Unveiling Novel Double-Negative Prostate Cancer Subtypes Through Single-Cell RNA Sequencing Analysis

Abstract

Androgen deprivation therapy has improved patient survival. Nevertheless, treatment resistance inevitably emerges due to the complex interplay of tumor heterogeneity and lineage plasticity. We integrated scRNAseq data from billions of cells, including both public cohorts and data generated in our laboratory, and generated the HuPSA (Human Prostate Single cell Atlas) and MoPSA (Mouse Prostate Single cell Atlas) datasets.Through unsupervised clustering and manually annotation, both atlases not only validated previously known prostate adenocarcinoma (AdPCa), neuroendocrine prostate cancer (NEPCa), stromal, and immune cell populations but also unearthed the less described populations including MMP7+ normal prostate club cells and two novel lineage plastic cancerous populations, namely progenitor-like and KRT7+ cells. Immunostaining experiments confirmed the presence of these populations in both human and mouse tissues, solidifying their significance in PCa biology. To unravel the drivers of these distinct cell populations, we calculated the upstream regulators of the genes enriched in these cells. Furthermore, leveraging the power of state-of-the-art bioinformatics analyses, we scrutinized over one thousand human PCa bulk RNAseq samples. Employing HuPSA-based deconvolution, we reclassified these samples into different molecular subtypes, including the newly discovered KRT7 and progenitor-like groups. The HuPSA and MoPSA provide invaluable blueprints for analyzing and interpreting external PCa single-cell RNAseq datasets. Our data elucidates the roadmap of PCa progression, showcasing the development of heterogeneous populations through lineage plasticity. This understanding holds promise for guiding the development of precise medicine in PCa field.

The expression of PKM1 and PKM2 in developing, benign, and cancerous prostatic tissues

“…RNAseq data in the CTPC collection were analyzed to assess the expression of PKM isoforms using STAR-Salmon based transcript level quantification method [58][59][60][61] . The raw Fastq files were downloaded from NCBI SRA database.…”