Risk and Response-Adapted Treatment in Multiple Myeloma

Myeloma therapeutic strategies have been adapted to patients' age and comorbidities for a long time. However, although cytogenetics and clinical presentations (plasmablastic cytology; extramedullary disease) are major prognostic factors, until recently, all patients received the same treatment whatever their initial risk. No strong evidence allows us to use a personalized treatment according to one cytogenetic abnormality in newly diagnosed myeloma. Retrospective studies showed a benefit of a double autologous transplant in high-risk cytogenetics according to the International Myeloma Working Group definition (t(4;14), t(14;16) or del(17p)). Moreover, this definition has to be updated since other independent abnormalities, namely gain 1q, del(1p32), and trisomies 5 or 21, as well as TP53 mutations, are also prognostic. Another very strong predictive tool is the response to treatment assessed by the evaluation of minimal residual disease (MRD). We are convinced that the time has come to use it to adapt the strategy to a dynamic risk. Many trials are ongoing to answer many questions: when and how should we adapt the therapy, its intensity and duration. Nevertheless, we also have to take into account the clinical outcome for one patient, especially adverse events affecting his or her quality of life and his or her preferences for continuous/fixed duration treatment

Multiple Myeloma: Heterogeneous in Every Way

Multiple myeloma (MM) is a hematological malignancy characterized by the accumulation of tumor plasma cells (PCs) in the bone marrow (BM). Despite considerable advances in terms of treatment, patients’ prognosis is still very heterogeneous. Cytogenetics and minimal residual disease both have a major impact on prognosis. However, they do not explain all the heterogeneity seen in the outcomes. Their limitations are the result of the emergence of minor subclones missed at diagnosis, detected by sensible methods such as single-cell analysis, but also the non-exploration in the routine practice of the spatial heterogeneity between different clones according to the focal lesions. Moreover, biochemical parameters and cytogenetics do not reflect the whole complexity of MM. Gene expression is influenced by a tight collaboration between cytogenetic events and epigenetic regulation. The microenvironment also has an important impact on the development and the progression of the disease. Some of these determinants have been described as independent prognostic factors and could be used to more accurately predict patient prognosis and response to treatment

Primary plasma cell leukemias displaying t(11;14) have specific genomic, transcriptional, and clinical features

International audiencePrimary plasma cell leukemia (pPCL) is an aggressive form of multiple myeloma (MM) that has not benefited from recent therapeutic advances in the field. Because it is very rare and heterogeneous, it remains poorly understood at the molecular level. To address this issue, we performed DNA and RNA sequencing of sorted plasma cells from a large cohort of 90 newly diagnosed pPCL and compared with MM. We observed that pPCL presents a specific genomic landscape with a high prevalence of t(11;14) (about half) and high-risk genomic features such as del(17p), gain 1q, and del(1p32). In addition, pPCL displays a specific transcriptome when compared with MM. We then wanted to characterize specifically pPCL with t(11;14). We observed that this subentity displayed significantly fewer adverse cytogenetic abnormalities. This translated into better overall survival when compared with pPCL without t(11;14) (39.2 months vs 17.9 months, P 5 .002). Finally, pPCL with t(11;14) displayed a specific transcriptome, including differential expression of BCL2 family members. This study is the largest series of patients with pPCL reported so far

Biallelic deletion of 1p32 defines ultra-high-risk myeloma, but monoallelic del(1p32) remains a strong prognostic factor

Cytogenetics abnormalities (CA) are known to be the preponderant prognostic factor in multiple myeloma (MM). Our team has recently developed a prognostic score based on 6 CA, where del(1p32) appears to be the second worst abnormality after del(17p). The aim of this study was to confirm the adverse impact of 1p32 deletion on newly-diagnosed multiple myeloma (NDMM) patients. Among 2551 NDMM patients, 11% were harboring del(1p32). Their overall survival (OS) was significantly inferior compared to patients without del(1p32) (median OS: 49 months vs. 124 months). Likewise, progression-free survival was significantly shorter. More importantly, biallelic del(1p32) conferred a dramatically poorer prognosis than a monoallelic del(1p32) (median OS: 25 months vs. 60 months). As expected, the OS of del(1p32) patients significantly decreased when this abnormality was associated with other high-risk CA (del(17p), t(4;14) or gain(1q)). In the multivariate analysis, del(1p32) appeared as a negative prognostic factor; after adjustment for age and treatment, the risk of progression was 1.3 times higher among patients harboring del(1p32), and the risk of death was 1.9 times higher. At the dawn of risk-adapted treatment strategies, we have confirmed the adverse impact of del(1p32) in MM and the relevance of its assessment at diagnosis

Heterogeneity in Long Term Outcomes for R-ISS Stage II in Newly Diagnosed Multiple Myeloma Patients.

International audienceIn the era of personalized treatment in multiple myeloma, high-risk patients must be accurately defined. The International Myeloma Working Group recommends using the Revised International Staging System (R-ISS) to identify high-risk patients. The main purpose of our work was to explore the heterogeneity of outcome among R-ISS stage II patients assessing the impact of ISS, chromosomal abnormalities (CA) and LDH level in this subgroup. Data were issued from 1,343 newly diagnosed myeloma patients up to 65 years, enrolled in 3 clinical trials implemented by the Intergroupe Francophone du Myelome. All patients were eligible to an intensive treatment. Patients R-ISS stage II but ISS stage I had 1.6 times more risk of death than patients R-ISS stage I (adjusted HR 1.6; 95% CI, 1.1 to 2.2; P = .01) and patients R-ISS stage II but ISS stage III had a better overall survival than patients R-ISS stage III (adjusted HR 0.7; 95% CI, 0.4 to 0.9, P = .02). However, among patients classified in R-ISS II, ISS stage and CA (del(17p) and t(4;14)) were still relevant prognostic factors for death. Dividing R-ISS stage II into 3 subgroups: ISS I with standard risk CA, ISS II or III with standard risk CA and, high risk CA patients, median overall survivals were respectively not reached, 112 and 71 months (P < 0.001). In conclusion, stratification of patients in the R-ISS stage II group can be improved by taking into account CA and ISS. However, this does not improve predictive performance of survival models