Somatic Mutations in Individual Prostate Cancer Cells from scRNA-seq Datasets

Abstract

Conventional RNA sequencing has not been efficient in detecting all cancer-related genes and mutations. Instead, an expanding corpus of research has found single-cell RNA sequencing (scRNA) to be more effective in characterizing the transcriptional profiles in different cells, and thus in exploring the heterogeneous nature of tumors and the evolutionary lineage of uncommon subpopulations. As virtually all tumors are genetically heterogeneous, i.e., containing cells with different genotypes and different sets of somatic mutations, the mutations often determine distinct cellular phenotypes, including tumor growth and response to treatment. Prostate cancer (PCa) is a disease that involves multiple cells and heterogeneous tumors including fibroblasts, epithelial cells, muscle, and immune cells. Therefore, genomic and proteomic studies are needed for the early detection and confirmation of prostate cancer. We conducted this study to explore mutations in non-reproductive somatic cells and their role in prostate cancer. We performed the genetic analysis of somatic cells to analyze prostate cancer mutagenesis. The integration of genetic variation and gene-expression from single-cell RNA- sequencing helped to identify Single Nucleotide Variants (SNVs) affecting the expression of their harboring gene. It was found that calling variants from individual alignments was an effective way to analyze somatic mutations, which led to the identification of high number of novel somatic mutation. Quantitative two-dimensional maps based on gene expression were mostly used to determine somatic mutations specific for cell clusters. Overall, our study shows a large potential of SNV calls from the datasets of individual cell scRNA-seq and highlights the importance and need of variant or mutation detecting tools and methods at a cellular level. Observing the increasing concentration of scRNA- seq datasets, mutation assessments conducted at the cellular level helps us to better understand the extent of heterogeneity of cells and the interaction between genetic mutations and their relevant functional phenotypes. It has also been found that mutation assessments at a cellular level from scRNA-seq can provide us with such information regarding cancer that it can help us to understand and explain the evolution and progression of somatic mutations.