"Direct to Drug" screening as a precision medicine tool in multiple myeloma

Transcriptional Heterogeneity Overcomes Super-Enhancer Disrupting Drug Combinations in Multiple Myeloma

Multiple myeloma (MM) is a malignancy that is often driven by MYC and that is sustained by IRF4, which are upregulated by super-enhancers. IKZF1 and IKZF3 bind to super-enhancers and can be degraded using immunomodulatory imide drugs (IMiD). Successful IMiD responses downregulate MYC and IRF4; however, this fails in IMiD-resistant cells. MYC and IRF4 downregulation can also be achieved in IMiD-resistant tumors using inhibitors of BET and EP300 transcriptional coactivator proteins; however, in vivo these drugs have a narrow therapeutic window. By combining IMiDs with EP300 inhibition, we demonstrate greater downregulation of MYC and IRF4, synergistic killing of myeloma in vitro and in vivo, and an increased therapeutic window. Interestingly, this potent combination failed where MYC and IRF4 expression was maintained by high levels of the AP-1 factor BATF. Our results identify an effective drug combination and a previously unrecognized mechanism of IMiD resistance.

SIGNIFICANCE

These results highlight the dependence of MM on IKZF1-bound super-enhancers, which can be effectively targeted by a potent therapeutic combination pairing IMiD-mediated degradation of IKZF1 and IKZF3 with EP300 inhibition. They also identify AP-1 factors as an unrecognized mechanism of IMiD resistance in MM. See related article by Neri, Barwick, et al., p. 56. See related commentary by Yun and Cleveland, p. 5. This article is featured in Selected Articles from This Issue, p. 4.

Mechanisms of action of the BCL-2 inhibitor venetoclax in multiple myeloma: a literature review

Abnormal cellular apoptosis plays a pivotal role in the pathogenesis of Multiple Myeloma (MM). Over the years, BCL-2, a crucial anti-apoptotic protein, has garnered significant attention in MM therapeutic research. Venetoclax (VTC), a small-molecule targeted agent, effectively inhibits BCL-2, promoting the programmed death of cancerous cells. While VTC has been employed to treat various hematological malignancies, its particular efficacy in MM has showcased its potential for broader clinical applications. In this review, we delve into the intricacies of how VTC modulates apoptosis in MM cells by targeting BCL-2 and the overarching influence of the BCL-2 protein family in MM apoptosis regulation. Our findings highlight the nuanced interplay between VTC, BCL-2, and MM, offering insights that may pave the way for optimizing therapeutic strategies. Through this comprehensive analysis, we aim to lay a solid groundwork for future explorations into VTC's clinical applications and the profound effects of BCL-2 on cellular apoptosis.

Definers and drivers of functional high-risk multiple myeloma: insights from genomic, transcriptomic, and immune profiling

Traditional prognostic models for newly diagnosed patients with multiple myeloma (MM), including International Staging System criteria and number of high-risk chromosomal abnormalities, are based on disease characteristics at diagnosis. However, the identification of patients at risk of more rapidly progressive MM is inherently a dynamic assessment. In a subset of patients with MM, adverse disease biology only becomes evident after the failure of first-line therapy. We define this entity as functional high-risk MM (FHRMM), encompassing relapse within 18 months of treatment initiation and/or within 12 months of frontline autologous stem cell transplantation. FHRMM is not adequately captured by traditional prognostic models, and there is a need for better understanding of mechanisms or risk factors for early relapse or progression. In this review, we explore potential definitions of FHRMM before delving into its underlying drivers based on genetic, transcriptomic, and immune cell profiling studies. Emerging data suggest that specific features of both myeloma cells and immune cells can enable the FHRMM phenotype. We conclude our review by discussing ongoing and future studies that seek to identify and intervene upon patients with FHRMM preemptively.

PLK1 as a cooperating partner for BCL2-mediated antiapoptotic program in leukemia

The deregulation of BCL2 family proteins plays a crucial role in leukemia development. Therefore, pharmacological inhibition of this family of proteins is becoming a prevalent treatment method. However, due to the emergence of primary and acquired resistance, efficacy is compromised in clinical or preclinical settings. We developed a drug sensitivity prediction model utilizing a deep tabular learning algorithm for the assessment of venetoclax sensitivity in T-cell acute lymphoblastic leukemia (T-ALL) patient samples. Through analysis of predicted venetoclax-sensitive and resistant samples, PLK1 was identified as a cooperating partner for the BCL2-mediated antiapoptotic program. This finding was substantiated by additional data obtained through phosphoproteomics and high-throughput kinase screening. Concurrent treatment using venetoclax with PLK1-specific inhibitors and PLK1 knockdown demonstrated a greater therapeutic effect on T-ALL cell lines, patient-derived xenografts, and engrafted mice compared with using each treatment separately. Mechanistically, the attenuation of PLK1 enhanced BCL2 inhibitor sensitivity through upregulation of BCL2L13 and PMAIP1 expression. Collectively, these findings underscore the dependency of T-ALL on PLK1 and postulate a plausible regulatory mechanism.

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.

Exploring the current molecular landscape and management of multiple myeloma patients with the t(11;14) translocation

Multiple myeloma (MM) is a genetically complex disease. The key myeloma-initiating genetic events are hyperdiploidy and translocations involving the immunoglobulin heavy chain (IgH) enhancer on chromosome 14, which leads to the activation of oncogenes (e.g., CCND1, CCND3, MAF, and MMSET). The t(11;14) translocation is the most common in MM (15%–20%) and results in cyclin D1 (CCND1) upregulation, which leads to kinase activation and tumor cell proliferation. Notably, t(11;14) occurs at a higher rate in patients with plasma cell leukemia (40%) and light chain amyloidosis (50%). Patients with myeloma who harbor the t(11;14) translocation have high levels of the anti-apoptotic protein B-cell lymphoma 2 (BCL2). Multiple studies demonstrated that the presence of t(11;14) was predictive of BCL2 dependency, suggesting that BCL2 could be a target in this subtype of myeloma. Venetoclax, an oral BCL2 inhibitor, has shown remarkable activity in treating relapsed/refractory MM patients with t(11;14) and BCL2 overexpression, either as monotherapy or in combination with other anti-myeloma agents. In this review, we describe the molecular defects associated with the t(11;14), bring into question the standard cytogenetic risk of myeloma patients harboring t(11;14), summarize current efficacy and safety data of targeted venetoclax-based therapies, and discuss the future of individualized or precision medicine for this unique myeloma subgroup, which will guide optimal treatment.

Transcriptional profiles define drug refractory disease in myeloma

Identifying biomarkers associated with disease progression and drug resistance are important for personalized care. We investigated the expression of 121 curated genes, related to immunomodulatory drugs (IMiDs) and proteasome inhibitors (PIs) responsiveness. We analyzed 28 human multiple myeloma (MM) cell lines with known drug sensitivities and 130 primary MM patient samples collected at different disease stages, including newly diagnosed (ND), on therapy (OT), and relapsed and refractory (RR, collected within 12 months before the patients' death) timepoints. Our findings led to the identification of a subset of genes linked to clinical drug resistance, poor survival, and disease progression following combination treatment containing IMIDs and/or PIs. Finally, we built a seven-gene model (MM-IMiD and PI sensitivity-7 genes [IP-7]) using digital gene expression profiling data that significantly separates ND patients from IMiD- and PI-refractory RR patients. Using this model, we retrospectively analyzed RNA sequcencing (RNAseq) data from the Mulltiple Myeloma Research Foundation (MMRF) CoMMpass (n = 578) and Mayo Clinic MM patient registry (n = 487) to divide patients into probabilities of responder and nonresponder, which subsequently correlated with overall survival, disease stage, and number of prior treatments. Our findings suggest that this model may be useful in predicting acquired resistance to treatments containing IMiDs and/or PIs.

Single-Cell Proteomics and Tumor RNAseq Identify Novel Pathways Associated With Clofazimine Sensitivity in PI- and IMiD- Resistant Myeloma, and Putative Stem-Like Cells

Multiple myeloma (MM) is an incurable plasma cell malignancy with dose-limiting toxicities and inter-individual variation in response/resistance to the standard-of-care/primary drugs, proteasome inhibitors (PIs), and immunomodulatory derivatives (IMiDs). Although newer therapeutic options are potentially highly efficacious, their costs outweigh the effectiveness. Previously, we have established that clofazimine (CLF) activates peroxisome proliferator-activated receptor-γ, synergizes with primary therapies, and targets cancer stem-like cells (CSCs) in drug-resistant chronic myeloid leukemia (CML) patients. In this study, we used a panel of human myeloma cell lines as in vitro model systems representing drug-sensitive, innate/refractory, and clonally-derived acquired/relapsed PI- and cereblon (CRBN)-negative IMiD-resistant myeloma and bone marrow-derived CD138+ primary myeloma cells obtained from patients as ex vivo models to demonstrate that CLF shows significant cytotoxicity against drug-resistant myeloma as single-agent and in combination with PIs and IMiDs. Next, using genome-wide transcriptome analysis (RNA-sequencing), single-cell proteomics (CyTOF; Cytometry by time-of-flight), and ingenuity pathway analysis (IPA), we identified novel pathways associated with CLF efficacy, including induction of ER stress, autophagy, mitochondrial dysfunction, oxidative phosphorylation, enhancement of downstream cascade of p65-NFkB-IRF4-Myc downregulation, and ROS-dependent apoptotic cell death in myeloma. Further, we also showed that CLF is effective in killing rare refractory subclones like side populations that have been referred to as myeloma stem-like cells. Since CLF is an FDA-approved drug and also on WHO's list of safe and effective essential medicines, it has strong potential to be rapidly re-purposed as a safe and cost-effective anti-myeloma drug.

Upgrade of an old drug: Auranofin in innovative cancer therapies to overcome drug resistance and to increase drug effectiveness

Auranofin is an oral gold(I) compound, initially developed for the treatment of rheumatoid arthritis. Currently, Auranofin is under investigation for oncological application within a drug repurposing plan due to the relevant antineoplastic activity observed both in vitro and in vivo tumor models. In this review, we analysed studies in which Auranofin was used as a single drug or in combination with other molecules to enhance their anticancer activity or to overcome chemoresistance. The analysis of different targets/pathways affected by this drug in different cancer types has allowed us to highlight several interesting targets and effects of Auranofin besides the already well-known inhibition of thioredoxin reductase. Among these targets, inhibitory-κB kinase, deubiquitinates, protein kinase C iota have been frequently suggested. To rationalize the effects of Auranofin by a system biology-like approach, we exploited transcriptomic data obtained from a wide range of cell models, extrapolating the data deposited in the Connectivity Maps website and we attempted to provide a general conclusion and discussed the major points that need further investigation.

secDrug: a pipeline to discover novel drug combinations to kill drug-resistant multiple myeloma cells using a greedy set cover algorithm and single-cell multi-omics

Multiple myeloma, the second-most common hematopoietic malignancy in the United States, still remains an incurable disease with dose-limiting toxicities and resistance to primary drugs like proteasome inhibitors (PIs) and Immunomodulatory drugs (IMiDs).We have created a computational pipeline that uses pharmacogenomics data-driven optimization-regularization/greedy algorithm to predict novel drugs ("secDrugs") against drug-resistant myeloma. Next, we used single-cell RNA sequencing (scRNAseq) as a screening tool to predict top combination candidates based on the enrichment of target genes. For in vitro validation of secDrugs, we used a panel of human myeloma cell lines representing drug-sensitive, innate/refractory, and acquired/relapsed PI- and IMiD resistance. Next, we performed single-cell proteomics (CyTOF or Cytometry time of flight) in patient-derived bone marrow cells (ex vivo), genome-wide transcriptome analysis (bulk RNA sequencing), and functional assays like CRISPR-based gene editing to explore molecular pathways underlying secDrug efficacy and drug synergy. Finally, we developed a universally applicable R-software package for predicting novel secondary therapies in chemotherapy-resistant cancers that outputs a list of the top drug combination candidates with rank and confidence scores.Thus, using 17AAG (HSP90 inhibitor) + FK866 (NAMPT inhibitor) as proof of principle secDrugs, we established a novel pipeline to introduce several new therapeutic options for the management of PI and IMiD-resistant myeloma.

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.

Ex vivo propagation in a novel 3D high-throughput co-culture system for multiple myeloma

Purpose

Multiple myeloma (MM) remains an incurable hematologic malignancy which ultimately develops drug resistance and evades treatment. Despite substantial therapeutic advances over the past years, the clinical failure rate of preclinically promising anti-MM drugs remains substantial. More realistic in vitro models are thus required to better predict clinical efficacy of a preclinically active compound.

Methods

Here, we report on the establishment of a conical agarose 3D co-culture platform for the preclinical propagation of primary MM cells ex vivo. Cell growth was compared to yet established 2D and liquid overlay systems. MM cell lines (MMCL: RPMI-8226, U266, OPM-2) and primary patient specimens were tested. Drug sensitivity was examined by exploring the cytotoxic effect of bortezomib and the deubiquitinase inhibitor auranofin under various conditions.

Results

In contrast to 2D and liquid overlay, cell proliferation in the 3D array followed a sigmoidal curve characterized by an initial growth delay but more durable proliferation of MMCL over 12 days of culture. Primary MM specimens did not expand in ex vivo monoculture, but required co-culture support by a human stromal cell line (HS-5, MSP-1). HS-5 induced a > fivefold increase in cluster volume and maintained long-term viability of primary MM cells for up to 21 days. Bortezomib and auranofin induced less cytotoxicity under 3D vs. 2D condition and in co- vs. monoculture, respectively.

Conclusions

This study introduces a novel model that is capable of long-term propagation and drug testing of primary MM specimens ex vivo overcoming some of the pitfalls of currently available in vitro models.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00432-021-03854-6.

Combining Radiation Therapy with ALK Inhibitors in Anaplastic Lymphoma Kinase-Positive Non-Small Cell Lung Cancer (NSCLC): A Clinical and Preclinical Overview

Over the past years, the identification of genetic alterations in oncogenic drivers in non-small cell lung cancer (NSCLC) has significantly and favorably transformed the outcome of patients who can benefit from targeted therapies such as tyrosine kinase inhibitors. Among these genetic alterations, anaplastic lymphoma kinase (ALK) rearrangements were discovered in 2007 and are present in 3-5% of patients with NSCLC. In addition, radiotherapy remains one of the cornerstones of NSCLC treatment. Moreover, improvements in the field of radiotherapy with the use of hypofractionated or ablative stereotactic radiotherapy have led to a better outcome for localized or oligometastatic NSCLC. To date, the effects of the combination of ALK inhibitors and radiotherapy are unclear in terms of safety and efficacy but could potently improve treatment. In this manuscript, we provide a clinical and preclinical overview of combining radiation therapy with ALK inhibitors in anaplastic lymphoma kinase-positive non-small cell lung cancer.

High-Throughput Drug Screening and Multi-Omic Analysis to Guide Individualized Treatment for Multiple Myeloma

Multiple myeloma (MM) is a genetically heterogeneous malignancy characterized by variable treatment responses. Although numerous drugs have been approved in recent years, the ability to predict treatment response and tailor individual therapy is limited by the absence of robust predictive biomarkers. The goal of this clinical trial was to use ex vivo, high-throughput screening (HTS) of 170 compounds to predict response among patients with relapsed or refractory MM and inform the next treatment decisions. Additionally, we integrated HTS with multi-omic analysis to uncover novel associations between in vitro drug sensitivity and gene expression and mutation profiles.

MATERIALS AND METHODS

Twenty-five patients with relapsed or refractory MM underwent a screening bone marrow or soft tissue biopsy. Sixteen patients were found to have sufficient plasma cells for HTS. Targeted next-generation sequencing was performed on plasma cell-free DNA from all patients who underwent HTS. RNA and whole-exome sequencing of bone marrow plasma cells were performed on eight and seven patients, respectively.

RESULTS

Results of HTS testing were made available to treating physicians within a median of 5 days from the biopsy. An actionable treatment result was identified in all 16 patients examined. Among the 13 patients who received assay-guided therapy, 92% achieved stable disease or better. The expression of 105 genes and mutations in 12 genes correlated with in vitro cytotoxicity.

CONCLUSION

In patients with relapsed or refractory MM, we demonstrate the feasibility of ex vivo drug sensitivity testing on isolated plasma cells from patient bone marrow biopsies or extramedullary plasmacytomas to inform the next line of therapy.

The Landscape of Signaling Pathways and Proteasome Inhibitors Combinations in Multiple Myeloma

Multiple myeloma is a malignancy of terminally differentiated plasma cells, characterized by an extreme genetic heterogeneity that poses great challenges for its successful treatment. Due to antibody overproduction, MM cells depend on the precise regulation of the protein degradation systems. Despite the success of PIs in MM treatment, resistance and adverse toxic effects such as peripheral neuropathy and cardiotoxicity could arise. To this end, the use of rational combinatorial treatments might allow lowering the dose of inhibitors and therefore, minimize their side-effects. Even though the suppression of different cellular pathways in combination with proteasome inhibitors have shown remarkable anti-myeloma activities in preclinical models, many of these promising combinations often failed in clinical trials. Substantial progress has been made by the simultaneous targeting of proteasome and different aspects of MM-associated immune dysfunctions. Moreover, targeting deranged metabolic hubs could represent a new avenue to identify effective therapeutic combinations with PIs. Finally, epigenetic drugs targeting either DNA methylation, histone modifiers/readers, or chromatin remodelers are showing pleiotropic anti-myeloma effects alone and in combination with PIs. We envisage that the positive outcome of patients will probably depend on the availability of more effective drug combinations and treatment of early MM stages. Therefore, the identification of sensitive targets and aberrant signaling pathways is instrumental for the development of new personalized therapies for MM patients.

Use of artificial intelligence to enhance phenotypic drug discovery

Research and development (R&D) productivity across the pharmaceutical industry has received close scrutiny over the past two decades, especially taking into consideration reports of attrition rates and the colossal cost for drug development. The respective merits of the two main drug discovery approaches, phenotypic and target based, have divided opinion across the research community, because each hold different advantages for identifying novel molecular entities with a successful path to the market. Nevertheless, both have low translatability in the clinic. Artificial intelligence (AI) and adoption of machine learning (ML) tools offer the promise of revolutionising drug development, and overcoming obstacles in the drug discovery pipeline. Here, we assess the potential of target-driven and phenotypic-based approaches and offer a holistic description of the current state of the field, from both a scientific and industry perspective. With the emerging partnerships between AI/ML and pharma still in their relative infancy, we investigate the potential and current limitations with a particular focus on phenotypic drug discovery. Finally, we emphasise the value of public-private partnerships (PPPs) and cross-disciplinary collaborations to foster innovation and facilitate efficient drug discovery programmes.

In Vitro Modeling of Non-Solid Tumors: How Far Can Tissue Engineering Go?

In hematological malignancies, leukemias or myelomas, malignant cells present bone marrow (BM) homing, in which the niche contributes to tumor development and drug resistance. BM architecture, cellular and molecular composition and interactions define differential microenvironments that govern cell fate under physiological and pathological conditions and serve as a reference for the native biological landscape to be replicated in engineered platforms attempting to reproduce blood cancer behavior. This review summarizes the different models used to efficiently reproduce certain aspects of BM in vitro; however, they still lack the complexity of this tissue, which is relevant for fundamental aspects such as drug resistance development in multiple myeloma. Extracellular matrix composition, material topography, vascularization, cellular composition or stemness vs. differentiation balance are discussed as variables that could be rationally defined in tissue engineering approaches for achieving more relevant in vitro models. Fully humanized platforms closely resembling natural interactions still remain challenging and the question of to what extent accurate tissue complexity reproduction is essential to reliably predict drug responses is controversial. However, the contributions of these approaches to the fundamental knowledge of non-solid tumor biology, its regulation by niches, and the advance of personalized medicine are unquestionable.