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The impact of response kinetics for multiple myeloma in the era of novel agents.

ABSTRACT: Rapid remission by induction therapy has long been recognized as an important predictor for long-time survival in acute leukemia. However, the impact of response kinetics on multiple myeloma (MM) seems to be different and remains unexplored. The relationship between response kinetics and outcome were assessed in 626 patients with newly diagnosed MM who were included in a prospective, nonrandomized clinical trial (BDH 2008/02). Patients were assigned to either immunomodulatory drug- or proteasome inhibitor-based therapy. The response depth, time to best response (T_BR) and duration of best response (D_BR) were collected. Depth of response was associated with superior outcomes, consistent with findings from other studies. However, the early responders (defined as T_BR ?3 months) showed significantly worse survival compared with late responders. We found that patients with rapid complete remission experienced inferior survivals comparable to those attaining a gradual partial remission. Moreover, 4 distinct response kinetics patterns were identified. Patients with gradual and sustained remission ("U-valley" pattern) experienced superior outcomes, whereas poor outcomes were observed in rapid and transient responders ("roller coaster" pattern) (median overall survival, 126 vs 30 months). The effects of response patterns on survival were confirmed in patients at different stages of disease and cytogenetic risk, including transplant-eligible patients and those attaining different extents of response depth. Collectively, our data indicated that slow and gradual response is a favorable prognostic factor in MM. In addition to response depth, the kinetic pattern of response is a simple and powerful predictor for survival even in the era of novel agents.

SUBMITTER: Yan Y

PROVIDER: S-EPMC6784522 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Project description:IntroductionCurrent risk predictors of multiple myeloma do not integrate ethnicity-specific information. However, the impact of ethnicity on disease biology cannot be overlooked. In this study, we have investigated the impact of ethnicity in multiple myeloma risk prediction. In addition, an efficient and robust artificial intelligence (AI)-enabled risk-stratification system is developed for newly diagnosed multiple myeloma (NDMM) patients that utilizes ethnicity-specific cutoffs of key prognostic parameters.MethodsK-adaptive partitioning is used to propose new cutoffs of parameters for two different datasets-the MMIn (MM Indian dataset) dataset and the MMRF (Multiple Myeloma Research Foundation) dataset belonging to two different ethnicities. The Consensus-based Risk-Stratification System (CRSS) is designed using the Gaussian mixture model (GMM) and agglomerative clustering. CRSS is validated via Cox hazard proportional methods, Kaplan-Meier analysis, and log-rank tests on progression-free survival (PFS) and overall survival (OS). SHAP (SHapley Additive exPlanations) is utilized to establish the biological relevance of the risk prediction by CRSS.ResultsThere is a significant variation in the key prognostic parameters of the two datasets belonging to two different ethnicities. CRSS demonstrates superior performance as compared with the R-ISS in terms of C-index and hazard ratios on both the MMIn and MMRF datasets. An online calculator has been built that can predict the risk stage of a multiple myeloma (MM) patient based on the values of parameters and ethnicity.ConclusionOur methodology discovers changes in the cutoffs with ethnicities from the established cutoffs of prognostic features. The best predictor model for both cohorts was obtained with the new ethnicity-specific cutoffs of clinical parameters. Our study also revealed the efficacy of AI in building a deployable risk prediction system for MM. In the future, it is suggested to use the CRSS risk calculator on a large dataset as the cohort size of the present study is 25% of the cohort used in the R-ISS reported in 2015.

Project description:Extramedullary disease is an uncommon manifestation in multiple myeloma and can either accompany newly diagnosed disease or develop with disease progression or relapse. We evaluated the impact of this disease feature on patients' outcome in the context of novel agents.We analyzed clinical and biological features of extramedullary disease in 936 patients with multiple myeloma enrolled in Total Therapy protocols, 240 patients in non-Total Therapy protocols, and 789 non-protocol patients, all of whom had baseline positron emission tomography scans to document extramedullary disease at diagnosis and its subsequent development at the time of disease progression or relapse.The most common sites for extramedullary disease at diagnosis were skin and soft tissue whereas liver involvement was the striking feature in extramedullary disease at disease relapse or progression. Regardless of therapy, extramedullary disease was associated with shorter progression-free and overall survival, as well as the presence of anemia, thrombocytopenia, elevated serum lactate dehydrogenase, cytogenetic abnormalities, and high-risk features in 70-and 80-gene risk models in univariate analysis. Multivariate analysis with logistic regression revealed that this disease feature was more prevalent in patients with an elevated centrosome index, as determined by gene expression profiling, as well as in myeloma molecular subtypes that are more prone to relapse. These include the MF subtype (also called the "MAF" subtype, associated with over-expression of the MAF gene seen with chromosome translocation 14;16 or 14;20) and the PR subtype (also called the "Proliferation" subtype, associated with overexpression of pro-proliferative genes).These data show that extramedullary disease is more prevalent in genomically defined high-risk multiple myeloma and is associated with shorter progression-free survival and overall survival, even in the era of novel agents. All clinical trials included in the analyses were registered with www.clinicaltrials.gov (NCT00083551, NCT00083876, NCT00081939, NCT00572169, NCT00644228,NCT00002548,NCT00734877).

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The impact of response kinetics for multiple myeloma in the era of novel agents.

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