Comparison of tumors from the Cancer Genome Atlas (TCGA) reveals that head and neck squamous cell carcinomas (HNSCC) harbor the most frequent genomic amplifications of Fas-associated death domain (FADD), with or without Baculovirus Inhibitor of Apoptosis repeat containing BIRC2 (cIAP1), affecting ~30% of patients in association with worse prognosis. Here, we identified HNSCC cell lines harboring FADD/BIRC2 amplifications and overexpression by exome sequencing, RT-PCR and Western blot. In vitro, FADD or BIRC2 siRNA knockdown inhibited HNSCC displaying amplification and increased expression of these genes, supporting their functional importance in promoting proliferation. Birinapant, a novel SMAC mimetic, sensitized multiple HNSCC lines to cell death by agonists TNFα or TRAIL, and inhibited cIAP1>XIAP>IAP2. Combination of birinapant and TNFα induced sub-G0 DNA fragmentation in sensitive lines, and birinapant alone also induced significant G2/M cell cycle arrest and cell death in UM-SCC-46 cells. Gene transfer and expression of FADD sensitized resistant UM-SCC-38 cells lacking FADD amplification to birinapant and TNFα, supporting a role for FADD in sensitization to IAP inhibitor and death ligands. HNSCC varied in mechanisms of cell death, as indicated by reversal by inhibitors or protein markers of caspase-dependent apoptosis and/or RIPK1/MLKL-mediated necroptosis. In vivo, birinapant inhibited tumor growth and enhanced radiation induced TNFα, tumor responses, and host survival in UM-SCC-46 and -11B xenograft models displaying amplification and overexpression of FADD+/−BIRC2. These findings suggest that combination of SMAC mimetics such as birinapant plus radiation may be particularly active in HNSCC, which harbor frequent FADD/BIRC2 genomic alterations.

During Fc receptor-mediated phagocytosis in macrophages, PI 3-kinase mediates transitions in the signaling by Rho-family GTPases. Receptor-activated Cdc42 increases PI 3-kinase activity. Increased 3′ phosphoinositide concentrations in phagocytic cups then deactivate Cdc42.

Head and neck squamous cell carcinoma (HNSCC) is one of the most morbid, mortal, and genetically diverse malignancies. Although HNSCC is heterogeneous in nature, alterations in major components of the PI3K/ Akt/mTOR pathway are consistently observed throughout the majority of HNSCC cases. These alterations include genetic aberrations, such as mutations or DNA copy number variations, and dysregulation of mRNA or protein expression. In normal physiology, the PI3K/Akt/ mTOR axis regulates cell survival, growth, and metabolism. However, alterations in this pathway lead to the malignant phenotype which characterizes HNSCC, among many other cancers. For this reason, both pharmaceutical companies and academic institutions are actively developing and investigating inhibitors of PI3K, Akt, and mTOR in preclinical and clinical studies of HNSCC. Many of these inhibitors have shown promise, while the effects of others are tempered by the mechanisms through which HNSCC can evade therapy. As such, current research aimed at elucidating the interactions between PI3K/Akt/mTOR and other important signaling pathways which may drive resistance in HNSCC, such as p53, NF-jB, and MAPK, has become a prominent focus toward better understanding how to most effectively treat HNSCC. Oral Diseases (2015) 21, 815-825

word count: 344/350Question: Can recent developments in machine learning and computer vision be used to develop an objective and automatic system for computer-aided assessment in facial palsy? Findings:In this research article, we found that by using a relatively small number of manually annotated photographs for a patient specific database it is possible to obtain significant improvement in the accuracy of facial measurements provided by a popular machine learning algorithm. Meaning: The results presented in the article represent the first steps towards the development of an automatic system for computer-aided assessment in facial palsy. Abstract (words = 344 / 350) Importance: Quantitative assessment of facial function is challenging, and subjective grading scales such as House-Brackmann, Sunnybrook, and eFACE have well-recognized limitations. Machine learning approaches to facial landmark localization carry great clinical potential as they enable high-throughput automated quantification of relevant facial metrics from photographs and videos. However, the translation from research settings to clinical application still requires important improvements. Objective: To develop a novel machine learning algorithm for fast and accurate localization of facial landmarks in photographs of facial palsy patients and utilize this technology as part of an automated computer-aided diagnosis system. Design, Setting, and Participants: Portrait photographs of eight expressions obtained from 200 facial palsy patients and 10 healthy participants were manually annotated, by localizing 68 facial landmarks in each photograph, by three trained clinicians using a custom graphical user interface. A novel machine learning model for automated facial landmark localization was trained using this disease-specific database. Algorithm accuracy was compared to manual markings and the output of a model trained using a larger database consisting only of healthy subjects. Main Outcomes and measurements: Root mean square error normalized by the inter-ocular distance (NRMSE) of facial landmark localization between prediction of machine learning algorithm and manually localized landmarks.