GOPC

Glioblastoma is the most frequent and malignant primary neoplasm of the central nervous system. In a recent breakthrough study on a prospective Discovery cohort, I proposed the first all-inclusive molecular classification of glioblastoma into seven subgroups, G1-G7, based on MAPK pathway activation. New data from a WHO-grade-4 diffuse glioma prospective Validation cohort offers, in this study, an integrated demographic-molecular analysis of a 213-patient Combined cohort. Despite cohort differences in the median age and molecular subgroup distribution, all the prospectively-acquired cases from the Validation cohort mapped into one of the G1-G7 subgroups defined in the Discovery cohort. A younger age of onset, higher tumor mutation burden and expanded G1/EGFR-mutant and G3/NF1 glioblastoma subgroups characterized the glioblastomas from African American/Black relative to Caucasian/White patients. The three largest molecular subgroups were G1/EGFR, G3/NF1 and G7/Other. The fourth largest subgroup, G6/Multi-RTK, was detailed by describing a novel gene fusion ST7-MET, rare PTPRZ1-MET, LMNA-NTRK1 and GOPC-ROS1 fusions and their overexpression mechanisms in glioblastoma. The correlations between the MAPK pathway G1-G7 subgroups and the PI3-kinase/PTEN, TERT, cell cycle G1 phase and p53 pathways defined characteristic subgroup pathway profiles amenable to personalized targeted therapy. This analysis validated the first all-inclusive molecular classification of glioblastoma, showed significant demographic and molecular differences between subgroups, and provided the first ethnic molecular comparison of glioblastoma.

Mitogen-activated protein kinase (MAPK) pathway alteration is a major oncogenic driver in paediatric low-grade gliomas (LGG) and some adult gliomas, encompassing BRAF (most common) and non-BRAF alterations. The aim was to determine the frequency, molecular spectrum and clinicopathological features of MAPK-altered gliomas in paediatric and adult patients at our neuropathology site in Kuwait.

We retrospectively searched the data of molecularly sequenced gliomas between 2018 and 2023 for MAPK alterations, revised the pathology in view of the 2021 WHO classification and evaluated the clinicopathological data for possible correlations.

Of 272 gliomas, 40 (15%) harboured a MAPK pathway alteration in 19 paediatric (median 9.6 years; 1.2-17.6) and 21 adult patients (median 37 years; 18.9-89.2), comprising 42% and 9% of paediatric and adult cases, respectively. Pilocytic astrocytoma and glioblastoma were the most frequent diagnoses in children (47%) and adults (43%), respectively. BRAF V600E (n=17, 43%) showed a wide distribution across age groups, locations and pathological diagnoses while KIAA1549::BRAF fusion (n=8, 20%) was spatially and histologically restricted to cerebellar paediatric LGGs. Non-V600E variants and BRAF amplifications accompanied other molecular aberrations in high-grade tumours. Non-BRAF MAPK alterations (n=8) included mutations and gene fusions involving FGFR1, NTRK2, NF1, ROS1 and MYB. Fusions included KANK1::NTRK2, GOPC::ROS1 (both infant hemispheric gliomas), FGFR1::TACC1 (diffuse LGG), MYB::QKI (angiocentric glioma) and BCR::NTRK2 (glioblastoma). Paradoxical H3 K27M/MAPK co-mutations were observed in two LGGs.

The study provided insights into MAPK-altered gliomas in Kuwait highlighting the differences among paediatric and adult patients and providing a framework for planning therapeutic polices.

The use of peptide drugs to treat cancer is gaining popularity because of their efficacy, fewer side effects, and several advantages over other properties. Identifying the peptides that interact with cancer proteins is crucial in drug discovery. Several approaches related to predicting peptide-protein interactions have been conducted. However, problems arise due to the high costs of resources and time and the smaller number of studies. This study predicts peptide-protein interactions using Random Forest, XGBoost, and SAE-DNN. Feature extraction is also performed on proteins and peptides using intrinsic disorder, amino acid sequences, physicochemical properties, position-specific assessment matrices, amino acid composition, and dipeptide composition. Results show that all algorithms perform equally well in predicting interactions between peptides derived from venoms and target proteins associated with cancer. However, XGBoost produces the best results with accuracy, precision, and area under the receiver operating characteristic curve of 0.859, 0.663, and 0.697, respectively. The enrichment analysis revealed that peptides from the Calloselasma rhodostoma venom targeted several proteins (ESR1, GOPC, and BRD4) related to cancer.