The serine/threonine kinase MINK1 directly regulates the function of promigratory proteins.

Upregulation of the developmental Wnt planar cell polarity (Wnt/PCP) pathway is observed in many cancers and is associated with cancer development. We have recently shown that PRICKLE1, a core Wnt/PCP pathway component, is a marker of poor prognosis in triple-negative breast cancer (TNBC). PRICKLE1 is phosphorylated by the serine/threonine kinase MINK1 and contributes to TNBC cell motility and invasiveness. However, the identity of the substrates of MINK1 and the role of MINK1 enzymatic activity in this process remain to be addressed. We used a phosphoproteomic strategy to identify MINK1 substrates, including LL5ß (also known as PHLDB2). LL5ß anchors microtubules at the cell cortex through its association with CLASP proteins to trigger focal adhesion disassembly. LL5ß is phosphorylated by MINK1, promoting its interaction with CLASP proteins. Using a kinase inhibitor, we demonstrate that the enzymatic activity of MINK1 is involved in PRICKLE1-LL5ß complex assembly and localization, as well as in cell migration. Analysis of gene expression data reveals that the concomitant upregulation of levels of mRNA encoding PRICKLE1 and LL5ß, which are MINK1 substrates, is associated with poor metastasis-free survival in TNBC patients. Taken together, our results suggest that MINK1 may represent a potential target for treatment of TNBC.

Patients' perceptions of the definition of a textbook outcome following liver transplantation.

BACKGROUND: A textbook outcome (TO) describes the results of a successful liver transplantation (LT) in which all aspects of the LT and posttransplant courses were uneventful. We compared patient perceived experience of a TO with clinically defined TO. METHODS: This was a single-institution cohort study with retrospective chart review including patients who underwent LT from 2019 to 2021. Patients were asked to complete the survey at a scheduled posttransplant visit. The survey was designed to assess their viewpoints on the definition of a TO. A clinically defined TO was defined as no mortality, no severe complications, no need for reintervention, no prolonged hospital and intensive care unit stays, and no readmission. RESULTS: Of the 182 patients who were contacted, 132 (72.5%) completed the survey. Overall, 98 patients (74%) considered that they had experienced a TO. The clinically defined TO rate was 22.0%. Multivariate analysis showed that patients who did not experience severe complications were more likely to consider that they had a TO (P = 0.01; odds ratio: 3.2; 95% confidence interval: 1.3-7.9). CONCLUSIONS: From patients' perspectives, survival and avoidance of complications were the major characteristics of a TO.

Role of AxyZ Transcriptional Regulator in Overproduction of AxyXY-OprZ Multidrug Efflux System in Achromobacter Species Mutants Selected by Tobramycin.

AxyXY-OprZ is an RND-type efflux system that confers innate aminoglycoside resistance to Achromobacter spp. We investigated here a putative TetR family transcriptional regulator encoded by the axyZ gene located upstream of axyXY-oprZ An in-frame axyZ gene deletion assay led to increased MICs of antibiotic substrates of the efflux system, including aminoglycosides, cefepime, fluoroquinolones, tetracyclines, and erythromycin, indicating that the product of axyZ negatively regulates expression of axyXY-oprZ Moreover, we identified an amino acid substitution at position 29 of AxyZ (V29G) in a clinical Achromobacter strain that occurred during the course of chronic respiratory tract colonization in a cystic fibrosis (CF) patient. This substitution, also detected in three other strains exposed in vitro to tobramycin, led to an increase in the axyY transcription level (5- to 17-fold) together with an increase in antibiotic resistance level. This overproduction of AxyXY-OprZ is the first description of antibiotic resistance acquisition due to modification of a chromosomally encoded mechanism in Achromobacter and might have an impact on the management of infected CF patients. Indeed, tobramycin is widely used for aerosol therapy within this population, and we have demonstrated that it easily selects mutants with increased MICs of not only aminoglycosides but also fluoroquinolones, cefepime, and tetracyclines.

GDNF/GFRA1 signaling contributes to chemo- and radioresistance in glioblastoma.

Glioblastoma is the most common primary brain tumor in adults, characterized by an inherent aggressivity and resistance to treatment leading to poor prognoses. While some resistance mechanisms have been elucidated, a deeper understanding of these mechanisms is needed to increase therapeutic efficacy. In this study we first discovered glial-cell derived neurotrophic factor (GDNF) to be upregulated in patient-derived glioblastoma spheroid cultures after chemotherapeutic temozolomide treatment, through RNA-Seq experiments. Therefore, we investigated the role of the GDNF/GDNF receptor alpha 1 (GFRA1) signaling pathway as a resistance mechanism to chemotherapy with temozolomide and lomustine, as well as irradiation using patient-derived glioblastoma spheroid cultures. With qPCR experiments we showed a consistent upregulation of GDNF and its primary receptor GFRA1 following all three lines of treatment. Moreover, CRISPR/Cas9 knock-outs of GDNF in two patient-derived models sensitized these cells to chemotherapy treatment, but not radiotherapy. The increased sensitivity was completely reversed by the addition of exogeneous GDNF, confirming the key role of this factor in chemoresistance. Finally, a CRISPR KO of GFRA1 demonstrated a similar increased sensitivity to temozolomide and lomustine treatment, as well as radiotherapy. Together, our findings support the role of the GDNF/GFRA1 signaling pathway in glioblastoma chemo and radioresistance.

Combination drug screen targeting glioblastoma core vulnerabilities reveals pharmacological synergisms.

BACKGROUND: Pharmacological synergisms are an attractive anticancer strategy. However, with more than 5000 approved-drugs and compounds in clinical development, identifying synergistic treatments represents a major challenge. METHODS: High-throughput screening was combined with target deconvolution and functional genomics to reveal targetable vulnerabilities in glioblastoma. The role of the top gene hit was investigated by RNA interference, transcriptomics and immunohistochemistry in glioblastoma patient samples. Drug combination screen using a custom-made library of 88 compounds in association with six inhibitors of the identified glioblastoma vulnerabilities was performed to unveil pharmacological synergisms. Glioblastoma 3D spheroid, organotypic ex vivo and syngeneic orthotopic mouse models were used to validate synergistic treatments. FINDINGS: Nine targetable vulnerabilities were identified in glioblastoma and the top gene hit RRM1 was validated as an independent prognostic factor. The associations of CHK1/MEK and AURKA/BET inhibitors were identified as the most potent amongst 528 tested pairwise drug combinations and their efficacy was validated in 3D spheroid models. The high synergism of AURKA/BET dual inhibition was confirmed in ex vivo and in vivo glioblastoma models, without detectable toxicity. INTERPRETATION: Our work provides strong pre-clinical evidence of the efficacy of AURKA/BET inhibitor combination in glioblastoma and opens new therapeutic avenues for this unmet medical need. Besides, we established the proof-of-concept of a stepwise approach aiming at exploiting drug poly-pharmacology to unveil druggable cancer vulnerabilities and to fast-track the identification of synergistic combinations against refractory cancers. FUNDING: This study was funded by institutional grants and charities.

In silico molecular target prediction unveils mebendazole as a potent MAPK14 inhibitor.

The concept of polypharmacology involves the interaction of drug molecules with multiple molecular targets. It provides a unique opportunity for the repurposing of already-approved drugs to target key factors involved in human diseases. Herein, we used an in silico target prediction algorithm to investigate the mechanism of action of mebendazole, an antihelminthic drug, currently repurposed in the treatment of brain tumors. First, we confirmed that mebendazole decreased the viability of glioblastoma cells in vitro (IC50 values ranging from 288 nm to 2.1 µm). Our in silico approach unveiled 21 putative molecular targets for mebendazole, including 12 proteins significantly upregulated at the gene level in glioblastoma as compared to normal brain tissue (fold change > 1.5; P < 0.0001). Validation experiments were performed on three major kinases involved in cancer biology: ABL1, MAPK1/ERK2, and MAPK14/p38α. Mebendazole could inhibit the activity of these kinases in vitro in a dose-dependent manner, with a high potency against MAPK14 (IC50  = 104 ± 46 nm). Its direct binding to MAPK14 was further validated in vitro, and inhibition of MAPK14 kinase activity was confirmed in live glioblastoma cells. Consistent with biophysical data, molecular modeling suggested that mebendazole was able to bind to the catalytic site of MAPK14. Finally, gene silencing demonstrated that MAPK14 is involved in glioblastoma tumor spheroid growth and response to mebendazole treatment. This study thus highlighted the role of MAPK14 in the anticancer mechanism of action of mebendazole and provides further rationale for the pharmacological targeting of MAPK14 in brain tumors. It also opens new avenues for the development of novel MAPK14/p38α inhibitors to treat human diseases.

Predicting Synergism of Cancer Drug Combinations Using NCI-ALMANAC Data.

Drug combinations are of great interest for cancer treatment. Unfortunately, the discovery of synergistic combinations by purely experimental means is only feasible on small sets of drugs. In silico modeling methods can substantially widen this search by providing tools able to predict which of all possible combinations in a large compound library are synergistic. Here we investigate to which extent drug combination synergy can be predicted by exploiting the largest available dataset to date (NCI-ALMANAC, with over 290,000 synergy determinations). Each cell line is modeled using primarily two machine learning techniques, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), on the datasets provided by NCI-ALMANAC. This large-scale predictive modeling study comprises more than 5,000 pair-wise drug combinations, 60 cell lines, 4 types of models, and 5 types of chemical features. The application of a powerful, yet uncommonly used, RF-specific technique for reliability prediction is also investigated. The evaluation of these models shows that it is possible to predict the synergy of unseen drug combinations with high accuracy (Pearson correlations between 0.43 and 0.86 depending on the considered cell line, with XGBoost providing slightly better predictions than RF). We have also found that restricting to the most reliable synergy predictions results in at least 2-fold error decrease with respect to employing the best learning algorithm without any reliability estimation. Alkylating agents, tyrosine kinase inhibitors and topoisomerase inhibitors are the drugs whose synergy with other partner drugs are better predicted by the models. Despite its leading size, NCI-ALMANAC comprises an extremely small part of all conceivable combinations. Given their accuracy and reliability estimation, the developed models should drastically reduce the number of required in vitro tests by predicting in silico which of the considered combinations are likely to be synergistic.