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Neurofibromatosis Type 1 (NF1) patients develop an array of benign and malignant tumors, of which Malignant Peripheral Nerve Sheath Tumors (MPNST) and High Grade Gliomas (HGG) have a dismal prognosis. About 15–20% of individuals with NF1 develop brain tumors and one third of these occur outside of the optic pathway. These non-optic pathway gliomas are more likely to progress to malignancy, especially in adults. Despite their low frequency, high grade gliomas have a disproportional effect on the morbidity of NF1 patients. In vitro drug combination screens have not been performed on NF1-associated HGG, hindering our ability to develop informed clinical trials. Here we present the first in vitro drug combination screen (21 compounds alone or in combination with MEK or PI3K inhibitors) on the only human NF1 patient derived HGG cell line available and on three mouse glioma cell lines derived from the NF1-P53 genetically engineered mouse model, which sporadically develop HGG. These mouse glioma cell lines were never exposed to serum, grow as spheres and express markers that are consistent with an Oligodendrocyte Precursor Cell (OPC) lineage origin. Importantly, even though the true cell of origin for HGG remains elusive, they are thought to arise from the OPC lineage. We evaluated drug sensitivities of the three murine glioma cell lines in a 3D spheroid growth assay, which more accurately reflects drug sensitivities in vivo . Excitingly, we identified six compounds targeting HDACs, BRD4, CHEK1, BMI-1, CDK1/2/5/9, and the proteasome that potently induced cell death in our NF1-associated HGG. Moreover, several of these inhibitors work synergistically with either MEK or PI3K inhibitors. This study forms the basis for further pre-clinical evaluation of promising targets, with an eventual hope to translate these to the clinic.

Alternative splicing of mRNA precursors represents a key gene expression regulatory step and permits the generation of distinct protein products with diverse functions. In a genome-scale expression screen for inducers of the epithelial-to-mesenchymal transition (EMT), we found a striking enrichment of RNA-binding proteins. We validated that QKI and RBFOX1 were necessary and sufficient to induce an intermediate mesenchymal cell state and increased tumorigenicity. Using RNA-seq and eCLIP analysis, we found that QKI and RBFOX1 coordinately regulated the splicing and function of the actin-binding protein FLNB, which plays a causal role in the regulation of EMT. Specifically, the skipping of FLNB exon 30 induced EMT by releasing the FOXC1 transcription factor. Moreover, skipping of FLNB exon 30 is strongly associated with EMT gene signatures in basal-like breast cancer patient samples. These observations identify a specific dysregulation of splicing, which regulates tumor cell plasticity and is frequently observed in human cancer. As the human body develops, countless cells change from one state into another. Two important cell states are known as epithelial and mesenchymal. Cells in the epithelial state tend to be tightly connected and form barriers, like skin cells. Mesenchymal state cells are loosely organized, move around more and make up connective tissues. Some cells alternate between these states via an epithelial-to-mesenchymal transition (EMT for short) and back again. Without this transition, certain organs would not develop and wounds would not heal. Yet, cancer cells also use this transition to spread to distant sites of the body. Such cancers are often the most aggressive, and therefore the most deadly. The epithelial-to-mesenchymal transition is dynamically regulated in a reversible manner. For example, the genes for some proteins might only be active in the epithelial state and further reinforce this state by turning on other ‘epithelial genes’. Alternatively, there might be differences in the processing of mRNA molecules – the intermediate molecules between DNA and protein – that result in the production of different proteins in epithelial and mesenchymal cells. Li, Choi et al. wanted to know which of the thousands of human genes can endow epithelial state cells with mesenchymal characteristics. A better understanding of the switch could help to prevent cancers undergoing an epithelial-to-mesenchymal transition. From a large-scale experiment in human breast cancer cells, Li, Choi et al. found that a group of proteins that bind and modify mRNA molecules are important for the epithelial-to-mesenchymal transition. Two proteins in particular promoted the transition, most likely by binding to the mRNA of a third protein called FLNB and removing a small piece of it. FLNB normally works to prevent the epithelial-to-mesenchymal transition, but the smaller protein encoded by the shorter mRNA promoted the transition by turning on ‘mesenchymal genes’. This switching between different FLNB proteins happens in some of the more aggressive breast cancers, which also contain mesenchymal cells. Finding out which FLNB protein is made in a given cancer may provide an indication of its aggressiveness. Also, looking for drugs that can target the mRNA-binding proteins or FLNB may one day lead to new treatments for some of the most aggressive breast cancers.

Renal medullary carcinoma (RMC) is a rare and deadly kidney cancer in patients of African descent with sickle cell trait. We have developed faithful patient-derived RMC models and using whole-genome sequencing, we identified loss-of-function intronic fusion events in one SMARCB1 allele with concurrent loss of the other allele. Biochemical and functional characterization of these models revealed that RMC requires the loss of SMARCB1 for survival. Through integration of RNAi and CRISPR-Cas9 loss-of-function genetic screens and a small-molecule screen, we found that the ubiquitin-proteasome system (UPS) was essential in RMC. Inhibition of the UPS caused a G2/M arrest due to constitutive accumulation of cyclin B1. These observations extend across cancers that harbor SMARCB1 loss, which also require expression of the E2 ubiquitin-conjugating enzyme, UBE2C . Our studies identify a synthetic lethal relationship between SMARCB1 -deficient cancers and reliance on the UPS which provides the foundation for a mechanism-informed clinical trial with proteasome inhibitors. Renal medullary carcinoma (RMC for short) is a rare type of kidney cancer that affects teenagers and young adults. These patients are usually of African descent and carry one of the two genetic changes that cause sickle cell anemia. RMC is an aggressive disease without effective treatments and patients survive, on average, for only six to eight months after their diagnosis. Recent genetic studies found that most RMC cells have mutations that prevent them from producing a protein called SMARCB1. SMARCB1 normally acts as a so-called tumor suppressor, preventing cells from becoming cancerous. However, it was not clear whether RMCs always have to lose SMARCB1 if they are to survive and grow. Often, diseases are studied using laboratory-grown cells and tissues that have certain features of the disease. No such models had been created for RMC, which has slowed efforts to understand how the disease develops and find new treatments for it. Hong et al. therefore worked with patients to develop new lines of cells that can be used to study RMC in the laboratory. These RMC cells started dying when they were given copies of the SMARCB1 gene, which supports the theory that RMCs have to lose SMARCB1 in order to grow. Hong et al. then used a set of genetic reagents that can suppress or delete genes that are targeted by drugs, and followed this by testing a range of drugs on the RMC cells. Drugs and genetic reagents that reduced the activity of the proteasome – the structure inside cells that gets rid of old or unwanted proteins – caused the RMC cells to die. These proteasome inhibitor drugs also killed other kinds of cancer cells with SMARCB1 mutations. Proteasome inhibitors are already used to treat different types of cancer. Potentially, a clinical trial could be run to see if they will treat patients whose cancers lack SMARCB1. Further work is also needed to determine the exact link between SMARCB1 and the proteasome.

Renal medullary carcinoma (RMC) is a rare and deadly kidney cancer in patients of African descent with sickle cell trait. Through direct-to-patient outreach, we developed genomically faithful patient-derived models of RMC. Using whole genome sequencing, we identified intronic fusion events in one SMARCB1 allele with concurrent loss of the other allele, confirming that SMARCB1 loss occurs in RMC. Biochemical and functional characterization of these RMC models revealed that RMC depends on the loss of SMARCB1 for survival and functionally resemble other cancers that harbor loss of SMARCB1, such as malignant rhabdoid tumors or atypical teratoid rhabdoid tumors. We performed RNAi and CRISPR-Cas9 loss of function genetic screens and a small-molecule screen and identified UBE2C as an essential gene in SMARCB1 deficient cancers. We found that the ubiquitin-proteasome pathway was essential for the survival of SMARCB1 deficient cancers in vitro and in vivo. Genetic or pharmacologic inhibition of this pathway leads to G2/M arrest due to constitutive accumulation of cyclin B1. These observations identify a synthetic lethal relationship that may serve as a therapeutic approach for patients with SMARCB1 deficient cancers.

Selected Issues in Sport-Related Concussion (SRC|Mild Traumatic Brain Injury) for the Team Physician: A Consensus Statement is title 22 in a series of annual consensus documents written for the practicing team physician. This document provides an overview of selected medical issues important to team physicians who are responsible for athletes with sports-related concussion (SRC). This statement was developed by the Team Physician Consensus Conference (TPCC), an annual project-based alliance of six major professional associations. The goal of this TPCC statement is to assist the team physician in providing optimal medical care for the athlete with SRC.

Concussions carry devastating potential for cognitive, neurologic, and socio-emotional disease, but no objective test reliably identifies a concussion and its severity. A variety of neurological insults compromise sound processing, particularly in complex listening environments that place high demands on brain processing. The frequency-following response captures the high computational demands of sound processing with extreme granularity and reliably reveals individual differences. We hypothesize that concussions disrupt these auditory processes, and that the frequency-following response indicates concussion occurrence and severity. Specifically, we hypothesize that concussions disrupt the processing of the fundamental frequency, a key acoustic cue for identifying and tracking sounds and talkers, and, consequently, understanding speech in noise. Here we show that children who sustained a concussion exhibit a signature neural profile. They have worse representation of the fundamental frequency, and smaller and more sluggish neural responses. Neurophysiological responses to the fundamental frequency partially recover to control levels as concussion symptoms abate, suggesting a gain in biological processing following partial recovery. Neural processing of sound correctly identifies 90% of concussion cases and clears 95% of control cases, suggesting this approach has practical potential as a scalable biological marker for sports-related concussion and other types of mild traumatic brain injuries.

Meningiomas are the most common primary intracranial tumors, frequently requiring radiotherapy as a part of management. Effective radiotherapy planning for meningiomas necessitates accurate and consistent segmentation of target volumes on MRI, a process that is complex, labor-intensive, and dependent on expert expertise. The 2024 Brain Tumor Segmentation Challenge Meningioma Radiotherapy (BraTS-MEN-RT) Dataset addresses this problem by providing the largest multi-institutional collection of systematically annotated radiotherapy planning MRIs for meningiomas. Publicly accessible, this dataset comprises 570 radiotherapy planning 3D T1-weighted post-contrast MRIs at native resolutions, with 500 cases featuring expert-annotated gross tumor volumes (GTV). Annotations follow standardized radiotherapy planning protocols and include both intact and postoperative meningioma cases, ensuring wide clinical relevance. Contributions from seven diverse medical centers across the United States and the United Kingdom enhance the dataset’s generalizability. The dataset aims to accelerate the development of automated segmentation methods for radiotherapy planning, improving workflow efficiency, reducing interobserver variability, and ultimately enhancing patient outcomes.

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well-annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied clinical environments and providing personalized volumetric reports to patients undergoing BM treatment.

The 2024 Brain Tumor Segmentation Meningioma Radiotherapy (BraTS-MEN-RT) challenge aimed to advance automated segmentation algorithms using the largest known multi-institutional dataset of 750 radiotherapy planning brain MRIs with expert-annotated target labels for patients with intact or postoperative meningioma that underwent either conventional external beam radiotherapy or stereotactic radiosurgery. Each case included a defaced 3D post-contrast T1-weighted radiotherapy planning MRI in its native acquisition space, accompanied by a single-label \"target volume\" representing the gross tumor volume (GTV) and any at-risk post-operative site. Target volume annotations adhered to established radiotherapy planning protocols, ensuring consistency across cases and institutions, and were approved by expert neuroradiologists and radiation oncologists. Six participating teams developed, containerized, and evaluated automated segmentation models using this comprehensive dataset. Team rankings were assessed using a modified lesion-wise Dice Similarity Coefficient (DSC) and 95% Hausdorff Distance (95HD). The best reported average lesion-wise DSC and 95HD was 0.815 and 26.92 mm, respectively. BraTS-MEN-RT is expected to significantly advance automated radiotherapy planning by enabling precise tumor segmentation and facilitating tailored treatment, ultimately improving patient outcomes. We describe the design and results from the BraTS-MEN-RT challenge.