The minimally—or non-invasive detection of circulating tumor-derived components in biofluids, such as blood, liquid biopsy is a revolutionary approach with significant potential for the management of cancer. Genomic and transcriptomic alterations can be accurately detected through liquid biopsies, which provide a more comprehensive characterization of the heterogeneous tumor profile than tissue biopsies alone. Liquid biopsies could assist diagnosis, prognosis, and treatment selection, and hold great potential to complement current surveilling strategies to monitor disease evolution and treatment response in real-time. In particular, these are able to detect minimal residual disease, to predict progression, and to identify mechanisms of resistance, allowing to re-orient treatment strategies in a timelier manner. In this review we gathered current knowledge regarding the role and potential of liquid biopsies for the diagnosis and follow-up of cancer patients. The presented findings emphasize the strengths of liquid biopsies, revealing their chance of improving the diagnosis and monitoring of several tumor types in the near future. However, despite growing evidence supporting their value as a management tool in oncology, some limitations still need to be overcome for their implementation in the routine clinical setting.

microRNAs (miRs) dysregulation have emerged as a crucial step in tumorigenesis, being related with cancer development, progression and response to treatment. In chronic myeloid leukaemia (CML), the resistance to tyrosine kinase inhibitors (TKI) is responsible for treatment failure and could be linked to changes in miRs expression. This work aimed to correlate the expression levels of 3 miRs, miR-21, miR-26b and miR-451, with response to TKI treatment in CML patients. miR-451 levels at diagnosis were significantly higher in patients with optimal response after 6 and 12 months of therapy. Conversely, patients without optimal response had highest levels of miR-21. miR-21 and miR-451 appear to be good biomarkers of response, able to predict optimal TKI responders (p < 0.05). Using the combined profile of both miRs, we create a predictive model of optimal response after one year of treatment. This study highlights the role of miR-21 and miR-451 expression levels at diagnosis in predicting which patients achieve the optimal response.

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