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

doi:10.1016/j.lungcan.2023.01.014

Liquid biopsy-based decision support algorithms for diagnosis and subtyping of lung cancer

Esther Visser (Corresponding author), 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

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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.

Original language	English
Pages (from-to)	28-36
Number of pages	9
Journal	Lung Cancer
Volume	178
DOIs	https://doi.org/10.1016/j.lungcan.2023.01.014
Publication status	Published - 1 Apr 2023

Funding

The study was supported by The Netherlands Organization for Scientific Research (NWO) via LIFT grant 731.017.405. Roche Diagnostics Netherlands partially sponsored the protein TM reagents used in the study. Supported by AstraZeneca grant AZNL201700295 and Catharina Onderzoeksfonds institutional fund grant 2017-02 (R.d.K.).

Funders	Funder number
AstraZeneca	AZNL201700295, 2017-02
Nederlandse Organisatie voor Wetenschappelijk Onderzoek	731.017.405

Keywords

circulating tumor DNA
Decision–support algorithm
Liquid biopsy
Lung cancer
Protein tumor markers

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.lungcan.2023.01.014

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Cite this

Visser, E., Genet, S. A. A. M., de Kock, R. P. P. A., van den Borne, B. E. E. M., Youssef-El Soud, M., Belderbos, H. N. A., Stege, G., de Saegher, M. E. A., van 't Westeinde, S. C., Brunsveld, L., Broeren, M. A. C., van de Kerkhof, D., Deiman, B. A. L. M., Eduati, F., & Scharnhorst, V. (2023). Liquid biopsy-based decision support algorithms for diagnosis and subtyping of lung cancer. Lung Cancer, 178, 28-36. https://doi.org/10.1016/j.lungcan.2023.01.014

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title = "Liquid biopsy-based decision support algorithms for diagnosis and subtyping of lung cancer",

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 = "circulating tumor DNA, Decision–support algorithm, Liquid biopsy, Lung cancer, Protein tumor markers",

author = "Esther Visser and Genet, {Sylvia A.A.M.} and {de Kock}, {Remco P.P.A.} and {van den Borne}, {Ben E.E.M.} and {Youssef-El Soud}, Maggy and Belderbos, {Huub N.A.} and Gerben Stege and {de Saegher}, {Marleen E.A.} and {van 't Westeinde}, {Susan C.} and Luc Brunsveld and Broeren, {Maarten A.C.} and {van de Kerkhof}, Daan and Deiman, {Birgit A L M} and Federica Eduati and Volkher Scharnhorst",

year = "2023",

month = apr,

day = "1",

doi = "10.1016/j.lungcan.2023.01.014",

language = "English",

volume = "178",

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journal = "Lung Cancer",

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publisher = "Elsevier Ireland Ltd",

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Visser, E , Genet, SAAM, de Kock, RPPA, van den Borne, BEEM, Youssef-El Soud, M, Belderbos, HNA, Stege, G, de Saegher, MEA, van 't Westeinde, SC, Brunsveld, L , Broeren, MAC , van de Kerkhof, D, Deiman, BALM, Eduati, F & Scharnhorst, V 2023, 'Liquid biopsy-based decision support algorithms for diagnosis and subtyping of lung cancer', Lung Cancer, vol. 178, pp. 28-36. https://doi.org/10.1016/j.lungcan.2023.01.014

TY - JOUR

T1 - Liquid biopsy-based decision support algorithms for diagnosis and subtyping of lung cancer

AU - Visser, Esther

AU - Genet, Sylvia A.A.M.

AU - de Kock, Remco P.P.A.

AU - van den Borne, Ben E.E.M.

AU - Youssef-El Soud, Maggy

AU - Belderbos, Huub N.A.

AU - Stege, Gerben

AU - de Saegher, Marleen E.A.

AU - van 't Westeinde, Susan C.

AU - Brunsveld, Luc

AU - Broeren, Maarten A.C.

AU - van de Kerkhof, Daan

AU - Deiman, Birgit A L M

AU - Eduati, Federica

AU - Scharnhorst, Volkher

PY - 2023/4/1

Y1 - 2023/4/1

N2 - 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.

AB - 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.

KW - circulating tumor DNA

KW - Decision–support algorithm

KW - Liquid biopsy

KW - Lung cancer

KW - Protein tumor markers

UR - http://www.scopus.com/inward/record.url?scp=85147709115&partnerID=8YFLogxK

U2 - 10.1016/j.lungcan.2023.01.014

DO - 10.1016/j.lungcan.2023.01.014

M3 - Article

C2 - 36773458

SN - 0169-5002

VL - 178

SP - 28

EP - 36

JO - Lung Cancer

JF - Lung Cancer

ER -

Liquid biopsy-based decision support algorithms for diagnosis and subtyping of lung cancer

Abstract

Funding

Keywords

UN SDGs

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Cardiovascular Medicine

Cite this

Liquid biopsy-based decision support algorithms for diagnosis and subtyping of lung cancer

Abstract

Funding

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Research areas

Cardiovascular Medicine

Cite this