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Published on: July 25, 2020
Simulated annealing based algorithm for identifying mutated driver pathways in cancer
Hai-Tao Li1, Yu-Lang Zhang2, Chun-Hou Zheng3
1College of Information and Communication Technology, Qufu Normal University, Rizhao 276826, China.
Abstract:
With the development of next-generation DNA sequencing technologies, large-scale cancer genomics projects can be implemented to help researchers to identify driver genes, driver mutations, and driver pathways, which promote cancer proliferation in large numbers of cancer patients. Hence, one of the remaining challenges is to distinguish functional mutations vital for cancer development, and filter out the unfunctional and random "passenger mutations." In this study, we introduce a modified method to solve the so-called maximum weight submatrix problem which is used to identify mutated driver pathways in cancer. The problem is based on two combinatorial properties, that is, coverage and exclusivity. Particularly, we enhance an integrative model which combines gene mutation and expression data. The experimental results on simulated data show that, compared with the other methods, our method is more efficient. Finally, we apply the proposed method on two real biological datasets. The results show that our proposed method is also applicable in real practice.
Insights
This study presents a new computational method to identify cancer driver pathways by distinguishing functional mutations from passenger mutations using gene expression and mutation data. The enhanced approach proves efficient and applicable to real biological datasets.
Area of Science:
- Computational biology
- Genomics
- Cancer research
Background:
- Next-generation sequencing enables large-scale cancer genomics.
- Distinguishing driver mutations from passenger mutations is a key challenge in cancer research.
Purpose of the Study:
- To introduce a modified method for the maximum weight submatrix problem to identify cancer driver pathways.
- To enhance an integrative model combining gene mutation and expression data.
Main Methods:
- Modified maximum weight submatrix algorithm.
- Integration of gene mutation and expression data.
- Evaluation on simulated and real biological datasets.
Main Results:
- The proposed method demonstrates higher efficiency compared to existing approaches on simulated data.
- The method is successfully applied to real biological datasets, showing practical applicability.
Conclusions:
- The developed method effectively identifies cancer driver pathways.
- This approach aids in distinguishing functional mutations crucial for cancer development.
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