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Characterization of cancer-driving nucleotides (CDNs) across genes, cancer types, and patients

Authors :
Lingjie Zhang
Tong Deng
Zhongqi Liufu
Xiangnyu Chen
Shijie Wu
Xueyu Liu
Changhao Shi
Bingjie Chen
Zheng Hu
Qichun Cai
Chenli Liu
Mengfeng Li
Miles E Tracy
Xuemei Lu
Chung-I Wu
Hai-Jun Wen
Source :
eLife, Vol 13 (2024)
Publication Year :
2024
Publisher :
eLife Sciences Publications Ltd, 2024.

Abstract

A central goal of cancer genomics is to identify, in each patient, all the cancer-driving mutations. Among them, point mutations are referred to as cancer-driving nucleotides (CDNs), which recur in cancers. The companion study shows that the probability of i recurrent hits in n patients would decrease exponentially with i; hence, any mutation with i ≥ 3 hits in The Cancer Genome Atlas (TCGA) database is a high-probability CDN. This study characterizes the 50–150 CDNs identifiable for each cancer type of TCGA (while anticipating 10 times more undiscovered ones) as follows: (i) CDNs tend to code for amino acids of divergent chemical properties. (ii) At the genic level, far more CDNs (more than fivefold) fall on noncanonical than canonical cancer-driving genes (CDGs). Most undiscovered CDNs are expected to be on unknown CDGs. (iii) CDNs tend to be more widely shared among cancer types than canonical CDGs, mainly because of the higher resolution at the nucleotide than the whole-gene level. (iv) Most important, among the 50–100 coding region mutations carried by a cancer patient, 5–8 CDNs are expected but only 0–2 CDNs have been identified at present. This low level of identification has hampered functional test and gene-targeted therapy. We show that, by expanding the sample size to 105, most CDNs can be identified. Full CDN identification will then facilitate the design of patient-specific targeting against multiple CDN-harboring genes.

Details

Language :
English
ISSN :
2050084X
Volume :
13
Database :
Directory of Open Access Journals
Journal :
eLife
Publication Type :
Academic Journal
Accession number :
edsdoj.277e2022dba457ba1aa1ec3bbb6ae22
Document Type :
article
Full Text :
https://doi.org/10.7554/eLife.99341