Individualized Treatment-Adjusted Risk Stratification in Newly Diagnosed Multiple Myeloma

Maura, Francesco; Rajanna, Arjun Raj; Derkach, Andriy; Ziccheddu, Bachisio; Weinhold, Niels; Maclachlan, Kylee; Diamond, Benjamin; Davies, Faith E.; Boyle, Eileen M.; Walker, Brian A; Poos, Alexandra M; Hultcrantz, Malin; Silva, Ariosto Siqueira; Hampton, Oliver A.; Teer, Jamie K.; Bolli, Niccolò; Jackson, Graham; Kaiser, Martin; Pawlyn, Charlotte; Cook, Gordon; Verducci, Dennis; Kazandjian, Dickran; Rhee, Frits van; Usmani, Saad Z.; Shain, Ken; Raab, Marc S.; Morgan, Gareth J.; Landgren, Ola

doi:10.1182/blood-2022-160215

Cited by 8 publications

(16 citation statements)

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Section: Resultsmentioning

confidence: 99%

“…We next interrogated the landscape of driver events across the cohorts using a catalog of 91 known genes involved by mutations and focal copy number aberrations derived from de novo driver discovery across 1933 NDMM 29 . We then combined mutations in driver genes with copy number aberrations (CNA; Supplemental Figure 2 ), including known GISTIC hotspots 20, 29 , to define inactivation of tumor suppressor genes ( Supplemental Figure 3, Supplemental Methods ). Consistent with their later role in MM evolution 16, 30 , a large number of tumor suppressor genes were less frequently inactivated, including CDKN2C, CYLD, TENT5C, FUBP1, MAX, NCOR1, NF1, NFKBIA, PRDM1, RB1, RPL5 , and TRAF3 (p < 0.05; Supplemental Table 3 ).…”

Section: Resultsmentioning

confidence: 99%

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Genomic Profiling to Contextualize the Results of Intervention for High-Risk Smoldering Myeloma

Kazandjian,

Diamond,

Papadimitriou

et al. 2023

Preprint

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Early intervention for High-Risk Smoldering Multiple Myeloma (HR-SMM) achieves deeper and more prolonged responses compared to Newly Diagnosed (ND) MM. It is unclear if beneficial outcomes of interventional studies in HR-SMM are due to treatment of less complex, susceptible disease or inaccuracy in clinical definition of cases entered. Here, to gain greater biologic insight into treatment outcomes, we performed the first whole genome sequencing analysis of treated HR-SMM for 27 patients treated with carfilzomib, lenalidomide, and dexamethasone and lenalidomide maintenance (NCT01572480). Genomic features were pooled with another contemporary HR-SMM interventional study (E-PRISM;NCT02279394) and compared to those of NDMM. We reveal that across interventional cohorts, the genomic landscape of HR-SMM is uniformly simple as compared to NDMM counterparts, with fewer inactivation events of tumor suppressor genes, fewer RAS pathway mutations, lower frequency of MYC disruption, and lower APOBEC contribution. The absence of these genomic events parallels that of indolent precursor conditions with low chance of progression, possibly explaining the overall superior outcomes across these trials. However, there remains a subgroup of patients harboring genomic complexity for whom early intervention with potent triplet therapy fails to sustain response and who experience resistant, progressive disease. Overall, these results suggest that clinical risk scores do not effectively discriminate between genomically indolent and aggressive disease. Furthermore, our study supports the use of genomics to contextualize the advantage of early intervention in SMM and to consider novel approaches for those with the most aggressive precursor states.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Genomic Profiling to Contextualize the Results of Intervention for High-Risk Smoldering Myeloma

Kazandjian,

Diamond,

Papadimitriou

et al. 2023

Preprint

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“…In the article that accompanies this editorial, Maura et al, 1 assisted by artificial intelligence and deep neuronal networks, have introduced the first individualized risk-prediction model for newly diagnosed multiple myeloma. This risk-prediction approach is the way forward for the dynamic integration of an ever-widening array of complex genomic, biologic, and soon immunologic features and will permit us to offer patients with myeloma a comprehensive individualized risk prediction adapted to the therapy they will receive.…”

Section: The Takeawaymentioning

confidence: 99%

Artificial Intelligence Individualized Risk Classifier in Multiple Myeloma

Neri,

Lee,

Bahlis

2024

JCO

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“…that encompasses mutations, mutational signatures, recurrent aneuploidies, and canonical translocations (Methods and Supplemental Table 4). Among the catalogues of 90 nonsynonymous MM driver mutations (Maura et al, 2019a;Maura et al, 2022b;Walker et al, 2018), we found 31 mutated genes in the RRMM patients treated with Dara-Rd (Supplemental Figure 1a). Genes in MAPK pathway (NRAS, KRAS and BRAF) were the most frequently mutated, with 61% of RRMM cases (treated with Dara-Rd) carrying at least one mutation in one of these genes (Supplemental Figure 1a).…”

Section: Genomic Determinant Of Resistance and Progression To Dara-rdmentioning

confidence: 99%

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

Ziccheddu,

Giannotta,

D’Agostino

et al. 2023

Preprint

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SUMMARYAnti-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, includingRPL5loss 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.

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Individualized Treatment-Adjusted Risk Stratification in Newly Diagnosed Multiple Myeloma

Cited by 8 publications

References 0 publications

Genomic Profiling to Contextualize the Results of Intervention for High-Risk Smoldering Myeloma

Genomic Profiling to Contextualize the Results of Intervention for High-Risk Smoldering Myeloma

Artificial Intelligence Individualized Risk Classifier in Multiple Myeloma

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

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