Samir Hanash

MD PhD

University of Texas at MD Anderson

director

PLCO (Learn more about this study)

PLCO-549

Nov 4, 2019

Incorporating Biomarkers to Improve Lung Cancer Risk Prediction

This summary relates to Grant U01CA194733. Lung cancer is the leading cause of cancer death worldwide, with overall 5-year survival rates in the United States of 15% but approaching 50% when diagnosed at an early stage. The National Lung Screening Trial (NLST) reported that low-dose computed tomography (LDCT) screening reduced lung cancer mortality by 20% in adults who were at high risk of lung cancer. These dramatic results come with high human and societal cost because of the extremely low yield associated with the screening criteria and high false positive rate by LDCT. NLST entry criteria, based on smoking history and age, yielded 1 lung cancer for every 156 screened. One quarter of those screened required expensive, sometimes invasive diagnostic work-up, yet 96.4% of them turned out to be false positives. A better lung cancer risk prediction model can provide better selection criteria and make LDCT more effective in balancing benefit versus harm. This study team has developed 10 blood-based biomarkers (protein: pro-SFTPB, HE4, IGFBP2, LRG1; lipid: DAS; autoantibody: LTF, ADCK1, STK10, TRIM10, KM2) and validated them using pre-diagnostic sera from the Beta-Carotene and Retinol Efficacy Trial. Pro-SFTPB was further validated on the Pan-Canadian Early Detection of Lung Cancer Screening Study and Physician Health Study and shown to complement lung cancer risk prediction models based on epidemiologic data. Recently, 4 circulating inflammation biomarkers (CRP, IL-1RA, BCA- 1/CXCL, MDC/CCL22) were found to be independently associated with lung cancer risk. The proposed study will incorporate these 14 biomarkers into PLCOm2012, a 6-year lung cancer risk prediction model developed and validated by this team using PLCO epidemiological data, to improve lung cancer risk prediction. In Aim 1, using a nested case-control study design these 14 biomarkers will be assayed using sera collected at baseline from 549 lung cancer patients diagnosed within 6 years after baseline and 1,098 matched controls, and then incorporated into the PLCOm2012 model to improve lung cancer risk prediction and the selection criteria for LDCT. In Aim 2, sera collected annually up to five years since baseline for these subjects will be analyzed using two Bayesian models to incorporate biomarker trajectories to improve early detection of lung cancer. In Aim 3, the models from Aim 1&2 will be evaluated for their potential clinical utilities. If successful, the proposed study will challenge the paradigm of epidemiological modeling (age, smoking history, etc.) for lung cancer risk prediction and single threshold for early detection, improve selection criteria for LDCT screening, increase yield of lung cancer by LDCT screening, reduce LDCT-associated harm, and improve early detection of lung cancer.

Aim1: Using a nested case-control study design, 14 biomarkers (protein: pro-SFTPB, HE4, IGFBP2, LRG1; lipid: DAS; autoantibody: LTF, ADCK1, STK10, TRIM10, KM2; inflammatory markers CRP, IL-1RA, BCA- 1/CXCL, MDC/CCL22) will be assayed using sera collected at baseline from 549 lung cancer patients diagnosed within 6 years after baseline and 1,098 matched controls, and then incorporated into the PLCOm2012 model to improve lung cancer risk prediction and the selection criteria for LDCT.

Aim2: Sera collected annually up to five years since baseline for these subjects will be analyzed using two Bayesian models to incorporate biomarker trajectories to improve early detection of lung cancer.

Aim3: The models from Aim 1&2 will be evaluated for their potential clinical utilities. If successful, the proposed study will challenge the paradigm of epidemiological modeling (age, smoking history, etc.) for lung cancer risk prediction and single threshold for early detection, improve selection criteria for LDCT screening, increase yield of lung cancer by LDCT screening, reduce LDCT-associated harm, and improve early detection of lung cancer.

Aim4: In our prior work, we leveraged plasma samples from Prostate, Lung, Colorectal, and Ovarian Trial (PLCO) to validate blood-based biomarker panels associated with 6-year risks of developing various forms of cancers including lung, breast, pancreatic, and ovarian cancers.One of the key components of the PLCO cohort is the comprehensive collection of patient characteristics, including smoking behavior and other self-reported factors like exercise habits. In a recent study, Dr. Lee Jones and colleagues utilized the PLCO dataset to demonstrate that self-reported exercise was a significant predictor of the risk for various types of cancer. Specifically, for individual cancers, exercisers had a 26% lower risk of head and neck cancer, 20% lower risk of lung cancer, and 11% lower risk of breast cancer compared with non-exercisers. With a view toward future intervention trials, we believe that integrating blood biomarkers with exercise and its association with cancer risk would improve the identification of individuals at risk and enhance recommendations for screening and intervention.