Kalari, Krishna R.

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]

Abstract Motivation Both single-cell RNA sequencing (scRNA-seq) and DNA sequencing (scDNA-seq) have been applied for cell-level genomic profiling. For mutation profiling, the latter seems more natural. However, the task is highly challenging due to the limited input materials from only two copies of DNA molecules, while whole-genome amplification generates biases and other technical noises. ScRNA-seq starts with a higher input amount, so generally has better data quality. There exists various methods for mutation detection from DNA sequencing, it is not clear whether these methods work for scRNA-seq data. Results Mutation detection methods developed for either bulk-cell sequencing data or scDNA-seq data do not work well for the scRNA-seq data, as they produce substantial numbers of false positives. We develop a novel and robust statistical method—called SCmut—to identify specific cells that harbor mutations discovered in bulk-cell data. Statistically SCmut controls the false positives using the 2D local false discovery rate method. We apply SCmut to several scRNA-seq datasets. In scRNA-seq breast cancer datasets SCmut identifies a number of highly confident cell-level mutations that are recurrent in many cells and consistent in different samples. In a scRNA-seq glioblastoma dataset, we discover a recurrent cell-level mutation in the PDGFRA gene that is highly correlated with a well-known in-frame deletion in the gene. To conclude, this study contributes a novel method to discover cell-level mutation information from scRNA-seq that can facilitate investigation of cell-to-cell heterogeneity. Availability and implementation The source codes and bioinformatics pipeline of SCmut are available at https://github.com/nghiavtr/SCmut. Supplementary information Supplementary data are available at Bioinformatics online.

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.

533 Background: Current guidelines define TNBC as complete absence of estrogen (ER) and progesterone receptor (PR), without HER2 amplification. However, the prognostic impact of clinical and histopathological factors, sTILs, and AdjCT in TNBC meeting these strict criteria is unknown. Methods: From a cohort of 9985 women who underwent upfront surgery for M0 breast cancer (BC) at Mayo Clinic Rochester from 1985-2012, 1159 pts with ER negative or low (≤10%) BC were identified for central ER/PR/HER2 staining and HER2 FISH (IHC2+ only) to select those with TNBC by modern definitions. Cox proportional hazards models were used to assess the impact of clinicopathological variables on invasive disease-free (IDFS) and overall survival (OS). Results: Tumors from 605 pts (median age 56.3 yrs) met criteria for TNBC (ER < 1%, PR < 1% and HER2 0, 1 or 2+ and FISH negative). 51% were T1, 65% N0, 88% grade 3, and 75% had Ki67 > 15%. Histologically, 39% were anaplastic, 26% invasive ductal, 16% medullary, 8% metaplastic, 6% apocrine and 5% others. Median sTILs was 20% (0-90%). 55% pts received AdjCT [21% anthracycline (A), 19% A + taxane, and 15% other]. Median follow-up for IDFS and OS were 7.4 and 10.6 yrs, respectively. Multivariate analyses demonstrated that higher N stage (p < 0.01), lower sTILs (p = 0.01) and no AdjCT (p < 0.01) were independently associated with worse IDFS and OS. Histology (medullary subtype) was associated with better IDFS in univariate (HR 0.56, 95% CI, 0.35-0.89) but not in multivariate analyses, once sTILs were accounted for. Among systemically untreated pts (n = 182), higher N (p < 0.01) and lower sTILs (p = 0.04) were associated with worse IDFS. For systemically untreated T1N0 pts (n = 111), the 5-yr IDFS was 70% (95% CI, 61-81) [T1a: 83% (95% CI, 63-100), T1b: 68% (95% CI, 52-88) and T1c: 67% (95% CI, 55-83)], compared to 78% (95% CI, 68-84) for T1N0 pts treated with AdjCT. Conclusions: In TNBC pts, N stage, sTILs and receipt of AdjCT were independently prognostic for IDFS and OS. sTILs remained prognostic for IDFS in systemically untreated TNBC. In N0 TNBC, the risk of recurrence or death was substantial in the absence of chemotherapy, even for those with T1 tumors.