Second primary malignancies in multiple myeloma: A review

Poh, Christina; Keegan, Theresa H.M.; Rosenberg, Aaron S

doi:10.1016/j.blre.2020.100757

Cited by 40 publications

(43 citation statements)

References 70 publications

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“…With the increasingly longer survival of MM, second primary malignancies gained more significance 38 . In a population-based study of Sweden, the incidence of second primary malignancy was 5.5% after a median follow-up of 2.5 years 39 .…”

Section: Discussionmentioning

confidence: 99%

Addition of daratumumab to multiple myeloma backbone regimens significantly improves clinical outcomes: a systematic review and meta-analysis of randomised controlled trials

Kiss

Gede

Hegyi

et al. 2021

Sci Rep

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Daratumumab has shown clinical benefit in multiple myeloma. We aimed to evaluate the safety and efficacy of adding daratumumab to backbone anti-myeloma treatments. Systematic search was performed up to August 2021 to identify randomised controlled trials comparing the outcomes of backbone therapy with and without daratumumab in relapsed/refractory and newly diagnosed myeloma (RRMM and NDMM, respectively). Odds ratios (ORs) and hazard ratios (HRs) were calculated with 95% confidence intervals (CIs). Primary outcomes were death or disease progression, minimal residual disease (MRD) negativity, and stringent complete response (sCR). Secondary outcomes were complete response or better and safety endpoints prespecified in the study protocol: PROSPERO (CRD42020222904). In NDMM, MRD negativity [OR = 3.61 (CI 2.33–5.61)] and sCR [OR = 2.29 (CI 1.49–3.51)] were more likely and death or disease progression [HR = 0.47 (CI 0.39–0.57)] was less likely to occur with daratumumab compared to control. Regarding RRMM, MRD negativity [OR = 5.43 (CI 2.76–10.66)] and sCR [OR = 3.08 (CI 2.00–4.76)] were more likely and death or disease progression was less likely [HR = 0.50 (CI 0.37–0.67)] with daratumumab compared to control. The addition of daratumumab has shown high clinical efficacy and acceptable toxicity profile for the treatment of NDMM and RRMM regarding the endpoints examined.

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Section: Discussionmentioning

confidence: 99%

Addition of daratumumab to multiple myeloma backbone regimens significantly improves clinical outcomes: a systematic review and meta-analysis of randomised controlled trials

Kiss

Gede

Hegyi

et al. 2021

Sci Rep

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“…It is possible that the change from the main clone to subclones during MM treatment causes a change in the clinical and hematological phenotype of MM and, thus, explains the inefficiency of the previously conducted therapy. Noteworthy, patients with MM or its precursors have an increased risk of developing secondary primary malignancies (SPMs), such as myelodysplastic syndrome (MDS), acute myeloid leukemia (AML), and others [ 64 , 65 , 66 , 67 ]. The origin of these tumors is likely related to the genotoxic action of some therapeutic agents [ 65 , 68 , 69 , 70 , 71 , 72 , 73 , 74 ], although an excess risk for hematopoietic neoplasms other than MM in MGUS patients supports an idea that endogenous factors play an essential role in SPMs’ development [ 9 , 75 , 76 , 77 ].…”

Section: Clinical Manifestation Of Multiple Myeloma and Recent Research Approachesmentioning

confidence: 99%

“…Notably, a significant increase in mutation burden and specific mutational signature have been reported in MM patients exposed to high-dose melphalan [ 54 , 59 , 63 , 145 , 146 ]. Melphalan (and to a lesser extent cyclophosphamide) is also associated with an increased risk of SPMs in MM patients [ 64 , 68 , 69 , 71 , 72 , 147 , 148 , 149 , 150 ]. The line of chemotherapeutic drugs has been consistently elaborated upon with novel therapeutic agents in recent years.…”

Section: Clinical Manifestation Of Multiple Myeloma and Recent Research Approachesmentioning

confidence: 99%

Genome Instability in Multiple Myeloma: Facts and Factors

Aksenova

Zhuk

Lada

et al. 2021

Cancers

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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.

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“…Multiple myeloma (MM) is a hematological cancer which arises from abnormal antibody producing white blood plasma cells (1). MM affects approximately 500,000 people and results iñ 100,000 deaths annually (2,3) being currently considered treatable but rarely curable (4,5). There are several MM chemotherapy treatment regimens currently in use, among which eleven include bortezomib (6) (Table 1).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Applicability for Classification of PAD/VCD Chemotherapy Response Using 53 Multiple Myeloma RNA Sequencing Profiles

et al. 2021

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Multiple myeloma (MM) affects ~500,000 people and results in ~100,000 deaths annually, being currently considered treatable but incurable. There are several MM chemotherapy treatment regimens, among which eleven include bortezomib, a proteasome-targeted drug. MM patients respond differently to bortezomib, and new prognostic biomarkers are needed to personalize treatments. However, there is a shortage of clinically annotated MM molecular data that could be used to establish novel molecular diagnostics. We report new RNA sequencing profiles for 53 MM patients annotated with responses on two similar chemotherapy regimens: bortezomib, doxorubicin, dexamethasone (PAD), and bortezomib, cyclophosphamide, dexamethasone (VCD), or with responses to their combinations. Fourteen patients received both PAD and VCD; six received only PAD, and 33 received only VCD. We compared profiles for the good and poor responders and found five genes commonly regulated here and in the previous datasets for other bortezomib regimens (all upregulated in the good responders): FGFR3, MAF, IGHA2, IGHV1-69, and GRB14. Four of these genes are linked with known immunoglobulin locus rearrangements. We then used five machine learning (ML) methods to build a classifier distinguishing good and poor responders for two cohorts: PAD + VCD (53 patients), and separately VCD (47 patients). We showed that the application of FloWPS dynamic data trimming was beneficial for all ML methods tested in both cohorts, and also in the previous MM bortezomib datasets. However, the ML models build for the different datasets did not allow cross-transferring, which can be due to different treatment regimens, experimental profiling methods, and MM heterogeneity.

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Second primary malignancies in multiple myeloma: A review

Cited by 40 publications

References 70 publications

Addition of daratumumab to multiple myeloma backbone regimens significantly improves clinical outcomes: a systematic review and meta-analysis of randomised controlled trials

Addition of daratumumab to multiple myeloma backbone regimens significantly improves clinical outcomes: a systematic review and meta-analysis of randomised controlled trials

Genome Instability in Multiple Myeloma: Facts and Factors

Machine Learning Applicability for Classification of PAD/VCD Chemotherapy Response Using 53 Multiple Myeloma RNA Sequencing Profiles

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