Single-Cell Exome Sequencing Reveals Single-Nucleotide Mutation Characteristics of a Kidney Tumor
Menée sur un échantillon tumoral et un échantillon de tissu sain adjacent prélevés sur un patient atteint d'un carcinome rénal à cellules claires, cette étude de séquençage des exons à l'échelle d'une seule cellule met en évidence l'hétérogénéité de la population cellulaire de la tumeur
Clear cell renal cell carcinoma (ccRCC) is the most common kidney cancer and has very few mutations that are shared between different patients. To better understand the intratumoral genetics underlying mutations of ccRCC, we carried out single-cell exome sequencing on a ccRCC tumor and its adjacent kidney tissue. Our data indicate that this tumor was unlikely to have resulted from mutations in VHL and PBRM1. Quantitative population genetic analysis indicates that the tumor did not contain any significant clonal subpopulations and also showed that mutations that had different allele frequencies within the population also had different mutation spectrums. Analyses of these data allowed us to delineate a detailed intratumoral genetic landscape at a single-cell level. Our pilot study demonstrates that ccRCC may be more genetically complex than previously thought and provides information that can lead to new ways to investigate individual tumors, with the aim of developing more effective cellular targeted therapies. º We present the genetic landscape of 25 single cells from a ccRCC patient º No significant subpopulation of tumor cells could be observed within this tumor º Different types of genetic lesion occurred depending on frequency of mutation º Recurrent genes in patient population do not predict mutations in an individual tumor Whole-exome sequencing of 25 tumor and somatic cells from a single patient with clear cell renal cell carcinoma (ccRCC) reveals the heterogeneity of cancer cells in a tumor that does not exhibit driver mutations in the two most commonly mutated genes in ccRCC. This analysis points to the importance of understanding the mutation landscape in individual patients to make informed treatment decisions and may pave the way to discovery of new driver mutations.