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Poster session 07

23P - MRE-seq based cancer screening for lung and colorectal cancer by deep learning analysis of cfDNA methylation pattern cancer screening

Date

10 Sep 2022

Session

Poster session 07

Topics

Clinical Research;  Cancer Biology;  Molecular Oncology

Tumour Site

Small Cell Lung Cancer;  Non-Small Cell Lung Cancer;  Colon and Rectal Cancer

Presenters

Sun Hye Shin

Citation

Annals of Oncology (2022) 33 (suppl_7): S4-S18. 10.1016/annonc/annonc1035

Authors

S.H. Shin1, H. Kwon2, H.H. Kim3, N.Y. Min2, Y. Lim2, T. Joo2, K.J. Lee2, M. Jeong2, H. Kim2, S. Um1, C. An4, S. Lee2

Author affiliations

  • 1 Division Of Pulmonary And Critical Care Medicine, Samsung Medical Center (SMC), 06351 - Seoul/KR
  • 2 Global R&d Department, Eone Diagnomics Genome Center, 22014 - Incheon/KR
  • 3 Department Of Surgery, The Catholic University of Korea - Bucheon St. Mary's Hospital, 420-717 - Bucheon/KR
  • 4 General Surgery, The Catholic University of Korea Bucheon?St. Mary's Hospital, 420-717 - Bucheon/KR

Resources

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Abstract 23P

Background

Among various genomic features in the blood, methylation pattern could be useful not only in determining whether cancer is present, but in revealing its origin. The purposes of this prospective study were to evaluate the diagnostic performance of methylation sensitive restriction enzyme digestion followed by sequencing method (MRE-seq) using cell-free DNA (cfDNA) in the diagnosis of cancer and to investigate tissue-of-origin (TOO) using deep neural network (DNN).

Methods

We have developed a deep learning-based prediction model with the hypomethylation data obtained from MRE-seq. One hundred ninety-one cancer patients with stages I–IV (95 lung cancer, 96 colorectal cancer) and 126 non-cancer participants were enrolled in this study. DNA isolated from each sample was subjected to MRE-seq to analyze the difference in methylation patterns between cancer tissues and normal tissues.

Results

We achieved the overall area under the ROC curve (AUC) 0.978 with sensitivity 78.1% at specificity 99.2% for colorectal cancer, AUC 0.956 with sensitivity 66.3% at specificity 99.2% for lung cancer. In colorectal cancer, the sensitivity is 76.5% in stage I, 76.2% in stage II, 78.3% in stage III and 83.3% in stage IV at specificity 99.2%. In lung cancer, the sensitivity is 48.5% in stage I, 44.4% in stage II, 78.9% in stage III and 76.0%enl in stage IV at specificity 99.2 %. The true positive rate (TPR) of TOO model was 94.4% (85/90), 89.9% (80/89) for colorectal cancer and lung cancer respectively.

Conclusions

Global hypomethylation pattern obtained by MRE-seq was useful in the diagnosis of cancer with high accuracy and in the determination of TOO through DNN. The role of MRE-seq needs to be validate for various solid tumors in the future multicenter studies.

Clinical trial identification

NCT 04253509.

Editorial acknowledgement

Legal entity responsible for the study

Eone Diagnomics Genome Center, Republic of Korea.

Funding

Has not received any funding.

Disclosure

All authors have declared no conflicts of interest.

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