Immunotherapy with immune checkpoint inhibitors (ICIs) for solid tumors had significantly improved overall survival. This type of therapy is still not available for acute myeloid leukemia (AML). One major issue is the lack of knowledge for the expression patterns of immune checkpoints (IC) in AML. In this study, we first explored the prognostic value of ICs for AML patients by analyzing RNA-seq and mutation data from 176 AML patients from the Cancer Genome Atlas (TCGA) database. We further validated the results of the database analysis by analyzing bone marrow (BM) samples from 62 patients with de novo AML. Both TCGA data and validation results indicated that high expression of PD-1, PD-L1, and PD-L2 was associated with poor overall survival (OS) in AML patients. In addition, increased co-expression of PD-1/CTLA-4 or PD-L2/CTLA-4 correlated with poor OS in AML patients (3-year OS: TGCA data 30% vs 0% and 20% vs 0%, validation group 57% vs 31% and 57% vs 33%, respectively) (P < 0.05).

In fully-automatic radiomics model for predicting overall survival (OS) of glioblastoma multiforme (GBM) patients, the effect of image standardization parameters such as voxel size, quantization method and gray level on model reproducibility and prognostic performance are still unclear. In this study, 45792 multiregional radiomics features were automatically extracted from multi-modality MR images with different voxel sizes, quantization methods, and gray levels. The feature reproducibility and prognostic performance were assessed. Multiparametric and fixed-parameter radiomics signatures were constructed based on a training cohort (60 patients). In an independent validation cohort (32 patients), the multiparametric signature achieved better performance for OS prediction (C-Index = 0.705, 95% CI: 0.672, 0.738) and significant stratification of patients into high- and low-risk groups (P = 0.0040, HR = 3.29, 95% CI: 1.40, 7.70), which outperformed the fixed-parameter signatures and conventional factors such as age, Karnofsky Performance Score and tumor volume. This study demonstrated that voxel size, quantization method and gray level had influence on reproducibility and prognosis of radiomics features for GBM OS prediction. An automatic method to determine the optimal parameter settings was provided. It indicated that multiparametric radiomics signature had the potential of offering better prognostic performance than fixed-parameter signatures.

• Multiregional and multiparametric MRI features reliably predicted MGMT methylation in multicentre cohorts. • All-relevant imaging features predicted MGMT methylation better than univariately-predictive and non-redundant features. • Combing clinical factors with radiomics features did not benefit the prediction performance.

PurposeIsocitrate dehydrogenase 1 (IDH1) has been proven as a prognostic and predictive marker in glioblastoma (GBM) patients. The purpose was to preoperatively predict IDH mutation status in GBM using multiregional radiomics features from multiparametric magnetic resonance imaging (MRI).MethodsIn this retrospective multicenter study, 225 patients were included. A total of 1614 multiregional features were extracted from enhancement area, non‐enhancement area, necrosis, edema, tumor core, and whole tumor in multiparametric MRI. Three multiregional radiomics models were built from tumor core, whole tumor, and all regions using an all‐relevant feature selection and a random forest classification for predicting IDH1. Four single‐region models and a model combining all‐region features with clinical factors (age, sex, and Karnofsky performance status) were also built. All models were built from a training cohort (118 patients) and tested on an independent validation cohort (107 patients).ResultsAmong the four single‐region radiomics models, the edema model achieved the best accuracy of 96% and the best F1‐score of 0.75 while the non‐enhancement model achieved the best area under the receiver operating characteristic curve (AUC) of 0.88 in the validation cohort. The overall performance of the tumor‐core model (accuracy 0.96, AUC 0.86 and F1‐score 0.75) and the whole‐tumor model (accuracy 0.96, AUC 0.88 and F1‐score 0.75) was slightly better than the single‐regional models. The 8‐feature all‐region radiomics model achieved an improved overall performance of an accuracy 96%, an AUC 0.90, and an F1‐score 0.78. Among all models, the model combining all‐region imaging features with age achieved the best performance of an accuracy 97%, an AUC 0.96, and an F1‐score 0.84.ConclusionsThe radiomics model built with multiregional features from multiparametric MRI has the potential to preoperatively detect the IDH1 mutation status in GBM patients. The multiregional model built with all‐region features performed better than the single‐region models, while combining age with all‐region features achieved the best performance.

Glioma proliferation is a multistep process during which a sequence of genetic and epigenetic alterations randomly occur to affect the genes controlling cell proliferation, cell death and genetic stability. microRNAs are emerging as important epigenetic modulators of multiple target genes, leading to abnormal cellular signaling involving cellular proliferation in cancers.In the present study, we found that expression of miR-195 was markedly downregulated in glioma cell lines and human primary glioma tissues, compared to normal human astrocytes and matched non-tumor associated tissues. Upregulation of miR-195 dramatically reduced the proliferation of glioma cells. Flow cytometry analysis showed that ectopic expression of miR-195 significantly decreased the percentage of S phase cells and increased the percentage of G1/G0 phase cells. Overexpression of miR-195 dramatically reduced the anchorage-independent growth ability of glioma cells. Furthermore, overexpression of miR-195 downregulated the levels of phosphorylated retinoblastoma (pRb) and proliferating cell nuclear antigen (PCNA) in glioma cells. Conversely, inhibition of miR-195 promoted cell proliferation, increased the percentage of S phase cells, reduced the percentage of G1/G0 phase cells, enhanced anchorage-independent growth ability, upregulated the phosphorylation of pRb and PCNA in glioma cells. Moreover, we show that miR-195 inhibited glioma cell proliferation by downregulating expression of cyclin D1 and cyclin E1, via directly targeting the 3′-untranslated regions (3′-UTR) of cyclin D1 and cyclin E1 mRNA. Taken together, our results suggest that miR-195 plays an important role to inhibit the proliferation of glioma cells, and present a novel mechanism for direct miRNA-mediated suppression of cyclin D1 and cyclin E1 in glioma.