Glioblastoma multiforme (GBM) is the most common and devastating type of primary brain tumor, with a median survival time of only 15 months. Having a clinically applicable genetic biomarker would lead to a paradigm shift in precise diagnosis, personalized therapeutic decisions, and prognostic prediction for GBM. Radiogenomic profiling connecting radiological imaging features with molecular alterations will offer a noninvasive method for genomic studies of GBM. To this end, we analyzed over 3800 glioma and GBM cases across four independent datasets. The Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases were employed for RNA-Seq analysis, whereas the Ivy Glioblastoma Atlas Project (Ivy-GAP) and The Cancer Imaging Archive (TCIA) provided clinicopathological data. The Clinical Proteomic Tumor Analysis Consortium Glioblastoma Multiforme (CPTAC-GBM) was used for proteomic analysis. We identified a simple three-gene transcriptome signature—SOCS3, VEGFA, and TEK—that can connect GBM’s overall prognosis with genes’ expression and simultaneously correlate radiographical features of perfusion imaging with SOCS3 expression levels. More importantly, the rampant development of neovascularization in GBM offers a promising target for therapeutic intervention. However, treatment with bevacizumab failed to improve overall survival. We identified SOCS3 expression levels as a potential selection marker for patients who may benefit from early initiation of angiogenesis inhibitors.

As a dynamic regulator for short-lived protein degradation and turnover, the ubiquitin-proteasome system (UPS) plays important roles in various biological processes, including response to cellular stress, regulation of cell cycle progression, and carcinogenesis. Over the past decade, research on targeting the cullin-RING (really interesting new gene) E3 ligases (CRLs) in the UPS has gained great momentum with the entry of late-phase clinical trials of its novel inhibitors MLN4924 (pevonedistat) and TAS4464. Several preclinical studies have demonstrated the efficacy of MLN4924 as a radiosensitizer, mainly due to its unique cytotoxic properties, including induction of DNA damage response, cell cycle checkpoints dysregulation, and inhibition of NF-κB and mTOR pathways. Recently, the PROteolysis TArgeting Chimeras (PROTACs) technology was developed to recruit the target proteins for CRL-mediated polyubiquitination, overcoming the resistance that develops inevitably with traditional targeted therapies. First-in-class cell-permeable PROTACs against critical radioresistance conferring proteins, including the epidermal growth factor receptor (EGFR), androgen receptor (AR) and estrogen receptor (ER), cyclin-dependent kinases (CDKs), MAP kinase kinase 1 (MEK1), and MEK2, have emerged in the past 5 years. In this review article, we will summarize the most important research findings of targeting CRLs for radiosensitization.

Background: In this study, we evaluated the potential of DNA polymerase epsilon (POLE) and DNA polymerase delta 1 (POLD1) as prognostic biomarkers for immune checkpoint inhibitor (ICI) treatment in patients with advanced stage non‐small cell lung cancer (NSCLC).Methods: Disease stage, PD‐L1 positivity, histological subtypes, POLE/POLD1 mutation status, tumor mutation burden (TMB), and response to ICIs in NSCLC cases were derived from AACR GENIE dataset (n = 24 120), TCGA‐Pan Lung Cancer dataset (n = 1144), AACR GENIE BPC NSCLC v2.0‐public (n = 2004), and Memorial Sloan Kettering‐Integrated Mutation Profiling of Actionable Cancer Targets dataset (n = 350). The smoking history from TCGA and AACR GENIE datasets was grouped into current, former or never‐smokers.Results: POLE and POLD1 genetic alterations were identified in 5% and 2.6% of NSCLC patients, respectively. Current smokers had 9% and 4% of POLE/POLD1 mutations, respectively, versus 1.7% for both POLE and POLD1 mutations prevalence in never‐smokers. POLE/POLD1 mutations were associated with elevated mutation counts than those with wild‐type (median mutation counts 16 vs. 7, p < 0.0001), more advanced disease stages (stage I disease 15.19% vs. 29.42%), more prevalent squamous histology subtype (21.69% vs. 9.05%, p = 0.0427), and a higher percentage of PD‐L1 positivity (66.67% vs. 43.87%, p < 0.001). Treatment with ICIs improved survival in patients with both POLE/POLD1 mutated and those with TMB > 18 (p < 0.001).Conclusion: Current smokers have a five‐fold increased risk of having POLE mutations than never‐smokers. POLE/POLD1 mutation status and TMB > 18 can be a composite biomarker for selecting NSCLC patients with survival benefits to ICI treatment.

Glioblastoma multiforme (GBM) is the deadliest type of brain tumor. The median survival time for patients with GBM is only 15 months, even following maximal surgical resection and chemotherapy and radiation therapy. A genetic biomarker could enable a paradigm shift in precise diagnosis, personalized therapeutics and prognosis. In this study, we employed the Chinese Glioma Genome Atlas, The Cancer Genome Atlas, and the Ivy Glioblastoma Atlas Project databases for RNA sequencing (RNA-seq) analysis and clinicopathological studies. We demonstrated that elevated expression of the RNF7, TCEB1, SOCS1 and SOCS3 genes, which encode components of cullin5-RING E3 ligase (CRL5), predict unfavorable GBM prognoses. In GBM and glioma cases carrying IDH1 mutations, SOCS1 and SOCS3 methylation was increased and their expression was downregulated. This study has thus identified a simple transcriptome signature for GBM prognosis.

Identification of isocitrate dehydrogenase (IDH)-mutant glioma patients at high risk of early progression is critical for radiotherapy treatment planning. Currently tools to stratify risk of early progression are lacking. We sought to identify a combination of molecular markers that could be used to identify patients who may have a greater need for adjuvant radiation therapy machine learning technology. 507 WHO Grade 2 and 3 glioma cases from The Cancer Genome Atlas, and 1309 cases from AACR GENIE v13.0 datasets were studied for genetic disparities between IDH1-wildtype and IDH1-mutant cohorts, and between different age groups. Genetic features such as mutations and copy number variations (CNVs) correlated with IDH1 mutation status were selected as potential inputs to train artificial neural networks (ANNs) to predict IDH1 mutation status. Grade 2 and 3 glioma cases from the Memorial Sloan Kettering dataset (n = 404) and Grade 3 glioma cases with subtotal resection (STR) from Northwestern University (NU) (n = 21) were used to further evaluate the best performing ANN model as independent datasets. IDH1 mutation is associated with decreased CNVs of EGFR (21% vs. 3%), CDKN2A (20% vs. 6%), PTEN (14% vs. 1.7%), and increased percentage of mutations for TP53 (15% vs. 63%), and ATRX (10% vs. 54%), which were all statistically significant (p < 0.001). Age > 40 was unable to identify high-risk IDH1-mutant with early progression. A glioma early progression risk prediction (GlioPredictor) score generated from the best performing ANN model (6/6/6/6/2/1) with 6 inputs, including CNVs of EGFR, PTEN and CDKN2A, mutation status of TP53 and ATRX, patient’s age can predict IDH1 mutation status with over 90% accuracy. The GlioPredictor score identified a subgroup of high-risk IDH1-mutant in TCGA and NU datasets with early disease progression (p = 0.0019, 0.0238, respectively). The GlioPredictor that integrates age at diagnosis, CNVs of EGFR, CDKN2A, PTEN and mutation status of TP53, and ATRX can identify a small cohort of IDH-mutant with high risk of early progression. The current version of GlioPredictor mainly incorporated clinically often tested genetic biomarkers. Considering complexity of clinical and genetic features that correlate with glioma progression, future derivatives of GlioPredictor incorporating more inputs can be a potential supplement for adjuvant radiotherapy patient selection of IDH-mutant glioma patients.