Systemic responses
Cancer is associated with widespread systemic perturbations such as inflammation that may be captured in the molecular profiles of genes, proteins, metabolites and DNA methylation in peripheral and target tissues. The Systemic Responses theme investigates these systemic perturbations to inform strategies for the early detection, precision prevention, and treatment of cancer.
Highlights include:
Six inflammatory cytokines may have roles in cancer risk
We explored the causal role of inflammatory cytokines and anti-inflammatory drugs in cancer risk using Mendelian randomization. We uncovered evidence for a causal role for six cytokines in breast, endometrial, lung, ovarian, and prostate cancer. More information: doi.org/10.1186/s12916-021-02193-0
Circulating proteins predict lung cancer diagnosis 2 years earlier
We have developed and published evaluations of proteins measured in blood for estimating lung cancer risk. We show that these biomarkers outperform existing prediction tools (PLCOm2012 and EarlyCDT-Lung) and are capable of predicting a lung cancer diagnosis 2 years before the diagnosis. More information: https://doi.org/10.1038/s41467-023-37979-8; https://doi.org/10.1093/jnci/djad071
Most circulatory inflammatory markers are not causally linked to cancer risk
Given previous findings suggesting associations and possible roles for some inflammatory markers in cancer risk, we investigated 66 circulating inflammatory markers in 30 adult cancers using genetic data for 340K cancer cases and 1.2M controls. We uncover evidence of causal roles using Mendelian randomization for only 4 inflammatory markers in the risk of 4 different cancers. More information: https://doi.org/10.1016/j.ebiom.2024.104991
Plasma IGF1 may mediate the effect of body mass index on colorectal cancer
It is well-known that higher body mass index increases risk of colorectal cancer. In this study we investigated a comprehensive set of hypothesised mediators of this relationship from molecular markers to lifestyle exposures using Mendelian randomization. We uncover evidence for mediation through plasma IGF1. More information: https://doi.org/10.1093/ije/dyae067
Accelerated biological aging may increase the risk of colorectal cancer
Observational evidence suggests that epigenetic age acceleration (i.e. when an individual’s biological age is greater than their chronological age) may be associated with an increased risk of mortality and age-related diseases, including cancer. We investigated the role of epigenetic age acceleration on the risk of five cancers, and we uncovered causal evidence using Mendelian randomization for risk of colorectal cancer. More information: https://doi.org/10.7554/elife.75374
Cancer is associated with widespread systemic perturbations that may be captured in molecular profiles such as the proteomes, transcriptomes and methylomes of peripheral and target tissues. Inflammation, for example, is prominent in cancer, contributing to microenvironments that foster tumorigenesis and metastasis. Consequently, understanding cancer risk, progression and survival requires a deep understanding of these systemic perturbations beyond the contributions of individual genes.
Toward this goal, we systematically characterise systemic perturbations linked to cancer using multi-omic profiles at the level of molecular pathways and high-level processes, distinguishing between mere associations and causal relationships using recently-developed hypothesis-free causal inference methods.
Building on known associations between aging and inflammation on cancer risk, the Systemic responses theme aims to use these associations to discover (1) molecular biomarkers of cancer risk for risk stratification and early detection and (2) therapeutic targets for preventing and treating cancer.
Candidate molecular markers have been selected from among those known to be associated with ageing (e.g. epigenetic clocks) and with inflammation (e.g. inflammatory proteins) in peripheral tissues. These will be integrated into models of cancer risk using machine learning algorithms using molecular data from case-control studies. These markers will in parallel be evaluated for their potential as therapeutic targets by applying Mendelian randomization to evaluate evidence for causal relationships between these markers and cancer risk.
