Assessment of Bone Marrow Biopsy and Cytogenetic Findings in Patients with Multiple Myeloma

Objective

Multiple myeloma (MM) is a malignant condition that is characterized by the accumulation of malignant plasma cells. Although MM remains incurable, the survival of MM patients has improved considerably due to applied autologous stem cell transplantation (ASCT), novel agents, and treatment strategies. This study aimed to determine the cytogenetic characterization and bone marrow features of Turkish patients with MM.

Materials and Methods

Eighty-five MM patients were admitted to the 9 Eylul University Hospital in Turkey. Bone marrow (BM) samples MM patients were subject to cytogenetic analyses on diagnosis and during therapy as part of therapeutical and clinical evaluation. A complete cytogenetic study was performed using the G-banding technique. The Fluorescent in situ hybridization (FISH) analysis was performed using cytoplasmic immunoglobulin (cIg)-FISH. The degree of bone marrow fibrosis was determined using the histochemical stain of reticulin. We determined the percentage of bone marrow plasma cells based on the extent of CD38 staining.

Results

Eighty-five MM patients were retrospectively identified between 2015 and 2021. The median age was 63 (38-90) years. Of the 85 patients, 60 (70.6%) were male, and 25 (29.4%) were female. Seventy-two (84.7%) cases had bone marrow fibrosis at the time of diagnosis. The most common was grade-2 fibrosis, recorded in 35 patients (41.2%). About 72.9% of the patients showed more than 50% plasma cells. The FISH analysis indicated the presence of abnormal chromosomes in 37% (32/85) of the patients. The most frequent abnormality was IGH translocation (21.3%).

Conclusion

Subgroup analysis of IGH mutations is crucial in the identification of high-risk MM patients. We believe that our study will contribute to the determination of bone marrow biopsy and cytogenetic features of MM patients in our country.

Current approaches to management of high-risk multiple myeloma

The median overall survival in multiple myeloma is rapidly approaching 10 years; however, in nearly a fifth of patients the prognosis remains poor. Therefore, the modern-day management of myeloma patients should be individualized, with a more intense and continuous approach in these high-risk patients. This includes first-line treatment based on multi-drug combinations employing the most effective drug combinations, upfront autologous stem cell transplantation (in eligible patients with tandem transplantation being a consideration), and maintenance based on proteasome inhibitor-based combinations. This paper reviews the results of recent retrospective analyses and clinical trials, but also gives a glance into the future by presenting the ongoing trials.

Gene Networks Constructed Through Simulated Treatment Learning can Predict Proteasome Inhibitor Benefit in Multiple Myeloma

PURPOSE

Proteasome inhibitors are widely used in treating multiple myeloma, but can cause serious side effects and response varies among patients. It is, therefore, important to gain more insight into which patients will benefit from proteasome inhibitors.

EXPERIMENTAL DESIGN

We introduce simulated treatment learned signatures (STLsig), a machine learning method to identify predictive gene expression signatures. STLsig uses genetically similar patients who have received an alternative treatment to model which patients will benefit more from proteasome inhibitors than from an alternative treatment. STLsig constructs gene networks by linking genes that are synergistic in their ability to predict benefit.

RESULTS

In a dataset of 910 patients with multiple myeloma, STLsig identified two gene networks that together can predict benefit to the proteasome inhibitor, bortezomib. In class "benefit," we found an HR of 0.47 (P = 0.04) in favor of bortezomib, while in class "no benefit," the HR was 0.91 (P = 0.68). Importantly, we observed a similar performance (HR class benefit, 0.46; P = 0.04) in an independent patient cohort. Moreover, this signature also predicts benefit for the proteasome inhibitor, carfilzomib, indicating it is not specific to bortezomib. No equivalent signature can be found when the genes in the signature are excluded from the analysis, indicating that they are essential. Multiple genes in the signature are linked to working mechanisms of proteasome inhibitors or multiple myeloma disease progression.

CONCLUSIONS

STLsig can identify gene signatures that could aid in treatment decisions for patients with multiple myeloma and provide insight into the biological mechanism behind treatment benefit.