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Several factors can increase the risk of cancer in an individual. About 50% of cancer cases can be prevented by adopting a healthy lifestyle, regular exercise, eating healthy, and following a modest cancer prevention diet. Epidemiological studies have consistently shown that populations with vegetable and fruit-rich diets have reduced the incidence of cancer. On the other hand, populations who have a diet rich in animal fat, red meat, junk food, or high calories are predisposed to cancer.
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Cancer driver mutations: predictions and reality.

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  • 1Department of Pathology and Molecular Medicine, Queen's University, Kingston, ON, Canada.

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|April 19, 2023
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Identifying cancer driver mutations is challenging due to tumor heterogeneity. Computational methods show success in finding novel cancer biomarkers, including in circulating tumor DNA (ctDNA), aiding clinical research.

Keywords:
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Area of Science:

  • Oncology
  • Genetics
  • Bioinformatics

Background:

  • Cancer cells accumulate numerous genetic alterations, but only a subset, known as driver mutations, actively promote tumor progression.
  • Driver mutations exhibit significant variability across cancer types and individuals, potentially remaining dormant or acting synergistically with other mutations.

Purpose of the Study:

  • To review recent advancements in identifying cancer driver mutations and characterizing their functional impacts.
  • To highlight the utility of computational approaches in discovering novel cancer biomarkers, particularly within circulating tumor DNA (ctDNA).

Main Methods:

  • Review of recent literature on computational methods for driver mutation identification.
  • Analysis of the application of these methods in cancer research and clinical settings.

Main Results:

  • Computational methods have proven effective in predicting driver mutations.
  • These methods have led to the discovery of new cancer biomarkers, including those detectable in ctDNA.
  • The review also addresses the limitations of these computational tools in clinical research.

Conclusions:

  • Despite challenges posed by tumor heterogeneity, computational strategies are crucial for identifying cancer driver mutations.
  • The application of these methods, especially in ctDNA analysis, offers promising avenues for biomarker discovery and clinical research.