Multianalyte Serum Biomarker Panel for Early Detection of Pancreatic Adenocarcinoma.

Autor:	Firpo MA; Department of Surgery, School of Medicine, University of Utah, Salt Lake City, UT., Boucher KM; Department of Oncological Sciences, School of Medicine, University of Utah, Salt Lake City, UT., Bleicher J; Department of Surgery, School of Medicine, University of Utah, Salt Lake City, UT., Khanderao GD; Department of Surgery, School of Medicine, University of Utah, Salt Lake City, UT., Rosati A; BIOUNIVERSA s.r.l., Baronissi, Italy.; Department of Medicine, Surgery and Dentistry 'Scuola Medica Salernitana' University of Salerno, Baronissi, Italy., Poruk KE; Department of Surgery, School of Medicine, University of Utah, Salt Lake City, UT., Kamal S; Department of Surgery, School of Medicine, University of Utah, Salt Lake City, UT., Marzullo L; BIOUNIVERSA s.r.l., Baronissi, Italy.; Department of Medicine, Surgery and Dentistry 'Scuola Medica Salernitana' University of Salerno, Baronissi, Italy., De Marco M; BIOUNIVERSA s.r.l., Baronissi, Italy.; Department of Medicine, Surgery and Dentistry 'Scuola Medica Salernitana' University of Salerno, Baronissi, Italy., Falco A; BIOUNIVERSA s.r.l., Baronissi, Italy.; Department of Medicine, Surgery and Dentistry 'Scuola Medica Salernitana' University of Salerno, Baronissi, Italy., Genovese A; University Hospital 'San Giovanni di Dio e Ruggi D'Aragona,' Salerno, Italy., Adler JM; Department of Surgery, School of Medicine, University of Utah, Salt Lake City, UT., De Laurenzi V; BIOUNIVERSA s.r.l., Baronissi, Italy.; Department of Medicine and Biotechnology, University G d'Annunzio and CeSI-MeT, Chieti, Italy., Adler DG; Department of Internal Medicine, School of Medicine, University of Utah, Salt Lake City, UT., Affolter KE; Department of Pathology, School of Medicine, University of Utah, Salt Lake City, UT., Garrido-Laguna I; Department of Oncological Sciences, School of Medicine, University of Utah, Salt Lake City, UT., Scaife CL; Department of Surgery, School of Medicine, University of Utah, Salt Lake City, UT., Turco MC; BIOUNIVERSA s.r.l., Baronissi, Italy.; Department of Medicine, Surgery and Dentistry 'Scuola Medica Salernitana' University of Salerno, Baronissi, Italy., Mulvihill SJ; Department of Surgery, School of Medicine, University of Utah, Salt Lake City, UT.
Jazyk:	angličtina
Zdroj:	JCO clinical cancer informatics [JCO Clin Cancer Inform] 2023 Mar; Vol. 7, pp. e2200160.
DOI:	10.1200/CCI.22.00160
Abstrakt:	Purpose: We determined whether a large, multianalyte panel of circulating biomarkers can improve detection of early-stage pancreatic ductal adenocarcinoma (PDAC). Materials and Methods: We defined a biologically relevant subspace of blood analytes on the basis of previous identification in premalignant lesions or early-stage PDAC and evaluated each in pilot studies. The 31 analytes that met minimum diagnostic accuracy were measured in serum of 837 subjects (461 healthy, 194 benign pancreatic disease, and 182 early-stage PDAC). We used machine learning to develop classification algorithms using the relationship between subjects on the basis of their changes across the predictors. Model performance was subsequently evaluated in an independent validation data set from 186 additional subjects. Results: A classification model was trained on 669 subjects (358 healthy, 159 benign, and 152 early-stage PDAC). Model evaluation on a hold-out test set of 168 subjects (103 healthy, 35 benign, and 30 early-stage PDAC) yielded an area under the receiver operating characteristic curve (AUC) of 0.920 for classification of PDAC from non-PDAC (benign and healthy controls) and an AUC of 0.944 for PDAC versus healthy controls. The algorithm was then validated in 146 subsequent cases presenting with pancreatic disease (73 benign pancreatic disease and 73 early- and late-stage PDAC cases) and 40 healthy control subjects. The validation set yielded an AUC of 0.919 for classification of PDAC from non-PDAC and an AUC of 0.925 for PDAC versus healthy controls. Conclusion: Individually weak serum biomarkers can be combined into a strong classification algorithm to develop a blood test to identify patients who may benefit from further testing.
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu