Carlson, Erin E.

Abstract Purpose: To determine genetic variants that could differentiate aromatase inhibitors (AIs) efficacy in early stage breast cancer. Experimental Design: We performed a stratified cox-proportional hazards analysis utilizing stratification factors and other covariates examining Breast Cancer Free Interval (BCFI) SNP-treatment interaction in Caucasian patients entered on the MA.27 trial. This analysis involved 4465 patients (2226 on anastrozole and 2239 on exemestane arm) including 245 (121 on anastrozole and 124 events on exemestane arm) with a breast event. Preclinical laboratory studies included luciferase activity, chromatin immunoprecipitation (ChIP) assay, and cell migration assays. Results: We identified 887 SNPs with a p-value <1E-4 that could differentiate anastrozole from exemestane efficacy. We next used GTEx databases to determine whether these SNPs might be cis-eQTLs with nearby genes, and found that 95 SNPs were eQTL with 14 genes. Functional validation of SNP effect in these 14 genes on response to anastrozole and exemestane revealed that 3 SNPs showed genotype-dependent differences between anastrozole and exemestane. Two of the three SNPs, rs1877193 and rs6735923 located upstream of LY75 gene, were associated with higher LY75 gene expression. Our drug-SNP interaction GWAS showed that both SNPs were associated with better BCFI for exemestane compared to anastrozole (HR= 0.447, 0.458). The SNP rs62293499 located downstream of the GPR160 gene was also associated with better BCFI for exemestane compared to anastrozole (HR=0.433). Interestingly, all 3 SNPs were associated with worse outcome (shorter BCFI) in anastrozole treated patients (HR=1.39~1.58), but with longer BCFI in the exemestane treated patients (HR=0.58~0.71) based on our GWAS results in MA.27 trial. However, all 3 SNPs showed no association with BCFI if the two treatment arms, anastrozole and exemestane, were combined (HR=0.92~1.08). Consistently, cells with Ly75 or GPR160 SNP variant genotypes were more sensitive to exemestane compared to anastrozole. Mechanistically, the 3 SNPs regulated estrogen receptor-dependant LY75 and GPR160 expression. LY75 suppression induced epithelial-to-mesenchymal transition (EMT) in breast cancer cell lines, accompanied by increased migratory capacity in vitro. LY75 knockdown also resulted in predominant downregulation of functional pathways such as cell proliferation, while pathways associated with mesenchymal stimulation were generally increased. GPR160 knockdown also resulted in downregulation of cell proliferation pathways. Conclusions: This SNP genotype and AI treatment interaction clinical study revealed unique genetic variants that differentiate anastrozole and exemestane efficacy. The signals were lost in the GWAS analysis when anastrozole and exemestane were combined. Preclinical laboratory studies revealed novel functions of LY75 and GPR160 in breast cancer. These findings represent potential steps towards individualized AI therapy. Citation Format: Junmei Cairns, James N. Ingle, Krishna R. Kalari, Lois E. Shepherd, Matthew J. Ellis, Paul E. Goss, Poulami Barman, Erin E. Carlson, Matthew P. Goetz, Richard M. Weinshilboum, Liewei Wang. The interaction between SNP genotype and aromatase inhibitor treatment response in early breast cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr LB-103.

Aromatase inhibitors (AIs) reduce breast cancer recurrence and prolong survival, but up to 30% of patients exhibit recurrence. Using a genome-wide association study of patients entered on MA.27, a phase III randomized trial of anastrozole versus exemestane, we identified a single nucleotide polymorphism (SNP) in CUB And Sushi multiple domains 1 ( CSMD1) associated with breast cancer–free interval, with the variant allele associated with fewer distant recurrences. Mechanistically, CSMD1 regulates CYP19 expression in an SNP- and drug-dependent fashion, and this regulation is different among 3 AIs: anastrozole, exemestane, and letrozole. Overexpression of CSMD1 sensitized AI-resistant cells to anastrozole but not to the other 2 AIs. The SNP in CSMD1 that was associated with increased CSMD1 and CYP19 expression levels increased anastrozole sensitivity, but not letrozole or exemestane sensitivity. Anastrozole degrades estrogen receptor α (ERα), especially in the presence of estradiol (E2). ER + breast cancer organoids and AI- or fulvestrant-resistant breast cancer cells were more sensitive to anastrozole plus E2 than to AI alone. Our findings suggest that the CSMD1 SNP might help to predict AI response, and anastrozole plus E2 serves as a potential new therapeutic strategy for patients with AI- or fulvestrant-resistant breast cancers.

Objectives Based on our previous findings that postmenopausal women with estrone (E1) and estradiol (E2) concentrations at or above 1.3 pg/ml and 0.5 pg/ml, respectively, after 6 months of adjuvant anastrozole therapy had a three-fold risk of recurrence, we aimed to identify a single-nucleotide polymorphism (SNP)-based model that would predict elevated E1 and E2 and then validate it in an independent dataset. Patients and methods The test set consisted of 322 women from the M3 study and the validation set consisted of 152 patients from MA.27. All patients were treated with adjuvant anastrozole, had on-anastrozole E1 and E2 concentrations and genome-wide genotyping. Results SNPs were identified from the M3 genome-wide association study. The best model to predict the E1-E2 phenotype with high balanced accuracy was a support vector machine model using clinical factors plus 46 SNPs. We did not have an independent cohort that is similar to the M3 study with clinical, E1-E2 phenotypes and genotype data to test our model. Hence, we chose a nested matched case-control cohort (MA.27 study) for testing. Our E1-E2 model was not validated but we found the MA.27 validation cohort was both clinically and genomically different. Conclusions We identified a SNP-based model that had excellent performance characteristics for predicting the phenotype of elevated E1 and E2 in women treated with anastrozole. This model was not validated in an independent dataset but that dataset was clinically and genomically substantially different. The model will need validation in a prospective study.

Abstract Background: The combination of cyclin dependent 4/6 kinase inhibitors (CDK4/6i) with endocrine therapy (ET) has resulted in clinically significant improvements in progression-free survival (PFS) and overall survival (OS) in hormone-receptor (HR)-positive metastatic breast cancer (MBC). However, most patients’ disease ultimately progresses on CDK4/6i and ET. Therefore, further research is necessary to understand the mechanisms driving primary and secondary resistance. PROMISE is a multicenter prospective cohort study enrolling women with HR-positive MBC commencing treatment with palbociclib + letrozole (1st line) or palbociclib + fulvestrant (2nd Line). The study provides a comprehensive “omic” assessment of blood, tumor, urine and the fecal microbiome to identify molecular or cellular features associated with primary endocrine resistance (e.g. disease progression ≤ 12 months on treatment) and acquired resistance to CDK 4/6i. Additionally, patient derived xenografts and organoids are created to test new drug strategies designed to overcome resistance to CDK 4/6i and ET. Here, we present initial sequencing results from pretreatment biospecimens collected from PROMISE study participants. Methods: On-study tumor biopsies and blood samples were collected for DNA/RNA sequencing (TempusTM). The analyzed biospecimens were all obtained prior to initiation of palbociclib and ET. We correlated patient clinical characteristics (phenotypes) with molecular data and responses to protocol treatment. The data were analyzed using a series of cutting-edge bioinformatics pipelines for somatic and germline mutations in addition to copy number alterations (CNAs). The study database was locked for analysis on 06/20/2019. Results: We analyzed the somatic single nucleotide variants/INDELs (sSNV/INDEL) profiles across the tumor samples to determine the genes that were least likely to occur as a result of background mutation processes. Twenty-six patients had somatic copy number alterations (sCNA) and/or sSNV/INDEL in at least one of 18 genes with the most significant sSNV/INDEL profiles (p < 0.03) which included clinically and biologically relevant genes. The genes with the most statistically significant sSNV/INDEL mutation profiles were GATA3, PIK3CA, CDH1, and ESR1 (p < 0.0009). We observed a high percentage of tumors with somatic alterations in GATA3 (23% sSNV/INDEL, 15% sCNA), PIK3CA (38%, 12%), CDH1 (19%, 50%) and ESR1 (19%, 58%). ESR1 mutations were more frequent in patients receiving 2nd line treatment. Other frequently altered genes included TP53 (15%, 46%), MAP2K4 (8%, 50%), DNAAF1 (8%, 50%), and CDKN1B (8%, 35%). Further, ZNF317 and F3 were altered in 9 and 7 patients, respectively. Twenty-four samples had alterations in at least one of the CDK4/6 pathway genes (RB1, CCNE2, CCND1, CDK6, ESR1, CDKN2A, CCND3, CDK4, CDK2 and CCNE1). Four patients progressed on therapy; three of the four patients had mutations in PIK3CA, and one had a mutation in ESR1. Results of the RNA sequencing data (N=26) will be presented at the SABCS meeting. Conclusions: This is the first report of a prospective study designed to characterize the genomic landscape of ER+/HER2- MBC prior to palbociclib treatment. We observed high frequencies of known targetable alterations in PIK3CA and ESR1, including in patients that progress, which is consistent with previous reports. RNA sequencing data will be presented at the meeting. Citation Format: Ciara C O Sullivan, Krishna R Kalari, Vera J Suman, Peter T Vedell, Ann Moyer, Erin Carlson, Jason Sinnwell, Tejaswi Alaparthi, Xiaojia Tang, Kevin Thompson, Jaeyun Sung, Alvaro Moreno-Aspitia, Donald Northfelt, Minetta C Liu, Tufia C Haddad, Prema Peethambaram, Saranya Chumsri, Kathryn J Ruddy, Karthik V Giridhar, Roberto A Leon-Ferre, Paula Gill, Mohammad Ranginwala, Asad Javed, Sameer Batoo, Brendan P. McMenomy, Richard Weinshilboum, Liewei Wang, Matthew P Goetz. Comprehensive tumor sequencing to identify biomarkers of palbociclib response: First report of the PROMISE study [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P2-11-07.

Abstract Purpose: To determine if the degree of estrogen suppression with aromatase inhibitors (AI: anastrozole, exemestane, letrozole) is associated with efficacy in early-stage breast cancer, and to examine for differences in the mechanism of action between the three AIs. Experimental Design: Matched case–control studies [247 matched sets from MA.27 (anastrozole vs. exemestane) and PreFace (letrozole) trials] were undertaken to assess whether estrone (E1) or estradiol (E2) concentrations after 6 months of adjuvant therapy were associated with risk of an early breast cancer event (EBCE). Preclinical laboratory studies included luciferase activity, cell proliferation, radio-labeled ligand estrogen receptor binding, surface plasmon resonance ligand receptor binding, and nuclear magnetic resonance assays. Results: Women with E1 ≥1.3 pg/mL and E2 ≥0.5 pg/mL after 6 months of AI treatment had a 2.2-fold increase in risk (P = 0.0005) of an EBCE, and in the anastrozole subgroup, the increase in risk of an EBCE was 3.0-fold (P = 0.001). Preclinical laboratory studies examined mechanisms of action in addition to aromatase inhibition and showed that only anastrozole could directly bind to estrogen receptor α (ERα), activate estrogen response element-dependent transcription, and stimulate growth of an aromatase-deficient CYP19A1−/− T47D breast cancer cell line. Conclusions: This matched case–control clinical study revealed that levels of estrone and estradiol above identified thresholds after 6 months of adjuvant anastrozole treatment were associated with increased risk of an EBCE. Preclinical laboratory studies revealed that anastrozole, but not exemestane or letrozole, is a ligand for ERα. These findings represent potential steps towards individualized anastrozole therapy.

Abstract Abstract 873 Introduction: Clonal chromosomal anomalies have been well-characterized in the blood cells of patients with Chronic Lymphocytic Leukemia (CLL) and other hematological malignancies. However, the frequencies and types of chromosomal anomalies found in “normal” people (i.e. no history of hematological cancer) are much less well known. Here we compare chromosomal anomalies in untreated CLL patients with those found in a large sample of people (n>50,000) recruited for genome-wide association studies in the Gene Environment Association Studies (GENEVA) consortium. This comparison provides information about the extent to which people without a prior history of hematological cancer have chromosomal anomalies that are characteristic of CLL. Methods and Results: The anomaly detection method focused on acquired, rather than inherited, chromosomal anomalies identified in whole blood samples. The method and results for the GENEVA subjects were published recently (Laurie et al. 2012, Nat. Gen. 44:642–650). The same method was applied to pre-treatment samples from 220 CLL patients from US Intergroup Trial E2997 of the Eastern Cooperative Oncology Group (ECOG). Acquired anomalies were detected as mosaic mixtures of normal disomic cells along with cells containing autosomal anomalies consisting of large (generally > 1 Mb) deletions, duplications and/or acquired uniparental disomy (UPD). Detection requires a large fraction of cells with the same abnormal karyotype (>5–10%), which is presumably of clonal origin. The subjects (of all ages) came from 12 different GENEVA studies, which addressed a variety of health conditions not directly related to hematological cancer (e.g. addiction and glaucoma). DNA was extracted mainly from blood samples (or, in 8% of subjects, from buccal/saliva samples) and genotyped on Illumina SNP mircroarrays. Clonal mosaic anomalies were detected from change points in intensity and genotype data (B Allele Frequency and Log R Ratio). We found 514 anomalies in 404 GENEVA subjects out of 50,222 examined (0.8% of subjects), although the prevalence increased with age. Clinical data from about 75% of subjects with a clonal chromosomal abnormality revealed that only a small fraction (2.8%, 95% CI=1.0 – 4.7%) had a record of prior hematological cancer. In the ECOG study, blood samples from 220 CLL patients enrolled in protocol E2997 were genotyped on the Illumina HumanOmni1-Quad_v1 array and clonal mosaic chromosome anomalies were detected using the GENEVA method. Chromosomal anomalies typical of CLL also were identified using interphase FISH (i.e. 6q23.3-, 11q22-, 12q15+, 13q14-, 17p13-) as previously reported (Grever, JCO 25:799-804). We found 489 anomalies in 148 patients of the 220 examined (67% of patients). The anomalies detected by FISH and SNP microarray were highly concordant (>98%). Although, the most common recurrent anomalies occurred on chromosomes 6, 11, 12, 13 and 17 in regions that span the FISH probes (except for chromosome 6), a majority of the anomalies detected by SNP data (74% of 489) were not detected by the CLL FISH panel. The frequency of large chromosomal anomalies was much higher in CLL patients than in GENEVA (67% versus 0.8% of subjects) and the overall composition of anomaly types found was also different. The fraction of anomalies consisting of acquired UPD was substantially higher in GENEVA subjects than in ECOG (34.0% versus 10.6%), while deletions were less common in GENEVA (50.1% versus 72.4%) and duplications were similar (15.6% versus 17.0%) (Chi-squared test p-value=3 × 10−18). We compared the most commonly deleted regions (CDR) on each chromosome across studies. In the ECOG CLL patients, four chromosomes each had 10 or more overlapping deletions in their top CDR (6, 11, 13 and 17), which span the FISH probes (except for chromosome 6). The GENEVA subjects also had CDR on chromosomes 11, 13 and 17, which overlap those in ECOG. In addition, GENEVA had CDR spanning genes or regions implicated in myeloid malignancies, including DNMT3a on 2p, TET2 on 4q, PRAME on 22q, and a region on 20q13. Conclusions: These genotyping results of >50,000 subjects suggest clonal chromosomal anomalies may represent precursor states, or perhaps undiagnosed or unrecorded cases, of a variety of hematological malignancies. Further studies to validate these findings and explore the natural history of these genetic defects in individuals without hematologic malignancy are warranted. Disclosures: Shanafelt: Genentech: Research Funding; Glaxo-Smith-Kline: Research Funding; Cephalon: Research Funding; Hospira: Research Funding; Celgene: Research Funding; Polyphenon E International: Research Funding.

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.

Anastrozole is a widely prescribed aromatase inhibitor for the therapy of estrogen receptor positive (ER+) breast cancer. We performed a genome‐wide association study (GWAS) for plasma anastrozole concentrations in 687 postmenopausal women with ER+ breast cancer. The top single‐nucleotide polymorphism (SNP) signal mapped across SLC38A7 (rs11648166, P = 2.3E‐08), which we showed to encode an anastrozole influx transporter. The second most significant signal (rs28845026, P = 5.4E‐08) mapped near ALPPL2 and displayed epistasis with the SLC38A7 signal. Both of these SNPs were cis expression quantitative trait loci (eQTL)s for these genes, and patients homozygous for variant genotypes for both SNPs had the highest drug concentrations, the highest SLC38A7 expression, and the lowest ALPPL2 expression. In summary, our GWAS identified a novel gene encoding an anastrozole transporter, SLC38A7, as well as epistatic interaction between SNPs in that gene and SNPs near ALPPL2 that influenced both the expression of the transporter and anastrozole plasma concentrations.

Purpose The majority of patients with cancer receive treatments that are minimally informed by omics data. We propose a precision medicine computational framework, PANOPLY (Precision Cancer Genomic Report: Single Sample Inventory), to identify and prioritize drug targets and cancer therapy regimens. Materials and Methods The PANOPLY approach integrates clinical data with germline and somatic features obtained from multiomics platforms and applies machine learning and network analysis approaches in the context of the individual patient and matched controls. The PANOPLY workflow uses the following four steps: selection of matched controls to the patient of interest; identification of patient-specific genomic events; identification of suitable drugs using the driver-gene network and random forest analyses; and provision of an integrated multiomics case report of the patient with prioritization of anticancer drugs. Results The PANOPLY workflow can be executed on a stand-alone virtual machine and is also available for download as an R package. We applied the method to an institutional breast cancer neoadjuvant chemotherapy study that collected clinical and genomic data as well as patient-derived xenografts to investigate the prioritization offered by PANOPLY. In a chemotherapy-resistant patient-derived xenograft model, we found that that the prioritized drug, olaparib, was more effective than placebo in treating the tumor ( P < .05). We also applied PANOPLY to in-house and publicly accessible multiomics tumor data sets with therapeutic response or survival data available. Conclusion PANOPLY shows promise as a means to prioritize drugs on the basis of clinical and multiomics data for an individual patient with cancer. Additional studies are needed to confirm this approach.