Multiple myeloma (MM) is a complex and heterogeneous hematologic malignancy characterized by clonal evolution, genetic instability, and interactions with a supportive tumor microenvironment. These factors contribute to treatment resistance, disease progression, and significant variability in clinical outcomes among patients. This review explores the mechanisms underlying MM progression, including the genetic and epigenetic changes that drive clonal evolution, the role of the bone marrow microenvironment in supporting tumor growth and immune evasion, and the impact of genomic instability. We highlight the critical insights gained from single-cell technologies, such as single-cell transcriptomics, genomics, and multiomics, which have enabled a detailed understanding of MM heterogeneity at the cellular level, facilitating the identification of rare cell populations and mechanisms of drug resistance. Despite the promise of advanced technologies, MM remains an incurable disease and challenges remain in their clinical application, including high costs, data complexity, and the need for standardized bioinformatics and ethical considerations. This review emphasizes the importance of continued research and collaboration to address these challenges, ultimately aiming to enhance personalized treatment strategies and improve patient outcomes in MM.

Immunomodulatory drug (IMiD) resistance is a key clinical challenge in myeloma treatment. Previous data suggests almost one third of myeloma patients acquire mutations in the key IMiD effector cereblon by the time they are pomalidomide refractory. Some events, including stop codons/frameshift mutations and copy loss, having clearly explicable effects on cereblon function. Missense mutations have also been reported throughout the length of cereblon but their functional impact has not been systematically studied. This study modelled selected missense mutations and examined their effect on cereblon function also analysing whether any mutations deleterious to IMiD action could be overcome using the novel cereblon binding agents (CELMoDs). Three patterns of response to missense mutations were apparent, mutations that led to complete loss of CRBN function for all agents, those that had no effect on CRBN function and those with agent-dependent effect on CRBN function. The latter group of 4 mutations were profiled in more detail with confirmatory experiments demonstrating an ability of the more potent CELMoDs to lead to neosubstrate degradation and cell death even though IMiDs were not active. Dynamic modelling based on a newly generated crystal structure of the DDB1/CRBN/lenalidomide complex, with greater resolution than those published to date, helped to understand the impact of these mutations. These results have important implications for the interpretation of CRBN sequencing results from patients for future therapy decisions, particularly differentiating those who may, despite relapsing on IMiDs with CRBN mutations, have the potential to still benefit from the use of CELMoD agents.

Introduction Advances in newly diagnosed multiple myeloma (MM) have resulted in high rates of MRD- negativity independent of transplant status. However, most patients eventually relapse while on lenalidomide-based maintenance therapy. The ubiquitous use of this immunomodulatory imide drug (IMiD) in frontline and maintenance settings leads to early IMiD resistance in most patients. While T-cell redirecting therapies are gaining prominence in the early relapse setting (i.e., following lenalidomide resistance; 1-3 prior lines), more options are needed in this space for balancing efficacy and toxicity as well as to provide avenues for de-escalation. In this context, Iberdomide is a potent cereblon E3 ligase modulator (CELMoD) with demonstrated activity in IMiD-resistant disease (Lonial et al., Lancet Haematology. 2022). We were motivated to develop a quadruplet reinduction strategy for early relapsed MM: ReKInDLE (Reinduction with Carfilzomib (K), Iberdomide (Iber), and Daratumumab (Dara) for Long-Term Efficacy). Methods This is a single-center, phase II study (NCT05896228) of Iber with a combination phase and a monotherapy phase. Eligible patients have ECOG PS 0-2 and have received 1-3 prior lines of therapy inclusive of lenalidomide. Prior treatment with K and CD38-directed therapy is allowed if they were not discontinued for toxicity or disease progression. The primary endpoint is the rate of MRD-negativity (10-5, NGS) at end of combination therapy. Key secondary endpoints include safety and tolerability (CTCAE v5), overall response rate, duration of response, progression-free; event-free; and overall survival (PFS, EFS, OS), and annual rate of MRD-negativity. For the combination phase, patients receive up to eight 28-day cycles of Iber (1mg oral 21/28 days), Dara [1800mg SC days 1, 8, 15, and 22 (C1-2), days 1 and 15 (C3-6), day 1 (C7-8)], K (20/56 mg/m2 IV, days 1, 8, and 15) and dexamethasone (d; 40mg C1-4 and 20 mg C5-8; on days of parenteral therapy). Patients with a response of ≥SD may de-escalate to Iber monotherapy (1mg 21/28 days) for up to 3 years. The two-stage design stipulated treatment of 15 patients and if 10-5MRD-negativity is achieved by at least one, the study continued to full accrual (n=30). Results The study opened for enrollment on February 2, 2024 and as of July 20, 2025, the study is fully enrolled. Median age is 63 (44-77); 17% are Black and 53% are Hispanic/Latino; 33% are high-risk per IMWG 2025 criteria; 17% have extramedullary disease. Median prior lines of therapy is 1 (1-3); 93% are lenalidomide-refractory. 27% had received a prior anti-CD38 antibody, 30% had received prior K, and 47% had prior autologous stem cell transplantation. As of the data cutoff, 12 patients (40%) have had an MRD assessment on combination therapy and 8 have MRD-negative (10-5) CR & PET-negativity (66.7%; 95% CI 34.9-90.1%). Of 26 response evaluable patients, 10 achieved ≥CR (38%) and 9 VGPR (35%); ORR (92.3%). 27 patients (90%) remain on treatment; 1 patient experienced extramedullary disease progression, 1 patient went off treatment for toxicity, and 1 withdrew consent for social reasons. 8 patients (27%) have so far de-escalated to Iber monotherapy with longest total duration of treatment 17 months. Hematologic AEs were relatively common. Grade 3 or greater events included neutropenia (50%), lymphocytopenia (40%), leukopenia (23%), thrombocytopenia (10%), and anemia (3%) but were manageable with growth factor support and/or iber dose-reduction, as needed. 6 Serious (S)AE occurred including 3 lung infections and 1 incidence each of febrile neutropenia, presyncope, and myocardial infarction, for which the latter patient was taken off-treatment. There were no deaths in the study. Discussion IberKDd is a novel, effective quadruplet combination-therapy which delivers high rates of MRD-negativity (~2/3 of patients) in early relapse/refractory MM. Updated data and MRD at 10-6 will be presented at the meeting. This quadruplet reinduction strategy adds another treatment alternative in the early relapse setting (i.e., following lenalidomide resistance; 1-3 prior lines) where parenteral (SC or IV) T-cell redirecting therapies are gaining increased prominence. IberKDd is safe with a manageable toxicity profile, and it affords the opportunity for de-escalation to oral monotherapy; decreasing the burden of continuous parenteral therapy which is common in this setting of MM.

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.

Background: Clinical outcomes for newly diagnosed multiple myeloma (NDMM) patients are heterogenous with survival ranging from months to > 10 years. Though several clinical and genomic features predict outcomes, the “one-size-fits-all” treatment paradigm remains dominant for NDMM. Hypothesis: By integrating clinical, genomic and therapeutic data, using artificial intelligence, an individualized risk-prediction model for NDMM (IRM) can facilitate individually-tailored therapeutic decisions. Methods: We included 1933 patients with clinical and genomic data from 5 cohorts: MMRF CoMMpass (n=1062), MGP (n=492), Moffit AVATAR (n=177), UAMS (n=93), and MSKCC (n=109). The median follow-up was 43 months. Overall, we considered 160 clinical (e.g., age, ECOG, race), therapeutics, and genomic variables. To correct for time-dependent variables such as autologous stem cell transplant (ASCT) and continuous treatment, a multi-state model was designed across two phases: induction (phase 1), and post-induction (phase 2). Neural Cox Non-proportional-hazards (NCNPH) was used to integrate the data and build the model. Results: Overall, the 5-year overall survival (OS) c-index for IRM was 0.73, significantly higher than all existing prognostic models: R2-ISS (0.62), ISS (0.61) and R-ISS (0.56). The overall model accuracy was significantly improved by the inclusion of 12 genomic features, including 1q21 gain/amp, TP53 loss, t(4;14)(NSD2;IGH), complex copy number signatures, APOBEC mutational signature contribution, and del1p. Prescribed therapy emerged as a key determinant of risk, suggesting that effective combinations may have a different impact in the context of individual patient features, with the potential to significantly change clinical outcomes despite poor historical prognostication (i.e., treatment variance). Leveraging these concepts, we interrogated the clinical impact of ASCT and continuous treatment in the context of NDMM treated with bortezomib, lenalidomide and dexamethasone (VRd). Integrating predicted outcomes and treatment variance for all 4 possible treatment combinations (i.e., VRd +/- ASCT +/- continuous treatment) we identified 3 patient groups. In the first group (n=632), patients were characterized by complex genomic features, older age, high ISS, poor outcomes and limited treatment variance, reflecting aggressive and refractory myeloma. The second group (n=571) was characterized by high treatment variance, with favorable outcomes if ASCT and continuous treatment are provided. The last group (n=730) included patients with favorable clinical and genomic profiles, achieving good outcomes, with minimal advantage from ASCT. Conclusion: Integrating historical and emerging genomic features with clinical and therapeutic data, we developed the first individualized risk-prediction model for personally-tailored therapeutic decisions in NDMM. Citation Format: Arjun Raj Rajanna, Francesco Maura, Andriy Derkach, Bachisio Ziccheddu, Niels Weinhold, Kylee Maclachlan, Benjamin Diamond, Faith Davies, Eileen Boyle, Brian Walker, Alexandra Pos, Malin Hulcrantz, Ariosto Silva, Oliver Hampton, Jamie K. Teer, Niccolò Bolli, Graham Jackson, Martin Kaiser, Charlotte Pawlyn, Gordon Cook, Dennis Verducci, Dickran Kazandjian, Fritz Van Rhee, Saad Usmani, Kenneth H. Shain, Marc S. Raab, Gareth Morgan, Ola Landgren. Individualized risk stratification in newly diagnosed multiple myeloma. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5453.

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.

2017 to 2022. Treatment drop-out decreased in 2L to 3L and 3L to 4L+, with a drop-out rate of 17% and 19% per switch subgroup, respectively, in 2022. Main 1L regimens in SCT-E (VCd and VTd) and SCT-NE subgroups (VMP and VCd) remained stable, with daratumumab-based regimens increase in 2022 in all subgroups.Patient share in maintenance therapy increased from 5% to 16% in the overall treated population, dominated by lenalidomide in 2022.The 2L and 3L treatment pattern changed considerably, with daratumumab-based regimens dominating patient share in 2022 (2L-43%; 3L-35%).In 4L+ a heterogenous treatment pattern emerged, with no standard of care identified, being carfilzomib-and daratumumab-based regimens the most relevant in 2022.Overall median TD increased in SCT-E subgroup from 5 to 19 months between time series, driven by maintenance therapy, and remained stable for SCT-NE patients (10 months).Regarding TTNT, median time increased between 3L to 4L+ in SCT-E patients from 5.5 to 9 months.Conclusions: MM treatment landscape changed during a 5-year period, characterized by an increase in treated patients, patients achieving advanced treatment lines, and improved access to therapeutic innovation in earlier treatment stages of the disease.

Introduction. For newly diagnosed multiple myeloma (NDMM) patients, median overall survival (OS) and progression free survival (PFS) have dramatically improved during recent years due to the introduction of novel agents. Unfortunately, a subset of patients with NDMM does not benefit from newer therapies reflected in persisting poor outcomes. In contrast, another subset has favorable outcomes despite receiving limited therapy. Though having demonstrated that several genomic events contribute to clinical outcomes, yet the "one-size-fits-all" treatment paradigm remains dominant for NDMM. Here, we present the first artificial intelligence individualized prediction model for NDMM to facilitate individually tailored therapeutic decisions. Methods. We included 1840 patients with available clinical and genomic data from the following cohorts: MMRF CoMMpass (n=1062), MGP (n=492), Moffit AVATAR (n=177), and MSKCC (n=109). The median follow up was 42 months. To build a treatment-adjusted predictive model of individualized risk for patients with NDMM, we considered 160 variables across clinical (e.g., age, ECOG, sex, ISS), therapeutics, genomics, and time-dependent treatments such as autologous stem cell transplant (ASCT) and continuous treatment (Palumbo et al. JCO 2015). A multi-state model was designed across two phases: induction (phase 1), and post-induction (phase 2). Phase 1 included patients that: 1) completed the induction without progression (PD); 2) PD of failed to respond during induction remaining alive after; 3) PD during induction and subsequently died. Phase 2 have patients that: 4) PD after induction and were alive; 5) PD after induction and died; 6) reached remission after induction and were alive; 7) responded after induction and died due to other causes. We leveraged and compared survival methods based on deep neural networks (Neural Cox Non-proportional-hazards;NCNPH), Random Survival Forest (RSF), and Cox proportional-hazard(CPH). Results. NCNPH showed the best cross-validated prognostic performance for OS with median Uno's concordance (C=0.67), followed by RSF (C=0.65) and CPH (C=0.64; Fig. 1a-b). Overall, the model significantly outperformed R2-ISS (C=0.6), ISS (C=0.59) and R-ISS (C=0.57; Fig. 1b). Additionally, the model found 28 genomic features to increase concordance accuracy for PFS by (3% in phase 1 and 1.5% in phase 2), and with greater effect on OS (10% in phase 1 and 5% in phase 2). The 14% of patients who did not respond in phase 1 were enriched for ISS3, age >75 y, 1q amp, NSD2 translocation, TP53 mutations, and deletions on 17p13 and 1p. Varying therapies emerged as a key determinant of risk, in particular, in phase 2, supporting the idea that effective combinations can have a different impact in individual patients and have the potential to significantly change the clinical outcome despite poor prognostication. The model not only predicts patient risk but also the impact of various treatment strategies (i.e., treatment variance). We identified 9 distinct clusters (C#1-9) based on predicted risk and treatment variance. C#8 and C#9 show favorable outcome, especially with ASCT and continuous treatment. C#4 and C#5 included patients with favorable outcome but low treatment variance where ASCT had marginal impact. C#6 had a substantial presence of high-risk patients associated with low variance, age >75y, 1q gain, and NSD2 translocations. Interestingly, NSD2 translocated patients not included in C#6 had an intermediate favorable outcome (p<0.0001). Early PD was observed in C#1 and C#7, both enriched for low treatment variance, and high risk genomic and clinical features. Finally, we identified a group of high-risk patients (C#2 and C#3) with high treatment variance whose poor outcome was mostly driven by a suboptimal treatment (i.e., use of doublet, no ASCT, no continuous treatment) rather than presence of distinct clinical and genomic features, highlighting the need for treatment to be considered to build more robust and accurate prognostic models. Conclusion: This work shows the first comprehensive model integrating new and historical features to train a neural network for individualized risk prediction in NDMM. Utilizing data from 1840 patients that received a variety of therapies, the model captures the interaction of genomic, clinical and therapy factors, enabling personally-tailored therapeutic decisions in NDMM. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

The trypsin-like proteases (TLPs) play widespread and diverse roles, in a host of physiological and pathological processes including clot dissolution, extracellular matrix remodelling, infection, angiogenesis, wound healing and tumour invasion/metastasis. Moreover, these enzymes are involved in the disruption of normal lung function in a range of respiratory diseases including allergic asthma where several allergenic proteases have been identified. Here, we report the synthesis of a series of peptide derivatives containing an N-alkyl glycine analogue of arginine, bearing differing electrophilic leaving groups (carbamate and triazole urea), and demonstrate their function as potent, irreversible inhibitors of trypsin and TLPs, to include activities from cockroach extract. As such, these inhibitors are suitable for use as activity probes (APs) in activity-based profiling (ABP) applications.

In the last two decades, proteases have become a primary and vital target in drug discovery [...]

Clean energy research and development (R and D) leading to commercial technologies is vital to economic development, technology competitiveness, and reduced environmental impact. Over the past 30 years, such efforts have advanced technology performance and reduced cost by leveraging network effects and economies of scale. After demonstrating promise in applied R and D, successful clean energy and energy efficiency technologies are incorporated into an initial product sold by the private sector. Despite its importance, processes by which first commercialization occurs are difficult to generalize while capturing specific insights from practitioners in markets and technologies. This paper presents a policy-focused qualitative assessment of the first commercialization of four diverse energy technologies: thin film photovoltaics, wind turbine blades, dual-stage refrigeration evaporators, and fuel cells for material handling equipment. Each technology presents distinct value propositions, markets, and regulatory drivers. The case studies indicate three common characteristics of successful first commercialization for new energy technologies: 1) good fit between the technology, R&D infrastructure, and public-private partnership models; 2) high degree of alignment of government regulations and R&D priorities with market forces; and 3) compatibility between time scales required for R&D, product development, and opportunities. These findings may inform energy investment decision-making, maximize benefits from R&D, and advance the transition to a low-emission future.

Introduction: Multiple myeloma (MM) remains an incurable disease with a median overall survival of approximately 5 years. Gain or amplification of 1q21 (1q21+) occurs in around 40% of patients with MM and generally portends a poor prognosis. Patients with MM who harbor 1q21+ are at increased risk of drug resistance, disease progression, and death. New pharmacotherapies with novel modes of action are required to overcome the negative prognostic impact of 1q21+. Areas covered: This review discusses the detection, biology, prognosis, and therapeutic targeting of 1q21+ in newly diagnosed and relapsed MM. Patients with MM and 1q21+ tend to present with higher tumor burden, greater end-organ damage, and more co-occurring high-risk cytogenetic abnormalities than patients without 1q21+. The chromosomal rearrangements associated with 1q21+ result in dysregulation of genes involved in oncogenesis. Identification and characterization of the 1q21+ molecular targets are needed to inform on prognosis and treatment strategy. Clinical trial data are emerging that addition of isatuximab to combination therapies may improve outcomes in patients with 1q21+ MM. Expert opinion: In the next 5 years, the results of ongoing research and trials are likely to focus on the therapeutic impact and treatment decisions associated with 1q21+ in MM.

Introduction: There is considerable heterogeneity in the clinical outcome of newly diagnosed multiple-myeloma (NDMM) with some patients having a good prognosis while others fail to respond or relapse quickly after therapy progressing rapidly to death. Using risk scores based on clinical, biochemical and genetic features it is possible to predict some of this variation giving an ability to segment the disease into risk strata. Clinical studies have suggested that patients with standard-risk disease have benefited more from the recent advances in therapy compared to those with high-risk disease. The development of clinical trials specifically recruiting patients with high-risk disease features offers the potential to improve the outcome of a subgroup of patients with a very poor clinical outcome. To perform such studies is it important to have a unifying definition of high-risk including standard parameters, group size and outcome of individual risk strata so that clinical trial rigor can be achieved (e.g., common entry criteria, statistical power). In order to understand the size and feasibility of such studies we analyzed the Myeloma Genome Project (MGP) dataset to assess multiple risk factors and scores to determine and compare how they perform as risk stratifiers with each other.

Introduction Copy number abnormalities (CNA) and structural variants (SV) are crucial to driving cancer progression and in multiple myeloma (MM). Chr1 CNA are seen in up to 40% of cases and associate with poor prognosis. Variants include deletions, gains, translocations and complex SV events such as chromothripsis (CT), chromoplexy (CP) and templated insertions (TI) which result in aberrant transcriptional patterns. Abnormal expression of genes on chr1 lead to the adverse clinical outcome and studies focussed on 1p12, 1p32.3 and 1q12-21 identified potential causal genes including TENT5C, CDKN2C, CKS1B, PDZK1, BCL9, ANP32E, ILF2, ADAR, MDM2 and MCL1 but none fully explain the clinical behavior. To address this deficiency and to relate chromatin structure to gene deregulation we present a multiomic bioinformatic analysis of SV, CNA, mutation and expression changes in relation to the chromatin structure of chr1.

Objectives Two promising epigenetic therapeutic targets have emerged for the treatment of hematologic malignancies, BET and CBP/EP300 proteins. Several studies have shown that targeting these individual classes of proteins has anti-tumor activity in multiple myeloma (MM), as well as other cancers. Here, we present the first data exploring the anti-tumor activity of two novel dual inhibitors, NEO2734 and NEO1132, of both BET and CBP/EP300 proteins in MM. Methods Sixteen MM cell lines (MMCLs) were treated with the dual inhibitors NEO2734 and NEO1132, the single BET inhibitors JQ1, OTX015, IBET-762, and IBET-151, and a single CBP/EP300 inhibitor CPI-637. Results The dual inhibitor NEO2734 showed strong anti-tumor activity and was consistently highly active against all MMCLs, being as potent as JQ1 and more so than other single inhibitors. NEO2734 and NEO11132 induced a significant G1 cell cycle arrest and decreased c-MYC and IRF4 protein levels in MMCLs compared to the other single inhibitors. Sensitivity to the dual inhibitors was not dependent on a specific MM molecular subgroup but correlated with c-MYC protein expression levels. Conclusions The dual inhibition of BET and CBP/EP300 has potential therapeutic benefits for patients with MM.