Genome-Wide Somatic Alterations in Multiple Myeloma Reveal a Superior Outcome Group

G-Quadruplex Forming DNA Sequence Context Is Enriched around Points of Somatic Mutations in a Subset of Multiple Myeloma Patients

Multiple myeloma (MM) is the second most common hematological malignancy, which remains incurable despite recent advances in treatment strategies. Like other forms of cancer, MM is characterized by genomic instability, caused by defects in DNA repair. Along with mutations in DNA repair genes and genotoxic drugs used to treat MM, non-canonical secondary DNA structures (four-stranded G-quadruplex structures) can affect accumulation of somatic mutations and chromosomal abnormalities in the tumor cells of MM patients. Here, we tested the hypothesis that G-quadruplex structures may influence the distribution of somatic mutations in the tumor cells of MM patients. We sequenced exomes of normal and tumor cells of 11 MM patients and analyzed the data for the presence of G4 context around points of somatic mutations. To identify molecular mechanisms that could affect mutational profile of tumors, we also analyzed mutational signatures in tumor cells as well as germline mutations for the presence of specific SNPs in DNA repair genes or in genes regulating G-quadruplex unwinding. In several patients, we found that sites of somatic mutations are frequently located in regions with G4 context. This pattern correlated with specific germline variants found in these patients. We discuss the possible implications of these variants for mutation accumulation and specificity in MM and propose that the extent of G4 context enrichment around somatic mutation sites may be a novel metric characterizing mutational processes in tumors.

The genomic landscape of Vk*MYC myeloma highlights shared pathways of transformation between mice and humans

Multiple myeloma (MM) is a heterogeneous disease characterized by frequent MYC translocations. Sporadic MYC activation in the germinal center of genetically engineered Vk*MYC mice is sufficient to induce plasma cell tumors in which a variety of secondary mutations are spontaneously acquired and selected over time. Analysis of 119 Vk*MYC myeloma reveals recurrent copy number alterations, structural variations, chromothripsis, driver mutations, apolipoprotein B mRNA-editing enzyme, catalytic polypeptide (APOBEC) mutational activity, and a progressive decrease in immunoglobulin transcription that inversely correlates with proliferation. Moreover, we identify frequent insertional mutagenesis by endogenous retro-elements as a murine specific mechanism to activate NF-kB and IL6 signaling pathways shared with human MM. Despite the increased genomic complexity associated with progression, advanced tumors remain dependent on MYC. In summary, here we credential the Vk*MYC mouse as a unique resource to explore MM genomic evolution and describe a fully annotated collection of diverse and immortalized murine MM tumors.

Genomic Classification and Individualized Prognosis in Multiple Myeloma

PURPOSE

Outcomes for patients with newly diagnosed multiple myeloma (NDMM) are heterogenous, with overall survival (OS) ranging from months to over 10 years.

METHODS

To decipher and predict the molecular and clinical heterogeneity of NDMM, we assembled a series of 1,933 patients with available clinical, genomic, and therapeutic data.

RESULTS

Leveraging a comprehensive catalog of genomic drivers, we identified 12 groups, expanding on previous gene expression-based molecular classifications. To build a model predicting individualized risk in NDMM (IRMMa), we integrated clinical, genomic, and treatment variables. To correct for time-dependent variables, including high-dose melphalan followed by autologous stem-cell transplantation (HDM-ASCT), and maintenance therapy, a multi-state model was designed. The IRMMa model accuracy was significantly higher than all comparator prognostic models, with a c-index for OS of 0.726, compared with International Staging System (ISS; 0.61), revised-ISS (0.572), and R2-ISS (0.625). Integral to model accuracy was 20 genomic features, including 1q21 gain/amp, del 1p, TP53 loss, NSD2 translocations, APOBEC mutational signatures, and copy-number signatures (reflecting the complex structural variant chromothripsis). IRMMa accuracy and superiority compared with other prognostic models were validated on 256 patients enrolled in the GMMG-HD6 (ClinicalTrials.gov identifier: NCT02495922) clinical trial. Individualized patient risks were significantly affected across the 12 genomic groups by different treatment strategies (ie, treatment variance), which was used to identify patients for whom HDM-ASCT is particularly effective versus patients for whom the impact is limited.

CONCLUSION

Integrating clinical, demographic, genomic, and therapeutic data, to our knowledge, we have developed the first individualized risk-prediction model enabling personally tailored therapeutic decisions for patients with NDMM.

Genomic and immune determinants of resistance to anti-CD38 monoclonal antibody-based therapy in relapsed refractory multiple myeloma

Anti-CD38 antibody therapies have transformed multiple myeloma (MM) treatment. However, a large fraction of patients inevitably relapses. To understand this, we investigated 32 relapsed MM patients treated with daratumumab, lenalidomide, and dexamethasone (Dara-Rd; NCT03848676 ). Whole genome sequencing (WGS) before and after treatment pinpointed genomic drivers associated with early progression, including RPL5 loss and APOBEC mutagenesis. Flow cytometry on 202 blood samples, collected every three months until progression for 31 patients, revealed distinct immune changes significantly impacting clinical outcomes. Progressing patients exhibited significant depletion of CD38+ NK cells, persistence of T cell exhaustion, and reduced depletion of T-reg cells over time. These findings underscore the influence of immune composition and daratumumab-induced immune changes in promoting MM resistance. Integrating genomics and flow cytometry unveiled associations between adverse genomic features and immune patterns. Overall, this study sheds light on the intricate interplay between genomic complexity and the immune microenvironment driving resistance to Dara-Rd.

Determining the prognosis of Lung cancer from mutated genes using a deep learning survival model: a large multi-center study

BACKGROUND

Gene status has become the focus of prognosis prediction. Furthermore, deep learning has frequently been implemented in medical imaging to diagnose, prognosticate, and evaluate treatment responses in patients with cancer. However, few deep learning survival (DLS) models based on mutational genes that are directly associated with patient prognosis in terms of progression-free survival (PFS) or overall survival (OS) have been reported. Additionally, DLS models have not been applied to determine IO-related prognosis based on mutational genes. Herein, we developed a deep learning method to predict the prognosis of patients with lung cancer treated with or without immunotherapy (IO).

METHODS

Samples from 6542 patients from different centers were subjected to genome sequencing. A DLS model based on multi-panels of somatic mutations was trained and validated to predict OS in patients treated without IO and PFS in patients treated with IO.

RESULTS

In patients treated without IO, the DLS model (low vs. high DLS) was trained using the training MSK-MET cohort (HR = 0.241 [0.213-0.273], P < 0.001) and tested in the inter-validation MSK-MET cohort (HR = 0.175 [0.148-0.206], P < 0.001). The DLS model was then validated with the OncoSG, MSK-CSC, and TCGA-LUAD cohorts (HR = 0.420 [0.272-0.649], P < 0.001; HR = 0.550 [0.424-0.714], P < 0.001; HR = 0.215 [0.159-0.291], P < 0.001, respectively). Subsequently, it was fine-tuned and retrained in patients treated with IO. The DLS model (low vs. high DLS) could predict PFS and OS in the MIND, MSKCC, and POPLAR/OAK cohorts (P < 0.001, respectively). Compared with tumor-node-metastasis staging, the COX model, tumor mutational burden, and programmed death-ligand 1 expression, the DLS model had the highest C-index in patients treated with or without IO.

CONCLUSIONS

The DLS model based on mutational genes can robustly predict the prognosis of patients with lung cancer treated with or without IO.

The Vk*MYC Mouse Model recapitulates human multiple myeloma evolution and genomic diversity

Despite advancements in profiling multiple myeloma (MM) and its precursor conditions, there is limited information on mechanisms underlying disease progression. Clincal efforts designed to deconvolute such mechanisms are challenged by the long lead time between monoclonal gammopathy and its transformation to MM. MM mouse models represent an opportunity to overcome this temporal limitation. Here, we profile the genomic landscape of 118 genetically engineered Vk*MYC MM and reveal that it recapitulates the genomic heterogenenity and life history of human MM. We observed recurrent copy number alterations, structural variations, chromothripsis, driver mutations, APOBEC mutational activity, and a progressive decrease in immunoglobulin transcription that inversely correlates with proliferation. Moreover, we identified frequent insertional mutagenesis by endogenous retro-elements as a murine specific mechanism to activate NF-kB and IL6 signaling pathways shared with human MM. Despite the increased genomic complexity associated with progression, advanced tumors remain dependent on MYC expression, that drives the progression of monoclonal gammopathy to MM.

Single-cell profiling in multiple myeloma: insights, problems, and promises

In a short time, single-cell platforms have become the norm in many fields of research, including multiple myeloma (MM). In fact, the large amount of cellular heterogeneity in MM makes single-cell platforms particularly attractive because bulk assessments can miss valuable information about cellular subpopulations and cell-to-cell interactions. The decreasing cost and increasing accessibility of single-cell platform, combined with breakthroughs in obtaining multiomics data for the same cell and innovative computational programs for analyzing data, have allowed single-cell studies to make important insights into MM pathogenesis; yet, there is still much to be done. In this review, we will first focus on the types of single-cell profiling and the considerations for designing a single-cell profiling experiment. Then, we will discuss what have learned from single-cell profiling about myeloma clonal evolution, transcriptional reprogramming, and drug resistance, and about the MM microenvironment during precursor and advanced disease.

Identification of potential resistance mechanisms and therapeutic targets for the relapse of BCMA CAR-T therapy in relapsed/refractory multiple myeloma through single-cell sequencing

BACKGROUND

BCMA CAR-T is highly effective for relapsed/refractory multiple myeloma(R/R-MM) and significantly improves the survival of patients. However, the short remission time and high relapse rate of MM patients treated with BCMA CAR-T remain bottlenecks that limit long-term survival. The immune microenvironment of the bone marrow (BM) in R/R-MM may be responsible for this. The present study aims to present an in-depth analysis of resistant mechanisms and to explore potential novel therapeutic targets for relapse of BCMA CAR-T treatment via single-cell RNA sequencing (scRNA-seq) of BM plasma cells and immune cells.

METHODS

This study used 10X Genomic scRNA-seq to identify cell populations in R/R-MM CD45⁺ BM cells before BCMA CAR-T treatment and relapse after BCMA CAR-T treatment. Cell Ranger pipeline and CellChat were used to perform detailed analysis.

RESULTS

We compared the heterogeneity of CD45⁺ BM cells before BCMA CAR-T treatment and relapse after BCMA CAR-T treatment. We found that the proportion of monocytes/macrophages increased, while the percentage of T cells decreased at relapse after BCMA CAR-T treatment. We then reclustered and analyzed the alterations in plasma cells, T cells, NK cells, DCs, neutrophils, and monocytes/macrophages in the BM microenvironment before BCMA CAR-T treatment and relapse after BCMA CAR-T treatment. We show here that the percentage of BCMA positive plasma cells increased at relapse after BCMA CAR-T cell therapy. Other targets such as CD38, CD24, SLAMF7, CD138, and GPRC5D were also found to be expressed in plasma cells of the R/R-MM patient at relapse after BCMA CAR-T cell therapy. Furthermore, exhausted T cells, TIGIT⁺NK cells, interferon-responsive DCs, and interferon-responsive neutrophils, increased in the R/R-MM patient at relapse after BCMA CAR-T cell treatment. Significantly, the proportion of IL1β^hi Mφ, S100A9^hi Mφ, interferon-responsive Mφ, CD16^hi Mφ, MARCO ^hi Mφ, and S100A11^hi Mφ significantly increased in the R/R-MM patient at relapse after BCMA CAR-T cell therapy. Cell-cell communication analysis indicated that monocytes/macrophages, especially the MIF and APRIL signaling pathway are key players in R/R-MM patient at relapse after BCMA CAR-T cell therapy.

CONCLUSION

Taken together, our data extend the understanding of intrinsic and extrinsic relapse of BCMA CAR-T treatment in R/R-MM patient and the potential mechanisms involved in the alterations of antigens and the induced immunosuppressive microenvironment, which may provide a basis for the optimization of BCMA CAR-T strategies. Further studies should be performed to confirm these findings.

High-dose melphalan treatment significantly increases mutational burden at relapse in multiple myeloma

High-dose melphalan (HDM) improves progression-free survival in multiple myeloma (MM), yet melphalan is a DNA-damaging alkylating agent; therefore, we assessed its mutational effect on surviving myeloma cells by analyzing paired MM samples collected at diagnosis and relapse in the IFM 2009 study. We performed deep whole-genome sequencing on samples from 68 patients, 43 of whom were treated with RVD (lenalidomide, bortezomib, and dexamethasone) and 25 with RVD + HDM. Although the number of mutations was similar at diagnosis in both groups (7137 vs 7230; P = .67), the HDM group had significantly more mutations at relapse (9242 vs 13 383, P = .005). No change in the frequency of copy number alterations or structural variants was observed. The newly acquired mutations were typically associated with DNA damage and double-stranded breaks and were predominantly on the transcribed strand. A machine learning model, using this unique pattern, predicted patients who would receive HDM with high sensitivity, specificity, and positive prediction value. Clonal evolution analysis showed that all patients treated with HDM had clonal selection, whereas a static progression was observed with RVD. A significantly higher percentage of mutations were subclonal in the HDM cohort. Intriguingly, patients treated with HDM who achieved complete remission (CR) had significantly more mutations at relapse yet had similar survival rates as those treated with RVD who achieved CR. This similarity could have been due to HDM relapse samples having significantly more neoantigens. Overall, our study identifies increased genomic changes associated with HDM and provides rationale to further understand clonal complexity.

In Multiple Myeloma, High-Risk Secondary Genetic Events Observed at Relapse Are Present From Diagnosis in Tiny, Undetectable Subclonal Populations

PURPOSE

Multiple myeloma (MM) is characterized by copy number abnormalities (CNAs), some of which influence patient outcomes and are sometimes observed only at relapse(s), suggesting their acquisition during tumor evolution. However, the presence of micro-subclones may be missed in bulk analyses. Here, we use single-cell genomics to determine how often these high-risk events are missed at diagnosis and selected at relapse.

MATERIALS AND METHODS

We analyzed 81 patients with plasma cell dyscrasias using single-cell CNA sequencing. Sixty-six patients were selected at diagnosis, nine at first relapse, and six in presymptomatic stages. A total of 956 newly diagnosed patients with MM and patients with first relapse MM have been identified retrospectively with required cytogenetic data to evaluate enrichment of CNA risk events and survival impact.

RESULTS

A total of 52,176 MM cells were analyzed. Seventy-four patients (91%) had 2-16 subclones. Among these patients, 28.7% had a subclone with high-risk features (del(17p), del(1p32), and 1q gain) at diagnosis. In a patient with a subclonal 1q gain at diagnosis, we analyzed the diagnosis, postinduction, and first relapse samples, which showed a rise of the high-risk 1q gain subclone (16%, 70%, and 92%, respectively). In our clinical database, we found that the 1q gain frequency increased from 30.2% at diagnosis to 43.6% at relapse (odds ratio, 1.78; 95% CI, 1.58 to 2.00). We subsequently performed survival analyses, which showed that the progression-free and overall survival curves were superimposable between patients who had the 1q gain from diagnosis and those who seemingly acquired it at relapse. This strongly suggests that many patients had 1q gains at diagnosis in microclones that were missed by bulk analyses.

CONCLUSION

These data suggest that identifying these scarce aggressive cells may necessitate more aggressive treatment as early as diagnosis to prevent them from becoming the dominant clone.

Epigenetic regulation in hematopoiesis and its implications in the targeted therapy of hematologic malignancies

Hematologic malignancies are one of the most common cancers, and the incidence has been rising in recent decades. The clinical and molecular features of hematologic malignancies are highly heterogenous, and some hematologic malignancies are incurable, challenging the treatment, and prognosis of the patients. However, hematopoiesis and oncogenesis of hematologic malignancies are profoundly affected by epigenetic regulation. Studies have found that methylation-related mutations, abnormal methylation profiles of DNA, and abnormal histone deacetylase expression are recurrent in leukemia and lymphoma. Furthermore, the hypomethylating agents and histone deacetylase inhibitors are effective to treat acute myeloid leukemia and T-cell lymphomas, indicating that epigenetic regulation is indispensable to hematologic oncogenesis. Epigenetic regulation mainly includes DNA modifications, histone modifications, and noncoding RNA-mediated targeting, and regulates various DNA-based processes. This review presents the role of writers, readers, and erasers of DNA methylation and histone methylation, and acetylation in hematologic malignancies. In addition, this review provides the influence of microRNAs and long noncoding RNAs on hematologic malignancies. Furthermore, the implication of epigenetic regulation in targeted treatment is discussed. This review comprehensively presents the change and function of each epigenetic regulator in normal and oncogenic hematopoiesis and provides innovative epigenetic-targeted treatment in clinical practice.

Whole-genome analysis identifies novel drivers and high-risk double-hit events in relapsed/refractory myeloma

Large-scale analyses of genomic data from patients with newly diagnosed multiple myeloma (ndMM) have been undertaken, however, large-scale analysis of relapsed/refractory MM (rrMM) has not been performed. We hypothesize that somatic variants chronicle the therapeutic exposures and clonal structure of myeloma from ndMM to rrMM stages. We generated whole-genome sequencing (WGS) data from 418 tumors (386 patients) derived from 6 rrMM clinical trials and compared them with WGS from 198 unrelated patients with ndMM in a population-based case-control fashion. We identified significantly enriched events at the rrMM stage, including drivers (DUOX2, EZH2, TP53), biallelic inactivation (TP53), noncoding mutations in bona fide drivers (TP53BP1, BLM), copy number aberrations (CNAs; 1qGain, 17pLOH), and double-hit events (Amp1q-ISS3, 1qGain-17p loss-of-heterozygosity). Mutational signature analysis identified a subclonal defective mismatch repair signature enriched in rrMM and highly active in high mutation burden tumors, a likely feature of therapy-associated expanding subclones. Further analysis focused on the association of genomic aberrations enriched at different stages of resistance to immunomodulatory agent (IMiD)-based therapy. This analysis revealed that TP53, DUOX2, 1qGain, and 17p loss-of-heterozygosity increased in prevalence from ndMM to lenalidomide resistant (LENR) to pomalidomide resistant (POMR) stages, whereas enrichment of MAML3 along with immunoglobulin lambda (IGL) and MYC translocations distinguished POM from the LEN subgroup. Genomic drivers associated with rrMM are those that confer clonal selective advantage under therapeutic pressure. Their role in therapy evasion should be further evaluated in longitudinal patient samples, to confirm these associations with the evolution of clinical resistance and to identify molecular subsets of rrMM for the development of targeted therapies.

Genetic Alterations in Members of the Proteasome 26S Subunit, AAA-ATPase (PSMC) Gene Family in the Light of Proteasome Inhibitor Resistance in Multiple Myeloma

For the treatment of Multiple Myeloma, proteasome inhibitors are highly efficient and widely used, but resistance is a major obstacle to successful therapy. Several underlying mechanisms have been proposed but were only reported for a minority of resistant patients. The proteasome is a large and complex machinery. Here, we focus on the AAA ATPases of the 19S proteasome regulator (PSMC1-6) and their implication in PI resistance. As an example of cancer evolution and the acquisition of resistance, we conducted an in-depth analysis of an index patient by applying FISH, WES, and immunoglobulin-rearrangement sequencing in serial samples, starting from MGUS to newly diagnosed Multiple Myeloma to a PI-resistant relapse. The WES analysis uncovered an acquired PSMC2 Y429S mutation at the relapse after intensive bortezomib-containing therapy, which was functionally confirmed to mediate PI resistance. A meta-analysis comprising 1499 newly diagnosed and 447 progressed patients revealed a total of 36 SNVs over all six PSMC genes that were structurally accumulated in regulatory sites for activity such as the ADP/ATP binding pocket. Other alterations impact the interaction between different PSMC subunits or the intrinsic conformation of an individual subunit, consequently affecting the folding and function of the complex. Interestingly, several mutations were clustered in the central channel of the ATPase ring, where the unfolded substrates enter the 20S core. Our results indicate that PSMC SNVs play a role in PI resistance in MM.

Genomic profiling for clinical decision making in lymphoid neoplasms

With the introduction of large-scale molecular profiling methods and high-throughput sequencing technologies, the genomic features of most lymphoid neoplasms have been characterized at an unprecedented scale. Although the principles for the classification and diagnosis of these disorders, founded on a multidimensional definition of disease entities, have been consolidated over the past 25 years, novel genomic data have markedly enhanced our understanding of lymphomagenesis and enriched the description of disease entities at the molecular level. Yet, the current diagnosis of lymphoid tumors is largely based on morphological assessment and immunophenotyping, with only few entities being defined by genomic criteria. This paper, which accompanies the International Consensus Classification of mature lymphoid neoplasms, will address how established assays and newly developed technologies for molecular testing already complement clinical diagnoses and provide a novel lens on disease classification. More specifically, their contributions to diagnosis refinement, risk stratification, and therapy prediction will be considered for the main categories of lymphoid neoplasms. The potential of whole-genome sequencing, circulating tumor DNA analyses, single-cell analyses, and epigenetic profiling will be discussed because these will likely become important future tools for implementing precision medicine approaches in clinical decision making for patients with lymphoid malignancies.

Analytical demands to use whole-genome sequencing in precision oncology

Interrogating the tumor genome in its entirety by whole-genome sequencing (WGS) offers an unprecedented insight into the biology and pathogenesis of cancer, with potential impact on diagnostics, prognostication and therapy selection. WGS is able to detect sequence as well as structural variants and thereby combines central domains of cytogenetics and molecular genetics. Given the potential of WGS in directing targeted therapeutics and clinical decision-making, we envision a gradual transition of the method from research to clinical routine. This review is one out of three within this issue aimed at facilitating this effort, by discussing in-depth analytical validation, clinical interpretation and clinical utility of WGS. The review highlights the requirements for implementing, validating and maintaining a clinical WGS pipeline to obtain high-quality patient-specific data in accordance with the local regulatory landscape. Every step of the WGS pipeline, which includes DNA extraction, library preparation, sequencing, bioinformatics analysis, and data storage, is considered with respect to its logistics, necessities, potential pitfalls, and the required quality management. WGS is likely to drive clinical diagnostics and patient care forward, if requirements and challenges of the technique are recognized and met.

Promises and Challenges of Immunogenic Chemotherapy in Multiple Myeloma

Immunological tolerance of myeloma cells represents a critical obstacle in achieving long-term disease-free survival for multiple myeloma (MM) patients. Over the past two decades, remarkable preclinical efforts to understand MM biology have led to the clinical approval of several targeted and immunotherapeutic agents. Among them, it is now clear that chemotherapy can also make cancer cells “visible” to the immune system and thus reactivate anti-tumor immunity. This knowledge represents an important resource in the treatment paradigm of MM, whereas immune dysfunction constitutes a clear obstacle to the cure of the disease. In this review, we highlight the importance of defining the immunological effects of chemotherapy in MM with the goal of enhancing the clinical management of patients. This area of investigation will open new avenues of research to identify novel immunogenic anti-MM agents and inform the optimal integration of chemotherapy with immunotherapy.

Multiple myeloma with high-risk cytogenetics and its treatment approach

Despite substantial advances in anti-myeloma treatments, early recurrence and death remain an issue in certain subpopulations. Cytogenetic abnormalities (CAs) are the most widely accepted predictors for poor prognosis in multiple myeloma (MM), such as t(4;14), t(14;16), t(14;20), gain/amp(1q21), del(1p), and del(17p). Co-existing high-risk CAs (HRCAs) tend to be associated with an even worse prognosis. Achievement of sustained minimal residual disease (MRD)-negativity has recently emerged as a surrogate for longer survival, regardless of cytogenetic risk. Information from newer clinical trials suggests that extended intensified treatment can help achieve MRD-negativity in patients with HRCAs, which may lead to improved outcomes. Therapy should be considered to include a 3- or 4-drug induction regimen (PI/IMiD/Dex or PI/IMiD/Dex/anti-CD38 antibody), auto-transplantation, and consolidation/maintenance with lenalidomide ± a PI. Results from ongoing clinical trials for enriched high-risk populations will reveal the precise efficacy of the investigated regimens. Genetic abnormalities of MM cells are intrinsic critical factors determining tumor characteristics, which reflect the natural course and drug sensitivity of the disease. This paper reviews the clinicopathological features of genomic abnormalities related to adverse prognosis, focusing on HRCAs that are the most relevant in clinical practice, and outline current optimal therapeutic approaches for newly diagnosed MM with HRCAs.

Adoptive Cellular Therapy for Multiple Myeloma Using CAR- and TCR-Transgenic T Cells: Response and Resistance

Despite the substantial improvement of therapeutic approaches, multiple myeloma (MM) remains mostly incurable. However, immunotherapeutic and especially T cell-based approaches pioneered the therapeutic landscape for relapsed and refractory disease recently. Targeting B-cell maturation antigen (BCMA) on myeloma cells has been demonstrated to be highly effective not only by antibody-derived constructs but also by adoptive cellular therapies. Chimeric antigen receptor (CAR)-transgenic T cells lead to deep, albeit mostly not durable responses with manageable side-effects in intensively pretreated patients. The spectrum of adoptive T cell-transfer covers synthetic CARs with diverse specificities as well as currently less well-established T cell receptor (TCR)-based personalized strategies. In this review, we want to focus on treatment characteristics including efficacy and safety of CAR- and TCR-transgenic T cells in MM as well as the future potential these novel therapies may have. ACT with transgenic T cells has only entered clinical trials and various engineering strategies for optimization of T cell responses are necessary to overcome therapy resistance mechanisms. We want to outline the current success in engineering CAR- and TCR-T cells, but also discuss challenges including resistance mechanisms of MM for evading T cell therapy and point out possible novel strategies.

Functional Impact of Genomic Complexity on the Transcriptome of Multiple Myeloma

PURPOSE

Multiple myeloma is a biologically heterogenous plasma-cell disorder. In this study, we aimed at dissecting the functional impact on transcriptome of gene mutations, copy-number abnormalities (CNA), and chromosomal rearrangements (CR). Moreover, we applied a geno-transcriptomic approach to identify specific biomarkers for personalized treatments.

EXPERIMENTAL DESIGN

We analyzed 514 newly diagnosed patients from the IA12 release of the CoMMpass study, accounting for mutations in multiple myeloma driver genes, structural variants, copy-number segments, and raw-transcript counts. We performed an in silico drug sensitivity screen (DSS), interrogating the Cancer Dependency Map (DepMap) dataset after anchoring cell lines to primary tumor samples using the Celligner algorithm.

RESULTS

Immunoglobulin translocations, hyperdiploidy and chr(1q)gain/amps were associated with the highest number of deregulated genes. Other CNAs and specific gene mutations had a lower but very distinct impact affecting specific pathways. Many recurrent genes showed a hotspot (HS)-specific effect. The clinical relevance of double-hit multiple myeloma found strong biological bases in our analysis. Biallelic deletions of tumor suppressors and chr(1q)-amplifications showed the greatest impact on gene expression, deregulating pathways related to cell cycle, proliferation, and expression of immunotherapy targets. Moreover, our in silico DSS showed that not only t(11;14) but also chr(1q)gain/amps and CYLD inactivation predicted differential expression of transcripts of the BCL2 axis and response to venetoclax.

CONCLUSIONS

The multiple myeloma genomic architecture and transcriptome have a strict connection, led by CNAs and CRs. Gene mutations impacted especially with HS-mutations of oncogenes and biallelic tumor suppressor gene inactivation. Finally, a comprehensive geno-transcriptomic analysis allows the identification of specific deregulated pathways and candidate biomarkers for personalized treatments in multiple myeloma.

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).

Branching clonal evolution patterns predominate mutational landscape in multiple myeloma

Multiple Myeloma (MM) arises from malignant transformation and deregulated proliferation of clonal plasma cells (PCs) harbouring heterogeneous molecular anomalies. The effect of evolving mutations on clone fitness and their cellular prevalence shapes the progressing myeloma genome and impacts clinical outcomes. Although clonal heterogeneity in MM is well established, which subclonal mutations emerge/persist/perish with progression in MM and which of these can be targeted therapeutically remains an open question. In line with this, we have sequenced pairwise whole exomes of 62 MM patients collected at two time points, i.e., at diagnosis and on progression. Somatic variants were called using a novel ensemble approach where a consensus was deduced from four variant callers (Illumina's Dragen, Strelka2, SomaticSniper and SpeedSeq) and actionable/druggable gene targets were identified. A marked intraclonal heterogeneity was observed. Branching evolution was observed among 72.58% patients, of whom 64.51% had low TMBs (<10) and 61.29% had 2 or more founder clones. The hypermutator patients (with high TMB levels ≥10 to ≤100) showed a significant decrease in their TMBs from diagnosis (median TMB 77.11) to progression (median TMB 31.22). A distinct temporal fall in subclonal driver mutations was identified recurrently across diagnosis to progression e.g., in PABPC1, BRAF, KRAS, CR1, DIS3 and ATM genes in 3 or more patients suggesting such patients could be treated early with target specific drugs like Vemurafenib/Cobimetinib. An analogous rise in driver mutations was observed in KMT2C, FOXD4L1, SP140, NRAS and other genes. A few drivers such as FAT4, IGLL5 and CDKN1A retained consistent distribution patterns at two time points. These findings are clinically relevant and point at consideration of evaluating multi time point subclonal mutational landscapes for designing better risk stratification strategies and tailoring time to time risk adapted combination therapies in future.

Dysregulated APOBEC3G causes DNA damage and promotes genomic instability in multiple myeloma

Multiple myeloma (MM) is a heterogeneous disease characterized by significant genomic instability. Recently, a causal role for the AID/APOBEC deaminases in inducing somatic mutations in myeloma has been reported. We have identified APOBEC/AID as a prominent mutational signature at diagnosis with further increase at relapse in MM. In this study, we identified upregulation of several members of APOBEC3 family (A3A, A3B, A3C, and A3G) with A3G, as one of the most expressed APOBECs. We investigated the role of APOBEC3G in MM and observed that A3G expression and APOBEC deaminase activity is elevated in myeloma cell lines and patient samples. Loss-of and gain-of function studies demonstrated that APOBEC3G significantly contributes to increase in DNA damage (abasic sites and DNA breaks) in MM cells. Evaluation of the impact on genome stability, using SNP arrays and whole genome sequencing, indicated that elevated APOBEC3G contributes to ongoing acquisition of both the copy number and mutational changes in MM cells over time. Elevated APOBEC3G also contributed to increased homologous recombination activity, a mechanism that can utilize increased DNA breaks to mediate genomic rearrangements in cancer cells. These data identify APOBEC3G as a novel gene impacting genomic evolution and underlying mechanisms in MM.

Prognostic Impact of Metabolic Heterogeneity in Patients With Newly Diagnosed Multiple Myeloma Using 18F-FDG PET/CT

PURPOSE

This study aimed to investigate the prognostic impact of metabolic heterogeneity (MH) in patients with multiple myeloma (MM).

PATIENTS AND METHODS

We retrospectively analyzed MH with 18F-FDG PET/CT in 203 patients with newly diagnosed MM. Metabolic heterogeneity was estimated using the area under the curve of the cumulative SUV volume histogram. To evaluate MH, we selected 2 lesions: "MH-SUVmax," a lesion with SUVmax, and "MH-metabolic tumor volume (MTV)," a lesion with the largest MTV.

RESULTS

Metabolic heterogeneity from an MH-SUVmax lesion showed more prognostic relevance than that from a lesion with the largest MTV. The progression-free survival (PFS) and overall survival (OS) rates were significantly lower in the high-MH-SUVmax group than in the low-MH-SUVmax group (median PFS: 25.2 vs 33.9 months; median OS: 41.6 vs 112.0 months; P = 0.004 and 0.046, respectively), whereas high MH-SUVmax retained independent prognostic power on multivariate analysis. Even among patients with high whole-body MTV, those with high MH-SUVmax tended to show poorer prognosis than those without (median PFS, 23.8 vs 30.2 months; P = 0.085). Moreover, patients with high MH-SUVmax and high-risk cytogenetic abnormalities showed dismal outcomes even with standard treatment (median PFS and OS, 10.0 and 33.3 months, respectively).

CONCLUSIONS

Our results suggested that high MH-SUVmax based on pretreatment with 18F-FDG PET/CT is a novel prognostic factor for cases of MM.

The Agony of Choice-Where to Place the Wave of BCMA-Targeted Therapies in the Multiple Myeloma Treatment Puzzle in 2022 and Beyond

Simple Summary

There is no doubt that immunotherapeutic approaches will change the current treatment landscape of multiple myeloma in the near future; in particular, a wave of BCMA-targeted therapies is currently entering clinical routine. Although the increasing availability of different therapeutic approaches is highly welcome, it also increases the daily challenges in clinical decision making if they all use the same target. Here, we provide a comprehensive summary of BCMA-targeted approaches in myeloma and aim to share some basic concepts in clinical decision making.

Abstract

Since the introduction of first-generation proteasome inhibitors and immunomodulatory agents, the multiple myeloma (MM) treatment landscape has undergone a remarkable development. Most recently, immunotherapeutic strategies targeting the B cell maturation antigen (BCMA) entered the clinical stage providing access to highly anticipated novel treatment strategies. At present, numerous different approaches investigate BCMA as an effective multi-modal target. Currently, BCMA-directed antibody–drug conjugates, bispecific and trispecific antibodies, autologous and allogeneic CAR-T cell as well as CAR-NK cell constructs are either approved or in different stages of clinical and preclinical development for the treatment of MM. This armamentarium of treatment choices raises several challenges for clinical decision making, particularly in the absence of head-to-head comparisons. In this review, we provide a comprehensive overview of BCMA-targeting therapeutics, deliver latest updates on clinical trial data, and focus on potential patient selection criteria for different BCMA-targeting immunotherapeutic strategies.

Copy number signatures predict chromothripsis and clinical outcomes in newly diagnosed multiple myeloma

Chromothripsis is detectable in 20–30% of newly diagnosed multiple myeloma (NDMM) patients and is emerging as a new independent adverse prognostic factor. In this study we interrogate 752 NDMM patients using whole genome sequencing (WGS) to investigate the relationship of copy number (CN) signatures to chromothripsis and show they are highly associated. CN signatures are highly predictive of the presence of chromothripsis (AUC = 0.90) and can be used identify its adverse prognostic impact. The ability of CN signatures to predict the presence of chromothripsis is confirmed in a validation series of WGS comprised of 235 hematological cancers (AUC = 0.97) and an independent series of 34 NDMM (AUC = 0.87). We show that CN signatures can also be derived from whole exome data (WES) and using 677 cases from the same series of NDMM, we are able to predict both the presence of chromothripsis (AUC = 0.82) and its adverse prognostic impact. CN signatures constitute a flexible tool to identify the presence of chromothripsis and is applicable to WES and WGS data.

Chromothripsis is associated with unfavourable outcomes in multiple myeloma (MM), but its detection usually requires whole genome sequencing. Here the authors develop an approach to detect chromothripsis in MM based on copy-number signatures that also works with whole exome sequencing data.

Genetic Predictors of Mortality in Patients with Multiple Myeloma

Multiple myeloma (MM) is a heterogeneous disease featured by clonal plasma cell proliferation and genomic instability. The advent of next-generation sequencing allowed unraveling the complex genomic landscape of the disease. Several recurrent genomic aberrations including immunoglobulin genes translocations, copy number abnormalities, complex chromosomal events, transcriptomic and epigenomic deregulation, and mutations define various molecular subgroups with distinct outcomes. In this review, we describe the recurrent genomic events identified in MM impacting patients’ outcome and survival. These genomic aberrations constitute new markers that could be incorporated into a prognostication model to eventually guide therapy at every stage of the disease.

Risk factors in multiple myeloma: is it time for a revision?

Although therapeutic strategies have been adapted to age and comorbidities for a long time, almost all multiple myeloma (MM) patients currently receive similar treatment, whatever their disease risk category. However, high-risk MM patients still constitute an unmet medical need and should benefit from the most efficient drug combinations. Herein, we review and discuss how to optimally define risk and why a revision of the current definition is urgently needed.

MUKnine OPTIMUM protocol: a screening study to identify high-risk patients with multiple myeloma suitable for novel treatment approaches combined with a phase II study evaluating optimised combination of biological therapy in newly diagnosed high-risk multiple myeloma and plasma cell leukaemia

INTRODUCTION

Multiple myeloma (MM) is a plasma cell tumour with over 5800 new cases each year in the UK. The introduction of biological therapies has improved outcomes for the majority of patients with MM, but in approximately 20% of patients the tumour is characterised by genetic changes which confer a significantly poorer prognosis, generally termed high-risk (HR) MM. It is important to diagnose these genetic changes early and identify more effective first-line treatment options for these patients.

METHODS AND ANALYSIS

The Myeloma UK nine OPTIMUM trial (MUKnine) evaluates novel treatment strategies for patients with HRMM. Patients with suspected or newly diagnosed MM, fit for intensive therapy, are offered participation in a tumour genetic screening protocol (MUKnine a), with primary endpoint proportion of patients with molecular screening performed within 8 weeks. Patients identified as molecularly HR are invited into the phase II, single-arm, multicentre trial (MUKnine b) investigating an intensive treatment schedule comprising bortezomib, lenalidomide, daratumumab, low-dose cyclophosphamide and dexamethasone, with single high-dose melphalan and autologous stem cell transplantation (ASCT) followed by combination consolidation and maintenance therapy. MUKnine b primary endpoints are minimal residual disease (MRD) at day 100 post-ASCT and progression-free survival. Secondary endpoints include response, safety and quality of life. The trial uses a Bayesian decision rule to determine if this treatment strategy is sufficiently active for further study. Patients identified as not having HR disease receive standard treatment and are followed up in a cohort study. Exploratory studies include longitudinal whole-body diffusion-weighted MRI for imaging MRD testing.

ETHICS AND DISSEMINATION

Ethics approval London South East Research Ethics Committee (Ref: 17/LO/0022, 17/LO/0023). Results of studies will be submitted for publication in a peer-reviewed journal.

TRIAL REGISTRATION NUMBER

ISRCTN16847817, May 2017; Pre-results.

Multi-omics tumor profiling technologies to develop precision medicine in multiple myeloma

Multiple myeloma (MM), the second most common hematologic cancer, is caused by accumulation of aberrant plasma cells in the bone marrow. Its molecular causes are not fully understood and its great heterogeneity among patients complicates therapeutic decision-making. In the past decades, development of new therapies and drugs have significantly improved survival of MM patients. However, resistance to drugs and relapse remain the most common causes of mortality and are the major challenges to overcome. The advent of high throughput omics technologies capable of analyzing big amount of clinical and biological data has changed the way to diagnose and treat MM. Integration of omics data (gene mutations, gene expression, epigenetic information, and protein and metabolite levels) with clinical histories of thousands of patients allows to build scores to stratify the risk at diagnosis and predict the response to treatment, helping clinicians to make better educated decisions for each particular case. There is no doubt that the future of MM treatment relies on personalized therapies based on predictive models built from omics studies. This review summarizes the current treatments and the use of omics technologies in MM, and their importance in the implementation of personalized medicine.

Multi-omics tumor profiling technologies to develop precision medicine in multiple myeloma

Multiple myeloma (MM), the second most common hematologic cancer, is caused by accumulation of aberrant plasma cells in the bone marrow. Its molecular causes are not fully understood and its great heterogeneity among patients complicates therapeutic decision-making. In the past decades, development of new therapies and drugs have significantly improved survival of MM patients. However, resistance to drugs and relapse remain the most common causes of mortality and are the major challenges to overcome. The advent of high throughput omics technologies capable of analyzing big amount of clinical and biological data has changed the way to diagnose and treat MM. Integration of omics data (gene mutations, gene expression, epigenetic information, and protein and metabolite levels) with clinical histories of thousands of patients allows to build scores to stratify the risk at diagnosis and predict the response to treatment, helping clinicians to make better educated decisions for each particular case. There is no doubt that the future of MM treatment relies on personalized therapies based on predictive models built from omics studies. This review summarizes the current treatments and the use of omics technologies in MM, and their importance in the implementation of personalized medicine.

Biallelic loss of BCMA as a resistance mechanism to CAR T cell therapy in a patient with multiple myeloma

BCMA targeting chimeric antigen receptor (CAR) T cell therapy has shown deep and durable responses in multiple myeloma. However, relapse following therapy is frequently observed, and mechanisms of resistance remain ill-defined. Here, we perform single cell genomic characterization of longitudinal samples from a patient who relapsed after initial CAR T cell treatment with lack of response to retreatment. We report selection, following initial CAR T cell infusion, of a clone with biallelic loss of BCMA acquired by deletion of one allele and a mutation that creates an early stop codon on the second allele. This loss leads to lack of CAR T cell proliferation following the second infusion and is reflected by lack of soluble BCMA in patient serum. Our analysis suggests the need for careful detection of BCMA gene alterations in multiple myeloma cells from relapse following CAR T cell therapy.

Relapse following BCMA targeted CAR T-cell therapy is frequently observed in patients with multiple myeloma (MM). Here, by single cell transcriptome profiling on serially collected bone marrow samples, the authors report biallelic loss of BCMA as the mechanism of resistance underlying both relapse and lack of response to a second CAR T infusion in a patient with MM.

A Tangle of Genomic Aberrations Drives Multiple Myeloma and Correlates with Clinical Aggressiveness of the Disease: A Comprehensive Review from a Biological Perspective to Clinical Trial Results

Multiple myeloma (MM) is a genetically heterogeneous disease, in which the process of tumorigenesis begins and progresses through the appearance and accumulation of a tangle of genomic aberrations. Several are the mechanisms of DNA damage in MM, varying from single nucleotide substitutions to complex genomic events. The timing of appearance of aberrations is well studied due to the natural history of the disease, that usually progress from pre-malignant to malignant phase. Different kinds of aberrations carry different prognostic significance and have been associated with drug resistance in some studies. Certain genetic events are well known to be associated with prognosis and are incorporated in risk evaluation in MM at diagnosis in the revised International Scoring System (R-ISS). The significance of some other aberrations needs to be further explained. Since now, few phase 3 randomized trials included analysis on patient's outcomes according to genetic risk, and further studies are needed to obtain useful data to stratify the choice of initial and subsequent treatment in MM.