MRI-Based Radiomics and Radiogenomics in the Management of Low-Grade Gliomas: Evaluating the Evidence for a Paradigm Shift

Low-grade gliomas (LGGs) are tumors that affect mostly adults. These neoplasms are comprised mainly of oligodendrogliomas and diffuse astrocytomas. LGGs remain vexing to current management and therapeutic modalities although they exhibit more favorable survival rates compared with high-grade gliomas (HGGs). The specific genetic subtypes that these tumors exhibit result in variable clinical courses and the need to involve multidisciplinary teams of neurologists, epileptologists, neurooncologists and neurosurgeons. Currently, the diagnosis of an LGG pivots mainly around the preliminary radiological findings and the subsequent definitive surgical diagnosis (via surgical sampling). The introduction of radiomics as a high throughput quantitative imaging technique that allows for improved diagnostic, prognostic and predictive indices has created more interest for such techniques in cancer research and especially in neurooncology (MRI-based classification of LGGs, predicting Isocitrate dehydrogenase (IDH) and Telomerase reverse transcriptase (TERT) promoter mutations and predicting LGG associated seizures). Radiogenomics refers to the linkage of imaging findings with the tumor/tissue genomics. Numerous applications of radiomics and radiogenomics have been described in the clinical context and management of LGGs. In this review, we describe the recently published studies discussing the potential application of radiomics and radiogenomics in LGGs. We also highlight the potential pitfalls of the above-mentioned high throughput computerized techniques and, most excitingly, explore the use of machine learning artificial intelligence technologies as standalone and adjunct imaging tools en route to enhance a personalized MRI-based tumor diagnosis and management plan design

Characterization of neural mechanotransduction response in human traumatic brain injury organoid model

Abstract The ability to model physiological systems through 3D neural in-vitro systems may enable new treatments for various diseases while lowering the need for challenging animal and human testing. Creating such an environment, and even more impactful, one that mimics human brain tissue under mechanical stimulation, would be extremely useful to study a range of human-specific biological processes and conditions related to brain trauma. One approach is to use human cerebral organoids (hCOs) in-vitro models. hCOs recreate key cytoarchitectural features of the human brain, distinguishing themselves from more traditional 2D cultures and organ-on-a-chip models, as well as in-vivo animal models. Here, we propose a novel approach to emulate mild and moderate traumatic brain injury (TBI) using hCOs that undergo strain rates indicative of TBI. We subjected the hCOs to mild (2 s $$^{-1}$$ - 1 ) and moderate (14 s $$^{-1}$$ - 1 ) loading conditions, examined the mechanotransduction response, and investigated downstream genomic effects and regulatory pathways. The revealed pathways of note were cell death and metabolic and biosynthetic pathways implicating genes such as CARD9, ENO1, and FOXP3, respectively. Additionally, we show a steeper ascent in calcium signaling as we imposed higher loading conditions on the organoids. The elucidation of neural response to mechanical stimulation in reliable human cerebral organoid models gives insights into a better understanding of TBI in humans

DataSheet_1_TP53-PTEN-NF1 depletion in human brain organoids produces a glioma phenotype in vitro.zip

Glioblastoma (GBM) is fatal and the study of therapeutic resistance, disease progression, and drug discovery in GBM or glioma stem cells is often hindered by limited resources. This limitation slows down progress in both drug discovery and patient survival. Here we present a genetically engineered human cerebral organoid model with a cancer-like phenotype that could provide a basis for GBM-like models. Specifically, we engineered a doxycycline-inducible vector encoding shRNAs enabling depletion of the TP53, PTEN, and NF1 tumor suppressors in human cerebral organoids. Designated as inducible short hairpin-TP53-PTEN-NF1 (ish-TPN), doxycycline treatment resulted in human cancer-like cerebral organoids that effaced the entire organoid cytoarchitecture, while uninduced ish-TPN cerebral organoids recapitulated the normal cytoarchitecture of the brain. Transcriptomic analysis revealed a proneural GBM subtype. This proof-of-concept study offers a valuable resource for directly investigating the emergence and progression of gliomas within the context of specific genetic alterations in normal cerebral organoids.</p

Table_1_TP53-PTEN-NF1 depletion in human brain organoids produces a glioma phenotype in vitro.docx

Glioblastoma (GBM) is fatal and the study of therapeutic resistance, disease progression, and drug discovery in GBM or glioma stem cells is often hindered by limited resources. This limitation slows down progress in both drug discovery and patient survival. Here we present a genetically engineered human cerebral organoid model with a cancer-like phenotype that could provide a basis for GBM-like models. Specifically, we engineered a doxycycline-inducible vector encoding shRNAs enabling depletion of the TP53, PTEN, and NF1 tumor suppressors in human cerebral organoids. Designated as inducible short hairpin-TP53-PTEN-NF1 (ish-TPN), doxycycline treatment resulted in human cancer-like cerebral organoids that effaced the entire organoid cytoarchitecture, while uninduced ish-TPN cerebral organoids recapitulated the normal cytoarchitecture of the brain. Transcriptomic analysis revealed a proneural GBM subtype. This proof-of-concept study offers a valuable resource for directly investigating the emergence and progression of gliomas within the context of specific genetic alterations in normal cerebral organoids.</p

Extracellular vesicle PD-L1 dynamics predict durable response to immune-checkpoint inhibitors and survival in patients with non-small cell lung cancer.

Immune-checkpoint inhibitors (ICIs) changed the therapeutic landscape of patients with lung cancer. However, only a subset of them derived clinical benefit and evidenced the need to identify reliable predictive biomarkers. Liquid biopsy is the non-invasive and repeatable analysis of biological material in body fluids and a promising tool for cancer biomarkers discovery. In particular, there is growing evidence that extracellular vesicles (EVs) play an important role in tumor progression and in tumor-immune interactions. Thus, we evaluated whether extracellular vesicle PD-L1 expression could be used as a biomarker for prediction of durable treatment response and survival in patients with non-small cell lung cancer (NSCLC) undergoing treatment with ICIs. Dynamic changes in EV PD-L1 were analyzed in plasma samples collected before and at 9 ± 1 weeks during treatment in a retrospective and a prospective independent cohorts of 33 and 39 patients, respectively. As a result, an increase in EV PD-L1 was observed in non-responders in comparison to responders and was an independent biomarker for shorter progression-free survival and overall survival. To the contrary, tissue PD-L1 expression, the commonly used biomarker, was not predictive neither for durable response nor survival. These findings indicate that EV PD-L1 dynamics could be used to stratify patients with advanced NSCLC who would experience durable benefit from ICIs