Next-Generation Sequencing and the Clinical Oncology Workflow

Data Challenges, Proposed Solutions, and a Call to Action

Jake R. Conway; Jeremy L. Warner, MD; Wendy S. Rubinstein, MD, PhD; Robert S. Miller, MD

Disclosures

JCO Precis Oncol. 2019;2019(3) 

In This Article

Abstract and Introduction

Abstract

Purpose: Next-generation sequencing (NGS) of tumor and germline DNA is foundational for precision oncology, with rapidly expanding diagnostic, prognostic, and therapeutic implications. Although few question the importance of NGS in modern oncology care, the process of gathering primary molecular data, integrating it into electronic health records, and optimally using it as part of a clinical workflow remains far from seamless. Numerous challenges persist around data standards and interoperability, and clinicians frequently face difficulties in managing the growing amount of genomic knowledge required to care for patients and keep up to date.

Methods: This review provides a descriptive analysis of genomic data workflows for NGS data in clinical oncology and issues that arise from the inconsistent use of standards for sharing data across systems. Potential solutions are described.

Results: NGS technology, especially for somatic genomics, is well established and widely used in routine patient care, quality measurement, and research. Available genomic knowledge bases play an evolving role in patient management but lack harmonization with one another. Questions about their provenance and timeliness of updating remain. Potentially useful standards for sharing genomic data, such as HL7 FHIR and mCODE, remain primarily in the research and/or development stage. Nonetheless, their impact will likely be seen as uptake increases across care settings and laboratories. The specific use case of ASCO CancerLinQ, as a clinicogenomic database, is discussed.

Conclusion: Because the electronic health records of today seem ill suited for managing genomic data, other solutions are required, including universal data standards and applications that use application programming interfaces, along with a commitment on the part of sequencing laboratories to consistently provide structured genomic data for clinical use.

Introduction

Next-generation sequencing (NGS) of tumor and inherited (germline) genomes has revolutionized and refined cancer treatment during the past two decades and is now vital for evaluating therapeutic opportunities in many solid and hematologic malignancies.[1] Currently, NGS panels including sets of genes are the most widespread method of rapidly identifying sequence variation in patients with cancer. NGS panels provide information for a variety of purposes, including diagnostics (eg, determination of sarcoma subtype), hereditary risk assessment (eg, Lynch syndrome), prognosis (eg, KRAS mutations in lung adenocarcinoma), and treatment selection (eg, biomarkers for immunotherapy responsiveness, such as tumor mutational burden and microsatellite instability; therapeutic selection for clinically actionable alterations, such as BRAF V600E in melanoma; and biomarkers of resistance, such as loss of B2M for immunotherapy).[2,3] As a metric of the significance of NGS in oncology care, 29 of the 43 National Comprehensive Cancer Network clinical practice guidelines denote specific sequence-based biomarkers important for clinical care (Table 1). Of these 29 guidelines for treatment of cancer by site, 12 include both somatic and germline biomarkers, 16 include somatic only, and one includes germline only.

Recently, there has been an increased focus on performing NGS on matched normal samples (either adjacent tissue or blood) to compare with tumor biopsies. The use of paired tumor and normal samples supports improved fidelity of variant calling,[4] increased sensitivity in low-purity tumor samples,[5,6] and unambiguous delineation of germline mutations.[4] The identification of pathogenic germline variation not only supports patient and familial risk management but is also embedded in guidelines and reimbursement for certain treatments (eg, poly [ADP-ribose] polymerase inhibitors for ovarian cancer). This is an example of how clinical practice guidelines[7] and reimbursement[8] can lag behind the evidence.

As the cost of sequencing continues to decrease, the identification of actionable variants improves, and oncogenic pathways become more targetable, whole-exome sequencing and whole-genome sequencing will likely gain more traction over NGS panels.[9] For instance, active mutational processes can now be determined from the mutational spectrum of single-patient samples that have undergone whole-exome sequencing, allowing for more personal dissection of the biology of a tumor.[10] Tumor signatures predicting response to immune checkpoint inhibitors will likely be refined soon, placing pressure on NGS and biomarker data management to support translation into evidence-based clinical care.[11] Sequencing of heterogeneous tumor tissues containing many cell types may eventually be supplemented by single-cell sequencing, and this will magnify data complexity.

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