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Related Concept Videos

Tumor Progression02:07

Tumor Progression

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Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
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Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
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Related Experiment Video

Updated: Jan 18, 2026

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
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TMBquant: an explainable AI-powered caller advancing tumor mutation burden quantification across heterogeneous

Shenjie Wang1,2,3, Xiaonan Wang2,4, Xiaoyan Zhu2,3

  • 1Department of Respiratory Medicine, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 157, Xiwu Road, Xincheng District, Xi'an 710004, China.

Briefings in Bioinformatics
|September 8, 2025
PubMed
Summary

TMBquant, an AI-powered tool, accurately estimates tumor mutation burden (TMB) for improved immunotherapy patient stratification. It outperforms existing methods in diverse cancer types, offering a reliable solution for precision oncology.

Keywords:
counting-based biomarkerheterogeneous samplesimmunotherapytumor mutation burdenvariant calling

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Area of Science:

  • Computational biology
  • Genomics
  • Artificial intelligence in oncology

Background:

  • Accurate tumor mutation burden (TMB) quantification is crucial for predicting immunotherapy response.
  • Existing variant callers exhibit variability, impacting TMB estimation accuracy and clinical utility.
  • Challenges include sequencing platform differences, tumor heterogeneity, and diverse variant calling pipelines.

Purpose of the Study:

  • To develop and validate TMBquant, an explainable AI-powered variant caller for optimized TMB estimation.
  • To enhance TMB quantification accuracy, stability, and reproducibility across diverse datasets.
  • To demonstrate TMBquant's superior performance in patient stratification for immunotherapy.

Main Methods:

  • TMBquant utilizes H2O AutoML for dynamic feature selection and ensemble learning.
  • It integrates variant features and minimizes classification errors for robust TMB estimation.
  • Benchmarking involved comparison with nine established variant callers on 706 whole-exome sequencing tumor-control pairs.

Main Results:

  • TMBquant consistently achieved the highest hazard ratios in survival analyses across NSCLC and NPC cohorts.
  • It demonstrated superior patient stratification compared to all benchmarked methods.
  • Robust performance was observed across high-TMB (NSCLC) and low-TMB (NPC) settings, indicating generalizability.

Conclusions:

  • TMBquant is a reliable, reproducible, and clinically actionable tool for precision oncology.
  • Its AI-driven approach optimizes TMB estimation, enhancing immunotherapy stratification.
  • The open-source software offers a significant advancement in cancer genomics analysis.