Population-scale plasma proteomics reveals pre-diagnostic biomarkers and shared pathways of cancer risk across 25 cancers

@misc{yarmolinsky2026populationscale,
  title = {{Population-scale plasma proteomics reveals pre-diagnostic biomarkers and shared pathways of cancer risk across 25 cancers}},
  author = {J. Yarmolinsky and V. Viallon and V. A. Burk and D. C. Muller and P. M. Kolijn and C. M. Lill and K. Tsilidis and P. Ferrari and G. Richenberg and Z. Wang and M. D. Chirlaque Lopez and A. Jimenez-Zabala and R. Turmino and C. Sacerdote and N. Wareham and Z. Huang and K. Smith-Byrne and R. Travis and N. Chatterjee and E. A. Platz and R. C. Vermeulen and E. Riboli and M. J. Gunter},
  howpublished = {medRxiv},
  year = {2026},
  month = sep,
  doi = {10.64898/2026.09.22.26363676},
  url = {https://www.medrxiv.org/content/10.64898/2026.09.22.26363676v1},
  note = {CC BY-NC-ND},
}

TY  - UNPB
TI  - Population-scale plasma proteomics reveals pre-diagnostic biomarkers and shared pathways of cancer risk across 25 cancers
AU  - J. Yarmolinsky
AU  - V. Viallon
AU  - V. A. Burk
AU  - D. C. Muller
AU  - P. M. Kolijn
AU  - C. M. Lill
AU  - K. Tsilidis
AU  - P. Ferrari
AU  - G. Richenberg
AU  - Z. Wang
AU  - M. D. Chirlaque Lopez
AU  - A. Jimenez-Zabala
AU  - R. Turmino
AU  - C. Sacerdote
AU  - N. Wareham
AU  - Z. Huang
AU  - K. Smith-Byrne
AU  - R. Travis
AU  - N. Chatterjee
AU  - E. A. Platz
AU  - R. C. Vermeulen
AU  - E. Riboli
AU  - M. J. Gunter
PY  - 2026
DA  - 2026/09/22
PB  - medRxiv
DO  - 10.64898/2026.09.22.26363676
UR  - https://www.medrxiv.org/content/10.64898/2026.09.22.26363676v1
AB  - The causes of most cancers remain unknown because the biological pathways underlying cancer development are poorly understood. Large-scale plasma proteomics offers a powerful strategy to uncover these pathways, identify biomarkers for cancer early detection, and discover novel therapeutic targets but has rarely been applied in prospective cohorts. Here, we profiled 6,402 plasma proteins using the SomaScanTM 7K Assay in baseline samples from 9,828 participants in the European Prospective Investigation into Cancer and Nutrition (EPIC), including 6,073 incident cancer cases within a case-cohort design. We identified 1,732 protein-cancer associations (FDR P < 0.05) across 25 cancer endpoints in multivariable-adjusted models, including 60 proteins associated with overall cancer risk. Confirmation analyses in the Atherosclerosis Risk in Communities (ARIC) Study cohort showed directional concordance for 78% of associations and statistical replication of 49% of findings. Most proteins were associated with a single cancer type, whereas 284 were associated with multiple cancers, indicating both shared and distinct biological pathways across cancer sites. Stratification by follow-up time revealed 523 protein associations that were restricted to specific time windows after blood collection (<2 years, <5 years, and >5 years), including 447 that were not apparent in primary analyses, highlighting proteins with potential roles as early detection or aetiological biomarkers. Proteins associated with cancer risk were enriched across diverse biological pathways and 315 corresponded to targets of approved cancer and non-cancer medications, while most proteins represent previously unrecognised links to cancer risk. As the most comprehensive prospective investigation of the plasma proteome and cancer risk to date, our study significantly expands our understanding of key biological pathways shaping cancer development and provides a resource for the scientific community to accelerate development of early detection strategies, biomarkers for risk stratification, and novel therapeutic interventions for cancer prevention.
ER  -