Thompson, KJ

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 Background: The LAR subtype is a genomically distinct subset of TNBC. Using a large cohort of non-metastatic TNBC patients (pts) with long term follow-up, we sought to further characterize the clinicopathologic features and outcomes of LAR vs non-LAR TNBC. Methods: From a cohort of 9982 women with surgically-treated non-metastatic breast cancer, 605 met criteria for TNBC (ER/PR<1% and HER2-negative) by central pathology. RNA extracted from 304 FFPE tumor specimens using the HighPure RNA extraction kit was subjected to TruSeq RNA Access library preparation and sequencing on a HiSeq2500. Adequate RNA was available for 283 pts. Tumors were classified as LAR or non-LAR using a shrunken centroid model, CABAL (Clustering Among BAsal and Luminal androgen receptor). In addition to previously described analyses [Leon-Ferre et al, Breast Cancer Res Treat 2017], immunohistochemical (IHC) androgen receptor (AR) staining was performed and the impact of various parameters on invasive disease-free survival (IDFS) and overall survival (OS) was assessed using Cox proportional hazards models. Results: 58 (20%) tumors were classified as LAR and 225 (80%) as non-LAR. Compared to non-LAR, LAR pts were older (mean age 65 vs 54) and more often postmenopausal (79%vs53%), both p=0.01. Apocrine histology was more common among LAR tumors (21%vs0%), which were also lower grade (grade3: 69%vs95%) and had lower Ki-67 (Ki-67>15%: 64%vs82%), all p<0.01. Additionally, LAR tumors had lower median stromal tumor infiltrating lymphocytes (TILs, 20%vs25%) and were less frequently lymphocyte-predominant [≥50% stromal or intratumoral TILs (19%vs32%)], although neither reached statistical significance. AR IHC was available for 223 of 283 tumors. Median AR IHC score in LAR was 65% (range 0-100%) vs 0% (range 0-90%) in non-LAR. T/N stage, surgery type, and receipt of adjuvant chemotherapy (AdjCT) or radiotherapy were similar between LAR and non-LAR. LAR pts had shorter IDFS and OS compared to non-LAR (5.6 vs 11.8 yrs and 10.8 vs 20.8 yrs, respectively), although this did not reach statistical significance. Test of proportional hazard assumption was not significant for IDFS or OS (p = 0.30 and 0.09). IDFS estimates were numerically higher in LAR vs non-LAR (80.2%vs70.5%,p = 0.92) at 3yrs post-diagnosis; whereas the opposite was true (40.9%vs55.6%,p = 0.07) after 10yrs. OS estimates at 3 and 5yrs were similar between LAR and non-LAR, but at 10yrs OS was inferior in LAR (40.9%vs66.4%,p = 0.24). In a univariate analysis including both LAR and non-LAR, older age, higher N stage, lower TILs and absence of AdjCT were associated with poorer IDFS and OS. In a multivariate analysis, higher N stage and absence of AdjCT remained associated with both poorer IDFS and OS; while lower stromal TILs were associated with poorer IDFS (p=0.01), and with a trend towards poorer OS (p=0.07). Conclusions: LAR TNBCs occurred in older women, were lower grade, and had lower TIL density than nonLAR tumors. While significant differences in IDFS or OS were not demonstrated, LAR pts exhibited a numerically lower risk of a disease event at 3yrs, but higher risk by 10yrs compared to nonLAR pts. In the entire cohort, higher N stage, absence of AdjCT and lower TILs were independently associated with poorer outcomes. Citation Format: Leon-Ferre RA, Polley M-Y, Liu H, Kalari KR, Boughey JC, Liu MC, Cafourek V, Negron V, Ingle JN, Thompson KJ, Tang X, Barman P, Carlson E, Visscher DW, Carter JC, Couch FJ, Goetz MP. Characteristics, outcomes and prognostic factors of luminal androgen receptor (LAR) triple-negative breast cancer (TNBC) [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 P3-08-01.

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: 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.