A protocol for using human genetic data to identify circulating protein level changes that are the causal consequence of cancer processes

  1. Lisa M Hobson
  2. Richard M Martin
  3. Karl Smith-Byrne
  4. George Davey Smith
  5. Gibran Hemani
  6. Joseph H Gilbody
  7. James Yarmolinsky
  8. Sarah ER Bailey
  9. Lucy J. Goudswaard
  10. Philip C Haycock
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Abstract

Introduction

Cancer is a leading cause of death worldwide. Early detection of cancer improves treatment options and patient survival but detecting cancer at the earliest stage presents challenges. Identification of circulating protein biomarkers for cancer risk stratification and early detection is an attractive avenue for potentially minimally invasive screening and early detection methods. We hypothesise that protein level changes resulting from cancer development can be identified via an individual’s polygenic risk score (PRS) for the disease, representing their genetic liability to developing that cancer.

Methods and analysis

PRS will be calculated using the PRS continuous shrinkage approach (PRS-CS and PRS-CSx) for colorectal and lung cancer risk. This methodology utilises effect sizes from summary statistics from genome-wide association studies (GWAS) available for the cancers of interest to generate weights via the continuous shrinkage approach which incorporates the strengths of the GWAS associations into the shrinkage applied (1). This methodology both improves upon previous PRS methods in accuracy as well as improving cross-ancestry application in the PRS-CSx approach. GWAS summary statistics will be from the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO) and the International Lung Cancer Consortium (ILCCO). The association between the polygenic risk scores and 2923 proteins measured by the Olink platform in UK Biobank (UKB) participants with protein measures available will be assessed using linear regression under the assumption of linearity in the proteomic data. The proteins identified could represent several different scenarios of association such as forward causation (protein causes cancer), reverse causation (cancer genetic liability causes protein level change), or horizontal pleiotropy bias (no causal relationship exists between the protein and cancer). Forward and reverse Mendelian randomization sensitivity analyses, as well as colocalization analysis, will be performed in efforts to distinguish between these three scenarios. Protein changes identified as causally downstream of genetic liability to cancer could reflect processes occurring prior to, or after, disease onset. Due to individuals in the UKB having proteins measures at only one timepoint, and because UKB contains a mix of incident and prevalent cases, some protein measures will have been made prior to a cancer diagnosis while others will have been made after a cancer diagnosis. We will explore the strength of association in relation to the time between protein measurement and prevalent or incident cancer diagnosis.

Ethics and Disseminations

No additional ethical approval is required for Genome Wide Association (GWAS) data used in this analysis as all data from GWAS has undergone individual ethical approval prior to this study. UK Biobank protein measure data will be obtained under application ID: 15825/81499.

Results produced from these analyses will be submitted as an open-access manuscript to journals for review and all code will be made publicly available using GitHub. The PRS we generate and the results of the PRS-protein associations will be returned to the UK Biobank.

Strengths and limitations of this study

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    A strength of the proposed PRS method in this study is the use of all available SNPs from a GWAS, which may increase power to identify proteins in comparison with conventional Mendelian Randomisation (MR) methods that use only those SNPs that are genome-wide significant.

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    Limitations of the study:

    • Lack of protein data for diverse population groups within available datasets; therefore, results may not be generalisable to ancestries outside of the European population for whom sufficient protein data was available for this study.

    • UKB participants reflect a subset of the population from a higher socioeconomic position than average.

    • Prevalent cancer cases will reflect a specific subset of the general population with cancer, individuals who have survived cancer and were able to volunteer for the study; potentially introducing survivorship bias.

    • It cannot be ruled out that proteins may reflect effects of processes beyond cancer liability to protein pathways.

    • Lack of staging information for cancer cases within the UKB limiting our ability to distinguish early versus more advanced cancers.

    • The proteomic technology currently used measures protein binding as opposed to protein levels

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    1. Version published to 10.1101/2024.10.18.24315725v1 on medRxiv