Artificial intelligence and machine learning (ML) promise to transform cancer therapies by accurately predicting the most appropriate therapies to treat individual patients. Here, we present an approach, named Drug Ranking Using ML (DRUML), which uses omics data to produce ordered lists of >400 drugs based on their anti-proliferative efficacy in cancer cells. To reduce noise and increase predictive robustness, instead of individual features, DRUML uses internally normalized distance metrics of drug response as features for ML model generation. DRUML is trained using in-house proteomics and phosphoproteomics data derived from 48 cell lines, and it is verified with data comprised of 53 cellular models from 12 independent laboratories. We show that DRUML predicts drug responses in independent verification datasets with low error (mean squared error < 0.1 and mean Spearman’s rank 0.7). In addition, we demonstrate that DRUML predictions of cytarabine sensitivity in clinical leukemia samples are prognostic of patient survival (Log rankp < 0.005). Our results indicate that DRUML accurately ranks anti-cancer drugs by their efficacy across a wide range of pathologies.

Altered metabolism is a defining hallmark of cancer. Metabolic adaptations are often linked to a reprogramming of the mitochondria due to the importance of these organelles in energy production and biosynthesis. Cancer cells present heterogeneous metabolic phenotypes that can be modulated by signals originating from the tumor microenvironment. Extracellular vesicles (EVs) are recognized as key players in intercellular communications and mediate many of the hallmarks of cancer via the delivery of their diverse biological cargo molecules. Firstly, this review introduces the most characteristic changes that the EV-biogenesis machinery and mitochondria undergo in the context of cancer. Then, it focuses on the EV-driven processes which alter mitochondrial structure, composition, and function to provide a survival advantage to cancer cells in the context of the hallmarks of cancers, such as altered metabolic strategies, migration and invasiveness, immune surveillance escape, and evasion of apoptosis. Finally, it explores the as yet untapped potential of targeting mitochondria using EVs as delivery vectors as a promising cancer therapeutic strategy.

Coronavirus disease 2019 (COVID‐19) caused by a novel coronavirus called severe acute respiratory syndrome coronavirus‐2 (SARS‐CoV‐2) is driving a present day global pandemic. Immunosuppressed patients are regarded as a high‐risk cohort. The following is a short report on COVID‐19 in liver transplant recipients (n = 5) from a high volume UK liver transplant unit with a large follow‐up cohort (n = 4500). Based on this limited data, liver transplant recipients appear to have a low incidence of COVID‐19, with less severe symptoms than expected, when compared with the general population and other solid organ recipients. This possibly could be related to self‐isolation adherence and/or the ‘ideal’ level of immunosuppression that favourably modulates the immune response to COVID‐19.

The aim of the study was to assess the UK DCD liver transplant experience from donors ≥70 years. Nationwide UK DCD retrospective analysis, period 2001‐2015 (n=1163). Recipients were divided into group1 vs. group2 (donors 70≥ vs. <70 years, respectively). Group1 (n=69, 5.9%) recipients were older (median 59 vs. 55 years, P=0.001) and had longer wait list time (128 vs. 84 days; P=0.039). 94.2% of group1 clustered in London and Birmingham, where the two busiest centers are located. Group1 allografts had higher UKDRI and UK DCD Risk Scores but similar WIT and CIT and were more likely to have been imported. Both groups had similar 1‐, 3‐ and 5‐year graft survival (group1, 90%, 81.4% and 74% vs. group2, 88.6%, 81.4% and 78.6 %, respectively; P= 0.54). Both groups had similar ICU stay length (P=0.22), 3‐month hepatic artery thrombosis rates (4.4% vs 4.0%; P=0.9), and 12‐month readmission rates for all biliary complications (20.3% vs 25.7%; P=0.32). This study demonstrates that acceptable outcomes are achievable using older grafts in a highly selected cohort at experienced centers. Advanced age should not be an absolute contraindication to utilizing a DCD graft from donors aged ≥70 years.