Related Experiment Video
Updated: Dec 9, 2025
Author Spotlight: Exploring the Role of Inflammation in the Co-occurrence of Primary Sjogren's Syndrome and Lung Adenocarcinoma
Published on: September 20, 2024
Integration of multiomic annotation data to prioritize and characterize inflammation and immune-related risk variants
Ryan Sun1, Miao Xu2,3, Xihao Li2
1Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Abstract:
Clinical trial results have recently demonstrated that inhibiting inflammation by targeting the interleukin-1β pathway can offer a significant reduction in lung cancer incidence and mortality, highlighting a pressing and unmet need to understand the benefits of inflammation-focused lung cancer therapies at the genetic level. While numerous genome-wide association studies (GWAS) have explored the genetic etiology of lung cancer, there remains a large gap between the type of information that may be gleaned from an association study and the depth of understanding necessary to explain and drive translational findings. Thus, in this study we jointly model and integrate extensive multiomics data sources, utilizing a total of 40 genome-wide functional annotations that augment previously published results from the International Lung Cancer Consortium (ILCCO) GWAS, to prioritize and characterize single nucleotide polymorphisms (SNPs) that increase risk of squamous cell lung cancer through the inflammatory and immune responses. Our work bridges the gap between correlative analysis and translational follow-up research, refining GWAS association measures in an interpretable and systematic manner. In particular, reanalysis of the ILCCO data highlights the impact of highly associated SNPs from nuclear factor-κB signaling pathway genes as well as major histocompatibility complex mediated variation in immune responses. One consequence of prioritizing likely functional SNPs is the pruning of variants that might be selected for follow-up work by over an order of magnitude, from potentially tens of thousands to hundreds. The strategies we introduce provide informative and interpretable approaches for incorporating extensive genome-wide annotation data in analysis of genetic association studies.
Insights
Targeting inflammation genetically can reduce lung cancer. This study integrates multi-omics data to identify specific genetic variants, like those in nuclear factor-κB signaling, that influence lung cancer risk via immune responses.
Area of Science:
- Genetics and Genomics
- Cancer Research
- Immunology
Background:
- Inflammation, particularly via the interleukin-1β pathway, is linked to reduced lung cancer incidence and mortality.
- Genome-wide association studies (GWAS) have identified genetic risk factors for lung cancer, but understanding their functional impact remains challenging.
- A gap exists between correlative genetic association findings and the deep understanding needed for translational therapies.
Purpose of the Study:
- To understand the genetic underpinnings of inflammation-focused lung cancer therapies.
- To prioritize and characterize single nucleotide polymorphisms (SNPs) associated with squamous cell lung cancer risk through inflammatory and immune responses.
- To bridge the gap between genetic association studies and translational research by integrating multi-omics data.
Main Methods:
- Jointly modeled and integrated extensive multi-omics data, including 40 genome-wide functional annotations.
- Augmented previously published International Lung Cancer Consortium (ILCCO) GWAS results.
- Prioritized and characterized SNPs influencing lung cancer risk via immune and inflammatory pathways.
Main Results:
- Reanalysis of ILCCO data highlighted significant SNPs in nuclear factor-κB signaling pathway genes.
- Identified major histocompatibility complex mediated variation in immune responses as a key factor.
- Reduced the number of potential candidate SNPs for follow-up research by over tenfold, from thousands to hundreds.
Conclusions:
- The study provides a systematic and interpretable method for incorporating genome-wide annotation data into genetic association studies.
- The findings refine GWAS association measures, facilitating a deeper understanding of genetic risk factors in lung cancer.
- The introduced strategies enable more focused and efficient follow-up research for lung cancer genetic therapies.
