The impact of tumor profiling approaches and genomic data strategies for cancer precision medicine

Abstract

BackgroundThe diversity of clinical tumor profiling approaches (small panels to whole exomes with matched or unmatched germline analysis) may engender uncertainty about their benefits and liabilities, particularly in light of reported germline false positives in tumor-only profiling and use of global mutational and/or neoantigen data. The goal of this study was to determine the impact of genomic analysis strategies on error rates and data interpretation across contexts and ancestries.MethodsWe modeled common tumor profiling modalities—large (n = 300 genes), medium (n = 48 genes), and small (n = 15 genes) panels—using clinical whole exomes (WES) from 157 patients with lung or colon adenocarcinoma. We created a tumor-only analysis algorithm to assess germline false positive rates, the impact of patient ancestry on tumor-only results, and neoantigen detection.ResultsAfter optimizing a germline filtering strategy, the germline false positive rate with tumor-only large panel sequencing was 14 % (144/1012 variants). For patients whose tumor-only results underwent molecular pathologist review (n = 91), 50/54 (93 %) false positives were correctly interpreted as uncertain variants. Increased germline false positives were observed in tumor-only sequencing of non-European compared with European ancestry patients (p < 0.001; Fisher’s exact) when basic germline filtering approaches were used; however, the ExAC database (60,706 germline exomes) mitigated this disparity (p = 0.53). Matched and unmatched large panel mutational load correlated with WES mutational load (r2 = 0.99 and 0.93, respectively; p < 0.001). Neoantigen load also correlated (r2 = 0.80; p < 0.001), though WES identified a broader spectrum of neoantigens. Small panels did not predict mutational or neoantigen load.ConclusionsLarge tumor-only targeted panels are sufficient for most somatic variant identification and mutational load prediction if paired with expanded germline analysis strategies and molecular pathologist review. Paired germline sequencing reduced overall false positive mutation calls and WES provided the most neoantigens. Without patient-matched germline data, large germline databases are needed to minimize false positive mutation calling and mitigate ethnic disparities.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-016-0333-9) contains supplementary material, which is available to authorized users.

Integrating cancer genomic data into electronic health records

“…WES and WGS will likely be most useful for determining factors that may indicate response to immunotherapy, such as predicted formation of neoantigens [48]. While these techniques also offer a highly accurate measure of mutational burden, it has recently been shown that NGS panels may suffice for this [49, 50]. Tarczy-Hornoch et al [51] have surveyed potential methods for properly integrating WES and WGS information within EHRs.…”

Technological considerations for genome-guided diagnosis and management of cancer

“…Recent work has shown that mutational load and neoantigen load might be more useful biomarkers of response than specific driver gene mutations [47]. Similarly, the determination of mutational load and neoantigen expression is more predictive when whole exome data are used compared to large or small gene panels [48]. …”

“…Some groups do not use matched normal material. In order to minimize false-positive calls, these groups either focus on calling previously characterized driver events in known oncogenes (in the case of hotspot panels), or use advanced filtering methods—unmatched normal, PoN, large germline databases (for example, 1000 Genomes, ExAc)—to remove non-somatic variants [48]. Specificity can be further increased by review of candidate mutations by an experienced molecular pathologist and cross-referencing somatic mutation databases such as COSMIC for pathogenicity information [48].…”

Delivering on the promise of precision cancer medicine

“…Furthermore, although the molecular landscapes of many cancers have been revealed using precision oncology approaches, many of the alterations observed in patients remain undruggable, and viable targets are incompletely characterized. In addition, a considerable amount of diversity exists regarding the types of molecular tests being offered clinically [8]; combined with the knowledge gap regarding the interpretation of genomic test results within the field of clinical oncology, this diversity has fostered confusion among physicians about the meaning and the clinical utility of genomic data at the point of care [9]. Perhaps most critically, prospective trials of molecular profiling across cancers in an unselected manner are not yet mature.…”