Pathology, diagnostics, and classification of medulloblastoma.
Ontology highlight
ABSTRACT:
SUBMITTER: Orr BA
PROVIDER: S-EPMC7317787 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:AimsApplication of advanced molecular pathology in rare tumours is hindered by low sample numbers, access to specialised expertise/technologies and tissue/assay QC and rapid reporting requirements. We assessed the feasibility of co-ordinated real-time centralised pathology review (CPR), encompassing molecular diagnostics and contemporary genomics (RNA-seq/DNA methylation-array).MethodsThis nationwide trial in medulloblastoma (<80 UK diagnoses/year) introduced a national reference centre (NRC) and assessed its performance and reporting to World Health Organisation standards. Paired frozen/formalin-fixed, paraffin-embedded tumour material were co-submitted from 135 patients (16 referral centres).ResultsComplete CPR diagnostics were successful for 88% (120/135). Inadequate sampling was the most common cause of failure; biomaterials were typically suitable for methylation-array (129/135, 94%), but frozen tissues commonly fell below RNA-seq QC requirements (53/135, 39%). Late reporting was most often due to delayed submission. CPR assigned or altered histological variant (vs local diagnosis) for 40/135 tumours (30%). Benchmarking/QC of specific biomarker assays impacted test results; fluorescent in-situ hybridisation most accurately identified high-risk MYC/MYCN amplification (20/135, 15%), while combined methods (CTNNB1/chr6 status, methylation-array subgrouping) best defined favourable-risk WNT tumours (14/135; 10%). Engagement of a specialist pathologist panel was essential for consensus assessment of histological variants and immunohistochemistry. Overall, CPR altered clinical risk-status for 29% of patients.ConclusionNational real-time CPR is feasible, delivering robust diagnostics to WHO criteria and assignment of clinical risk-status, significantly altering clinical management. Recommendations and experience from our study are applicable to advanced molecular diagnostics systems, both local and centralised, across rare tumour types, enabling their application in biomarker-driven routine diagnostics and clinical/research studies.
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Project description:Germline alterations of the tumor suppressor PTEN have been extensively characterized in patients with PTEN hamartoma tumor syndromes, encompassing subsets of Cowden syndrome, Bannayan-Riley-Ruvalcaba syndrome, Proteus and Proteus-like syndromes, as well as autism spectrum disorder. Studies have shown an increase in the risk of developing specific cancer types in the presence of a germline PTEN mutation. Furthermore, outside of the familial setting, somatic variants of PTEN occur in numerous malignancies. Here we introduce and discuss the prospect of moving toward a systems pathology approach for PTEN diagnostics, incorporating clinical and molecular pathology data with the goal of improving the clinical management of patients with a PTEN mutation. Detection of a germline PTEN mutation can inform cancer surveillance and in the case of somatic mutation, have value in predicting disease course. Given that PTEN functions in the PI3K/AKT/mTOR pathway, identification of a PTEN mutation may highlight new therapeutic opportunities and/or inform therapeutic choices.
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Project description:Recent advances in cancer genomics have revealed 4 distinct subgroups of medulloblastomas, each with unique transcription profiles, DNA alterations and clinical outcome. Molecular classification of medulloblastomas improves predictions of clinical outcome, allowing more accurate matching of intensity of conventional treatments with chemotherapy and radiation to overall prognosis and setting the stage for the introduction of targeted therapies.
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Project description:Medulloblastoma (MB) is the commonest primary malignant brain cancer. The current treatment of MB is usually surgical resection combined with radiotherapy or chemotherapy. Although great progress has been made in the clinical management of MB, tumor metastasis and recurrence are still the main cause of death. Therefore, definitive and timely diagnosis is of great importance for improving therapeutic effects on MB. In 2016, the World Health Organization (WHO) divided MB into four subtypes: wingless-type mouse mammary tumor virus integration site (WNT), sonic hedgehog (SHH), non-WNT/non-SHH group 3, and group 4. Each subtype of MB has a unique profile in copy number variation, DNA alteration, gene transcription, or post-transcriptional/translational modification, all of which are associated with different biological manifestations, clinical features, and prognosis. This article reviewed the research progress of different molecular pathology markers in MB and summarized some targeted drugs against these molecular markers, hoping to stimulate the clinical application of these molecular markers in the classification, diagnosis, and treatment of MB.
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Project description:Relapsed medulloblastoma (rMB) accounts for a considerable, and disproportionate amount of childhood cancer deaths. Recent advances have gone someway to characterising disease biology at relapse including second malignancies that often cannot be distinguished from relapse on imaging alone. Furthermore, there are now multiple international early-phase trials exploring drug-target matches across a range of high-risk/relapsed paediatric tumours. Despite these advances, treatment at relapse in pre-irradiated patients is typically non-curative and focuses on providing life-prolonging and symptom-modifying care that is tailored to the needs and wishes of the individual and their family. Here, we describe the current understanding of prognostic factors at disease relapse such as principal molecular group, adverse molecular biology, and timing of relapse. We provide an overview of the clinical diagnostic process including signs and symptoms, staging investigations, and molecular pathology, followed by a summary of treatment modalities and considerations. Finally, we summarise future directions to progress understanding of treatment resistance and the biological mechanisms underpinning early therapy-refractory and relapsed disease. These initiatives include development of comprehensive and collaborative molecular profiling approaches at relapse, liquid biopsies such as cerebrospinal fluid (CSF) as a biomarker of minimal residual disease (MRD), modelling strategies, and the use of primary tumour material for real-time drug screening approaches.
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Project description:Traditionally, tumors are classified by histopathological criteria, i.e., based on their specific morphological appearances. Consequently, current therapeutic decisions in oncology are strongly influenced by histology rather than underlying molecular or genomic aberrations. The increase of information on molecular changes however, enabled by the Human Genome Project and the International Cancer Genome Consortium as well as the manifold advances in molecular biology and high-throughput sequencing techniques, inaugurated the integration of genomic information into disease classification. Furthermore, in some cases it became evident that former classifications needed major revision and adaption. Such adaptations are often required by understanding the pathogenesis of a disease from a specific molecular alteration, using this molecular driver for targeted and highly effective therapies. Altogether, reclassifications should lead to higher information content of the underlying diagnoses, reflecting their molecular pathogenesis and resulting in optimized and individual therapeutic decisions. The objective of this article is to summarize some particularly important examples of genome-based classification approaches and associated therapeutic concepts. In addition to reviewing disease specific markers, we focus on potentially therapeutic or predictive markers and the relevance of molecular diagnostics in disease monitoring.
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Project description:Lens-free digital in-line holography (LDIH) is a promising microscopic tool that overcomes several drawbacks (e.g., limited field of view) of traditional lens-based microcopy. However, extensive computation is required to reconstruct object images from the complex diffraction patterns produced by LDIH. This limits LDIH utility for point-of-care applications, particularly in resource limited settings. We describe a deep transfer learning (DTL) based approach to process LDIH images in the context of cellular analyses. Specifically, we captured holograms of cells labeled with molecular-specific microbeads and trained neural networks to classify these holograms without reconstruction. Using raw holograms as input, the trained networks were able to classify individual cells according to the number of cell-bound microbeads. The DTL-based approach including a VGG19 pretrained network showed robust performance with experimental data. Combined with the developed DTL approach, LDIH could be realized as a low-cost, portable tool for point-of-care diagnostics.
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Project description:In December 2019, physicians reported numerous patients showing pneumonia of unknown origin in the Chinese region of Wuhan. Following the spreading of the infection over the world, The World Health Organization (WHO) on 11 March 2020 declared the novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) outbreak a global pandemic. The scientific community is exerting an extraordinary effort to elucidate all aspects related to SARS-CoV-2, such as the structure, ultrastructure, invasion mechanisms, replication mechanisms, or drugs for treatment, mainly through in vitro studies. Thus, the clinical in vivo data can provide a test bench for new discoveries in the field of SARS-CoV-2, finding new solutions to fight the current pandemic. During this dramatic situation, the normal scientific protocols for the development of new diagnostic procedures or drugs are frequently not completely applied in order to speed up these processes. In this context, interdisciplinarity is fundamental. Specifically, a great contribution can be provided by the association and interpretation of data derived from medical disciplines based on the study of images, such as radiology, nuclear medicine, and pathology. Therefore, here, we highlighted the most recent histopathological and imaging data concerning the SARS-CoV-2 infection in lung and other human organs such as the kidney, heart, and vascular system. In addition, we evaluated the possible matches among data of radiology, nuclear medicine, and pathology departments in order to support the intense scientific work to address the SARS-CoV-2 pandemic. In this regard, the development of artificial intelligence algorithms that are capable of correlating these clinical data with the new scientific discoveries concerning SARS-CoV-2 might be the keystone to get out of the pandemic.
| S-EPMC7554796 | biostudies-literature
Project description:Mendelian diseases have shown to be an and efficient model for connecting genotypes to phenotypes and for elucidating the function of genes. Whole-exome sequencing (WES) accelerated the study of rare Mendelian diseases in families, allowing for directly pinpointing rare causal mutations in genic regions without the need for linkage analysis. However, the low diagnostic rates of 20-30% reported for multiple WES disease studies point to the need for improved variant pathogenicity classification and causal variant prioritization methods. Here, we present the exome Disease Variant Analysis (eDiVA; http://ediva.crg.eu), an automated computational framework for identification of causal genetic variants (coding/splicing single-nucleotide variants and small insertions and deletions) for rare diseases using WES of families or parent-child trios. eDiVA combines next-generation sequencing data analysis, comprehensive functional annotation, and causal variant prioritization optimized for familial genetic disease studies. eDiVA features a machine learning-based variant pathogenicity predictor combining various genomic and evolutionary signatures. Clinical information, such as disease phenotype or mode of inheritance, is incorporated to improve the precision of the prioritization algorithm. Benchmarking against state-of-the-art competitors demonstrates that eDiVA consistently performed as a good or better than existing approach in terms of detection rate and precision. Moreover, we applied eDiVA to several familial disease cases to demonstrate its clinical applicability.
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Project description:This will be a pilot study involving 5 patients diagnosed with colorectal carcinoma and treated with pre-operative chemotherapy and external beam radiation therapy at the Jewish General Hospital, whom will very soon undergo surgery. Participants will be sensitized by the instillation of a 250 mL enema containing 1.6 mmol of HAL. The enema will be administered with a plastic tube with an inflatable blocking balloon to prevent leakage of the enema. Fluorescence sigmoidoscopy will be performed with white light then blue excitation light after retention of the enema for 60 minutes, followed by a rest time of up to 30 minutes before rectoscopy. Red fluorescence should be induced by illumination with blue light. Pictures with and without fluorescence will be taken. The patients will undergo a colectomy (partial or complete) within the next 2-3 days and the surgical specimens will be collected for further fluorescence microscopy studies and pathological correlation of fluoresce with malignant pathology/histology as the gold standard. The total concentration of porphyrins in the patients’ urine and serum will be recorded before sensitization, immediately after sensitization (instillation of the enema), and approximately 24 hours after sensitization. The patients’ pre-and-post operative liver function tests will be measured. Adverse events will be reported by direct questioning of all patients with regards to photosensitivity and gastrointestinal symptoms (nausea, vomiting), and by measuring blood pressure and heart rate. Our objectives and endpoints are: 1) to determine if fluorescence with photodynamic diagnostics is selective for colorectal cancer, 2) to determine if photodynamic diagnostics has the potential to improve the detection of malignant cell after neoadjuvant chemotherapy and radiation, and 3) to determine if photodynamic diagnostics can provide an accurate depiction of the extent of disease burden not visible with normal white light sigmoidoscopy to the naked human eye.
| 2253555 | ecrin-mdr-crc