Biomarkers, omics and artificial intelligence for early detection of pancreatic cancer

Kate Murray; Lucy Oldfield; Irena Stefanova; Manuel Gentiluomo; Paolo Aretini; Rachel O’Sullivan; William Greenhalf; Salvatore Paiella; Mateus N Aoki; Aldo Pastore; James Birch-Ford; Bhavana Hemantha Rao; Pinar Uysal-Onganer; Caoimhe M Walsh; George B Hanna; Jagriti Narang; Pradakshina Sharma; Daniele Campa; Cosmeri Rizzato; Andrei Turtoi; Elif Arik Sever; Alessio Felici; Ceren Sucularli; Giulia Peduzzi; Elif Öz; Osman Uğur Sezerman; Robert Van der Meer; Nathan Thompson; Eithne Costello

doi:10.1016/j.semcancer.2025.02.009

Biomarkers, omics and artificial intelligence for early detection of pancreatic cancer

Kate Murray, Lucy Oldfield, Irena Stefanova, Manuel Gentiluomo, Paolo Aretini, Rachel O’Sullivan, William Greenhalf, Salvatore Paiella, Mateus N Aoki, Aldo Pastore, James Birch-Ford, Bhavana Hemantha Rao, Pinar Uysal-Onganer, Caoimhe M Walsh, George B Hanna, Jagriti Narang, Pradakshina Sharma, Daniele Campa, Cosmeri Rizzato, Andrei TurtoiElif Arik Sever, Alessio Felici, Ceren Sucularli, Giulia Peduzzi, Elif Öz, Osman Uğur Sezerman, Robert Van der Meer, Nathan Thompson, Eithne Costello^*

^*Corresponding author for this work

Management Science

Research output: Contribution to journal › Article › peer-review

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is frequently diagnosed in its late stages when treatment options are limited. Unlike other common cancers, there are no population-wide screening programmes for PDAC. Thus, early disease detection, although urgently needed, remains elusive. Individuals in certain high-risk groups are, however, offered screening or surveillance. Here we explore advances in understanding high-risk groups for PDAC and efforts to implement biomarker-driven detection of PDAC in these groups. We review current approaches to early detection biomarker development and the use of artificial intelligence as applied to electronic health records (EHRs) and social media. Finally, we address the cost-effectiveness of applying biomarker strategies for early detection of PDAC.

Original language	English
Pages (from-to)	76-88
Number of pages	13
Journal	Seminars in Cancer Biology
Volume	111
Early online date	20 Feb 2025
DOIs	https://doi.org/10.1016/j.semcancer.2025.02.009
Publication status	E-pub ahead of print - 20 Feb 2025

Funding

This article is based upon work from COST Action “Identification of biological markers for prevention and translational medicine in pancreatic cancer (TRANSPAN)”, CA21116, supported by COST (European Cooperation in Science and Technology). Eithne Costello is supported by Pancreatic Cancer UK and Cancer Research UK, grant number C7690/A26881.

Keywords

pancreatic cancer
early detection
biomarkers
artificial intelligence
omics

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/j.semcancer.2025.02.009Licence: CC BY 4.0

Murray-etal-SCB-2025-Biomarkers-omics-and-artificial-intelligence-for-early-detection-of-pancreatic-cancerFinal published version, 606 KBLicence: CC BY 4.0

1 Active

Detecting pancreatic cancer in the largest high-risk group for this disease: A top-down approach (CRUK Early Detection Programme)
van der Meer, R. (Principal Investigator) & Morton, A. (Co-investigator)
Cancer Research UK CRUK
1/04/19 → 30/06/25
Project: Research

1 Article

United Kingdom Early Detection Initiative (UK-EDI): protocol for establishing a national multicentre cohort of individuals with new-onset diabetes for early detection of pancreatic cancer
Oldfield, L., Stott, M., Hanson, R., Jackson, R. J., Reynolds, W., Chandran-Gorner, V., Van Der Meer, R., Alison, L., Tejeiro, R., Purewal, T., Ghaneh, P., Palmer, D., Greenhalf, W., Halloran, C. & Costello, E., 10 Oct 2022, In: BMJ open. 12, 10, e068010.
Research output: Contribution to journal › Article › peer-review

Open Access
File
8 Citations (Scopus)

26 Downloads (Pure)

Improved patient outcomes through evidence-based decisions by Scottish and global health organisations
Morton, A. (Main contact), van der Meer, R. (Participant), Megiddo, I. (Participant) & Howick, S. (Participant)
Impact: Impact - for External Portal › Health and welfare - new products, guidelines and services

File

1 Participation in workshop, seminar, course

Inspiring Innovation in Cancer Care III
van der Meer, R. (Participant)
14 Nov 2023
Activity: Participating in or organising an event types › Participation in workshop, seminar, course

Cite this

Murray, K., Oldfield, L., Stefanova, I., Gentiluomo, M., Aretini, P., O’Sullivan, R., Greenhalf, W., Paiella, S., Aoki, M. N., Pastore, A., Birch-Ford, J., Rao, B. H., Uysal-Onganer, P., Walsh, C. M., Hanna, G. B., Narang, J., Sharma, P., Campa, D., Rizzato, C., ... Costello, E. (2025). Biomarkers, omics and artificial intelligence for early detection of pancreatic cancer. Seminars in Cancer Biology, 111, 76-88. Advance online publication. https://doi.org/10.1016/j.semcancer.2025.02.009

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abstract = "Pancreatic ductal adenocarcinoma (PDAC) is frequently diagnosed in its late stages when treatment options are limited. Unlike other common cancers, there are no population-wide screening programmes for PDAC. Thus, early disease detection, although urgently needed, remains elusive. Individuals in certain high-risk groups are, however, offered screening or surveillance. Here we explore advances in understanding high-risk groups for PDAC and efforts to implement biomarker-driven detection of PDAC in these groups. We review current approaches to early detection biomarker development and the use of artificial intelligence as applied to electronic health records (EHRs) and social media. Finally, we address the cost-effectiveness of applying biomarker strategies for early detection of PDAC.",

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Murray, K, Oldfield, L, Stefanova, I, Gentiluomo, M, Aretini, P, O’Sullivan, R, Greenhalf, W, Paiella, S, Aoki, MN, Pastore, A, Birch-Ford, J, Rao, BH, Uysal-Onganer, P, Walsh, CM, Hanna, GB, Narang, J, Sharma, P, Campa, D, Rizzato, C, Turtoi, A, Sever, EA, Felici, A, Sucularli, C, Peduzzi, G, Öz, E, Sezerman, OU, Van der Meer, R, Thompson, N & Costello, E 2025, 'Biomarkers, omics and artificial intelligence for early detection of pancreatic cancer', Seminars in Cancer Biology, vol. 111, pp. 76-88. https://doi.org/10.1016/j.semcancer.2025.02.009

TY - JOUR

T1 - Biomarkers, omics and artificial intelligence for early detection of pancreatic cancer

AU - Murray, Kate

AU - Oldfield, Lucy

AU - Stefanova, Irena

AU - Gentiluomo, Manuel

AU - Aretini, Paolo

AU - O’Sullivan, Rachel

AU - Greenhalf, William

AU - Paiella, Salvatore

AU - Aoki, Mateus N

AU - Pastore, Aldo

AU - Birch-Ford, James

AU - Rao, Bhavana Hemantha

AU - Uysal-Onganer, Pinar

AU - Walsh, Caoimhe M

AU - Hanna, George B

AU - Narang, Jagriti

AU - Sharma, Pradakshina

AU - Campa, Daniele

AU - Rizzato, Cosmeri

AU - Turtoi, Andrei

AU - Sever, Elif Arik

AU - Felici, Alessio

AU - Sucularli, Ceren

AU - Peduzzi, Giulia

AU - Öz, Elif

AU - Sezerman, Osman Uğur

AU - Van der Meer, Robert

AU - Thompson, Nathan

AU - Costello, Eithne

PY - 2025/2/20

Y1 - 2025/2/20

N2 - Pancreatic ductal adenocarcinoma (PDAC) is frequently diagnosed in its late stages when treatment options are limited. Unlike other common cancers, there are no population-wide screening programmes for PDAC. Thus, early disease detection, although urgently needed, remains elusive. Individuals in certain high-risk groups are, however, offered screening or surveillance. Here we explore advances in understanding high-risk groups for PDAC and efforts to implement biomarker-driven detection of PDAC in these groups. We review current approaches to early detection biomarker development and the use of artificial intelligence as applied to electronic health records (EHRs) and social media. Finally, we address the cost-effectiveness of applying biomarker strategies for early detection of PDAC.

AB - Pancreatic ductal adenocarcinoma (PDAC) is frequently diagnosed in its late stages when treatment options are limited. Unlike other common cancers, there are no population-wide screening programmes for PDAC. Thus, early disease detection, although urgently needed, remains elusive. Individuals in certain high-risk groups are, however, offered screening or surveillance. Here we explore advances in understanding high-risk groups for PDAC and efforts to implement biomarker-driven detection of PDAC in these groups. We review current approaches to early detection biomarker development and the use of artificial intelligence as applied to electronic health records (EHRs) and social media. Finally, we address the cost-effectiveness of applying biomarker strategies for early detection of PDAC.

KW - pancreatic cancer

KW - early detection

KW - biomarkers

KW - artificial intelligence

KW - omics

U2 - 10.1016/j.semcancer.2025.02.009

DO - 10.1016/j.semcancer.2025.02.009

M3 - Article

SN - 1044-579X

VL - 111

SP - 76

EP - 88

JO - Seminars in Cancer Biology

JF - Seminars in Cancer Biology

ER -

Biomarkers, omics and artificial intelligence for early detection of pancreatic cancer

Abstract

Funding

Keywords

UN SDGs

Access to Document

Fingerprint

Projects

Detecting pancreatic cancer in the largest high-risk group for this disease: A top-down approach (CRUK Early Detection Programme)

Research output

United Kingdom Early Detection Initiative (UK-EDI): protocol for establishing a national multicentre cohort of individuals with new-onset diabetes for early detection of pancreatic cancer

Impacts

Improved patient outcomes through evidence-based decisions by Scottish and global health organisations

Activities

Inspiring Innovation in Cancer Care III

Cite this