Hepatocellular Carcinoma Detection by Plasma Methylated DNA: Discovery, Phase I Pilot, and Phase II Clinical Validation
Early detection improves hepatocellular carcinoma (HCC) outcomes, but better noninvasive surveillance tools are needed. We aimed to identify and validate methylated DNA markers (MDMs) for HCC detection. Reduced representation bisulfite sequencing was performed on DNA extracted from 18 HCC and 35 con...
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| Published in | Hepatology (Baltimore, Md.) Vol. 69; no. 3; pp. 1180 - 1192 |
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| Main Authors | , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Wiley Subscription Services, Inc
01.03.2019
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Early detection improves hepatocellular carcinoma (HCC) outcomes, but better noninvasive surveillance tools are needed. We aimed to identify and validate methylated DNA markers (MDMs) for HCC detection. Reduced representation bisulfite sequencing was performed on DNA extracted from 18 HCC and 35 control tissues. Candidate MDMs were confirmed by quantitative methylation‐specific PCR in DNA from independent tissues (74 HCC, 29 controls). A phase I plasma pilot incorporated quantitative allele‐specific real‐time target and signal amplification assays on independent plasma‐extracted DNA from 21 HCC cases and 30 controls with cirrhosis. A phase II plasma study was then performed in 95 HCC cases, 51 controls with cirrhosis, and 98 healthy controls using target enrichment long‐probe quantitative amplified signal (TELQAS) assays. Recursive partitioning identified best MDM combinations. The entire MDM panel was statistically cross‐validated by randomly splitting the data 2:1 for training and testing. Random forest (rForest) regression models performed on the training set predicted disease status in the testing set; median areas under the receiver operating characteristics curve (AUCs; and 95% confidence interval [CI]) were reported after 500 iterations. In phase II, a six‐marker MDM panel (homeobox A1 [HOXA1], empty spiracles homeobox 1 [EMX1], AK055957, endothelin‐converting enzyme 1 [ECE1], phosphofructokinase [PFKP], and C‐type lectin domain containing 11A [CLEC11A]) normalized by beta‐1,3‐galactosyltransferase 6 (B3GALT6) level yielded a best‐fit AUC of 0.96 (95% CI, 0.93‐0.99) with HCC sensitivity of 95% (88%‐98%) at specificity of 92% (86%‐96%). The panel detected 3 of 4 (75%) stage 0, 39 of 42 (93%) stage A, 13 of 14 (93%) stage B, 28 of 28 (100%) stage C, and 7 of 7 (100%) stage D HCCs. The AUC value for alpha‐fetoprotein (AFP) was 0.80 (0.74‐0.87) compared to 0.94 (0.9‐0.97) for the cross‐validated MDM panel (P < 0.0001). Conclusion: MDMs identified in this study proved to accurately detect HCC by plasma testing. Further optimization and clinical testing of this promising approach are indicated. |
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| AbstractList | Early detection improves hepatocellular carcinoma (HCC) outcomes, but better noninvasive surveillance tools are needed. We aimed to identify and validate methylated DNA markers (MDMs) for HCC detection. Reduced representation bisulfite sequencing was performed on DNA extracted from 18 HCC and 35 control tissues. Candidate MDMs were confirmed by quantitative methylation-specific PCR in DNA from independent tissues (74 HCC, 29 controls). A phase I plasma pilot incorporated quantitative allele-specific real-time target and signal amplification assays on independent plasma-extracted DNA from 21 HCC cases and 30 controls with cirrhosis. A phase II plasma study was then performed in 95 HCC cases, 51 controls with cirrhosis, and 98 healthy controls using target enrichment long-probe quantitative amplified signal (TELQAS) assays. Recursive partitioning identified best MDM combinations. The entire MDM panel was statistically cross-validated by randomly splitting the data 2:1 for training and testing. Random forest (rForest) regression models performed on the training set predicted disease status in the testing set; median areas under the receiver operating characteristics curve (AUCs; and 95% confidence interval [CI]) were reported after 500 iterations. In phase II, a six-marker MDM panel (homeobox A1 [HOXA1], empty spiracles homeobox 1 [EMX1], AK055957, endothelin-converting enzyme 1 [ECE1], phosphofructokinase [PFKP], and C-type lectin domain containing 11A [CLEC11A]) normalized by beta-1,3-galactosyltransferase 6 (B3GALT6) level yielded a best-fit AUC of 0.96 (95% CI, 0.93-0.99) with HCC sensitivity of 95% (88%-98%) at specificity of 92% (86%-96%). The panel detected 3 of 4 (75%) stage 0, 39 of 42 (93%) stage A, 13 of 14 (93%) stage B, 28 of 28 (100%) stage C, and 7 of 7 (100%) stage D HCCs. The AUC value for alpha-fetoprotein (AFP) was 0.80 (0.74-0.87) compared to 0.94 (0.9-0.97) for the cross-validated MDM panel (P < 0.0001). Conclusion: MDMs identified in this study proved to accurately detect HCC by plasma testing. Further optimization and clinical testing of this promising approach are indicated. Early detection improves hepatocellular carcinoma (HCC) outcomes, but better noninvasive surveillance tools are needed. We aimed to identify and validate methylated DNA markers (MDMs) for HCC detection. Reduced representation bisulfite sequencing was performed on DNA extracted from 18 HCC and 35 control tissues. Candidate MDMs were confirmed by quantitative methylation specific PCR in DNA from independent tissues (74 HCC, 29 controls). A phase I plasma pilot incorporated quantitative allele-specific real time target and signal amplification assays on independent plasma-extracted DNA from 21 HCC cases and 30 cirrhotic controls. A phase II plasma study was then performed in 95 HCC cases, 51 cirrhosis controls, and 98 healthy controls using target enrichment long-probe quantitative amplified signal (TELQAS) assays. Recursive partitioning identified best MDM combinations. The entire MDM panel was statistically cross-validated by randomly splitting the data 2:1 for training and testing. Random forest regression models performed on the training set predicted disease status in the testing set; the median AUC (and 95% CI) were reported after 500 iterations. In phase II, a 6-marker MDM panel (HOXA1, EMX1, AK055957, ECE1, PFKP and CLEC11A , normalized by B3GALT6 level yielded a best fit AUC of 0.96 (95% CI, 0.93–0.99) with HCC sensitivity of 95% (88–98%) at specificity of 92% (86–96%). The panel detected 3/4 (75%) stage 0, 39/42 (93%) stage A, 13/14 (93%) ge B, 28/28 (100%) stage C and 7/7 (100%) stage D HCC. The AUC value for AFP was 0.80 (0.74–0.87) compared to 0.94 (0.9–0.97) for the cross-validated MDM panel, P<0.0001. Early detection improves hepatocellular carcinoma (HCC) outcomes, but better noninvasive surveillance tools are needed. We aimed to identify and validate methylated DNA markers (MDMs) for HCC detection. Reduced representation bisulfite sequencing was performed on DNA extracted from 18 HCC and 35 control tissues. Candidate MDMs were confirmed by quantitative methylation-specific PCR in DNA from independent tissues (74 HCC, 29 controls). A phase I plasma pilot incorporated quantitative allele-specific real-time target and signal amplification assays on independent plasma-extracted DNA from 21 HCC cases and 30 controls with cirrhosis. A phase II plasma study was then performed in 95 HCC cases, 51 controls with cirrhosis, and 98 healthy controls using target enrichment long-probe quantitative amplified signal (TELQAS) assays. Recursive partitioning identified best MDM combinations. The entire MDM panel was statistically cross-validated by randomly splitting the data 2:1 for training and testing. Random forest (rForest) regression models performed on the training set predicted disease status in the testing set; median areas under the receiver operating characteristics curve (AUCs; and 95% confidence interval [CI]) were reported after 500 iterations. In phase II, a six-marker MDM panel (homeobox A1 [HOXA1], empty spiracles homeobox 1 [EMX1], AK055957, endothelin-converting enzyme 1 [ECE1], phosphofructokinase [PFKP], and C-type lectin domain containing 11A [CLEC11A]) normalized by beta-1,3-galactosyltransferase 6 (B3GALT6) level yielded a best-fit AUC of 0.96 (95% CI, 0.93-0.99) with HCC sensitivity of 95% (88%-98%) at specificity of 92% (86%-96%). The panel detected 3 of 4 (75%) stage 0, 39 of 42 (93%) stage A, 13 of 14 (93%) stage B, 28 of 28 (100%) stage C, and 7 of 7 (100%) stage D HCCs. The AUC value for alpha-fetoprotein (AFP) was 0.80 (0.74-0.87) compared to 0.94 (0.9-0.97) for the cross-validated MDM panel (P < 0.0001). Conclusion: MDMs identified in this study proved to accurately detect HCC by plasma testing. Further optimization and clinical testing of this promising approach are indicated.Early detection improves hepatocellular carcinoma (HCC) outcomes, but better noninvasive surveillance tools are needed. We aimed to identify and validate methylated DNA markers (MDMs) for HCC detection. Reduced representation bisulfite sequencing was performed on DNA extracted from 18 HCC and 35 control tissues. Candidate MDMs were confirmed by quantitative methylation-specific PCR in DNA from independent tissues (74 HCC, 29 controls). A phase I plasma pilot incorporated quantitative allele-specific real-time target and signal amplification assays on independent plasma-extracted DNA from 21 HCC cases and 30 controls with cirrhosis. A phase II plasma study was then performed in 95 HCC cases, 51 controls with cirrhosis, and 98 healthy controls using target enrichment long-probe quantitative amplified signal (TELQAS) assays. Recursive partitioning identified best MDM combinations. The entire MDM panel was statistically cross-validated by randomly splitting the data 2:1 for training and testing. Random forest (rForest) regression models performed on the training set predicted disease status in the testing set; median areas under the receiver operating characteristics curve (AUCs; and 95% confidence interval [CI]) were reported after 500 iterations. In phase II, a six-marker MDM panel (homeobox A1 [HOXA1], empty spiracles homeobox 1 [EMX1], AK055957, endothelin-converting enzyme 1 [ECE1], phosphofructokinase [PFKP], and C-type lectin domain containing 11A [CLEC11A]) normalized by beta-1,3-galactosyltransferase 6 (B3GALT6) level yielded a best-fit AUC of 0.96 (95% CI, 0.93-0.99) with HCC sensitivity of 95% (88%-98%) at specificity of 92% (86%-96%). The panel detected 3 of 4 (75%) stage 0, 39 of 42 (93%) stage A, 13 of 14 (93%) stage B, 28 of 28 (100%) stage C, and 7 of 7 (100%) stage D HCCs. The AUC value for alpha-fetoprotein (AFP) was 0.80 (0.74-0.87) compared to 0.94 (0.9-0.97) for the cross-validated MDM panel (P < 0.0001). Conclusion: MDMs identified in this study proved to accurately detect HCC by plasma testing. Further optimization and clinical testing of this promising approach are indicated. Early detection improves hepatocellular carcinoma (HCC) outcomes, but better noninvasive surveillance tools are needed. We aimed to identify and validate methylated DNA markers (MDMs) for HCC detection. Reduced representation bisulfite sequencing was performed on DNA extracted from 18 HCC and 35 control tissues. Candidate MDMs were confirmed by quantitative methylation‐specific PCR in DNA from independent tissues (74 HCC, 29 controls). A phase I plasma pilot incorporated quantitative allele‐specific real‐time target and signal amplification assays on independent plasma‐extracted DNA from 21 HCC cases and 30 controls with cirrhosis. A phase II plasma study was then performed in 95 HCC cases, 51 controls with cirrhosis, and 98 healthy controls using target enrichment long‐probe quantitative amplified signal (TELQAS) assays. Recursive partitioning identified best MDM combinations. The entire MDM panel was statistically cross‐validated by randomly splitting the data 2:1 for training and testing. Random forest (rForest) regression models performed on the training set predicted disease status in the testing set; median areas under the receiver operating characteristics curve (AUCs; and 95% confidence interval [CI]) were reported after 500 iterations. In phase II, a six‐marker MDM panel (homeobox A1 [ HOXA1 ], empty spiracles homeobox 1 [ EMX1 ], AK055957 , endothelin‐converting enzyme 1 [ ECE1 ], phosphofructokinase [ PFKP ], and C‐type lectin domain containing 11A [ CLEC11A ]) normalized by beta‐1,3‐galactosyltransferase 6 ( B3GALT6 ) level yielded a best‐fit AUC of 0.96 (95% CI, 0.93‐0.99) with HCC sensitivity of 95% (88%‐98%) at specificity of 92% (86%‐96%). The panel detected 3 of 4 (75%) stage 0, 39 of 42 (93%) stage A, 13 of 14 (93%) stage B, 28 of 28 (100%) stage C, and 7 of 7 (100%) stage D HCCs. The AUC value for alpha‐fetoprotein (AFP) was 0.80 (0.74‐0.87) compared to 0.94 (0.9‐0.97) for the cross‐validated MDM panel ( P < 0.0001). Conclusion : MDMs identified in this study proved to accurately detect HCC by plasma testing. Further optimization and clinical testing of this promising approach are indicated. Early detection improves hepatocellular carcinoma (HCC) outcomes, but better noninvasive surveillance tools are needed. We aimed to identify and validate methylated DNA markers (MDMs) for HCC detection. Reduced representation bisulfite sequencing was performed on DNA extracted from 18 HCC and 35 control tissues. Candidate MDMs were confirmed by quantitative methylation‐specific PCR in DNA from independent tissues (74 HCC, 29 controls). A phase I plasma pilot incorporated quantitative allele‐specific real‐time target and signal amplification assays on independent plasma‐extracted DNA from 21 HCC cases and 30 controls with cirrhosis. A phase II plasma study was then performed in 95 HCC cases, 51 controls with cirrhosis, and 98 healthy controls using target enrichment long‐probe quantitative amplified signal (TELQAS) assays. Recursive partitioning identified best MDM combinations. The entire MDM panel was statistically cross‐validated by randomly splitting the data 2:1 for training and testing. Random forest (rForest) regression models performed on the training set predicted disease status in the testing set; median areas under the receiver operating characteristics curve (AUCs; and 95% confidence interval [CI]) were reported after 500 iterations. In phase II, a six‐marker MDM panel (homeobox A1 [HOXA1], empty spiracles homeobox 1 [EMX1], AK055957, endothelin‐converting enzyme 1 [ECE1], phosphofructokinase [PFKP], and C‐type lectin domain containing 11A [CLEC11A]) normalized by beta‐1,3‐galactosyltransferase 6 (B3GALT6) level yielded a best‐fit AUC of 0.96 (95% CI, 0.93‐0.99) with HCC sensitivity of 95% (88%‐98%) at specificity of 92% (86%‐96%). The panel detected 3 of 4 (75%) stage 0, 39 of 42 (93%) stage A, 13 of 14 (93%) stage B, 28 of 28 (100%) stage C, and 7 of 7 (100%) stage D HCCs. The AUC value for alpha‐fetoprotein (AFP) was 0.80 (0.74‐0.87) compared to 0.94 (0.9‐0.97) for the cross‐validated MDM panel (P < 0.0001). Conclusion: MDMs identified in this study proved to accurately detect HCC by plasma testing. Further optimization and clinical testing of this promising approach are indicated. |
| Author | Berger, Calise K. Giama, Nasra H. Lidgard, Graham P. Kisiel, John B. Foote, Patrick H. Lewis, Jason T. Ahlquist, David A. Abdallah, Mohamed A. Taylor, William R. Slettedahl, Seth W. Burger, Kelli N. Yab, Tracy C. Giakoumopoulos, Maria Smyrk, Thomas C. Ghoz, Hassan M. Onyirioha, Kristeen Dukek, Brian A. V.S.R. Kanipakam, Reddappa Mahoney, Douglas W. Allawi, Hatim T. Roberts, Lewis R. |
| AuthorAffiliation | 1. Mayo Clinic, 200 First Street SW, Rochester, MN 55905 2. Exact Sciences Development Company, LLC, 441 Charmany Drive, Madison, WI 53719 |
| AuthorAffiliation_xml | – name: 1. Mayo Clinic, 200 First Street SW, Rochester, MN 55905 – name: 2. Exact Sciences Development Company, LLC, 441 Charmany Drive, Madison, WI 53719 |
| Author_xml | – sequence: 1 givenname: John B. surname: Kisiel fullname: Kisiel, John B. email: kisiel.john@mayo.edu organization: Mayo Clinic – sequence: 2 givenname: Brian A. surname: Dukek fullname: Dukek, Brian A. organization: Mayo Clinic – sequence: 3 givenname: Reddappa surname: V.S.R. Kanipakam fullname: V.S.R. Kanipakam, Reddappa organization: Mayo Clinic – sequence: 4 givenname: Hassan M. surname: Ghoz fullname: Ghoz, Hassan M. organization: Mayo Clinic – sequence: 5 givenname: Tracy C. surname: Yab fullname: Yab, Tracy C. organization: Mayo Clinic – sequence: 6 givenname: Calise K. surname: Berger fullname: Berger, Calise K. organization: Mayo Clinic – sequence: 7 givenname: William R. surname: Taylor fullname: Taylor, William R. organization: Mayo Clinic – sequence: 8 givenname: Patrick H. surname: Foote fullname: Foote, Patrick H. organization: Mayo Clinic – sequence: 9 givenname: Nasra H. surname: Giama fullname: Giama, Nasra H. organization: Mayo Clinic – sequence: 10 givenname: Kristeen surname: Onyirioha fullname: Onyirioha, Kristeen organization: Mayo Clinic – sequence: 11 givenname: Mohamed A. surname: Abdallah fullname: Abdallah, Mohamed A. organization: Mayo Clinic – sequence: 12 givenname: Kelli N. surname: Burger fullname: Burger, Kelli N. organization: Mayo Clinic – sequence: 13 givenname: Seth W. surname: Slettedahl fullname: Slettedahl, Seth W. organization: Mayo Clinic – sequence: 14 givenname: Douglas W. surname: Mahoney fullname: Mahoney, Douglas W. organization: Mayo Clinic – sequence: 15 givenname: Thomas C. surname: Smyrk fullname: Smyrk, Thomas C. organization: Mayo Clinic – sequence: 16 givenname: Jason T. surname: Lewis fullname: Lewis, Jason T. organization: Mayo Clinic – sequence: 17 givenname: Maria surname: Giakoumopoulos fullname: Giakoumopoulos, Maria organization: Exact Sciences Development Company, LLC – sequence: 18 givenname: Hatim T. surname: Allawi fullname: Allawi, Hatim T. organization: Exact Sciences Development Company, LLC – sequence: 19 givenname: Graham P. surname: Lidgard fullname: Lidgard, Graham P. organization: Exact Sciences Development Company, LLC – sequence: 20 givenname: Lewis R. surname: Roberts fullname: Roberts, Lewis R. organization: Mayo Clinic – sequence: 21 givenname: David A. surname: Ahlquist fullname: Ahlquist, David A. organization: Mayo Clinic |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30168613$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | 2018 by the American Association for the Study of Liver Diseases. 2019 by the American Association for the Study of Liver Diseases. |
| Copyright_xml | – notice: 2018 by the American Association for the Study of Liver Diseases. – notice: 2019 by the American Association for the Study of Liver Diseases. |
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| DOI | 10.1002/hep.30244 |
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| SubjectTerms | Bisulfite Carcinoma, Hepatocellular Cirrhosis Deoxyribonucleic acid DNA DNA Methylation DNA sequencing DNA, Neoplasm - blood DNA, Neoplasm - metabolism Early Detection of Cancer - methods Endothelins Female Hepatocellular carcinoma Hepatology Homeobox HOXA1 protein Humans Liver cancer Liver cirrhosis Liver Neoplasms - blood Male Middle Aged Phosphofructokinase Pilot Projects Plasma Regression analysis Single-Blind Method Spiracles |
| Title | Hepatocellular Carcinoma Detection by Plasma Methylated DNA: Discovery, Phase I Pilot, and Phase II Clinical Validation |
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