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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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Related Experiment Video

Updated: Dec 12, 2025

Simple and Rapid Method to Obtain High-quality Tumor DNA from Clinical-pathological Specimens Using Touch Imprint Cytology
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Predicting Cancer Tissue-of-Origin by a Machine Learning Method Using DNA Somatic Mutation Data.

Xiaojun Liu1, Lianxing Li2, Lihong Peng1

  • 1School of Computer Science, Hunan University of Technology, Zhuzhou, China.

Frontiers in Genetics
|August 8, 2020
PubMed
Summary

Identifying cancer

Keywords:
machine learningpatients with carcinoma of unknown primaryrandom forestsomatic mutationtissue of origin

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Area of Science:

  • Genomics
  • Oncology
  • Bioinformatics

Background:

  • Carcinoma of unknown primary (CUP) constitutes 3-5% of all cancer diagnoses.
  • Accurate identification of the primary tumor site is crucial for effective cancer treatment but remains challenging.
  • Molecular profiling offers a promising avenue for determining tumor tissue of origin.

Purpose of the Study:

  • To assess the efficacy of using somatic mutations in identifying the tissue of origin for metastatic cancers.
  • To develop a computational method for cancer tissue-of-origin inference.

Main Methods:

  • Somatic mutation data from the International Cancer Genome Consortium were analyzed.
  • A random forest algorithm was employed for feature extraction.
  • A logistic regression classifier was built using mutations from 300 cell adhesion-related genes.

Main Results:

  • The developed classifier achieved 81% prediction accuracy for cancer tissue of origin.
  • The model was validated on 3,374 cancer samples across 13 distinct cancer types using 10-fold cross-validation.
  • Somatic mutations in genes related to cell-to-cell adhesion were found to be significant predictors.

Conclusions:

  • Somatic mutation analysis provides a robust method for inferring cancer tissue of origin.
  • This approach can aid in the diagnosis and treatment of cancers with unknown primary sites.
  • The findings support the integration of molecular profiling into clinical cancer diagnostics.