Publications

The androgen receptor (AR) is a validated therapeutic target for prostate cancer and has been a focus for drug development for more than six decades. Currently approved therapies that inhibit AR signaling, such as enzalutamide, rely solely on targeting the AR ligand-binding domain and, therefore, have limited efficacy on prostate cancer cells that express truncated, constitutively active AR splice variants (AR-Vs). The LNCaP95 cell line is a human prostate cancer cell line that expresses both functional full-length AR and AR-V7. LNCaP95 is a heterogeneous cell population that is resistant to enzalutamide, with its proliferation dependent on transcriptionally active AR-V7. The purpose of this study was to identify a LNCaP95 clone that would be useful for evaluating therapies for their effectiveness against enzalutamide-resistant prostate cancer cells. Seven clones from the LNCaP95 cell line were isolated and characterized using morphology, in vitro growth rate, and response to ralaniten (AR N-terminal domain inhibitor) and enzalutamide (antiandrogen). In vivo growth of the clones as subcutaneous xenografts was evaluated in castrated immunodeficient mice. All of the clones maintained the expression of full-length AR and AR-V7. Cell proliferation of the clones was insensitive to androgen and enzalutamide but importantly was inhibited by ralaniten, which is consistent with AR-Vs driving the proliferation of parental LNCaP95 cells. In castrated immunodeficient animals, the growth of subcutaneous xenografts of the D3 clone was the most reproducible compared to the parental cell line and other clones. These data support that the enzalutamide-resistant LNCaP95-D3 subline may be suitable as a xenograft tumor model for preclinical drug development with improved reproducibility.

EGFR T790M testing is the standard of care for activating EGFR mutation (EGFRm) non-small cell lung cancer (NSCLC) progressing on 1st/2nd generation TKIs to select patients for osimertinib. Despite sensitive assays, detection of circulating tumour deoxyribonucleic acid (ctDNA) is variable and influenced by clinical factors. The number and location of sites of progressive disease at time of testing were reviewed to explore the effect on EGFR ctDNA detection. The prognostic value of EGFR ctDNA detection on survival outcomes was assessed.

The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts.

We present Epiclomal, a probabilistic clustering method arising from a hierarchical mixture model to simultaneously cluster sparse single-cell DNA methylation data and impute missing values. Using synthetic and published single-cell CpG datasets, we show that Epiclomal outperforms non-probabilistic methods and can handle the inherent missing data characteristic that dominates single-cell CpG genome sequences. Using newly generated single-cell 5mCpG sequencing data, we show that Epiclomal discovers sub-clonal methylation patterns in aneuploid tumour genomes, thus defining epiclones that can match or transcend copy number-determined clonal lineages and opening up an important form of clonal analysis in cancer. Epiclomal is written in R and Python and is available at https://github.com/shahcompbio/Epiclomal.

Sex differences have been observed in multiple facets of cancer epidemiology, treatment and biology, and in most cancers outside the sex organs. Efforts to link these clinical differences to specific molecular features have focused on somatic mutations within the coding regions of the genome. Here we report a pan-cancer analysis of sex differences in whole genomes of 1983 tumours of 28 subtypes as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We both confirm the results of exome studies, and also uncover previously undescribed sex differences. These include sex-biases in coding and non-coding cancer drivers, mutation prevalence and strikingly, in mutational signatures related to underlying mutational processes. These results underline the pervasiveness of molecular sex differences and strengthen the call for increased consideration of sex in molecular cancer research.

Aim: We examined methylation changes in cell-free DNA (cfDNA) in metastatic castration-resistant prostate cancer (mCRPC) during treatment. Patients & methods: Genome-wide methylation analysis of sequentially collected cfDNA samples derived from mCRPC patients undergoing androgen-targeting therapy was performed. Results: Alterations in methylation states of genes previously implicated in prostate cancer progression were observed and patients that maintained methylation changes throughout therapy tended to have a longer time to clinical progression. Importantly, we also report that markers associated with a highly aggressive form of the disease, neuroendocrine-CRPC, were associated with a faster time to clinical progression. Conclusion: Our findings highlight the potential of monitoring the cfDNA methylome during therapy in mCRPC, which may serve as predictive markers of response to androgen-targeting agents.

Despite the rapid advance in single-cell RNA sequencing (scRNA-seq) technologies within the last decade, single-cell transcriptome analysis workflows have primarily used gene expression data while isoform sequence analysis at the single-cell level still remains fairly limited. Detection and discovery of isoforms in single cells is difficult because of the inherent technical shortcomings of scRNA-seq data, and existing transcriptome assembly methods are mainly designed for bulk RNA samples. To address this challenge, we developed RNA-Bloom, an assembly algorithm that leverages the rich information content aggregated from multiple single-cell transcriptomes to reconstruct cell-specific isoforms. Assembly with RNA-Bloom can be either reference-guided or reference-free, thus enabling unbiased discovery of novel isoforms or foreign transcripts. We compared both assembly strategies of RNA-Bloom against five state-of-the-art reference-free and reference-based transcriptome assembly methods. In our benchmarks on a simulated 384-cell data set, reference-free RNA-Bloom reconstructed 37.9%-38.3% more isoforms than the best reference-free assembler, whereas reference-guided RNA-Bloom reconstructed 4.1%-11.6% more isoforms than reference-based assemblers. When applied to a real 3840-cell data set consisting of more than 4 billion reads, RNA-Bloom reconstructed 9.7%-25.0% more isoforms than the best competing reference-based and reference-free approaches evaluated. We expect RNA-Bloom to boost the utility of scRNA-seq data beyond gene expression analysis, expanding what is informatically accessible now.