Diagnostic classification of childhood cancer using multiscale transcriptomics.

The causes of pediatric cancers' distinctiveness compared to adult-onset tumors of the same type are not completely clear and not fully explained by their genomes. In this study, we used an optimized multilevel RNA clustering approach to derive molecular definitions for most childhood cancers. Applying this method to 13,313 transcriptomes, we constructed a pediatric cancer atlas to explore age-associated changes. Tumor entities were sometimes unexpectedly grouped due to common lineages, drivers or stemness profiles. Some established entities were divided into subgroups that predicted outcome better than current diagnostic approaches. These definitions account for inter-tumoral and intra-tumoral heterogeneity and have the potential of enabling reproducible, quantifiable diagnostics. As a whole, childhood tumors had more transcriptional diversity than adult tumors, maintaining greater expression flexibility. To apply these insights, we designed an ensemble convolutional neural network classifier. We show that this tool was able to match or clarify the diagnosis for 85% of childhood tumors in a prospective cohort. If further validated, this framework could be extended to derive molecular definitions for all cancer types.

An Informatics Analysis to Identify Sex Disparities and Healthcare Needs for Autism across the United States.

Autism is among the most common neurodevelopmental conditions. Timely diagnosis and access to therapeutic resources are essential for positive prognoses, yet long queues and unevenly dispersed resources leave many untreated. Without granular estimates of autism prevalence by geographic area, it is difficult to identify unmet needs and mechanisms to address them. Mining a dataset of 53M children using meaningful geographic regions, we computed autism prevalence across the country. We then performed comparative analysis against 50,000 resources to identify the type and extent of gaps in access to autism services. We find a steady increase in autism diagnoses from K-5, supporting delayed diagnosis of autism, and consistent under-diagnosis of females. We find a significant inverse relationship between prevalence and availability of resources (p < 0.001). While more work is needed to characterize additional trends including racial and ethnicity-based disparities, the identification of resource gaps can direct and prioritize new innovations.

Indels in SARS-CoV-2 occur at template-switching hotspots.

The evolutionary dynamics of SARS-CoV-2 have been carefully monitored since the COVID-19 pandemic began in December 2019. However, analysis has focused primarily on single nucleotide polymorphisms and largely ignored the role of insertions and deletions (indels) as well as recombination in SARS-CoV-2 evolution. Using sequences from the GISAID database, we catalogue over 100 insertions and deletions in the SARS-CoV-2 consensus sequences. We hypothesize that these indels are artifacts of recombination events between SARS-CoV-2 replicates whereby RNA-dependent RNA polymerase (RdRp) re-associates with a homologous template at a different loci ("imperfect homologous recombination"). We provide several independent pieces of evidence that suggest this. (1) The indels from the GISAID consensus sequences are clustered at specific regions of the genome. (2) These regions are also enriched for 5' and 3' breakpoints in the transcription regulatory site (TRS) independent transcriptome, presumably sites of RNA-dependent RNA polymerase (RdRp) template-switching. (3) Within raw reads, these indel hotspots have cases of both high intra-host heterogeneity and intra-host homogeneity, suggesting that these indels are both consequences of de novo recombination events within a host and artifacts of previous recombination. We briefly analyze the indels in the context of RNA secondary structure, noting that indels preferentially occur in "arms" and loop structures of the predicted folded RNA, suggesting that secondary structure may be a mechanism for TRS-independent template-switching in SARS-CoV-2 or other coronaviruses. These insights into the relationship between structural variation and recombination in SARS-CoV-2 can improve our reconstructions of the SARS-CoV-2 evolutionary history as well as our understanding of the process of RdRp template-switching in RNA viruses.