Tumor heterogeneity assessment using single-cell RNA sequencing (scRNA-seq): applications in lung cancer for diagnosis and treatment

TL;DR

Single-cell RNA sequencing helps understand lung cancer by revealing tumor diversity and guiding better diagnosis and treatment.

Contribution

This paper reviews how scRNA-seq uncovers tumor heterogeneity and identifies therapeutic targets in lung cancer.

Findings

scRNA-seq reveals cellular heterogeneity and rare cell populations in tumors.

It identifies interactions between tumor cells and the tumor microenvironment.

scRNA-seq provides molecular indicators for diagnosis and treatment response prediction in NSCLC.

Abstract

Recent progress in single-cell RNA sequencing has led to mechanistic and clinically actionable insight into genetic heterogeneity and tumor progression via transcriptome profiling at single-cell level, applied to identification of cell types, gene expression patterns, and signaling pathways involved in cancer development. In this work, we review the use of single-cell RNA sequencing (scRNA-seq) applications to gain insights into tumor molecular and cellular characteristics, such as cellular heterogeneity, rare cell populations, characteristic pathogenic cell populations, cells of the immune tumor microenvironment, and information regarding clonal evolution, none of which can be observed using bulk RNA-seq. We describe how this set of methods facilitates a better understanding of tumor heterogeneity, interactions between the tumor cells and the cells of the tumor microenvironment (TME), and can elucidate potential therapeutic targets. From the applied clinical perspective, we summarise the ability of scRNA-seq data to identify molecular indicators for diagnosis, outcome, and prediction of response to therapy. This is particularly relevant due to the low response rate to therapy of non-small cell lung cancer (NSCLC) and acquired resistance, including in immunotherapy.

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