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Dhanasekaran, S. M.

Publications and source records attributed to Dhanasekaran, S. M..

3 recordsLinked to original sources

A harmonized single-cell RNA-seq atlas of human localized and metastatic prostate cancers and benign tissues

Single-cell RNA sequencing (scRNA-seq) effectively captures the differences in transcriptomic landscape of cell types and cell states between benign and cancer tissues. Pooling publicly available datasets distributed across independent studies enables increased sample representation and cross-study comparisons. Here we present a harmonized scRNA-seq atlas of the human prostate constructed by integrating 17 available studies, comprising 163 samples from 106 donors. The dataset contains benign tissue, primary tumors, and metastatic disease profiles. Raw sequencing FASTQ data files were uniformly reprocessed to minimize technical variability. Study metadata were curated and standardized using a unified schema capturing donor identity, tissue site, disease context, and histologic grade. Post quality control, the integrated dataset contains 754,000 high-quality cells. Harmonized cell type annotations were generated using a pseudobulk correlation framework informed by multiple reference resources. The workflow identified 17 distinct cell types representing epithelial, mesenchymal, and immune compartments of the prostate. The processed expression matrices, standardized metadata, and analysis workflows are publicly available to support reproducible analysis and enable exploration of heterogeneity across prostate disease states.

cancer biology↗

Cellular cartography reveals mouse prostate organization and determinants of castration resistance

Inadequate response to androgen deprivation therapy (ADT) frequently arises in prostate cancer, driven by cellular mechanisms that remain poorly understood. Here, we integrated single-cell RNA sequencing, single-cell multiomics, and spatial transcriptomics to define the transcriptional, epigenetic, and spatial basis of cell identity and castration response in the mouse prostate. Leveraging these data along with a meta-analysis of human prostates and prostate cancer, we identified cellular orthologs and key determinants of ADT response and resistance. Our findings reveal that mouse prostates harbor lobe-specific luminal epithelial cell types distinguished by unique gene regulatory modules and anatomically defined androgen-responsive transcriptional programs, indicative of divergent developmental origins. Androgen-insensitive, stem-like epithelial populations - resembling human club and hillock cells - are notably enriched in the urethra and ventral prostate but are rare in other lobes. Within the ventral prostate, we also uncovered two additional androgen-responsive luminal epithelial cell types, marked by Pbsn or Spink1 expression, which align with human luminal subsets and may define the origin of distinct prostate cancer subtypes. Castration profoundly reshaped luminal epithelial transcriptomes, with castration-resistant luminal epithelial cells activating stress-responsive and stemness programs. These transcriptional signatures are enriched in tumor cells from ADT-treated and castration-resistant prostate cancer patients, underscoring their likely role in driving treatment resistance. Collectively, our comprehensive cellular atlas of the mouse prostate illuminates the importance of lobe-specific contexts for prostate cancer modeling and reveals potential therapeutic targets to counter castration resistance. Significance StatementAndrogen deprivation therapy is a mainstay in prostate cancer treatment, yet many patients eventually develop castration-resistant disease--a lethal progression driven by poorly understood cellular mechanisms. Our study provides a comprehensive cellular map of the prostate, identifying key determinants of normal organization and castration-induced remodeling. By pinpointing the cell types and molecular programs that confer ADT responsiveness or resistance, our findings offer new directions for prostate cancer modeling and pave the way toward novel therapeutic strategies aimed at enhancing ADT efficacy and preventing the emergence of castration-resistant prostate cancer.

cancer biology↗

ProTrack: An Interactive Multi-Omics Data Browser for Proteogenomic Studies

The Clinical Proteomic Tumor Analysis Consortium (CPTAC) initiative has generated extensive multi-omics data resources of deep proteogenomic profiles for multiple cancer types. To enable the broader community of biological and medical researchers to intuitively query, explore, and download data and analysis results from various CPTAC projects, we built a prototype user-friendly web application called "ProTrack" with the CPTAC clear cell renal cell carcinoma (ccRCC) data set (http://ccrcc.cptac-data-view.org). Here we describe the salient features of this application which provides a dynamic, comprehensive, and granular visualization of the rich proteogenomic data. Statement of SignificanceThe CPTAC initiative (https://proteomics.cancer.gov/) has generated multi-omics data for multiple cancer types to understand the proteogenomic aberrations of these malignancies. Collectively this effort has so far produced a large data resource for the research community, including high-throughput profiles for proteome, phosphoproteome, whole exome, whole genome, transcriptome, and DNA methylome. To make this valuable data-resource useful to the larger research community, there is a pressing need for development of user-friendly, readily accessible, and easily shared analytic and visualization tools for aligning multi-omics data and exploring alterations in key cancer genes, to drive and support new biological hypotheses. To bridge this gap, we have developed CPTAC ProTrack, an interactive web application which uses a multilayered, client-server architecture in order to deliver an interactive web experience to any user of a modern web-browser. This tool is intentionally designed accessible for researchers, biologists, and clinicians who are interested in multi-omic data without any need to code.

bioinformatics↗