Abstract
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.
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