Investigators have developed a genomic score, called multiple myeloma-like (MM-like), for predicting progression from multiple myeloma precursor conditions to active cancer, study results published in Nature Genetics showed. The researchers created a map of the genome of multiple myeloma and its precursor conditions that helped to identify and model the timing of disease progression, demonstrating the clinical potential of genome profiling.
“In patients with a precursor condition for multiple myeloma, the multiple myeloma–like score helps to predict who is at a higher risk of progressing to active cancer,” stated co-senior author Jean-Baptiste Alberge, PhD, Instructor in Medicine, Dana-Farber Cancer Institute, Boston. “This study brings us closer to more personalized care for patients with a precursor stage of cancer and could better inform early intervention strategies in the future.”
Study Methods and Results
Currently, there are no strategies to determine when patients with multiple myeloma precursor conditions will develop active disease, as about 1% to 10% of patients with these conditions will ultimately progress to active disease stages. In the case of smoldering multiple myeloma, about 50% of patients with high-risk disease will progress within 2 years.
“There has been an urgent need to identify genomic risk factors that predict disease progression in smoldering myeloma,” stated Irene Ghobrial, MD, Director of the Center for Early Detection and Interception of Blood Cancers, Dana-Farber Cancer Institute, Boston. “This is especially true as we start early therapeutic interception for high-risk smoldering multiple myeloma. Our data provide a simple genomic score that can help predict progression and can improve on our clinical markers to help stratify risk for our patients.”
The researchers pooled genomic sequencing data from bone marrow biopsies from 1,030 patients with multiple myeloma or its precursor conditions (n = 218). This represented one of the largest analyses of whole-genome data for multiple myeloma and its precursor conditions to date.
The data were used to identify recurrent coding and noncoding drivers and significant hotspots of structural variation across the patients’ genomes. These drivers were used to create the MM-like score, which assessed the severity of the disease as well as the rate of progression.
The researchers mapped out an axis of progression from monoclonal gammopathy of undetermined significance to smoldering multiple myeloma and on to active multiple myeloma. With this map and score, the researchers gained insights into the time of disease initiation and cell of origin of the disease as well as the order that genomic alterations and mutations were acquired that led to the transformation to active disease.
The score was then validated against 47 tumor samples from various points of disease progression in 20 patients. The MM-like score was able to indicate the course of disease for individual patients in multiple cases, with stable scores indicating no disease progression and increasing scores indicating disease progression.
The study findings regarding genomic changes in the disease also suggested that these changes may appear earlier on in the patient’s life than previously thought. “This study greatly improves our ability to discover both potentially clinically actionable cancer-driving mutations in multiple myeloma and the timing of when these events occur across disease states,” says Gad Getz, PhD, Director of Cancer Genome Computational Analysis at the Broad Institute of MIT and Harvard, and Director of Bioinformatics in the Krantz Family Center for Cancer Research and Department of Pathology at Massachusetts General Hospital.
The researchers are working to develop a liquid biopsy test for the MM-like score going forward and to continue to improve the score over time with further study.
Disclosure: For full disclosures of the study authors, visit nature.com.
