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Related Concept Videos

Cancer02:18

Cancer

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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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Cancer-Critical Genes II: Tumor Suppressor Genes01:05

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Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
When the function of certain critical genes, especially those involved in cell cycle regulation and cell growth signaling cascades, gets disrupted, it upsets the cell cycle progression. Such cells with unchecked cell cycles start proliferating uncontrollably and eventually develop into tumors.
Such genes that act...
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Cancer-Critical Genes I: Proto-oncogenes01:33

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Cancers Originate from Somatic Mutations in a Single Cell02:21

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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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Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors
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Cancer Driver Log (CanDL): Catalog of Potentially Actionable Cancer Mutations.

Senthilkumar Damodaran1, Jharna Miya2, Esko Kautto2

  • 1Division of Medical Oncology, Department of Internal Medicine, The Ohio State University, Columbus, Ohio.

The Journal of Molecular Diagnostics : JMD
|September 1, 2015
PubMed
Summary

A new database, the Cancer Driver Log (CanDL), aids molecular pathologists by curating functionally validated cancer driver mutations. This resource facilitates accurate annotation of genomic testing, improving cancer diagnostics and potential therapeutic targeting.

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Area of Science:

  • Genomics
  • Cancer Biology
  • Bioinformatics

Background:

  • Genomic alterations in tumors are increasingly characterized using massively parallel sequencing.
  • Identifying driver mutations is crucial for targeted cancer therapy, but distinguishing them from passenger mutations is challenging.
  • Existing in silico tools lack comprehensive functional characterization data for cancer driver mutations.

Purpose of the Study:

  • To create a comprehensive, expert-curated database of functionally characterized cancer driver mutations.
  • To provide molecular pathologists with a resource to facilitate the annotation of cancer genomic testing.
  • To support the identification of actionable mutations for targeted cancer therapies.

Main Methods:

  • Systematic review of scientific literature to identify functionally validated driver mutations.
  • Extraction of variant data, including chromosome location and amino acid changes.
  • Compilation of data into the Cancer Driver Log (CanDL) database with literature references.
  • Development of a user-friendly interface and data download options (CSV).

Main Results:

  • The Cancer Driver Log (CanDL) database was established, containing expert-curated, functionally characterized driver mutations.
  • The database includes detailed variant information and literature references supporting functional driver evidence.
  • CanDL offers a simple interface for data access and allows for bulk or selective gene downloads.

Conclusions:

  • The Cancer Driver Log (CanDL) database provides a valuable, freely accessible resource for molecular pathology.
  • This database aids in the rapid and accurate annotation of cancer genomic testing by identifying actionable driver mutations.
  • CanDL supports the advancement of precision oncology by facilitating the interpretation of cancer genomic data.