Axitinib had a tolerable safety profile and clinically meaningful activity in refractory and progressive thyroid cancer regardless of histology as first-line therapy. To our knowledge, this is the first time that cross-resistance between MKIs is suggested in thyroid cancer, highlighting the importance of prospective sequential clinical studies.

Cancer is often complicated by venous thromboembolism (VTE), a common and potentially fatal complication associated with poor prognosis in these patients. An increased incidence of VTE is being observed due to the advanced age of cancer patients, the thrombogenic effect of novel drugs and advances in the diagnosis of related complications. In this review, we look at five different risk groups of cancer patients with an increased probability of developing VTE, including hospitalized patients undergoing chemotherapy, patients undergoing a surgical procedure, ambulatory patients undergoing chemotherapy, patients with a central venous access and patients receiving antiangiogenic drugs or anticoagulant therapy due to previous chronic diseases. The aim of this review is to summarize the most important clinical evidence reported to date on the suitability of primary thromboprophylaxis to cancer patients. Recommendations have drawn up for each group based on current evidence and guidelines to facilitate decision-making in clinical practice.

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Triple negative breast cancer accounts for 15%–20% of all breast carcinomas and is clinically characterized by an aggressive phenotype and poor prognosis. Triple negative tumors do not benefit from targeted therapies, so further characterization is needed to define subgroups with potential therapeutic value. In this work, the proteomes of 125 formalin‐fixed paraffin‐embedded samples from patients diagnosed with non‐metastatic triple negative breast cancer were analyzed using data‐independent acquisition + in a LTQ‐Orbitrap Fusion Lumos mass spectrometer coupled to an EASY‐nLC 1000. 1206 proteins were identified in at least 66% of the samples. Hierarchical clustering, probabilistic graphical models and Significance Analysis of Microarrays were combined to characterize proteomics‐based molecular groups. Two molecular groups were defined with differences in biological processes such as glycolysis, translation and immune response. These two molecular groups showed also several differentially expressed proteins. This clinically homogenous dataset may serve to design new therapeutic strategies in the future.