Dataset Information

AI-supported modified risk staging for multiple myeloma cancer useful in real-world scenario.

ABSTRACT:

Introduction

An efficient readily employable risk prognostication method is desirable for MM in settings where genomics tests cannot be performed owing to geographical/economical constraints. In this work, a new Modified Risk Staging (MRS) has been proposed for newly diagnosed Multiple Myeloma (NDMM) that exploits six easy-to-acquire clinical parameters i.e. age, albumin, β2-microglobulin (β2M), calcium, estimated glomerular filtration rate (eGFR) and hemoglobin.

Materials and methods

MRS was designed using a training cohort of 716 NDMM patients of our inhouse MM Indian (MMIn) cohort and validated on MMIn (n=354) cohort and MMRF (n=900) cohort. K-adaptive partitioning (KAP) was used to find new thresholds for the parameters. Risk staging rules, obtained via training a J48 classifier, were used to build MRS.

Results

New thresholds were identified for albumin (3.6 g/dL), β2M (4.8 mg/L), calcium (11.13 mg/dL), eGFR (48.1 mL/min), and hemoglobin (12.3 g/dL) using KAP on the MMIn dataset. On the MMIn dataset, MRS outperformed ISS for OS prediction in terms of C-index, hazard ratios, and its corresponding p-values, but performs comparable in prediction of PFS. On both MMIn and MMRF datasets, MRS performed better than RISS in terms of C-index and p-values. A simple online tool was also designed to allow automated calculation of MRS based on the values of the parameters.

Discussion

Our proposed ML-derived yet simple staging system, MRS, although does not employ genetic features, outperforms RISS as confirmed by better separability in KM survival curves and higher values of C-index on both MMIn and MMRF datasets.

Funding

Grant: BT/MED/30/SP11006/2015 (Department of Biotechnology, Govt. of India), Grant: DST/ICPS/CPS-Individual/2018/279(G) (Department of Science and Technology, Govt. of India), UGC-Senior Research Fellowship.

SUBMITTER: Farswan A

PROVIDER: S-EPMC8278429 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Most patients cannot be included in randomized clinical trials. We report real-world outcomes of all Danish patients with multiple myeloma (MM) treated with daratumumab-based regimens until 1 January 2019.MethodsInformation of 635 patients treated with daratumumab was collected retrospectively and included lines of therapy (LOT), hematologic responses according to the International Myeloma Working Group recommendations, time to next treatment (TNT) and the cause of discontinuation of treatment. Baseline characteristics were acquired from the validated Danish Multiple Myeloma Registry (DMMR).ResultsDaratumumab was administrated as monotherapy (Da-mono) in 27.7%, in combination with immunomodulatory drugs (Da-IMiD) in 57.3%, in combination with proteasome inhibitors (Da-PI) in 11.2% and in other combinations (Da-other) in 3.8% of patients. The median number of lines of therapy given before daratumumab was 5 for Da-mono, 3 for Da-IMiD, 4 for Da-PI, and 2 for Da-other. In Da-mono, overall response rate (ORR) was 44.9% and median time to next treatment (mTNT) was 4.9 months. In Da-IMiD, ORR was 80.5%, and mTNT was 16.1 months. In Da-PI, OOR was 60.6% and mTNT was 5.3 months. In patients treated with Da-other, OOR was 54,2% and mTNT was 5.6 months. The use of daratumumab in early LOT was associated with longer TNT (p<0.0001). Patients with amplification 1q had outcome comparable to standard risk patients, while patients with t(4;14), t(14;16) or del17p had worse outcome (p = 0.0001). Multivariate analysis indicated that timing of treatment (timing of daratumumab in the sequence of all LOT that the patients received throughout the course of their disease) was the most important factor for outcome (p<0.0001).ConclusionThe real-world outcomes of multiple myeloma patients treated with daratumumab are worse than the results of clinical trials. Outcomes achieved with daratumumab were best when daratumumab was used in combination with IMIDs and in early LOT. Patients with high-risk CA had worse outcomes, but patients with amp1q had similar outcomes to standard-risk patients.