Rapalogs induce non-apoptotic,autophagy-dependent cell death in HPV-negative TP53 mutant head and neck squamous cell carcinoma

TP53 is the most frequently mutated gene in head and neck squamous cell carcinoma (HNSCC). Patients with HPV-negative TP53 mutant HNSCC have the worst prognosis, necessitating additional agents for treatment. Since mutant p53 causes sustained activation of the PI3K/AKT/mTOR signaling pathway, we investigated the effect of rapalogs RAD001 and CCI-779 on HPV-negative mutTP53 HNSCC cell lines and xenografts. Rapalogs significantly reduced cell viability and colony formation. Interestingly, rapalogs-induced autophagy with no effect on apoptosis. Pretreatment with autophagy inhibitors, 3-methyladenine (3-MA) and ULK-101 rescued the cell viability by inhibiting rapalog-induced autophagy, suggesting that both RAD001 and CCI-779 induce non-apoptotic autophagy-dependent cell death (ADCD). Moreover, rapalogs upregulated the levels of ULK1 and pULK1 S555 with concomitant downregulation of the mTORC1 pathway. However, pretreatment of cells with rapalogs prevented the ULK-101-mediated inhibition of ULK1 to sustained autophagy, suggesting that rapalogs induce ADCD through the activation of ULK1. To further translate our in vitro studies, we investigated the effect of RAD001 in HPV-negative mutTP53 (HN31 and FaDu) tumor cell xenograft model in nude mice. Mice treated with RAD001 exhibited a significant tumor volume reduction without induction of apoptosis, and with a concomitant increase in autophagy. Further, treatment with RAD001 was associated with a considerable increase in pULK1 S555 and ULK1 levels through the inhibition of mTORC1. 3-MA reversed the effect of RAD001 on FaDu tumor growth suggesting that RAD001 promotes ACDC in HPV-negative mutTP53 xenograft. This is the first report demonstrating that rapalogs promote non-apoptotic ADCD in HPV-negative mutTP53 HNSCC via the ULK1 pathway. Further studies are required to establish the promising role of rapalogs in preventing the regrowth of HPV-negative mutTP53 HNSCC.     

Seltene Superinfektion bei einem COVID-19-Patienten – Eine Chronologie

Die Anaesthesiologie - Bei einem 55-jährigen Mann, der sich zur elektiven Ablation bei Vorhofflimmern in der Klinik befand, wurde nach einer Reanimationssituation auf der peripheren Station...     

Anxiety Behavior of Parents during Formation of a Defensive Reactions in Offspring Affects EEG Parameters in Pied Flycatcher Chicks

Neuroscience and Behavioral Physiology - Anxiety behavior was studied in adult pied flycatchers raising broods of chicks from early and late broods laid to replace predated clutches; its effect on...     

Das prozessierte EEG zur personalisierten Dosierung von Anästhetika während Allgemeinanästhesie

Die Anaesthesiologie - Die Elektroenzephalogramm(EEG)-gesteuerte Anästhesie ist aus modernen Operationssälen nicht mehr wegzudenken und hat sich als Standard-Monitoring etabliert. Viele...     

Begutachtung von traumatischen Gehörschäden

HNO - Die gutachterliche Beurteilung von traumatischen Schäden im Kopf-Hals-Gebiet gliedert sich grundsätzlich in 2 Fragestellungen: Besteht ein ursächlicher Zusammenhang...     

Machine learning to predict myocardial injury and death after non-cardiac surgery

Myocardial injury due to ischaemia within 30 days of non-cardiac surgery is prognostically relevant. We aimed to determine the discrimination, calibration, accuracy, sensitivity and specificity of single-layer and multiple-layer neural networks for myocardial injury and death within 30 postoperative days. We analysed data from 24,589 participants in the Vascular Events in Non-cardiac Surgery Patients Cohort Evaluation study. Validation was performed on a randomly selected subset of the study population. Discrimination for myocardial injury by single-layer vs. multiple-layer models generated areas (95%CI) under the receiver operating characteristic curve of: 0.70 (0.69–0.72) vs. 0.71 (0.70–0.73) with variables available before surgical referral, p < 0.001; 0.73 (0.72–0.75) vs. 0.75 (0.74–0.76) with additional variables available on admission, but before surgery, p < 0.001; and 0.76 (0.75–0.77) vs. 0.77 (0.76–0.78) with the addition of subsequent variables, p < 0.001. Discrimination for death by single-layer vs. multiple-layer models generated areas (95%CI) under the receiver operating characteristic curve of: 0.71 (0.66–0.76) vs. 0.74 (0.71–0.77) with variables available before surgical referral, p = 0.04; 0.78 (0.73–0.82) vs. 0.83 (0.79–0.86) with additional variables available on admission but before surgery, p = 0.01; and 0.87 (0.83–0.89) vs. 0.87 (0.85–0.90) with the addition of subsequent variables, p = 0.52. The accuracy of the multiple-layer model for myocardial injury and death with all variables was 70% and 89%, respectively.