White, Kevin P.

2621 Background: TMB is an emerging predictor of survival with immunotherapy. TMB is determined by taking the difference between somatic and germline datasets when tumor-normal pairs are available. In the case of commonly utilized tumor-only sequencing, additional steps are needed to estimate the somatic alterations. Computational tools have been developed that determine germline contribution based on sample copy state, purity estimates and occurrence of the variant in population databases. Given the potential bias of population datasets, we hypothesized that tumor-only filtering approaches may overestimate the actual TMB. Methods: We assessed the TMB from 50 tumors in 10 diseases including all missense, indels, and frameshift variants with an allelic fraction (AF) ≥5% and Coverage ≥100X within the tumor. Tumor-only TMB was evaluated against the gold standard of matched germline subtracted TMB at three levels. Level 1 removed all the tumor-only variants with AF in the non-TCGA ExAC database ≥1%. Level 2 removed all variants observed in population databases simulating a naive approach of removing germline variation. Level 3 used an internal tumor-only pipeline for calculating TMB. Results: There were significantly higher estimates of TMB with Level 1, Level 2 and Level 3 tumor-only filtering approaches than that determined by germline subtraction, resulting in significant bias. Whereas there was no correlation between TMB estimates and tumor-germline TMB for Level 1 filtering, there were improvements in correlations for Level 2 and Level 3. Conclusions: The tumor-only approaches that filter variants in population databases overestimate TMB compared to that determined by germline subtraction. Despite improved correlations with more stringent filtering approaches, these falsely elevated estimates may result in the inappropriate categorization of tumor specimens and negatively impact clinical trial results and patient outcomes. [Table: see text]

INTRODUCTION The human cerebral cortex has undergone an extraordinary increase in size and complexity during mammalian evolution. Cortical cell lineages are specified in the embryo, and genetic and epidemiological evidence implicates early cortical development in the etiology of neuropsychiatric disorders such as autism spectrum disorder (ASD), intellectual disabilities, and schizophrenia. Most of the disease-implicated genomic variants are located outside of genes, and the interpretation of noncoding mutations is lagging behind owing to limited annotation of functional elements in the noncoding genome. RATIONALE We set out to discover gene-regulatory elements and chart their dynamic activity during prenatal human cortical development, focusing on enhancers, which carry most of the weight upon regulation of gene expression. We longitudinally modeled human brain development using human induced pluripotent stem cell (hiPSC)–derived cortical organoids and compared organoids to isogenic fetal brain tissue. RESULTS Fetal fibroblast–derived hiPSC lines were used to generate cortically patterned organoids and to compare oganoids’ epigenome and transcriptome to that of isogenic fetal brains and external datasets. Organoids model cortical development between 5 and 16 postconception weeks, thus enabling us to study transitions from cortical stem cells to progenitors to early neurons. The greatest changes occur at the transition from stem cells to progenitors. The regulatory landscape encompasses a total set of 96,375 enhancers linked to target genes, with 49,640 enhancers being active in organoids but not in mid-fetal brain, suggesting major roles in cortical neuron specification. Enhancers that gained activity in the human lineage are active in the earliest stages of organoid development, when they target genes that regulate the growth of radial glial cells. Parallel weighted gene coexpression network analysis (WGCNA) of transcriptome and enhancer activities defined a number of modules of coexpressed genes and coactive enhancers, following just six and four global temporal patterns that we refer to as supermodules, likely reflecting fundamental programs in embryonic and fetal brain. Correlations between gene expression and enhancer activity allowed stratifying enhancers into two categories: activating regulators (A-regs) and repressive regulators (R-regs). Several enhancer modules converged with gene modules, suggesting that coexpressed genes are regulated by enhancers with correlated patterns of activity. Furthermore, enhancers active in organoids and fetal brains were enriched for ASD de novo variants that disrupt binding sites of homeodomain, Hes1, NR4A2, Sox3, and NFIX transcription factors. CONCLUSION We validated hiPSC-derived cortical organoids as a suitable model system for studying gene regulation in human embryonic brain development, evolution, and disease. Our results suggest that organoids may reveal how noncoding mutations contribute to ASD etiology. Summary of the study, analyses, and main results. Data were generated for iPSC-derived human telencephalic organoids and isogenic fetal cortex. Organoids modeled embryonic and early fetal cortex and show a larger repertoire of enhancers. Enhancers could be divided into activators and repressors of gene expression. We derived networks of modules and supermodules with correlated gene and enhancer activities, some of which were implicated in autism spectrum disorders (ASD).