In-Vitro Drug Sensitivity Screening in Chronic Lymphocytic Leukemia (CLL) Primary Patient Samples Identifies Drug Candidates for Precision Cancer Therapy
Authors: Thimiri Govinda Raj Deepak Balaji balaji , Cremaschi Andrea , Skånland Sigrid Strand , Gade Alexandra , Schjesvold Fredrik H. , Tjønnfjord Geir Erland , A Munthe Ludvig , Tasken Kjetil 
Published: October 2019

Introduction

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. With the aim to support future introduction of individualized treatment for patients, we assessed the sensitivity of CLL patient samples to several drug candidates using our in vitro functional drug sensitivity screening platform.

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 26 patient samples and 10 healthy donors 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). DSSs for CLL patient samples were next compared with DSS of healthy donors for the full patient sample cohort screened so far to generate a selective DSS (sDSS = DSSpatient - DSShealthy) for each patient. Drugs which have 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 13 IgVH unmutated and 13 IgVH mutated CLL patient samples, as well as 10 healthy donors (due to the lower number of cells healthy donors were pooled into two samples of 5 donors each). Our in vitro assay showed that proteasome inhibitors, kinase inhibitors and several approved CLL drug candidates were considered sensitive in the majority of patient samples. This included venetoclax, the Bcl-2 inhibitor ABT-737, doxorubicin, acalabrutinib, other kinase inhibitors (sunitinib, volasertib, trametinib, copanlisib) and proteasome inhibitors (carfilzomib, bortezomib). Selective drug sensitivity scores of the top 5 drugs in all patient samples (73 drugs in total) are shown in the heatmap (Figure 1). Venetoclax showed a higher sDSS score in 10 of the 20 patients with an average sDSS score of 22.3 followed by ABT-737 (Bcl-2 inhibitor) with an average sDSS score of 19.7. By performing hierarchical clustering analysis (Euclidean distance, Ward linkage method), we observed unsupervised clustering of patient samples irrespective of the IgVH mutation status. We are currently expanding the analysis by classifying the patient samples by age, sex and mutation status.

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.

Figure 1: Selective Drug Sensitivity Screening (sDSS) score for top 3 drugs (44 drugs) for 20 CLL patient samples. Green label is IgVH mutated CLL patient samples and blue label is IgVH unmutated patient samples.

Disclosures

Schjesvold: Oncopeptides: Consultancy; Abbvie: Honoraria; Novartis: Honoraria; Janssen: Consultancy, Honoraria, Research Funding; Adaptive: Consultancy; Bayer: Consultancy; Bristol Myers Squibb: Consultancy; Takeda: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Research Funding.