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Updated: Aug 7, 2026
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Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
Published on: October 18, 2013
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Parameter estimation from single patient, single time-point sequencing data of recurrent tumors
Kevin Leder1, Ruping Sun2, Zicheng Wang3
1Department of Industrial and Systems Engineering, University of Minnesota, Twin Cities, MN, 55455, USA.
Journal of Mathematical Biology
|October 9, 2024
Summary
We developed new methods to estimate cancer cell population dynamics and clonal diversity after treatment. Our approach uses single-time point sequencing data to understand individual cancer recurrence.
Area of Science:
- Mathematical Biology
- Cancer Research
- Genomics
Background:
- Pharmacological treatments initially reduce drug-sensitive tumor cells.
- Drug-resistant cell populations emerge, leading to cancer recurrence.
- Recurrent tumor samples provide insights into cancer cell adaptation.
Purpose of the Study:
- To develop consistent estimators for tumor cell population dynamics under treatment.
- To quantify clonal diversity metrics at cancer recurrence.
- To enable individual-level understanding of cancer recurrence.
Main Methods:
- Derived large number limit theorems for clonal diversity metrics.
- Constructed estimators based on these theorems.
- Utilized single time-point sequencing data from individual tumors.
Main Results:
- Developed novel, consistent estimators for key cancer dynamics parameters.
- Quantified clonal diversity at cancer recurrence using derived theorems.
- Demonstrated practicality with single-tumor, single-time point data.
Conclusions:
- The developed estimators offer a practical approach to study cancer recurrence.
- Understanding clonal diversity is crucial for analyzing treatment resistance.
- This method facilitates personalized insights into individual cancer evolution.
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