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

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

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.

Keywords: cancer; cell-free DNA; methylation; next-generation sequencing.

Conflict of interest statement

Disclosures The Mayo Clinic was compensated for MCL's advisory board activities for GRAIL, Inc. GRO reports personal fees from GRAIL, Inc. during the conduct of the study as well as personal fees from Inivata, Sysmex, AstraZeneca, Janssen, Illumina, and Foundation Medicine outside the submitted work. EAK reports personal fees from GRAIL, Inc. during the conduct of the study. MVS reports personal fees and other from McKesson and personal fees from GRAIL, Inc. during the conduct of the study as well as other from Merck and Bristol-Myers Squibb outside the submitted work. CS reports grants from Pfizer, AstraZeneca, BMS, Roche-Ventana, and Boehringer-Ingelheim; has consulted for Pfizer, Novartis, GlaxoSmithKline, MSD, BMS, Celgene, AstraZeneca, Illumina, Genentech, Roche-Ventana, GRAIL, Inc., Medicxi, and the Sarah Cannon Research Institute; has stock options of Apogen Biotechnologies, Epic Bioscience, GRAIL, Inc., and has stock options in and is co-founder of Achilles Therapeutics. CS is Royal Society Napier Research Professor. HA reports personal fees from GRAIL, Inc., during the conduct of the study as well as other from Illumina Inc. outside the submitted work; in addition, HA has patents pending to GRAIL, Inc. DAB reports grants from the National Cancer Institute, other from Berry Consultants, LLC, outside the submitted work. TCC reports personal fees and other from Illumina, Inc., outside the submitted work. CC reports personal fees and other (stock options) from GRAIL, Inc., during the conduct of the study as well as personal fees from Genentech outside the submitted work; in addition, CC has a patent pending outside the submitted work. KD reports personal fees from GRAIL, Inc. during the conduct of the study and other from Alphabet outside the submitted work. FJC reports research support from GRAIL, Inc. MD reports personal fees from GRAIL, Inc. during the conduct of the study; in addition, MD has patents pending to GRAIL, Inc. SF reports personal fees and other from GRAIL, Inc., during the conduct of the study as well as personal fees and other from 23andMe and other from Illumina outside the submitted work. APF reports personal fees from GRAIL, Inc. during the conduct of the study; in addition, APF has patents pending to GRAIL, Inc. DF reports personal fees from GRAIL, Inc. during the conduct of the study as well as personal fees from Roche Sequencing Solutions outside the submitted work; in addition, DF has patents pending to GRAIL, Inc. SG reports personal fees and other from GRAIL, Inc. during the conduct of the study as well as other from Illumina outside the submitted work. S. Gross reports personal fees from GRAIL, Inc. during the conduct of the study; in addition, S. Gross has patents pending to GRAIL, Inc. MPH reports personal fees and other from GRAIL, Inc. during the conduct of the study as well as other from Jazz Pharmaceuticals and Natera outside the submitted work. SAP reports personal fees and other from GRAIL, Inc. during the conduct of the study as well as other from Natera, Inc. outside of the submitted work. EH reports personal fees and other from GRAIL, Inc. during the conduct of the study; in addition, EH has patents pending to GRAIL, Inc. NH reports personal fees from GRAIL, Inc. during the conduct of the study; in addition, NH has patents pending to GRAIL, Inc. CH reports personal fees and other from GRAIL, Inc. during the conduct of the study as well as other from Illumina outside the submitted work. QL reports personal fees from GRAIL, Inc. during the conduct of the study; in addition, QL has patents pending to GRAIL, Inc. AJ reports personal fees from GRAIL, Inc. during the conduct of the study and personal fees from Illumina outside the submitted work; in addition, AJ has patents pending to GRAIL, Inc. and a patent (differential tagging of RNA for preparation of a cell-free DNA/RNA sequencing library) issued to GRAIL, Inc. RK reports personal fees from GRAIL, Inc. during the conduct of the study as well as personal fees from Mindstrong Health, personal fees from Lyell Immunopharma, personal fees from LifeMine, personal fees from Wisdo, personal fees from Medical Creations/Extremity, and personal fees from FOG Pharma all outside the submitted work. KNK reports personal fees and other from GRAIL, Inc. during the conduct of the study as well as other from Illumina outside the submitted work. ML reports personal fees and other from GRAIL, Inc. during the conduct of the study as well as personal fees and other from Genentech, Inc., personal fees and other from Google Life Sciences, personal fees and other from Boreal Genomics, and personal fees and other from Genomic Health, Inc. outside the submitted work; in addition, ML has a patent arising from the CCGA work pending to GRAIL, Inc. MCM reports personal fees from GRAIL, Inc. during the conduct of the study; in addition, MCM has patents pending to GRAIL, Inc. CM reports personal fees and other from GRAIL, Inc. during the conduct of the study; in addition, CM has a patent pending to GRAIL, Inc. VD reports personal fees and other from GRAIL, Inc. during the conduct of the study. JN reports personal fees from GRAIL Inc. during the conduct of the study as well as personal fees from Verily Life Sciences (formerly part of Google) outside the submitted work. Joshua N. reports personal fees and other from GRAIL, Inc. during the conduct of the study. VN reports personal fees from GRAIL Inc. during the conduct of the study; in addition, VN has patents pending to GRAIL, Inc. RVS reports personal fees from GRAIL, Inc. during the conduct of the study as well as personal fees from Guardant Health outside the submitted work; in addition, RVS has a patent pending to GRAIL, Inc. AS reports personal fees from GRAIL, Inc. during the conduct of the study and is the owner of Illumina stock. LS reports personal fees from GRAIL, Inc. during the conduct of the study; in addition, LS has a patent pending to GRAIL, Inc. MS reports personal fees from Celgene, personal fees from Millenium/Takeda, and personal fees from Syros outside the submitted work. DS reports other from US Oncology during the conduct of the study. AV reports personal fees and other from GRAIL, Inc. during the conduct of the study as well as other from Illumina outside the submitted work. OV reports personal fees from GRAIL, Inc. during the conduct of the study; in addition, OV has patents pending to GRAIL, Inc. SRC reports research support from GRAIL, Inc. JY reports personal fees and other from GRAIL Inc. during the conduct of the study as well as personal fees and other from Acerta Pharma B.V., personal fees from Forty Seven Inc., other from BeiGene, Ltd., other from Celgene Corporation, other from Loxo Oncology, Inc., other from Nektar Therapeutics, other from Corvus Pharmaceuticals, Inc., and other from Illumina, Inc., all outside the submitted work. AB reports a financial interest in GRAIL, Inc. via Foresite Capital’s funds and personal equity. AMA is a founder, employee, and shareholder at GRAIL, Inc. and a paid advisor to Foresite Capital and Myst Therapeutics. JB reports personal fees from GRAIL, Inc. as well as patents pending to GRAIL, Inc. during the conduct of the study; JB also has patents issued to Roche and Philips Medical Systems outside of this work. PF reports personal fees from GRAIL, Inc. as well as patents pending to GRAIL, Inc. during the conduct of the study. WL reports personal fees and other from GRAIL, Inc. during the conduct of the study and personal fees from Genentech outside of this work. TM reports personal fees and other from GRAIL, Inc. and a patent pending to GRAIL, Inc. during the conduct of the study; TM also reports personal fees and other from Lexent Bio, HTG Molecular, and NDA Partners, as well as other from Genomic Health and personal fees from Terumo Medical outside of this work. RS, RL, TW, AS, ON, LZ, RC, CY, PS, NR, CC, AY, A. Shanmugam, JS, GA, AM, JZ, HC, GC, KCS, XC, BA, JL, JY, FA, LB, J. Berman, JC, TK, SB, JFB, CB, TCC, DC, ZD, ETF, A-RH, RJ, BJ, QL, SN, CN, SP, RR, OS, ES, AJS, SS, KKS, ST, JMT, RTW, XY, JY, and NZ report personal fees from GRAIL, Inc. during the conduct of the study. The remaining authors have declared no conflict of interest.

Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.

Figures

Figure 1.. The CCGA study for development…
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 WBCs 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 2.. Pre-classifier sample preparation and preprocessing…
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 3.. Participant disposition.
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 4.. Targeted methylation cfDNA test performance.
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 5.. Sensitivity in individual tumors by…
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 6.. Tissue of origin accuracy by…
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.

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