Expression quantitative trait loci of genes predicting outcome are associated with survival of multiple myeloma patients

Multiple myeloma

Multiple myeloma (MM) is a haematological lymphoid malignancy involving tumoural plasma cells and is usually characterized by the presence of a monoclonal immunoglobulin protein. MM is the second most common haematological malignancy, with an increasing global incidence. It remains incurable because most patients relapse or become refractory to treatments. MM is a genetically complex disease with high heterogeneity that develops as a multistep process, involving acquisition of genetic alterations in the tumour cells and changes in the bone marrow microenvironment. Symptomatic MM is diagnosed using the International Myeloma Working Group criteria as a bone marrow infiltration of ≥10% clonal plasma cells, and the presence of at least one myeloma-defining event, either standard CRAB features (hypercalcaemia, renal failure, anaemia and/or lytic bone lesions) or biomarkers of imminent organ damage. Younger and fit patients are considered eligible for transplant. They receive an induction, followed by consolidation with high-dose melphalan and autologous haematopoietic cell transplantation, and maintenance therapy. In older adults (ineligible for transplant), the combination of daratumumab, lenalidomide and dexamethasone is the preferred option. If relapse occurs and requires further therapy, the choice of therapy will be based on previous treatment and response and now includes immunotherapies, such as bi-specific monoclonal antibodies and chimeric antigen receptor T cell therapy.

Utilizing 3D Models to Unravel the Dynamics of Myeloma Plasma Cells' Escape from the Bone Marrow Microenvironment

Recent therapeutic advancements have markedly increased the survival rates of individuals with multiple myeloma (MM), doubling survival compared to pre-2000 estimates. This progress, driven by highly effective novel agents, suggests a growing population of MM survivors exceeding the 10-year mark post-diagnosis. However, contemporary clinical observations indicate potential trends toward more aggressive relapse phenotypes, characterized by extramedullary disease and dominant proliferative clones, despite these highly effective treatments. To build upon these advances, it is crucial to develop models of MM evolution, particularly focusing on understanding the biological mechanisms behind its development outside the bone marrow. This comprehensive understanding is essential to devising innovative treatment strategies. This review emphasizes the role of 3D models, specifically addressing the bone marrow microenvironment and development of extramedullary sites. It explores the current state-of-the-art in MM modelling, highlighting challenges in replicating the disease's complexity. Recognizing the unique demand for accurate models, the discussion underscores the potential impact of these advanced 3D models on understanding and combating this heterogeneous and still incurable disease.

FASTK family of genes linked to cancer

Fas Activated Serine/Threonine Kinase (FASTK) family is a protein family encoded in the nuclear genome that spans the mitochondria and executes numerous functions, and consists of FASTK, the founding member along with 5 homologous proteins FASTKD1-5. Up regulation of FASTK family members have not only been implicated in tumour progression and invasion but also in increased resistance to chemotherapy proven by their knockdown leading to increased sensitivity to drugs. Thus, this review reports the implication of FASTK proteins in cancer and hence provides a scope to emphasise the role of these proteins in Oral Cancer.

Personalized 3-Gene Panel for Prostate Cancer Target Therapy

Many years and billions spent for research did not yet produce an effective answer to prostate cancer (PCa). Not only each human, but even each cancer nodule in the same tumor, has unique transcriptome topology. The differences go beyond the expression level to the expression control and networking of individual genes. The unrepeatable heterogeneous transcriptomic organization among men makes the quest for universal biomarkers and “fit-for-all” treatments unrealistic. We present a bioinformatics procedure to identify each patient’s unique triplet of PCa Gene Master Regulators (GMRs) and predict consequences of their experimental manipulation. The procedure is based on the Genomic Fabric Paradigm (GFP), which characterizes each individual gene by the independent expression level, expression variability and expression coordination with each other gene. GFP can identify the GMRs whose controlled alteration would selectively kill the cancer cells with little consequence on the normal tissue. The method was applied to microarray data on surgically removed prostates from two men with metastatic PCas (each with three distinct cancer nodules), and DU145 and LNCaP PCa cell lines. The applications verified that each PCa case is unique and predicted the consequences of the GMRs’ manipulation. The predictions are theoretical and need further experimental validation.