Dr. Luca Agnelli E-Mail
Pathology Department, IRCCS National Cancer Institute (NCI), Milan, Italy
Research Keywords: lymphoid malignancies; bioinformatics and biostatistics in cancer; -omics analyses; NGS; non-coding RNA
Dr. Giancarlo Pruneri E-Mail
Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
The knowledge about cancer has improved on the basis of -omics studies. The integration of genetic information about cancers with data on how the cancers respond to target based-therapy help to define optimum cancer treatment. As a consequence, in the last decades, targeted therapies led to unprecedented clinical benefit in first-line therapies, as well as in patients with aggressive or advanced neoplasms bearing specific genomic alterations (gene mutations, amplifications, translocations, microsatellite instability). Although it would be desirable to increase the number of clinical trials and standardized treatments, several factors influence so far the choice of therapies, including the non-trivial aspect of the cost-effective manageability by the Healthcare National Institutions. For this reason, several non-standard treatments are commonly considered, and off-label drug usage is a common practice in treating cancer, in most cases based on physician choice. This common clinical practice suffers from a lack of knowledge base for proper cancer drug selections. The present issue is aimed at focusing on all the aspects (clinical evidence, procedures, scientific hypothesis, and investigations) involving off-label drugs for cancer treatment in the context of -omics data.
Keywords: Off-label drugs; omics data; real-world cancer treatments; (targeted) NGS; biomarkers
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.