Weinshilboum, RM

This study uncovers a previously unrecognized role for NAV3 in TGF-β1-driven cardiac fibroblast activation. We show that NAV3 facilitates profibrotic remodeling through noncanonical signaling and cell cycle arrest, independently of SMAD2/3. These findings position NAV3 as a novel regulator of fibroblast phenotype and a potential modulator of cardiac fibrosis.

Abstract 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 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 < 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 Background: Combining cyclin dependent 4/6 kinase inhibitors (CDK4/6i) with endocrine therapy (ET) has resulted in clinically significant improvements in progression-free survival (PFS) in persons with hormone-receptor (HR)-positive metastatic breast cancer (MBC). However, nearly all patients will progress on CDK4/6i and ET and the mechanisms associated with primary and secondary resistance are mostly unknown. The identification of biomarkers associated with response to CDK4/6i is therefore a major research priority. PROMISE is a multicenter prospective cohort study designed to perform a comprehensive “omic” assessment of blood, tumor, urine and the fecal microbiome to identify alterations in molecular or cellular features associated with primary endocrine resistance (e.g. disease progression ≤ 12 months on treatment) and acquired resistance to CDK4/6i. Additionally, patient derived xenografts (PDX) and organoids are generated to test new drug strategies designed to reverse resistance to CDK4/6i and ET. Methods: Eligible participants include women for whom palbociclib is recommended in combination with either letrozole (first-line) or fulvestrant (second-line) therapy for HR-positive MBC. As of July 2018, 14 patients have been enrolled; the target accrual is 250 patients, who will be followed over the course of 36 months and followed for at least 12 months after the close of enrollment. Blood and tumor biopsies are obtained at baseline, at the end of cycle 2, and at progression. Whole exome sequencing (germline and tumor) and RNAseq (whole transcriptome) are performed on tumor samples in a CAP/CLIA lab (TEMPUS) and results will be provided back to all patients for later use. Additionally, PDX and organoids are generated from tumor biopsies at baseline and during progression. Blood, stool, and urine samples are collected for additional correlative studies. The primary objective of this trial is to identify novel genomic variants and pathways associated with early progression (≤ 12 months) among women with advanced HR-positive breast cancer treated with palbociclib and ET. Secondary objectives include identification of the biomolecular features within the gut (stool) microbiome and exploration of the metabolomics and proteomics of ER-positive MBC. Results from the interrogation of these biospecimens will be critical to gain insights into treatment resistance. Funding is provided by Mayo Clinic Center for Individualized Medicine, TEMPUS, and an ASCO Career Development Award (COS). ClinicalTrials.gov Identifier: NCT03281902. Citation Format: O'Sullivan CC, Suman VJ, Kalari KR, Moyer A, Sung J, Moreno-Aspitia A, Northfelt DW, Liu MC, Haddad TC, Chumsri S, Couch FJ, Weinshilboum RM, Wang L, Goetz MP. Genetic analysis in blood, urine, stool and tumor samples from patients with advanced or metastatic estrogen receptor positive and HER2 negative breast cancer receiving palbociclib and endocrine therapy (PROMISE) [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 OT3-02-08.

Abstract 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.

Advances in pharmacogenomics (PGx) have the potential to transform healthcare by allowing precision medicine to become a reality. However, PGx knowledge is new, complex, and evolving, and relying on the cognition of clinicians alone is insufficient for clinical implementation. Integrating clinical decision support (CDS) tools in the electronic health record (EHR) is critical for translating PGx into clinical practice. Herein, we review current strategies to implement PGx using EHR‐CDS functionalities.

Numerous pharmacogenetic clinical guidelines and recommendations have been published, but barriers have hindered the clinical implementation of pharmacogenetics. The Translational Pharmacogenetics Program (TPP) of the National Institutes of Health (NIH) Pharmacogenomics Research Network was established in 2011 to catalog and contribute to the development of pharmacogenetic implementations at eight US healthcare systems, with the goal to disseminate real‐world solutions for the barriers to clinical pharmacogenetic implementation. The TPP collected and normalized pharmacogenetic implementation metrics through June 2015, including gene–drug pairs implemented, interpretations of alleles and diplotypes, numbers of tests performed and actionable results, and workflow diagrams. TPP participant institutions developed diverse solutions to overcome many barriers, but the use of Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines provided some consistency among the institutions. The TPP also collected some pharmacogenetic implementation outcomes (scientific, educational, financial, and informatics), which may inform healthcare systems seeking to implement their own pharmacogenetic testing programs.

Abstract Background: Mutations in BRCA1 and BRCA2 influence the molecular pathogenesis of breast cancer. However, little is known about the association between mutations, response to therapy, and prognosis. We therefore analysed these associations in triple negative breast cancer (TNBC) patients in the neoadjuvant GeparQuinto Study. Methods: The GeparQuinto study recruited 1956 patients with HER2 negative disease including a prespecified group of 671 TNBC patients with untreated breast cancer. Patients were randomized to receive four cycles EC (90/600 mg/m2; q3w) followed by four cycles docetaxel (100 mg/m2; q3w), with or without bevacizumab (15 mg/kg). Here we present the analysis for both randomization arms combined. Sufficient blood was available of 482 TNBC patients. BRCA1 and BRCA2 genotyping was successful in 469 patients with available germline DNA and was conducted by SureSelect custom capture and sequencing on HiSeq 2500. Mutation status was correlated with pathological complete response rates (pCR) and disease free survival (DFS). Results: A total of 74 (15.8%) mutations in BRCA1 (n=61) and BRCA2 (n=13) were detected after initial sequencing. A pCR (ypT0/ypN0) was observed in 50% (n=37) of mutation carriers but only 31.1% (n=123) of patients without mutations (p=0.002). Similar results were observed for pCR (pT0is/pN0) (52.7% vs. 36.5%; p=0.010). pCR (ypT0/ypN0) was predictive of disease free survival (DFS) in all patients (Hazard Ratio, HR=0.23; 95%CI: 0.15-0.37; p<0.001) and in patients without BRCA mutations pCR (ypT0/ypN0) (HR= 0.20; 95%CI: 0.11-0.34; p<0.001). However in mutation carriers this effect was reduced (HR=0.48; 95%CI: 0.18 1.27; p=0.129). Conclusion: TNBC Patients with BRCA mutations showed a higher frequency of pCR than patients without BRCA mutations after treatment with epirubicin, cyclophosphamide and docetaxel (+/ bevacizumab), suggesting that BRCA mutations may influence pCR in response to treatment regimens that do not include platin chemotherapy. pCR as a surrogate marker for DFS was also confirmed in patients without BRCA mutations. In addition, the effect of pCR on prognosis seemed to be smaller among the mutation carriers, although the number of mutation carriers was too low to test for differences between mutation carriers and wildtype patients. The project has partly been funded within NIH-NIGMS U19 GM61388 and the Breast Cancer Research Foundation. Citation Format: Fasching PA, Loibl S, Eidtmann H, Tesch H, Untch M, Hilfrich J, Schem C, Rezai M, Gerber B, Costa SD, Blohmer JU, Fehm TN, Huober J, Liedtke C, Müller V, Nekljudova V, Weber K, Rack B, Rübner M, Wang L, Ingle JN, Weinshilboum RM, von Minckwitz G, Couch F. BRCA mutations, therapy response and prognosis in the neoadjuvant GeparQuinto study. [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 S5-06.

Abstract 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->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 > 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.