Pan-cancer analysis of intratumor heterogeneity associated with patient prognosis using multidimensional measures

Chie Kikutake, Minako Yoshihara, Tetsuya Sato, Daisuke Saito, Mikita Suyama

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Human cancers accumulate various mutations during development and consist of highly heterogeneous cell populations. This phenomenon is called intratumor heterogeneity (ITH). ITH is known to be involved in tumor growth, progression, invasion, and metastasis, presenting obstacles to accurate diagnoses and effective treatments. Numerous studies have explored the dynamics of ITH, including constructions of phylogenetic trees in cancer samples using multiregional ultradeep sequencing and simulations of evolution using statistical models. Although ITH is associated with prognosis, it is still challenging to use the characteristics of ITH as prognostic factors because of difficulties in quantifying ITH precisely. In this study, we analyzed the relationship between patient prognosis and the distribution of variant allele frequencies (VAFs) in cancer samples (n = 6,064) across 16 cancer types registered in The Cancer Genome Atlas. To measure VAF distributions multidimensionally, we adopted parameters that define the shape of VAF distributions and evaluated the relationships between these parameters and prognosis. In seven cancer types, we found significant relationships between prognosis and VAF distributions. Moreover, we observed that samples with a larger amount of mutations were not necessarily linked to worse prognosis. By evaluating the ITH from multidimensional viewpoints, it will be possible to provide a more accurate prediction of cancer prognosis.

Original languageEnglish
Pages (from-to)37689-37699
Number of pages11
JournalOncotarget
Volume9
Issue number102
DOIs
Publication statusPublished - Dec 1 2018

All Science Journal Classification (ASJC) codes

  • Oncology

Fingerprint Dive into the research topics of 'Pan-cancer analysis of intratumor heterogeneity associated with patient prognosis using multidimensional measures'. Together they form a unique fingerprint.

  • Cite this