Despite scientific evidence, use of liquid biopsy (LB) in diagnosis and monitoring of lung cancer (LC) is limited since it requires major changes in diagnostic and care pathways. Analyzing tumor markers (TMs), circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) in blood (LB) can inform about the nature of the tumor, the most appropriate therapy, therapy response and resistance.
Lungmarker2 is a multicenter, prospective, implementation and diagnostic cohort study. This study aims to implement up-front ctDNA analysis ('plasma first approach') into routine diagnostic work-up of all advanced stage LC patients in the Southeast of the Netherlands (the participating hospitals in the OncoZON region). Thereby, additional information about the molecular make-up of the tumor becomes available, the number of tissue Next-Generation Sequencing (NGS) analyses will decrease and time to therapeutic decision making is shortened. Next, using ctDNA, TM and other information, multi-parametric decision support models are built and validated that may support diagnosis, predict the outcome of the next imaging procedure and progression-free survival during follow-up. The final goal is to develop a super-resolution microscopy test that can detect PD-L1 expression on CTCs.
Aged 18 or above and suspected of having lung cancer
Presence of another malignant tumor, i.e. diagnosed with a tumor in the past 5 years
800 individuals, \>18 years of age, suspected of lung cancer and referred to a lung physician in one of the participating centers are included in the study.
Cohort
Prospective
Not listed
Up-front ctDNA analysis is implemented into routine clinical care in the participating OncoZoN hospitals
After the Lungmarker2 study has proven up-front ctDNA analysis to be feasible and successful for diagnosis and monitoring of lung cancer, hospitals participating in this project will adopt this as routine practice. Up-front ctDNA analysis will be adopted by the other OncoZON hospitals outside the consortium through transfer of the clinical practice during the regular tumor board meetings where shared care decisions are made for individual patients.
Evaluate the number of driver mutations detected by up-front ctDNA analysis compared to tumor NGS analysis
Determine and compare the number of driver mutations detected by up-front ctDNA analysis with the number of driver mutations detected by tumor NGS analysis.
Develop decision support algorithms for the diagnosis and monitoring of lung cancer patients
Develop decision support algorithms using information from CT scans, measured tumor markers (CA125, CA15.3, CEA, CYFRA 21.1, HE-4, NSE, proGRP, SCCA) and ctDNA analysis to identify small-cell lung cancer and non-small-cell lung cancer patients (classification, diagnosis). Develop decision support algorithms to predict therapy response, expressed as a threshold for durable clinical benefit: the progression free survival (PFS) at 6 months (as probability %), in lung cancer patients (monitoring). Different metrics regarding model performances will be reported: area under the receiver operating characteristic curve (AUC), area under the precision- recall curve (AUC-PR), sensitivity, specificity, negative predictive value (NPV) and positive predictive value (PPV).
Develop an analytical protocol for the analysis of PD-L1 expression on circulating tumor cells (CTCs) by super-resolution microscopy
Develop an analytical protocol for the isolation of CTCs from whole blood samples (measured as the number of cells per mL) and to quantify PD-L1 expression (reported as a percentage on a scale of 0% to 100%) on the isolated CTCs using super-resolution microscopy.
Evaluate the number of tumor NGS analyses and time to diagnosis when up-front ctDNA analysis is introduced
Determine the time to diagnosis (in days) of lung cancer (e.g., time between first visit at lung physician and the diagnosis) when using up-front ctDNA analysis compared to tumor NGS analysis, and determine the number of tumor NGS analyses that can be replaced by up-front ctDNA analysis.
A study database is filled with all relevant clinical and diagnostic information
For the Lungmarker2 study, all information will be entered into the study database (Research manager) by personnel of the participating hospitals (research nurse, physician or lung oncology nurse) using a digital CRF. Research manager offers eCRF possibilities and enables safe and anonymous data collection, storage and management. The data collected include patient data, detailed results of diagnostic procedures and tests including imaging procedures, treatment regime and response evaluation.
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Catharina Ziekenhuis Eindhoven
Lead sponsor
Eindhoven University of Technology
Collaborator
Roche BV Netherlands
Collaborator
Maxima Medical Center
Collaborator
Zuyderland Medisch Centrum
Collaborator
Maastricht University Medical Center
Collaborator
St. Anna Ziekenhuis, Geldrop, Netherlands
Collaborator
The Netherlands Cancer Institute
Collaborator
