Published December 17, 2024
| Version v1
Journal article
Open
The theory of massively repeated evolution and full identifications of cancer-driving nucleotides (CDNs)
Creators
- 1. Sun Yat-sen University
- 2. Chinese Academy of Sciences
- 3. University of Chicago
Description
Tumorigenesis, like most complex genetic traits, is driven by the joint actions of many mutations. At the nucleotide level, such mutations are cancer-driving nucleotides (CDNs). The full sets of CDNs are necessary, and perhaps even sufficient, for the understanding and treatment of each cancer patient. Currently, only a small fraction of CDNs is known as most mutations accrued in tumors are not drivers. We now develop the theory of CDNs on the basis that cancer evolution is massively repeated in millions of individuals. Hence, any advantageous mutation should recur frequently and, conversely, any mutation that does not is either a passenger or deleterious mutation. In the TCGA cancer database (sample size n=300–1000), point mutations may recur in i out of n patients. This study explores a wide range of mutation characteristics to determine the limit of recurrences (i*) driven solely by neutral evolution. Since no neutral mutation can reach i*=3, all mutations recurring at i≥3 are CDNs. The theory shows the feasibility of identifying almost all CDNs if n increases to 100,000 for each cancer type. At present, only <10% of CDNs have been identified. When the full sets of CDNs are identified, the evolutionary mechanism of tumorigenesis in each case can be known and, importantly, gene targeted therapy will be far more effective in treatment and robust against drug resistance.
Data availability
The key scripts used in this study are available at GitLab, copy archived at Zhang, 2024. A subset of key example files for breast cancer analysis can be found in the "/example_data_files" directory. The complete list of CDNs analyzed in this study is provided in Supplementary file 1.Files
elife-99340-v1.pdf
Files
(3.8 MB)
| Name | Size | Download all |
|---|---|---|
|
Article md5:07b95d9f6aeb2f4947f40aa4268633e7 |
3.2 MB | Preview Download |
|
md5:9d8b7731b9ff02bdc31552ab63f3508a
|
560.7 kB | Preview Download |
Additional details
Identifiers
- DOI
- 10.7554/eLife.99340.3
- Other
- oai:uchicago.tind.io:14285
Funding
- National Natural Science Foundation of China
- 32150006
- Guangdong Key R&D Project of China
- 2022B1111030001
- National Natural Science Foundation of China
- 32293193
- National Natural Science Foundation of China
- 32293190
- Yunnan Revitalization Talent Support Program Top Team
- 202405AS350022
- National Natural Science Foundation of China
- 82341092
- National Natural Science Foundation of China
- 32200493
- National Key Research and Development Program of China
- 2021YFC2301300
- National Key Research and Development Program of China
- 2021YFC0863400
- Yunnan Revitalization Talent Support Program Yunling Scholar Project
- National Natural Science Foundation of China
- 32370659
- Guangdong Basic and Applied Basic Research Foundation
- 2023A1515010016