Outcome data from >10 000 multiple myeloma patients in the Danish and Swedish national registries

The establishment of a multiple myeloma clinical registry in the Asia-Pacific region: The Asia-Pacific Myeloma and Related Diseases Registry (APAC MRDR)

BACKGROUND

Multiple myeloma (MM) is the second most common haematological cancer worldwide. Along with related diseases including monoclonal gammopathy of undetermined significance (MGUS), plasma cell leukaemia (PCL) and plasmacytoma, MM incidence is rising, yet it remains incurable and represents a significant disease burden. Clinical registries can provide important information on management and outcomes, and are vital platforms for clinical trials and other research. The Asia-Pacific Myeloma and Related Diseases Registry (APAC MRDR) was developed to monitor and explore variation in epidemiology, treatment regimens and their impact on clinical outcomes across this region. Here we describe the registry's design and development, initial data, progress and future plans.

METHODS

The APAC MRDR was established in 2018 as a multicentre collaboration across the Asia-Pacific, collecting prospective data on patients newly diagnosed with MM, MGUS, PCL and plasmacytoma in Korea, Singapore, Malaysia and Taiwan, with China recently joining. Development of the registry required a multidisciplinary team of clinicians, researchers, legal and information technology support, and financial resources, as well as local clinical context from key opinion leaders in the APAC region. Written informed consent is obtained and data are routinely collected throughout treatment by hospital staff. Data are stored securely, meeting all local privacy and ethics requirements. Data were collected from October 2018 to March 2024.

RESULTS

Over 1700 patients from 24 hospitals have been enrolled onto the APAC MRDR to date, with the majority (86%) being newly diagnosed with MM. Bortezomib with an immunomodulatory drug was most frequently used in first-line MM therapy, and lenalidomide-based therapy was most common in second-line. Establishment and implementation challenges include regulatory and a range of operational issues.

CONCLUSION

The APAC MRDR is providing 'real-world' data to participating sites, clinicians and policy-makers to explore factors influencing outcomes and survival, and to support high quality studies. It is already a valuable resource that will continue to grow and support research and clinical collaboration in MM and related diseases across the APAC region.

Recent trends in incidence, survival and treatment of multiple myeloma in Finland - a nationwide cohort study

This study aimed to determine the incidence and prevalence of multiple myeloma (MM) in Finland in 2015-2019, to characterize adult patients newly diagnosed with MM, and to follow-up their overall survival (OS) and treatment patterns until the end of 2020. We sourced the data on inpatient and outpatient diagnoses, outpatient medication use, and date of death from comprehensive, nationwide registers. We identified 2037 incident patients with MM in 2015-2019. On average, the annual crude incidence was 8.8 and the age-standardized incidence (World Standard Population) was 3.3 per 100,000. The crude prevalence at the end of 2019 was 32.7 cases per 100,000 inhabitants ≥ 18 years of age. Median age of the patients at first diagnosis (index date) was 71 years, and 48% were female, the median follow-up being 2.4 years. The median OS was estimated at 4.5 years. The proportion of the patients receiving autologous stem cell transplantation (ASCT) within one year since the index date was 24%, with little variation across study years. Conversely, the proportion of all patients receiving lenalidomide within one year since the index date increased from 27 to 48% overall, and from 39 to 81% among ASCT recipients. The estimated median relapse-free survival after ASCT was 2.9 years. Information on in-hospital MM medication administrations was available for a subset of the study cohort. In this subset, 85.8% of the patients received immunomodulatory drugs and/or proteasome inhibitors within the first year after the index date.

Clinical Outcomes and Serum B-Cell Maturation Antigen Levels in a Real-World Unselected Population of Newly Diagnosed Multiple Myeloma Patients

BACKGROUND

Progression-free survival (PFS) and overall survival (OS) of newly diagnosed multiple myeloma (MM) patients have been widely published in the clinical trials setting, but data published from real-world settings are limited.

OBJECTIVE

We determined the survival and factors that predict outcomes among 161 unselected, newly diagnosed MM patients whose frontline therapy was started at a single clinic specializing in the treatment of this B-cell malignancy.

PATIENTS AND METHODS

None of these patients underwent an autologous stem cell transplantation as part of their initial therapy and the population had a high proportion (35%) of cytogenetic high-risk patients.

RESULTS

With a median follow-up of 42.7 months, the cohort had a median PFS of 22.8 months and a median OS of 136.2 months. The 1-, 3-, and 5-year survival rates were 97.5%, 85.3%, and 76.2%, respectively. These results are considerably better than those reported from patients enrolled in clinical trials and those from countries with national registries. Age <65 years predicted for a longer OS (p = 0.0004). Baseline serum B-cell maturation antigen (sBCMA) levels were also assessed and showed median and mean levels of 320.3 ng/mL and 551.1 ng/mL, respectively. Furthermore, patients with baseline sBCMA levels in the lowest quartile (≤136.2 ng/mL) showed a longer PFS (p = 0.0262).

CONCLUSION

These results provide clinicians with a real-world understanding of the survival of unselected, newly diagnosed patients initiating therapy in a clinic specializing in the care of MM patients.

Real-world evidence of multiple myeloma treated from 2013 to 2019 in the Hospital District of Helsinki and Uusimaa, Finland

Background: The rapid development of multiple myeloma (MM) management underscores the value of real-world data. In our study we examined 509 adult MM patients treated with immunochemotherapy (ICT) with/without stem cell transplantation (SCT) from 2013 to 2019 in the Hospital District of Helsinki and Uusimaa, Finland. Materials & methods: Our study was based on computational analyses of data integrated into the hospital data lake. Results: After 2017, treatment pattern diversity increased with improved access to novel treatments. 5-year survivals were 74.4% (95% CI: 65.5-84.5) in SCT-eligible and 44.0% (95% CI: 37.6-51.4) in non-SCT subgroups. In the SCT-eligible subgroup, high first-year hospitalization costs were followed by stable resource requirements. Conclusion: Hospital data lakes can be adapted to carry out complex analysis of large MM cohorts.

Anti-CD38 antibody isatuximab monotherapy for Japanese individuals with relapsed/refractory multiple myeloma: An update of the phase 1/2 ISLANDs study

The primary analysis of the phase 1/2 ISLANDs study in Japanese individuals with relapsed/refractory multiple myeloma (RRMM) showed that isatuximab monotherapy was well tolerated and effective, even in participants with high-risk cytogenetic abnormalities. Here, we report a prespecified second analysis conducted 20 months after the first dosing of the last participant (ClinicalTrials.gov identifier: NCT02812706). The primary objectives were to evaluate the safety and tolerability of isatuximab in phase 1 and to evaluate the efficacy of isatuximab, including assessment of overall response rate (ORR) at the recommended dose (RD), in phase 2. In phase 1, three participants received isatuximab 10 mg/kg every week (QW) for 4 weeks/cycle followed by every 2 weeks (Q2W) and five participants received 20 mg/kg QW/Q2W. Since no dose-limiting toxicities occurred in phase 1, 20 mg/kg QW/Q2W was identified as the RD for the phase 2 study (n = 28). At the time of data cut-off, three participants (one in phase 1 and two in phase 2) continued to receive isatuximab; disease progression and treatment-related adverse events were the most common reasons for treatment discontinuation. The overall safety profile was consistent with the primary analysis. One death, not related to isatuximab treatment, was reported since the first analysis. The ORR and clinical benefit rate remained unchanged from the primary analysis at 36.4% (95% confidence interval [CI]: 20.4%-54.9%) and 54.5% (95% CI: 36.4%-71.9%), respectively. The median progression-free survival (PFS) was 5.6 months, longer than the median PFS reported in the primary analysis (4.7 months), whereas median overall survival was not reached. Overall, isatuximab 20 mg/kg QW/Q2W had an acceptable safety and tolerability profile and showed promising antitumor activity in Japanese individuals with RRMM.

Machine Learning and Deep Learning Applications in Multiple Myeloma Diagnosis, Prognosis, and Treatment Selection

Simple Summary

Multiple myeloma is a malignant neoplasm of plasma cells with complex pathogenesis. With major progresses in multiple myeloma research, it is essential that we reconsider our methods for diagnosing and monitoring multiple myeloma disease. This fact needs the integration of serology, histology, radiology, and genetic data; therefore, multiple myeloma study has generated massive quantities of granular high-dimensional data exceeding human understanding. With improved computational techniques, artificial intelligence tools for data processing and analysis are becoming more and more relevant. Artificial intelligence represents a wide set of algorithms for which machine learning and deep learning are presently among the most impactful. This review focuses on artificial intelligence applications in multiple myeloma research, first illustrating machine learning and deep learning procedures and workflow, followed by how these algorithms are used for multiple myeloma diagnosis, prognosis, bone lesions identification, and evaluation of response to the treatment.

Abstract

Artificial intelligence has recently modified the panorama of oncology investigation thanks to the use of machine learning algorithms and deep learning strategies. Machine learning is a branch of artificial intelligence that involves algorithms that analyse information, learn from that information, and then employ their discoveries to make abreast choice, while deep learning is a field of machine learning basically represented by algorithms inspired by the organization and function of the brain, named artificial neural networks. In this review, we examine the possibility of the artificial intelligence applications in multiple myeloma evaluation, and we report the most significant experimentations with respect to the machine and deep learning procedures in the relevant field. Multiple myeloma is one of the most common haematological malignancies in the world, and among them, it is one of the most difficult ones to cure due to the high occurrence of relapse and chemoresistance. Machine learning- and deep learning-based studies are expected to be among the future strategies to challenge this negative-prognosis tumour via the detection of new markers for their prompt discovery and therapy selection and by a better evaluation of its relapse and survival.