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Genomic Model Predicts Outcomes Better Than Existing Prognostic Methods in Newly Diagnosed Multiple Myeloma

March 15, 2024

April 2024

Khylia Marshall

Khylia Marshall is a freelance journalist based in Tucson, Arizona.

A recent publication by Francesco Maura, MD, of the University of Miami in Florida, and colleagues describes a model that aims to predict individualized risk in newly diagnosed multiple myeloma (IRMMa), incorporating clinical, demographic, genomic, and therapeutic data. The IRMMa model demonstrated superior performance compared to existing predictive models, according to a study published in the Journal of Clinical Oncology.

Because myeloma is a heterogeneous disease, "we aimed to develop more biology-driven approaches to create subclasses [of MM] that can be attacked through specific therapies,” said corresponding author Francesco Maura, MD, of the University of Miami in Florida.

The study included a training set of 1,933 patients and a validation set of 256 patients with newly diagnosed multiple myeloma (NDMM) enrolled in the GMMG-HD6 clinical trial. Researchers conducted a pairwise analysis between each single genomic event and combined genomic events with strong patterns of co-occurrence, developing a genomic classification that expands previous fluorescence in situ hybridization (FISH) and gene expression models. They also integrated 20 genomic features to improve the accuracy of their prediction model, including the identification of primary refractory and early progressiveness (often termed functional high-risk MM).

IRMMa’s accuracy was significantly higher than the current FISH and gene expression–based predictive models. IRMMa’s c-index for overall survival (OS) was 0.726, higher than the International Staging System (ISS; 0.61), revised-ISS (0.572), and R2-ISS (0.625). Furthermore, IRMMa’s ability to predict OS and event-free survival (EFS), even without genomic data, outperformed existing prognostic methods. Also, unlike other prognostic models for NDMM, IRMMa can predict the risk for 32 possible treatment courses.

Additionally, IRMMa can estimate the risk of progression and death by adjusting for time-dependent treatment and consolidation strategies such as high-dose melphalan with autologous hematopoietic cell transplantation and maintenance or continuous treatment, which have not been previously considered. “The model demonstrates that genomics can be used to identify populations that can benefit from transplant and populations that cannot,” Dr. Maura said. Including time-dependent features improved the prognostic accuracy for EFS, which can prevent unnecessary overtreatment.

Moreover, findings underscore the importance of including genomic and treatment features in NDMM patient outcomes. For example, researchers found that biallelic loss was associated with shorter EFS and OS compared to monoallelic events; researchers used copy number variant to predict chromothripsis, which is associated with shorter EFS and OS; and researchers found patients with hyper-APOBEC (n=154) had significantly worse OS compared to patients with evidence of some APOBEC activity (n=598; p=0.0013) and patients without (p<0.0001).

One limitation to the model is that IRMMa cannot provide estimates for newer agents such as anti-CD38 antibodies or specific time-dependent features such as measurable residual disease. However, because IRMMa is a flexible and knowledge-driven model, it can develop over time to integrate additional genomic drivers, new treatments, and the effect on treatment variance as prognostic markers.

Dr. Maura emphasized that “this work is a multicenter collaboration. Over time, if the community gets engaged and sets up a productive way to share these kinds of data and models, we can probably create something very important for our patients and physicians around the world.”

“People need to be prepared for the slow but imminent revolution where genomics will replace cytogenetics and FISH, and our clinical decisions will be more and more integrated with prediction models developed on big data. We need more genomics, we need more genetics, we need more complex statistical modeling that helps doctors to accurately predict outcomes in MM and to eventually use this to adjust our therapy for each patient. That is really a priority of our community,” Dr. Maura said.

Any conflicts of interest declared by the authors can be found in the original article. Researchers wish to acknowledge the collaboration of Memorial Sloan Kettering Cancer Center, NYU Langone Health, UK Myeloma Group, Moffitt Cancer Center, University of Arkansas for Medical Sciences, Heidelberg University Hospital, the Multiple Myeloma Research Foundation, and Sylvester Comprehensive Cancer Center at the University of Miami.

Reference

Maura F, Rajanna AR, Ziccheddu B, et al. Genomic classification and individualized prognosis in multiple myeloma [published online ahead of print, 2024 Jan 9]. J Clin Oncol. doi: 10.1200/JCO.23.01277.

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