Molecular and genetic research has advanced the categorization of different risk groups in patients with smoldering myeloma. Notable presentations at the 2021 American Society of Hematology (ASH) Annual Meeting & Exposition included reports on biomarkers to predict response to immunotherapy in patients with high-risk multiple myeloma; genomic characteristics of smoldering myeloma in patients who are unlikely to develop myeloma; and identification of distinct molecular subgroups of patients with smoldering myeloma with varying risks of progression to myeloma.
Novel Biomarkers May Predict Immunotherapy Response
In a presentation by Sklavenitis-Pistofidis et al (Abstract 330), researchers identified novel biomarkers in the immune environment of smoldering myeloma cells that may predict patients’ likelihood of responding to treatment with immunotherapy.
The scientists performed single-cell RNA sequencing on CD138 immune cells from 40 samples of 14 patients with high-risk smoldering myeloma enrolled in a phase II trial evaluating the combination of elotuzumab, lenalidomide, and dexamethasone. Elotuzumab is an immunotherapy agent that stimulates the immune response and activates natural killer cells. The researchers profiled the immune cells collected at baseline, at day 1 of the ninth cycle of treatment, and at the end of treatment.
Their study showed that patients whose immune responses at baseline were “not normal-like”—meaning they had changes in abundance of certain cells and changes in gene expression signatures—had longer periods of survival without the disease progressing.
“This model suggests that at least some of the compositional changes observed in disease may reflect the immune system’s capacity to react successfully to the immune challenge posed by the tumor, which we termed ‘immune reactivity,’” the researchers said. This measure of baseline immune reactivity may help to identify patients who will benefit the most from early treatment, they added.
Patients whose immune profile showed other changes in samples taken at day 1 of the ninth treatment cycle had shorter progression-free survival. Additionally, patients whose immune profile was “normal-like” at the end of treatment, potentially reflecting the resolution of the immune challenge, had significantly longer progression-free survival.
“Our study has nominated novel immune biomarkers for optimal patient selection and assessment of response to immunotherapy,” said the investigators. “Our findings may usher in a next generation of clinical assays that assess both tumor biology and immune state, as well as common clinical biomarkers, in the marrow or blood, to accurately predict who may benefit from early treatment, monitor response to immunotherapy, and improve patient outcomes.”
Genomic Analysis to Predict Progression to Multiple Myeloma
On average, 50% of patients with smoldering myeloma progress to symptomatic multiple myeloma within the first 5 years of diagnosis, and a high-risk group of patients is prone to progressing within 2 years of diagnosis. On the other hand, patients who don’t progress within the first 5 years after diagnosis have a significantly lower risk of developing full-blown multiple myeloma. Aktas-Samur et al presented the results of a study (Abstract 545) using genomic analysis to identify these good-risk patients “who have nothing to worry about; they have no need for intervention, it is not going to progress to cancer,” said senior study author Nikhil Munshi, MD, Director of Basic and Correlative Science at the Jerome Lipper Multiple Myeloma Center at Dana-Farber Cancer Institute.
Nikhil Munshi, MD
Dr. Munshi estimates that one-third of patients with smoldering myeloma are at low risk for progression. In an effort to identify these patients in advance, he and his colleagues looked for changes in the DNA and RNA complement of cells of nonprogressors (patients with smoldering myeloma who did not progress after 5 years) and compared them with changes observed in newly diagnosed patients with multiple myeloma. They also studied samples from 18 patients at their precursor stage and when they progressed to multiple myeloma. The goal was to spot particular DNA and RNA changes in the cells of patients with smoldering myeloma that were associated with low progression risk.
One of the most significant findings was that the cells of nonprogressors exhibited a lower mutational load—fewer DNA mutations in the myeloma cells—compared with those from patients who progressed to myeloma. The scientists also developed a “deep learning” mathematical model that differentiated the patterns of mutations between the nonprogressors and those with multiple myeloma. In addition, the genomes of nonprogressors were less likely to have a gain or loss of chromosome 8 than the progressors had.
Using these and other genomic differences, “we have identified a simple genomic model only involving chromosome 8 copy number changes and overall mutational burden” that identified the patients at low risk of progression, said the investigators.
“These patients have low chromosomal instability and a small likelihood of progressing to multiple myeloma,” said Dr. Munshi. This would be reassuring, and such patients would be advised to have follow-up monitoring, but not as frequently as most patients with smoldering myeloma, he said. Dr. Munshi noted that the findings from this study were not yet ready for clinical application, in part because whole-genome sequencing is not yet routinely available. “But it is almost time to think about making it routine,” he said.
Genomic Subtypes of Smoldering Myeloma
In another report, Anand et al (Abstract 723) reported the identification of six genomic subtypes of smoldering myeloma. Studying 214 samples from patients with smoldering myeloma, the researchers used analytic techniques including whole-exome sequencing and RNA sequencing to identify molecular clusters that enabled stratification of risk groups. The subgroups had distinct, differing DNA alterations, transcriptional profiles, and dysregulated molecular signaling pathways associated with progression and myeloma-associated proteins during a median follow-up period of 7 years. When the team applied these classification factors to a cohort of 74 patients with smoldering myeloma, they successfully helped to predict the clinical outcomes—the risk of progression and time to progression.
“Our results underscore the importance of molecular classification in addition to clinical evaluation in better identifying high-risk [patients with] smoldering myeloma,” the investigators said.
Disclosure: For full disclosures of the study authors, visit ash.confex.com.
