Proteomics-inspired precision medicine for treating and understanding multiple myeloma

Efficacy and Safety of Anti-CD38 Monoclonal Antibodies in Patients with Relapsed or Refractory Multiple Myeloma: A Meta-Analysis of Randomized Clinical Trials

The benefit of associating anti-CD38 monoclonal antibodies to proteasome inhibitor (PI)/immunomodulatory agent (IA) and dexamethasone in the treatment of patients with relapsed or refractory multiple myeloma (MM) remains unclear. PubMed, Embase, and Cochrane Library databases were searched for randomized controlled trials that investigated the addition of anti-CD38 monoclonal antibodies to a therapy composed of PI/IA and dexamethasone versus PI/IA and dexamethasone alone for treating relapsed or refractory MM. Hazard ratios (HRs) or risk ratios (RRs) were computed for binary endpoints, with 95% confidence intervals (CIs). Six studies comprising 2191 patients were included. Anti-CD38 monoclonal antibody significantly improved progression-free survival (HR 0.52; 95% CI 0.43-0.61; p < 0.001) and overall survival (HR 0.72; 95% CI 0.63-0.83; p < 0.001). There was a significant increase in hematological adverse events, such as neutropenia (RR 1.41; 95% CI 1.26-1.58; p < 0.01) and thrombocytopenia (RR 1.14; 95% CI 1.02-1.27; p = 0.02), in the group treated with anti-CD38 monoclonal antibody. Also, there was a significant increase in non-hematological adverse events, such as dyspnea (RR 1.72; 95% CI 1.38-2.13; p < 0.01) and pneumonia (RR 1.34; 95% CI 1.13-1.59; p < 0.01), in the group treated with anti-CD38 monoclonal antibody. In conclusion, the incorporation of an anti-CD38 monoclonal antibody demonstrated a promising prospect for reshaping the established MM treatment paradigms.

Pain Management in Multiple Myeloma Patients: A Literature Review

Managing pain in cancer patients with multiple myeloma (MM) poses a considerable challenge. This review thoroughly investigates current pain management strategies, difficulties, and future directions in the field. The review divides pain treatment strategies into pharmaceutical and non-pharmacological therapies. Looking ahead, promising areas for future study and development are mentioned, such as incorporating precision medicine into pain management and investigating innovative therapeutics. Despite existing limitations, advances in pain management provide great opportunities to improve the quality of life and overall results for MM patients.

Proteomics analysis of serum and urine identifies VCP and CTSA as potential biomarkers associated with multiple myeloma

AIMS

We analyzed the differentially expressed proteins (DEPs) in serum and urine in order to provide new potential biomarkers for MM.

METHODS

Data-Independent Acquisition-based proteomics of serum and urine was performed to identify potential biomarkers for MM patients. Then we performed Western Blotting (WB), ELISA along with their ROC curve analysis to confirm DEPs.

RESULTS

A total of 1653 proteins in serum and 4519 proteins in urine were identified using Data-Dependent Acquisition method. VCP was the only protein that showed significant differences in different comparison groups in both serum and urine. Pathway analysis revealed that protein processing in the endoplasmic reticulum was the most relevant pathway associated with MM. Furthermore, the increased expression of HSP90B1, VCP, CTSA, HYOU1, PDIA4, and RAB7A was detected by WB. The results of ELISA indicated that a combination of VCP and CTSA provided a high area under curve (AUC) value of 0.883 (95 % CI, 0.769-0.997, p < 0.001) to diagnose NDMM. The combination of VCP, CTSA, ALB, and HGB exhibited better performance (AUC = 0.981), with 100 % specificity and 86.7 % sensitivity.

CONCLUSION

These findings suggest VCP and CTSA exhibit potential as biomarkers for MM, which may be helpful in the molecular mechanisms and pathogenesis upon further investigation.

Using Proteomics Data to Identify Personalized Treatments in Multiple Myeloma: A Machine Learning Approach

This paper describes a machine learning (ML) decision support system to provide a list of chemotherapeutics that individual multiple myeloma (MM) patients are sensitive/resistant to, based on their proteomic profile. The methodology used in this study involved understanding the parameter space and selecting the dominant features (proteomics data), identifying patterns of proteomic profiles and their association to the recommended treatments, and defining the decision support system of personalized treatment as a classification problem. During the data analysis, we compared several ML algorithms, such as linear regression, Random Forest, and support vector machines, to classify patients as sensitive/resistant to therapeutics. A further analysis examined data-balancing techniques that emerged due to the small cohort size. The results suggest that utilizing proteomics data is a promising approach for identifying effective treatment options for patients with MM (reaching on average an accuracy of 81%). Although this pilot study was limited by the small patient cohort (39 patients), which restricted the training and validation of the explored ML solutions to identify complex associations between proteins, it holds great promise for developing personalized anti-MM treatments using ML approaches.

Proteomic Alteration in the Progression of Multiple Myeloma: A Comprehensive Review

Multiple myeloma (MM) is an incurable hematologic malignancy. Most MM patients are diagnosed at a late stage because the early symptoms of the disease can be uncertain and nonspecific, often resembling other, more common conditions. Additionally, MM patients are commonly associated with rapid relapse and an inevitable refractory phase. MM is characterized by the abnormal proliferation of monoclonal plasma cells in the bone marrow. During the progression of MM, massive genomic alterations occur that target multiple signaling pathways and are accompanied by a multistep process involving differentiation, proliferation, and invasion. Moreover, the transformation of healthy plasma cell biology into genetically heterogeneous MM clones is driven by a variety of post-translational protein modifications (PTMs), which has complicated the discovery of effective treatments. PTMs have been identified as the most promising candidates for biomarker detection, and further research has been recommended to develop promising surrogate markers. Proteomics research has begun in MM, and a comprehensive literature review is available. However, proteomics applications in MM have yet to make significant progress. Exploration of proteomic alterations in MM is worthwhile to improve understanding of the pathophysiology of MM and to search for new treatment targets. Proteomics studies using mass spectrometry (MS) in conjunction with robust bioinformatics tools are an excellent way to learn more about protein changes and modifications during disease progression MM. This article addresses in depth the proteomic changes associated with MM disease transformation.

Multi-omics tumor profiling technologies to develop precision medicine in multiple myeloma

Multiple myeloma (MM), the second most common hematologic cancer, is caused by accumulation of aberrant plasma cells in the bone marrow. Its molecular causes are not fully understood and its great heterogeneity among patients complicates therapeutic decision-making. In the past decades, development of new therapies and drugs have significantly improved survival of MM patients. However, resistance to drugs and relapse remain the most common causes of mortality and are the major challenges to overcome. The advent of high throughput omics technologies capable of analyzing big amount of clinical and biological data has changed the way to diagnose and treat MM. Integration of omics data (gene mutations, gene expression, epigenetic information, and protein and metabolite levels) with clinical histories of thousands of patients allows to build scores to stratify the risk at diagnosis and predict the response to treatment, helping clinicians to make better educated decisions for each particular case. There is no doubt that the future of MM treatment relies on personalized therapies based on predictive models built from omics studies. This review summarizes the current treatments and the use of omics technologies in MM, and their importance in the implementation of personalized medicine.

Multi-omics tumor profiling technologies to develop precision medicine in multiple myeloma

Multiple myeloma (MM), the second most common hematologic cancer, is caused by accumulation of aberrant plasma cells in the bone marrow. Its molecular causes are not fully understood and its great heterogeneity among patients complicates therapeutic decision-making. In the past decades, development of new therapies and drugs have significantly improved survival of MM patients. However, resistance to drugs and relapse remain the most common causes of mortality and are the major challenges to overcome. The advent of high throughput omics technologies capable of analyzing big amount of clinical and biological data has changed the way to diagnose and treat MM. Integration of omics data (gene mutations, gene expression, epigenetic information, and protein and metabolite levels) with clinical histories of thousands of patients allows to build scores to stratify the risk at diagnosis and predict the response to treatment, helping clinicians to make better educated decisions for each particular case. There is no doubt that the future of MM treatment relies on personalized therapies based on predictive models built from omics studies. This review summarizes the current treatments and the use of omics technologies in MM, and their importance in the implementation of personalized medicine.