Phosphatidylinositol-3-kinase (PI3K)/AKT/mammalian target of rapamycin (mTOR) signaling is one of the most important intracellular pathways, which can be considered as a master regulator for cancer. Enormous efforts have been dedicated to the development of drugs targeting PI3K signaling, many of which are currently employed in clinical trials evaluation, and it is becoming increasingly clear that PI3K inhibitors are effective in inhibiting tumor progression. PI3K inhibitors are subdivided into dual PI3K/mTOR inhibitors, pan-PI3K inhibitors and isoform-specific inhibitors. In this review, we performed a critical review to summarize the role of the PI3K pathway in tumor development, recent PI3K inhibitors development based on clinical trials, and the mechanisms of resistance to PI3K inhibition.

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Metastatic melanoma is the most aggressive and obstinate skin cancer with poor prognosis. Variant novel applicable regimens have emerged during the past decades intensively, while the most profound approaches are oncogene-targeted therapy and T-lymphocyte mediated immunotherapy. Although targeted therapies generated remarkable and rapid clinical responses in the majority of patients, acquired resistance was developed promptly within months leading to tumor relapse. By contrast, immunotherapies elicited long-term tumor regression. However, the overall response rate was limited. In view of the above, either targeted therapy or immunotherapy cannot elicit durable clinical responses in large range of patients. Interestingly, the advantages and limitations of these regimens happened to be complementary. An increasing number of preclinical studies and clinical trials proved a synergistic antitumor effect with the combination of targeted therapy and immunotherapy, implying a promising prospect for the treatment of metastatic melanoma. In order to achieve a better therapeutic effectiveness and reduce toxicity in patients, great efforts need to be made to illuminate multifaceted interplay between targeted therapy and immunotherapy.

This study was to evaluate the prognostic value of pretreatment inflammatory indexes including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and systemic immune-inflammation index (SII) in colorectal cancer (CRC) patients receiving neoadjuvant chemoradiotherapy (CRT). We enrolled 98 eligible CRC patients and divided them into high or low NLR, PLR, LMR, and SII groups according to their median index value, respectively. Univariate and multivariate analysis were performed to identify the potential predictors of progression-free survival (PFS) and overall survival (OS). In the univariate analysis, ECOG performance status, distant metastasis, NLR, PLR, LMR, and SII were found to be significantly associated with PFS and OS. In the multivariate analysis, ECOG performance status, distant metastasis, and NLR were identified to be independent predictors of PFS (HR 2.487, p = 0.012; HR 2.422, p = 0.042; HR 2.243, p = 0.034, respectively), and OS (HR 2.237, p = 0.018; HR 2.757, p = 0.020; HR 2.336, p = 0.017, respectively). The results of our study revealed that ECOG performance status, distant metastasis and NLR were independent prognostic factors of PFS and OS in CRC patients receiving neoadjuvant CRT.

Purpose: To investigative the diagnostic performance of radiomics-based machine learning in differentiating glioblastomas (GBM) from metastatic brain tumors (MBTs). Method: The current study involved 134 patients diagnosed and treated in our institution between April 2014 and December 2018. Radiomics features were extracted from contrast-enhanced T1 weighted imaging (T1C). Thirty diagnostic models were built based on five selection methods and six classification algorithms. The sensitivity, specificity, accuracy, and area under curve (AUC) of each model were calculated, and based on these the optimal model was chosen. Result : Two models represented promising diagnostic performance with AUC of 0.80. The first model was a combination of Distance Correlation as the selection method and Linear Discriminant Analysis (LDA) as the classification algorithm. In the training group, the sensitivity, specificity, accuracy, and AUC were 0.75, 0.85, 0.80, and 0.80, respectively; and in the testing group, the sensitivity, specificity, accuracy, and AUC of the model were 0.69, 0.86, 0.78, and 0.80, respectively. The second model was the Distance Correlation as the selection method and logistic regression (LR) as the classification algorithm, with sensitivity, specificity, accuracy, and AUC of 0.75, 0.85, 0.80, 0.80 in the training group and 0.69, 0.86, 0.78, 0.80 in the testing group. Conclusion: Radiomic-based machine learning has potential to be utilized in differentiating GBM from MBTs.