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Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

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Published on: April 11, 2016

Bootstrap confidence for molecular evolutionary estimates from tumor bulk sequencing data.

Jared Huzar¹, Madelyn Shenoy¹, Maxwell D Sanderford¹

¹Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, United States.

Frontiers in Bioinformatics

|June 1, 2023

Summary

A new bootstrap method enhances cancer clone analysis by providing statistical confidence for variant assignments. This tool improves the reliability of cancer evolution studies and metastasis research.

Keywords:

bootstrap bulk sequencing driver mutation metastasis tumor evolution

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Area of Science:

Computational Biology
Cancer Genomics
Evolutionary Medicine

Background:

Bulk sequencing is standard for analyzing tumor genetic diversity and cancer clone evolution.
Current methods infer cancer clones but lack statistical confidence measures for variant assignments.
Accurate clone identification is crucial for understanding cancer progression and metastasis.

Purpose of the Study:

To introduce a novel bootstrap resampling approach for predicting cancer clones and calculating statistical confidence.
To assess the reliability of clone prediction and downstream inferences, such as mapping metastatic pathways.
To apply the method to empirical metastatic cancer data and quantify confidence in mutation counts during metastasis.

Main Methods:

Developed a bootstrap resampling approach integrating clone prediction with statistical confidence calculation for variant assignments.
Validated the method using computer-simulated datasets to evaluate clone prediction reliability and downstream inference accuracy.
Applied the bootstrap approach to analyze empirical datasets from metastatic cancers.

Main Results:

The bootstrap approach accurately assesses the reliability of predicted cancer clones and downstream inferences.
Analysis revealed that a significant fraction of inferences from real data may lack robust statistical support.
Empirical data analysis showed similar numbers of driver mutations in metastatic events originating from primary versus metastatic tumors, suggesting ongoing mutation evolution.

Conclusions:

The developed bootstrap method provides essential statistical confidence for variant assignments in cancer clone inference.
This approach enhances the reliability of evolutionary analyses and metastasis studies in cancer genomics.
The software implementation (CloneFinderPlus) is available for broader research application.