Expanding the Precision Medicine Toolkit With Circulating Tumor DNA

Circulating tumor DNA (ctDNA) is a powerful emerging technology that detects and quantifies fragments of cell-free, tumor-derived DNA and is a tumor-specific biomarker across a variety of cancers.¹ In this issue of JCO Oncology Practice, Huet and Salles² review data that support the diverse potential of ctDNA across lymphomas, with emphasis toward guiding clinical decision making. We agree that ctDNA is a versatile analyte that may transform current paradigms of lymphoma detection and response assessment.³ The enthusiasm is fueled by technical advances in detection capability and processing speed of next-generation sequencing–based assays along with enhanced bioinformatics pipelines. Furthermore, ctDNA directly im-proves upon the fundamental imprecision of tissue biopsies and imaging scans. To reach its potential, the collection and interpretation of ctDNA requires technical standardization and harmonization.⁴ Clinical validation studies are ongoing to confirm early seminal observations, but the clinical utility of ctDNA remains unproven. Clinical practitioners should not only familiarize themselves with the principles of ctDNA but also recognize that the understanding of lymphoma biology is evolving and that ctDNA detection methods continue to broaden and optimize.⁵ Serial sampling of ctDNA may be the most efficient method to gather critical data for precision medicine, but the information required will evolve and be shaped by the clinical situation.

The most pressing need for ctDNA as an emerging field is the development of technical standardization and harmonization across laboratories. Collection and specimen handling procedures should minimize DNA contamination and fragmentation. Specialized collection tubes that reduce DNA degradation should be used. Appropriate specimen handling and avoidance of processing delays must work in real time for the clinician. Once these goals are met, the following clinical applications can be addressed:

1. Establishing accurate diagnosis and prognostication

2. Monitoring tumor responsiveness

3. Determining depth of response and post-treatment monitoring

4. Selecting individual agents (ie, precision medicine).

Accurate diagnosis of lymphoma is complex and incorporates specific genetic information that affects clinical decision making. Tissue biopsies have inherent sampling errors, while liquid biopsies capture and integrate genetic information shed from all disease sites. Indeed, analysis of ctDNA detects somatic mutations not identified in tissue biopsies. The Cancer Personalized Profiling by Deep Sequencing method detects lymphoma-relevant single nucleotide variants, insertions/deletions, and breakpoints in BCL2, BCL6, and MYC and can accurately determine the cell-of-origin phenotype of diffuse large B-cell lymphoma (DLBCL).⁶ Recent studies have demonstrated that genetic subtypes of DLBCL exist within and across cell-of-origin phenotypes.⁷ The oncogenic driver mutations that define these genetic subtypes of DLBCL may be most easily accessed by ctDNA. Quantitative baseline levels of ctDNA may also be prognostic. For lymphomas with diverse clinical behavior, such as follicular lymphoma and mantle cell lymphoma, this may identify patients who can safely defer therapy and become a monitoring tool.

Assays for ctDNA directly measure tumor dynamics during therapy. Multiple studies have shown that early clearance of ctDNA or logarithmic reductions are associated with superior prognosis in DLBCL.^8,9 These tools overcome the lack of specificity from imaging scans and capture dynamic processes such as tumor response kinetics, clonal evolution, and cellular resistance. Therefore, comprehensive input from all available data, including after therapy initiates, may serve as the ultimate tool for personalized outcome prediction, as recently modeled.¹⁰ This forward-thinking model has the advantage of incorporating multiple different sources of input that adapt to changing circumstances and that underpins risk-adapted treatment.

Response assessment in lymphoma relies heavily on imaging scans that cannot capture dynamic biologic processes. Paradigms for surveillance monitoring differ on the basis of therapeutic goals. The goal of therapy in aggressive lymphomas is cure, so clearance of minimal residual disease after therapy is critical. Modern next-generation sequencing–based assays for the variable-dense joining regions of immunoglobulin receptors can detect minimal residual disease across multiple B-cell lymphomas and are in clinical use for multiple myeloma.⁸ These assays detect subclinical disease months before imaging scans, which has intuitive appeal because some patients may yet be cured. For indolent lymphomas, the goal is disease control, but clinical decisions are usually made without direct measures of disease.

Lymphoma therapy will undoubtedly change, but the monitoring tools may stay constant. Precision medicine is the selection of targeted or immunotherapy on the basis of genetic and/or functional characteristics of the tumor. Barriers to precision treatment include spatial and temporal tumor heterogeneity. As novel agents become more available, the treatment decisions become more complicated. Liquid biopsies address tumor heterogeneity, clonal evolution, and mechanisms of resistance. DLBCL is molecularly heterogeneous, but its molecular profile can predict responsiveness to targeted agents.¹¹ Indeed, ctDNA will likely be an essential component of precision medicine strategies for lymphoma.

AUTHOR CONTRIBUTIONS

Conception and design: Mark Roschewski

Provision of study material or patients: Wyndham H. Wilson

Collection and assembly of data: Wyndham H. Wilson

Data analysis and interpretation: Wyndham H. Wilson

Manuscript writing: All authors

Final approval of manuscript: All authors

Accountable for all aspects of the work: All authors

AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST

Expanding the Precision Medicine Toolkit With Circulating Tumor DNA

The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated unless otherwise noted. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO's conflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/op/authors/author-center.

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No potential conflicts of interest were reported.