Implementation of Up-front ctDNA Into Lung Cancer Care and Development of Liquid Biopsy-based Decision Support Models - LM² Study

Condition(s) studied

Study summary

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.

Eligibility

Primary outcome measure(s)

• Up-front ctDNA analysis is implemented into routine clinical care in the participating OncoZoN hospitals — Up to 3 years
  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 — Up to 3 years
  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 — Up to 3 years
  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).

Trial sites (5)

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