Citations

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).

Abstract Over 50 genetic variants have been associated with colorectal cancer (CRC) risk through genome-wide association studies (GWAS), yet these variants represent only a fraction of the total estimated heritability. CRC is a heterogenous disease with diverse tumor etiology. Assessing genetic risk in molecular subtypes may help to identify novel loci and characterize genetic risk among tumor subtypes. We used microsatellite instability (MSI), an established CRC classifier with etiological and therapeutic relevance, to define CRC subtypes for GWAS analyses. We conducted a case-case analysis to estimate odds ratios (OR) and 95% confidence intervals (CI) for association of genome-wide variants with microsatellite stable (MSS) versus unstable (MSI) carcinomas. We ran an inverse-variance weighted fixed-effects meta-analysis across GWAS in a discovery set of 4,163 population-based CRC cases with harmonized microsatellite instability (MSI) marker and imputed genotype data. For each analysis, we used log-additive logistic regression, adjusting for age, sex, and principal components to account for population substructure. We then followed up with replication of 102 SNPs that reached p-values less than 5x10-6 in 1,698 cases. A total of 845 (20.3%) cancer cases were microsatellite unstable in the discovery population and 174 (10.2%) were unstable in the replication population. No variants reached the genome-wide significance level of 5x10-8 in the discovery set. However, we identified two variants that reached a Bonferroni corrected p-value of 4.0x10-4 in the replication set. This included one variant in MLH1 (Replication: OR=1.74, 95% CI=1.53-1.98, p=1.63x10-5; Discovery+Replication: OR=1.45, 95% CI=1.37-1.54, p=9.76x10-11) and one variant in LOC105377645 (Replication: OR=1.70, 95% CI=1.49-1.94, p=5.13x10-5; Discovery+Replication: OR=1.45, 95% CI=1.37-1.54, p=9.76 x 10-11). The MLH1 gene is a DNA mismatch repair gene implicated in Lynch Syndrome, the hallmark of which is microsatellite instability. This is the first genome-wide scan to identify a common variant in MLH1 that is associated with CRC. This variant (minor allele frequency, MAF = 23% in this all European ancestry population) is located in the 5'-untranslated region of MLH1 and is thought to act as a long-range regulator of DCLK3, a potential tumor driver gene. The second variant, located in LOC105377645 with an MAF of 22%, is in an uncharacterized region of the genome and has not previously been implicated in cancer development. These findings suggest that accounting for molecular heterogeneity is important for discovery and characterization of genetic variants associated with CRC risk. We plan to run polytomous regression analyses, increase our sample size, and further investigate CRC subtypes by CIMP, BRAF mutation, KRAS mutation status. Citation Format: Tabitha A. Harrison, Yiwen Lu, Chenjie Zeng, Flora Qu, Kristin Anderson, Hermann Brenner, Daniel D. Buchanan, Peter T. Campbell, Andrew T. Chan, Jenny Chang-Claude, Graham G. Giles, Bethany Van Guelpen, Michael Hoffmeister, Mark A. Jenkins, Noralane M. Lindor, Roger L. Milne, Polly A. Newcomb, Reiko Nishihara, Michael O. Woods, Shuji Ogino, John D. Potter, Martha L. Slattery, Wei Sun, Stephen N. Thibodeau, Li Hsu, Ulrike Peters. Genome-wide association study by colorectal carcinoma subtype [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 229.

Brain mutations, young and old Most neurons that make up the human brain are postmitotic, living and functioning for a very long time without renewal (see the Perspective by Lee). Bae et al. examined the genomes of single neurons from the prenatal developing human brain. Both the type of mutation and the rates of accumulation changed between gastrulation and neurogenesis. These early mutations could be generating useful neuronal diversity or could predispose individuals to later dysfunction. Lodato et al. also found that neurons take on somatic mutations as they age by sequencing single neurons from subjects aged 4 months to 82 years. Somatic mutations accumulated with increasing age and accumulated faster in individuals affected by inborn errors in DNA repair. Postmitotic mutations might only affect one neuron, but the accumulated divergence of genomes across the brain could affect function. Science , this issue p. 550 , p. 555 ; see also p. 521

Abstract Patient-derived xenografts (PDX) are appearing as a prime approach for preclinical studies despite being insufficiently characterized as a model of the human disease and its diversity. In this work, 29 PDX were obtained from either surgery or endoscopic ultrasound-guided fine needle aspirate of pancreatic adenocarcinoma. The extensive genomic profiling of these pancreatic PDX, revealed two clinically-relevant subtypes having broad similarities with human primary tumors. These subtypes are defined by highly specific DNA methylation and transcriptomic profiles (mRNA, miRNA or lncRNA) but are not distinguishable by exonic mutations or copy number aberrations. Moreover, by specifically analyzing the stroma transcriptome, as defined by the expression of murine transcripts, we found that it is able to stratify the patients with the same efficiency than the analysis of grafted human tumor cells. This finding suggest that transformed pancreatic cells drive the composition of their own stroma. Finally, the multiomics analysis pinpoints novel therapeutic targets, one of which we demonstrate to be an efficient method for treating pancreatic cancer. Overall, we show that PDX are trustworthy pre-clinical models of pancreatic adenocarcinoma including of unresectable tumors. Their multiomics profiling allow the independent analysis of the uncontaminated cancer or stromal compartments and discloses several original therapeutics targets. Citation Format: Remy Nicolle, Yuna Blum, Laetitia Marisa, Celine Loncle, Odile Gayet, Vincent Moutardier, Olivier Turrini, Marc Giovannini, Benjamin Bian, Martin Bigonnet, Marion Rubis, Nabila Elarouci, Lucile Armenoult, Mira Ayadi, Pauline Duconseil, Mohamed Gasmi, Mehdi Ouaissi, Aurélie Maignan, Gwen Lomberk, Jean-Marie Boher, Jacques Ewald, Erwan Bories, Jonathan Garnier, Anthony Goncalves, Flora Poizat, Jean-Luc Raoul, Veronique Secq, Stephane Garcia, Philippe Grandval, Marine Barraud-Blanc, Emmanuelle Norguet, Marine Gilabert, Jean-Robert Delpero, Julie Roques, Ezequiel Calvo, Fabienne Guillaumond, Sophie Vasseur, Raul Urrutia, Aurélien de Reyniès, Nelson Dusetti, Juan Iovanna. Multiomics assessment of the cancer and stromal compartments of patient-derived pancreatic xenografts reveals clinically-relevant subtypes and novel targeted therapies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4396. doi:10.1158/1538-7445.AM2017-4396

Single-cell diversity in the brain The cells that make up an organism may all start from one genome, but somatic mutations mean that somewhere along the line of development, an organism's individual cellular genomes diverge. McConnell et al. review the implications and causes of single-cell genomic diversity for brain function. Somatic mutations caused by mobile genetic elements or errors in DNA repair may underlie certain neuropsychiatric disorders. Science , this issue p. eaal1641

SummaryObjectiveDetermine incidence of posttraumatic seizure (PTS) following traumatic brain injury (TBI) among individuals with moderate‐to‐severe TBI requiring rehabilitation and surviving at least 5 years.MethodsUsing the prospective TBI Model Systems National Database, we calculated PTS incidence during acute hospitalization, and at years 1, 2, and 5 postinjury in a continuously followed cohort enrolled from 1989 to 2000 (n = 795). Incidence rates were stratified by risk factors, and adjusted relative risk (RR) was calculated. Late PTS associations with immediate (<24 h), early (24 h–7 day), or late seizures (>7 day) versus no seizure prior to discharge from acute hospitalization was also examined.ResultsPTS incidence during acute hospitalization was highest immediately (<24 h) post‐TBI (8.9%). New onset PTS incidence was greatest between discharge from inpatient rehabilitation and year 1 (9.2%). Late PTS cumulative incidence from injury to year 1 was 11.9%, and reached 20.5% by year 5. Immediate/early PTS RR (2.04) was increased for those undergoing surgical evacuation procedures. Late PTS RR was significantly greater for individuals who self‐identified as a race other than black/white (year 1 RR = 2.22), and for black individuals (year 5 RR = 3.02) versus white individuals. Late PTS was greater for individuals with subarachnoid hemorrhage (year 1 RR = 2.06) and individuals age 23–32 (year 5 RR = 2.43) and 33–44 (year 5 RR = 3.02). Late PTS RR years 1 and 5 was significantly higher for those undergoing surgical evacuation procedures (RR: 3.05 and 2.72, respectively).SignificanceIn this prospective, longitudinal, observational study, PTS incidence was similar to that in studies published previously. Individuals with immediate/late seizures during acute hospitalization have increased late PTS risk. Race, intracranial pathologies, and neurosurgical procedures also influenced PTS RR. Further studies are needed to examine the impact of seizure prophylaxis in high‐risk subgroups and to delineate contributors to race/age associations on long‐term seizure outcomes.

SummaryObjectivePosttraumatic seizures (PTS) are well‐recognized acute and chronic complications of traumatic brain injury (TBI). Risk factors have been identified, but considerable variability in who develops PTS remains. Existing PTS prognostic models are not widely adopted for clinical use and do not reflect current trends in injury, diagnosis, or care. We aimed to develop and internally validate preliminary prognostic regression models to predict PTS during acute care hospitalization, and at year 1 and year 2 postinjury.MethodsPrognostic models predicting PTS during acute care hospitalization and year 1 and year 2 post‐injury were developed using a recent (2011–2014) cohort from the TBI Model Systems National Database. Potential PTS predictors were selected based on previous literature and biologic plausibility. Bivariable logistic regression identified variables with a p‐value < 0.20 that were used to fit initial prognostic models. Multivariable logistic regression modeling with backward‐stepwise elimination was used to determine reduced prognostic models and to internally validate using 1,000 bootstrap samples. Fit statistics were calculated, correcting for overfitting (optimism).ResultsThe prognostic models identified sex, craniotomy, contusion load, and pre‐injury limitation in learning/remembering/concentrating as significant PTS predictors during acute hospitalization. Significant predictors of PTS at year 1 were subdural hematoma (SDH), contusion load, craniotomy, craniectomy, seizure during acute hospitalization, duration of posttraumatic amnesia, preinjury mental health treatment/psychiatric hospitalization, and preinjury incarceration. Year 2 significant predictors were similar to those of year 1: SDH, intraparenchymal fragment, craniotomy, craniectomy, seizure during acute hospitalization, and preinjury incarceration. Corrected concordance (C) statistics were 0.599, 0.747, and 0.716 for acute hospitalization, year 1, and year 2 models, respectively.SignificanceThe prognostic model for PTS during acute hospitalization did not discriminate well. Year 1 and year 2 models showed fair to good predictive validity for PTS. Cranial surgery, although medically necessary, requires ongoing research regarding potential benefits of increased monitoring for signs of epileptogenesis, PTS prophylaxis, and/or rehabilitation/social support. Future studies should externally validate models and determine clinical utility.

AbstractAutomated methods are needed to facilitate high‐throughput and reproducible scoring of Ki67 and other markers in breast cancer tissue microarrays (TMAs) in large‐scale studies. To address this need, we developed an automated protocol for Ki67 scoring and evaluated its performance in studies from the Breast Cancer Association Consortium. We utilized 166 TMAs containing 16,953 tumour cores representing 9,059 breast cancer cases, from 13 studies, with information on other clinical and pathological characteristics. TMAs were stained for Ki67 using standard immunohistochemical procedures, and scanned and digitized using the Ariol system. An automated algorithm was developed for the scoring of Ki67, and scores were compared to computer assisted visual (CAV) scores in a subset of 15 TMAs in a training set. We also assessed the correlation between automated Ki67 scores and other clinical and pathological characteristics. Overall, we observed good discriminatory accuracy (AUC = 85%) and good agreement (kappa = 0.64) between the automated and CAV scoring methods in the training set. The performance of the automated method varied by TMA (kappa range= 0.37–0.87) and study (kappa range = 0.39–0.69). The automated method performed better in satisfactory cores (kappa = 0.68) than suboptimal (kappa = 0.51) cores (p‐value for comparison = 0.005); and among cores with higher total nuclei counted by the machine (4,000–4,500 cells: kappa = 0.78) than those with lower counts (50–500 cells: kappa = 0.41; p‐value = 0.010). Among the 9,059 cases in this study, the correlations between automated Ki67 and clinical and pathological characteristics were found to be in the expected directions. Our findings indicate that automated scoring of Ki67 can be an efficient method to obtain good quality data across large numbers of TMAs from multicentre studies. However, robust algorithm development and rigorous pre‐ and post‐analytical quality control procedures are necessary in order to ensure satisfactory performance.