Integrative Analysis of Gene Expression Through One-Class Logistic Regression Machine Learning Identifies Stemness Features in Multiple Myeloma

Targeting cancer stem cells in multiple myeloma

Multiple myeloma (MM) is a hematological malignancy of bone marrow (BM) plasma cells with excessive clonal expansion and is associated with the overproduction of light-chain or monoclonal immunoglobulins (Igs). MM remains incurable, with high rates of relapses and refractory disease after first-line treatment. Cancer stem cells (CSCs) have been implicated in drug resistance in MM; however, the evidence for CSCs in MM is not adequate, partly due to a lack of uniformity in the definitions of multiple myeloma stem cells (MMSCs). We review advances in understanding MMSCs and their role in drug resistance to MM therapies. We also discuss novel therapeutic strategies to overcome MMSC-mediated relapses and drug resistance.

The role of staging in multiple myeloma

INTRODUCTION

The importance of cancer staging is determined by how accurately it can predict prognosis, and how useful it is for treatment decisions. Compared to other malignancies, multiple myeloma (MM) staging proved more challenging because of unreliable prognostic factors and wide-ranging life expectancy. As traditional MM staging continues to evolve, it requires reassessment of its prognostic and predictive value.

AREAS COVERED

The studies that included prognostic and predictive value of MM stages from 1975 through 2023 were selected for this review using PubMed, MEDLINE platforms. The history and evolution of MM staging are revisited, including its role in predicting survival, treatment planning and potential practical implications for the future. The role of MM staging for oncological practice and patient counseling is discussed.

EXPERT OPINION

The utility of the traditional MM staging remains unsatisfactory because it lacks a strong connection with the disease biology, prognosis or treatment planning. Additionally, it demonstrates a modest value for patient counseling because individual prognosis is subject to under- or overestimation, and the median survival or survival rates are difficult concepts to grasp. Although the role of MM stages may change in the future, the current research upholds the notion that MM staging benefits more medical research and clinical trials than oncological practice.

Machine Learning and Deep Learning Applications in Multiple Myeloma Diagnosis, Prognosis, and Treatment Selection

Simple Summary

Multiple myeloma is a malignant neoplasm of plasma cells with complex pathogenesis. With major progresses in multiple myeloma research, it is essential that we reconsider our methods for diagnosing and monitoring multiple myeloma disease. This fact needs the integration of serology, histology, radiology, and genetic data; therefore, multiple myeloma study has generated massive quantities of granular high-dimensional data exceeding human understanding. With improved computational techniques, artificial intelligence tools for data processing and analysis are becoming more and more relevant. Artificial intelligence represents a wide set of algorithms for which machine learning and deep learning are presently among the most impactful. This review focuses on artificial intelligence applications in multiple myeloma research, first illustrating machine learning and deep learning procedures and workflow, followed by how these algorithms are used for multiple myeloma diagnosis, prognosis, bone lesions identification, and evaluation of response to the treatment.

Abstract

Artificial intelligence has recently modified the panorama of oncology investigation thanks to the use of machine learning algorithms and deep learning strategies. Machine learning is a branch of artificial intelligence that involves algorithms that analyse information, learn from that information, and then employ their discoveries to make abreast choice, while deep learning is a field of machine learning basically represented by algorithms inspired by the organization and function of the brain, named artificial neural networks. In this review, we examine the possibility of the artificial intelligence applications in multiple myeloma evaluation, and we report the most significant experimentations with respect to the machine and deep learning procedures in the relevant field. Multiple myeloma is one of the most common haematological malignancies in the world, and among them, it is one of the most difficult ones to cure due to the high occurrence of relapse and chemoresistance. Machine learning- and deep learning-based studies are expected to be among the future strategies to challenge this negative-prognosis tumour via the detection of new markers for their prompt discovery and therapy selection and by a better evaluation of its relapse and survival.