Weinshilboum, Richard M.

Abstract Background: In early stage, ERα-positive breast cancer, the concurrent use of endocrine therapy and chemotherapy has not been shown to be superior to sequential use. We hypothesized that genetic biomarkers can aid in selecting patients who would benefit from chemo-endocrine therapy. Our previous genome-wide association study (GWAS) and functional studies revealed that a single nucleotide polymorphism (SNP) in ZNF423, rs9940645, determines tamoxifen response. Specifically, 4-hydroxytamoxifen (4-OH-TAM) increases the gene expression of ZNF423, a transcription factor for BRCA1, in cells that are carry the rs9940645 variant, but not the wildtype, genotype. In this study, we set out to identify additional genes regulated by ZNF423, which, upon identifying a relationship between ZNF423 and cell cycle genes, led us to investigate taxane response in a SNP- and tamoxifen-dependent fashion. Method: Gene correlation analysis using The Cancer Genome Atlas (TCGA) breast cancer dataset was used to identify genes with expression correlated with that of ZNF423. Quantitative reverse transcription PCR, chromatin immunoprecipitation, and luciferase reporter assays were used to validate the gene associations. To test ZNF423 rs9940645-specific phenotypes, we used ZR75-1 cells, which are homozygous for the variant SNP, and ZR75-1 cells that were generated to be homozygous wild type (WT) using CRISPR-Cas9 technology, in addition to Hs578T breast cells with ERα overexpression (WT) and HCC1500 cells (variant). Cell cycle was assessed by propidium iodide staining. Cytotoxicity was assessed using a colorimetric assay. Results: Mitosis-related genes VRK1 and PBK, which encode histone H3 kinases, were experimentally validated to be regulated by ZNF423. Specifically, ZNF423 knockdown resulted in decreased VRK1 and PBK expression and activity. Additionally, ZNF423 knockdown resulted in enhanced docetaxel-induced G2/M arrest and cytotoxicity which could be rescued by VRK1 or PBK overexpression. Lastly, breast cancer cells carrying the rs9940645 variant SNP genotype had increased G2-M arrest and decreased cell viability when treated with docetaxel in combination with estradiol and 4-OH-TAM. Conclusion: We identified ZNF423regulated genes involved in the G2/M phase of the cell cycle. We found that 4-OH-TAM sensitized ERα-positive breast cancer cells to docetaxel treatment in a ZNF423 SNP-dependent manner. Our findings suggest that patients with rs9940645 variant genotype may benefit from concurrent tamoxifen endocrine therapy and docetaxel chemotherapy. This would impact a substantial proportion of patients given the minor allele frequency of 0.47 for this SNP. Citation Format: Gen Wang, Sisi QIN, Jacqueline Zayas, James N. Ingle, Mohan Liu, Richard Weinshilboum, Kunwei Shen, Liewei Wang. 4-Hydroxytamoxifen enhances sensitivity of estrogen receptor α-positive breast cancer to docetaxel in an estrogen and ZNF423 SNP-dependent fashion [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2940.

e16559 Background: ARVs that develop early during treatment with abiraterone acetate/ prednisone (AA/P) may play a role in treatment resistance. We evaluated metastatic site and whole blood mRNA expression of full-length AR (AR-FL) and ARVs to characterize treatment-emergent ARVs (TE-ARVs) from men with mCRPC pre- and post- 12 weeks of AA/P collected in a prospective clinical trial (NCT#01953640). Methods: RNAseq performed on paired metastatic site biopsy (n = 40) and whole blood (n = 25) passed quality control. Reads were aligned to the GRCh38 reference genome with the spliced-alignment TopHat2 package. AR-FL or ARVs were detected if ≥ 2 splice reads aligned to unique splice junctions for AR-FL, AR-8, AR-45, AR-23, AR-V3, AR-V5V6, AR-V7, AR-V8, AR-V9, AR-V10, AR-V12, AR-V13, and AR-V14, and normalized to splice reads per million (SRPM). Cox proportional hazard regression analysis was performed on AR-FL and AR-Vs with ≥1 SRPM for association with time to treatment change (TTTC). Results: In metastatic site biopsies post-AA/P, the average number of splice reads was 27,376,541 (range 7,753,998 to 62,456,773). The median number of ARVs detected was 2 (range 0-8), with a total of total of 110 ARVs identified (Table). Dynamic shifts in ARV profiles were observed post-AAP, with 41 TE-ARVs identified in 17 unique patients. The most common TE-ARV was AR-8 (n = 8), followed by AR-45 (n = 5), AR-23 (n = 5), and AR-V7 (n = 4). The presence of AR-V7 post-AAP was adversely associated with TTTC (hazard ratio 2.46, p = 0.013). The identification of early TE- ARV was not associated with TTTC. In whole blood samples, post-AA/P detection of AR-FL was low (n = 3) and no ARVs were detected. Conclusions: No specific patterns were observed in ARV profiles obtained in the metastatic biopsies after 12 weeks of treatment with AA/P. Clinical trial information: 01953640. [Table: see text]

We set out to determine whether machine learning–based algorithms that included functionally validated pharmacogenomic biomarkers joined with clinical measures could predict selective serotonin reuptake inhibitor (SSRI) remission/response in patients with major depressive disorder (MDD). We studied 1,030 white outpatients with MDD treated with citalopram/escitalopram in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN‐AMPS; n = 398), Sequenced Treatment Alternatives to Relieve Depression (STAR*D; n = 467), and International SSRI Pharmacogenomics Consortium (ISPC; n = 165) trials. A genomewide association study for PGRN‐AMPS plasma metabolites associated with SSRI response (serotonin) and baseline MDD severity (kynurenine) identified single nucleotide polymorphisms (SNPs) in DEFB1, ERICH3, AHR, and TSPAN5 that we tested as predictors. Supervised machine‐learning methods trained using SNPs and total baseline depression scores predicted remission and response at 8 weeks with area under the receiver operating curve (AUC) > 0.7 (P < 0.04) in PGRN‐AMPS patients, with comparable prediction accuracies > 69% (P ≤ 0.07) in STAR*D and ISPC. These results demonstrate that machine learning can achieve accurate and, importantly, replicable prediction of SSRI therapy response using total baseline depression severity combined with pharmacogenomic biomarkers.

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.