Abstract
Abstract
Background
Accurate clinical structural variant (SV) calling is essential for cancer target identification and diagnosis but has been historically challenging due to the lack of ground truth for clinical specimens. Meanwhile, reduced clinical-testing cost is the key to the widespread clinical utility.
Methods
We analyzed massive data from tumor samples of 476 patients and developed a computational framework for accurate and cost-effective detection of clinically-relevant SVs. In addition, standard materials and classical experiments including immunohistochemistry and/or fluorescence in situ hybridization were used to validate the developed computational framework.
Results
We systematically evaluated the common algorithms for SV detection and established an expert-reviewed SV call set of 1,303 tumor-specific SVs with high-evidence levels. Moreover, we developed a random-forest-based decision model to improve the true positive of SVs. To independently validate the tailored ‘two-step’ strategy, we utilized standard materials and classical experiments. The accuracy of the model was over 90% (92–99.78%) for all types of data.
Conclusion
Our study provides a valuable resource and an actionable guide to improve cancer-specific SV detection accuracy and clinical applicability.
Funder
Scientific Research Project of Cancer Foundation of China
Clinical Research Fund of Wu Jieping Medical Foundation
CSCO-Shiyao Tumor Research Fund
CSCO-Junshi Tumor Immunology Research Fund
Publisher
Springer Science and Business Media LLC
Subject
General Biochemistry, Genetics and Molecular Biology,General Medicine