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Purpose: MRI-based radiomics models can predict genetic markers in pediatric low-grade glioma (pLGG). These models usually require tumour segmentation, which is tedious and time consuming if done manually. We propose a deep learning (DL) model to automate tumour segmentation and build an end-to-end radiomics-based pipeline for pLGG classification. Methods: The proposed architecture is a 2-step U-Net based DL network. The first U-Net is trained on downsampled images to locate the tumour. The second U-Net is trained using image patches centred around the located tumour to produce more refined segmentations. The segmented tumour is then fed into a radiomics-based model to predict the genetic marker of the tumour. Results: Our segmentation model achieved a correlation value of over 80% for all volume-related radiomic features and an average Dice score of.795 in test cases. Feeding the auto-segmentation results into a radiomics model resulted in a mean area under the ROC curve (AUC) of.843, with 95% confidence interval (CI) [.78-.906] and.730, with 95% CI [.671-.789] on the test set for 2-class (BRAF V600E mutation BRAF fusion) and 3-class (BRAF V600E mutation BRAF fusion and Other) classification, respectively. This result was comparable to the AUC of.874, 95% CI [.829-.919] and.758, 95% CI [.724-.792] for the radiomics model trained and tested on the manual segmentations in 2-class and 3-class classification scenarios, respectively. Conclusion: The proposed end-to-end pipeline for pLGG segmentation and classification produced results comparable to manual segmentation when it was used for a radiomics-based genetic marker prediction model. [ABSTRACT FROM AUTHOR]

The use of targeted agents in the treatment of pediatric low-grade gliomas (pLGGs) relies on the determination of molecular status. It has been shown that genetic alterations in pLGG can be identified non-invasively using MRI-based radiomic features or convolutional neural networks (CNNs). We aimed to build and assess a combined radiomics and CNN non-invasive pLGG molecular status identification model. This retrospective study used the tumor regions, manually segmented from T2-FLAIR MR images, of 336 patients treated for pLGG between 1999 and 2018. We designed a CNN and Random Forest radiomics model, along with a model relying on a combination of CNN and radiomic features, to predict the genetic status of pLGG. Additionally, we investigated whether CNNs could predict radiomic feature values from MR images. The combined model (mean AUC: 0.824) outperformed the radiomics model (0.802) and CNN (0.764). The differences in model performance were statistically significant (p-values < 0.05). The CNN was able to learn predictive radiomic features such as surface-to-volume ratio (average correlation: 0.864), and difference matrix dependence non-uniformity normalized (0.924) well but was unable to learn others such as run-length matrix variance (− 0.017) and non-uniformity normalized (− 0.042). Our results show that a model relying on both CNN and radiomic-based features performs better than either approach separately in differentiating the genetic status of pLGGs, and that CNNs are unable to express all handcrafted features. [ABSTRACT FROM AUTHOR]

Intoduction: Diffuse leptomeningeal glioneuronal tumors (DLGNTs) pose a rare and challenging entity within pediatric central nervous system neoplasms. Despite their rarity, DLGNTs exhibit complex clinical presentations and unique molecular characteristics, necessitating a deeper understanding of their diagnostic and therapeutic nuances. Methods: This review synthesizes contemporary literature on DLGNT, encompassing epidemiology, clinical manifestations, pathological features, treatment strategies, prognostic markers, and future research directions. To compile the existing body of knowledge on DLGNT, a comprehensive search of relevant databases was conducted. Results: DLGNT primarily affects pediatric populations but can manifest across all age groups. Its diagnosis is confounded by nonspecific clinical presentations and overlapping radiological features with other CNS neoplasms. Magnetic resonance imaging (MRI) serves as a cornerstone for DLGNT diagnosis, revealing characteristic leptomeningeal enhancement and intraparenchymal involvement. Histologically, DLGNT presents with low to moderate cellularity and exhibits molecular alterations in the MAPK/ERK signalling pathway. Optimal management of DLGNT necessitates a multidisciplinary approach encompassing surgical resection, chemotherapy, radiotherapy, and emerging targeted therapies directed against specific genetic alterations. Prognostication remains challenging, with factors such as age at diagnosis, histological subtypes, and genetic alterations influencing disease progression and treatment response. Long-term survival data are limited, underscoring the need for collaborative research efforts. Conclusion: Advancements in molecular profiling, targeted therapies, and international collaborations hold promise for improving DLGNT outcomes. Harnessing the collective expertise of clinicians, researchers, and patient advocates, can advance the field of DLGNT research and optimize patient care paradigms. [ABSTRACT FROM AUTHOR]

Objectives: Currently, the BRAF status of pediatric low-grade glioma (pLGG) patients is determined through a biopsy. We established a nomogram to predict BRAF status non-invasively using clinical and radiomic factors. Additionally, we assessed an advanced thresholding method to provide only high-confidence predictions for the molecular subtype. Finally, we tested whether radiomic features provide additional predictive information for this classification task, beyond that which is embedded in the location of the tumor. Methods: Random forest (RF) models were trained on radiomic and clinical features both separately and together, to evaluate the utility of each feature set. Instead of using the traditional single threshold technique to convert the model outputs to class predictions, we implemented a double threshold mechanism that accounted for uncertainty. Additionally, a linear model was trained and depicted graphically as a nomogram. Results: The combined RF (AUC: 0.925) outperformed the RFs trained on radiomic (AUC: 0.863) or clinical (AUC: 0.889) features alone. The linear model had a comparable AUC (0.916), despite its lower complexity. Traditional thresholding produced an accuracy of 84.5%, while the double threshold approach yielded 92.2% accuracy on the 80.7% of patients with the highest confidence predictions. Conclusion: Models that included radiomic features outperformed, underscoring their importance for the prediction of BRAF status. A linear model performed similarly to RF but with the added benefit that it can be visualized as a nomogram, improving the explainability of the model. The double threshold technique was able to identify uncertain predictions, enhancing the clinical utility of the model. Clinical relevance statement: Radiomic features and tumor location are both predictive of BRAF status in pLGG patients. We show that they contain complementary information and depict the optimal model as a nomogram, which can be used as a non-invasive alternative to biopsy. Key Points: • Radiomic features provide additional predictive information for the determination of the molecular subtype of pediatric low-grade gliomas patients, beyond what is embedded in the location of the tumor, which has an established relationship with genetic status. • An advanced thresholding method can help to distinguish cases where machine learning models have a high chance of being (in)correct, improving the utility of these models. • A simple linear model performs similarly to a more powerful random forest model at classifying the molecular subtype of pediatric low-grade gliomas but has the added benefit that it can be converted into a nomogram, which may facilitate clinical implementation by improving the explainability of the model. [ABSTRACT FROM AUTHOR]

We describe a 31-year-old male who presented with progressive myelopathy from a thoracic pilocytic astrocytoma (PA). Following multiple recurrences and resections, 10 years after his index surgery, pathology revealed diffuse leptomeningeal glioneuronal tumor (DLGNT) with high-grade features. We discuss his clinical course, management, histopathological findings, and present a comprehensive review of spinal PA undergoing malignant transformation in adults and adult-onset spinal DLGNT. To our knowledge, we present the first reported case of adult-onset spinal PA malignant transformation to DLGNT. Our case adds to the paucity of clinical data characterizing such transformations and highlights the importance of developing novel management paradigms. [ABSTRACT FROM AUTHOR]

Pediatric high-grade glioma (pHGG) is highly malignant central nervous system tumor and constitute 10% of the pediatric gliomas. Effective treatment needs a functioning multi-disciplinary team including pediatric neuro oncologist, neurosurgeon, neuroradiologist, neuropathologist and radiation oncologist. Despite surgical resection, radiotherapy and chemotherapy, most HGG will recur resulting in early death. A significant proportion of HGG occurs in context of cancer predisposition syndromes like Constitutional Mismatch Repair Deficiency (CMMRD) also known as Biallelic Mismatch Repair Deficiency (bMMRD) characterized by high mutational burden. The incidence of HGG with CMMRD is one per million patients. bMMRD is caused by homozygous germline mutations in one of the four Mis Match Repair (MMR) genes (PMS2, MLH1, MSH2, and MSH6). The use of TMZ is now avoided in CMMRD related HGG due to its limited response and known ability to increase the accumulation of somatic mutations in these patients, increasing the risk of secondary tumors. HGG should be managed under the care of multidisciplinary team to receive optimum treatment. This is particularly important for low middle-income countries (LMIC) with limited resources like Pakistan. [ABSTRACT FROM AUTHOR]

Background: Studies to date have yielded inconclusive results as to whether maternal medical history during pregnancy, and a child's early-life medical history contribute to the development of childhood brain tumours (CBTs). This study examined associations between maternal and childhood medical history and the risk of CBTs. Methods: The Childhood Brain Tumour Epidemiology Study of Ontario (CBREO) examined children 0–15 years of age with newly diagnosed CBTs from 1997 to 2003. Multivariable logistic regression analysis determined associations for prenatal medications and childhood medical history, adjusted for child's demographics, and maternal education. Analyses were stratified by histology. A latency period analysis was conducted using 12- and 24-month lead times. Results: Maternal intake of immunosuppressants during the prenatal period was significantly associated with glial tumours (OR 2.73, 95% CI 1.17–6.39). Childhood intake of anti-epileptics was significantly associated with CBTs overall, after accounting for 12-month (OR 8.51, 95% CI 3.35–21.63) and 24-month (OR 6.04, 95% CI 2.06–17.70) lead time before diagnosis. No associations for other medications were found. Conclusions: This study underscores the need to examine potential carcinogenic effects of the medication classes highlighted and of the indication of medication use. Despite possible reverse causality, increased CBT surveillance for children with epilepsy might be warranted. [ABSTRACT FROM AUTHOR]

Constitutional mismatch repair deficiency (CMMRD) is an aggressive and highly penetrant cancer predisposition syndrome. Because of its variable clinical presentation and phenotypical overlap with neurofibromatosis, timely diagnosis remains challenging, especially in countries with limited resources. Since current tests are either difficult to implement or interpret or both we used a novel and relatively inexpensive functional genomic assay (LOGIC) which has been recently reported to have high sensitivity and specificity in diagnosing CMMRD. Here we report the clinical and molecular characteristics of nine patients diagnosed with cancer and suspected to have CMMRD and highlight the challenges with variant interpretation and immunohistochemical analysis that led to an uncertain interpretation of genetic findings in 6 of the 9 patients. Using LOGIC, we were able to confirm the diagnosis of CMMRD in 7 and likely exclude it in 2 patients, resolving ambiguous result interpretation. LOGIC also enabled predictive testing of asymptomatic siblings for early diagnosis and implementation of surveillance. This study highlights the varied manifestations and practical limitations of current diagnostic criteria for CMMRD, and the importance of international collaboration for implementing robust and low-cost functional assays for resolving diagnostic challenges. [ABSTRACT FROM AUTHOR]

The detection of gene fusions by RNA-based next-generation sequencing (NGS) is an emerging method in clinical genetic laboratories for oncology biomarker testing to direct targeted therapy selections. A recent Canadian study (CANTRK study) comparing the detection of NTRK gene fusions on different NGS assays to determine subjects' eligibility for tyrosine kinase TRK inhibitor therapy identified the need for recommendations for best practices for laboratory testing to optimize RNA-based NGS gene fusion detection. To develop consensus recommendations, representatives from 17 Canadian genetic laboratories participated in working group discussions and the completion of survey questions about RNA-based NGS. Consensus recommendations are presented for pre-analytic, analytic and reporting aspects of gene fusion detection by RNA-based NGS. [ABSTRACT FROM AUTHOR]

Purpose: Biopsy-based assessment of H3 K27 M status helps in predicting survival, but biopsy is usually limited to unusual presentations and clinical trials. We aimed to evaluate whether radiomics can serve as prognostic marker to stratify diffuse intrinsic pontine glioma (DIPG) subsets. Methods: In this retrospective study, diagnostic brain MRIs of children with DIPG were analyzed. Radiomic features were extracted from tumor segmentations and data were split into training/testing sets (80:20). A conditional survival forest model was applied to predict progression-free survival (PFS) using training data. The trained model was validated on the test data, and concordances were calculated for PFS. Experiments were repeated 100 times using randomized versions of the respective percentage of the training/test data. Results: A total of 89 patients were identified (48 females, 53.9%). Median age at time of diagnosis was 6.64 years (range: 1–16.9 years) and median PFS was 8 months (range: 1–84 months). Molecular data were available for 26 patients (29.2%) (1 wild type, 3 K27M-H3.1, 22 K27M-H3.3). Radiomic features of FLAIR and nonenhanced T1-weighted sequences were predictive of PFS. The best FLAIR radiomics model yielded a concordance of.87 [95% CI:.86–.88] at 4 months PFS. The best T1-weighted radiomics model yielded a concordance of.82 [95% CI:.8–.84] at 4 months PFS. The best combined FLAIR + T1-weighted radiomics model yielded a concordance of.74 [95% CI:.71–.77] at 3 months PFS. The predominant predictive radiomic feature matrix was gray-level size-zone. Conclusion: MRI-based radiomics may predict progression-free survival in pediatric diffuse midline glioma/diffuse intrinsic pontine glioma. [ABSTRACT FROM AUTHOR]

Background And Objectives: Langerhans cell histiocytosis (LCH) is an inflammatory myeloid neoplasm with a wide spectrum of clinical presentations. Programmed Cell Death-1 (PD- 1) receptor and its ligand (PD-L1) are overexpressed in LCH, but their clinical significance is unknown. We performed a clinical correlation study of PD-1/PD-L1 and VE1(BRAFp.V600E) expression in 131 children with LCH. Methods: A total of 111 samples were tested for PD-1/PD-L1 and 109 for VE1(BRAFp.V600E) mutant protein by immunohistochemistry. Results: PD-1, PD-L1 and VE1(BRAFp.V600E) positivity was observed in 40.5%, 31.53% and 55%, respectively. PD-1/PD-L1 expression showed no significant effect on the rate of disease reactivations, early response to therapy or late sequelae. The 5-year EFS was not statistically different between patients with PD-1 positive compared to those with PD-1 negative tumours (47.7% vs.58.8%, p=0.17). Similar 5-year EFS rates were also seen in those who were PD-L1 positive compared to PD-L1 negative cases (50.5% vs.55.5%, p=0.61). VE1(BRAFp.V600E) positivity was associated with a significantly higher frequency of risk-organ involvement (p=0.0053), but no significant effect on early response to therapy or rates of reactivations or late sequelae. Conclusions: Our study showed no significant correlation between VE1(BRAFp.V600E) expression, PD-1 and PD-L1 and clinical outcome in pediatric LCH. [ABSTRACT FROM AUTHOR]

Glioblastoma (GBM) is characterized by extensive cellular and genetic heterogeneity. Its initial presentation as primary disease (pGBM) has been subject to exhaustive molecular and cellular profiling. By contrast, our understanding of how GBM evolves to evade the selective pressure of therapy is starkly limited. The proteomic landscape of recurrent GBM (rGBM), which is refractory to most treatments used for pGBM, are poorly known. We, therefore, quantified the transcriptome and proteome of 134 patient-derived pGBM and rGBM samples, including 40 matched pGBM–rGBM pairs. GBM subtypes transition from pGBM to rGBM towards a preferentially mesenchymal state at recurrence, consistent with the increasingly invasive nature of rGBM. We identified immune regulatory/suppressive genes as important drivers of rGBM and in particular 2–5-oligoadenylate synthase 2 (OAS2) as an essential gene in recurrent disease. Our data identify a new class of therapeutic targets that emerge from the adaptive response of pGBM to therapy, emerging specifically in recurrent disease and may provide new therapeutic opportunities absent at pGBM diagnosis. [ABSTRACT FROM AUTHOR]

Histone H3 mutations at amino acids 27 (H3K27M) and 34 (H3G34R) are recurrent drivers of pediatric-type high-grade glioma (pHGG). H3K27M mutations lead to global disruption of H3K27me3 through dominant negative PRC2 inhibition, while H3G34R mutations lead to local losses of H3K36me3 through inhibition of SETD2. However, their broader oncogenic mechanisms remain unclear. We characterized the H3.1K27M, H3.3K27M and H3.3G34R interactomes, finding that H3K27M is associated with epigenetic and transcription factor changes; in contrast H3G34R removes a break on cryptic transcription, limits DNA methyltransferase access, and alters mitochondrial metabolism. All 3 mutants had altered interactions with DNA repair proteins and H3K9 methyltransferases. H3K9me3 was reduced in H3K27M-containing nucleosomes, and cis-H3K9 methylation was required for H3K27M to exert its effect on global H3K27me3. H3K9 methyltransferase inhibition was lethal to H3.1K27M, H3.3K27M and H3.3G34R pHGG cells, underscoring the importance of H3K9 methylation for oncohistone-mutant gliomas and suggesting it as an attractive therapeutic target. [ABSTRACT FROM AUTHOR]

Medulloblastoma (MB) is the most common primary malignant pediatric brain cancer. We recently identified novel roles for the MEK/MAPK pathway in regulating human Sonic Hedgehog (SHH) MB tumorigenesis. The MEK inhibitor, selumetinib, decreased SHH MB growth while extending survival in mouse models. However, the treated mice ultimately succumbed to disease progression. Here, we perform RNA sequencing on selumetinib-treated orthotopic xenografts to identify molecular pathways that compensate for MEK inhibition specifically in vivo. Notably, the JAK/STAT3 pathway exhibits increased activation in selumetinib-treated tumors. The combination of selumetinib and the JAK/STAT3 pathway inhibitor, pacritinib, further reduces growth in two xenograft models and also enhances survival. Multiplex spatial profiling of proteins in drug-treated xenografts reveals shifted molecular dependencies and compensatory changes following combination drug treatment. Our study warrants further investigation into MEK and JAK/STAT3 inhibition as a novel combinatory therapeutic strategy for SHH MB. RNA sequencing of MEK inhibitor (selumetinib)-treated tumors reveals an upregulation of the JAK/STAT3 pathway, with combinatorial therapeutic strategies of JAK/STAT3 inhibitors and selumetinib investigated for the SHH subgroup of medulloblastoma. [ABSTRACT FROM AUTHOR]

Objectives: In precision medicine, where oncologic management is tailored to the individual's clinical and genetic profiles, advanced diagnostic testing provides prognostic information and guides management in a growing number of malignancies. There is a need to capture the work pathologists perform to meet this demand by providing medically relevant, timely, and accurate testing results. This work includes not only direct patient consults (interpretation of results and issuing reports) but the administrative and medical oversight as well as the research needed to provide the necessary quality assurance, quality control, direction, and framework for the laboratory.Methods: An expert panel of Canadian pathologists involved in advanced diagnostics was convened to establish and beta test a model for workload assessment in advanced diagnostics.Results: All aspects of the advanced diagnostics workload were detailed and applied to models based on members' experience, including medical oversight, administration, and the introduction of new testing and platforms. Models for biomarker testing were developed for simple and complex or multiplexed assays, and a detailed model was developed to assess the workload for next-generation sequencing-based assays.Conclusions: This paper provides the first detailed proposal for capturing an advanced diagnostic workload to enable appropriate pathologist allotment for performing all the steps required to run an advanced diagnostic service. [ABSTRACT FROM AUTHOR]

Hippocampal sclerosis (HS) is a common surgical substrate in adult epilepsy surgery cohorts but variably reported in various pediatric cohorts. We aimed to study the epilepsy phenotype, radiological and pathological variability, seizure and neurocognitive outcomes in children with drug-resistant epilepsy and hippocampal sclerosis (HS) with or without additional subtle signal changes in anterior temporal lobe who underwent surgery. This retrospective study enrolled children with drug-resistant focal epilepsy and hippocampal sclerosis with or without additional subtle T2-Fluid Attenuated Inversion Recovery (FLAR)/Proton Density (PD) signal changes in anterior temporal lobe who underwent anterior temporal lobectomy with amygdalohippocampectomy. Their clinical, EEG, neuropsychological, radiological and pathological data were reviewed and summarized. Thirty-six eligible patients were identified. The mean age at seizure onset was 3.7 years; 25% had daily seizures at time of surgery. Isolated HS was noted in 22 (61.1%) cases and additional subtle signal changes in ipsilateral temporal lobe in 14 (38.9%) cases. Compared to the normative population, the group mean performance in intellectual functioning and most auditory and visual memory tasks were significantly lower than the normative sample. The mean age at surgery was 12.3 years; 22 patients (61.1%) had left hemispheric surgeries. ILAE class 1 outcomes was seen in 28 (77.8%) patients after a mean follow up duration of 2.3 years. Hippocampal sclerosis was noted pathologically in 32 (88.9%) cases; type 2 (54.5%) was predominant subtype where further classification was possible. Additional pathological abnormalities were seen in 11 cases (30.6%); these had had similar rates of seizure freedom as compared to children with isolated hippocampal sclerosis/gliosis (63.6% vs 84%, p=0.21). Significant reliable changes were observed across auditory and visual memory tasks at an individual level post surgery. Favourable seizure outcomes were seen in most children with isolated radiological hippocampal sclerosis. Patients with additional pathological abnormalities had similar rates of seizure freedom as compared to children with isolated hippocampal sclerosis/gliosis. • Subtle anterior temporal signal changes were noted in 38.9% children with hippocampal sclerosis (HS). • 77.8% children became seizure free after anterior temporal lobectomy with amygdalohippocampectomy. • Overall, 30.6% cases had additional pathological abnormalities. • Seizure freedom rates of children with additional pathological abnormalities similar to those with isolated HS. [ABSTRACT FROM AUTHOR]

Pediatric-type low-grade glioma (PLGG) encompasses a heterogeneous group of WHO grade 1 or 2 tumors and is the most common central nervous system tumor found in children. PLGG extends beyond pediatrics, into adolescents and young adults (AYA, ages 15–40). PLGG represents 25% of all gliomas diagnosed in AYA with differences in tumor location and molecular alterations compared to children, resulting in improved outcome for AYAs. Long-term outcome is excellent, though patients may suffer significant morbidity depending on tumor location. There are differences in treatment practices with radiation used to treat PLGG in AYAs more often than in children. Most PLGG in AYA harbor an alteration in the RAS/MAPK pathway, with limited insight into response to targeted therapy in this age group. This review discusses the epidemiology, current therapeutic approaches, and challenges in the management of PLGG in AYA. [ABSTRACT FROM AUTHOR]

Two targets with one arrow: #Immunotherapy as single treatment for synchronous RRD #glioblastoma and #metastatic cancers. [ABSTRACT FROM AUTHOR]

Two targets with one arrow: #Immunotherapy as single treatment for synchronous RRD #glioblastoma and #metastatic cancers. [ABSTRACT FROM AUTHOR]

Motivation Current fusion detection tools use diverse calling approaches and provide varying results, making selection of the appropriate tool challenging. Ensemble fusion calling techniques appear promising; however, current options have limited accessibility and function. Results MetaFusion is a flexible metacalling tool that amalgamates outputs from any number of fusion callers. Individual caller results are standardized by conversion into the new file type Common Fusion Format. Calls are annotated, merged using graph clustering, filtered and ranked to provide a final output of high-confidence candidates. MetaFusion consistently achieves higher precision and recall than individual callers on real and simulated datasets, and reaches up to 100% precision, indicating that ensemble calling is imperative for high-confidence results. MetaFusion uses FusionAnnotator to annotate calls with information from cancer fusion databases and is provided with a Benchmarking Toolkit to calibrate new callers. Availability and implementation MetaFusion is freely available at https://github.com/ccmbioinfo/MetaFusion. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]