22 January 2024

A computational model for newly-diagnosed multiple myeloma predicts an individual’s prognosis based on tumour genomics and treatments, researchers have reported.

Led by researchers at Sylvester Comprehensive Cancer Center, at the University of Miami Miller School of Medicine, the model, called IRMMa, improves on previous prognostic tools because it takes into account the biology of patients’ tumours, the researchers say.

Writing in the Journal of Clinical Oncology, senior author Dr C. Ola Landgren, chief of the Division of Myeloma and director of the Sylvester Myeloma Institute, said the being able to predict risk based on individual factors is important in many cancers, but especially in multiple myeloma because it is so variable.

In the study, the research team identified 12 distinct subtypes of the disease, a classification that had not been made before.

The original method for classifying multiple myeloma, developed in the 1970s, was based on staging for solid tumours and relied on the amount of cancer present. Thanks to newly developed therapies, especially immunotherapies, the amount of cancer is often less important than the nature of the cancerous cells, the researchers say.

Different kinds of driver mutations in the tumour genome affect the cancer’s growth, so certain subtypes of myeloma could have a very good outcome even if the patient is diagnosed when the cancer is widespread.

Although the number of treatment options for the disease has grown over the past 20 years, physicians need a way to predict which treatment will work best for each patient.

First author Dr Francesco Maura, an assistant professor at Sylvester, said that although prognostic tools have improved, they still lacked precise prediction.

“Our model is based on the idea of predicting the risk of the individual patient rather than that of the group,” Dr Maura said.

To build the model, the Sylvester researchers and their collaborators used genetic, treatment, and clinical data from nearly 2,000 patients newly diagnosed with multiple myeloma.

They first identified 90 “driver genes” and looked at the treatments each patient in their dataset received and how the patients fared on those treatments, matching treatment outcome to an individual’s tumour genetic sequences.

The model was built using deep learning and allows emerging datasets for future treatment strategies to be added, the researchers say. They are now working on including additional datasets from patients treated with new antibody-based multiple myeloma therapies.

“This model can only grow with the help of the research community,” Dr Maura said. “The next challenge is to keep feeding it with the right datasets so at a certain point it will be usable for clinical purposes.”


Maura F, Rajanna AR, Ziccheddu B, Poos AM, Derkach A, Maclachlan K, Durante M, Diamond B, Papadimitriou M, Davies F, Boyle EM, Walker B, Hultcrantz M, Silva A, Hampton O, Teer JK, Siegel EM, Bolli N, Jackson GH, Kaiser M, Pawlyn C, Cook G, Kazandjian D, Stein C, Chesi M, Bergsagel L, Mai EK, Goldschmidt H, Weisel KC, Fenk R, Raab MS, Van Rhee F, Usmani S, Shain KH, Weinhold N, Morgan G, Landgren O. (2024) “Genomic Classification and Individualized Prognosis in Multiple Myeloma.” Journal of Clinical Oncology, doi: 10.1200/JCO.23.01277

Links: https://ascopubs.org/doi/10.1200/JCO.23.01277


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