Daratumumab in high‐risk relapsed/refractory multiple myeloma patients: adverse effect of chromosome 1q21 gain/amplification and GEP70 status on outcome

Mohan, Meera; Weinhold, Niels; Schinke, Carolina; Thanedrarajan, Sharmilan; Rasche, Leo; Sawyer, Jeffrey R.; Tian, Erming; Rhee, Frits van; Zangari, Maurizio

doi:10.1111/bjh.16292

Cited by 45 publications

(45 citation statements)

References 26 publications

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“…Patients affected by monoclonal gammopathy of undetermined significance (MGUS), a precursor of MM, and smoldering MM (SMM) who carry gain(1q) have higher risk of progression to MM, with a median time to progression of two years 42,43 . Other studies with newly diagnosed and relapsed/refractory MM patients have confirmed the negative prognostic impact of gain(1q) in different therapeutic regimens 22,44 . MM-PSN identified six sub-groups (1c, 2b, 2c, 2d, 2e, 3c) enriched for gain(1q), all of them associated with other recurrent lesions and having the shortest median time to relapse and death compared to the other sub-groups.…”

Section: Discussionmentioning

confidence: 77%

Patient Similarity Network of Newly Diagnosed Multiple Myeloma Identifies Patient Sub-groups with Distinct Genetic Features and Clinical Implications

Bhalla

Melnekoff

Onel

et al. 2020

Preprint

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The remarkable genetic heterogeneity of Multiple Myeloma poses a significant challenge for proper prognostication and clinical management of patients. Here, we introduce MM-PSN, the first multi-omics Patient Similarity Network of Myeloma. MM-PSN enabled accurate dissection of the genetic and molecular landscape of the disease and determined twelve distinct sub-groups defined by five data types generated from genomic and transcriptomic patient profiling of 655 patients. MM-PSN revealed 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). Several sub-groups uncovered novel associations between the gain of 1q and other adverse secondary lesions, thereby identifying the chromosomal hallmarks of sub-clonal heterogeneity and tumor progression in MM. We also determined gene vulnerabilities and potential therapeutic options for each sub-group and validated our prognostic model in an independent dataset.

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Section: Discussionmentioning

confidence: 77%

Patient Similarity Network of Newly Diagnosed Multiple Myeloma Identifies Patient Sub-groups with Distinct Genetic Features and Clinical Implications

Bhalla

Melnekoff

Onel

et al. 2020

Preprint

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“…e ., anti-CD38 mAb). 52 Finally, we note that virtually all subtypes exhibit correlation between risk and the activity of the mitosis-related targets AURKA, AURKB, and KIF11, which may indicate that these therapies will be effective in high-risk MM.…”

Section: Discussionmentioning

confidence: 80%

Genetic program activity delineates risk, relapse, and therapy responsiveness in Multiple Myeloma

Wall

Turkarslan

et al. 2020

Preprint

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38Despite recent advancements in the treatment of multiple myeloma (MM), nearly all patients 39 ultimately relapse and many become refractory to their previous therapies. Although many 40 therapies exist with diverse mechanisms of action, it is not yet clear how the differences in MM 41 biology across patients impacts the likelihood of success for existing therapies and those in the 42 pipeline. Therefore, we not only need the ability to predict which patients are at high risk for 43 disease progression, but also a means to understand the mechanisms underlying their risk. We 44 hypothesized that knowledge of the biological networks that give rise to MM, specifically the 45 transcriptional regulatory network (TRN) and the mechanisms by which mutations impact gene 46 regulation, would enable improved predictions of disease progression and actionable insights for 47 treatment. Here we present a method to infer TRNs from multi-omics data and apply it to the 48 generation of a MM TRN that links chromosomal abnormalities and somatic mutations to 49 downstream effects on gene expression via perturbation of transcriptional regulators. We find that 50 141 genetic programs underlie the disease and that the activity profile of these programs fall into 51 one of 25 distinct transcriptional states. These transcriptional signatures prove to be more 52 predictive of outcomes than do mutations and reveal plausible mechanisms for relapse, including 53 the establishment of an immuno-suppressive microenvironment. Moreover, we observe subtype-54 specific vulnerabilities to interventions with existing drugs and motivate the development of new 55 targeted therapies that appear especially promising for relapsed refractory MM. 56 57 Introduction 58 59 Multiple Myeloma (MM) is a cancer of malignant plasma cells in the bone marrow (BM) that has 60 a prevalence of approximately 86,000 new cases per year. 1 Several clinical subtypes of MM have 61 been established on the basis of characteristic cytogenetic features, including various 62 translocations, gain or loss of chromosomal arms, deletion of specific chromosomes, and 63 hyperdiploidy. 2-4 Accordingly, MM is a complex disease of great heterogeneity that exhibits64 subtype-specific drivers of progression. 5,6 Efforts to better characterize the biology and 65 therapeutic vulnerabilities of MM have increased exponentially in recent years, as can be seen 66 from the number of research articles, clinical trials, and public availability of matched genomic, 67 transcriptomic, and patient data. However, the myriad combinations of chromosomal aberrations 68 and somatic mutations, coupled with the complex dependence of MM progression on the BM 69 microenvironment, have precluded a mechanistic understanding of the disease on a patient-70 specific level. 72The 5-year survival rate of MM is approximately 50% 7 , which is nearly double what it was in the 73 late 1980s. 8 The reason for the improved outcomes is a series of therapeutic advances that 74 include the introduction of autologous stem cell t...

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“…This is in contrast to the pomalidomide–dexamethasone group where patients with gain(1q21) had shorter mPFS (4·6 months) than the overall study population treated with Pd (6·5 months). This observation is of particular relevance in light of recent reports of significantly worse PFS outcomes in patients with gain(1q21) treated with daratumumab 29 . The gene coding for complement‐inhibitory protein CD55 is localised to 1q32·2 and increased expression of CD55 on myeloma plasma cells has been hypothesised as a resistance mechanism to daratumumab therapy 29,30 .…”

Section: Discussionmentioning

confidence: 92%

Subgroup analysis of ICARIA‐MM study in relapsed/refractory multiple myeloma patients with high‐risk cytogenetics

et al. 2021

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Summary Treatment benefit in multiple myeloma (MM) patients with high‐risk cytogenetics remains suboptimal. The phase 3 ICARIA‐MM trial (NCT02990338) showed that isatuximab plus pomalidomide–dexamethasone prolongs median progression‐free survival (mPFS) in patients with relapsed/refractory MM (RRMM). This subgroup analysis of ICARIA‐MM compared the benefit of isatuximab in high‐risk [defined by the presence of del(17p), t(4;14) or t(14;16)] versus standard‐risk patients. The efficacy of isatuximab in patients with gain(1q21) abnormality was also assessed in a retrospective subgroup analysis. In ICARIA‐MM, 307 patients received isatuximab–pomalidomide–dexamethasone (n = 154) or pomalidomide–dexamethasone (n = 153). Isatuximab (10 mg/kg intravenously) was given weekly in the first 28‐day cycle, and every other week thereafter. Standard pomalidomide–dexamethasone doses were given. Isatuximab–pomalidomide–dexamethasone improved mPFS (7·5 vs 3·7 months; HR, 0·66; 95% CI, 0·33–1·28) and overall response rate (ORR, 50·0% vs 16·7%) in high‐risk patients. In patients with isolated gain(1q21), isatuximab addition improved mPFS (11·2 vs 4·6 months; HR, 0·50; 95% CI, 0·28–0·88) and ORR (53·6% vs 27·6%). More grade ≥3 adverse events occurred in high‐risk patients receiving isatuximab (95·7%) versus the control group (67·6%); however, isatuximab did not increase events leading to discontinuation or treatment‐related mortality. Isatuximab–pomalidomide–dexamethasone provides a consistent benefit over pomalidomide–dexamethasone treatment in RRMM patients regardless of cytogenetic risk.

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Daratumumab in high‐risk relapsed/refractory multiple myeloma patients: adverse effect of chromosome 1q21 gain/amplification and GEP70 status on outcome

Cited by 45 publications

References 26 publications

Patient Similarity Network of Newly Diagnosed Multiple Myeloma Identifies Patient Sub-groups with Distinct Genetic Features and Clinical Implications

Patient Similarity Network of Newly Diagnosed Multiple Myeloma Identifies Patient Sub-groups with Distinct Genetic Features and Clinical Implications

Genetic program activity delineates risk, relapse, and therapy responsiveness in Multiple Myeloma

Subgroup analysis of ICARIA‐MM study in relapsed/refractory multiple myeloma patients with high‐risk cytogenetics

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