets to advance or enable precision breast cancer care.

Citation Format: Kalari KR, Sinnwell JP, Thompson KJ, Tang X, Carlson EE, Alaparthi T, Yu J, Vedell PT, Kalmbach MT, Bockol MA, Hossain A, Weinshilboum RM, Boughey JC, Wang L, Suman VJ, Goetz MP. Multiscale modeling of omics data for precision breast cancer treatment [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P3-06-10.

Abstract P5-07-01: LncRNA MIR2052HG regulates ERα level and endocrine resistance through LMTK3 by recruiting early growth response protein 1

BACKGROUND: A GWAS for the MA.27 aromatase inhibitors (AIs) adjuvant trial (4,406 controls and 252 cases) identified variant (V) SNPs in a long noncoding (lnc) RNA, MIR2052HG, that were associated with longer breast cancer free interval (HR= 0.37, P= 2.15E-07). V SNPs (MAF= 0.32 to 0.42) were associated with lower MIR2052HG and ERα expression in the presence of AIs. MIR2052HG maintained ERα both by promoting AKT/FOXO3-mediated ESR1 transcription and by limiting ubiquitin-mediated ERα degradation. (Cancer Res 76:7012-23, 2016). Our goal was to further elucidate MIR2052HG's mechanism of action.

METHODS: RNA-Binding Protein Immunoprecipitation (RBPI) assays were performed to demonstrate that the transcription factor, early growth response protein 1 (EGR1), worked together with MIR2052HG to regulate lemur tyrosine kinase-3 (LMTK3) transcription in MCF7/AC1 and CAMA-1 cells. The location of EGR1 on the LMTK3 gene locus was mapped using chromatin immunoprecipitation (ChIP) assays. The co-localization of MIR2052HG RNA and the LMTK3 gene locus was determined using RNA-DNA dual fluorescent in situ hybridization (FISH). SNP effects were evaluated using a panel of human lymphoblastoid cell lines.

RESULTS: TCGA analysis revealed LMTK3 and MIR2052HG expression were highly correlated in ERα-positive breast cancer patients. We found that the MIR2052HG transcript was located in the LMTK3 gene locus by RNA-DNA FISH. Among all of the 12 potential LMTK3 transcription factors identified in the Encode database that were examined by RBPI, only EGR1 showed an interaction with MIR2052HG. CHIP assays confirmed EGR1 binding to the two putative EGR1 binding sites in LMTK3 gene.Depletion of MIR2052HG reduced the binding of EGR1 to the LMTK3 promoter and decreased LMTK3 expression, suggesting that it might function as a scaffold. Mechanistically, decreased LMTK3 levels further increased protein kinase C (PKC) activity and downstream AKT activity, leading to reduced ESR1 mRNA levels via increased pFOXO3. At the protein level, in MIR2052HG depleted cells, increased PKC activity increased the phosphorylation of MEK, ERK, and RSK1, leading to increased ERα phosphorylation at Ser167 and increased ERα degradation. Conversely, overexpression of LMTK3 in MIR2052HG depleted cells reversed these phenotypes. MIR2052HG regulated LMTK3 and ERα expression in a SNP- dependent fashion: the MIR2052HG V SNP, relative to wild-type (WT) genotype, increased LMTK3/ERα expression in response to androstenedione due to increased binding between EGR1 and the LMTK3 promoter in LCLs. However, AI treatment reduced this binding in MIR2052HG variant cells but increased binding in WT cells, resulting in decreased LMTK3/ERα in V cells and increased expression in WT cells.

CONCLUSIONS: Our findings support a model in which the protective MIR2052HG variant genotype regulates LMTK3 via MIR2052HG/EGR1, and LMTK3 regulates ERα stability via the PKC/MEK/ERK/RSK1 axis. This regulation may explain the effect of the MIR2052HG variant genotype on cell proliferation and response to AIs in MA.27. These findings provide new insight into the mechanism of action of MIR2052HG and suggest that LMTK3 may be a new therapeutic target in ERα-positive breast cancer patients treated with AIs.

Citation Format: Cairns J, Ingle JN, Shepherd LE, Kubo M, Goetz MP, Weinshilboum RM, Kalari KR, Wang L. LncRNA MIR2052HG regulates ERα level and endocrine resistance through LMTK3 by recruiting early growth response protein 1 [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P5-07-01.

Genetic Breast Cancer Susceptibility Variants and Prognosis in the Prospectively Randomized SUCCESS A Study

Large-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 SNPs' 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.

Abstract PD1-04: CSMD1 SNPs selectively affect anastrozole response in postmenopausal breast cancer patients

BACKGROUND: Based on prospective clinical trials, there is no evidence for differences in efficacy between the 3 aromatase inhibitors (AIs) anastrozole, exemestane, and letrozole. The purpose of this study was to identify germline genetic variants associated with response to AIs and to help identify novel mechanisms associated with drug disease efficacy.

METHODS: A genome-wide association study (GWAS) was performed for 624 patients (Steroids 2015;99:32-38) to identify SNPs associated with estrogen level change in women with estrogen receptor (ER) positive breast cancer treated with anastrozole. Replication of associated SNPs was performed in a GWAS from the MA.27 trial that compared adjuvant anastrozole and exemestane treatment of post-menopausal women with ER+ breast cancer. Functional studies were subsequently performed to determine SNP effects and underlying mechanisms.

RESULTS: Our initial GWAS identified SNPs within CSMD1 that were associated with changes in estrogen levels during anastrozole therapy. An additional SNP in CSMD1 was also associated with breast cancer events in CCTG MA.27. Functionally, we showed that CSMD1 regulates CYP19 expression in a SNP-, and in an anastrozole- dependent fashion. These phenomena were not observed for either letrozole or exemestane. In MA.27, an anastrozole- specific effect was also seen with the minor allele having a protective effect on time to distant metastasis (HR=0.49, p=0.00259), but this was not the case for exemestane (HR=0.71, p=0.111). Our in vitro functional studies indicated that overexpression of CSMD1 sensitized anastrozole or letrozole resistant cells to anastrozole but not to the other two AIs. The SNP in CSMD1 that was associated with increased CSMD1 and CYP19 expression levels increased anastrozole sensitivity, but not letrozole or exemestane in lymphoblastoid cell lines (LCLs) homozygous for either WT or variant CSMD1 SNP genotypes. Based on these observations, we explored whether anastrozole has additional mechanisms beyond its function as a CYP19 inhibitor. Utilizing an estrogen response element (ERE) luciferase reporter assay in a CYP19 CRISPR knockout breast cancer T47D cell line and a surface plasmon resonance (SPR) assay, we found that anastrozole can also function as an ERα agonist, and can bind to, and result in, proteasome dependent ERα degradation, especially in the presence of E2. Treatment of these CYP19 CRISPR knockout cells with anastrozole in the presence of increasing concentrations of E2 results in greater sensitivity compared with anastrozole alone, while the addition of E2, as expected, does not improve letrozole or exemestane sensitivity. These same observations were also seen in letrozole and anastrazole resistant cells.

CONCLUSIONS: Our findings suggest that anastrozole might be more effective than letrozole or exemestane in patients with the CSMD1 SNP. Furthermore, anastrozole can function as an ERα agonist, binding to ERα and resulting in its degradation, especially in the presence of E2. These findings should help to make it possible to develop precision endocrine therapies for women who are candidates for AIs.

Citation Format: Cairns J, Ingle J, Dudenkov T, Kalari K, Buzdar A, Kubo M, Robson M, Ellis M, Goss P, Shepherd L, Goetz M, Weinshilboum R, Wang L. CSMD1 SNPs selectively affect anastrozole response in postmenopausal breast cancer patients [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr PD1-04.

Polymorphic variation in TPMT is the principal determinant of TPMT phenotype: A meta-analysis of three genome-wide association studies

Thiopurine-related hematotoxicity in pediatric acute lymphoblastic leukemia (ALL) and inflammatory bowel diseases has been linked to genetically defined variability in thiopurine S-methyltransferase (TPMT) activity. While gene testing of TPMT is being clinically implemented, it is unclear if additional genetic variation influences TPMT activity with consequences for thiopurine-related toxicity. To examine this possibility, we performed a genome-wide association study (GWAS) of red blood cell TPMT activity in 844 Estonian individuals and 245 pediatric ALL cases. Additionally, we correlated genome-wide genotypes to human hepatic TPMT activity in 123 samples. Only genetic variants mapping to chromosome 6, including the TPMT gene region, were significantly associated with TPMT activity (P < 5.0 × 10^-8 ) in each of the three GWAS and a joint meta-analysis of 1,212 cases (top hit P = 1.2 × 10^-72 ). This finding is consistent with TPMT genotype being the primary determinant of TPMT activity, reinforcing the rationale for genetic testing of TPMT alleles in routine clinical practice to individualize mercaptopurine dosage.

TSPAN5, ERICH3 and selective serotonin reuptake inhibitors in major depressive disorder: pharmacometabolomics-informed pharmacogenomics

Millions of patients suffer from major depressive disorder (MDD), but many do not respond to selective serotonin reuptake inhibitor (SSRI) therapy. We used a pharmacometabolomics-informed pharmacogenomics research strategy to identify genes associated with metabolites that were related to SSRI response. Specifically, 306 MDD patients were treated with citalopram or escitalopram and blood was drawn at baseline, 4 and 8 weeks for blood drug levels, genome-wide single nucleotide polymorphism (SNP) genotyping and metabolomic analyses. SSRI treatment decreased plasma serotonin concentrations (P<0.0001). Baseline and plasma serotonin concentration changes were associated with clinical outcomes (P<0.05). Therefore, baseline and serotonin concentration changes were used as phenotypes for genome-wide association studies (GWAS). GWAS for baseline plasma serotonin concentrations revealed a genome-wide significant (P=7.84E-09) SNP cluster on chromosome four 5' of TSPAN5 and a cluster across ERICH3 on chromosome one (P=9.28E-08) that were also observed during GWAS for change in serotonin at 4 (P=5.6E-08 and P=7.54E-07, respectively) and 8 weeks (P=1.25E-06 and P=3.99E-07, respectively). The SNPs on chromosome four were expression quantitative trait loci for TSPAN5. Knockdown (KD) and overexpression (OE) of TSPAN5 in a neuroblastoma cell line significantly altered the expression of serotonin pathway genes (TPH1, TPH2, DDC and MAOA). Chromosome one SNPs included two ERICH3 nonsynonymous SNPs that resulted in accelerated proteasome-mediated degradation. In addition, ERICH3 and TSPAN5 KD and OE altered media serotonin concentrations. Application of a pharmacometabolomics-informed pharmacogenomic research strategy, followed by functional validation, indicated that TSPAN5 and ERICH3 are associated with plasma serotonin concentrations and may have a role in SSRI treatment outcomes.

Genomewide Association Studies in Pharmacogenomics: Meeting Report of the NIH Pharmacogenomics Research Network-RIKEN (PGRN-RIKEN) Collaboration

Genomewide association studies (GWAS) have resulted in the identification of many heritable genetic factors that underlie risk for human disease or variation in physiologic traits. In contrast, there are fewer GWAS of drug response phenotypes, despite extensive unexplained interindividual variability. To address this urgent need, the NIH Pharmacogenomics Research Network (PGRN) and the Center for Integrative Medical Sciences (IMS) at RIKEN support a collaboration, PGRN-RIKEN, with the goal of accelerating GWAS of drug response phenotypes.

Human genome meeting 2016 : Houston, TX, USA. 28 February - 2 March 2016

O1 The metabolomics approach to autism: identification of biomarkers for early detection of autism spectrum disorder A. K. Srivastava, Y. Wang, R. Huang, C. Skinner, T. Thompson, L. Pollard, T. Wood, F. Luo, R. Stevenson O2 Phenome-wide association study for smoking- and drinking-associated genes in 26,394 American women with African, Asian, European, and Hispanic descents R. Polimanti, J. Gelernter O3 Effects of prenatal environment, genotype and DNA methylation on birth weight and subsequent postnatal outcomes: findings from GUSTO, an Asian birth cohort X. Lin, I. Y. Lim, Y. Wu, A. L. Teh, L. Chen, I. M. Aris, S. E. Soh, M. T. Tint, J. L. MacIsaac, F. Yap, K. Kwek, S. M. Saw, M. S. Kobor, M. J. Meaney, K. M. Godfrey, Y. S. Chong, J. D. Holbrook, Y. S. Lee, P. D. Gluckman, N. Karnani, GUSTO study group O4 High-throughput identification of specific qt interval modulating enhancers at the SCN5A locus A. Kapoor, D. Lee, A. Chakravarti O5 Identification of extracellular matrix components inducing cancer cell migration in the supernatant of cultivated mesenchymal stem cells C. Maercker, F. Graf, M. Boutros O6 Single cell allele specific expression (ASE) IN T21 and common trisomies: a novel approach to understand DOWN syndrome and other aneuploidies G. Stamoulis, F. Santoni, P. Makrythanasis, A. Letourneau, M. Guipponi, N. Panousis, M. Garieri, P. Ribaux, E. Falconnet, C. Borel, S. E. Antonarakis O7 Role of microRNA in LCL to IPSC reprogramming S. Kumar, J. Curran, J. Blangero O8 Multiple enhancer variants disrupt gene regulatory network in Hirschsprung disease S. Chatterjee, A. Kapoor, J. Akiyama, D. Auer, C. Berrios, L. Pennacchio, A. Chakravarti O9 Metabolomic profiling for the diagnosis of neurometabolic disorders T. R. Donti, G. Cappuccio, M. Miller, P. Atwal, A. Kennedy, A. Cardon, C. Bacino, L. Emrick, J. Hertecant, F. Baumer, B. Porter, M. Bainbridge, P. Bonnen, B. Graham, R. Sutton, Q. Sun, S. Elsea O10 A novel causal methylation network approach to Alzheimer’s disease Z. Hu, P. Wang, Y. Zhu, J. Zhao, M. Xiong, David A Bennett O11 A microRNA signature identifies subtypes of triple-negative breast cancer and reveals MIR-342-3P as regulator of a lactate metabolic pathway A. Hidalgo-Miranda, S. Romero-Cordoba, S. Rodriguez-Cuevas, R. Rebollar-Vega, E. Tagliabue, M. Iorio, E. D’Ippolito, S. Baroni O12 Transcriptome analysis identifies genes, enhancer RNAs and repetitive elements that are recurrently deregulated across multiple cancer types B. Kaczkowski, Y. Tanaka, H. Kawaji, A. Sandelin, R. Andersson, M. Itoh, T. Lassmann, the FANTOM5 consortium, Y. Hayashizaki, P. Carninci, A. R. R. Forrest O13 Elevated mutation and widespread loss of constraint at regulatory and architectural binding sites across 11 tumour types C. A. Semple O14 Exome sequencing provides evidence of pathogenicity for genes implicated in colorectal cancer E. A. Rosenthal, B. Shirts, L. Amendola, C. Gallego, M. Horike-Pyne, A. Burt, P. Robertson, P. Beyers, C. Nefcy, D. Veenstra, F. Hisama, R. Bennett, M. Dorschner, D. Nickerson, J. Smith, K. Patterson, D. Crosslin, R. Nassir, N. Zubair, T. Harrison, U. Peters, G. Jarvik, NHLBI GO Exome Sequencing Project O15 The tandem duplicator phenotype as a distinct genomic configuration in cancer F. Menghi, K. Inaki, X. Woo, P. Kumar, K. Grzeda, A. Malhotra, H. Kim, D. Ucar, P. Shreckengast, K. Karuturi, J. Keck, J. Chuang, E. T. Liu O16 Modeling genetic interactions associated with molecular subtypes of breast cancer B. Ji, A. Tyler, G. Ananda, G. Carter O17 Recurrent somatic mutation in the MYC associated factor X in brain tumors H. Nikbakht, M. Montagne, M. Zeinieh, A. Harutyunyan, M. Mcconechy, N. Jabado, P. Lavigne, J. Majewski O18 Predictive biomarkers to metastatic pancreatic cancer treatment J. B. Goldstein, M. Overman, G. Varadhachary, R. Shroff, R. Wolff, M. Javle, A. Futreal, D. Fogelman O19 DDIT4 gene expression as a prognostic marker in several malignant tumors L. Bravo, W. Fajardo, H. Gomez, C. Castaneda, C. Rolfo, J. A. Pinto O20 Spatial organization of the genome and genomic alterations in human cancers K. C. Akdemir, L. Chin, A. Futreal, ICGC PCAWG Structural Alterations Group O21 Landscape of targeted therapies in solid tumors S. Patterson, C. Statz, S. Mockus O22 Genomic analysis reveals novel drivers and progression pathways in skin basal cell carcinoma S. N. Nikolaev, X. I. Bonilla, L. Parmentier, B. King, F. Bezrukov, G. Kaya, V. Zoete, V. Seplyarskiy, H. Sharpe, T. McKee, A. Letourneau, P. Ribaux, K. Popadin, N. Basset-Seguin, R. Ben Chaabene, F. Santoni, M. Andrianova, M. Guipponi, M. Garieri, C. Verdan, K. Grosdemange, O. Sumara, M. Eilers, I. Aifantis, O. Michielin, F. de Sauvage, S. Antonarakis O23 Identification of differential biomarkers of hepatocellular carcinoma and cholangiocarcinoma via transcriptome microarray meta-analysis S. Likhitrattanapisal O24 Clinical validity and actionability of multigene tests for hereditary cancers in a large multi-center study S. Lincoln, A. Kurian, A. Desmond, S. Yang, Y. Kobayashi, J. Ford, L. Ellisen O25 Correlation with tumor ploidy status is essential for correct determination of genome-wide copy number changes by SNP array T. L. Peters, K. R. Alvarez, E. F. Hollingsworth, D. H. Lopez-Terrada O26 Nanochannel based next-generation mapping for interrogation of clinically relevant structural variation A. Hastie, Z. Dzakula, A. W. Pang, E. T. Lam, T. Anantharaman, M. Saghbini, H. Cao, BioNano Genomics O27 Mutation spectrum in a pulmonary arterial hypertension (PAH) cohort and identification of associated truncating mutations in TBX4 C. Gonzaga-Jauregui, L. Ma, A. King, E. Berman Rosenzweig, U. Krishnan, J. G. Reid, J. D. Overton, F. Dewey, W. K. Chung O28 NORTH CAROLINA macular dystrophy (MCDR1): mutations found affecting PRDM13 K. Small, A. DeLuca, F. Cremers, R. A. Lewis, V. Puech, B. Bakall, R. Silva-Garcia, K. Rohrschneider, M. Leys, F. S. Shaya, E. Stone O29 PhenoDB and genematcher, solving unsolved whole exome sequencing data N. L. Sobreira, F. Schiettecatte, H. Ling, E. Pugh, D. Witmer, K. Hetrick, P. Zhang, K. Doheny, D. Valle, A. Hamosh O30 Baylor-Johns Hopkins Center for Mendelian genomics: a four year review S. N. Jhangiani, Z. Coban Akdemir, M. N. Bainbridge, W. Charng, W. Wiszniewski, T. Gambin, E. Karaca, Y. Bayram, M. K. Eldomery, J. Posey, H. Doddapaneni, J. Hu, V. R. Sutton, D. M. Muzny, E. A. Boerwinkle, D. Valle, J. R. Lupski, R. A. Gibbs O31 Using read overlap assembly to accurately identify structural genetic differences in an ashkenazi jewish trio S. Shekar, W. Salerno, A. English, A. Mangubat, J. Bruestle O32 Legal interoperability: a sine qua non for international data sharing A. Thorogood, B. M. Knoppers, Global Alliance for Genomics and Health - Regulatory and Ethics Working Group O33 High throughput screening platform of competent sineups: that can enhance translation activities of therapeutic target H. Takahashi, K. R. Nitta, A. Kozhuharova, A. M. Suzuki, H. Sharma, D. Cotella, C. Santoro, S. Zucchelli, S. Gustincich, P. Carninci O34 The undiagnosed diseases network international (UDNI): clinical and laboratory research to meet patient needs J. J. Mulvihill, G. Baynam, W. Gahl, S. C. Groft, K. Kosaki, P. Lasko, B. Melegh, D. Taruscio O36 Performance of computational algorithms in pathogenicity predictions for activating variants in oncogenes versus loss of function mutations in tumor suppressor genes R. Ghosh, S. Plon O37 Identification and electronic health record incorporation of clinically actionable pharmacogenomic variants using prospective targeted sequencing S. Scherer, X. Qin, R. Sanghvi, K. Walker, T. Chiang, D. Muzny, L. Wang, J. Black, E. Boerwinkle, R. Weinshilboum, R. Gibbs O38 Melanoma reprogramming state correlates with response to CTLA-4 blockade in metastatic melanoma T. Karpinets, T. Calderone, K. Wani, X. Yu, C. Creasy, C. Haymaker, M. Forget, V. Nanda, J. Roszik, J. Wargo, L. Haydu, X. Song, A. Lazar, J. Gershenwald, M. Davies, C. Bernatchez, J. Zhang, A. Futreal, S. Woodman O39 Data-driven refinement of complex disease classification from integration of heterogeneous functional genomics data in GeneWeaver E. J. Chesler, T. Reynolds, J. A. Bubier, C. Phillips, M. A. Langston, E. J. Baker O40 A general statistic framework for genome-based disease risk prediction M. Xiong, L. Ma, N. Lin, C. Amos O41 Integrative large-scale causal network analysis of imaging and genomic data and its application in schizophrenia studies N. Lin, P. Wang, Y. Zhu, J. Zhao, V. Calhoun, M. Xiong O42 Big data and NGS data analysis: the cloud to the rescue O. Dobretsberger, M. Egger, F. Leimgruber O43 Cpipe: a convergent clinical exome pipeline specialised for targeted sequencing S. Sadedin, A. Oshlack, Melbourne Genomics Health Alliance O44 A Bayesian classification of biomedical images using feature extraction from deep neural networks implemented on lung cancer data V. A. A. Antonio, N. Ono, Clark Kendrick C. Go O45 MAV-SEQ: an interactive platform for the Management, Analysis, and Visualization of sequence data Z. Ahmed, M. Bolisetty, S. Zeeshan, E. Anguiano, D. Ucar O47 Allele specific enhancer in EPAS1 intronic regions may contribute to high altitude adaptation of Tibetans C. Zeng, J. Shao O48 Nanochannel based next-generation mapping for structural variation detection and comparison in trios and populations H. Cao, A. Hastie, A. W. Pang, E. T. Lam, T. Liang, K. Pham, M. Saghbini, Z. Dzakula O49 Archaic introgression in indigenous populations of Malaysia revealed by whole genome sequencing Y. Chee-Wei, L. Dongsheng, W. Lai-Ping, D. Lian, R. O. Twee Hee, Y. Yunus, F. Aghakhanian, S. S. Mokhtar, C. V. Lok-Yung, J. Bhak, M. Phipps, X. Shuhua, T. Yik-Ying, V. Kumar, H. Boon-Peng O50 Breast and ovarian cancer prevention: is it time for population-based mutation screening of high risk genes? I. Campbell, M.-A. Young, P. James, Lifepool O53 Comprehensive coverage from low DNA input using novel NGS library preparation methods for WGS and WGBS C. Schumacher, S. Sandhu, T. Harkins, V. Makarov O54 Methods for large scale construction of robust PCR-free libraries for sequencing on Illumina HiSeqX platform H. DoddapaneniR. Glenn, Z. Momin, B. Dilrukshi, H. Chao, Q. Meng, B. Gudenkauf, R. Kshitij, J. Jayaseelan, C. Nessner, S. Lee, K. Blankenberg, L. Lewis, J. Hu, Y. Han, H. Dinh, S. Jireh, K. Walker, E. Boerwinkle, D. Muzny, R. Gibbs O55 Rapid capture methods for clinical sequencing J. Hu, K. Walker, C. Buhay, X. Liu, Q. Wang, R. Sanghvi, H. Doddapaneni, Y. Ding, N. Veeraraghavan, Y. Yang, E. Boerwinkle, A. L. Beaudet, C. M. Eng, D. M. Muzny, R. A. Gibbs O56 A diploid personal human genome model for better genomes from diverse sequence data K. C. C. Worley, Y. Liu, D. S. T. Hughes, S. C. Murali, R. A. Harris, A. C. English, X. Qin, O. A. Hampton, P. Larsen, C. Beck, Y. Han, M. Wang, H. Doddapaneni, C. L. Kovar, W. J. Salerno, A. Yoder, S. Richards, J. Rogers, J. R. Lupski, D. M. Muzny, R. A. Gibbs O57 Development of PacBio long range capture for detection of pathogenic structural variants Q. Meng, M. Bainbridge, M. Wang, H. Doddapaneni, Y. Han, D. Muzny, R. Gibbs O58 Rhesus macaques exhibit more non-synonymous variation but greater impact of purifying selection than humans R. A. Harris, M. Raveenedran, C. Xue, M. Dahdouli, L. Cox, G. Fan, B. Ferguson, J. Hovarth, Z. Johnson, S. Kanthaswamy, M. Kubisch, M. Platt, D. Smith, E. Vallender, R. Wiseman, X. Liu, J. Below, D. Muzny, R. Gibbs, F. Yu, J. Rogers O59 Assessing RNA structure disruption induced by single-nucleotide variation J. Lin, Y. Zhang, Z. Ouyang P1 A meta-analysis of genome-wide association studies of mitochondrial dna copy number A. Moore, Z. Wang, J. Hofmann, M. Purdue, R. Stolzenberg-Solomon, S. Weinstein, D. Albanes, C.-S. Liu, W.-L. Cheng, T.-T. Lin, Q. Lan, N. Rothman, S. Berndt P2 Missense polymorphic genetic combinations underlying down syndrome susceptibility E. S. Chen P4 The evaluation of alteration of ELAM-1 expression in the endometriosis patients H. Bahrami, A. Khoshzaban, S. Heidari Keshal P5 Obesity and the incidence of apolipoprotein E polymorphisms in an assorted population from Saudi Arabia population K. K. R. Alharbi P6 Genome-associated personalized antithrombotical therapy for patients with high risk of thrombosis and bleeding M. Zhalbinova, A. Akilzhanova, S. Rakhimova, M. Bekbosynova, S. Myrzakhmetova P7 Frequency of Xmn1 polymorphism among sickle cell carrier cases in UAE population M. Matar P8 Differentiating inflammatory bowel diseases by using genomic data: dimension of the problem and network organization N. Mili, R. Molinari, Y. Ma, S. Guerrier P9 Vulnerability of genetic variants to the risk of autism among Saudi children N. Elhawary, M. Tayeb, N. Bogari, N. Qotb P10 Chromatin profiles from ex vivo purified dopaminergic neurons establish a promising model to support studies of neurological function and dysfunction S. A. McClymont, P. W. Hook, L. A. Goff, A. McCallion P11 Utilization of a sensitized chemical mutagenesis screen to identify genetic modifiers of retinal dysplasia in homozygous Nr2e3^rd7 mice Y. Kong, J. R. Charette, W. L. Hicks, J. K. Naggert, L. Zhao, P. M. Nishina P12 Ion torrent next generation sequencing of recessive polycystic kidney disease in Saudi patients B. M. Edrees, M. Athar, F. A. Al-Allaf, M. M. Taher, W. Khan, A. Bouazzaoui, N. A. Harbi, R. Safar, H. Al-Edressi, A. Anazi, N. Altayeb, M. A. Ahmed, K. Alansary, Z. Abduljaleel P13 Digital expression profiling of Purkinje neurons and dendrites in different subcellular compartments A. Kratz, P. Beguin, S. Poulain, M. Kaneko, C. Takahiko, A. Matsunaga, S. Kato, A. M. Suzuki, N. Bertin, T. Lassmann, R. Vigot, P. Carninci, C. Plessy, T. Launey P14 The evolution of imperfection and imperfection of evolution: the functional and functionless fractions of the human genome D. Graur P16 Species-independent identification of known and novel recurrent genomic entities in multiple cancer patients J. Friis-Nielsen, J. M. Izarzugaza, S. Brunak P18 Discovery of active gene modules which are densely conserved across multiple cancer types reveal their prognostic power and mutually exclusive mutation patterns B. S. Soibam P19 Whole exome sequencing of dysplastic leukoplakia tissue indicates sequential accumulation of somatic mutations from oral precancer to cancer D. Das, N. Biswas, S. Das, S. Sarkar, A. Maitra, C. Panda, P. Majumder P21 Epigenetic mechanisms of carcinogensis by hereditary breast cancer genes J. J. Gruber, N. Jaeger, M. Snyder P22 RNA direct: a novel RNA enrichment strategy applied to transcripts associated with solid tumors K. Patel, S. Bowman, T. Davis, D. Kraushaar, A. Emerman, S. Russello, N. Henig, C. Hendrickson P23 RNA sequencing identifies gene mutations for neuroblastoma K. Zhang P24 Participation of SFRP1 in the modulation of TMPRSS2-ERG fusion gene in prostate cancer cell lines M. Rodriguez-Dorantes, C. D. Cruz-Hernandez, C. D. P. Garcia-Tobilla, S. Solorzano-Rosales P25 Targeted Methylation Sequencing of Prostate Cancer N. Jäger, J. Chen, R. Haile, M. Hitchins, J. D. Brooks, M. Snyder P26 Mutant TPMT alleles in children with acute lymphoblastic leukemia from México City and Yucatán, Mexico S. Jiménez-Morales, M. Ramírez, J. Nuñez, V. Bekker, Y. Leal, E. Jiménez, A. Medina, A. Hidalgo, J. Mejía P28 Genetic modifiers of Alström syndrome J. Naggert, G. B. Collin, K. DeMauro, R. Hanusek, P. M. Nishina P31 Association of genomic variants with the occurrence of angiotensin-converting-enzyme inhibitor (ACEI)-induced coughing among Filipinos E. M. Cutiongco De La Paz, R. Sy, J. Nevado, P. Reganit, L. Santos, J. D. Magno, F. E. Punzalan , D. Ona , E. Llanes, R. L. Santos-Cortes , R. Tiongco, J. Aherrera, L. Abrahan, P. Pagauitan-Alan; Philippine Cardiogenomics Study Group P32 The use of “humanized” mouse models to validate disease association of a de novo GARS variant and to test a novel gene therapy strategy for Charcot-Marie-Tooth disease type 2D K. H. Morelli, J. S. Domire, N. Pyne, S. Harper, R. Burgess P34 Molecular regulation of chondrogenic human induced pluripotent stem cells M. A. Gari, A. Dallol, H. Alsehli, A. Gari, M. Gari, A. Abuzenadah P35 Molecular profiling of hematologic malignancies: implementation of a variant assessment algorithm for next generation sequencing data analysis and clinical reporting M. Thomas, M. Sukhai, S. Garg, M. Misyura, T. Zhang, A. Schuh, T. Stockley, S. Kamel-Reid P36 Accessing genomic evidence for clinical variants at NCBI S. Sherry, C. Xiao, D. Slotta, K. Rodarmer, M. Feolo, M. Kimelman, G. Godynskiy, C. O’Sullivan, E. Yaschenko P37 NGS-SWIFT: a cloud-based variant analysis framework using control-accessed sequencing data from DBGAP/SRA C. Xiao, E. Yaschenko, S. Sherry P38 Computational assessment of drug induced hepatotoxicity through gene expression profiling C. Rangel-Escareño, H. Rueda-Zarate P40 Flowr: robust and efficient pipelines using a simple language-agnostic approach;ultraseq; fast modular pipeline for somatic variation calling using flowr S. Seth, S. Amin, X. Song, X. Mao, H. Sun, R. G. Verhaak, A. Futreal, J. Zhang P41 Applying “Big data” technologies to the rapid analysis of heterogenous large cohort data S. J. Whiite, T. Chiang, A. English, J. Farek, Z. Kahn, W. Salerno, N. Veeraraghavan, E. Boerwinkle, R. Gibbs P42 FANTOM5 web resource for the large-scale genome-wide transcription start site activity profiles of wide-range of mammalian cells T. Kasukawa, M. Lizio, J. Harshbarger, S. Hisashi, J. Severin, A. Imad, S. Sahin, T. C. Freeman, K. Baillie, A. Sandelin, P. Carninci, A. R. R. Forrest, H. Kawaji, The FANTOM Consortium P43 Rapid and scalable typing of structural variants for disease cohorts W. Salerno, A. English, S. N. Shekar, A. Mangubat, J. Bruestle, E. Boerwinkle, R. A. Gibbs P44 Polymorphism of glutathione S-transferases and sulphotransferases genes in an Arab population A. H. Salem, M. Ali, A. Ibrahim, M. Ibrahim P46 Genetic divergence of CYP3A5*3 pharmacogenomic marker for native and admixed Mexican populations J. C. Fernandez-Lopez, V. Bonifaz-Peña, C. Rangel-Escareño, A. Hidalgo-Miranda, A. V. Contreras P47 Whole exome sequence meta-analysis of 13 white blood cell, red blood cell, and platelet traits L. Polfus, CHARGE and NHLBI Exome Sequence Project Working Groups P48 Association of adipoq gene with type 2 diabetes and related phenotypes in african american men and women: The jackson heart study S. Davis, R. Xu, S. Gebeab, P Riestra, A Gaye, R. Khan, J. Wilson, A. Bidulescu P49 Common variants in casr gene are associated with serum calcium levels in koreans S. H. Jung, N. Vinayagamoorthy, S. H. Yim, Y. J. Chung P50 Inference of multiple-wave population admixture by modeling decay of linkage disequilibrium with multiple exponential functions Y. Zhou, S. Xu P51 A Bayesian framework for generalized linear mixed models in genome-wide association studies X. Wang, V. Philip, G. Carter P52 Targeted sequencing approach for the identification of the genetic causes of hereditary hearing impairment A. A. Abuzenadah, M. Gari, R. Turki, A. Dallol P53 Identification of enhancer sequences by ATAC-seq open chromatin profiling A. Uyar, A. Kaygun, S. Zaman, E. Marquez, J. George, D. Ucar P54 Direct enrichment for the rapid preparation of targeted NGS libraries C. L. Hendrickson, A. Emerman, D. Kraushaar, S. Bowman, N. Henig, T. Davis, S. Russello, K. Patel P56 Performance of the Agilent D5000 and High Sensitivity D5000 ScreenTape assays for the Agilent 4200 Tapestation System R. Nitsche, L. Prieto-Lafuente P57 ClinVar: a multi-source archive for variant interpretation M. Landrum, J. Lee, W. Rubinstein, D. Maglott P59 Association of functional variants and protein physical interactions of human MUTY homolog linked with familial adenomatous polyposis and colorectal cancer syndrome Z. Abduljaleel, W. Khan, F. A. Al-Allaf, M. Athar , M. M. Taher, N. Shahzad P60 Modification of the microbiom constitution in the gut using chicken IgY antibodies resulted in a reduction of acute graft-versus-host disease after experimental bone marrow transplantation A. Bouazzaoui, E. Huber, A. Dan, F. A. Al-Allaf, W. Herr, G. Sprotte, J. Köstler, A. Hiergeist, A. Gessner, R. Andreesen, E. Holler P61 Compound heterozygous mutation in the LDLR gene in Saudi patients suffering severe hypercholesterolemia F. Al-Allaf, A. Alashwal, Z. Abduljaleel, M. Taher, A. Bouazzaoui, H. Abalkhail, A. Al-Allaf, R. Bamardadh, M. Athar

Abstract P4-04-05: Differential mRNA expression patterns in breast tumors with high vs. low quantity of stromal tumor–Infiltrating lymphocytes

Background: Tumor-infiltrating lymphocytes (TIL) have prognostic and potentially predictive significance in the (neo)adjuvant treatment of high-risk breast cancer. However, quantitative TIL measurement is not routinely performed. It is unclear why some tumors attract large quantities of TIL while others do not. We sought to confirm the association between TIL and pathologic complete response rate (pCR) and to further use next generation sequencing (NGS) to identify genes and gene pathways associated with the presence/absence of TIL.

Methods: We studied 140 women with high risk stage I-III breast cancer, enrolled in the Breast Cancer Genome Guided Therapy Study (BEAUTY), obtaining serial biopsies for DNA/RNA sequencing and MRI imaging to assess response to neoadjuvant chemotherapy (NAC) with taxane (+/- trastuzumab+/-pertuzumab for HER2+ disease) followed by AC or (F)EC. Diagnostic pre-NAC core needle biopsies and surgical resection specimens post-NAC were available from 110 patients. Stromal TIL were semi-quantitated on a scale of 1-4 (with 1: ≤10/hpf, 2: subtle infiltrate &gt;10/hpf, 3: moderate infiltrate readily visible at low power magnification, 4: dense infiltrate with innumerable lymphocytes). For this analysis, low TIL was defined as scores of 1-2 vs. high defined as 3-4. Using pre-NAC biopsies, RNAseq was performed using the Illumina HiSeq2000 and the Mayo Analysis Pipeline for RNAseq (MAP-Rseq) for quality control, sequence alignment, and gene counts. The quantity of TIL was associated with transcripts across the transcriptome after conditional quantile normalization. Differentially expressed genes were obtained using EdgeR analysis, using a false discovery rate of 0.05, and pathways were evaluated using GAGE methods.

Results: The pCR and residual cancer burden (RCB)-0/I rates by stromal TIL status within each molecular subtype are presented in the table. A diverse spectrum of 1344 genes with differential expression between tumors with high vs. low stromal TIL was identified. The genes with &gt;2.0-fold change (FC) and p&lt;1e-09 included S100A7 (4.49 FC), LCN2 (2.48 FC), and ART3 (2.82 FC) (genes known to be involved in immune regulation), as well as TDRD1 (2.71 FC) (a gene related to ERG [ETS-related gene] expression). In addition, the "regulation of actin cytoskeleton" pathway was upregulated in tumors with high TIL, while the "Hedgehog signaling" and "Wnt signaling" pathways were downregulated.

Molecular SubtypeStromal TILspCR rate n (%)RCB-0/I rateLuminal AHigh------Luminal ALow0/9 (0%)0/9 (0%)Luminal BHigh1/9 (11.1%)1/8 (12.5%)Luminal BLow3/24 (12.5%)6/23 (26.1%)ER+/HER2+High3/9 (33.3%)4/9 (44.4%)ER+/HER2+Low1/6 (16.7%)1/6 (16.7%)ER-/HER2+High8/9 (88.9%)7/7 (100%)ER-/HER2+Low4/8 (50.0%)6/8 (75.0%)Triple NegativeHigh10/19 (52.6%)13/19 (68.4%)Triple NegativeLow7/14 (50.0%)9/13 (69.2%)

Conclusions: We identified genes and gene pathways associated with high TIL expression in breast tumors prior to NAC that provide insight into the interactions between TIL and tumors. TIL can be easily semi-quantitated on H&E and along with these novel biomarkers, may contribute to the personalization of breast cancer therapy.

Citation Format: Moyer AM, Boughey JC, Kalari KR, Suman VJ, McLaughlin SA, Moreno-Aspitia A, Northfelt DW, Gray RJ, Sinnwell JP, Carlson EE, Dockter TJ, Jones KN, Felten SJ, Conners AL, Wieben ED, Ingle JN, Wang L, Weinshilboum RM, Visscher DW, Goetz MP. Differential mRNA expression patterns in breast tumors with high vs. low quantity of stromal tumor–Infiltrating lymphocytes. [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 P4-04-05.

Abstract P3-07-51: Regulation of DNA methyltransferases via TRAF6 determines breast cancer response to decitabine

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.

Abstract P4-10-07: Genetic predictors of chemotherapy-related amenorrhea

Background: Chemotherapy-related amenorrhea (CRA) impacts quality of life and reproductive options, and may play a role in treatment decision-making for young breast cancer patients. Although age, increasing doses of alkylating agents, and tamoxifen are known to increase the risk of chemotherapy-related amenorrhea (CRA), it is difficult to predict which women will develop CRA in order to inform fertility preservation and treatment decisions. We sought to investigate whether single nucleotide polymorphisms (SNPs) that have been associated with a younger age at natural menopause and ovarian reserve in non-oncologic populations were predictive of CRA in two large German clinical trials that administered gonadotoxic chemotherapy.

Methods: A total of 1322 premenopausal participants under age 50 who enrolled in SUCCESS B (a phase 3 trial of FEC-docetaxel-trastuzumab +/- gemcitabine for high risk early stage breast cancer) or SUCCESS C (a phase 3 trial of FEC-docetaxel-cyclophosphamide vs. docetaxel-cyclophosphamide that also tested the efficacy of a weight loss intervention in early stage breast cancer patients) were eligible for this study. We excluded patients who were receiving ovarian suppression. On these trials, menstrual status was self-reported as amenorrheic or not every three months after chemotherapy. Bloods were stored and patients provided consent for DNA analysis. We genotyped nine candidate SNPs in six genes (rs2002555 and rs11170547 in AMHR2, rs6166 in FSHR, rs10852344 &lt;60kb from TNFRSF17/RUNDC2A/ GSPT1, rs12461110 in NLRP11, rs1801133 in MTHFR, rs2066470 in MTHRF, rs3810682 in BMP15, and rs615942 in COASY), and examined their association with CRA at 12-month and 24-month time points using logistic regression adjusted for age, BMI, and tamoxifen.

Results: Of the 554 eligible women with SNP data and menstrual data at 12 months, 70% reported CRA; of the 458 eligible women with SNP data and menstrual data at 24 months, 69% reported CRA. The two SNPs in AMHR2 showed suggestive associations with CRA at 12 and 24 months. [Final results pending].

Conclusions: Genetic variation in AMHR2 may be associated with CRA. Additional research will be needed to validate these findings in other breast cancer survivors and to identify additional SNPs that may be associated with CRA. Together with other clinical factors, genetic variation may improve our ability to predict who will experience CRA, and may inform reproductive and treatment decision-making in cancer patients.

Citation Format: Ruddy KJ, Rack B, Schwitulla J, Lambrechts D, Haeberle L, Schramm A, Trapp E, Scholz C, Beutler AS, Ginsburg E, Couch F, Partridge AH, Wang L, Weinshilboum RM, Janni W, Vachon C, Fasching P. Genetic predictors of chemotherapy-related amenorrhea. [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 P4-10-07.

Abstract P6-03-09: Genetic polymorphism and correlation with treatment induced cardiotoxicity and prognosis in HER2 amplified early breast cancer patients

Introduction: Small studies have indicated a possible correlation between a HER2 gene polymorphism at codon 655 and trastuzumab-associated cardiotoxicity. Association between a synonymous coding variant rs7853758 within the SLC28A3 gene and anthracycline induced cardiotoxicity has also been reported. This study aimed to validate these correlations and assess for any relationship with prognosis.

Methods: Genomic DNA was isolated from 666 patients enrolled in a large trial of adjuvant chemotherapy in HER2 amplified early breast cancer (BCIRG 006). Genotyping was conducted using Sequenom MassARRAY System for HER2 G-&gt;A polymorphism at amino acid codon 655 (rs1136201) and variant rs7853758 (L461L) within the SLC28A3 gene.

Results: Of the 666 patients analyzed, 216 patients were treated with anthracycline based therapy, 226 with trastuzumab based therapy, and 224 with regimens containing both an anthracycline and trastuzumab. Compared with the overall results of the BCIRG006 study (N=3,222), in the subset of patients genotyped in this analysis, a less robust improvement in disease free survival (DFS) was observed for the trastuzumab arms than control arm (HR, 0.821). When stratified for prognostic features, the hazard ratio in favor of trastuzumab was consistent with that of the overall study (HR, 0.674). Samples from 662 patients were successfully genotyped for rs1136201. Of these, 424 (64%) were AA, 30 (4.5%) were GG, 208 (31%) were AG genotype. Samples from 665 patients were successfully genotyped for rs7853758. Of these, 19 (3%) were AA, 475 (71%) were GG, and 171 (26%) were AG genotype. There was no correlation seen between mean left ventricular ejection fraction (LVEF) and HER2 genotype at codon 655 in patients treated with trastuzumab. Of patients tested for the HER2 polymorphism, cardiac dysfunction [defined as &gt; 10% decline in LVEF or clinical congestive heart failure (CHF)] developed in 16% of patients with AA, 17% of patients with GG and 20% of patients with AG. There was also no correlation between mean LVEF and variant rs7853758 in patients treated with anthracyclines. The percentage of patients who developed cardiac dysfunction was 13%, 17% and 21% in AA, GG, and AG genotypes respectively. No correlation between disease free survival and any of the genotypes was seen.

Conclusion: In the largest analysis to date to evaluate for relationship between cardiac toxicity and HER2 polymorphism, we did not find a correlation with rs1136201 HER2 polymorphism and trastuzumab induced cardiac toxicity. Our study also did not show a correlation between variant rs7853758 (L461L) and anthracycline induced cardiotoxicity. Neither polymorphism correlated with prognosis.

Citation Format: Peddi PF, Hurvitz SA, Fasching PA, Wang L, Cunningham J, Weinshilboum RM, Liu D, Quinaux E, Fourmanoir H, Robert NJ, Valero V, Crown J, Falkson C, Brufsky A, Pienkowski T, Eiermann W, Martin M, Bee V, Slamon DJ. Genetic polymorphism and correlation with treatment induced cardiotoxicity and prognosis in HER2 amplified early breast cancer patients. [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 P6-03-09.

Metabolomic Signatures for Drug Response Phenotypes: Pharmacometabolomics Enables Precision Medicine

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.

The International SSRI Pharmacogenomics Consortium (ISPC): a genome-wide association study of antidepressant treatment response

Response to treatment with selective serotonin reuptake inhibitors (SSRIs) varies considerably between patients. The International SSRI Pharmacogenomics Consortium (ISPC) was formed with the primary goal of identifying genetic variation that may contribute to response to SSRI treatment of major depressive disorder. A genome-wide association study of 4-week treatment outcomes, measured using the 17-item Hamilton Rating Scale for Depression (HRSD-17), was performed using data from 865 subjects from seven sites. The primary outcomes were percent change in HRSD-17 score and response, defined as at least 50% reduction in HRSD-17. Data from two prior studies, the Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomics Study (PGRN-AMPS) and the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study, were used for replication, and a meta-analysis of the three studies was performed (N=2394). Although many top association signals in the ISPC analysis map to interesting candidate genes, none were significant at the genome-wide level and the associations were not replicated using PGRN-AMPS and STAR*D data. The top association result in the meta-analysis of response represents SNPs 5′ upstream of the neuregulin-1 gene, NRG1 (P = 1.20E - 06). NRG1 is involved in many aspects of brain development, including neuronal maturation and variations in this gene have been shown to be associated with increased risk for mental disorders, particularly schizophrenia. Replication and functional studies of these findings are warranted.

Genetic markers associated with abstinence length in alcohol-dependent subjects treated with acamprosate

Acamprosate supports abstinence in some alcohol-dependent subjects, yet predictors of response are unknown. To identify response biomarkers, we investigated associations of abstinence length with polymorphisms in candidate genes in glycine and glutamate neurotransmission pathways and genes previously implicated in acamprosate response. Association analyses were conducted in the discovery sample of 225 alcohol-dependent subjects treated with acamprosate for 3 months in community-based treatment programs in the United States. Data from 110 alcohol-dependent males treated with acamprosate in the study PREDICT were used for replication of the top association findings. Statistical models were adjusted for relevant covariates, including recruitment site and baseline clinical variables associated with response. In the discovery sample, shorter abstinence was associated with increased intensity of alcohol craving and lower number of days between the last drink and initiation of acamprosate treatment. After adjustment for covariates, length of abstinence was associated with the GRIN2B rs2058878 (P=4.6 × 10(-5)). In the replication sample, shorter abstinence was associated with increased craving, increased depressive mood score and higher alcohol consumption. Association of abstinence length with GRIN2B rs2058878 was marginally significant (P=0.0675); as in the discovery sample, the minor A allele was associated with longer abstinence. Furthermore, rs2300272, which is in strong linkage disequilibrium with rs2058878, was also associated with abstinence length (P=0.049). This is the first report of a replicated association of genetic markers with the length of abstinence in acamprosate-treated alcoholics. Investigation of the underlying mechanisms of this association and its usefulness for individualized treatment selection should follow.

Inflammation-related genetic variants predict toxicity following definitive radiotherapy for lung cancer

Definitive radiotherapy improves locoregional control and survival in inoperable non-small cell lung cancer (NSCLC) patients. However, radiation-induced toxicities (pneumonitis/esophagitis) are common dose-limiting inflammatory conditions. We therefore conducted a pathway-based analysis to identify inflammation-related SNPs associated with radiation-induced pneumonitis or esophagitis. 11,930 SNPs were genotyped in 201 stage I-III NSCLC patients treated with definitive radiotherapy. Validation was performed in an additional 220 NSCLC cases. After validation, 19 SNPs remained significant. A polygenic risk score (PRS) was generated to summarize the effect from validated SNPs. Significant improvements in discriminative ability were observed by adding the PRS into the clinical/epidemiological variable-based model. We then used 277 lymphoblastoid cell-lines to assess radiation sensitivity and eQTL relationships of the identified SNPs. Three genes (PRKCE,DDX58 and TNFSF7) were associated with radiation sensitivity. We concluded that inflammation-related genetic variants could contribute to the development of radiation-induced toxicities. These loci could assist in predicting those unfavorable events.

Pharmacogenetics of the mycophenolic acid targets inosine monophosphate dehydrogenases IMPDH1 and IMPDH2: gene sequence variation and functional genomics

BACKGROUND AND PURPOSE

Inosine monophosphate dehydrogenases, encoded by IMPDH1 and IMPDH2, are targets for the important immunosuppressive drug, mycophenolic acid (MPA). Variation in MPA response may result, in part, from genetic variation in IMPDH1 and IMPDH2.

EXPERIMENTAL APPROACH

We resequenced IMPDH1 and IMPDH2 using DNA from 288 individuals from three ethnic groups and performed functional genomic studies of the sequence variants observed.

KEY RESULTS

We identified 73 single nucleotide polymorphisms (SNPs) in IMPDH1, 59 novel, and 25 SNPs, 24 novel, in IMPDH2. One novel IMPDH1 allozyme (Leu275) had 10.2% of the wild-type activity as a result of accelerated protein degradation. Decreased activity of the previously reported IMPDH2 Phe263 allozyme was primarily due to decreased protein quantity, also with accelerated degradation. These observations with regard to the functional implications of variant allozymes were supported by the IMPDH1 and IMPDH2 X-ray crystal structures. A novel IMPDH2 intron 1 SNP, G > C IVS1(93), was associated with decreased mRNA quantity, possibly because of altered transcription.

CONCLUSIONS AND IMPLICATIONS

These results provide insight into the nature and extent of sequence variation in the IMPDH1 and IMPDH2 genes. They also describe the influence of gene sequence variation that alters the encoded amino acids on IMPDH function and provide a foundation for future translational studies designed to correlate sequence variation in these genes with outcomes in patients treated with MPA.

Abstract P2-09-03: Mammographic Density Response to Aromatase Inhibitor Therapy

Background: Mammographic density, the variation in fat, epithelial and stromal tissues seen on screening mammography, is a strong risk factor for breast cancer and can be modified by hormonal agents. Changes in density from tamoxifen or postmenopausal hormone (PMH) use are associated with risk, suggesting that density may be a surrogate marker of therapeutic efficacy. Aromatase inhibitors (AIs) are given as adjuvant therapy in hormone receptor positive postmenopausal breast cancer and are known to decrease levels of estrone and estradiol in both serum and breast tissue. Our goal here was to examine the influence of AIs on mammographic density in women with early breast cancer.

Methods: We conducted a case-control study of postmenopausal breast cancer patients initiating adjuvant AI therapy (anastrozole or exemestane) on protocols NCIC CTG MA27, NCCTG N063I and MC (Mayo Clinic) 0532. Eligibility included; an intact contralateral breast with no prior surgery; a screening mammogram within twelve months before AI initiation and at 9-15 months on therapy; no prior endocrine therapy and informed consent. Controls were sampled from the Mayo Mammography Health Study, a cohort of 19,924 receiving screening mammography at the Mayo Clinic, and matched to cases on age, prior PMH use, baseline body mass index (BMI) and interval between mammograms. Pre-treatment and on-study mammograms for cases (corresponding mammograms for controls) were digitized. Change in percent density was estimated on the craniocaudal view of the non-cancerous breast using two methods: a subjective assessment of change by an expert radiologist (within 5%; 5-10% increase, 10-25% increase, 25%+ increase, 5-10% decrease, 10-25% decrease and 25%+ decrease) and a quantitative assessment of absolute change using a computer-assisted thresholding program (Cumulus). Analyses compared magnitude of change in density by both the subjective and quantitative methods between cases and matched controls. Results: 574 pairs were eligible for analyses (MA27-505 cases; N063I-12 cases; MC0532-57 cases). Characteristics of the two groups are shown in the table below. Using either density estimation method, there was a greater decrease in density among women on AI therapy vs. matched controls. In 33% (95% CI: 29-37%) of pairs, there was at least a one greater category decrease for the case relative to her control by subjective estimation. In 14% (95% CI: 11-18%) of the pairs, there was at least a 5% greater decrease for the case relative to her control by quantitative estimation. Data will be available according to AI class (non-steroidal versus steroidal) in November.

Conclusions: In the largest report to date to examine the influence of AI therapy on mammographic density, we provide evidence that AI is associated with decreases in density in a small subgroup of women. We are currently examining factors that influence these AI-associated decreases in density and whether these differences are unique to one class of AI. (Supported in part by NIH grants P50CA116201, U01GM61388, U10CA77202, U10CA25224)

Glycine and a glycine dehydrogenase (GLDC) SNP as citalopram/escitalopram response biomarkers in depression: pharmacometabolomics-informed pharmacogenomics

Major depressive disorder (MDD) is a common psychiatric disease. Selective serotonin reuptake inhibitors (SSRIs) are an important class of drugs used in the treatment of MDD. However, many patients do not respond adequately to SSRI therapy. We used a pharmacometabolomics-informed pharmacogenomic research strategy to identify citalopram/escitalopram treatment outcome biomarkers. Metabolomic assay of plasma samples from 20 escitalopram remitters and 20 nonremitters showed that glycine was negatively associated with treatment outcome (P = 0.0054). This observation was pursued by genotyping tag single-nucleotide polymorphisms (SNPs) for genes encoding glycine synthesis and degradation enzymes, using 529 DNA samples from SSRI-treated MDD patients. The rs10975641 SNP in the glycine dehydrogenase (GLDC) gene was associated with treatment outcome phenotypes. Genotyping for rs10975641 was carried out in 1,245 MDD patients in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study, and its presence was significant (P = 0.02) in DNA taken from these patients. These results highlight a possible role for glycine in SSRI response and illustrate the use of pharmacometabolomics to "inform" pharmacogenomics.

Multivariate models to detect genomic signatures for a class of drugs: application to thiopurines pharmacogenomics

Often, analysis for pharmacogenomic studies involving multiple drugs from the same class is completed by analyzing each drug individually for association with genomic variation. However, by completing the analysis of each drug individually, we may be losing valuable information. When studying multiple drugs from the same drug class, one may wish to determine genomic variation that explains the difference in response between individuals for the drug class, as opposed to each individual drug. Therefore, we have developed a multivariate model to assess whether genomic variation impacts a class of drugs. In addition to determine genomic effects that are similar for the drugs, we will also be able to determine genomic effects that differ between the drugs (that is, interaction). We will illustrate the utility of this multivariate model for cytotoxicity and genomic data collected on the Coriell Human Variation Panel for the class of anti-purine metabolites (6-mercaptopurine and 6-thioguanine).

Catechol O-methyltransferase pharmacogenomics and selective serotonin reuptake inhibitor response

We applied a systematic pharmacogenetic approach to investigate the role of genetic variation in the gene encoding catechol O-methyltransferase (COMT) in individual variation in selective serotonin reuptake inhibitor (SSRI) response among depressed patients. In all, 23 single-nucleotide polymorphisms (SNPs) in COMT were genotyped using DNA from the Sequenced Treatment Alternatives to Relieve Depression (STAR(*)D) study (N=1914). One SNP, rs13306278, located in the distal promoter region of COMT, showed significant association with remission in White non-Hispanic (WNH) subjects (P=0.038). Electromobility shift assay for rs13306278 showed alternation in the ability of the variant sequence to bind nuclear proteins. A replication study was performed using samples from the Mayo Clinic Pharmacogenetics Research Network Citalopram/Escitalopram Pharmacogenomic study (N=422) that demonstrated a similar trend for association. Our findings suggest that novel genetic markers in the COMT distal promoter may influence SSRI response phenotypes.

A Genome-Wide Association Study in Patients Experiencing Musculoskeletal Adverse Events on Aromatase Inhibitors as Adjuvant Therapy in Early Breast Cancer Entered on NCIC CTG Trial MA.27. A Pharmacogenetics Research Network-RIKEN Collaboration

Background: The third-generation aromatase inhibitors (AIs) are established adjuvant therapy for postmenopausal women with early stage breast cancer. Musculoskeletal adverse events (MS AE) have become increasingly recognized as an issue for many women receiving AIs resulting in decreased quality of life and, in some instances, discontinuation of the AI. MA.27 is an ongoing randomized clinical trial comparing anastrozole with exemestane that accrued 7576 patients (pts), including 6827 from North America, about 79% of whom provided DNA and consent for genetic testing. A review of withdrawals related to adverse events revealed that the most common cause was MS AE. The hypothesis for our study was that a genome-wide association (GWA) case control study would identify single nucleotide polymorphisms (SNPs) associated with MS AE.Methods: Cases were defined as pts with grade 3 or 4 MS AE (NCI CTCAE v3.0) or who went off treatment for any grade MS AE within the first 2 years and were matched to 2 controls based on treatment arm, length of follow-up, prior chemotherapy, age and prior celecoxib (at least 3 months prior to MS AE). The study was restricted to the 94% of pts self-identified as Caucasian. Genotyping was performed with the Illumina Human610-Quad. Genotype quality control prior to analysis required call rates ≥98%. Eigenstrat analyses were performed to control for population stratification.Results: The GWA study included 293 cases and 586 controls with 6 pts (0.7%) (4 cases, 2 controls) excluded for call rates &lt;98%. 551,358 SNPs were used in the analyses after removing 29,439 for a minor allele frequency &lt;1% and 82 with Hardy Weinberg Equilibrium p-values &lt;10-6. Eigenvectors did not impact the results when used as covariates. Four SNPs with the lowest p-values (3.3-8.2 x 10-6, Armitage test), 3 in high LD (r2&gt;.8) on chromosome (ch) 14 (rs7158782, rs7159713, rs2369049) and 1 on the X ch (rs6637820) were identified. The gene closest (4000-7000 bp) to the 3 SNPs on ch 14 was T-Cell Leukemia 1A (TCL1A), encoding a protein known to augment AKT kinase activity. Expression array data from lymphoblastoid cell lines from 300 subjects of 3 ethnicities revealed that all 3 SNPs were associated with decreased TCL1A expression after adjusting for ethnicity using 2 probe sets (p=0.006 to 0.03). Electromobility shift assay (EMSA) showed that 2 of these SNPs (rs7158782, rs7159713) displayed a shift and the former SNP showed substantially less binding for variant than wild type. The gene closest to the X ch SNP was Immunoglobulin Superfamily 1 (IGSF1), but EMSA did not show a shift for this SNP.Conclusions: MS AEs represent a major impediment to optimal use of AIs in women with breast cancer and this GWA study identified SNPs on ch 14 that provide a focus for further research to identify pts at risk for, and means to ameliorate, this adverse event.(Supported in part by NIH grants U01GM61388, U01GM63173, P50CA116201 and U10CA77202)

Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 15.

SLC6A4 variation and citalopram response

The influence of genetic variations in SLC6A4 (serotonin transporter gene) on citalopram treatment of depression using the Sequenced Treatment to Relieve Depression (STAR*D) sample was assessed. Of primary interest were three previously studied polymorphisms: 1) the VNTR variation of the second intron, 2) the indel promoter polymorphism (5HTTLPR or SERT), and 3) a single nucleotide polymorphism (SNP) rs25531. Additionally, SLC6A4 was resequenced to identify new SNPs for exploratory analyses. DNA from 1914 subjects in the STAR*D study were genotyped for the intron 2 VNTR region, the indel promoter polymorphism, and rs25531. Associations of these variants with remission of depressive symptoms were evaluated following citalopram treatment. In white non-Hispanic subjects, variations in the intron 2 VNTR (point-wise P = 0.041) and the indel promoter polymorphism (point-wise P = 0.039) were associated with remission following treatment with citalopram. The haplotype composed of the three candidate loci was also associated with remission, with a global p-value of 0.040 and a maximum statistic simulation p-value of 0.0031 for the S-a-12 haplotype, under a dominant model. One SNP identified through re-sequencing the SLC6A4 gene, Intron7-83-TC, showed point-wise evidence of association, which did not remain significant after correction for the number of SNPs evaluated in this exploratory analysis. No associations between these SLC6A4 variations and remission were found in the white Hispanic or black subjects. These findings suggest that multiple variations in the SLC6A4 gene are associated with remission in white non-Hispanic depressed adults treated with citalopram. The mechanism of action of these variants remains to be determined.

Sulfotransferase gene copy number variation: pharmacogenetics and function

Pharmacogenetics is the study of the role of inheritance in variation to drug response. Drug response phenotypes can vary from adverse drug reactions at one end of the spectrum to equally serious lack of the desired effect of drug therapy at the other. Many of the current important examples of pharmacogenetics involve inherited variation in drug metabolism. Sulfate conjugation catalyzed by cytosolic sulfotransferase (SULT) enzymes, particularly SULT1A1, is a major pathway for drug metabolism in humans. Pharmacogenetic studies of SULT1A1 began over a quarter of a century ago and have advanced from biochemical genetic experiments to include cDNA and gene cloning, gene resequencing, and functional studies of the effects of single nucleotide polymorphisms (SNPs). SNP genotyping, in turn, led to the discovery of functionally important copy number variations (CNVs) in the SULT1A1 gene. This review will briefly describe the evolution of our understanding of SULT1A1 pharmacogenetics and CNV, as well as challenges involved in utilizing both SNP and CNV data in an attempt to predict SULT1A1 function. SULT1A1 represents one example of the potential importance of CNV for the evolving disciplines of pharmacogenetics and pharmacogenomics.

Thiopurine methyltransferase activity is related to the risk of relapse of childhood acute lymphoblastic leukemia: results from the NOPHO ALL-92 study

Myelotoxicity during thiopurine therapy is enhanced in patients, who because of single nucleotide polymorphisms have decreased activity of the enzyme thiopurine methyltransferase (TPMT) and thus more thiopurine converted into 6-thioguanine nucleotides. Of 601 children with acute lymphoblastic leukemia (ALL) who were treated by the NOPHO ALL-92 protocol, 117 had TPMT genotype determined, whereas for 484 patients only erythrocyte TPMT activity was available. The latter were classified as heterozygous, if TPMT activity was <14 IU/ml, or deficient (<1.0 IU/ml). 526 patients had TPMT wild type, 73 were presumed heterozygous, and two were TPMT deficient. Risk of relapse was higher for the 526 TPMT wild type patients than for the remaining 75 patients (18 vs 7%, P=0.03). In Cox multivariate regression analysis, sex (male worse; P=0.06), age (higher age worse, P=0.02), and TPMT activity (wild type worse; P=0.02) were related to risk of relapse. Despite a lower probability of relapse, patients in the low TPMT activity group did not have superior survival (P=0.82), possibly because of an excess of secondary cancers among these 75 patients (P=0.07). These data suggest that children with ALL and TPMT wild type might have their cure rate improved, if the pharmacokinetics/-dynamics of TPMT low-activity patients could be mimicked without a concurrent excessive risk of second cancers.

The pharmacogenetics research network: from SNP discovery to clinical drug response

The NIH Pharmacogenetics Research Network (PGRN) is a collaborative group of investigators with a wide range of research interests, but all attempting to correlate drug response with genetic variation. Several research groups concentrate on drugs used to treat specific medical disorders (asthma, depression, cardiovascular disease, addiction of nicotine, and cancer), whereas others are focused on specific groups of proteins that interact with drugs (membrane transporters and phase II drug-metabolizing enzymes). The diverse scientific information is stored and annotated in a publicly accessible knowledge base, the Pharmacogenetics and Pharmacogenomics Knowledge base (PharmGKB). This report highlights selected achievements and scientific approaches as well as hypotheses about future directions of each of the groups within the PGRN. Seven major topics are included: informatics (PharmGKB), cardiovascular, pulmonary, addiction, cancer, transport, and metabolism.

Genetic diversity and function in the human cytosolic sulfotransferases

Amino-acid substitutions, which result from common nonsynonymous (NS) polymorphisms, may dramatically alter the function of the encoded protein. Gaining insight into how these substitutions alter function is a step toward acquiring predictability. In this study, we incorporated gene resequencing, functional genomics, amino-acid characterization and crystal structure analysis for the cytosolic sulfotransferases (SULTs) to attempt to gain predictability regarding the function of variant allozymes. Previously, four SULT genes were resequenced in 118 DNA samples. With additional resequencing of the remaining eight SULT family members in the same DNA samples, a total of 217 polymorphisms were revealed. Of 64 polymorphisms identified within 8785 bp of coding regions from SULT genes examined, 25 were synonymous and 39 were NS. Overall, the proportion of synonymous changes was greater than expected from a random distribution of mutations, suggesting the presence of a selective pressure against amino-acid substitutions. Functional data for common variants of five SULT genes have been previously published. These data, together with the SULT1A1 variant allozyme data presented in this paper, showed that the major mechanism by which amino acid changes altered function in a transient expression system was through decreases in immunoreactive protein rather than changes in enzyme kinetics. Additional insight with regard to mechanisms by which NS single nucleotide polymorphisms alter function was sought by analysis of evolutionary conservation, physicochemical properties of the amino-acid substitutions and crystal structure analysis. Neither individual amino-acid characteristics nor structural models were able to accurately and reliably predict the function of variant allozymes. These results suggest that common amino-acid substitutions may not dramatically alter the protein structure, but affect interactions with the cellular environment that are currently not well understood.

Thiopurine S-methyltransferase pharmacogenetics: insights, challenges and future directions

The thiopurine S-methyltransferase (TPMT) genetic polymorphism is one of the most 'mature' examples in pharmacogenetics. That is true because of its importance clinically for the individualization of thiopurine drug therapy and also because TPMT has provided novel insights into molecular mechanisms responsible for the functional effects of common genetic polymorphisms. This review will summarize the development of our understanding of the role of inheritance in the regulation of TPMT as well as the clinical implications of that genetic regulation. It will also summarize recent studies in which TPMT pharmacogenetics has enhanced our understanding of molecular mechanisms by which common polymorphisms influence or alter function. TPMT pharmacogenetics highlights the potential clinical importance of the translation of pharmacogenetics from bench to bedside, the potential for basic pharmacogenetic research to provide insight into mechanisms by which genetic polymorphisms can alter function, and the challenges associated with the achievement of both of those goals.

Human catechol O-methyltransferase genetic variation: gene resequencing and functional characterization of variant allozymes

Catechol O-methyltransferase (COMT) plays an important role in the metabolism of catecholamines, catecholestrogens and catechol drugs. A common COMT G472A genetic polymorphism (Val108/158Met) that was identified previously is associated with decreased levels of enzyme activity and has been implicated as a possible risk factor for neuropsychiatric disease. We set out to 'resequence' the human COMT gene using DNA samples from 60 African-American and 60 Caucasian-American subjects. A total of 23 single nucleotide polymorphisms (SNPs), including a novel nonsynonymous cSNP present only in DNA from African-American subjects, and one insertion/deletion were observed. The wild type (WT) and two variant allozymes, Thr52 and Met108, were transiently expressed in COS-1 and HEK293 cells. There was no significant change in level of COMT activity for the Thr52 variant allozyme, but there was a 40% decrease in the level of activity in cells transfected with the Met108 construct. Apparent K(m) values of the WT and variant allozymes for the two reaction cosubstrates differed slightly, but significantly, for 3,4-dihydroxybenzoic acid but not for S-adenosyl-L-methionine. The Met108 allozyme displayed a 70-90% decrease in immunoreactive protein when compared with WT, but there was no significant change in the level of immunoreactive protein for Thr52. A significant decrease in the level of immunoreactive protein was also observed in hepatic biopsy samples from patients homozygous for the allele encoding Met108. These observations represent steps toward an understanding of molecular genetic mechanisms responsible for variation in COMT level and/or properties, variation that may contribute to the pathophysiology of neuropsychiatric disease.

Human cytosolic sulfotransferase database mining: identification of seven novel genes and pseudogenes

A total of 10 SULT genes are presently known to be expressed in human tissues. We performed a comprehensive genome-wide search for novel SULT genes using two different but complementary approaches, and developed a novel graphical display to aid in the annotation of the hits. Seven novel human SULT genes were identified, five of which were predicted to be pseudogenes, including two processed pseudogenes and three pseudogenes that contained introns. Those five pseudogenes represent the first unambiguous SULT pseudogenes described in any species. Expression-profiling studies were conducted for one novel gene, SULT6B1, and a series of alternatively spliced transcripts were identified in the human testis. SULT6B1 was also present in chimpanzee and gorilla, differing at only seven encoded amino-acid residues among the three species. The results of these database mining studies will aid in studies of the regulation of these SULT genes, provide insights into the evolution of this gene family in humans, and serve as a starting point for comparative genomic studies of SULT genes.

L-DOPA biotransformation: correlations of dosage, erythrocyte catechol O-methyltransferase and platelet SULT1A3 activities with metabolic pathways in Parkinsonian patients

The objectives of this study were to determine (1) the effects of dose and drug absorption on pathways of biotransformation of L-DOPA in Parkinsonian patients treated with Sinemet, and (2) the extent to which genetically-determined variations in the activities of erythrocyte catechol O-methyltransferase and/or platelet phenol sulfotransferase might be reflected in individual differences in L-DOPA metabolism. In the 19 patients studied, there were negative correlations between dosage or absorption and extent of O-methylation and of sulfation of L-DOPA or its metabolites. Levels of activity for erythrocyte COMT were also reflected in individual variation in the metabolism of L-DOPA. In contrast, differences in platelet phenol sulfotransferase were not reflected in differences in sulfation of L-DOPA or of its metabolites. If such a relationship did exist, it might have been obscured by the effects of high dosage of L-DOPA, effects which might have resulted from a deficiency of the sulfation cosubstrate 3'-phosphoadenosine 5'-phosphosulfate in patients taking higher doses of drug.

Human sulfotransferase SULT2A1 pharmacogenetics: genotype-to-phenotype studies

SULT2A1 catalyzes the sulfate conjugation of dehydroepiandrosterone (DHEA) as well as other steroids. As a step toward pharmacogenetic studies, we have 'resequenced' SULT2A1 using 60 DNA samples from African-American and 60 samples from Caucasian-American subjects. All exons, splice junctions and approximately 370 bp located 5' of the site of transcription initiation were sequenced. We observed 15 single nucleotide polymorphisms (SNPs), including three non-synonymous coding SNPs (cSNPs) that were present only in DNA from African-American subjects. Linkage analysis revealed that two of the nonsynonymous cSNPs were tightly linked. Expression constructs were created for all nonsynonymous cSNPs observed, including a 'double variant' construct that included the two linked cSNPs, and those constructs were expressed in COS-1 cells. SULT2A1 activity was significantly decreased for three of the four variant allozymes. Western blot analysis demonstrated that decreased levels of immunoreactive protein appeared to be the major mechanism responsible for decreases in activity, although apparent Km values also varied among the recombinant allozymes. In addition, the most common of the nonsynonymous cSNPs disrupted the portion of SULT2A1 involved with dimerization, and this variant allozyme behaved as a monomer rather than a dimer during gel filtration chromatography. These observations indicate that common genetic polymorphisms for SULT2A1 can result in reductions in levels of both activity and enzyme protein. They also raise the possibility of ethnic-specific pharmacogenetic variation in SULT2A1-catalyzed sulfation of both endogenous and exogenous substrates for this phase II drug-metabolizing enzyme.

Human sulfotransferase SULT1C1 pharmacogenetics: gene resequencing and functional genomic studies

Sulfotransferase (SULT) enzymes catalyze an important phase II reaction in the biotransformation of many drugs and other xenobiotics. We previously cloned the human SULT1C1 cDNA and gene as steps toward pharmacogenetic studies. We have now 'resequenced' the exons, portions of introns flanking exons and approximately 315 bp of the 5' flanking region of SULT1C1 in 89 DNA samples from Caucasian subjects to identify common genetic polymorphisms. Nineteen separate polymorphisms were observed, including four nonsynonymous coding region single nucleotide polymorphisms (cSNPs) and five insertions/deletions. These data were also used to determine and/or infer common SULT1C1 haplotypes. Three of the four nonsynonymous cSNPs had allele frequencies greater than 1%, including one with a frequency of 6.7%. Expression constructs were created for all of the nonsynonymous cSNPs observed, and those constructs were used to transfect COS-1 cells. Three of the four SULT1C1 variant allozymes had significantly reduced enzyme activity when compared with the wild-type enzyme. Among the variant allozymes, apparent Km values for 3'-phosphoadenosine 5'-phosphosulfate (PAPS), the sulfate donor for the reaction, varied 7-fold, and quantitative Western blot analysis showed variable levels of immunoreactive protein when compared to the wild-type enzyme. Therefore, mechanisms responsible for decreased activity involved both alterations in levels of enzyme protein and alterations in substrate kinetics. In summary, application of a 'genotype to phenotype' strategy has resulted in the identification of a series of functionally significant common genetic polymorphisms for SULT1C1. It will now be possible to evaluate the possible contribution of these polymorphisms to variation in the sulfate conjugation of drugs, other xenobiotics and/or disease pathophysiology.

Endonuclease-mediated long PCR and its application to restriction mapping

The polymerase chain reaction (PCR) is the most widely used technique for the study of DNA. Applications for PCR have been extended significantly by the development of "long" PCR, a technique that makes it possible to amplify DNA fragments up to 40 kb in length. This article describes two novel applications of the long PCR technique, one which simplifies restriction mapping and another which enhances amplification specificity and yield. The same primers used to perform the long PCR amplification can be used as probes to perform restriction mapping of the DNA fragment amplified. Restriction digestion performed prior to long PCR amplification can be used to selectively suppress the amplification of members of families of closely related DNA sequences, thereby making it possible to selectively amplify one of a group of highly homologous sequences. These two complimentary techniques, both involving use of the long PCR paired with restriction digestion, have potential application in any laboratory in which PCR is performed.