Abstract 4931: Pan-cancer proteomic profiling of the EPIC cohort identifies novel plasma biomarkers of cancer risk

Plasma proteins play critical roles in various cancer-related processes, are frequently dysregulated in cancer, and represent a key source of druggable targets. The integration of high-throughput proteomic assays into large-scale population-based studies may provide important insights into mechanisms of cancer pathogenesis, identify biomarkers for cancer risk and early detection, and prioritize novel therapeutic targets for cancer prevention. Here, we describe initial findings from proteomic profiling of baseline plasma samples from a case-cohort of 6,073 incident cancer cases and 4,115 sub-cohort members within the European Prospective Investigation into Cancer and Nutrition (EPIC) using the SomaScan 7K assay. Participants at baseline were 53.8 (SD 8.7) years of age and 57.3% were women. Prentice weights and robust variance were used to account for the case-cohort design of the study. Models were stratified on sex, center, and age at recruitment and adjusted for education status and cancer type-specific risk factors. Pre-diagnostic plasma concentrations of 7,596 protein aptamers (representing 6,432 proteins) were evaluated for association with 24 cancer types. In total, 2,264 protein aptamer-cancer associations were observed at FDR P < 0.05, including 53 for overall cancer risk. The majority of aptamers (80%, 1,422/1,770) were associated with one cancer type with 11 associated with 4 or more types, including aptamers binding to GDF15, MMP12, and TNFRSF1B. Proteins represented among FDR-significant aptamers map to a diverse set of biological processes including the epithelial-mesenchymal transition, coagulation, and inflammatory signaling pathways. Around half of all aptamer-cancer associations (52%, 1,173/2,264) remained FDR-significant after excluding the first 5 years of follow-up, supporting the potential role of proteins in etiological processes, as opposed to being markers of latent/undiagnosed cancer at baseline. The number of FDR-significant associations per cancer type varied substantially, ranging from 885 for hepatocellular carcinoma and 403 for chronic lymphocytic leukemia to 3 for both pancreatic and thyroid cancer and 2 for endometrial cancer (median 23 aptamers per cancer type, IQR: 6-65). Our plasma-based proteomic profiling of a large case-cohort analysis established within the EPIC study has identified potentially novel biomarkers across a range of cancers, with evidence of both shared and distinct proteomic profiles across cancer types. Replication of findings in independent cohort studies and further interrogation of mechanistic pathways will help to validate proteins identified in this analysis and clarify their roles as potential markers for the early detection, prediction, and prevention of cancer. Citation Format: James Yarmolinsky, Vivian Viallon, David C. Muller, Antonio Agudo, Pietro Ferrari, Giovanna Masala, Salvatore Panico, Carlotta Sacerdote, Karl Smith-Byrne, Ruth C. Travis, Rosario Turmino, P.Martijn Kolijn, Roel C. Vermeulen, Monique Verschure, Nicholas Wareham, Elio Riboli, Marc J. Gunter. Pan-cancer proteomic profiling of the EPIC cohort identifies novel plasma biomarkers of cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4931.

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Journal
Cancer Research
Published
2025-04-21
DOI
https://doi.org/10.1158/1538-7445.am2025-4931
Primary Topic
Radiomics and Machine Learning in Medical Imaging
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article

Abstract 4931: Pan-cancer proteomic profiling of the EPIC cohort identifies novel plasma biomarkers of cancer risk

James Yarmolinsky, Salvatore Panico, Roel C.H. Vermeulen, Ruth C. Travis et al.
Cancer Research
Radiomics and Machine Learning in Medical Imaging
article

Abstract 4931: Pan-cancer proteomic profiling of the EPIC cohort identifies novel plasma biomarkers of cancer risk

James Yarmolinsky, Salvatore Panico, Roel C.H. Vermeulen, Ruth C. Travis, Carlotta Sacerdote, Monique Verschure, Karl Smith-Byrne, Giovanna Masala, P. Martijn Kolijn, Elio Ríboli, Marc J. Gunter, David C. Muller, Pietro Ferrari, Nicholas J. Wareham, Vivian Viallon, Antonio Agudo, Rosario Turmino
article en

Abstract

Plasma proteins play critical roles in various cancer-related processes, are frequently dysregulated in cancer, and represent a key source of druggable targets. The integration of high-throughput proteomic assays into large-scale population-based studies may provide important insights into mechanisms of cancer pathogenesis, identify biomarkers for cancer risk and early detection, and prioritize novel therapeutic targets for cancer prevention. Here, we describe initial findings from proteomic profiling of baseline plasma samples from a case-cohort of 6,073 incident cancer cases and 4,115 sub-cohort members within the European Prospective Investigation into Cancer and Nutrition (EPIC) using the SomaScan 7K assay. Participants at baseline were 53.8 (SD 8.7) years of age and 57.3% were women. Prentice weights and robust variance were used to account for the case-cohort design of the study. Models were stratified on sex, center, and age at recruitment and adjusted for education status and cancer type-specific risk factors. Pre-diagnostic plasma concentrations of 7,596 protein aptamers (representing 6,432 proteins) were evaluated for association with 24 cancer types. In total, 2,264 protein aptamer-cancer associations were observed at FDR P < 0.05, including 53 for overall cancer risk. The majority of aptamers (80%, 1,422/1,770) were associated with one cancer type with 11 associated with 4 or more types, including aptamers binding to GDF15, MMP12, and TNFRSF1B. Proteins represented among FDR-significant aptamers map to a diverse set of biological processes including the epithelial-mesenchymal transition, coagulation, and inflammatory signaling pathways. Around half of all aptamer-cancer associations (52%, 1,173/2,264) remained FDR-significant after excluding the first 5 years of follow-up, supporting the potential role of proteins in etiological processes, as opposed to being markers of latent/undiagnosed cancer at baseline. The number of FDR-significant associations per cancer type varied substantially, ranging from 885 for hepatocellular carcinoma and 403 for chronic lymphocytic leukemia to 3 for both pancreatic and thyroid cancer and 2 for endometrial cancer (median 23 aptamers per cancer type, IQR: 6-65). Our plasma-based proteomic profiling of a large case-cohort analysis established within the EPIC study has identified potentially novel biomarkers across a range of cancers, with evidence of both shared and distinct proteomic profiles across cancer types. Replication of findings in independent cohort studies and further interrogation of mechanistic pathways will help to validate proteins identified in this analysis and clarify their roles as potential markers for the early detection, prediction, and prevention of cancer. Citation Format: James Yarmolinsky, Vivian Viallon, David C. Muller, Antonio Agudo, Pietro Ferrari, Giovanna Masala, Salvatore Panico, Carlotta Sacerdote, Karl Smith-Byrne, Ruth C. Travis, Rosario Turmino, P.Martijn Kolijn, Roel C. Vermeulen, Monique Verschure, Nicholas Wareham, Elio Riboli, Marc J. Gunter. Pan-cancer proteomic profiling of the EPIC cohort identifies novel plasma biomarkers of cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4931.

Cancer ResearchVol. 85(8_Supplement_1)
Utrecht University (NL), University of Cambridge (GB), Codarts Rotterdam (NL), Erasmus MC (NL), University Medical Center Utrecht (NL), University of Oxford (GB), Institut Català d'Oncologia (ES), Italian Association for Cancer Research (IT), The London College (GB), Piedmont Reference Center for Epidemiology and Cancer Prevention (IT), Centre international de recherche sur le cancer (FR), Imperial College London (GB), University of Turin (IT), University of Naples Federico II (IT)
Openalex Percentile: Top 87%
Radiomics and Machine Learning in Medical Imaging
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