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Published on: April 11, 2016
Batch Effects in Tumor-Only Next-Generation Sequencing Panel Sequencing and Implications for Copy Number Variant
Chung Lee1, Sejoon Lee2, Hyun-Hee Koh1
1Department of Pathology, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
Abstract:
Next-generation sequencing is routinely performed in clinical practice to detect various types of mutations for targeted therapy, diagnosis, and prognosis. Actionable alterations detected by next-generation sequencing include not only nonsynonymous mutations that lead to functional or structural changes of proteins but also copy number variants (CNVs) that affect gene dosage, such as gene copy gains, amplifications, or deletions. Among tumor-only CNV detection methods, the use of a panel of normals for relative comparison has become a common practice, largely because of the lack of matched normal samples. It was therefore hypothesized, once established, a panel of normals and CNV caller may not fully compensate for all experimental variations, such as differences in probe efficiency across reagent lots. To investigate this, 12,104 clinical sequencing data sets from 1454 sequencing batches were analyzed over a 4-year period. This analysis revealed batch-associated fluctuation patterns in gene-level fold changes that could potentially lead to misinterpretation, such as the incorrect classification of gene copy deletions or gains. In this study, a strategy is presented that calculates the median and median absolute deviation of gene-level fold changes across all samples within each sequencing batch and incorporates these measures into the result interpretation. By providing batch-level reference metrics, putative batch-driven artifacts can be identified, reducing false-positive CNV calls and supporting more reliable interpretation in comprehensive genomic profiling.
Insights
Next generation sequencing (NGS) detects mutations using a Panel of Normals (PoN). A new method identifies and reduces batch-associated artifacts in copy number variant (CNV) detection for improved clinical genomic profiling.
Area of Science:
- Genomic Medicine
- Bioinformatics
- Clinical Diagnostics
Background:
- Next generation sequencing (NGS) is crucial for clinical detection of mutations, including copy number variants (CNVs).
- Tumor-only CNV detection often relies on a Panel of Normals (PoN) due to the absence of matched normal samples.
- Experimental variations, such as reagent lot differences, can impact PoN-based CNV analysis accuracy.
Purpose of the Study:
- To investigate batch-associated fluctuations in gene-level fold changes during NGS.
- To develop a strategy for identifying and mitigating batch-driven artifacts in CNV calling.
- To enhance the reliability of comprehensive genomic profiling.
Main Methods:
- Analysis of 12,104 clinical NGS datasets across 1,454 sequencing batches over four years.
- Calculation of median and median absolute deviation of gene-level fold changes within each batch.
- Incorporation of batch-level reference metrics into CNV result interpretation.
Main Results:
- Batch-associated fluctuations in gene-level fold changes were identified, potentially leading to misclassification of CNVs.
- The proposed strategy effectively identified putative batch-driven artifacts.
- Implementation of batch-level metrics reduced false-positive CNV calls.
Conclusions:
- A novel strategy using batch-level metrics improves the accuracy of CNV detection in NGS.
- This approach enhances the reliability of comprehensive genomic profiling by reducing false positives.
- The findings support more confident clinical interpretation of genomic alterations.
