RESUMEN
Medulloblastoma is an aggressively growing tumour, arising in the cerebellum or medulla/brain stem. It is the most common malignant brain tumour in children, and shows tremendous biological and clinical heterogeneity. Despite recent treatment advances, approximately 40% of children experience tumour recurrence, and 30% will die from their disease. Those who survive often have a significantly reduced quality of life. Four tumour subgroups with distinct clinical, biological and genetic profiles are currently identified. WNT tumours, showing activated wingless pathway signalling, carry a favourable prognosis under current treatment regimens. SHH tumours show hedgehog pathway activation, and have an intermediate prognosis. Group 3 and 4 tumours are molecularly less well characterized, and also present the greatest clinical challenges. The full repertoire of genetic events driving this distinction, however, remains unclear. Here we describe an integrative deep-sequencing analysis of 125 tumour-normal pairs, conducted as part of the International Cancer Genome Consortium (ICGC) PedBrain Tumor Project. Tetraploidy was identified as a frequent early event in Group 3 and 4 tumours, and a positive correlation between patient age and mutation rate was observed. Several recurrent mutations were identified, both in known medulloblastoma-related genes (CTNNB1, PTCH1, MLL2, SMARCA4) and in genes not previously linked to this tumour (DDX3X, CTDNEP1, KDM6A, TBR1), often in subgroup-specific patterns. RNA sequencing confirmed these alterations, and revealed the expression of what are, to our knowledge, the first medulloblastoma fusion genes identified. Chromatin modifiers were frequently altered across all subgroups. These findings enhance our understanding of the genomic complexity and heterogeneity underlying medulloblastoma, and provide several potential targets for new therapeutics, especially for Group 3 and 4 patients.
Asunto(s)
Neoplasias Cerebelosas/genética , Genoma Humano/genética , Meduloblastoma/genética , Envejecimiento/genética , Secuencia de Aminoácidos , Transformación Celular Neoplásica , Neoplasias Cerebelosas/clasificación , Neoplasias Cerebelosas/diagnóstico , Neoplasias Cerebelosas/patología , Niño , Cromatina/metabolismo , Cromosomas Humanos/genética , ARN Helicasas DEAD-box/genética , ADN Helicasas/genética , Proteínas de Unión al ADN/genética , Genómica , Proteínas Hedgehog/metabolismo , Secuenciación de Nucleótidos de Alto Rendimiento , Histona Demetilasas/genética , Humanos , Meduloblastoma/clasificación , Meduloblastoma/diagnóstico , Meduloblastoma/patología , Metilación , Mutación/genética , Tasa de Mutación , Proteínas de Neoplasias/genética , Proteínas Nucleares/genética , Proteínas de Fusión Oncogénica/genética , Receptores Patched , Receptor Patched-1 , Fosfoproteínas Fosfatasas/genética , Poliploidía , Receptores de Superficie Celular/genética , Análisis de Secuencia de ARN , Transducción de Señal , Proteínas de Dominio T Box/genética , Factores de Transcripción/genética , Proteínas Wnt/metabolismo , beta Catenina/genéticaRESUMEN
Cell-counting is critical for a wide range of applications, e.g., life sciences, medicine, or pharmacology. Hemocytometry is a classical method that requires manual counting of cells under a microscope. This methodology is low-cost but manual counting is slow, and the test accuracy is limited by the operator experience. Accuracy and throughput of such application could be improved with the use of automated cell-counting devices. Possessing the ability of recording and processing cell images, devices employing these technologies could dramatically improve the accuracy of the counting results. However, accuracy of these devices still requires further improvement as the counting results rely only on 100-200 cells. Furthermore, the test cost of these devices increases due to the need for a counting chamber or consumables compatible with their hardware settings. Herein, in order to address these drawbacks, we introduced an optofluidic cell-counting platform that could scan more than 2000 cells, which dramatically improves the test accuracy. Our technology could yield an error rate below 1% for cell viability, and below 5% for cell concentration. The platform could deliver the count results within only â¼1 minute, including sample loading, autofocusing, recording images, and image processing. The presented platform also benefits from a built-in fluidic component that eliminates the need for an external counting chamber, and allows fully automated sample loading and self-cleaning modality compatible with any solutions that are typically used for cell-counting tests. Providing an easy-to-use and rapid feature from sample loading to image analyses, our optofluidic platform could be a critical asset for accurate and low cost cell-counting applications.
Asunto(s)
Medicina , Microscopía , Recuento de Células/métodos , Procesamiento de Imagen Asistido por ComputadorRESUMEN
Early-onset prostate cancer (EO-PCA) represents the earliest clinical manifestation of prostate cancer. To compare the genomic alteration landscapes of EO-PCA with "classical" (elderly-onset) PCA, we performed deep sequencing-based genomics analyses in 11 tumors diagnosed at young age, and pursued comparative assessments with seven elderly-onset PCA genomes. Remarkable age-related differences in structural rearrangement (SR) formation became evident, suggesting distinct disease pathomechanisms. Whereas EO-PCAs harbored a prevalence of balanced SRs, with a specific abundance of androgen-regulated ETS gene fusions including TMPRSS2:ERG, elderly-onset PCAs displayed primarily non-androgen-associated SRs. Data from a validation cohort of > 10,000 patients showed age-dependent androgen receptor levels and a prevalence of SRs affecting androgen-regulated genes, further substantiating the activity of a characteristic "androgen-type" pathomechanism in EO-PCA.