Advances in Molecular Pathogenesis and Targeted Therapies for Multiple Myeloma

Dear Colleagues,

We are delighted to announce a call for submissions to a Topic on the theme of “Advances in Molecular Pathogenesis and Targeted Therapies for Multiple Myeloma”.

Multiple myeloma (MM) is the second most common hematological malignancy globally, and despite enormous progress in its treatment armamentarium, MM remains incurable. Among the main limitations and barriers to cure are the heterogeneity of phase III studies and the absence of real-world data to address its considerable clinical and molecular heterogeneity, precluding its accurate prognostication and the development of optimal personalized treatment strategies. Current prognostication models (most commonly R-ISS) incorporate clinical and molecular features for risk stratification, with well-recognized knowledge gaps and technical limitations with current methodologies, and do not incorporate adverse non-genomic prognostic features including extramedullary disease and circulating tumor cells. To enable clinicians to select the optimal risk-adapted therapy for individual patients, there is a critical need to integrate all of the relevant clinical, biological, and molecular features, which can potentially be achieved with evolving technologies such as artificial intelligence (AI). Within this Topic, we outline recent advances in our understanding of the molecular pathogenesis of MM, focusing on the improvement of risk stratification and the integration of complex information and emerging targeted therapies to bring us closer to a future where personalized treatment for individual patients may become the new standard of care of patients with MM.

Topics may include but are not limited to the following:

• The application of genomics and proteomics in the understanding of MM pathogenesis and its resistance mechanisms, with the identification of novel therapeutic targets and biomarker-driven treatment strategies.
• The role of MM microbiome in identifying future biomarkers or therapeutic agents
• The use of bioinformatics and artificial intelligence including machine learning in the integration of big data, multi-omics, and the development of predictive models in the management of MM patients
• Precision medicine strategies for patients at high risk of progression, including early intervention and personalized therapeutic approaches
• Clinical implications of predicting survival and progression risk for patient management and outcomes, including risk-adapted treatment strategies and quality of life measures.

We encourage the submission of both original research articles and reviews. All submitted articles will be considered to undergo peer review.

Dr. Chung Hoow Kok
Dr. Cindy H. S. Lee
Dr. Claudio Cerchione
Topic Editors