Assessing matched normal and tumor pairs in next-generation sequencing studies

Area of Science:

• Genomics
• Cancer Research
• Bioinformatics

Background:

• Next-generation sequencing (NGS) transformed cancer studies by enabling identification of germline and somatic mutations using matched normal-tumor pairs.
• Current analytical pipelines for NGS data involve rigorous quality checks but implicitly assume accurate matching of normal and tumor samples.
• This assumption is critical, as germline and rare somatic variant identification relies on the correct pairing of normal and tumor samples.

Purpose of the Study:

• To introduce a quantitative method for assessing the matchedness of tumor-normal pairs in sequencing data.
• To highlight the implications of assuming pair matching without explicit quality control.
• To propose integrating a relatedness metric into standard bioinformatics quality protocols for cancer sequencing analysis.

Main Methods:

• Applied a genetics-based concept for measuring individual relatedness to quantify the sequence similarity between tumor-normal pairs.
• Compared sequence similarity between genuinely matched pairs and non-matched pairs.
• Evaluated the utility of this relatedness metric for assessing the mutation landscape.

Main Results:

• Demonstrated that the matchedness of tumor-normal pairs can be reliably quantified.
• Showed that even with cancer-induced mutations, matched pairs exhibit higher sequence similarity than non-matched pairs.
• Validated the approach's capability to assess inter-individual mutation landscapes.

Conclusions:

• The implicit assumption of matched pairs in cancer sequencing analysis lacks a robust quality check.
• Quantifying sequence relatedness between tumor-normal pairs should be a standard quality control step.
• This method enhances the reliability of identifying germline and somatic mutations and understanding cancer's mutation landscape.