Receptor Tyrosine Kinase Interaction with the Tumor Microenvironment in Malignant Progression of Human Glioblastoma

Glioblastoma (GBM) is the most malignant brain tumor, characterized with a rapid progression and poor prognosis despite modern therapies. Receptor tyrosine kinase (RTK) is a membrane tyrosine kinase that could be activated by binding ligands with the extracellular domain, and communicating signals according to the tyrosine kinase activity of the intracellular domain. Recent studies revealed that RTKs such as EGFR, PDGFR and MET play key roles in cancer progression through regulation of abundant cellular processes. As transmembrane proteins, RTKs work as a mediator between the extracellular environment and intracellular compartments, translating the tumor microenvironment (TME) signals into the tumor cells. TME is also a critical regulator for the malignant process, lately receiving considerable attention. It is composed of extracellular matrix (ECM), the stromal cells (i.e., endothelial cells, microglia and fibroblasts), secreted factors, and hypoxia environment, etc. Among these, the strong invasion and sustained angiogenesis of GBM are closely related to ECM-receptor interaction and -associated signaling events. In this chapter, we consider the interaction and mechanisms of RTKs and TME in GBM progression, especially the role of ECM-receptor mediated signaling in tumor invasion, hypoxia and angiogenesis, glioma stem cells and tumor metabolism. We then summarize and discuss recent improvements on the approaches of targeting RTK and TME as the therapy in the primary GBM

Image_1_Radiomics features from perihematomal edema for prediction of prognosis in the patients with basal ganglia hemorrhage.TIFF

ObjectiveWe developed and validated a clinical-radiomics nomogram to predict the prognosis of basal ganglia hemorrhage patients.MethodsRetrospective analyses were conducted in 197 patients with basal ganglia hemorrhage (training cohort: n = 136, test cohort: n = 61) who were admitted to The First Affiliated Hospital of Shandong First Medical University (Shandong Provincial Qianfoshan Hospital) and underwent computed tomography (CT) scan. According to different prognoses, patients with basal ganglia hemorrhage were divided into two groups. Independent clinical risk factors were derived with univariate and multivariate regression analysis. Radiomics signatures were obtained using least absolute shrinkage and selection operator. A radiomics score (Rad-score) was generated by 12 radiomics signatures of perihematomal edema (PHE) from CT images that were correlated with the prognosis of basal ganglia hemorrhage patients. A clinical-radiomics nomogram was conducted by combing the Rad-score and clinical risk factors using logistic regression analysis. The prediction performance of the nomogram was tested in the training cohort and verified in the test cohort.ResultsThe clinical model conducted by four clinical risk factors and 12 radiomcis features were used to establish the Rad-score. The clinical-radiomics nomogram outperformed the clinical model in the training cohort [area under the curve (AUC), 0.92 vs. 0.85] and the test cohort (AUC, 0.91 vs 0.85). The clinical-radiomics nomogram showed good calibration and clinical benefit in both the training and test cohorts.ConclusionRadiomics features of PHE in patients with basal ganglia hemorrhage could contribute to the outcome prediction. The clinical-radiomics nomogram may help first-line clinicians to make individual clinical treatment decisions for patients with basal ganglia hemorrhage.</p

Table_2_Radiomics features from perihematomal edema for prediction of prognosis in the patients with basal ganglia hemorrhage.XLSX

ObjectiveWe developed and validated a clinical-radiomics nomogram to predict the prognosis of basal ganglia hemorrhage patients.MethodsRetrospective analyses were conducted in 197 patients with basal ganglia hemorrhage (training cohort: n = 136, test cohort: n = 61) who were admitted to The First Affiliated Hospital of Shandong First Medical University (Shandong Provincial Qianfoshan Hospital) and underwent computed tomography (CT) scan. According to different prognoses, patients with basal ganglia hemorrhage were divided into two groups. Independent clinical risk factors were derived with univariate and multivariate regression analysis. Radiomics signatures were obtained using least absolute shrinkage and selection operator. A radiomics score (Rad-score) was generated by 12 radiomics signatures of perihematomal edema (PHE) from CT images that were correlated with the prognosis of basal ganglia hemorrhage patients. A clinical-radiomics nomogram was conducted by combing the Rad-score and clinical risk factors using logistic regression analysis. The prediction performance of the nomogram was tested in the training cohort and verified in the test cohort.ResultsThe clinical model conducted by four clinical risk factors and 12 radiomcis features were used to establish the Rad-score. The clinical-radiomics nomogram outperformed the clinical model in the training cohort [area under the curve (AUC), 0.92 vs. 0.85] and the test cohort (AUC, 0.91 vs 0.85). The clinical-radiomics nomogram showed good calibration and clinical benefit in both the training and test cohorts.ConclusionRadiomics features of PHE in patients with basal ganglia hemorrhage could contribute to the outcome prediction. The clinical-radiomics nomogram may help first-line clinicians to make individual clinical treatment decisions for patients with basal ganglia hemorrhage.</p

Table_1_Radiomics features from perihematomal edema for prediction of prognosis in the patients with basal ganglia hemorrhage.DOCX

ObjectiveWe developed and validated a clinical-radiomics nomogram to predict the prognosis of basal ganglia hemorrhage patients.MethodsRetrospective analyses were conducted in 197 patients with basal ganglia hemorrhage (training cohort: n = 136, test cohort: n = 61) who were admitted to The First Affiliated Hospital of Shandong First Medical University (Shandong Provincial Qianfoshan Hospital) and underwent computed tomography (CT) scan. According to different prognoses, patients with basal ganglia hemorrhage were divided into two groups. Independent clinical risk factors were derived with univariate and multivariate regression analysis. Radiomics signatures were obtained using least absolute shrinkage and selection operator. A radiomics score (Rad-score) was generated by 12 radiomics signatures of perihematomal edema (PHE) from CT images that were correlated with the prognosis of basal ganglia hemorrhage patients. A clinical-radiomics nomogram was conducted by combing the Rad-score and clinical risk factors using logistic regression analysis. The prediction performance of the nomogram was tested in the training cohort and verified in the test cohort.ResultsThe clinical model conducted by four clinical risk factors and 12 radiomcis features were used to establish the Rad-score. The clinical-radiomics nomogram outperformed the clinical model in the training cohort [area under the curve (AUC), 0.92 vs. 0.85] and the test cohort (AUC, 0.91 vs 0.85). The clinical-radiomics nomogram showed good calibration and clinical benefit in both the training and test cohorts.ConclusionRadiomics features of PHE in patients with basal ganglia hemorrhage could contribute to the outcome prediction. The clinical-radiomics nomogram may help first-line clinicians to make individual clinical treatment decisions for patients with basal ganglia hemorrhage.</p

Image_4_Radiomics features from perihematomal edema for prediction of prognosis in the patients with basal ganglia hemorrhage.TIF

ObjectiveWe developed and validated a clinical-radiomics nomogram to predict the prognosis of basal ganglia hemorrhage patients.MethodsRetrospective analyses were conducted in 197 patients with basal ganglia hemorrhage (training cohort: n = 136, test cohort: n = 61) who were admitted to The First Affiliated Hospital of Shandong First Medical University (Shandong Provincial Qianfoshan Hospital) and underwent computed tomography (CT) scan. According to different prognoses, patients with basal ganglia hemorrhage were divided into two groups. Independent clinical risk factors were derived with univariate and multivariate regression analysis. Radiomics signatures were obtained using least absolute shrinkage and selection operator. A radiomics score (Rad-score) was generated by 12 radiomics signatures of perihematomal edema (PHE) from CT images that were correlated with the prognosis of basal ganglia hemorrhage patients. A clinical-radiomics nomogram was conducted by combing the Rad-score and clinical risk factors using logistic regression analysis. The prediction performance of the nomogram was tested in the training cohort and verified in the test cohort.ResultsThe clinical model conducted by four clinical risk factors and 12 radiomcis features were used to establish the Rad-score. The clinical-radiomics nomogram outperformed the clinical model in the training cohort [area under the curve (AUC), 0.92 vs. 0.85] and the test cohort (AUC, 0.91 vs 0.85). The clinical-radiomics nomogram showed good calibration and clinical benefit in both the training and test cohorts.ConclusionRadiomics features of PHE in patients with basal ganglia hemorrhage could contribute to the outcome prediction. The clinical-radiomics nomogram may help first-line clinicians to make individual clinical treatment decisions for patients with basal ganglia hemorrhage.</p