Pangenome References Improve Biomarker Estimation from Tumor Sequencing Data

Abstract It has recently been shown that patients from non-European ancestries are at a higher risk of inappropriate clinical intervention because of inaccurate biomarker estimation, arising from the reference bias inherent in standard methods for determining the tumor genome from sequencing data. Here we demonstrate that these inaccuracies can be reduced by using a pangenome reference appropriate for the patient’s population. We constructed a novel secondary analysis workflow where the pangenome reference serves as a scaffold for mapping the sequencing reads, and is also included in the relevant ‘panel of normals’ needed to discriminate between germline and somatic mutations in tumor-only sequencing. This approach detects known somatic mutations in tumor-only sequencing more accurately than the standard GATK somatic calling workflow, prevalent in diagnostic settings for analysis of sequencing data from tumor-only assays, on a standard benchmark tumor sample, HCC1395 (33% relative increase in F1 score). We also assessed the expected clinical impact of our approach by comparing the Tumor Mutational Burden (TMB) calculated from missense somatic mutations called in tumor/normal samples from 6 patients self-reported as belonging to African, 1 to Asian and 3 to European populations respectively. We find that the TMB values calculated from the tumor-only sequencing data analyzed by our workflow more closely approximate the TMB values calculated from the tumor-normal analysis of the same sample, being 35% higher on average, whereas GATK tumor-only analysis generates TMB values 56% higher on average than the tumor-normal analysis of the same sample. Tumor-normal TMB values calculated by the two methods do not vary as drastically, GATK generated values being 13% higher on average, indicating that GATK tumor-only analysis leads to significant overestimation of TMB values, which can be largely corrected by using our workflow when tumor-normal sequencing is not available. These results indicate that pangenome based analysis has the potential to become the new standard for unbiased processing of somatic sequencing samples, following on from its increased adoption for germline sequencing analysis.