Metastatic State of Colorectal Cancer can be Accurately Predicted with Methylome

Somayah Albaradei; Maha Thafar; Christophe Van Neste; Magbubah Essack; Vladimir B. Bajic

doi:10.1145/3383783.3383792

Metastatic State of Colorectal Cancer can be Accurately Predicted with Methylome

Somayah Albaradei, Maha Thafar, Christophe Van Neste, Magbubah Essack, Vladimir B. Bajic

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Scopus citations

Abstract

Colorectal cancer (CRC) appears to be the third most common cancer as well as the fourth most common cause of cancer deaths in the world. Its most lethal states are when it becomes metastatic. It is of interest to find tests that can quickly and accurately determine if the patient has already developed metastasis. Changes in methylation profiles have been found to be characteristic of cancers at different stages and can therefore be used to develop diagnostic panels. We developed a deep learning (DL) model (Deep2Met) using methylation profiles of patients with CRC to predict if the cancer is in its metastatic state. Results suggest that our method achieves an AUPR and an average F-score of 96.99% and 94.71%, respectively, making Deep2Met potentially useful for diagnostic purposes. The DL model Deep2Met we developed, shows promise in the diagnosis of CRC based on methylation profiles of individual patients.

Original language	English (US)
Title of host publication	Proceedings of the 2019 6th International Conference on Bioinformatics Research and Applications
Publisher	ACM
Pages	125-130
Number of pages	6
ISBN (Print)	9781450372183
DOIs	https://doi.org/10.1145/3383783.3383792
State	Published - May 4 2020

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): BAS/1/1606-01-01, URF/1/1976
Acknowledgements: This work has been supported by the King Abdullah University of Science and Technology (KAUST) Base Research Fund (BAS/1/1606-01-01) to VBB, and KAUST Office of Sponsored Research (OSR) under Awards No CCF ? URF/1/1976-30-01

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10.1145/3383783.3383792

https://repository.kaust.edu.sa/bitstream/10754/664683/1/metastatic.pdf

Cite this

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title = "Metastatic State of Colorectal Cancer can be Accurately Predicted with Methylome",

abstract = "Colorectal cancer (CRC) appears to be the third most common cancer as well as the fourth most common cause of cancer deaths in the world. Its most lethal states are when it becomes metastatic. It is of interest to find tests that can quickly and accurately determine if the patient has already developed metastasis. Changes in methylation profiles have been found to be characteristic of cancers at different stages and can therefore be used to develop diagnostic panels. We developed a deep learning (DL) model (Deep2Met) using methylation profiles of patients with CRC to predict if the cancer is in its metastatic state. Results suggest that our method achieves an AUPR and an average F-score of 96.99% and 94.71%, respectively, making Deep2Met potentially useful for diagnostic purposes. The DL model Deep2Met we developed, shows promise in the diagnosis of CRC based on methylation profiles of individual patients.",

author = "Somayah Albaradei and Maha Thafar and {Van Neste}, Christophe and Magbubah Essack and Bajic, {Vladimir B.}",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledged KAUST grant number(s): BAS/1/1606-01-01, URF/1/1976 Acknowledgements: This work has been supported by the King Abdullah University of Science and Technology (KAUST) Base Research Fund (BAS/1/1606-01-01) to VBB, and KAUST Office of Sponsored Research (OSR) under Awards No CCF ? URF/1/1976-30-01",

year = "2020",

month = may,

day = "4",

doi = "10.1145/3383783.3383792",

language = "English (US)",

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pages = "125--130",

booktitle = "Proceedings of the 2019 6th International Conference on Bioinformatics Research and Applications",

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AU - Albaradei, Somayah

AU - Thafar, Maha

AU - Van Neste, Christophe

AU - Essack, Magbubah

AU - Bajic, Vladimir B.

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledged KAUST grant number(s): BAS/1/1606-01-01, URF/1/1976 Acknowledgements: This work has been supported by the King Abdullah University of Science and Technology (KAUST) Base Research Fund (BAS/1/1606-01-01) to VBB, and KAUST Office of Sponsored Research (OSR) under Awards No CCF ? URF/1/1976-30-01

PY - 2020/5/4

Y1 - 2020/5/4

N2 - Colorectal cancer (CRC) appears to be the third most common cancer as well as the fourth most common cause of cancer deaths in the world. Its most lethal states are when it becomes metastatic. It is of interest to find tests that can quickly and accurately determine if the patient has already developed metastasis. Changes in methylation profiles have been found to be characteristic of cancers at different stages and can therefore be used to develop diagnostic panels. We developed a deep learning (DL) model (Deep2Met) using methylation profiles of patients with CRC to predict if the cancer is in its metastatic state. Results suggest that our method achieves an AUPR and an average F-score of 96.99% and 94.71%, respectively, making Deep2Met potentially useful for diagnostic purposes. The DL model Deep2Met we developed, shows promise in the diagnosis of CRC based on methylation profiles of individual patients.

AB - Colorectal cancer (CRC) appears to be the third most common cancer as well as the fourth most common cause of cancer deaths in the world. Its most lethal states are when it becomes metastatic. It is of interest to find tests that can quickly and accurately determine if the patient has already developed metastasis. Changes in methylation profiles have been found to be characteristic of cancers at different stages and can therefore be used to develop diagnostic panels. We developed a deep learning (DL) model (Deep2Met) using methylation profiles of patients with CRC to predict if the cancer is in its metastatic state. Results suggest that our method achieves an AUPR and an average F-score of 96.99% and 94.71%, respectively, making Deep2Met potentially useful for diagnostic purposes. The DL model Deep2Met we developed, shows promise in the diagnosis of CRC based on methylation profiles of individual patients.

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DO - 10.1145/3383783.3383792

M3 - Conference contribution

SN - 9781450372183

SP - 125

EP - 130

BT - Proceedings of the 2019 6th International Conference on Bioinformatics Research and Applications

PB - ACM

ER -

Metastatic State of Colorectal Cancer can be Accurately Predicted with Methylome

Abstract

Bibliographical note

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