Qian, D. Z. | bioRxiv Science

bioRxiv2024

Understanding the development of enzalutamide resistance based on a functional single-cell approach

Most metastatic prostate cancers (PCa) initially depend on androgen for survival and proliferation. Thus, anti-androgen or castration therapies are the mainstay treatment. Although effective at first, androgen-dependent PCa (ADPC) universally develops therapy resistance, thereby evolving to the incurable disease, called castration resistant PCa (CRPC). Currently, mechanisms underlying the emergence of CRPC from ADPC are largely unclear. We used single-cell RNA-sequencing (scRNA-Seq) to determine how a therapy-naive ADPC cell line - LNCaP responds to the anti-androgen drug, enzalutamide. We found that most cells expressed the drug-target androgen receptor (AR+), while a small subpopulation ([~]12%) expressed low or no AR (ARlow/-). Gene set enrichment analysis (GSEA) revealed that AR+ and ARlow/- cells were enriched with significantly different gene expressions and signaling pathways. Unexpectedly, ARlow/- cells displayed robust transcriptional response, including upregulations of genes and pathways involved in clinical CRPC. Next, we isolate ARlow/- and AR+ cells from the LNCaP cell line, and functionally confirmed the enzalutamide resistant phenotype of ARlow/- cells in vitro and in xenograft models in vivo. Finally, to explore a therapeutic option for ARlow/- cells, we found that ARlow/- cells expressed low levels of NAD+ biosynthesis genes, notably NAPRT, indicating a possible vulnerability to inhibitors blocking NAD+ synthesis. Indeed, treating ARlow/- cells with NAD+ synthesis inhibitors, FK866 and OT-82, significantly inhibited the survival and proliferation of ARlow/- cells, thus suggesting a possible novel therapeutic option for ADT and enzalutamide resistant PCa. SUMMARYSingle-cell RNA-Sequencing reveals heterogeneities of tumor cell populations. In most cases, however, the functional significance of the observed heterogeneity is not tested. In this study, we first identified a possible therapy-resistant prostate cancer cell subpopulation with scRNA-Seq, then confirmed the resistant phenotype with single cell and colony - based cloning and functional testing. In addition, we also identified a therapeutic vulnerability of the resistant cells.

cancer biology↗

bioRxiv2020

Phenotype-guided subpopulation identification from single-cell sequencing data

Single-cell sequencing yields novel discoveries by distinguishing cell types, states and lineages within the context of heterogeneous tissues. However, interpreting complex single-cell data from highly heterogeneous cell populations remains challenging. Currently, most existing single-cell data analyses focus on cell type clusters defined by unsupervised clustering methods, which cannot directly link cell clusters with specific biological and clinical phenotypes. Here we present Scissor, a novel approach that utilizes disease phenotypes to identify cell subpopulations from single-cell data that most highly correlate with a given phenotype. This "phenotype-to-cell within a single step" strategy enables the utilization of a large amount of clinical information that has been collected for bulk assays to identify the most highly phenotype-associated cell subpopulations. When applied to a lung cancer single-cell RNA-seq (scRNA-seq) dataset, Scissor identified a subset of cells exhibiting high hypoxia activities, which predicted worse survival outcomes in lung cancer patients. Furthermore, in a melanoma scRNA-seq dataset, Scissor discerned a T cell subpopulation with low PDCD1/CTLA4 and high TCF7 expressions, which is associated with a favorable immunotherapy response. Thus, Scissor provides a novel framework to identify the biologically and clinically relevant cell subpopulations from single-cell assays by leveraging the wealth of phenotypes and bulk-omics datasets.

bioinformatics↗