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Myeloma News

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Myeloma News

SRRM2 Is a Potential Biomarker and Immunotherapy Target in Multiple Myeloma

Source: Myeloma – Hematology Advisor

The splicing factor protein serine/arginine repetitive matrix 2 (SRRM2) may serve as a novel biomarker and immunotherapeutic target in multiple myeloma (MM), according to research published in Clinical and Experimental Medicine.

“New drugs and immunotherapy bring new changes to the prognosis of MM and need to be supplemented with more diagnostic, risk stratification, and prognostic markers for clinical application,” the authors wrote in their report. “There are data sets showing that increased expression of SRRM2 is associated with poor survival in acute myeloid leukemia, renal, and hepatocellular carcinoma.”

The researchers investigated whether SRRM2 could serve as a potential biomarker and immunotherapeutic target in MM by comparing its expression on plasma cells and normal blood cells using flow cytometry. The team also evaluated its correlation with other clinical data and treatment response.

The study included 102 bone marrow or peripheral blood samples from 95 patients with plasma cell disease, including 80 patients with MM. The analysis revealed SRRM2 expression on plasma cells was significantly higher than that on normal blood cells in all subgroups analyzed, and its expression on aberrant plasma cells was higher than that on normal plasma cells. 

We demonstrated that SRRM2 is a novel biomarker for MM and has the potential to serve as a target for immunotherapy in this disease.

SRRM2 expression on plasma cells was correlated with MM treatment response. In patients with newly diagnosed MM, those with high SRRM2 expression (vs SRRM2-negative) had higher levels of serum β2-microglobulin and lactate dehydrogenase, International Staging System stage, and plasma cell infiltration, as well a high-risk mSMART 3.0 stratification and more cytogenetic abnormalities.

Similar results were observed in patients with previous MM. Those with high SRRM2 expression on plasma cells had higher plasma cell infiltration, high-risk mSMART 3.0 risk stratification, more cytogenetic abnormalities, more relapses, and fewer autologous stem cell transplant treatments. 

“In summary, we demonstrated that SRRM2 is a novel biomarker for MM and has the potential to serve as a target for immunotherapy in this disease,” the authors concluded. “The expression level of SRRM2 on plasma cells can aid in risk stratification and monitoring of treatment responses in MM.”

The investigators noted that no significant differences were observed in the expression of some inflammatory and iron metabolism-related markers associated with prognosis in MM between the SRRM2-negative and SRRM2-positive groups, potentially limiting the prognostic relevance of SRRM2 in MM.

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Linvoseltamab Gets Priority Review for Relapsed/Refractory Multiple Myeloma

Source: Myeloma – Hematology Advisor

The Food and Drug Administration (FDA) has accepted for Priority Review the Biologics License Application (BLA) for linvoseltamab for the treatment of adults with relapsed/refractory multiple myeloma.

The BLA is supported by data from the phase 1/2 LINKER-MM1 trial ( Identifier: NCT03761108), which evaluated linvoseltamab, a B-cell maturation antigen (BCMA) CD3-targeted bispecific antibody, in patients with relapsed/refractory multiple myeloma who have progressed after at least 3 prior lines of therapy including a proteasome inhibitor, an immunomodulatory drug, and an anti-CD38 antibody. 

Data from 117 evaluable patients (27% of whom were over 75 years old) showed that at a median duration of follow-up of 11 months, the objective response rate was 71%, with 46% of patients achieving a complete response or better. Patients who achieved a very good partial response or better after at least 24 weeks of treatment were allowed to switch from every 2 week dosing to every 4 week dosing. 

As for safety, 85% of patients treated with linvoseltamab experienced a grade 3 or higher adverse event. The most common adverse event reported was cytokine release syndrome (46%). Adjudicated immune effector cell-associated neurotoxicity syndrome (ICANS) events occurred in 9 patients (8% all grades), of which 3 patients experienced grade 3 ICANS.

A Prescription Drug User Fee Act target date of August 22, 2024 has been set for this application.

Regeneron is currently enrolling patients with relapsed/refractory multiple myeloma in the confirmatory phase 3 LINKER-MM3 trial ( Identifier: NCT05730036), which is comparing the efficacy and safety of linvoseltamab to a combination therapy consisting of elotuzumab, pomalidomide, and dexamethasone.

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Social Support Appears to Affect HRQoL Among Patients With Multiple Myeloma

Source: Myeloma – Hematology Advisor

Screening for social support and psycho-oncological care interventions may be beneficial for patients with multiple myeloma (MM) or its precursor diseases, such as monoclonal gammopathy of undetermined significance, according to research published in the Journal of Cancer Research and Clinical Oncology.

There has been an increased clinical focus on health-related quality of life (HRQoL) in this patient population, and among patients with the precursor diseases to MM, which include monoclonal gammopathy of undetermined significance and smoldering MM. HRQoL, moreover, has been linked with survival outcomes in multiple cancer settings, highlighting the need for improving it across patient populations.

Whether social support may have a positive or negative effect — depending on the form of interaction — on HRQoL was previously unestablished. For this survey-based study, researchers aimed to evaluate the effects of social support on HRQoL among patients with MM or its precursor diseases.

In the future, the influence of social support on the HRQoL of MM patients should also be investigated in longitudinal studies.

Overall, of 170 patients were approached for enrollment and survey data from 126 were included. In this cohort, the mean age was 64.1 years, 56.7% of patients were male sex, and 81% of patients were married or in a partnership. Most (77%) patients had MM. All patients were sent the EORTC QLQ-C30, EORTC QLQ-MY20, and Illness-specific Social Support Scale questionnaires for completion to assess the effects of social support on HRQoL.

Multiple linear regression analysis suggested that positive social support benefited both patient emotional function (beta, 0.323; P <.001) and social function (beta, 0.251; P =.007). Negative social interactions, however, appeared to damage social function (beta, –0.209; P =.027) and increase the risk of treatment side effects (beta, 0.266; P =.004).

“In the future, the influence of social support on the HRQoL of MM patients should also be investigated in longitudinal studies,” the authors wrote in their report. “This would provide important information over the course of the disease.”

Disclosures: Some study authors declared affiliations with biotech, pharmaceutical, or device companies. Please see the original reference for a full list of disclosures.

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The Role of Physician-AI Interaction in the Treatment of Multiple Myeloma

Source: Myeloma – Hematology Advisor

The treatment paradigm for multiple myeloma has shifted in recent years. Notably, the gap between clinical trials and real-world practice continues to expand.

A drawback of randomized controlled trials (RCTs) is their inability to address every clinical scenario — patient care is often far more nuanced and complex. With the advent of artificial intelligence (AI) technologies, the integration of machine learning models in clinical decision support systems (CDSS) could be a potential solution to this problem.

In a report published in Blood, Barbara D. Lam, MD, of the department of medicine at Beth Israel Deaconess Medical Center in Boston, Massachusetts, and colleagues developed a CDSS that displays simulated survival and adverse event data from a clinical trial and machine learning model.

In a pilot study, Dr Lam and her colleagues evaluated how physicians utilize the available data to make treatment decisions for patients with multiple myeloma.

Testing the System

To test the system, physicians were recruited from the internal medicine and hematology-oncology departments at an academic medical center. They were presented with varying combinations of RCT and machine learning data in increasing “tiers” of information for 12 patients with multiple myeloma.

In tier 1, only RCT data was presented. In tier 2, participants were shown outcomes of a machine learning model, and in tier 3, they were provided with information about how the machine learning model was trained and validated. 

At each tier, clinicians were asked to choose a treatment (“red pill” or “blue pill”), rate their confidence in treatment on a scale from 1-10, and when machine learning data was available, rate their perceived reliability of the model.

Out of 284 physicians who were invited to participate, 32 (11.3%) took part in the study. Among the participants, 50% were internal medicine residents and 50% were hematology-oncology fellows and attendings. Most were White (69.0%), male (72.0%),  and all were less than 40 years of age.

Participants preferred the treatment that demonstrated a survival benefit, regardless of whether it was supported by RCT data or [a machine learning] model.

Across various clinical scenarios, a few trends were observed. “Confidence in treatment was highest when RCT and [machine learning] findings were concordant,” the study authors wrote in their report. “Participants preferred the treatment that demonstrated a survival benefit, regardless of whether it was supported by RCT data or [a machine learning] model.” This was the case even before participants learned how the model was trained or validated.

Finally, participants chose the treatment that showed a survival benefit, regardless of whether it was supported by RCT data or a machine learning model.

Results in Context 

Overall, there has been limited investigation into how clinicians reconcile RCT and machine learning data, especially when the results are conflicting. Larger prospective randomized trials are necessary to bring more clarity to this question.

Undoubtedly, the integration of machine learning models into modern CDSS is intriguing and may offer a new path towards precision oncology. Dr Lam and her colleagues have showcased a prime example in this single-center pilot experience involving patients with multiple myeloma.

A broader question is how to best implement CDSS into clinical workflows. The sample size of the existing research is quite small, and some have questioned the clinical efficiency of such systems in their present state. Despite these difficulties, there is still ample opportunity to further develop and improve the utility of these systems.2

Notably, CDSS are currently unable to replace oncologists. The value of these systems lies in their ability to support clinical decision making and train young physicians.2-3

Even if AI technology provides treatment suggestions, the most appropriate treatment needs to consider the patient’s physical and mental wellbeing, financial status, complications, and willingness to receive such treatment.2,4

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Recent Publications

KDM6A Regulates Immune Response Genes in Multiple Myeloma

bioRxiv [Preprint]. 2024 Feb 12:2024.02.12.579179. doi: 10.1101/2024.02.12.579179. ABSTRACT The histone H3K27 demethylase KDM6A is a tumor suppressor in multiple cancers, including multiple myeloma (MM). We created isogenic MM cells disrupted for KDM6A and tagged the endogenous protein to...