Proteogenomic and metabolomic characterization of human glioblastoma

An Update on Glioblastoma Biology, Genetics, and Current Therapies: Novel Inhibitors of the G Protein-Coupled Receptor CCR5

“…Another approach distinguished among three subtypes of GBM based on phenotypes and prognosis referred to as mitotic (or favorable), intermediate, and invasive (or poor prognosis) [23]. These categories were verified by distinct genetic fingerprints showing similar but not identical trends as the TCGA-based classification [22,25]. However, the GBM subtype-specific markers are variable and simultaneously expressed across individual cells even within a single tumor [26,27].…”

“…Short-term relapse after radiation therapy is characterized by both increased expression of MES–GBM subtype genes and a gene signature based on TME inference, associated with decreased monocyte invasion, increased macrophages/microglial cells, and CD8 + T cell enrichment. A recent pathogenetic characterization of GBM revealed that four immune GBM subtypes could be identified in IDH -wild-type GBM and IDH -mutant astrocytoma [ 25 ]. Taken together, higher intertumor and intratumor heterogeneity are associated with lower overall patient survival, in spite of modern modalities of standard-of-care GBM therapy [ 32 ], and novel targeted approaches are undoubtedly needed.…”

“…Since several studies confirmed that GBM therapy results in recurrences with more invasive mesenchymal subtypes, it has been hypothesized that this process involves a proneural (PN) to mesenchymal (MES) GSC subtype reprogramming, or “PMT transition” [ 22 , 24 , 25 ].…”

Altered Metabolism in Glioblastoma: Myeloid-Derived Suppressor Cell (MDSC) Fitness and Tumor-Infiltrating Lymphocyte (TIL) Dysfunction

“…To date, metabolic reprogramming of GBM cells has only been considered a consequence of hard nutrient-restricted conditions within the tumor microenvironment. Recently, new molecular subtypes of GBM were delineated by integrating not only proteomic and genomic but also metabolomic analyses [ 2 ]. Indeed, in an elegant and meticulous new study, the use of single-cell mRNA sequencing enabled us to revise the GBM subclassification into four subtypes based on biological and metabolic characteristics, not just on genetic alterations, allowing the prediction of patient outcome.…”

Predicting Endometrial Cancer Subtypes and Molecular Features from Histopathology Images Using Multi-resolution Deep Learning Models

“…An InceptionV3-based model achieves a high level of accuracy in determining melanoma possibility, exhibiting significant diagnostic potential (Louis et al, 2016). Moreover, successful deep learning models have also been built to predict molecular and genomic features in cancer, such as microsatellite instability (MSI), immune subtypes, and somatic mutation status, suggesting that machine learning techniques may be able to assist human experts to further exploit clinically relevant information in pathological images (Coudray et al, 2018; Gillette et al, 2020; Hong et al, 2020; Kather et al, 2019; Kim et al, 2019; Wang et al, 2021). In addition, studies have also demonstrated that deep neural network models have the potentials in capturing features across cancer and tissue types (Fu et al, 2020; Kather et al, 2020).…”