Date of Award

18-6-2025

Document Type

Doctoral Thesis

Degree Name

Doctor of Philosophy

First Advisor

Dr. Vitaliy Mykytiv

Second Advisor

Dr. Fiona O'Halloran

Abstract

Multiple Myeloma (MM) remains an incurable haematological malignancy; however, continuous advancements in treatment strategies have significantly improved patient survival. There is now a pressing need for enhanced detection methods to ensure optimal patient outcomes. This includes rapid diagnosis, early detection of relapses, and identification of high-risk patients. Diagnosis: For optimal patient results, initiating early MM diagnosis at the primary care level is essential. Delays in diagnosis can result from nonspecific presenting symptoms such as anaemia and bone pain. A retrospective observational study identified calculated globulin as a promising clinical biomarker to indicate MM in primary care. Subsequent implementation of calculated globulin in the study site significantly improved the referral pathway for newly diagnosed MM patients. Monitoring: Following treatment, MM relapse can occur due to residual malignant cells, known as measurable residual disease (MRD). Blood-based methods for monitoring MM MRD are gaining interest as a less invasive alternative to bone marrow (BM) assays. Detecting MRD in MM requires highly sensitive methods, particularly when examining PB. Following method optimisation, serial BM and PB samples were analysed over a two-year period from MM patients in biochemical remission (n=45). The samples were analysed for malignant plasma cells in BM and circulating tumour plasma cells (CTPCs), total immunoglobulins, paraproteins, and serum-free light chains in PB. Clinical data was evaluated in conjunction with these findings to determine if CTPCs, alternative paraprotein assays, or immunoparesis in PB can be clinically useful indicators for MM relapse. Risk Stratification: MM is preceded by an asymptomatic stage called Smouldering Multiple Myeloma. Several risk stratification models are currently used in clinical practice to identify those at high risk of rapid disease progression. This study investigated whether CTPCs and immunoparesis levels correspond with these models and if incorporating these factors could improve their predictive capabilities

Access Level

info:eu-repo/semantics/openAccess

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