ctDNA monitoring using patient-specific sequencing and integration of variant reads
Circulating tumor-derived DNA (ctDNA) can be used to monitor cancer dynamics noninvasively. Detection of ctDNA can be challenging in patients with low-volume or residual disease, where plasma contains very few tumor-derived DNA fragments. We show that sensitivity for ctDNA detection in plasma can be...
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| Published in: | Science translational medicine Vol. 12; no. 548 |
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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17.06.2020
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| ISSN: | 1946-6242, 1946-6242 |
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| Abstract | Circulating tumor-derived DNA (ctDNA) can be used to monitor cancer dynamics noninvasively. Detection of ctDNA can be challenging in patients with low-volume or residual disease, where plasma contains very few tumor-derived DNA fragments. We show that sensitivity for ctDNA detection in plasma can be improved by analyzing hundreds to thousands of mutations that are first identified by tumor genotyping. We describe the INtegration of VAriant Reads (INVAR) pipeline, which combines custom error-suppression methods and signal-enrichment approaches based on biological features of ctDNA. With this approach, the detection limit in each sample can be estimated independently based on the number of informative reads sequenced across multiple patient-specific loci. We applied INVAR to custom hybrid-capture sequencing data from 176 plasma samples from 105 patients with melanoma, lung, renal, glioma, and breast cancer across both early and advanced disease. By integrating signal across a median of >10
informative reads, ctDNA was routinely quantified to 1 mutant molecule per 100,000, and in some cases with high tumor mutation burden and/or plasma input material, to parts per million. This resulted in median area under the curve (AUC) values of 0.98 in advanced cancers and 0.80 in early-stage and challenging settings for ctDNA detection. We generalized this method to whole-exome and whole-genome sequencing, showing that INVAR may be applied without requiring personalized sequencing panels so long as a tumor mutation list is available. As tumor sequencing becomes increasingly performed, such methods for personalized cancer monitoring may enhance the sensitivity of cancer liquid biopsies. |
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| AbstractList | Circulating tumor-derived DNA (ctDNA) can be used to monitor cancer dynamics noninvasively. Detection of ctDNA can be challenging in patients with low-volume or residual disease, where plasma contains very few tumor-derived DNA fragments. We show that sensitivity for ctDNA detection in plasma can be improved by analyzing hundreds to thousands of mutations that are first identified by tumor genotyping. We describe the INtegration of VAriant Reads (INVAR) pipeline, which combines custom error-suppression methods and signal-enrichment approaches based on biological features of ctDNA. With this approach, the detection limit in each sample can be estimated independently based on the number of informative reads sequenced across multiple patient-specific loci. We applied INVAR to custom hybrid-capture sequencing data from 176 plasma samples from 105 patients with melanoma, lung, renal, glioma, and breast cancer across both early and advanced disease. By integrating signal across a median of >105 informative reads, ctDNA was routinely quantified to 1 mutant molecule per 100,000, and in some cases with high tumor mutation burden and/or plasma input material, to parts per million. This resulted in median area under the curve (AUC) values of 0.98 in advanced cancers and 0.80 in early-stage and challenging settings for ctDNA detection. We generalized this method to whole-exome and whole-genome sequencing, showing that INVAR may be applied without requiring personalized sequencing panels so long as a tumor mutation list is available. As tumor sequencing becomes increasingly performed, such methods for personalized cancer monitoring may enhance the sensitivity of cancer liquid biopsies.Circulating tumor-derived DNA (ctDNA) can be used to monitor cancer dynamics noninvasively. Detection of ctDNA can be challenging in patients with low-volume or residual disease, where plasma contains very few tumor-derived DNA fragments. We show that sensitivity for ctDNA detection in plasma can be improved by analyzing hundreds to thousands of mutations that are first identified by tumor genotyping. We describe the INtegration of VAriant Reads (INVAR) pipeline, which combines custom error-suppression methods and signal-enrichment approaches based on biological features of ctDNA. With this approach, the detection limit in each sample can be estimated independently based on the number of informative reads sequenced across multiple patient-specific loci. We applied INVAR to custom hybrid-capture sequencing data from 176 plasma samples from 105 patients with melanoma, lung, renal, glioma, and breast cancer across both early and advanced disease. By integrating signal across a median of >105 informative reads, ctDNA was routinely quantified to 1 mutant molecule per 100,000, and in some cases with high tumor mutation burden and/or plasma input material, to parts per million. This resulted in median area under the curve (AUC) values of 0.98 in advanced cancers and 0.80 in early-stage and challenging settings for ctDNA detection. We generalized this method to whole-exome and whole-genome sequencing, showing that INVAR may be applied without requiring personalized sequencing panels so long as a tumor mutation list is available. As tumor sequencing becomes increasingly performed, such methods for personalized cancer monitoring may enhance the sensitivity of cancer liquid biopsies. Circulating tumor-derived DNA (ctDNA) can be used to monitor cancer dynamics noninvasively. Detection of ctDNA can be challenging in patients with low-volume or residual disease, where plasma contains very few tumor-derived DNA fragments. We show that sensitivity for ctDNA detection in plasma can be improved by analyzing hundreds to thousands of mutations that are first identified by tumor genotyping. We describe the INtegration of VAriant Reads (INVAR) pipeline, which combines custom error-suppression methods and signal-enrichment approaches based on biological features of ctDNA. With this approach, the detection limit in each sample can be estimated independently based on the number of informative reads sequenced across multiple patient-specific loci. We applied INVAR to custom hybrid-capture sequencing data from 176 plasma samples from 105 patients with melanoma, lung, renal, glioma, and breast cancer across both early and advanced disease. By integrating signal across a median of >10 informative reads, ctDNA was routinely quantified to 1 mutant molecule per 100,000, and in some cases with high tumor mutation burden and/or plasma input material, to parts per million. This resulted in median area under the curve (AUC) values of 0.98 in advanced cancers and 0.80 in early-stage and challenging settings for ctDNA detection. We generalized this method to whole-exome and whole-genome sequencing, showing that INVAR may be applied without requiring personalized sequencing panels so long as a tumor mutation list is available. As tumor sequencing becomes increasingly performed, such methods for personalized cancer monitoring may enhance the sensitivity of cancer liquid biopsies. |
| Author | Barker, Emily Heider, Katrin Corrie, Pippa G Rosenfeld, Nitzan Gale, Davina Mair, Richard Parkinson, Christine Young, Gemma Smith, Christopher G Murphy, Suzanne McDermott, Ultan Fisher, Eyal Chandrananda, Dineika Ruiz-Valdepenas, Andrea Caldas, Carlos Abraham, Jean E Gallagher, Ferdia A Rintoul, Robert C Morris, James Brindle, Kevin M Durrani, Amer Massie, Charles Mouliere, Florent Alifrangis, Constantine Marshall, Andrea Gill, Andrew B Rassl, Doris M Chan, Pui Ying Hudecova, Irena Stewart, Grant D Wan, Jonathan C M Santonja, Angela Cooper, Wendy N Marass, Francesco Middleton, Mark R |
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0000-0003-3546-6153 surname: Fisher fullname: Fisher, Eyal organization: Cancer Research UK Major Centre-Cambridge, Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK – sequence: 6 givenname: Florent orcidid: 0000-0001-7043-0514 surname: Mouliere fullname: Mouliere, Florent organization: Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Pathology, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, Netherlands – sequence: 7 givenname: Andrea surname: Ruiz-Valdepenas fullname: Ruiz-Valdepenas, Andrea organization: Cancer Research UK Major Centre-Cambridge, Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK – sequence: 8 givenname: Angela orcidid: 0000-0001-9367-7632 surname: Santonja fullname: Santonja, Angela organization: Cancer Research UK Major Centre-Cambridge, Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK – sequence: 9 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organization: National Institute for Health Research Biomedical Research Centre, Oxford OX4 2PG, UK – sequence: 28 givenname: Ferdia A orcidid: 0000-0003-4784-5230 surname: Gallagher fullname: Gallagher, Ferdia A organization: Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Pathology, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, Netherlands – sequence: 29 givenname: Christine surname: Parkinson fullname: Parkinson, Christine organization: Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK – sequence: 30 givenname: Amer surname: Durrani fullname: Durrani, Amer organization: Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK – sequence: 31 givenname: Ultan orcidid: 0000-0001-9032-4700 surname: McDermott fullname: McDermott, Ultan organization: Wellcome Sanger Institute, Hinxton CB10 1SA, UK – sequence: 32 givenname: Christopher G orcidid: 0000-0001-7357-2737 surname: Smith fullname: Smith, Christopher G organization: Cancer Research UK Major Centre-Cambridge, Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK – sequence: 33 givenname: Charles orcidid: 0000-0003-2314-4843 surname: Massie fullname: Massie, Charles organization: Department of Oncology, University of Cambridge Hutchison-MRC Research Centre, Box 197, Cambridge Biomedical Campus, Cambridge CB2 0XZ, UK – sequence: 34 givenname: Pippa G orcidid: 0000-0003-4875-7021 surname: Corrie fullname: Corrie, Pippa G organization: Cancer Research UK Major Centre-Cambridge, Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK – sequence: 35 givenname: Nitzan orcidid: 0000-0002-2825-4788 surname: Rosenfeld fullname: Rosenfeld, Nitzan email: nitzan.rosenfeld@cruk.cam.ac.uk organization: Cancer Research UK Major Centre-Cambridge, Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32554709$$D View this record in MEDLINE/PubMed |
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| Snippet | Circulating tumor-derived DNA (ctDNA) can be used to monitor cancer dynamics noninvasively. Detection of ctDNA can be challenging in patients with low-volume... |
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| SubjectTerms | Biomarkers, Tumor Circulating Tumor DNA - genetics DNA, Neoplasm - genetics High-Throughput Nucleotide Sequencing Humans Liquid Biopsy Mutation - genetics |
| Title | ctDNA monitoring using patient-specific sequencing and integration of variant reads |
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