Citations

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 Cyclin dependent kinase 4 and 6 inhibitors (CDK4/6i) are standard first line (1L) and second line (2L) treatments of ER+ MBC. However, the optimal treatment strategy after progression on a CDK 4/6i is unknown. Given concern for rapid disease progression after discontinuation of CDK4/6is (Bashour, J Cancer 2017), further data on responses to subsequent lines of therapy post CDK4/6i are needed. We performed a single institution retrospective review of patients (pts) with ER+ MBC who received 1L or 2L CDK4/6i to examine the prescribing patterns and clinical responses to post-CDK4/6i treatment. Methods: We identified 136 ER+ MBC pts prescribed a CDK4/6i at Mayo Clinic Rochester (from 12/2014 to 2/2018) on 1L or 2L who received at least 30 days of therapy and ≥ two clinic visits during treatment. For the 1L and 2L cohorts we assessed the time to treatment failure during (TTF-1) and after CDK4/6i therapy (TTF-2). TTF was defined as time from start of therapy to discontinuation for any cause. Additionally, we assessed overall survival (OS) post CDK4/6i discontinuation (defined as the date of TTF-1 to death or last follow up). Results: The study cohort included 81 and 55 pts treated with 1L and 2L CDK4/6is, respectively. In the 1L cohort, palbociclib/letrozole (82.3%) and palbociclib/fulvestrant (13.6%) were most commonly prescribed. Treatment was discontinued in 39/81 pts due to: progression (84.6%), intolerability (7.7%), and other (7.7%). The median TTF-1 was 19.7 mo (95% CI 11.2 – 25.4 mo). Subsequent treatment and response data were available in 37/39 pts that progressed on 1L CDK4/6i. The most commonly prescribed regimens included single-agent hormonal therapy (29.7%), everolimus/exemestane (27.0%), and single agent chemotherapy (21.7%). The overall median TTF-2 were as follows: everolimus/exemestane 13.2 mo [95%CI 0.33 mo – not reached (NR)]; single agent hormonal therapy 3.1 mo (95%CI 1.4 – 5.4 mo); and single agent chemotherapy 4.1 mo (95%CI 1.4 - 5.4 mo). With a median post-CDK4/6i follow up of 11.2 mo, we observed 8 deaths and the median OS-post CDK4/6i was NR. In the 2L cohort, common regimens were palbociclib/letrozole (63.6%) and palbociclib/fulvestrant (27.3%). Treatment was discontinued in 30/55 pts due to: progression (73.3%), intolerability (13.3%), and other (13.3%). The median TTF-1 in the 2L cohort was 8.3 mo (95%CI 4.6 – 12.7 mo). Subsequent treatment data were available in 24/30 pts and included single-agent hormonal therapy [n=7 (29.2%); median TTF-2 4.7 mo; 95%CI 1.9 – 14.0 mo], everolimus/exemestane [n=6 (25.0%); median TTF-2 3.2 mo, 95% CI 0.8-10.1 mo)] and single agent chemotherapy [n=6 (25.0%); median TTF-2 2.6 mo; 95%CI 1.7 mo – NR)]. With a median post-CDK4/6i follow up 18.7 mo in the 2L cohort, we observed 11 deaths, and the median OS-post CDK4/6i was 11.8 mo (95%CI 11.8 mo – NR). Conclusions: Single and combination-based endocrine strategies are commonly administered post-CDK4/6i without clinical evidence for rapid deterioration. The observation of a median treatment duration lasting >12 months in patients receiving everolimus/exemestane post-CDK4/6i supports ongoing studies evaluating drugs that target PI3K/mTOR pathway in combination and following progression on a CDK4/6i. Citation Format: Giridhar KV, Choong GM, Leon-Ferre RA, O'Sullivan CC, Ruddy KJ, Haddad TC, Hobday TJ, Peethambaram PP, Moynihan TJ, Loprinzi CL, Liu MC, Suman VJ, Goetz MP. Clinical management of metastatic breast cancer (MBC) after CDK 4/6 inhibitors: A retrospective single-institution study [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 P6-18-09.

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: MicroRNAs (miRNA) are small, noncoding RNAs with important regulatory roles in tissue development, differentiation, proliferation, and when deregulated tumor formation. The goal of this study was to characterize the miRNA profile of normal breast tissue from carriers of either BRCA1 or BRCA2 mutations, with and without a prior history of cancer, for biomarker discovery for future prevention studies in high risk patient populations. Methods: MicroRNA profiling was conducted using a panel of breast candidate miRNAs and normal ‘non-cancerous’ tissue biopsy from 15 BRCA1 mutation carriers, 15 BRCA2 mutation carriers and 15 BRCA screened mutation negative subjects from high risk families. Results: While small differences were observed for any individual miRNA between groups, the most striking observation was the presence of a clear BRCA1 miRNA signature that was distinct from that in BRCA2 subjects or in the high-risk, non-mutation carriers. The BRCA1 pattern was present in normal appearing tissue biopsy independent of previous history of cancer, treatment for cancer, age, menopausal status or oophorectomy. BRCA2 carriers and high risk, mutation negative subjects showed an overall shared miRNA expression pattern. MicroRNA species that distinguished the groups included differences in the expressed levels of let-7a, let-7b, miR-125b, miR-145, miR-15b and members of the miR-181 family. Ongoing efforts include pathway modeling to gain insight on novel targets for prevention along with RT-PCR confirmation of differentiating microRNA species as well the potential downstream targets for example of miR-181b target genes including HOXA7, HOXA9, HOXA11 and PBX3. Conclusion: The presence of a distinctive miRNA profile in normal appearing tissue of BRCA1 mutation carriers suggest a major role of BRCA1 mutation status on the normal tissue not previously reported. If corroborated, miRNA profiling studies of normal breast tissue may prove useful in both risk assessment as well as providing insight on deregulated pathways for pharmacologic intervention for risk reduction in the BRCA carrier population. Citation Format: Sandhya Pruthi, Julie Buckmeier, Christina Preese, Vera J. Suman, Rachel M. Hurley, Patricia Thompson. microRNA expression profiling in normal breast epithelium of BRCA1 and BRCA2 carriers as prevention targets. [abstract]. In: Proceedings of the Twelfth Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2013 Oct 27-30; National Harbor, MD. Philadelphia (PA): AACR; Can Prev Res 2013;6(11 Suppl): Abstract nr C47.

Abstract Purpose: BRCA1 and BRCA2 mutation carriers develop breast cancers with a tumor phenotype unique from spontaneous tumors. We surveyed documented BRCA mutation carriers to determine willingness to be contacted about participation in future breast cancer prevention research studies directed to their unique phenotype that may or may not require breast biopsies. Methods: At each of the 3 participating institutions, women at least 20 years of age with a documented germline deleterious mutation in BRCA1 or BRCA2 and no prior history of breast cancer were contacted to participate in a survey to ascertain their willingness to be contacted to participate in future prevention trials. Survey results were summarized using descriptive statistics. Results: Among 56 BRCA1 and BRCA2 mutation carriers who responded, 55.4% of women reported high or very high interest in participating in randomized control study of chemoprevention agent vs. placebo. Within this population, post-menopausal women demonstrated a higher interested in study participation (64.5%) versus pre-menopausal women (38.9%). When examining willingness to undergo breast biopsy for a chemoprevention study, women expressed near equal willingness (42.9%) and unwillingness (44.6%) for biopsy. Conclusions: BRCA1 and BRCA2 mutation carriers demonstrated significant interest in breast cancer prevention study participation involving trial agent versus placebo, and an equal expression of willingness and unwillingness to undergo breast biopsy, establishing the feasibility for future research elucidating targeted chemoprevention agents. Citation Format: Rachel M. Hurley, Vera J. Suman, Mary Daly, Funmi Olopade, Paul J. Limburg, Sandhya Pruthi. Assessment of interest for breast cancer prevention trial participation among BRCA mutation carriers. [abstract]. In: Proceedings of the Twelfth Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2013 Oct 27-30; National Harbor, MD. Philadelphia (PA): AACR; Can Prev Res 2013;6(11 Suppl): Abstract nr C01.

Abstract Introduction Quantitative in vivo measurement of brain amyloid burden is important for both research and clinical purposes. However, the existence of multiple imaging tracers presents challenges to the interpretation of such measurements. This study presents a direct comparison of Pittsburgh compound B–based and florbetapir‐based amyloid imaging in the same participants from two independent cohorts using a crossover design. Methods Pittsburgh compound B and florbetapir amyloid PET imaging data from three different cohorts were analyzed using previously established pipelines to obtain global amyloid burden measurements. These measurements were converted to the Centiloid scale to allow fair comparison between the two tracers. The mean and inter‐individual variability of the two tracers were compared using multivariate linear models both cross‐sectionally and longitudinally. Results Global amyloid burden measured using the two tracers were strongly correlated in both cohorts. However, higher variability was observed when florbetapir was used as the imaging tracer. The variability may be partially caused by white matter signal as partial volume correction reduces the variability and improves the correlations between the two tracers. Amyloid burden measured using both tracers was found to be in association with clinical and psychometric measurements. Longitudinal comparison of the two tracers was also performed in similar but separate cohorts whose baseline amyloid load was considered elevated (i.e., amyloid positive). No significant difference was detected in the average annualized rate of change measurements made with these two tracers. Discussion Although the amyloid burden measurements were quite similar using these two tracers as expected, difference was observable even after conversion into the Centiloid scale. Further investigation is warranted to identify optimal strategies to harmonize amyloid imaging data acquired using different tracers.