Tuberous sclerosis complex (TSC) is a rare genetic disease causing multisystem growth of benign tumours and other hamartomatous lesions, which leads to diverse and debilitating clinical symptoms. Patients are born with TSC1 or TSC2 mutations, and somatic inactivation of wild-type alleles drives MTOR activation; however, second hits to TSC1/TSC2 are not always observed. Here, we present the genomic landscape of TSC hamartomas. We determine that TSC lesions contain a low somatic mutational burden relative to carcinomas, a subset feature large-scale chromosomal aberrations, and highly conserved molecular signatures for each type exist. Analysis of the molecular signatures coupled with computational approaches reveals unique aspects of cellular heterogeneity and cell origin. Using immune data sets, we identify significant neuroinflammation in TSC-associated brain tumours. Taken together, this molecular catalogue of TSC serves as a resource into the origin of these hamartomas and provides a framework that unifies genomic and transcriptomic dimensions for complex tumours.

Background Diagnosis of significant coronary artery disease (CAD) in at risk patients can be challenging, typically including non-invasive imaging modalities and ultimately the gold standard of coronary angiography. Previous studies suggested that peripheral blood gene expression can reflect the presence of CAD. Objective To validate a previously developed 23-gene expression-based classifier for diagnosis of obstructive CAD in non-diabetic patients. Design Multi-center prospective trial with blood samples drawn prior to coronary angiography. Setting Thirty-nine US centers. Patients An independent validation cohort of 526 non-diabetic patients clinically-indicated for coronary angiography Intervention None. Measurements Receiver-operator characteristics (ROC) analysis of classifier score measured by real-time polymerase chain reaction (RT-PCR), additivity to clinical factors, and reclassification of patient disease likelihood vs disease status defined by quantitative coronary angiography (QCA). Obstructive CAD defined as ≥50% stenosis in ≥1 major coronary artery by QCA. Results The overall ROC curve area (AUC) was 0.70 ±0.02, (p<0.001); the classifier added to clinical variables (Diamond-Forrester method) (AUC 0.72 with classifier vs 0.66 without, p = 0.003). Net reclassification was improved by the classifier over Diamond-Forrester and an expanded clinical model (both p<0.001). At a score threshold corresponding to 20% obstructive CAD likelihood (14.75), the sensitivity and specificity were 85% and 43%, yielding NPV of 83% and PPV 46%, with 33% of patient scores below this threshold. Limitations The study excluded patients with chronic inflammatory disorders, elevated white blood counts or cardiac protein markers, and diabetes. Conclusions This non-invasive whole blood test, based on gene expression and demographics, may be useful for assessment of obstructive CAD in non-diabetic patients without known CAD. Primary Funding Source CardioDx, Inc.

To understand how genomic heterogeneity of glioblastoma contributes to the poor response to therapy characteristic of this disease, we performed DNA and RNA sequencing on GBM tumor samples and the neurospheres and orthotopic xenograft models derived from them. We used the resulting data set to show that somatic driver alterations including single nucleotide variants, focal DNA alterations, and oncogene amplification on extrachromosomal DNA (ecDNA) elements were in majority propagated from tumor to model systems. In several instances, ecDNAs and chromosomal alterations demonstrated divergent inheritance patterns and clonal selection dynamics during cell culture and xenografting. We infer that ecDNA inherited unevenly between offspring cells, a characteristic that affects the oncogenic potential of cells with more or fewer ecDNAs. Longitudinal patient tumor profiling found that oncogenic ecDNAs are frequently retained throughout the course of disease. Our analysis shows that extrachromosomal elements allow rapid increase of genomic heterogeneity during glioblastoma evolution, independent of chromosomal DNA alterations.

Autism is a highly heritable neurodevelopmental disorder, yet the genetic underpinnings of the disorder are largely unknown. Aberrant brain overgrowth is a well-replicated observation in the autism literature; but association, linkage, and expression studies have not identified genetic factors that explain this trajectory. Few studies have had sufficient statistical power to investigate whole-genome gene expression and genotypic variation in the autistic brain, especially in regions that display the greatest growth abnormality. Previous functional genomic studies have identified possible alterations in transcript levels of genes related to neurodevelopment and immune function. Thus, there is a need for genetic studies involving key brain regions to replicate these findings and solidify the role of particular functional pathways in autism pathogenesis. We therefore sought to identify abnormal brain gene expression patterns via whole-genome analysis of mRNA levels and copy number variations (CNVs) in autistic and control postmortem brain samples. We focused on prefrontal cortex tissue where excess neuron numbers and cortical overgrowth are pronounced in the majority of autism cases. We found evidence for dysregulation in pathways governing cell number, cortical patterning, and differentiation in young autistic prefrontal cortex. In contrast, adult autistic prefrontal cortex showed dysregulation of signaling and repair pathways. Genes regulating cell cycle also exhibited autism-specific CNVs in DNA derived from prefrontal cortex, and these genes were significantly associated with autism in genome-wide association study datasets. Our results suggest that CNVs and age-dependent gene expression changes in autism may reflect distinct pathological processes in the developing versus the mature autistic prefrontal cortex. Our results raise the hypothesis that genetic dysregulation in the developing brain leads to abnormal regional patterning, excess prefrontal neurons, cortical overgrowth, and neural dysfunction in autism.