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Updated: May 23, 2025
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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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The application of compressed sensing on tumor mutation burden calculation from overlapped pooling sequencing data
Yue Cui1, Yi Qiao1, Rongming An1,2
1State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China.
BMC Bioinformatics
|May 20, 2025
Summary
This study introduces a cost-effective method for calculating Tumor Mutational Burden (TMB) using compressed sensing on pooled sequencing data. This approach significantly reduces sequencing costs while maintaining high accuracy in detecting mutations for cancer immunotherapy prediction.
Area of Science:
- Genomics
- Bioinformatics
- Cancer Research
Background:
- Tumor Mutational Burden (TMB) is a key biomarker for predicting cancer immunotherapy response.
- Current methods for TMB assessment are labor-intensive and costly due to extensive sequencing library preparation.
- Accurate TMB profiling is crucial for personalized cancer treatment strategies.
Purpose of the Study:
- To develop a cost-effective method for TMB calculation using pooled sequencing data.
- To reduce the financial and labor burden associated with TMB screening.
- To evaluate the feasibility and accuracy of compressed sensing for TMB analysis.
Main Methods:
- Employed compressed sensing (CS) techniques to analyze overlapped pooled sequencing data.
- Utilized orthogonal matching pursuit (OMP) and basic pursuit (BP) algorithms for TMB reconstruction.
- Assessed the performance and accuracy of CS-based TMB calculation from pooled samples.
Main Results:
- Over 90% of single nucleotide polymorphisms (SNPs) were detectable in pooled data from ten samples with minimal information loss.
- The basic pursuit (BP) algorithm demonstrated consistent performance for TMB reconstruction.
- Achieved a 40% reduction in sequencing costs by reducing sequencing runs to 0.6 times the total number of samples.
Conclusions:
- Compressed sensing enables accurate TMB calculation from overlapped pooled sequencing data, significantly reducing costs.
- SNP calling from pooled sequencing data of up to ten samples is feasible.
- The study validates the utility of CS strategies for efficient and economical TMB assessment in cancer research.
