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Insight into the critical role of B-cell receptor signaling for the pathogenesis of chronic lymphocytic leukemia (CLL) led to the development of targeted therapies directed at key regulators of cell survival. Agents targeting B-cell lymphoma-2 protein, Bruton’s tyrosine kinase (BTK), and phosphatidylinositol 3-kinase are approved for treatment of CLL, and have significantly improved the disease management. Nevertheless, acquired resistance to the targeted therapies is a challenge still to be resolved. The mechanisms underlying resistance are becoming clearer, and include secondary mutations within the drug target and activation of bypass pathways. This knowledge has allowed development of strategies to prevent and overcome treatment resistance. Approaches to prevent resistance include targeting bypass mechanisms by combination therapies, temporally sequencing of therapies, improved clinical trial designs, and real-time monitoring of patient response. A rational design of drug sequencing may secure effective treatment options at the relapsed setting. Next-generation inhibitors and bispecific antibodies have the potential to overcome resistance to the BTK inhibitor ibrutinib. Immunotherapy, including chimeric antigen receptor-modified T-cell therapy, is explored for relapsed CLL. Here, recent advances that have contributed to the understanding of resistance to targeted therapies in CLL are discussed. Strategies for managing resistance are reviewed, including translational, real-world, and clinical perspectives.

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.

Introduction Multiple Myeloma (MM) is considered incurable and MM patients eventually relapse despite use of many promising approved drugs in standard-of-care treatment. It has been challenging to design precision medicine protocols to tailor personalized treatment for MM patients that relapse despite availability of novel drugs. In-vitro drug screening has been hampered by lack of in-vitro culture protocols that mimic tumor microenvironment and that accommodates for low cell number. Here, we report our novel MM proliferation protocol along with an in-vitro functional screening platform, that allow us to assess drug sensitivity on MM patient samples with a customized panel of 30 myeloma drugs. Using our novel drug sensitivity screening platform, we aim to identify efficient drugs for individual patients with progressive disease and select the best treatment option. Methods Previously, we have established culture settings that mimic the tumor microenvironment for MM (Wang D. et al Leukemia 2017). Here, we implemented a novel protocol that allowed primary MM cells to proliferate in a 384 well-format. Stimulated CD138+ MM cells were tested against a customized library of 30 clinically approved drugs including proteasome inhibitors (PI) and drugs that are in clinical trials. CD138+ MM cells were cultured in 384-well format in the presence of individual drugs in a concentration range over 6 logs for 72 hours (3 days). To define drugs that inhibit malignant plasma cell growth, we used the cell-based assays CellTiter-Glo® luminescent cell viability assay and CellTox™ green cytotoxicity assay as readouts by assessing drug sensitivity at day 3. We performed MM drug screening on 18 patient samples and 6 healthy B-cell (BC) control samples. We performed drug screening on myeloma cells SK-MM2 (patient derived cell line) for 527 drugs at 5 concentrations. We are currently performing drug screening on 11 MM cell lines which represents diverse cancer stage. For each patient sample, a Drug Sensitivity Score (DSS) was calculated for every drug using the IC50 value, slope and the area under the curve (AUC). Next, DSS values for the full MM patient cohort were compared to those of healthy controls to generate a selective DSS (sDSS) for each drug (sDSS = DSSpatient - average DSShealthy). Drugs which had sDSS >5 were considered clearly more effective for patient samples in the in vitro test. MM patient samples were assessed for sDSS score using our screening data and we ranked all the drugs by their sDSS score. We have generated sDSS score for both CTG (cell viability) and CTxG (cell toxicity) datasets. Results and conclusion To date we have performed MM drug sensitivity screening on 18 MM patient samples and 6 healthy B cells donors. We adopted a quantitative scoring approach using sDSS to rank drugs that are selective and effective in inhibiting myeloma cells. Based on our drug sensitivity analysis, proteasome inhibitors such as bortezomib and carfilzomib were more effective in inhibiting myeloma cell proliferation compared to other drugs in all 18 patient samples as well as in the 6 healthy donors. Surprising, doxorubicin showed the highest average sDSS score in 10 patients with score 12.96 followed by prednisolone with average sDSS score 6.73 (Figure 1), while proteasome inhibitor bortezomib showed average sDSS score of 4.14 and carfilzomib showed average sDSS score of 1. In addition, we observed that samples from dexamethasone-treated patients showed lower sDSS score for dexamethasone in the in vitro drug screening compared to samples from untreated patients (MM0905 and MM0706). Based on the screening data and clustering analysis, we concluded that the observed diversity in drug effectiveness between patient samples supports the hypothesis of tumor heterogeneity and creates a basis for exploring the possibility to individualize treatment choices. Figure 1: Selective Drug Sensitivity Screening (sDSS) score for 30 drugs for 13 MM patient samples. HB, Healthy donor B cells (Euclidean distance, Ward linkage method) Disclosures Schjesvold: Novartis: Honoraria; Oncopeptides: Consultancy; Janssen: Consultancy, Honoraria, Research Funding; Adaptive: Consultancy; Bayer: Consultancy; Bristol Myers Squibb: Consultancy; Takeda: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Research Funding; Abbvie: Honoraria.