PF360 IN-VITRO DRUG SENSITIVITY SCREENING IN CHRONIC LYMPHOCYTIC LEUKEMIA (CLL) PATIENT SAMPLES IDENTIFIES DRUG CANDIDATES FOR PRECISION CANCER THERAPY
Authors: D.B. Thimiri Govinda Raj, A. Cremaschi, S. Skånland, A. Gade, F. Schjesvold, G.E. Tjønnfjord, L.A. Munthe, K. Taskén
Journal: HemaSphere (2019)

Background: Chronic Lymphocytic Leukemia (CLL) is the most common leukemia in adults and is currently considered incurable. Although current treatment regimens prolong life for patients, CLL eventually relapses. Efficient therapies may require a personalized approach that combines targeting cancer cells and the tumor microenvironment by restoring the patient's own anti-tumor immunity. However, a major limitation is that no efficient approach exists to identify the most effective drugs for each patient and cancer stage. Aims: To assess drug sensitivity for future introduction of individualized treatment for patients. Methods: We have established novel in vitro culture settings that mimic the CLL tumor microenvironment and allow proliferation of CLL cells for 5 days. Using our unique method, we performed drug screening on 24 patient samples and 10 healthy donors (due to the lower number of cells healthy donors were pooled into two samples of 5 donors each) against a customized, annotated library of 516 drugs including kinase inhibitors, proteasome inhibitors, B-cell pathway inhibitors and several other approved drug classes. Primary patient samples were cultured in 384 well-plates with the presence of individual drugs over a concentration range over 5 logs. Drug sensitivity was assessed using CellTiter-Glo® luminescent cell viability assay and CellTox™ green cytotoxicity assay on day 5. Drug Sensitivity Score (DSS) was then calculated for each drug using the IC50 value, slope and area under the curve (AUC). The DSSs of the CLL patient samples were next compared with the DSSs of the healthy donors in order to to generate a selective DSS (sDSS = DSSpatient – DSShealthy) for each patient. Drugs with sDSS > 5 were considered clearly more effective for patient samples in the in vitro test system. CLL samples were assessed for sDSS using our screening data and we ranked all the drugs by their score. Results: In order to find drug candidates for targeted therapies in CLL patients, we performed in vitro drug sensitivity screening on 12 IgVH unmutated and 12 IgVH mutated CLL patient samples, as well as 10 healthy donors. Our in vitro assay showed that proteasome inhibitors, kinase inhibitors and several approved CLL drugs were considered sensitive in the majority of patient samples. This included the Bcl-2 inhibitors venetoclax and ABT-737, the BMP signaling inhibitor LDN193189, other kinase inhibitors (sunitinib, volasertib) and proteasome inhibitors (carfilzomib, bortezomib). Venetoclax showed a higher sDSS score in 10 of the 20 patients with an average sDSS score of 22.8 followed by LDN193189 with an average sDSS score of 17,6. By performing hierarchical clustering analysis, we observed unsupervised clustering of patient samples irrespective of the IgVH mutation status. We currently expand the analysis by classifying the patients by age, sex and mutation status. Summary/Conclusion: Our novel CLL culture method that allows cell proliferation along with our established functional in-vitro drug sensitivity screening platform enabled us to screen a number of patient samples and evaluate the sensitivity of a library of approved drugs and investigational drug candidates for CLL. Our analysis shows that several drugs may be effective for CLL and can be tested in drug combinations in order to identify synergistic effects. As a future perspective, we want to combine machine learning strategies with the experimental drug screening strategies to identify drug combinations and validate drug candidates by xenografting and in precision medicine clinical trials.