Large Germline Databases Needed to Mitigate Ethnic Disparities

Large Germline Databases Needed to Mitigate Ethnic Disparities
Large Germline Databases Needed to Mitigate Ethnic Disparities

CHICAGO — “In the absence of patient-matched germline data, large germline databases are required in analysis workflows to minimize false positive mutation calling and mitigate ethnic disparities,” a study presented at the American Society of Clinical Oncology (ASCO) 2016 Annual Meeting concluded.1

“The expanding appeal of clinical tumor profiling has led to many sequencing strategies, ranging from small gene panels to exomes, with or without patient-matched germline data,” said Eliezer Van Allen, MD, Dana-Farber Cancer Institute, Boston, Massachusetts.

However, this diversity of approaches “may engender uncertainty about benefits and liabilities, particularly in light of reported germline false positives in tumor-only profiling, and emerging immunotherapies that leverage genome-wide data.”

The study modeled common tumor profiling modalities — both large (n = 300 genes) and small (n= 15 or 48 genes) panels — onto clinical whole exomes from 157 patients with lung or colon adenocarcinoma.

“We created a tumor-only analysis algorithm to assess germline false positives (variants erroneously called somatic), impact of patient ethnicity on results, and neoantigen detection,” he said.

Results showed the germline false positive rate with the tumor-only large panel sequencing to be 14% (144/1012 variants).

Among patients whose results underwent molecular pathologist review, 50 of 54 false positives (93%) were correctly interpreted as likely germline events.

“Using dbSNP, increased germline false positives were observed in tumor-only sequencing of ethnic minorities compared to white patients (P < .001),” he reported; however, use of ExAC (60 706 germline exomes) mitigated this disparity (P = .53).

Large panel mutational load correlated with whole exomes mutational load, with average nonsynonymous mutation rates of 5.5/Mb and 8.8/Mb in whole exomes and panels, respectively.

The neoantigen load and individual neoepitopes were more difficult to infer from panels, Dr. Van Allen said.

He concluded by noting that clinical tumor-only analysis was enhanced with use of the largest and most ancestrally diverse database, ExAC, and molecular pathology review, while “mutational load may be inferred from larger panels.”



1. Garofalo A, Sholl LM, Reardon B, et al. Performance of genomic data strategies for cancer precision medicine across distinct contexts and ethnicities. Oral presentation at: ASCO 2016 Annual Meeting; June 3-7, 2016; Chicago, IL

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