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An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage. ​

Literature Information ​

DOI10.1038/nm.3519
PMID24705333
JournalNature medicine
Impact Factor50.0
JCR QuartileQ1
Publication Year2014
Times Cited1089
Keywordscirculating tumor DNA, ultrasensitive method, personalized cancer detection
Literature TypeJournal Article, Research Support, N.I.H., Extramural, Research Support, U.S. Gov't, Non-P.H.S.
ISSN1078-8956
Pages548-54
Issue20(5)
AuthorsAaron M Newman, Scott V Bratman, Jacqueline To, Jacob F Wynne, Neville C W Eclov, Leslie A Modlin, Chih Long Liu, Joel W Neal, Heather A Wakelee, Robert E Merritt, Joseph B Shrager, Billy W Loo, Ash A Alizadeh, Maximilian Diehn

TL;DR ​

The study presents cancer personalized profiling by deep sequencing (CAPP-Seq), an economical and ultrasensitive method for quantifying circulating tumor DNA (ctDNA), which demonstrated over 95% mutation detection in non-small-cell lung cancer (NSCLC) patients and 100% detection in stages II-IV. CAPP-Seq not only correlates ctDNA levels with tumor volume but also allows for earlier response assessments and could be widely used for noninvasive cancer monitoring and personalized therapy.

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circulating tumor DNA · ultrasensitive method · personalized cancer detection

Abstract ​

Circulating tumor DNA (ctDNA) is a promising biomarker for noninvasive assessment of cancer burden, but existing ctDNA detection methods have insufficient sensitivity or patient coverage for broad clinical applicability. Here we introduce cancer personalized profiling by deep sequencing (CAPP-Seq), an economical and ultrasensitive method for quantifying ctDNA. We implemented CAPP-Seq for non-small-cell lung cancer (NSCLC) with a design covering multiple classes of somatic alterations that identified mutations in >95% of tumors. We detected ctDNA in 100% of patients with stage II-IV NSCLC and in 50% of patients with stage I, with 96% specificity for mutant allele fractions down to ∼0.02%. Levels of ctDNA were highly correlated with tumor volume and distinguished between residual disease and treatment-related imaging changes, and measurement of ctDNA levels allowed for earlier response assessment than radiographic approaches. Finally, we evaluated biopsy-free tumor screening and genotyping with CAPP-Seq. We envision that CAPP-Seq could be routinely applied clinically to detect and monitor diverse malignancies, thus facilitating personalized cancer therapy.

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Primary Questions Addressed ​

  1. How does CAPP-Seq compare to traditional ctDNA detection methods in terms of sensitivity and specificity?
  2. What are the potential implications of using CAPP-Seq for early detection of various types of cancer beyond NSCLC?
  3. In what ways can CAPP-Seq be integrated into existing clinical workflows for personalized cancer therapy?
  4. What challenges might arise in the implementation of CAPP-Seq across diverse patient populations and cancer types?
  5. How does the correlation between ctDNA levels and tumor volume influence treatment decisions in cancer management?

Key Findings ​

Research Background and Purpose ​

Circulating tumor DNA (ctDNA) is a promising biomarker for noninvasive cancer assessment. Existing methods for ctDNA quantification lack sensitivity and broad patient applicability. This study introduces a novel method called CAncer Personalized Profiling by deep Sequencing (CAPP-Seq), aimed at providing an economical and ultrasensitive approach to quantify ctDNA, particularly in non-small cell lung cancer (NSCLC).

Main Methods/Materials/Experimental Design ​

CAPP-Seq combines optimized library preparation with a multi-phase bioinformatics approach to detect recurrent mutations in tumors. The study involved the following key steps:

  1. Selector Design: A custom selector was created to capture recurrent mutations in NSCLC based on data from The Cancer Genome Atlas (TCGA).
  2. Sample Collection: Plasma and tumor samples were collected from patients with NSCLC.
  3. Sequencing: Deep sequencing was performed to analyze ctDNA and tumor DNA, achieving high coverage.
  4. Bioinformatics Analysis: Data processing included variant calling and quantification of ctDNA.

The technical workflow can be represented as follows:

Mermaid diagram

Key Results and Findings ​

  • CAPP-Seq successfully detected ctDNA in 100% of stage II–IV NSCLC patients and 50% of stage I patients, with a specificity of 96%.
  • The method correlated ctDNA levels with tumor volume and was able to distinguish between residual disease and treatment-related changes.
  • It provided earlier response assessments compared to traditional imaging methods.
  • CAPP-Seq was able to identify actionable mutations and demonstrated potential for biopsy-free tumor genotyping.

Main Conclusions/Significance/Innovation ​

CAPP-Seq represents a significant advancement in ctDNA analysis due to its high sensitivity, specificity, and broad applicability across various NSCLC cases. The method's ability to detect low levels of ctDNA opens avenues for personalized cancer therapy and monitoring. This approach could facilitate routine clinical use, enhancing noninvasive cancer diagnostics.

Research Limitations and Future Directions ​

  • Limitations: The study primarily focused on NSCLC, and further validation is needed for other cancer types. The method may have challenges in capturing all fusion events, potentially underestimating tumor burden.
  • Future Directions: Enhancements to capture efficiency for fusions and improvements in detection thresholds are necessary. Further studies should explore the application of CAPP-Seq in other malignancies and biological fluids.
AspectDetails
Sensitivity100% for stages II–IV, 50% for stage I
Specificity96%
ctDNA Detection LimitDown to ~0.02%
Clinical ApplicationMonitoring treatment response, minimal residual disease detection
Future EnhancementsImprove fusion capture efficiency, expand to other cancers

This structured summary encapsulates the core findings and implications of the research, highlighting CAPP-Seq's potential impact on cancer diagnostics and management.

References ​

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Literatures Citing This Work ​

  1. Detecting cancer by monitoring circulating tumor DNA. - Paul T Spellman;Joe W Gray - Nature medicine (2014)
  2. Circulating tumor DNA moves further into the spotlight. - Mark Sausen;Sonya Parpart;Luis A Diaz - Genome medicine (2014)
  3. Cancer biomarkers: Written in blood. - Ed Yong - Nature (2014)
  4. Lung cancer screening beyond low-dose computed tomography: the role of novel biomarkers. - Naveed Hasan;Rohit Kumar;Mani S Kavuru - Lung (2014)
  5. Liquid biopsy in gastrointestinal stromal tumors: a novel approach. - Margherita Nannini;Annalisa Astolfi;Milena Urbini;Guido Biasco;Maria A Pantaleo - Journal of translational medicine (2014)
  6. FACTERA: a practical method for the discovery of genomic rearrangements at breakpoint resolution. - Aaron M Newman;Scott V Bratman;Henning Stehr;Luke J Lee;Chih Long Liu;Maximilian Diehn;Ash A Alizadeh - Bioinformatics (Oxford, England) (2014)
  7. Strategies for modern biomarker and drug development in oncology. - Alan D Smith;Desam Roda;Timothy A Yap - Journal of hematology & oncology (2014)
  8. Single-tube, highly parallel mutation enrichment in cancer gene panels by use of temperature-tolerant COLD-PCR. - Elena Castellanos-Rizaldos;Katherine Richardson;Rui Lin;Grant Wu;Mike G Makrigiorgos - Clinical chemistry (2015)
  9. Current challenges for detection of circulating tumor cells and cell-free circulating nucleic acids, and their characterization in non-small cell lung carcinoma patients. What is the best blood substrate for personalized medicine? - Marius Ilie;Véronique Hofman;Elodie Long;Olivier Bordone;Eric Selva;Kevin Washetine;Charles Hugo Marquette;Paul Hofman - Annals of translational medicine (2014)
  10. Genomic profiling of breast cancers. - Christina Curtis - Current opinion in obstetrics & gynecology (2015)

... (1079 more literatures)

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