Ex Vivo Drug Response Profiling and Outcome Prediction in Lung Adenocarcinoma

Sheba Medical Center400 enrolled

Overview

This observational study evaluates if ex-vivo lung cancer drug sensitivity testing results correlate with the driver mutations found by genetic sequencing and if it can predict treatment response.

Non-small cell lung cancer (NSCLC) treatment decisions often rely on DNA sequencing, which only partially captures the tumor's mutational landscape and may miss other complex resistance mechanisms. Consequently, an unbiased functional drug screen is essential to rapidly identify optimal next-line therapies. Current drug sensitivity testing (DST) is limited by inefficient live tumor cells isolation. To address this, a rapid, unbiased DST platform utilizing FDA-approved targeted therapies was developed. Our approach uses malignant pleural effusions (MPEs) as a rich source of viable tumor cells. Because MPEs are highly heterogeneous, tumor cells are first identified via EpCAM flow cytometry, and the sample is enriched for tumor cells by depleting CD45+ immune cells using antibody-coated magnetic beads. These enriched tumor cells are then seeded into drug-containing plates. Following 72-hour incubation, cell viability is assessed using the CellTiter-Glo assay. Treatment efficacy is determined by the degree of reduction in cell viability at a range of drug concentrations, where the most effective treatment is the drug that reduces cell viability at the lowest concentration range. To link clinical outcome and ex-vivo drug response assays, the investigators systematically measure drug sensitivity and resistance of primary tumor cells ex-vivo using a diverse compound library for individual patients in need of treatment. By systematically analyzing ex-vivo drug response patterns, tumors should be functionally grouped, by response phenotype. While for the purpose of this study selection of a specific treatment will not be based on ex-vivo drug response assays, clinical response- and follow-up data of patients will be prospectively collected in parallel.

Study Type

OBSERVATIONAL

Enrollment

400

Conditions

Lung Adenocarcinoma Malignant Pleural Effusions (Mpe)

Interventions

Non-Interventional StudyOTHER

Non-Interventional Study, patients go through pleural drainage due to shortness of breath

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

Inclusion Criteria: * Presence of pleural effusion in patient with a diagnosis of a lung cancer or suspected to be diagnosed with lung cancer according to their CT imaging, who are in need for pleural effusion drainage due to shortness of breath, and are willing to donate effusion for ex-vivo drug sensitivity testing. * Patient's written informed consent present. * Ability to understand the nature of the trial and the trial related procedures and to comply with them. Exclusion Criteria: * negative cytology for malignancy in the last 6 months

Locations (1)

Sheba Medical Center

Ramat Gan, Israel, Israel

RECRUITING

Outcomes

Primary Outcomes

Statistically significant correlation between patients' drug response by ex-vivo drug profiling and expected drug sensitivity according to genetic sequencing

Ex-vivo drug sensitivity categorizes drugs as sensitive/not sensitive. Results will be compared with the expected sensitivity of drugs according to the driver mutation found be genetic sequencing. For example, in EGFR-driven lung cancer, the tumor cells are expected to be more sensitive to EGFR-targeting drugs than to ALK-targeting drugs.

Time frame: From enrollment until results of clinical genetic sequencing and drug sensitivity testing are obtained (within 3 weeks).

Secondary Outcomes

Accuracy of patients' drug response prediction by ex-vivo drug profiling

Ex-vivo drug sensitivity categorizes drugs as sensitive/not sensitive. Results will be compared with clinical outcome of patient (response vs. progressive disease as defined by RECISET score)

Time frame: from date of enrollment until progression (latest 24 months)

Data from ClinicalTrials.gov

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