Standardized assays to monitor drug sensitivity in hematologic cancers.

The principle of drug sensitivity testing is to expose cancer cells to a library of different drugs and measure its effects on cell viability. Recent technological advances, continuous approval of targeted therapies, and improved cell culture protocols have enhanced the precision and clinical relevance of such screens. Indeed, drug sensitivity testing has proven diagnostically valuable for patients with advanced hematologic cancers. However, different cell types behave differently in culture and therefore require optimized drug screening protocols to ensure that their ex vivo drug sensitivity accurately reflects in vivo drug responses. For example, primary chronic lymphocytic leukemia (CLL) and multiple myeloma (MM) cells require unique microenvironmental stimuli to survive in culture, while this is less the case for acute myeloid leukemia (AML) cells. Here, we present our optimized and validated protocols for culturing and drug screening of primary cells from AML, CLL, and MM patients, and a generic protocol for cell line models. We also discuss drug library designs, reproducibility, and quality controls. We envision that these protocols may serve as community guidelines for the use and interpretation of assays to monitor drug sensitivity in hematologic cancers and thus contribute to standardization. The read-outs may provide insight into tumor biology, identify or confirm treatment resistance and sensitivity in real time, and ultimately guide clinical decision-making.

Phenotypic deconvolution in heterogeneous cancer cell populations using drug-screening data.

Tumor heterogeneity is an important driver of treatment failure in cancer since therapies often select for drug-tolerant or drug-resistant cellular subpopulations that drive tumor growth and recurrence. Profiling the drug-response heterogeneity of tumor samples using traditional genomic deconvolution methods has yielded limited results, due in part to the imperfect mapping between genomic variation and functional characteristics. Here, we leverage mechanistic population modeling to develop a statistical framework for profiling phenotypic heterogeneity from standard drug-screen data on bulk tumor samples. This method, called PhenoPop, reliably identifies tumor subpopulations exhibiting differential drug responses and estimates their drug sensitivities and frequencies within the bulk population. We apply PhenoPop to synthetically generated cell populations, mixed cell-line experiments, and multiple myeloma patient samples and demonstrate how it can provide individualized predictions of tumor growth under candidate therapies. This methodology can also be applied to deconvolution problems in a variety of biological settings beyond cancer drug response.

Mutational analysis and protein profiling predict drug sensitivity in multiple myeloma cell lines.

Introduction: Multiple myeloma (MM) is a heterogeneous disease where cancer-driver mutations and aberrant signaling may lead to disease progression and drug resistance. Drug responses vary greatly, and there is an unmet need for biomarkers that can guide precision cancer medicine in this disease. Methods: To identify potential predictors of drug sensitivity, we applied integrated data from drug sensitivity screening, mutational analysis and functional signaling pathway profiling in 9 cell line models of MM. We studied the sensitivity to 33 targeted drugs and their association with the mutational status of cancer-driver genes and activity level of signaling proteins. Results: We found that sensitivity to mitogen-activated protein kinase kinase 1 (MEK1) and phosphatidylinositol-3 kinase (PI3K) inhibitors correlated with mutations in NRAS/KRAS, and PI3K family genes, respectively. Phosphorylation status of MEK1 and protein kinase B (AKT) correlated with sensitivity to MEK and PI3K inhibition, respectively. In addition, we found that enhanced phosphorylation of proteins, including Tank-binding kinase 1 (TBK1), as well as high expression of B cell lymphoma 2 (Bcl-2), correlated with low sensitivity to MEK inhibitors. Discussion: Taken together, this study shows that mutational status and signaling protein profiling might be used in further studies to predict drug sensitivities and identify resistance markers in MM.

Ex vivo drug sensitivity screening in multiple myeloma identifies drug combinations that act synergistically.

The management of multiple myeloma (MM) is challenging: An assortment of available drug combinations adds complexity to treatment selection, and treatment resistance frequently develops. Given the heterogeneous nature of MM, personalized testing tools are required to identify drug sensitivities. To identify drug sensitivities in MM cells, we established a drug testing pipeline to examine ex vivo drug responses. MM cells from 44 patients were screened against 30 clinically relevant single agents and 44 double- and triple-drug combinations. We observed variability in responses across samples. The presence of gain(1q21) was associated with low sensitivity to venetoclax, and decreased ex vivo responses to dexamethasone reflected the drug resistance observed in patients. Less heterogeneity and higher efficacy was detected with many combinations compared to the corresponding single agents. We identified new synergistic effects of melflufen plus panobinostat using low concentrations (0.1-10 nm and 8 nm, respectively). In agreement with clinical studies, clinically approved combinations, such as triple combination of selinexor plus bortezomib plus dexamethasone, acted synergistically, and synergies required low drug concentrations (0.1 nm bortezomib, 10 nm selinexor and 4 nm dexamethasone). In summary, our drug screening provided results within a clinically actionable 5-day time frame and identified synergistic drug efficacies in patient-derived MM cells that may aid future therapy choices.

Mcl-1 and Bcl-xL levels predict responsiveness to dual MEK/Bcl-2 inhibition in B-cell malignancies.

Most patients with chronic lymphocytic leukemia (CLL) initially respond to targeted therapies, but eventually relapse and develop resistance. Novel treatment strategies are therefore needed to improve patient outcomes. Here, we performed direct drug testing on primary CLL cells and identified synergy between eight different mitogen-activated protein kinase kinase (MEK) inhibitors and the B-cell lymphoma 2 (Bcl-2) antagonist venetoclax. Drug sensitivity was independent of immunoglobulin heavy-chain gene variable region (IGVH) and tumor protein p53 (TP53) mutational status, and CLL cells from idelalisib-resistant patients remained sensitive to the treatment. This suggests that combined MEK/Bcl-2 inhibition may be an option for high-risk CLL. To test whether sensitivity could be detected in other B-cell malignancies, we performed drug testing on cell line models of CLL (n = 4), multiple myeloma (MM; n = 8), and mantle cell lymphoma (MCL; n = 7). Like CLL, MM cells were sensitive to the MEK inhibitor trametinib, and synergy was observed with venetoclax. In contrast, MCL cells were unresponsive to MEK inhibition. To investigate the underlying mechanisms of the disease-specific drug sensitivities, we performed flow cytometry-based high-throughput profiling of 31 signaling proteins and regulators of apoptosis in the 19 cell lines. We found that high expression of the antiapoptotic proteins myeloid cell leukemia-1 (Mcl-1) or B-cell lymphoma-extra large (Bcl-xL) predicted low sensitivity to trametinib + venetoclax. The low sensitivity could be overcome by combined treatment with an Mcl-1 or Bcl-xL inhibitor. Our findings suggest that MEK/Bcl-2 inhibition has therapeutic potential in leukemia and myeloma, and demonstrate that protein expression levels can serve as predictive biomarkers for treatment sensitivities.

Hypoxia promotes IL-32 expression in myeloma cells, and high expression is associated with poor survival and bone loss.

Multiple myeloma (MM) is a hematologic cancer characterized by expansion of malignant plasma cells in the bone marrow. Most patients develop an osteolytic bone disease, largely caused by increased osteoclastogenesis. The myeloma bone marrow is hypoxic, and hypoxia may contribute to MM disease progression, including bone loss. Here we identified interleukin-32 (IL-32) as a novel inflammatory cytokine expressed by a subset of primary MM cells and MM cell lines. We found that high IL-32 gene expression in plasma cells correlated with inferior survival in MM and that IL-32 gene expression was higher in patients with bone disease compared with those without. IL-32 was secreted from MM cells in extracellular vesicles (EVs), and those EVs, as well as recombinant human IL-32, promoted osteoclast differentiation both in vitro and in vivo. The osteoclast-promoting activity of the EVs was IL-32 dependent. Hypoxia increased plasma-cell IL-32 messenger RNA and protein levels in a hypoxia-inducible factor 1α-dependent manner, and high expression of IL-32 was associated with a hypoxic signature in patient samples, suggesting that hypoxia may promote expression of IL-32 in MM cells. Taken together, our results indicate that targeting IL-32 might be beneficial in the treatment of MM bone disease in a subset of patients.