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A Strategy to Identify de Novo Mutations in Common Disorders such as Autism and Schizophrenia
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A NOVEL AND EFFICIENT ALGORITHM FOR DE NOVO DISCOVERY OF MUTATED DRIVER PATHWAYS IN CANCER

Binghui Liu1,2, Chong Wu2, Xiaotong Shen2

  • 1Northeast Normal University.

The Annals of Applied Statistics
|February 27, 2018
PubMed

Insights

New computational methods identify cancer driver pathways by analyzing mutation data. This approach efficiently discovers mutated pathways, improving our understanding of cancer development and offering new avenues for research.

Area of Science:

  • Computational Biology
  • Genomics
  • Cancer Research

Background:

  • Next-generation sequencing reveals driver mutations in cancer are common but rarely overlap within a single tumor.
  • This suggests single driver mutations can significantly impact entire biological pathways.

Purpose of the Study:

  • To develop novel computational methods for de novo discovery of mutated driver pathways in cancer.
  • To address the limitations of existing algorithms for high-dimensional combinatorial optimization problems in this domain.

Main Methods:

  • Formulated the driver pathway discovery as a non-convex programming and non-convex regularization problem.
  • Developed a new algorithm based on this formulation, offering improved computational efficiency, effectiveness, and scalability.
  • Extended the method for integrated analysis of both mutation and gene expression data.

Main Results:

  • The new algorithm is more efficient and scalable than existing methods like Monte Carlo searching.
  • Applied to The Cancer Genome Atlas (TCGA) data and three cancer datasets, demonstrating promising performance.
  • Successfully discovered de novo mutated driver pathways.

Conclusions:

  • The developed computational approach offers a powerful and efficient tool for identifying cancer driver pathways.
  • The method's scalability and effectiveness make it suitable for large-scale cancer genomics projects.
  • Integration with gene expression data enhances the discovery of complex cancer-related pathways.
Keywords:
DNA sequencingDriver mutationsOptimizationSubset selectionTruncated L1 penalty

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