Liquid biopsy-based decision support algorithms for diagnosis and subtyping of lung cancer
Esther Visser, Sylvia A A M Genet, Remco P P A de Kock, Ben E E M van den Borne, Maggy Youssef-El Soud, Huub N A Belderbos, Gerben Stege, Marleen E A de Saegher, Susan C van 't Westeinde, Luc Brunsveld, Maarten A C Broeren, Daan van de Kerkhof, Birgit A L M Deiman, Federica Eduati, Volkher Scharnhorst, Esther Visser, Sylvia A A M Genet, Remco P P A de Kock, Ben E E M van den Borne, Maggy Youssef-El Soud, Huub N A Belderbos, Gerben Stege, Marleen E A de Saegher, Susan C van 't Westeinde, Luc Brunsveld, Maarten A C Broeren, Daan van de Kerkhof, Birgit A L M Deiman, Federica Eduati, Volkher Scharnhorst
Abstract
Objectives: Pathologic subtyping of tissue biopsies is the gold standard for the diagnosis of lung cancer (LC), which could be complicated in cases of e.g. inconclusive tissue biopsies or unreachable tumors. The diagnosis of LC could be supported in a minimally invasive manner using protein tumor markers (TMs) and circulating tumor DNA (ctDNA) measured in liquid biopsies (LBx). This study evaluates the performance of LBx-based decision-support algorithms for the diagnosis of LC and subtyping into small- and non-small-cell lung cancer (SCLC and NSCLC) aiming to directly impact clinical practice.
Materials and methods: In this multicenter prospective study (NL9146), eight protein TMs (CA125, CA15.3, CEA, CYFRA 21-1, HE4, NSE, proGRP and SCCA) and ctDNA mutations in EGFR, KRAS and BRAF were analyzed in blood of 1096 patients suspected of LC. The performance of individual and combined TMs to identify LC, NSCLC or SCLC was established by evaluating logistic regression models at pre-specified positive predictive values (PPV) of ≥95% or ≥98%. The most informative protein TMs included in the multi-parametric models were selected by recursive feature elimination.
Results: Single TMs could identify LC, NSCLC and SCLC patients with 46%, 25% and 40% sensitivity, respectively, at pre-specified PPVs. Multi-parametric models combining TMs and ctDNA significantly improved sensitivities to 65%, 67% and 50%, respectively.
Conclusion: In patients suspected of LC, the LBx-based decision-support algorithms allowed identification of about two-thirds of all LC and NSCLC patients and half of SCLC patients. These models therefore show clinical value and may support LC diagnostics, especially in patients for whom pathologic subtyping is impossible or incomplete.
Keywords: Decision–support algorithm; Liquid biopsy; Lung cancer; Protein tumor markers; circulating tumor DNA.
Conflict of interest statement
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.
Source: PubMed