Proteomics biomarker discovery for individualized prevention of familial pancreatic cancer using statistical learning

• Published in: PloS one Vol. 18; no. 1; p. e0280399
• Main Authors: Ha, Chung Shing Rex, Müller-Nurasyid, Martina, Petrera, Agnese, Hauck, Stefanie M, Marini, Federico, Bartsch, Detlef K, Slater, Emily P, Strauch, Konstantin
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 26-01-2023; Public Library of Science (PLoS)
• Subjects: Adenocarcinoma; Analysis; Asymptomatic; Biology and Life Sciences; Biomarkers; Biomarkers, Tumor - genetics; Cancer; Carcinoma, Pancreatic Ductal - diagnosis; Carcinoma, Pancreatic Ductal - pathology; Care and treatment; Data structures; Datasets; Diagnosis; Feature selection; Genetic aspects; Humans; Hypothesis testing; Learning; Magnetic resonance imaging; Medical prognosis; Medicine and Health Sciences; Metabolism; Methods; Modelling; Mutation; Pancreatic cancer; Pancreatic Neoplasms; Pancreatic Neoplasms - diagnosis; Pancreatic Neoplasms - genetics; Pancreatic Neoplasms - pathology; Physical Sciences; Prostate cancer; Proteins; Proteomics; Research and Analysis Methods; Sample size; Screening; Simulation; Statistical analysis; Statistical learning (Psychology); Statistical methods; Statistics; Surgery; Survival; Tumors; Unbalance; Variables; Taiwan
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0280399

The low five-year survival rate of pancreatic ductal adenocarcinoma (PDAC) and the low diagnostic rate of early-stage PDAC via imaging highlight the need to discover novel biomarkers and improve the current screening procedures for early diagnosis. Familial pancreatic cancer (FPC) describes the cases of PDAC that are present in two or more individuals within a circle of first-degree relatives. Using innovative high-throughput proteomics, we were able to quantify the protein profiles of individuals at risk from FPC families in different potential pre-cancer stages. However, the high-dimensional proteomics data structure challenges the use of traditional statistical analysis tools. Hence, we applied advanced statistical learning methods to enhance the analysis and improve the results' interpretability.
We applied model-based gradient boosting and adaptive lasso to deal with the small, unbalanced study design via simultaneous variable selection and model fitting. In addition, we used stability selection to identify a stable subset of selected biomarkers and, as a result, obtain even more interpretable results. In each step, we compared the performance of the different analytical pipelines and validated our approaches via simulation scenarios.
In the simulation study, model-based gradient boosting showed a more accurate prediction performance in the small, unbalanced, and high-dimensional datasets than adaptive lasso and could identify more relevant variables. Furthermore, using model-based gradient boosting, we discovered a subset of promising serum biomarkers that may potentially improve the current screening procedure of FPC.
Advanced statistical learning methods helped us overcome the shortcomings of an unbalanced study design in a valuable clinical dataset. The discovered serum biomarkers provide us with a clear direction for further investigations and more precise clinical hypotheses regarding the development of FPC and optimal strategies for its early detection.