Identification of a Novel Recessive Ataxia Gene.

While the genes involved in most forms of sporadic or recessive ataxia with mental retardation are still unknown, exome sequencing is a promising tool to identify novel genes in rare disorders. Previously, two siblings in a consanguineous Turkish family were reported, who presented with a non- progressive ataxia syndrome including congenital truncal and extremity ataxia, cerebellar hypoplasia, hypotonia, developmental delay, mental retardation and nystagmus. After exome sequencing and filtering by homozygosity, we identified a homozygous mutation at the invariant +1 position (c. 964+1 G>A) in intron 9 of the CWF19L1 (complexed with cdc5 protein 19-like 1) gene. This mutation is absent in >6,500 European and African American individuals and 200 Turkish control DNAs. In lymphoblastoid cell lines from affected individuals, the mutation causes exon skipping, reduction in mRNA levels, and protein loss. Morpholino- mediated knockdown in a zebrafish model demonstrates that loss of the evolutionarily highly conserved CWF19L1, whose normal biological function is unknown, alters cerebellar morphology and causes movement abnormalities. Preliminary data suggests this protein is expressed in a tissue-specific manner and that this protein is localized in the nucleus. Our results suggest that we have identified a novel cause of recessive ataxia and developmental delay.PhDCellular & Molecular BiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/108924/1/randibu_1.pd

PRC2 loss induces chemoresistance by repressing apoptosis in T cell acute lymphoblastic leukemia

The tendency of mitochondria to undergo or resist BCL2-controlled apoptosis (so-called mitochondrial priming) is a powerful predictor of response to cytotoxic chemotherapy. Fully exploiting this finding will require unraveling the molecular genetics underlying phenotypic variability in mitochondrial priming. Here, we report that mitochondria) apoptosis resistance in T cell acute lymphoblastic leukemia (T-ALL) is mediated by inactivation of polycomb repressive complex 2 (PRC2). In T-ALL clinical specimens, loss-of-function mutations of PRC2 core components (EZH2, FED, or SUZ12) were associated with mitochondrial apoptosis resistance. In T-ALL cells, PRC2 depletion induced resistance to apoptosis induction by multiple chemotherapeutics with distinct mechanisms of action. PRC2 loss induced apoptosis resistance via transcriptional up-regulation of the LIM domain transcription factor CRIP2 and downstream up-regulation of the mitochondrial chaperone TRAP1. These findings demonstrate the importance of mitochondrial apoptotic priming as a prognostic factor in T-ALL and implicate mitochondrial chaperone function as a molecular determinant of chemotherapy response

Analyses of non-coding somatic drivers in 2,658 cancer whole genomes.

The discovery of drivers of cancer has traditionally focused on protein-coding genes1-4. Here we present analyses of driver point mutations and structural variants in non-coding regions across 2,658 genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium5 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). For point mutations, we developed a statistically rigorous strategy for combining significance levels from multiple methods of driver discovery that overcomes the limitations of individual methods. For structural variants, we present two methods of driver discovery, and identify regions that are significantly affected by recurrent breakpoints and recurrent somatic juxtapositions. Our analyses confirm previously reported drivers6,7, raise doubts about others and identify novel candidates, including point mutations in the 5' region of TP53, in the 3' untranslated regions of NFKBIZ and TOB1, focal deletions in BRD4 and rearrangements in the loci of AKR1C genes. We show that although point mutations and structural variants that drive cancer are less frequent in non-coding genes and regulatory sequences than in protein-coding genes, additional examples of these drivers will be found as more cancer genomes become available

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Measures of Intracranial Injury Size Do Not Improve Clinical Decision Making for Children With Mild Traumatic Brain Injuries and Intracranial Injuries

BackgroundWhen evaluating children with mild traumatic brain injuries (mTBIs) and intracranial injuries (ICIs), neurosurgeons intuitively consider injury size. However, the extent to which such measures (eg, hematoma size) improve risk prediction compared with the kids intracranial injury decision support tool for traumatic brain injury (KIIDS-TBI) model, which only includes the presence/absence of imaging findings, remains unknown.ObjectiveTo determine the extent to which measures of injury size improve risk prediction for children with mild traumatic brain injuries and ICIs.MethodsWe included children ≤18 years who presented to 1 of the 5 centers within 24 hours of TBI, had Glasgow Coma Scale scores of 13 to 15, and had ICI on neuroimaging. The data set was split into training (n = 1126) and testing (n = 374) cohorts. We used generalized linear modeling (GLM) and recursive partitioning (RP) to predict the composite of neurosurgery, intubation &gt;24 hours, or death because of TBI. Each model's sensitivity/specificity was compared with the validated KIIDS-TBI model across 3 decision-making risk cutoffs (&lt;1%, &lt;3%, and &lt;5% predicted risk).ResultsThe GLM and RP models included similar imaging variables (eg, epidural hematoma size) while the GLM model incorporated additional clinical predictors (eg, Glasgow Coma Scale score). The GLM (76%-90%) and RP (79%-87%) models showed similar specificity across all risk cutoffs, but the GLM model had higher sensitivity (89%-96% for GLM; 89% for RP). By comparison, the KIIDS-TBI model had slightly higher sensitivity (93%-100%) but lower specificity (27%-82%).ConclusionAlthough measures of ICI size have clear intuitive value, the tradeoff between higher specificity and lower sensitivity does not support the addition of such information to the KIIDS-TBI model

Development and external validation of the KIIDS-TBI tool for managing children with mild traumatic brain injury and intracranial injuries

Background Clinical decision support (CDS) may improve the postneuroimaging management of children with mild traumatic brain injuries (mTBI) and intracranial injuries. While the CHIIDA score has been proposed for this purpose, a more sensitive risk model may have broader use. Consequently, this study's objectives were to: (1) develop a new risk model with improved sensitivity compared to the CHIIDA model and (2) externally validate the new model and CHIIDA model in a multicenter data set. Methods We analyzed children ≤18 years old with mTBI and intracranial injuries included in the PECARN head injury data set (2004–2006). We used binary recursive partitioning to predict the composite outcome of neurosurgical intervention, intubation for > 24 h due to TBI, or death due to TBI. The new model was externally validated in a separate data set that included children treated at any one of six centers from 2006 to 2019. Results Based on 839 patients from the PECARN data set, a new risk model, the KIIDS-TBI model, was developed that incorporated imaging (e.g., midline shift) and clinical (e.g., Glasgow Coma Scale score) findings. Based on the model-predicted probability of the composite outcome, three cutoffs were evaluated to classify patients as “high risk” for level of care decisions. In the external validation data set consisting of 1,630 patients, the most conservative cutoff (i.e., any predictor present) identified 119 of 119 children with the composite outcome (sensitivity = 100%), but had the lowest specificity (26.3%). The other two decision-making cutoffs had worse sensitivity (94.1%–96.6%) but improved specificity (67.4%–81.3%). The CHIIDA model lacked the most conservative cutoff and otherwise showed the same or slightly worse performance compared to the other two cutoffs. Conclusions The KIIDS-TBI model has high sensitivity and moderate specificity for risk stratifying children with mTBI and intracranial injuries. Use of this CDS tool may help improve the safe, resource-efficient management of this important patient population