Design and methodological considerations for biomarker discovery and validation in the Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) Program

The Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) program is an NCI-funded initiative with an objective to develop tools to optimize lung cancer screening. Here, we describe the rationale and design for the Risk Biomarker and Nodule Malignancy projects within INTEGRAL. The overarching goal of these projects is to systematically investigate circulating protein markers to include on a panel for use (i) pre-LDCT, to identify people likely to benefit from screening, and (ii) post-LDCT, to differentiate benign versus malignant nodules. To identify informative proteins, the Risk Biomarker project measured 1,161 proteins in a nested-case control study within 2 prospective cohorts (n=252 lung cancer cases and 252 controls) and replicated associations for a subset of proteins in 4 cohorts (n=479 cases and 479 controls). Eligible participants had any history of smoking and cases were diagnosed within 3 years of blood draw. The Nodule Malignancy project measured 1,077 proteins among participants with a heavy smoking history within 4 LDCT screening studies (n=425 cases within 5 years of blood draw, 398 benign-nodule controls, and 430 nodule-free controls). The INTEGRAL panel will enable absolute quantification of 21 proteins. We will evaluate its lung cancer discriminative performance in the Risk Biomarker project using a case-cohort study including 14 cohorts (n=1,696 cases and 2,926 subcohort representatives), and in the Nodule Malignancy project within 5 LDCT screening studies (n=675 cases, 648 benign-nodule controls, and 680 nodule-free controls). Future progress to advance lung cancer early detection biomarkers will require carefully designed validation, translational, and comparative studies.

[1]  R. Hung Biomarker-Based Lung Cancer Screening Eligibility: Implementation Considerations. , 2022, Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology.

[2]  S. Lam,et al.  USPSTF2013 versus PLCOm2012 lung cancer screening eligibility criteria (International Lung Screening Trial): interim analysis of a prospective cohort study , 2021, The Lancet Oncology.

[3]  R. Prentice,et al.  Epidemiology of 40 blood biomarkers of one-carbon metabolism, vitamin status, inflammation, and renal and endothelial function among cancer-free older adults , 2021, Scientific Reports.

[4]  M. Cabana,et al.  Screening for Lung Cancer: US Preventive Services Task Force Recommendation Statement. , 2021, JAMA.

[5]  A. Swerdlow,et al.  Comparative performance of lung cancer risk models to define lung screening eligibility in the United Kingdom , 2021, British Journal of Cancer.

[6]  C. Berg,et al.  Using Prediction-Models to Reduce Persistent Racial/Ethnic Disparities in Draft 2020 USPSTF Lung-Cancer Screening Guidelines. , 2021, Journal of the National Cancer Institute.

[7]  R. Rintoul,et al.  Biomarkers in lung cancer screening: the importance of study design , 2021, European Respiratory Journal.

[8]  M. Pepe,et al.  Adding Rigor to Biomarker Evaluations—EDRN Experience , 2020, Cancer Epidemiology, Biomarkers & Prevention.

[9]  Jennifer B Dennison,et al.  Contribution of a blood-based protein biomarker panel to the classification of indeterminate pulmonary nodules. , 2020, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[10]  M. Oudkerk,et al.  Lung cancer LDCT screening and mortality reduction — evidence, pitfalls and future perspectives , 2020, Nature Reviews Clinical Oncology.

[11]  P. Brennan,et al.  Assessment of Biomarker Testing for Lung Cancer Screening Eligibility , 2020, JAMA network open.

[12]  Harry J de Koning,et al.  Reduced Lung-Cancer Mortality with Volume CT Screening in a Randomized Trial. , 2020, The New England journal of medicine.

[13]  C. Berg,et al.  Life-GainedBased Versus Risk-Based Selection of Smokers for Lung Cancer Screening , 2019, Annals of Internal Medicine.

[14]  J. Buring,et al.  Circulating markers of cellular immune activation in prediagnostic blood sample and lung cancer risk in the Lung Cancer Cohort Consortium (LC3) , 2019, International journal of cancer.

[15]  P. Massion,et al.  Biomarkers in Lung Cancer Screening: Achievements, Promises, and Challenges. , 2019, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[16]  J. Buring,et al.  Is high vitamin B12 status a cause of lung cancer? , 2019, International journal of cancer.

[17]  J. Buring,et al.  Circulating high sensitivity C reactive protein concentrations and risk of lung cancer: nested case-control study within Lung Cancer Cohort Consortium , 2019, British Medical Journal.

[18]  S. Hanash,et al.  Assessment of Lung Cancer Risk on the Basis of a Biomarker Panel of Circulating Proteins , 2018, JAMA oncology.

[19]  A. Jemal,et al.  Implications of Nine Risk Prediction Models for Selecting Ever-Smokers for Computed Tomography Lung Cancer Screening , 2018, Annals of Internal Medicine.

[20]  N. Rothman,et al.  No association between circulating concentrations of vitamin D and risk of lung cancer: an analysis in 20 prospective studies in the Lung Cancer Cohort Consortium (LC3) , 2018, Annals of oncology : official journal of the European Society for Medical Oncology.

[21]  Paul Kearney,et al.  Assessment of Plasma Proteomics Biomarker’s Ability to Distinguish Benign From Malignant Lung Nodules , 2018, Chest.

[22]  Frauke Degenhardt,et al.  Evaluation of variable selection methods for random forests and omics data sets , 2017, Briefings Bioinform..

[23]  Stephanie A Kovalchik,et al.  Development and Validation of Risk Models to Select Ever-Smokers for CT Lung Cancer Screening. , 2016, JAMA.

[24]  A. Scarsbrook,et al.  Risk of malignancy in pulmonary nodules: A validation study of four prediction models. , 2015, Lung cancer.

[25]  Harry J de Koning,et al.  Lung cancer probability in patients with CT-detected pulmonary nodules: a prespecified analysis of data from the NELSON trial of low-dose CT screening. , 2014, The Lancet. Oncology.

[26]  S. Lam,et al.  Probability of cancer in pulmonary nodules detected on first screening CT. , 2013, The New England journal of medicine.

[27]  C. Berg,et al.  Targeting of low-dose CT screening according to the risk of lung-cancer death. , 2013, The New England journal of medicine.

[28]  Timothy R Church,et al.  Selection criteria for lung-cancer screening. , 2013, The New England journal of medicine.

[29]  C. Gatsonis,et al.  Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening , 2012 .

[30]  Holly Janes,et al.  Pivotal Evaluation of the Accuracy of a Biomarker Used for Classification or Prediction: Standards for Study Design , 2008, Journal of the National Cancer Institute.

[31]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[32]  C. Begg,et al.  Variations in lung cancer risk among smokers. , 2003, Journal of the National Cancer Institute.

[33]  P. Royston,et al.  Flexible parametric proportional‐hazards and proportional‐odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects , 2002, Statistics in medicine.

[34]  J. Buring,et al.  Circulating Folate, Vitamin B6, and Methionine in Relation to Lung Cancer Risk in the Lung Cancer Cohort Consortium (LC3) , 2018, Journal of the National Cancer Institute.