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Generating and Imaging Mouse and Human Epithelial Organoids from Normal and Tumor Mammary Tissue Without Passaging
Published on: November 11, 2022
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DeTiN: overcoming tumor-in-normal contamination.
Amaro Taylor-Weiner1,2, Chip Stewart1, Thomas Giordano3
1Broad Institute of Harvard and MIT, Cambridge, MA, USA.
Nature Methods
|June 27, 2018
Summary
Accurate somatic mutation detection requires comparing tumor and germline samples. Our deTiN method quantifies tumor-in-normal (TiN) contamination, improving variant detection sensitivity in affected samples.
Area of Science:
- Genomics
- Cancer Research
- Bioinformatics
Background:
- Accurate somatic mutation detection is crucial for cancer research and clinical applications.
- Contamination in matched normal samples significantly reduces the sensitivity of somatic variant detection.
Purpose of the Study:
- To develop a computational method to estimate tumor-in-normal (TiN) contamination.
- To improve the sensitivity of somatic variant detection in the presence of TiN contamination.
Main Methods:
- Developed deTiN, a novel bioinformatics tool.
- Implemented a method to estimate TiN contamination levels from sequencing data.
- Incorporated a reclassification strategy for variants initially discarded due to contamination.
Main Results:
- deTiN accurately estimates TiN contamination levels.
- The method successfully improves sensitivity for detecting somatic variants in contaminated samples.
- Reclassification of discarded variants enhances the comprehensive identification of somatic mutations.
Conclusions:
- TiN contamination is a significant challenge in somatic mutation detection.
- deTiN provides a robust solution for estimating contamination and improving variant detection sensitivity.
- This method has the potential to enhance the accuracy of genomic profiling in cancer studies.
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