CoChat
Sign Up
Cited by 1,495Cancer Genomics and DiagnosticsEpigenetics and DNA MethylationAdvanced biosensing and bioanalysis techniques

The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.

Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA

Open access
Liu, M.C., Oxnard, G.R., Klein, E.A., Swanton, C., Seiden, M.V., Liu, Minetta C., Oxnard, Geoffrey R., Klein, Eric A., Smith, David, Richards, Donald, Yeatman, Timothy J., Cohn, Allen L., Lapham, Rosanna, Clement, Jessica, Parker, Alexander S., Tummala, Mohan K., McIntyre, Kristi, Sekeres, Mikkael A., Bryce, Alan H., Siegel, Robert, Wang, Xuezhong, Cosgrove, David P., Abu-Rustum, Nadeem R., Trent, Jonathan, Thiel, David D., Becerra, Carlos, Agrawal, Manish, Garbo, Lawrence E., Giguere, Jeffrey K., Michels, Ross M., Harris, Ronald P., Richey, Stephen L., McCarthy, Timothy A., Waterhouse, David M., Couch, Fergus J., Wilks, Sharon T., Krie, Amy K., Balaraman, Rama, Restrepo, Alvaro, Meshad, Michael W., Rieger-Christ, Kimberly, Sullivan, Travis, Lee, Christine M., Greenwald, Daniel R., Oh, William, Tsao, Che-Kai, Fleshner, Neil, Kennecke, Hagen F., Khalil, Maged F., Spigel, David R., Manhas, Atisha P., Ulrich, Brian K., Kovoor, Philip A., Stokoe, Christopher, Courtright, Jay G., Yimer, Habte A., Larson, Timothy G., Swanton, Charles, Seiden, Michael V., Cummings, Steven R., Absalan, Farnaz, Alexander, Gregory, Allen, Brian, Amini, Hamed, Aravanis, Alexander M., Bagaria, Siddhartha, Bazargan, Leila, Beausang, John F., Berman, Jennifer, Betts, Craig, Blocker, Alexander, Bredno, Joerg, Calef, Robert, Cann, Gordon, Carter, Jeremy, Chang, Christopher, Chawla, Hemanshi, Chen, Xiaoji, Chien, Tom C., Civello, Daniel, Davydov, Konstantin, Demas, Vasiliki, Desai, Mohini, Dong, Zhao, Fayzullina, Saniya, Fields, Alexander P., Filippova, Darya, Freese, Peter, Fung, Eric T., Gnerre, Sante, Gross, Samuel, Halks-Miller, Meredith, Hall, Megan P., Hartman, Anne-Renee, Hou, Chenlu, Hubbell, Earl, Hunkapiller, Nathan, Jagadeesh, Karthik, Jamshidi, Arash, Jiang, Roger, Jung, Byoungsok, Kim, TaeHyung, Klausner, Richard D., Kurtzman, Kathryn N., Lee, Mark, Lin, Wendy, Lipson, Jafi, Liu, Hai, Liu, Qinwen, Lopatin, Margarita, Maddala, Tara, Maher, M. Cyrus, Melton, Collin, Mich, Andrea, Nautiyal, Shivani, Newman, Jonathan, Newman, Joshua, Nicula, Virgil, Nicolaou, Cosmos, Nikolic, Ongjen, Pan, Wenying, Patel, Shilpen, Prins, Sarah A., Rava, Richard, Ronaghi, Neda, Sakarya, Onur, Satya, Ravi Vijaya, Schellenberger, Jan, Scott, Eric, Sehnert, Amy J., Shaknovich, Rita, Shanmugam, Avinash, Shashidhar, K.C., Shen, Ling, Shenoy, Archana, Shojaee, Seyedmehdi, Singh, Pranav, Steffen, Kristan K., Tang, Susan, Toung, Jonathan M., Valouev, Anton, Venn, Oliver, Williams, Richard T., Wu, Tony, Xu, Hui H., Yakym, Christopher, Yang, Xiao, Yecies, Jessica, Yip, Alexander S., Youngren, Jack, Yue, Jeanne, Zhang, Jingyang, Zhang, Lily, Zhang, Lori (Quan), Zhang, Nan, Curtis, Christina, Berry, Donald A.·2020·Annals of Oncology
DOI 10.1016/j.annonc.2020.02.011PMC PMC8274402
Open this paper in CoChat — free accountOpen PDF

Abstract

BACKGROUND: Early cancer detection could identify tumors at a time when outcomes are superior and treatment is less morbid. This prospective case-control sub-study (from NCT02889978 and NCT03085888) assessed the performance of targeted methylation analysis of circulating cell-free DNA (cfDNA) to detect and localize multiple cancer types across all stages at high specificity. PARTICIPANTS AND METHODS: The 6689 participants [2482 cancer (>50 cancer types), 4207 non-cancer] were divided into training and validation sets. Plasma cfDNA underwent bisulfite sequencing targeting a panel of >100 000 informative methylation regions. A classifier was developed and validated for cancer detection and tissue of origin (TOO) localization. RESULTS: Performance was consistent in training and validation sets. In validation, specificity was 99.3% [95% confidence interval (CI): 98.3% to 99.8%; 0.7% false-positive rate (FPR)]. Stage I-III sensitivity was 67.3% (CI: 60.7% to 73.3%) in a pre-specified set of 12 cancer types (anus, bladder, colon/rectum, esophagus, head and neck, liver/bile-duct, lung, lymphoma, ovary, pancreas, plasma cell neoplasm, stomach), which account for ∼63% of US cancer deaths annually, and was 43.9% (CI: 39.4% to 48.5%) in all cancer types. Detection increased with increasing stage: in the pre-specified cancer types sensitivity was 39% (CI: 27% to 52%) in stage I, 69% (CI: 56% to 80%) in stage II, 83% (CI: 75% to 90%) in stage III, and 92% (CI: 86% to 96%) in stage IV. In all cancer types sensitivity was 18% (CI: 13% to 25%) in stage I, 43% (CI: 35% to 51%) in stage II, 81% (CI: 73% to 87%) in stage III, and 93% (CI: 87% to 96%) in stage IV. TOO was predicted in 96% of samples with cancer-like signal; of those, the TOO localization was accurate in 93%. CONCLUSIONS: cfDNA sequencing leveraging informative methylation patterns detected more than 50 cancer types across stages. Considering the potential value of early detection in deadly malignancies, further evaluation of this test is justified in prospective population-level studies.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.annonc.2020.02.011

Cites

28 of 28 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1. The CCGA study for development and validation of a cfDNA-based assay for multi-cancer detection. (A) CCGA study design. The CCGA study included three pre-specified sub-studies designed to discover, train, and validate an assay for multi-cancer detection and localization. The burgundy, shaded boxes highlight the second sub-study, which is the focus of this report. (B) Methylation biology discriminates cancer from non-cancer. One circulating cfDNA fragment is represented on the top left; individual CpGs are indicated as burgundy (methylated) or teal (unmethylated) circles. This assay interrogated fragment-level methylation patterns as indicated on the bottom left (‘Fragment-Level CpG Sites’). In non-cancer participants (top right), cfDNA is shed from cells across the body including WBCs13 and is present in plasma. These DNA fragments retain methylation marks from the originating cells as indicated in this example from a region on chromosome 10. Individual cfDNA fragment sequencing reads are indicated as horizontal lines of differing sizes and are aligned vertically. In non-cancer participants, these fragments are largely unmethylated as indicated by the almost uniformly teal fragments. In a participant with lung cancer (bottom right), the plasma contains a mix of methylated (burgundy) and unmethylated (teal) fragments as the circulating cfDNA is a mixture of tumor cfDNA and cfDNA from other cells in the body. Sequencing of the tumor tissue sample confirms that this region is almost entirely methylated as indicated. Note that tumor tissue is not a requirement for this assay but is illustrative. (C) Target selection. A large database of methylation patterns (‘data input types’) was constructed from WGBS analysis of cfDNA and tissue samples from the CCGA study as well as WGBS analysis of a set of commercially sourced tissue samples. Systematic examination of the fragment-level methylation signature (‘methylation information type’) from these samples allowed the identification of a large number of genomic regions (‘target selection’) containing informative biological signatures of cancer and TOO.
Figure 1. The CCGA study for development and validation of a cfDNA-based assay for multi-cancer detection. (A) CCGA study design. The CCGA study included three pre-specified sub-studies designed to discover, train, and validate an assay for multi-cancer detection and localization. The burgundy, shaded boxes highlight the second sub-study, which is the focus of this report. (B) Methylation biology discriminates cancer from non-cancer. One circulating cfDNA fragment is represented on the top left; individual CpGs are indicated as burgundy (methylated) or teal (unmethylated) circles. This assay interrogated fragment-level methylation patterns as indicated on the bottom left (‘Fragment-Level CpG Sites’). In non-cancer participants (top right), cfDNA is shed from cells across the body including WBCs13 and is present in plasma. These DNA fragments retain methylation marks from the originating cells as indicated in this example from a region on chromosome 10. Individual cfDNA fragment sequencing reads are indicated as horizontal lines of differing sizes and are aligned vertically. In non-cancer participants, these fragments are largely unmethylated as indicated by the almost uniformly teal fragments. In a participant with lung cancer (bottom right), the plasma contains a mix of methylated (burgundy) and unmethylated (teal) fragments as the circulating cfDNA is a mixture of tumor cfDNA and cfDNA from other cells in the body. Sequencing of the tumor tissue sample confirms that this region is almost entirely methylated as indicated. Note that tumor tissue is not a requirement for this assay but is illustrative. (C) Target selection. A large database of methylation patterns (‘data input types’) was constructed from WGBS analysis of cfDNA and tissue samples from the CCGA study as well as WGBS analysis of a set of commercially sourced tissue samples. Systematic examination of the fragment-level methylation signature (‘methylation information type’) from these samples allowed the identification of a large number of genomic regions (‘target selection’) containing informative biological signatures of cancer and TOO.CC BY-NC-ND · PubMed Central
Figure 2. Pre-classifier sample preparation and preprocessing overview. Illustration of how cfDNA fragments from the blood are processed: cfDNA was extracted from plasma, subjected to bisulfite treatment, and regions of interest were pulled down, followed by sequencing and alignment. In this way the methylation state of fragments was obtained.
Figure 2. Pre-classifier sample preparation and preprocessing overview. Illustration of how cfDNA fragments from the blood are processed: cfDNA was extracted from plasma, subjected to bisulfite treatment, and regions of interest were pulled down, followed by sequencing and alignment. In this way the methylation state of fragments was obtained.CC BY-NC-ND · PubMed Central
Figure 3. Participant disposition. A total of 4841 participants (2836 cancer, 2005 non-cancer) from the CCGA study and 2202 non-cancer participants from the STRIVE study were included in this pre-specified analysis. Of these, 3133 samples from CCGA were allocated to training (1742 cancer, 1391 non-cancer) and 1354 were allocated to validation (740 cancer, 614 non-cancer); 1587 samples from STRIVE were allocated to training and 615 to validation. STRIVE non-cancer samples were used to train the classifier and to ensure >99% specificity was achieved with >90% confidence (see Methods, supplementary information, available at Annals of Oncology online). Participant disposition is indicated. Overall, 3052 samples in training (1531 cancer, 1521 non-cancer) and 1264 samples in validation (654 cancer, 610 non-cancer) were analyzable and in the pre-specified primary analysis population. Samples reserved for pre-specified future analyses (as indicated) included, for example, samples lacking 1-year follow-up and samples from participants with carcinoma in situ (CIS) (see Methods).
Figure 3. Participant disposition. A total of 4841 participants (2836 cancer, 2005 non-cancer) from the CCGA study and 2202 non-cancer participants from the STRIVE study were included in this pre-specified analysis. Of these, 3133 samples from CCGA were allocated to training (1742 cancer, 1391 non-cancer) and 1354 were allocated to validation (740 cancer, 614 non-cancer); 1587 samples from STRIVE were allocated to training and 615 to validation. STRIVE non-cancer samples were used to train the classifier and to ensure >99% specificity was achieved with >90% confidence (see Methods, supplementary information, available at Annals of Oncology online). Participant disposition is indicated. Overall, 3052 samples in training (1531 cancer, 1521 non-cancer) and 1264 samples in validation (654 cancer, 610 non-cancer) were analyzable and in the pre-specified primary analysis population. Samples reserved for pre-specified future analyses (as indicated) included, for example, samples lacking 1-year follow-up and samples from participants with carcinoma in situ (CIS) (see Methods).CC BY-NC-ND · PubMed Central
Figure 4. Targeted methylation cfDNA test performance. (A) Specificity. Specificity was >99% in the training and validation sets. Importantly, this represents a consistent, single false-positive rate (FPR) across the >50 cancer types in this study. (B) Sensitivity. Sensitivity (y-axis) is reported by clinical stage (x-axis) in the pre-specified cancer types (left panel) and in all cancer types (right panel) for training and validation. Numbers indicate samples in training|validation sets. It excludes 45 samples in training and 21 samples in validation without stage information (e.g. leukemias). (C) Tissue of origin. Tissue of origin (TOO) accuracy (y-axis) is reported by clinical stage (x-axis) in the pre-specified cancer types (left panel) and in all cancer types (right panel) for training and validation. Numbers indicate samples in training|validation sets.
Figure 4. Targeted methylation cfDNA test performance. (A) Specificity. Specificity was >99% in the training and validation sets. Importantly, this represents a consistent, single false-positive rate (FPR) across the >50 cancer types in this study. (B) Sensitivity. Sensitivity (y-axis) is reported by clinical stage (x-axis) in the pre-specified cancer types (left panel) and in all cancer types (right panel) for training and validation. Numbers indicate samples in training|validation sets. It excludes 45 samples in training and 21 samples in validation without stage information (e.g. leukemias). (C) Tissue of origin. Tissue of origin (TOO) accuracy (y-axis) is reported by clinical stage (x-axis) in the pre-specified cancer types (left panel) and in all cancer types (right panel) for training and validation. Numbers indicate samples in training|validation sets.CC BY-NC-ND · PubMed Central
Figure 5. Sensitivity in individual tumors by stage. Sensitivity at 99.8% specificity (training) or 99.3% specificity (validation) with 95% confidence intervals is reported for individual cancer types with at least 50 samples. Clinical stage is indicated below the plots as is the number of samples in training and validation (separated by a vertical line).
Figure 5. Sensitivity in individual tumors by stage. Sensitivity at 99.8% specificity (training) or 99.3% specificity (validation) with 95% confidence intervals is reported for individual cancer types with at least 50 samples. Clinical stage is indicated below the plots as is the number of samples in training and validation (separated by a vertical line).CC BY-NC-ND · PubMed Central
Figure 6. Tissue of origin accuracy by individual cancer type in the training and validation sets. Confusion matrices representing the accuracy of tissue of origin (TOO) localization in the (A) training and (B) validation sets. Agreement between the actual (x-axis) and predicted (y-axis) TOO per sample using the targeted methylation classifier is depicted. Color corresponds to the proportion of predicted TOO calls. Included participants (training: n = 844, validation: n = 359) are those with cancer predicted as having cancer at 99.8% specificity (training) or 99.3% specificity (validation). The TOO calls were assigned in 95% (806/844) of cases in training and in 96% (344/359) of cases in validation; calls were correct in 92% (744/806) of cases in training and in 93% (321/344) of cases in validation.
Figure 6. Tissue of origin accuracy by individual cancer type in the training and validation sets. Confusion matrices representing the accuracy of tissue of origin (TOO) localization in the (A) training and (B) validation sets. Agreement between the actual (x-axis) and predicted (y-axis) TOO per sample using the targeted methylation classifier is depicted. Color corresponds to the proportion of predicted TOO calls. Included participants (training: n = 844, validation: n = 359) are those with cancer predicted as having cancer at 99.8% specificity (training) or 99.3% specificity (validation). The TOO calls were assigned in 95% (806/844) of cases in training and in 96% (344/359) of cases in validation; calls were correct in 92% (744/806) of cases in training and in 93% (321/344) of cases in validation.CC BY-NC-ND · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 28 references

Research works printed but not linked: 1

  1. Cohen, 2018, Detection and localization of surgically resectable cancers with a multi-analyte blood test, Science, 359, 926, 10.1126/science.aar3247 · DOI · Paper page

Cited by

Sorted by citations

Items 301–400 of 1,000

Related

See all

Metadata

Authors
Liu, M. C., Oxnard, G. R., Klein, E. A., Swanton, C., Seiden, M. V., Liu, M. C., Oxnard, G. R., Klein, E. A., Smith, D., Richards, D., Yeatman, T. J., Cohn, A. L., Lapham, R., Clement, J., Parker, A. S., Tummala, M. K., McIntyre, K., Sekeres, M. A., Bryce, A. H., Siegel, R., Wang, X., Cosgrove, D. P., Abu-Rustum, N. R., Trent, J., Thiel, D. D., Becerra, C., Agrawal, M., Garbo, L. E., Giguere, J. K., Michels, R. M., Harris, R. P., Richey, S. L., McCarthy, T. A., Waterhouse, D. M., Couch, F. J., Wilks, S. T., Krie, A. K., Balaraman, R., Restrepo, A., Meshad, M. W., Rieger-Christ, K., Sullivan, T., Lee, C. M., Greenwald, D. R., Oh, W., Tsao, C.-K., Fleshner, N., Kennecke, H. F., Khalil, M. F., Spigel, D. R., Manhas, A. P., Ulrich, B. K., Kovoor, P. A., Stokoe, C., Courtright, J. G., Yimer, H. A., Larson, T. G., Swanton, C., Seiden, M. V., Cummings, S. R., Absalan, F., Alexander, G., Allen, B., Amini, H., Aravanis, A. M., Bagaria, S., Bazargan, L., Beausang, J. F., Berman, J., Betts, C., Blocker, A., Bredno, J., Calef, R., Cann, G., Carter, J., Chang, C., Chawla, H., Chen, X., Chien, T. C., Civello, D., Davydov, K., Demas, V., Desai, M., Dong, Z., Fayzullina, S., Fields, A. P., Filippova, D., Freese, P., Fung, E. T., Gnerre, S., Gross, S., Halks-Miller, M., Hall, M. P., Hartman, A.-R., Hou, C., Hubbell, E., Hunkapiller, N., Jagadeesh, K., Jamshidi, A., Jiang, R., Jung, B., Kim, T., Klausner, R. D., Kurtzman, K. N., Lee, M., Lin, W., Lipson, J., Liu, H., Liu, Q., Lopatin, M., Maddala, T., Maher, M. C., Melton, C., Mich, A., Nautiyal, S., Newman, J., Newman, J., Nicula, V., Nicolaou, C., Nikolic, O., Pan, W., Patel, S., Prins, S. A., Rava, R., Ronaghi, N., Sakarya, O., Satya, R. V., Schellenberger, J., Scott, E., Sehnert, A. J., Shaknovich, R., Shanmugam, A., Shashidhar, K. C., Shen, L., Shenoy, A., Shojaee, S., Singh, P., Steffen, K. K., Tang, S., Toung, J. M., Valouev, A., Venn, O., Williams, R. T., Wu, T., Xu, H. H., Yakym, C., Yang, X., Yecies, J., Yip, A. S., Youngren, J., Yue, J., Zhang, J., Zhang, L., Zhang, L. (., Zhang, N., Curtis, C., & Berry, D. A.
Affiliations
  • Compass Oncology (US)
  • Texas Oncology (US)
  • Rocky Mountain Cancer Centers (US)
  • Spartanburg Regional Healthcare System (US)
  • Hartford Hospital (US)
  • Mayo Clinic in Florida (US)
  • Texas Health Dallas (US)
  • Cleveland Clinic (US)
  • Mayo Clinic (US)
  • University of Miami Health System (US)
  • Maryland Oncology Hematology (US)
  • New York Oncology Hematology (US)
  • Prisma Health (US)
  • Virginia Cancer Specialists (US)
  • Oncology Hematology Care (US)
  • Avera Health (US)
  • Lahey Hospital and Medical Center (US)
  • Sansum Medical Clinic (US)
  • Mount Sinai Hospital (US)
  • University of Toronto (CA)
  • Benaroya Research Institute (US)
  • Lehigh Valley Health Network (US)
  • Sarah Cannon (US)
  • Minnesota Oncology (US)
  • Sutter Health (US)
  • Grail (United States) (US)
  • Stanford University (US)
  • The University of Texas MD Anderson Cancer Center (US)
ORCID
Venue
Annals of Oncology
Publisher
Elsevier BV
Published
2020-06
Volume
31
Issue
6
Pages
745-759
ISSN
0923-7534 (ISSN-L)
DOI
10.1016/j.annonc.2020.02.011
PMC
PMC8274402
OpenAlex
W3013924915
Cited by
1,495
References
28
Citations by year
2026: 202 · 2025: 245 · 2024: 253 · 2023: 267 · 2022: 248 · 2021: 205 · 2020: 74
FWCI
70.07
Type
journal article
Language
en
Open access
Licence
Locations
  • Annals of Oncology (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion, cc-by-nc-nd)
  • UCL Discovery (University College London) (submittedVersion)
  • Figshare (submittedVersion, cc-by-nc-nd)
Funders
  • GRAIL, Inc
  • Princess Margaret Cancer Centre’s McCain GU BioBank
  • Francis Crick Institute
  • Cancer Research UK (FC001169, FC001202)
  • UK Medical Research Council (FC001169, FC001202)
  • Wellcome Trust (FC001169, FC001202)
  • CRUK Lung Cancer Centre of Excellence
  • Rosetrees Trust
  • Breast Cancer Research Foundation
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Advanced biosensing and bioanalysis techniques
Keywords
multi-cancer early detection, cell-free DNA, DNA methylation signature, liquid biopsy, tumor stage, bisulfite sequencing, false positive rate, early-stage cancer detection, cancer mortality reduction
MeSH terms
Cell-Free Nucleic Acids, DNA, Neoplasm, Female, Humans, Neoplasms, Prospective Studies, Biomarkers, Tumor, DNA Methylation

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review