4.2 Review

A Portrait of Intratumoral Genomic and Transcriptomic Heterogeneity at Single-Cell Level in Colorectal Cancer

Journal

MEDICINA-LITHUANIA
Volume 57, Issue 11, Pages -

Publisher

MDPI
DOI: 10.3390/medicina57111257

Keywords

colorectal carcinoma; intratumor heterogeneity; single-cell next-generation sequencing; precision medicine

Funding

  1. Fondazione Banco di Sardegna, Italy
  2. Fondo di Beneficenza, Intesa Sanpaolo S.p.A. (Milano, Italy) [B/2020/0094]

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Omics technologies are driving the shift towards precision medicine in cancer research, with the study of intra-tumoral heterogeneity playing a key role in advancing CRC diagnosis and treatment. Single-cell technologies offer a promising approach to identifying cell subpopulations and uncovering new drug targets.
In the study of cancer, omics technologies are supporting the transition from traditional clinical approaches to precision medicine. Intra-tumoral heterogeneity (ITH) is detectable within a single tumor in which cancer cell subpopulations with different genome features coexist in a patient in different tumor areas or may evolve/differ over time. Colorectal carcinoma (CRC) is characterized by heterogeneous features involving genomic, epigenomic, and transcriptomic alterations. The study of ITH is a promising new frontier to lay the foundation towards successful CRC diagnosis and treatment. Genome and transcriptome sequencing together with editing technologies are revolutionizing biomedical research, representing the most promising tools for overcoming unmet clinical and research challenges. Rapid advances in both bulk and single-cell next-generation sequencing (NGS) are identifying primary and metastatic intratumoral genomic and transcriptional heterogeneity. They provide critical insight in the origin and spatiotemporal evolution of genomic clones responsible for early and late therapeutic resistance and relapse. Single-cell technologies can be used to define subpopulations within a known cell type by searching for differential gene expression within the cell population of interest and/or effectively isolating signal from rare cell populations that would not be detectable by other methods. Each single-cell sequencing analysis is driven by clustering of cells based on their differentially expressed genes. Genes that drive clustering can be used as unique markers for a specific cell population. In this review we analyzed, starting from published data, the possible achievement of a transition from clinical CRC research to precision medicine with an emphasis on new single-cell based techniques; at the same time, we focused on all approaches and issues related to this promising technology. This transition might enable noninvasive screening for early diagnosis, individualized prediction of therapeutic response, and discovery of additional novel drug targets.

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