Citations

Abstract Purpose: We hypothesized that integrated analysis of cancer types from different lineages would reveal novel molecularly defined subgroups with unique therapeutic vulnerabilities. On the basis of the molecular similarities between subgroups of breast and ovarian cancers, we analyzed these cancers as a single cohort to test our hypothesis. Experimental Design: Identification of transcriptional subgroups of cancers and drug sensitivity analyses were performed using mined data. Cell line sensitivity to Hsp90 inhibitors (Hsp90i) was tested in vitro. The ability of a transcriptional signature to predict Hsp90i sensitivity was validated using cell lines, and cell line- and patient-derived xenograft (PDX) models. Mechanisms of Hsp90i sensitivity were uncovered using immunoblot and RNAi. Results: Transcriptomic analyses of breast and ovarian cancer cell lines uncovered two mixed subgroups comprised primarily of triple-negative breast and multiple ovarian cancer subtypes. Drug sensitivity analyses revealed that cells of one mixed subgroup are significantly more sensitive to Hsp90i compared with cells from all other cancer lineages evaluated. A gene expression classifier was generated that predicted Hsp90i sensitivity in vitro, and in cell line- and PDXs. Cells from the Hsp90i-sensitive subgroup underwent apoptosis mediated by Hsp90i-induced upregulation of the proapoptotic proteins Bim and PUMA. Conclusions: Our findings identify Hsp90i as a potential therapeutic strategy for a transcriptionally defined subgroup of ovarian and breast cancers. This study demonstrates that gene expression profiles may be useful to identify therapeutic vulnerabilities in tumor types with limited targetable genetic alterations, and to identify molecularly definable cancer subgroups that transcend lineage.

Abstract Motivation Estimation of isoform-level gene expression from RNA-seq data depends on simplifying assumptions, such as uniform read distribution, that are easily violated in real data. Such violations typically lead to biased estimates. Most existing methods provide bias correction step(s), which is based on biological considerations—such as GC content—and applied in single samples separately. The main problem is that not all biases are known. Results We have developed a novel method called XAEM based on a more flexible and robust statistical model. Existing methods are essentially based on a linear model Xβ, where the design matrix X is known and is computed based on the simplifying assumptions. In contrast XAEM considers Xβ as a bilinear model with both X and β unknown. Joint estimation of X and β is made possible by a simultaneous analysis of multi-sample RNA-seq data. Compared to existing methods, XAEM automatically performs empirical correction of potentially unknown biases. We use an alternating expectation-maximization (AEM) algorithm, alternating between estimation of X and β. For speed XAEM utilizes quasi-mapping for read alignment, thus leading to a fast algorithm. Overall XAEM performs favorably compared to recent advanced methods. For simulated datasets, XAEM obtains higher accuracy for multiple-isoform genes. In a differential-expression analysis of a real single-cell RNA-seq dataset, XAEM achieves substantially better rediscovery rates in independent validation sets. Availability and implementation The method and pipeline are implemented as a tool and freely available for use at http://fafner.meb.ki.se/biostatwiki/xaem/. Supplementary information Supplementary data are available at Bioinformatics online.

Abstract Molecular and cellular diversity in cancer cells enables evolutionary processes that govern tumor progression and contributes to treatment failures, but the mechanisms governing this variability remain incompletely understood. We previously described tumor-specific epigenetic alterations in DNA devoid of canonical CpG methylation sites throughout the genomes of spontaneous breast tumors which arise in genetically uniform, yet heterozygous, [FVB X BALB/c MMTV-NeuT+] F1 mice. Those changes were detected in purified tumor DNA using a template-assisted quantitative ligation assay employing locus-specific and SNP-specific oligonucleotide genotyping probes, and the assay was subsequently shown to be sensitive to epigenetic modifications of the templates. We now describe losses of heterozygosity similarly distributed throughout the transcriptomes of tumors in this same model. We quantified expressed alleles across a population of clonal tumors and tested each expressed variant for the occurrence of allelic ratio outliers. The results revealed variegated patterns of expression in hundreds of genes, highly significantly enriched in pathways typically co-opted by tumors (Molecular Mechanisms of Cancer, P < 10-10, IPA analysis). An astounding 87% (2634 of 3044) of expressed genes that could be measured using this approach were represented in at least one tumor. Furthermore, the frequency of these outliers in any one individual tumor was found to correlate strongly with the transcriptional repression of an additional large set of non-polymorphic genes. These findings reveal underappreciated, latent mechanisms driving sporadic errors in epigenetic programming that promote repression of a multiple cis-linked transcripts. This genome-wide molecular chaos presents as dysregulated cellular homeostasis and is the foundation for heritable diversity as tumors evolve. Citation Format: Sara J. Felts, Xiaojia Tang, Virginia P. Van Keulen, Krishna R. Kalari, Larry R. Pease. Stochastic variation in gene expression is selected during clonal evolution of spontaneous mouse mammary tumors [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 2898.

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.