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Related Concept Videos

Cancers Originate from Somatic Mutations in a Single Cell02:21

Cancers Originate from Somatic Mutations in a Single Cell

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Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...
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Cancer02:18

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Cancers arise due to mutations in genes involved in the regulation of cell division, which leads to unrestricted cell proliferation. Modern science and medicine have made great strides in the understanding and treatment of cancer, including eradicating cancer in some patients. However, there is still no cure for cancer. This is largely due to the fact that cancer is a large group of many diseases.
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Comparative Lesions Analysis Through a Targeted Sequencing Approach
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Computational Methods for Characterizing Cancer Mutational Heterogeneity.

Fabio Vandin1

  • 1Department of Information Engineering, University of PadovaPadova, Italy.

Frontiers in Genetics
|June 30, 2017
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Summary
This summary is machine-generated.

Computational methods analyze DNA sequencing data to understand cancer

Keywords:
cancer heterogeneitycancer pathwaysclinical associationmutationsmutual exclusivity

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Area of Science:

  • Genomics and Computational Biology
  • Cancer Research
  • Bioinformatics

Background:

  • Next-generation DNA sequencing reveals extensive somatic mutations in cancer genomes.
  • Cancer exhibits genetic heterogeneity at both inter-tumor (patient-to-patient) and intra-tumor (within-tumor clones) levels.
  • Tumor heterogeneity significantly impacts clinical treatment strategies and outcomes.

Purpose of the Study:

  • To review computational methods for characterizing cancer heterogeneity using DNA sequencing data.
  • To explore methods for assessing the association between tumor heterogeneity and clinical variables.
  • To provide an overview of current computational approaches for analyzing inter- and intra-tumor heterogeneity.

Main Methods:

  • Review of computational algorithms for analyzing somatic alterations from next-generation sequencing data.
  • Focus on methods identifying commonly mutated pathways across patients (inter-tumor heterogeneity).
  • Examination of approaches for characterizing intra-tumor heterogeneity using bulk and single-cell sequencing data.

Main Results:

  • Identification of diverse computational strategies for dissecting cancer's genetic landscape.
  • Highlighting methods that link tumor heterogeneity patterns to clinical outcomes.
  • Summarizing advancements in analyzing both inter- and intra-tumor heterogeneity.

Conclusions:

  • Computational methods are essential for understanding complex cancer heterogeneity.
  • Characterizing tumor heterogeneity aids in personalized medicine and improved clinical decision-making.
  • Further development of computational tools is crucial for advancing cancer genomics research.