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Related Experiment Video

Updated: Mar 2, 2026

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

Progression inference for somatic mutations in cancer.

Leif E Peterson^1,2,3,4,5, Tatiana Kovyrshina^1,6

¹Center for Biostatistics, Houston Methodist Research Institute, Houston, TX 77030, USA.

|May 12, 2017

Summary

Computational methods reveal the evolutionary order of genomic alterations in common cancers. Understanding these cancer gene mutation pathways is crucial for advancing precision medicine and personalized cancer treatments.

Keywords:

Cancer research Computational biology Genetics Oncology

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Last Updated: Mar 2, 2026

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

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Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors

Published on: September 20, 2016

Area of Science:

Computational biology
Genomics
Cancer research

Background:

Genomic alterations drive cancer development.
Understanding the sequence of these alterations is key to cancer progression.

Purpose of the Study:

To infer the evolutionary trajectories of genomic alterations in common cancers.
To identify the order and selectivity relationships among driver gene alterations.

Main Methods:

Utilized The Cancer Genome Atlas (TCGA) cross-sectional data.
Employed computational methods to analyze gene-specific alterations (mutations, deletions, amplifications, etc.) in top driver genes.
Computed evolutionary pathways and selectivity relationships.

Main Results:

Identified hierarchical relationships among genomic alterations in driver genes.
TP53, PIK3CA, ERBB2, APC, KRAS, EGFR, IDH1, and VHL were frequently involved in these hierarchies.
Established progression inference for genomic alterations.

Conclusions:

The study provides insights into the order of genomic alterations in cancer.
This research supports the growing importance of understanding genomic landscapes for personalized medicine.
Advancements in next-generation sequencing and whole-genome scans will further enhance cancer diagnosis and treatment.