Weinshilboum, Richard

Abstract Introduction: Triple negative breast cancers (TNBC) are characterized as lacking estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) expression and TNBC patients have higher rates of recurrence and death compared with other breast cancer subtypes. For TNBC patients who fail standard chemotherapy, there are a lack of novel drug therapies, given the absence of well-defined molecular targets. Recently, a microarray meta-analysis identified 7 triple negative subtypes, including the validation of the luminal androgen receptor (LAR) positive subtype [Lehmann, 2011]. However, microarray technology is dependent on probe-target specificity and the 7 subtypes have yet to be validated using RNA sequencing data, and the presences of recurrent genomic alterations in the 7 subtypes are unknown. METHODS: We obtained 1106 breast cancer RNA-Seq bam files from The Cancer Genome Atlas (TCGA) and aligned with Tophat v1.3. The PAM50 intrinsic gene signature was used to extract a cohort of 128 TNBC samples. Consensus clustering of genes, greater than 75th percentile of variance, was performed using Kmeans clustering in Spearman's correlation space. A nearest centroid prediction model was developed from genes differentially expressed among the clusters. Eighty independent TNBC RNA sequencing samples were obtained (British Colombia; BC) [Shah, 2012] which were calibrated to our TNBC conditional quantile normalized cohort and sub-typed by our model. RESULTS: Using RNA-Seq gene expression count data, we identified 5 clusters, all of which were stable, including the LAR cluster. Signaling pathway impact analysis (SPIA) implicated cytokine-cytokine receptor interaction, leukocyte transendothelial migration, and regulation of actin cytoskeleton pathways commonly altered in the non-LAR TNBC subtypes. In contrast, cell cycle, ECM-receptor interaction, endocrine regulated calcium reabsorption, and insulin signaling pathways were altered in the LAR versus non-LAR subtypes. Neuroactive ligand-receptor interactions were observed to be altered commonly between all sub-types. We then applied our model to the Shah, et al cohort. In this cohort, the LAR subtype was consistent with Shah's classification of ‘other’ TNBC and contained no basal samples by PAM50 intrinsic modeling. Analysis of sub-type specific mutation data from the BC cohort demonstrates an increased mutational load in ECM-related proteins, particularly the myosins, along with increased TP53 clonality in the non-LAR subtypes. CONCLUSIONS: Using TCGA RNASeq data, we have confirmed the presence of 5 major TNBC subtypes, including the LAR; which was negligible in basal composition by PAM50 intrinsic modeling. SPIA pathway analysis indicates a core set of pathways demonstrating altered expression across the TNBC sub-types and the identification of molecular targets within each subtype is ongoing. Citation Format: Kevin J. Thompson, Xiaojia Tang, Zhifu Sun, Jason P. Sinnwell, Hugues Sicotte, Douglas W. Mahoney, Steven Hart, Peter T. Vedell, Poulami Barman, Jeanette E. Eckel Passow, Eric D. Wieben, James N. Ingle, Judy C. Boughey, Liewei Wang, Richard Weinshilboum, Krishna R. Kalari, Matthew P. Goetz. Molecular classification of triple negative breast cancer via RNA-sequencing data. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 5592. doi:10.1158/1538-7445.AM2014-5592

Abstract INTRODUCTION Next generation sequencing (NGS) of patients has significantly changed our ways to study cancer genomics as it provides precise estimates of gene expression, fusion transcripts, expressed single nucleotide variants (eSNVs), splice variants and copy number variants. The Breast Cancer Genome Guided Therapy (BEAUTY) is an ongoing clinical study in which RNA sequencing (RNAseq) and whole exome sequencing (WES) are performed prior to, during and after neoadjuvant chemotherapy. Here we report the use of these sequencing technologies to investigate gene expression levels and mutational profiles in a triple negative breast cancer (TNBC) patient enrolled in BEAUTY whose disease did not respond to neoadjuvant paclitaxel and anthracycline/cycphosphamide. METHODS Computational approaches were used to integrate WES and RNAseq data obtained before therapy (V1T), after 12 weekly paclitaxel treatments (V2T) and after anthracycline-based regimen at surgical resection (V3T) to study a single patient with persistent TNBC disease after neoadjuvant chemotherapy. RESULTS Using RNA-Seq data, we identified an inter-chromosomal fusion transcript between chromosome 20 and 22 (GNAS-TTC38) that was highly expressed at V1T, V2T and V3T. We also identified intra-chromosomal fusion transcripts that were expressed at two time points, such as fusion transcript (KANSL1-ARL17A) on chromosome 17 for V1T and V2T and fusion transcript (RBM12B-LINC00535) on chromosome 8 for V2T and V3T time points. Several gene expression changes were also observed. Gene expression analysis of V1T, V2T and V3T tumors was performed. Differential temporal gene expression profiles of 9884 genes that were significant and varying at different time points were obtained for pathway analysis. Pathway analysis of 9884 genes identified up regulation and down regulation of several transcription factors with a fold change of 2x or more. When compared to blood, DNA tumor and RNA-Seq data, we identified 81 common somatic eSNVs that were expressed in both V1T and V2T time points and we are in the process of investigating V3T data. We found alterations of key transporter domains (CD225, Coatamer_beta_C, DUF2435, Dynamitin, EI24, GLTP, LMF1, Porin_3, V-ATPase_C) in our V1T and V2T SNV data. Similar to gene expression analysis, we are in the process of obtaining the list of mutations at various time points to identify driver and passenger mutation candidate genes for this specific TNBC patient. CONCLUSIONS Our initial time-series analysis of eSNV, fusion transcripts and gene expression data demonstrate that intensive analysis for individual patients is feasible. Further investigation of drug transporters and transcription regulators may help develop personalized treatment strategies for patients with disease resistant to current regimens. Citation Format: Krishna R. Kalari, Xiaojia Tang, Kevin J. Thompson, Douglas W. Mahoney, Poulami Barman, Jason P. Sinnwell, Hugues Sicotte, Peter Vedell, Steven N. Hart, Travis J. Dockter, Katie N. Jones, Amy L. Conners, Ann M. Moyer, Daniel W. Visscher, Jia Yu, Bowen Gao, Sarah A. McLaughlin, John A. Copland, Alvaro Moreno-Aspitia, Donald W. Northfelt, Richard J. Gray, Vera J. Suman, Jeanette E. Eckel Passow, Jean-Pierre A. Kocher, Eric D. Wieben, Gianrico Farrugia, Cloann G. Schultz, James N. Ingle, Richard Weinshilboum, Matthew P. Goetz, Liewei Wang, Judy C. Boughey. Analysis of sequencing data to identify potential drug targets for an individual newly diagnosed with basal breast cancer who failed to respond to current standard neoadjuvant chemotherapy. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4185. doi:10.1158/1538-7445.AM2014-4185

Abstract INTRODUCTION Patient derived xenografts (PDX) may better reflect individual patient (pt) tumor biology; however, the feasibility of collecting PDX from percutaneous tumor biopsies (PTB) in the neoadjuvant setting is unknown. Furthermore, drug response phenotypes observed in PDX have not been prospectively compared to the corresponding pt clinical outcomes. METHODS The Breast Cancer Genome Guided Therapy Study (BEAUTY) is a prospective Mayo study of pts with high-risk breast cancer treated with neoadjuvant weekly paclitaxel (T) +/- trastuzumab followed by anthracycline based chemotherapy. PTB (at baseline) and residual surgical tissue (after all chemotherapy) are obtained for next generation sequencing (NGS) and PDX. Tumor biopsies (1-2 cores from 14 gauge needle) were implanted with Matrigel <1 hour of collection in the flanks of NOD-SCID or NSG mice. Low dose estradiol was supplemented in the drinking water. Primary outgrowth rate was defined as PDX tumor volume >50 mm3. Take rate was defined as development of at least 1 stably transplantable xenograft line/pt. To determine whether clinical T response assessed by MRI corresponded with in vivo T response, pretreatment PDX from 5 pts were injected into NOD-SCID mice (20 mice per pt PDX) and when tumors reached 100-200mm3, mice were randomized to no treatment vs T (20 mg/kg, ip. every 3-4 days). Two of these 5 patients had a MRI response defined as >30% decrease in longest lesion. RESULTS Pretreatment PTB from 81 unique pts were implanted in 251 mice (2-4 mice/pt). PDX outgrowth rates were 33.3% (27/81 pts) and 22 stable PDX were established (overall take rate 27.2%). Take rates were as follows: triple negative breast cancer (46%; 13/28); HER2 (27%; 6/22), Luminal B (13%; 3/22), and luminal A (0%; 0/9). Residual surgical tumor (after all treatment) from 17 pts was injected into 85 mice (average 5 mice/pt) and the initial outgrowth rate was 23% (4/17) with 3 stably transplantable lines established. PDX, derived from pretreatment PTB of 5 pts (2 responders and 3 non-responders), were assessed for in vivo T response. The size of the T treated group was significantly smaller than the no treatment group for the PDX derived from the 2 clinical responders, with complete disappearance of tumor by 18 days. In contrast, the PDX derived from the 3 clinical non-responders had no evidence for T response. CONCLUSIONS We have demonstrated the feasibility of using PTB to establish PDX in a prospective neoadjuvant clinical study and have demonstrated similar T drug response phenotypes in in the PDX as seen in the corresponding pt. These data suggest that PDX generated prospectively may be useful for biomarker validation and the development and individualization of new drug therapy. Funded by the Mayo Clinic Center for Individualized Medicine and the Mayo Clinic Cancer Center Citation Format: Jia Yu, Ping Yin, Bowen Gao, Jason P. Sinnwell, Ann M. Moyer, Daniel W. Visscher, Amy L. Conners, Travis J. Dockter, Krishna R. Kalari, Xiaojia Tang, Kevin J. Thompson, Hugues Sicotte, Douglas W. Mahoney, Steven N. Hart, Peter T. Vedell, Poulami Barman, Katie N. Jones, Sarah A. McLaughlin, John A. Copland, Alvaro Moreno Aspitia, Donald W. Northfelt, Richard J. Gray, Vera J. Suman, Jeanette E. Eckel Passow, Eric D. Wieben, James N. Ingle, Zhenkun Lou, Gianrico Farrugia, Richard Weinshilboum, Matthew P. Goetz, Judy C. Boughey, Liewei Wang. Feasibility of using percutaneous tumor biopsies from a prospective neoadjuvant breast cancer study to develop patient derived xenografts and assess in vivo chemotherapy sensitivity. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1195. doi:10.1158/1538-7445.AM2014-1195

Abstract Selective Estrogen Receptor Modulators (SERMs) such as tamoxifen and raloxifene can reduce the occurrence of breast cancer in high risk women by approximately 50%. This presentation will describe the use of a genome-wide association study (GWAS) to identify novel genes associated with breast cancer risk during SERM prevention therapy. Those results were pursued by functional genomic experiments that defined mechanisms responsible for the associations observed during the GWAS. These observations not only moved “beyond biomarkers” to function and underlying mechanism, but they also provided novel insight into mechanisms responsible for individual variation in SERM response phenotypes. Citation Format: Richard Weinshilboum. SERM breast cancer pharmaco-genomics: Beyond biomarkers. [abstract]. In: Proceedings of the Eleventh Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2012 Oct 16-19; Anaheim, CA. Philadelphia (PA): AACR; Cancer Prev Res 2012;5(11 Suppl):Abstract nr PL04-03.

Abstract 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) Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr P2-09-03.

Abstract 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 <98%. 551,358 SNPs were used in the analyses after removing 29,439 for a minor allele frequency <1% and 82 with Hardy Weinberg Equilibrium p-values <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>.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.

Abstract Abstract #57 Background: ABCSG 8 is the only prospective trial examining the value of switching patients (pts) to an aromatase inhibitor after tamoxifen (Tam) in which randomization occurred prior to therapy. This trial demonstrated that 2 years (yrs) of Tam followed by 3 yrs of anastrozole (TAM to A) was superior to 5 yrs of Tam (Jakesz SABC 2005). Tam is metabolically activated by CYP2D6 to endoxifen and pts with impaired CYP2D6 metabolism have a higher risk of recurrence (Goetz JCO 2005). The HOXB13/IL17BR (H/I) index (Ma Cancer Cell 2004) is associated with Tam resistance while a 5-gene molecular grade index (MGI) distinguishes outcome in grade II tumors. H/I and MGI are complementary prognostic factors that define low (low MGI), intermediate (low H/I, high MGI) and high risk groups (high H/I and MGI) (Ma Clin Cancer Res 2008). We sought to validate these biomarkers in ABCSG Trial 8, and hypothesized that they may predict the benefit of sequencing TAM to A versus Tam alone.
 Methods: From 1996-2004, ABCSG 8 randomized 3,901 pts with ER positive breast cancer. The primary endpoint was event-free survival. Assuming 40% of pts had reduced CYP2D6 metabolism, we designed a nested case control study in which 110 Tam treated cases matched to 2 controls would yield 80% power to detect a relative risk of 2.3 for pts with reduced CYP2D6 metabolism. Additionally, we sought to perform an exploratory analysis of the benefit of switching from Tam to A in pts with impaired vs non-impaired CYP2D6 metabolism. DNA and RNA derived from paraffin embedded sections were used to genotype for CYP2D6 and to obtain H/I and MGI RT-PCR profiles.
 Findings: Thus far, CYP2D6 genotyping is complete in 345 pts corresponding to 53 cases/106 controls (Tam) and 62 cases/124 controls (Tam to A). DNA and RNA yields were excellent with a CYP2D6 genotype call rate of >95%. CYP2D6 allele frequencies were as expected: *3 (.01), *4, (.23), *6 (.01), *10 (.02), *17 (.00), and *41 (.10). 44% of pts had reduced CYP2D6 metabolism (at least one CYP2D6 null allele or intermediate metabolizer) and 8% were CYP2D6 poor metabolizers. For H/I and MGI, 340 pts were profiled with 64%, 20% and 16% in the low, intermediate, and high risk groups, respectively. Tissue sectioning/genotyping for additional cases/controls is ongoing.
 Discussion: This is the first retrospective analysis of a large Tam versus Tam to A trial to evaluate the association between 1) CYP2D6, HOXB13/IL17BR, and MGI with TAM treatment outcome and 2) the predictive benefit of these biomarkers in pts randomized to switch from Tam to AI versus Tam alone. Final results will be presented at the meeting. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 57.

Clinical Pharmacology & Therapeutics (2003) 73, P29–P29; doi: