Precision Medicine for Relapsed Multiple Myeloma on the Basis of an Integrative Multiomics Approach

The Genetic and Molecular Drivers of Multiple Myeloma: Current Insights, Clinical Implications, and the Path Forward

Background

Multiple myeloma (MM) is a hematological malignancy characterized by the clonal proliferation of malignant plasma cells within the bone marrow. The disease's complexity is underpinned by a variety of genetic and molecular abnormalities that drive its progression.

Methods

This review was conducted through a state-of-The-art literature search, primarily utilizing PubMed to gather peer-reviewed articles. We focused on the most comprehensive and cited studies to ensure a thorough understanding of the genetic and molecular landscapes of MM.

Results

We detail primary and secondary alterations such as translocations, hyperdiploidy, single nucleotide variants (SNVs), copy number alterations (CNAs), gene fusions, epigenetic modifications, non-coding RNAs, germline predisposing variants, and the influence of the tumor microenvironment (TME). Our analysis highlights the heterogeneity of MM and the challenges it poses in treatment and prognosis, emphasizing the distinction between driver mutations, which actively contribute to oncogenesis, and passenger mutations, which arise due to genomic instability and do not contribute to disease progression.

Conclusion & Future Perspectives

We report key controversies and challenges in defining the genetic drivers of MM, and examine their implications for future therapeutic strategies. We discuss the importance of systems biology approaches in understanding the dependencies and interactions among these alterations, particularly highlighting the impact of double and triple-hit scenarios on disease outcomes. By advancing our understanding of the molecular drivers and their interactions, this review sets the stage for novel therapeutic targets and strategies, ultimately aiming to improve clinical outcomes in MM patients.

Multi-omic analysis of the tumor microenvironment shows clinical correlations in Ph1 study of atezolizumab +/- SoC in MM

Multiple myeloma (MM) remains incurable, and treatment of relapsed/refractory (R/R) disease is challenging. There is an unmet need for more targeted therapies in this setting; deep cellular and molecular phenotyping of the tumor and microenvironment in MM could help guide such therapies. This phase 1b study (NCT02431208) evaluated the safety and efficacy of the anti-programmed death-ligand 1 monoclonal antibody atezolizumab (Atezo) alone or in combination with the standard of care (SoC) treatments lenalidomide (Len) or pomalidomide (Pom) and/or daratumumab (Dara) in patients with R/R MM. Study endpoints included incidence of adverse events (AEs) and overall response rate (ORR). A novel unsupervised integrative multi-omic analysis was performed using RNA sequencing, mass cytometry immunophenotyping, and proteomic profiling of baseline and on-treatment bone marrow samples from patients receiving Atezo monotherapy or Atezo+Dara. A similarity network fusion (SNF) algorithm was applied to preprocessed data. Eighty-five patients were enrolled. Treatment-emergent deaths occurred in 2 patients; both deaths were considered unrelated to study treatment. ORRs ranged from 11.1% (Atezo+Len cohorts, n=18) to 83.3% (Atezo+Dara+Pom cohort, n=6). High-dimensional multi-omic profiling of the tumor microenvironment and integrative SNF analysis revealed novel correlations between cellular and molecular features of the tumor and immune microenvironment, patient selection criteria, and clinical outcome. Atezo monotherapy and SoC combinations were safe in this patient population and demonstrated some evidence of clinical efficacy. Integrative analysis of high dimensional genomics and immune data identified novel clinical correlations that may inform patient selection criteria and outcome assessment in future immunotherapy studies for myeloma.

Multiple myeloma with t(11;14): unique biology and evolving landscape

Multiple myeloma is characterized by heterogeneity in clinical presentation, response to treatment, and importantly, patient outcomes. The translocation of chromosomes 11 and 14 [t(11;14)(q13;32)], hereafter referred to as t(11;14), is the most common primary translocation event in multiple myeloma, occurring in approximately 16%-24% of patients. Multiple myeloma harboring t(11;14) represents a unique disease subset as t(11;14)-positive myeloma cells exhibit biological features that are distinct from t(11;14)-negative myeloma cells, including overexpression of cyclin D1, higher levels of the antiapoptotic protein BCL-2, and the frequent expression of the B-cell lineage protein CD20. Additionally, t(11;14) is associated with less common clinical features, such as immunoglobulin M and light chain disease. With the evolution of the treatment landscape, the prognostic significance of t(11;14) multiple myeloma remains debatable. However, it is clear that t(11;14) multiple myeloma represents a distinct subset and a rare opportunity for targeted therapy with BCL-2 inhibition. In this review, we first describe the underlying biology of t(11;14) multiple myeloma cells, then summarize the body of literature evaluating the prognosis of patients with t(11;14) multiple myeloma, and finally discuss therapeutic implications.

Single-Cell Sequencing Technologies in Precision Oncology

Single-cell sequencing technologies are revolutionizing cancer research and are poised to become the standard for translational cancer studies. Rapidly decreasing costs and increasing throughput and resolution are paving the way for the adoption of single-cell technologies in clinical settings for personalized medicine applications. In this chapter, we review the state of the art of single-cell DNA and RNA sequencing technologies, the computational tools to analyze the data, and their potential application to precision oncology. We also discuss the advantages of single-cell over bulk sequencing for the dissection of intra-tumor heterogeneity and the characterization of subclonal cell populations, the implementation of targeted drug repurposing approaches, and describe advanced methodologies for multi-omics data integration and to assess cell signaling at single-cell resolution.

Genome Instability in Multiple Myeloma: Facts and Factors

Multiple myeloma (MM) is a malignant neoplasm of terminally differentiated immunoglobulin-producing B lymphocytes called plasma cells. MM is the second most common hematologic malignancy, and it poses a heavy economic and social burden because it remains incurable and confers a profound disability to patients. Despite current progress in MM treatment, the disease invariably recurs, even after the transplantation of autologous hematopoietic stem cells (ASCT). Biological processes leading to a pathological myeloma clone and the mechanisms of further evolution of the disease are far from complete understanding. Genetically, MM is a complex disease that demonstrates a high level of heterogeneity. Myeloma genomes carry numerous genetic changes, including structural genome variations and chromosomal gains and losses, and these changes occur in combinations with point mutations affecting various cellular pathways, including genome maintenance. MM genome instability in its extreme is manifested in mutation kataegis and complex genomic rearrangements: chromothripsis, templated insertions, and chromoplexy. Chemotherapeutic agents used to treat MM add another level of complexity because many of them exacerbate genome instability. Genome abnormalities are driver events and deciphering their mechanisms will help understand the causes of MM and play a pivotal role in developing new therapies.

Patient similarity network of newly diagnosed multiple myeloma identifies patient subgroups with distinct genetic features and clinical implications

The remarkable genetic heterogeneity of multiple myeloma poses a substantial challenge for proper prognostication and clinical management of patients. Here, we introduce MM-PSN, the first multiomics patient similarity network of myeloma. MM-PSN enabled accurate dissection of the genetic and molecular landscape of the disease and determined 12 distinct subgroups defined by five data types generated from genomic and transcriptomic profiling of 655 patients. MM-PSN identified patient subgroups not previously described defined by specific patterns of alterations, enriched for specific gene vulnerabilities, and associated with potential therapeutic options. Our analysis revealed that co-occurrence of t(4;14) and 1q gain identified patients at significantly higher risk of relapse and shorter survival as compared to t(4;14) as a single lesion. Furthermore, our results show that 1q gain is the most important single lesion conferring high risk of relapse and that it can improve on the current International Staging Systems (ISS and R-ISS).

Proteomics and Drug Repurposing in CLL towards Precision Medicine

Simple Summary

Despite continued efforts, the current status of knowledge in CLL molecular pathobiology, diagnosis, prognosis and treatment remains elusive and imprecise. Proteomics approaches combined with advanced bioinformatics and drug repurposing promise to shed light on the complex proteome heterogeneity of CLL patients and mitigate, improve, or even eliminate the knowledge stagnation. In relation to this concept, this review presents a brief overview of all the available proteomics and drug repurposing studies in CLL and suggests the way such studies can be exploited to find effective therapeutic options combined with drug repurposing strategies to adopt and accost a more “precision medicine” spectrum.

Abstract

CLL is a hematological malignancy considered as the most frequent lymphoproliferative disease in the western world. It is characterized by high molecular heterogeneity and despite the available therapeutic options, there are many patient subgroups showing the insufficient effectiveness of disease treatment. The challenge is to investigate the individual molecular characteristics and heterogeneity of these patients. Proteomics analysis is a powerful approach that monitors the constant state of flux operators of genetic information and can unravel the proteome heterogeneity and rewiring into protein pathways in CLL patients. This review essences all the available proteomics studies in CLL and suggests the way these studies can be exploited to find effective therapeutic options combined with drug repurposing approaches. Drug repurposing utilizes all the existing knowledge of the safety and efficacy of FDA-approved or investigational drugs and anticipates drug alignment to crucial CLL therapeutic targets, leading to a better disease outcome. The drug repurposing studies in CLL are also discussed in this review. The next goal involves the integration of proteomics-based drug repurposing in precision medicine, as well as the application of this procedure into clinical practice to predict the most appropriate drugs combination that could ensure therapy and the long-term survival of each CLL patient.

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.

Combination of Dabrafenib and Trametinib for the Treatment of Relapsed and Refractory Multiple Myeloma Harboring BRAF V600E Mutation

Multiple myeloma (MM) is an incurable plasma cell neoplasia characterized by relapsed and/or refractory (R/R) disease course, which poses a major therapeutic challenge. New therapies, including BRAF V600E mutation targeting, may become a new treatment option for R/R MM. In combination with mitogen-activated protein kinase inhibitors (MEKi), BRAF inhibitors (BRAFi) could provide better tailored clinical management, although experience in this field is lacking. To this date, there is only one case describing R/R MM treatment with BRAFi vemurafenib and MEKi cobimetinib. This is the first case presenting a R/R MM patient treated with BRAFi dabrafenib and MEKi trametinib.

Multiple myeloma: the (r)evolution of current therapy and a glance into future

Over the past 20 years, the regulatory approval of several novel agents to treat multiple myeloma (MM) has prolonged median patient survival from 3 to 8-10 years. Increased understanding of MM biology has translated to advances in diagnosis, prognosis, and response assessment, as well as informed the development of targeted and immune agents. Here we provide an overview of the recent progress in MM, and highlight research areas of greatest promise to further improve patient outcome in the future.

The FMS like Tyrosine Kinase 3 (FLT3) Is Overexpressed in a Subgroup of Multiple Myeloma Patients with Inferior Prognosis

Therapy resistance remains a major challenge in the management of multiple myeloma (MM). We evaluated the expression of FLT3 tyrosine kinase receptor (FLT3, CD135) in myeloma cells as a possible clonal driver. FLT3 expression was analyzed in bone marrow biopsies of patients with monoclonal gammopathy of undetermined significance or smoldering myeloma (MGUS, SMM), newly diagnosed MM (NDMM), and relapsed/refractory MM (RRMM) by immunohistochemistry (IHC). FLT3 gene expression was analyzed by RNA sequencing (RNAseq) and real-time PCR (rt-PCR). Anti-myeloma activity of FLT3 inhibitors (midostaurin, gilteritinib) was tested in vitro on MM cell lines and primary MM cells by ³H-tymidine incorporation assays or flow cytometry. Semi-quantitative expression analysis applying a staining score (FLT3 expression IHC-score, FES, range 1-6) revealed that a high FES (>3) was associated with a significantly shorter progression-free survival (PFS) in NDMM and RRMM patients (p = 0.04). RNAseq and real-time PCR confirmed the expression of FLT3 in CD138-purified MM samples. The functional relevance of FLT3 expression was corroborated by demonstrating the in vitro anti-myeloma activity of FLT3 inhibitors on FLT3-positive MM cell lines and primary MM cells. FLT3 inhibitors might offer a new targeted therapy approach in a subgroup of MM patients displaying aberrant FLT3 signaling.

Development of a Precision Medicine Workflow in Hematological Cancers, Aalborg University Hospital, Denmark

Within recent years, many precision cancer medicine initiatives have been developed. Most of these have focused on solid cancers, while the potential of precision medicine for patients with hematological malignancies, especially in the relapse situation, are less elucidated. Here, we present a demographic unbiased and observational prospective study at Aalborg University Hospital Denmark, referral site for 10% of the Danish population. We developed a hematological precision medicine workflow based on sequencing analysis of whole exome tumor DNA and RNA. All steps involved are outlined in detail, illustrating how the developed workflow can provide relevant molecular information to multidisciplinary teams. A group of 174 hematological patients with progressive disease or relapse was included in a non-interventional and population-based study, of which 92 patient samples were sequenced. Based on analysis of small nucleotide variants, copy number variants, and fusion transcripts, we found variants with potential and strong clinical relevance in 62% and 9.5% of the patients, respectively. The most frequently mutated genes in individual disease entities were in concordance with previous studies. We did not find tumor mutational burden or micro satellite instability to be informative in our hematologic patient cohort.

Telescope: an interactive tool for managing large-scale analysis from mobile devices

BACKGROUND

In today's world of big data, computational analysis has become a key driver of biomedical research. High-performance computational facilities are capable of processing considerable volumes of data, yet often lack an easy-to-use interface to guide the user in supervising and adjusting bioinformatics analysis via a tablet or smartphone.

RESULTS

To address this gap we proposed Telescope, a novel tool that interfaces with high-performance computational clusters to deliver an intuitive user interface for controlling and monitoring bioinformatics analyses in real-time. By leveraging last generation technology now ubiquitous to most researchers (such as smartphones), Telescope delivers a friendly user experience and manages conectivity and encryption under the hood.

CONCLUSIONS

Telescope helps to mitigate the digital divide between wet and computational laboratories in contemporary biology. By delivering convenience and ease of use through a user experience not relying on expertise with computational clusters, Telescope can help researchers close the feedback loop between bioinformatics and experimental work with minimal impact on the performance of computational tools. Telescope is freely available at https://github.com/Mangul-Lab-USC/telescope.