Cairns, Junmei

201 Background: Previous studies have evaluated peripheral whole blood mRNA to identify prognostic biomarkers in metastatic prostate cancer, with no consistent genes of prognostic significance between studies and sample collection done at various points of therapy. We aim to identify the prognostic value of 20 previously identified genes in patients mCRPC from a prospective clinical trial. Methods: Between June 2013 and August 2015, whole blood was prospectively collected in PAXgene RNA tubes from 92 men with mCRPC (NCT01953640). Gene expression from samples with RIN > 4.0 were quantified using a customized NanoString CodeSet of 20 candidate genes (C1QA, MCM2, STOM, GABARAPL2, R10K3, HMBS, TFDP1, CDKN1A, ITGAL, TMCC2, TERF2IP, MMP9, SNCA, TIMP1, SLC4A1, SELENBP1, TMEM66, SEMA4D, PROS1, CD22). The raw data from the nCounter Analysis system were normalized to the positive controls and log2 transformed. Univariable Cox proportional hazards models were performed on each target gene sufficiently above negative control (5 times the maximum value of the negative control) to evaluate associations with overall survival (OS). For each gene we considered a linear, quadratic, or categorical model with quantile cutpoints. Categorical models with cutpoints at the 25th percentile for the genes CD22, GABARAPL2, R10K3, and STOM were used as there was a nonlinear relationship between gene expression and death hazard. The remaining genes were modeled with a linear model. Findings were considered statistically significant based on p-values adjusted using the Holm-Bonferroni correction. Results: 18/20 genes were identified and passed quality control (QC) measures, which excluded C1QA and MCM2. 72/92 men had an evaluable pretreatment specimen that passed QC. The median age was 72 years. The study cohort was evenly balanced in terms of Gleason score ( < = 7 v. > 7) and 60% had high volume disease. The median follow-up time was 5.73 years (IQR: 5.39, 6.49) and 58 patients had died. Of the 18 genes evaluated, STOM (Stomatin) [hazard ratio (HR) 3.00, adjusted p = 0.007] and GABARAPL2 (GABA Type A Receptor Associated Protein Like 2) [HR 2.62, adjusted p = 0.033] were significantly associated with overall survival in univariate analysis. In multivariate analysis, after adjusting for volume of metastatic disease and Gleason score, STOM [HR 2.89, adjusted p = 0.014] was significantly associated with overall survival. Conclusions: Elevated whole blood mRNA expression of STOM is adversely associated with OS in mCRPC. Further studies investigating elucidating the molecular significance of STOM in mCRPC are planned. Clinical trial information: NCT01953640 .

Abstract Prediction of the response of cancer patients to different treatments and identification of biomarkers of drug response are two major goals of individualized medicine. Here, we developed a deep learning framework called TINDL, completely trained on preclinical cancer cell lines (CCLs), to predict the response of cancer patients to different treatments. TINDL utilizes a tissue-informed normalization to account for the tissue type and cancer type of the tumors and to reduce the statistical discrepancies between CCLs and patient tumors. Moreover, by making the deep learning black box interpretable, this model identifies a small set of genes whose expression levels are predictive of drug response in the trained model, enabling identification of biomarkers of drug response. Using data from two large databases of CCLs and cancer tumors, we showed that this model can distinguish between sensitive and resistant tumors for 10 (out of 14) drugs, outperforming various other machine learning models. In addition, our small interfering RNA (siRNA) knockdown experiments on 10 genes identified by this model for one of the drugs (tamoxifen) confirmed that tamoxifen sensitivity is substantially influenced by all of these genes in MCF7 cells, and seven of these genes in T47D cells. Furthermore, genes implicated for multiple drugs pointed to shared mechanism of action among drugs and suggested several important signaling pathways. In summary, this study provides a powerful deep learning framework for prediction of drug response and identification of biomarkers of drug response in cancer. The code can be accessed at https://github.com/ddhostallero/tindl.

Abstract Our previous matched case–control study of postmenopausal women with resected early-stage breast cancer revealed that only anastrozole, but not exemestane or letrozole, showed a significant association between the 6-month estrogen concentrations and risk of breast cancer. Anastrozole, but not exemestane or letrozole, is a ligand for estrogen receptor α. The mechanisms of endocrine resistance are heterogenous and with the new mechanism of anastrozole, we have found that treatment of anastrozole maintains fatty acid synthase (FASN) protein level by limiting the ubiquitin-mediated FASN degradation, leading to increased breast cancer cell growth. Mechanistically, anastrozole decreases the guided entry of tail-anchored proteins factor 4 (GET4) expression, resulting in decreased BCL2-associated athanogene cochaperone 6 (BAG6) complex activity, which in turn, prevents RNF126-mediated degradation of FASN. Increased FASN protein level can induce a negative feedback loop mediated by the MAPK pathway. High levels of FASN are associated with poor outcome only in patients with anastrozole-treated breast cancer, but not in patients treated with exemestane or letrozole. Repressing FASN causes regression of breast cancer cell growth. The anastrozole-FASN signaling pathway is eminently targetable in endocrine-resistant breast cancer.

Aromatase inhibitors (AIs) are the treatment of choice for hormone receptor–positive early breast cancer in postmenopausal women. None of the third‐generation AIs are superior to the others in terms of efficacy. We attempted to identify genetic factors that could differentiate between the effectiveness of adjuvant anastrozole and exemestane by examining single‐nucleotide polymorphism (SNP)‐treatment interaction in 4,465 patients. A group of SNPs were found to be differentially associated between anastrozole and exemestane regarding outcomes. However, they showed no association with outcome in the combined analysis. We followed up common SNPs near LY75 and GPR160 that could differentiate anastrozole from exemestane efficacy. LY75 and GPR160 participate in epithelial‐to‐mesenchymal transition and growth pathways, in both cases with SNP‐dependent variation in regulation. Collectively, these studies identified SNPs that differentiate the efficacy of anastrozole and exemestane and they suggest additional genetic biomarkers for possible use in selecting an AI for a given patient.

Abstract Cell division cycle 25 (CDC25) dual specificity phosphatases positively regulate the cell cycle by activating cyclin-dependent kinase/cyclin complexes. Here, we demonstrate that in addition to its role in cell cycle regulation, CDC25B functions as a regulator of protein phosphatase 2A (PP2A), a major cellular Ser/Thr phosphatase, through its direct interaction with PP2A catalytic subunit. Importantly, CDC25B alters the regulation of AMP-activated protein kinase signaling (AMPK) by PP2A, increasing AMPK activity by inhibiting PP2A to dephosphorylate AMPK. CDC25B depletion leads to metformin resistance by inhibiting metformin-induced AMPK activation. Furthermore, dual inhibition of CDC25B and PP2A further inhibits growth of 3D organoids isolated from patient derived xenograft model of breast cancer compared to CDC25B inhibition alone. Our study identifies CDC25B as a regulator of PP2A, and uncovers a mechanism of controlling the activity of a key energy metabolism marker, AMPK.

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 &lt;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 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.

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.