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Detection of Solid Tumor Molecular Residual Disease (MRD) Using Circulating Tumor DNA (ctDNA)

  • Re-I Chin
  • Kevin Chen
  • Abul Usmani
  • Chanelle Chua
  • Peter K. Harris
  • Michael S. Binkley
  • Tej D. Azad
  • Jonathan C. Dudley
  • Aadel A. ChaudhuriEmail author
Review Article

Abstract

Circulating tumor DNA (ctDNA) is a component of cell-free DNA that is shed by malignant tumors into the bloodstream and other bodily fluids. Levels of ctDNA are typically low, particularly in patients with localized disease, requiring highly sophisticated methods for detection and quantification. Multiple liquid biopsy methods have been developed for ctDNA analysis in solid tumor malignancies and are now enabling detection and assessment of earlier stages of disease, post-treatment molecular residual disease (MRD), resistance to targeted systemic therapy, and tumor mutational burden. Understanding ctDNA biology, mechanisms of release, and clearance and size characteristics, in conjunction with the application of molecular barcoding and targeted error correction, have increased the sensitivity and specificity of ctDNA detection techniques. Combinatorial approaches including integration of ctDNA data with circulating protein biomarkers may further improve assay sensitivity and broaden the scope of ctDNA applications. Circulating viral DNA may be utilized to monitor disease in some virally induced malignancies. In spite of increasingly accurate methods of ctDNA detection, results need to be interpreted with caution given that somatic mosaicisms such as clonal hematopoiesis of indeterminate potential (CHIP) may give rise to genetic variants in the bloodstream unrelated to solid tumors, and the limited concordance observed between different commercial platforms. Overall, highly precise ctDNA detection and quantification methods have the potential to transform clinical practice via non-invasive monitoring of solid tumor malignancies, residual disease detection at earlier timepoints than standard clinical and/or imaging surveillance, and treatment personalization based on real-time assessment of the tumor genomic landscape.

Notes

Compliance with Ethical Standards

Funding

This work was funded by the NCI under award number K08CA238711 (A. A. Chaudhuri), a Cancer Research Foundation Young Investigator Award (A. A. Chaudhuri), the Washington University SPORE in Pancreatic Cancer Career Enhancement Program (A. A. Chaudhuri), and the Conquer Cancer Foundation ASCO Young Investigator Award (A. A. Chaudhuri), supported by Takeda Pharmaceuticals. Any opinions, findings and conclusions expressed in this material are those of the authors and do not necessarily reflect those of the American Society of Clinical Oncology®, Conquer Cancer®, or Takeda®.

Conflict of interest

Aadel A. Chaudhuri has received speaker honoraria and travel support from Varian Medical Systems, Roche Sequencing Solutions, and Foundation Medicine, Inc., has research support from Roche Sequencing Solutions, has served as a consultant for Tempus Labs and for Oscar Health, and is a scientific advisor for Roche Sequencing Solutions and for Geneoscopy, LLC. Jonathan C. Dudley has served as a consultant for Merck. Re-I Chin, Kevin Chen, Abul Usmani, Chanelle Chua, Peter K. Harris, Michael S. Binkley, and Tej D. Azad declare that they have no conflicts of interest that might be relevant to the contents of this review.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Radiation OncologyWashington University School of MedicineSt. LouisUSA
  2. 2.Department of Radiation OncologyStanford University School of MedicineStanfordUSA
  3. 3.Department of PathologyJohns Hopkins University School of MedicineBaltimoreUSA
  4. 4.Department of Computer Science and EngineeringWashington UniversitySt. LouisUSA
  5. 5.Alvin J. Siteman Cancer Center, Barnes-Jewish Hospital and Washington University School of MedicineSt. LouisUSA

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