Annual Meeting of the NCI Cohort Consortium (Abstract Submission): Submission #3

Submission information
Submission Number: 3
Submission ID: 127059
Submission UUID: 33483d60-ab2d-4385-8751-430feac2406b

Created: Tue, 09/10/2024 - 06:11
Completed: Tue, 09/10/2024 - 06:11
Changed: Mon, 09/16/2024 - 16:43

Remote IP address: 10.208.24.118
Submitted by: Anonymous
Language: English

Is draft: No
Presenter's First Name: Hana
Presenter's Last Name: Zahed
Title (eg: professor, assistant professor, chair, etc):
Degree(s) PhD
Contact Email: zahedh@iarc.who.int
Organization: International Agency for Research on Cancer (IARC/WHO)
Project Title: Biomarker-based eligibility for lung cancer screening. Validation of the INTEGRAL protein-based risk model in the LC3.
Additional Authors
Abstract: Background: Circulating protein biomarkers may improve smoking-based lung cancer risk models. The INTEGRAL-program aimed to identify risk biomarkers within the Lung Cancer Cohort Consortium (LC3), develop a fit-for-purpose protein panel, and train and validate a protein-based model for lung cancer risk assessment.

Methods: Participants had a smoking history and cases were diagnosed with incident lung cancer at most 3 years after blood-draw. We first carried out a proteomics discovery analysis using 731 case-control pairs from six LC3 cohorts and developed the INTEGRAL-panel that includes 21 proteins with non-redundant information on lung cancer risk. The model training and validation phase used the INTEGRAL panel and a case-cohort design (Robbins et al., Ann Epidemiology 2023), including 1,696 incident lung cancers and 2,926 cohort-representatives from 14 LC3 cohorts. Risk models were trained using flexible parametric survival regressions in 7 cohorts, with external validation in the remaining 7 cohorts.

Results: The initial discovery phase identified 36 robust markers of lung cancer risk (LC3, Nat Comm 2023). Preliminary results from the training-cohorts indicate that a protein-based risk model improves risk discrimination compared to the questionnaire-based PLCOm2012 model. Final results from the external validation will be presented at the meeting.

Conclusion/Discussion: Improved lung cancer risk assessment can optimize early detection by better identifying individuals who are likely to benefit from screening. Ongoing efforts within the LC3 consortium include assessing the acceptability and feasibility of using a protein-based risk model to assess eligibility for screening, as well as identifying novel markers in individuals without a smoking history.