Apitolisib

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide. Currently, therapeutic modalities such as surgery, chemotherapy, radiotherapy, and immunotherapy are being used to treat HNSCC. However, the treatment outcomes of most patients are dismal because they are already in middle or advanced stage by the time of diagnosis and poorly responsive to treatments. It is therefore of great interest to clarify mechanisms that contribute to the metastasis of cells to identify possible targets for therapy. In this study, we identified the Na+‐coupled bicarbonate transporter, SLC4A7, play essential roles in the metastasis of HNSCC. Our results showed that the relative expression of SLC4A7 messenger RNA was highly expressed in HNSCCs samples from TCGA, and compared with precancerous cells of human oral mucosa (DOK), SLC4A7 was highly expressed in HNSCC cell lines. In vitro and in vivo experiments showed that dysregulation of SLC4A7 had minor influence on the proliferation of HNSCC but impacted HNSCC's migration and invasion. Meanwhile, SLC4A7 could promote epithelial‐mesenchymal transition (EMT) in HNSCC. RNA‐seq, KEGG pathway enrichment analysis and Western blot further revealed that downregulation of SLC4A7 in HNSCC cells inhibited the PI3K/AKT pathway. These findings were further validated via rescue experiments using a small molecule inhibitor of PI3K/mTOR (GDC‐0980). Our findings suggest that SLC4A7 promotes EMT and metastasis of HNSCC through the PI3K/AKT/mTOR signaling pathway, which may be a valuable predictive biomarker and potential therapeutic target in HNSCC.

Glioblastoma (GBM) is the most common form of malignant primary brain tumor with a dismal prognosis. Currently, the standard treatments for GBM rarely achieve satisfactory results, which means that current treatments are not individualized and precise enough. In this study, a multiomics-based GBM classification was established and three subclasses (GPA, GPB, and GPC) were identified, which have different molecular features both in bulk samples and at single-cell resolution. A robust GBM poor prognostic signature (GPS) score model was then developed using machine learning method, manifesting an excellent ability to predict the survival of GBM. NVP-BEZ235, GDC-0980, dasatinib and XL765 were ultimately identified to have subclass-specific efficacy targeting patients with a high risk of poor prognosis. Furthermore, the GBM classification and GPS score model could be considered as potential biomarkers for immunotherapy response. In summary, an integrative genomic analysis was conducted to advance individual-based therapies in GBM.