This lecture, presented by Dr. Ya Fishell, is the third in a series on molecular pathology for surgical pathologists, focusing on the fundamentals of the bioinformatics pipeline used to analyze Next-Generation Sequencing (NGS) data. The presentation systematically explains the process that transforms raw sequencing output into an interpretable list of genetic variants. Key steps covered include base calling (generating FastQ files), alignment against a reference genome (creating BAM files), variant calling (producing VCF files), and annotation.
A significant portion of the lecture is dedicated to the practical application of the Integrative Genomics Viewer (IGV) for visualizing aligned sequencing reads in BAM files. Through several detailed clinical case studies—including colorectal carcinoma, Wilms tumor, and medullary thyroid carcinoma—Dr. Fishell demonstrates how to differentiate true variants from common sequencing artifacts. The cases highlight critical challenges such as pseudogene interference, variants on the anti-sense strand, errors in homopolymer regions, and deamination artifacts in old specimens that can lead to a falsely elevated tumor mutational burden (TMB). The talk emphasizes the necessity of careful visual inspection and data filtering to ensure the accuracy of variants before clinical interpretation.
An Introduction to the Clinical Bioinformatics Pipeline for Pathologists
0:00:44 - What is Bioinformatics?: Dr. Fishell defines bioinformatics as the discipline combining biology, computer science, and mathematics to analyze biological data, particularly DNA and protein sequences. For pathologists, it is essential for interpreting the large datasets produced by Next-Generation Sequencing (NGS).
0:06:21 - The Bioinformatics Pipeline: The lecture outlines the process of turning raw sequencing data into a list of variants. This involves:
Base Calling: Converting raw signals into DNA sequences stored in a FastQ file, which includes quality scores for each base.
Alignment: Arranging the sequenced reads against a reference genome (e.g., GRCH37), resulting in a BAM file.
Variant Calling: Identifying differences between the sample's DNA and the reference, generating a VCF file.
Annotation: Adding functional information (gene name, protein change, population frequency) to the variants.
0:19:51 - Using the Integrative Genomics Viewer (IGV): IGV is a crucial tool for visually inspecting the BAM file to verify variants. It allows pathologists to assess read quality, coverage depth, allele frequency, and check for potential artifacts.
0:39:29 - Case 1: Differentiating True Variants from Artifacts (Colorectal Carcinoma): A case with four variants is used to demonstrate common analysis pitfalls.
NRAS G12D: A true pathogenic variant. The IGV view initially appears incorrect until the reference sequence is flipped to account for the gene being on the anti-sense strand.
MSH6 Deletion: A likely artifact. The small deletion occurs in a homopolymer region (a long string of the same nucleotide), which is a common source of sequencing errors.
CREB3L1 Insertion: A benign polymorphism. Although it appears to be a frameshift, its presence in nearly 100% of the population (and 100% of the reads in the sample) indicates it is a common, non-pathogenic variant.
0:53:49 - Case 2: A Missense Variant Creating Artifacts (Wilms Tumor): An APC gene variant initially reported as a frameshift deletion is shown in IGV to be a missense variant. This true variant creates a new homopolymer sequence, which in turn causes the sequencing process to produce artificial deletion artifacts.
0:58:02 - Case 3: Deamination Artifacts in an Old Sample (Medullary Thyroid Carcinoma): An 8-year-old tissue block produced an extremely high tumor mutational burden (TMB) of 500.
Key takeaway: The vast majority of the mutations were C-to-T changes, characteristic of deamination, a chemical degradation that occurs in old, formalin-fixed tissue. These are artifacts, not true mutations.
By applying a stringent filter to remove these low-frequency artifacts, a single, high-confidence, and clinically actionable RET gene mutation was identified and confirmed.
1:08:21 - Filtering and Final Analysis: The lecture concludes by emphasizing that the annotated variant file is not the final result. It requires careful filtering to remove artifacts and common polymorphisms, leaving only the potentially significant variants for clinical classification and reporting.