Vedell, PT

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: Both HR deficiency and BRCA mutation status predict response to platinum-based therapy and BRCA mutation status predicts docetaxel resistance. However, the association of either biomarker with response to the individual elements of either AC or taxanes (T) is unknown since T is commonly given concomitantly with or after anthracyclines (A). We evaluated the association of HRD and BRCA mutation status with response to neoadjuvant weekly T followed by AC or (F)EC in high-risk breast cancer. Methods: We studied 140 high risk Stage I-III breast cancer patients (pts), enrolled in the breast cancer genome guided therapy study (BEAUTY), obtaining biopsies for DNA/RNA sequencing and MRI imaging to assess response to neoadjuvant weekly T (+trastuzumab+/-pertuzumab for HER2+ disease) followed by AC or (F)EC. Germline BRCA status and HR status of tumor samples (Myriad laboratories) were obtained. HR deficient tumor was defined as HRD score ≥42 or BRCA mutation. MRI response by changes in tumor size after 12 weeks of T was classified by WHO criteria. pCR was defined as ypT0/Tis ypN0. Both MRI response after T and pCR (after T and AC) were examined in terms of germline BRCA mutation (gBRCAmut vs. gBRCAwt) and tumor HR deficiency. Results: Of 140 pts enrolled, 8 withdrew consent and 2 carboplatin treated pts were excluded. Germline data were available for 124/130 pts. 12 patients had BRCA deleterious germline mutations (4 BRCA1, 8 BRCA2). MRI partial (PR)/complete response (CR) rate to T was 47.3% (95% CI: 37.8-57.0%) in the BRCAwt group and 66.7% (95% CI: 34.9-90.1%) in the BRCAmut group. No MRI CR's were observed in BRCA1 mut pts. In contrast, pCR rate was 50% in the 12 gBRCAmut pts (95% CI: 21.1-78.9%) and 31.3% in the 112 gBRCAwt pts (95% CI: 22.8-40.7%). HR deficiency status has thus far been determined for 74 pts: 26 pts have HD deficient tumors: 18 TNBC, 5 Luminal B, 2 ER-/HER2+; and 1 ER+/HER2+. Determination of HR deficiency is ongoing and will be reported for the full cohort in terms of 12 week MRI response to T and pCR to T+AC. HR deficientMolecular Subtypeyes (%)no (%)TBD (%)Luminal A0/112/11 (18.2)9/11 (81.8)Luminal B5/37 (13.5)13/37 (35.1)19/37 (51.3)Luminal NOS0/21/2 (50)1/2 (50)ER+/Her2+1/17 (5.8)14/17 (82.4)2/17 (11.8)ER-/Her2+2/20 (10)11/20 (55)7/20 (35)Triple Negative18/43 (41.9)6/43 (18.6)17/43 (39.5)germline BRCA statusMRI partial response after T (%)MRI complete response after T (%)pCR after T&AC (%)BRCA11/4 (25)0/42/4 (50)BRCA25/8 (62.5)2/8 (25)4/8 (50)BRCAwt35/112 (31.3)18/112 (16.1)35/112 (31.3) Conclusion: In the setting of neoadjuvant weekly T followed by AC, pCR rates were non-significantly higher in pts with BRCA1 mutations. While we observed no overall association between BRCA mutation status and response rates to taxanes; nearly all MRI responses to taxanes (partial and complete) were observed in the BRCA2 group. Prospective studies are needed to validate these findings and to determine whether BRCA status can be used to select therapy. HR deficiency is uncommon in luminal A and HER2+, frequent in TNBC, and the association of HRD with both MRI response to taxanes and pCR will be reported at the meeting. Citation Format: Boughey JC, Kalari KR, Suman VJ, McLaughlin SA, Moreno Aspitia A, Moyer AM, Northfelt DW, Gray RJ, Vedell PT, Tang X, Dockter TJ, Jones KN, Felten SJ, Conners AL, Hart SN, Visscher DW, Wieben ED, Ingle JN, Hartman A-R, Timms K, Elkin E, Jones J, Wang L, Weinshilboum RW, Goetz MP. Role of germline BRCA status and tumor homologous recombination (HR) deficiency in response to neoadjuvant weekly paclitaxel followed by anthracycline-based chemotherapy. [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 P3-07-29.

Abstract Background: Based upon the association between pathologic response and disease free survival, the neoadjuvant setting is increasingly being used for drug development. NGS has identified unique and recurrent genetic alterations in breast cancer (BC) that are potentially targetable; however, the clinical implications are mostly unknown. We developed a prospective neoadjuvant study (BEAUTY) in high risk BC patients (pts) using weekly T followed by anthracycline-based chemo wherein percutaneous tumor biopsies (PTB) are obtained before/during/after chemo for NGS and PDX. Our goal is to identify novel biomarkers/pathways and develop PDX to test new therapeutic approaches. Methods: Pts underwent PTB at baseline and after 12 wks of T. Response to T was defined based upon 12 week Ki-67: responder (<15%) vs non-responder (≥15%). Pts with histologic response and absence of invasive BC at 12 wks were classified as responders. NGS was performed using PTB/blood DNA (exome) and PTB (RNA seq). MRI response was classified using RECIST criteria. NGS data were used to identify somatic copy number variants (cnvs) and expressed single nucleotide variants (eSNVs). Non-SCID mice (estrogen supplemented) were implanted ≤ 30 minutes with PTB samples. Results: Of the first 78 pts, 44 have completed T. Here we focus on 18 pts with either triple negative or luminal B BC. Clinical characteristics according to Ki-67 response are shown in Table 1. Comparison of genomic alterations in BEAUTY pts with TCGA identified a greater overlap with copy number gains (73%) compared to deletions (40%), along with similar observations of mutations in TP53, PTEN, RYR2, and AKT1 genes. Association analysis of CNVs and eSNVs between responders/non-responders identified 33 genes (predominantly located in chromosomes 1, 8, 13) and 580 eSNVs (corresponding to 497 genes) with a p < 0.05. Differential gene expression (DGE) analysis of responders/non-responders identified 198 genes with a p-value < 0.05. Integrated analysis of 539 genes (CNVs, eSNVs and DGE) identified pathways such as TGF-beta, Jak-Stat, WNT and NOTCH signalling. PDX take rate was 44% [triple negative (6/10); Luminal B (2/8)]. PDX growth rate was significantly associated with clinical baseline Ki-67 (p = 0.00014). Conclusion: This is the first prospective study to demonstrate the feasibility of using PTB to obtain both NGS data and PDX in the neoadjuvant setting. PDX take rate is associated with BC subtype and baseline Ki-67. Studies are ongoing to 1) validate genes/pathways associated with treatment response in subsequent BEAUTY pts; 2) genomically characterize and assess PDX in vivo response to T and 3) Use NGS data to prioritize new drugs/drug combinations in PDX. Funded by Mayo Clinic Center for Individualized Medicine and MC Cancer Center. Clinical CharacteristicsOverallResponders: 12 week Ki-67 < 15% (n = 9)Non-Responders: 12 week Ki-67 ≥ 15% (n = 9)Median Age495345T stage T2/T314 (78%)7 (78%)7 (78%)Node Positive8 (44%)4 (44%)4 (44%)Triple negative10 (56%)6 (67%)4 (44%)Luminal B8 (44%)3 (33%)5 (56%)Ki-67 after 12 Weeks of T Median 5% (0-11%)Median 35% (17-60%)Complete/Partial MRI Response after T 6 (67%)2 (22%) Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P1-08-10.