ABSTRACT
Autism spectrum disorder (ASD) affects 1 in 44 children. Chromatin regulatory proteins are overrepresented among genes that contain high risk variants in ASD. Disruption of the chromatin environment leads to widespread dysregulation of gene expression, which is traditionally thought of as a mechanism of disease pathogenesis associated with ASD. Alternatively, alterations in chromatin dynamics could also lead to dysregulation of alternative splicing, which is understudied as a mechanism of ASD pathogenesis. The anticonvulsant valproic acid (VPA) is a well-known environmental risk factor for ASD that acts as a class I histone deacetylase inhibitor. However, the precise molecular mechanisms underlying defects in human neuronal development associated with exposure to VPA are understudied. To dissect how VPA exposure and subsequent chromatin hyperacetylation influence molecular signatures involved in ASD pathogenesis, we conducted RNA sequencing (RNA-seq) in human cortical neurons that were treated with VPA. We observed that differentially expressed genes (DEGs) were enriched for mRNA splicing, mRNA processing, histone modification and metabolism related gene sets. Furthermore, we observed widespread increases in the number and the type of alternative splicing events. Analysis of differential transcript usage (DTU) showed that exposure to VPA induces extensive alterations in transcript isoform usage across neurodevelopmentally important genes. Finally, we find that DEGs and genes that display DTU overlap with known ASD-risk genes. Altogether, these findings suggest that, in addition to differential gene expression, changes in alternative splicing correlated with alterations in the chromatin environment could act as an additional mechanism of disease in ASD.
Subject(s)
Autism Spectrum Disorder , Prenatal Exposure Delayed Effects , Child , Humans , Animals , Female , Autism Spectrum Disorder/etiology , Chromatin/genetics , Alternative Splicing/genetics , Valproic Acid/adverse effects , RNA, Messenger/metabolism , Disease Models, AnimalABSTRACT
Mismatch repair (MMR) alterations are important prognostic and predictive biomarkers in a variety of cancer subtypes, including colorectal and endometrial. However, in breast cancer (BC), the distinction and clinical significance of MMR are largely unknown. This may be due in part to the fact that genetic alterations in MMR genes are rare and only seen to occur in around 3% of BCs. In the present study, we analyzed TCGA data using a multi-sample protein-protein interaction (PPI) analysis tool, Proteinarium, and showed a distinct separation between specific MMR-deficient and -intact networks in a cohort of 994 BC patients. In the PPI networks specific to MMR deficiency, highly connected clusters of histone genes were identified. We also found the distribution of MMR-deficient BC to be more prevalent in HER2-enriched and triple-negative (TN) BC subtypes compared to luminal BCs. We recommend defining MMR-deficient BC by next-generation sequencing (NGS) when any somatic mutation is detected in one of the seven MMR genes.
Subject(s)
Colorectal Neoplasms , Triple Negative Breast Neoplasms , Humans , Histones/genetics , Proteomics , Triple Negative Breast Neoplasms/genetics , DNA Mismatch Repair/geneticsABSTRACT
BACKGROUND: Gastric cancer is a heterogeneous disease with poorly understood genetic and microenvironmental factors. Mutations in collagen genes are associated with genetic diseases that compromise tissue integrity, but their role in tumor progression has not been extensively reported. Aberrant collagen expression has been long associated with malignant tumor growth, invasion, chemoresistance, and patient outcomes. We hypothesized that somatic mutations in collagens could functionally alter the tumor extracellular matrix. METHODS: We used publicly available datasets including The Tumor Cancer Genome Atlas (TCGA) to interrogate somatic mutations in collagens in stomach adenocarcinomas. To demonstrate that collagens were significantly mutated above background mutation rates, we used a moderated Kolmogorov-Smirnov test along with combination analysis with a bootstrap approach to define the background accounting for mutation rates. Association between mutations and clinicopathological features was evaluated by Fisher or chi-squared tests. Association with overall survival was assessed by Kaplan-Meier and the Cox-Proportional Hazards Model. Gene Set Enrichment Analysis was used to interrogate pathways. Immunohistochemistry and in situ hybridization tested expression of COL7A1 in stomach tumors. RESULTS: In stomach adenocarcinomas, we identified individual collagen genes and sets of collagen genes harboring somatic mutations at a high frequency compared to background in both microsatellite stable, and microsatellite instable tumors in TCGA. Many of the missense mutations resemble the same types of loss of function mutations in collagenopathies that disrupt tissue formation and destabilize cells providing guidance to interpret the somatic mutations. We identified combinations of somatic mutations in collagens associated with overall survival, with a distinctive tumor microenvironment marked by lower matrisome expression and immune cell signatures. Truncation mutations were strongly associated with improved outcomes suggesting that loss of expression of secreted collagens impact tumor progression and treatment response. Germline collagenopathy variants guided interpretation of impactful somatic mutations on tumors. CONCLUSIONS: These observations highlight that many collagens, expressed in non-physiologically relevant conditions in tumors, harbor impactful somatic mutations in tumors, suggesting new approaches for classification and therapy development in stomach cancer. In sum, these findings demonstrate how classification of tumors by collagen mutations identified strong links between specific genotypes and the tumor environment.
Subject(s)
Adenocarcinoma/genetics , Collagen Type VII/genetics , Collagen/genetics , Stomach Neoplasms/genetics , Tumor Microenvironment/genetics , Adenocarcinoma/mortality , Computational Biology , Genotype , Humans , Kaplan-Meier Estimate , Mutation , Mutation Rate , Proportional Hazards Models , Stomach Neoplasms/mortalityABSTRACT
INTRODUCTION: Breast cancers are complex ecosystem like networks of malignant cells and their associated microenvironment. Applications for machine intelligence and the tumoral microenvironment are expanding frontiers in pathology. Previously, computational approaches have been developed to quantify and spatially analyze immune cells, proportionate stroma, and detect tumor budding. Little work has been done to analyze different types of tumor-associated stromata both quantitatively and computationally in relation to clinical endpoints. METHODS: We aimed to quantify stromal features from whole slide images (WSI) including stromata (myxoid, collagenous, immune) and tumoral components and combined them with traditional clinical and pathologic parameters in 120 triple-negative breast cancer (TNBC) patients treated with neoadjuvant chemotherapy (NAC) to predict pathologic complete response (pCR) and poor clinical outcomes. RESULTS: High collagenous stroma on WSI was best associated with lower rates of pCR, while combined high proportionated stroma (myxoid, collagenous, and immune) most optimally predicted worse clinical survival outcomes. When combining clinical, pathologic, and WSI features, Receiver Operator Characteristics (ROC) curves for LASSO features was up to 0.67 for pCR and 0.77 for poor outcomes. CONCLUSION: The techniques demonstrated in the present study can be performed with appropriate quality assurance. Future trials are needed to demonstrate whether coupling applications for machine intelligence, inclusive of the tumor mesenchyme, can improve outcomes prediction for patients with breast cancer.
Subject(s)
Breast Neoplasms , Triple Negative Breast Neoplasms , Humans , Female , Triple Negative Breast Neoplasms/pathology , Breast Neoplasms/pathology , Ecosystem , Antineoplastic Combined Chemotherapy Protocols/therapeutic use , Prognosis , Neoadjuvant Therapy/methods , Tumor MicroenvironmentABSTRACT
Advanced urothelial carcinoma continues to have a dismal prognosis despite several new therapies in the last 5 years. FGFR2 and FGFR3 mutations and fusions, PD-L1 expression, tumor mutational burden, and microsatellite instability are established predictive biomarkers in advanced urothelial carcinoma. Novel biomarkers can optimize the sequencing of available treatments and improve outcomes. We describe herein the clinical and pathologic features of patients with an emerging subtype of bladder cancer characterized by deletion of the gene MTAP encoding the enzyme S-Methyl-5'-thioadenosine phosphatase, a potential biomarker of response to pemetrexed. We performed a retrospective analysis of 61 patients with advanced urothelial carcinoma for whom demographics, pathologic specimens, next generation sequencing, and clinical outcomes were available. We compared the frequency of histology variants, upper tract location, pathogenic gene variants, tumor response, progression free survival (PFS) and overall survival (OS) between patients with tumors harboring MTAP deletion (MTAP-del) and wild type tumors (MTAP-WT). A propensity score matching of 5 covariates (age, gender, presence of variant histology, prior surgery, and prior non-muscle invasive bladder cancer) was calculated to compensate for disparity when comparing survival in these subgroups. Non-supervised clustering analysis of differentially expressed genes between MTAP-del and MTAP-WT urothelial carcinomas was performed. MTAP-del occurred in 19 patients (31%). Tumors with MTAP-del were characterized by higher prevalence of squamous differentiation (47.4 vs 11.9%), bone metastases (52.6 vs 23.5%) and lower frequency of upper urinary tract location (5.2% vs 26.1%). Pathway gene set enrichment analysis showed that among the genes upregulated in the MTAP-del cohort, at least 5 were linked to keratinization (FOXN1, KRT33A/B, KRT84, RPTN) possibly contributing to the higher prevalence of squamous differentiation. Alterations in the PIK3 and MAPK pathways were more frequent when MTAP was deleted. There was a trend to inferior response to chemotherapy among MTAP-del tumors, but no difference in the response to immune checkpoint inhibitors or enfortumab. Median progression free survival after first line therapy (PFS1) was 5.5 months for patients with MTAP-WT and 4.5 months for patients with MTAP-del (HR = 1.30; 95% CI, 0.64-2.63; P = 0.471). There was no difference in the time from metastatic diagnosis to death (P = 0.6346). Median OS from diagnosis of localized or de novo metastatic disease was 16 months (range 1.5-60, IQR 8-26) for patients with MTAP-del and 24.5 months (range 3-156, IQR 16-48) for patients with MTAP-WT (P = 0.0218), suggesting that time to progression to metastatic disease is shorter in MTAP-del patients. Covariates did not impact significantly overall survival on propensity score matching. In conclusion, MTAP -del occurs in approximately 30% of patients with advanced urothelial carcinoma and defines a subgroup of patients with aggressive features, such as squamous differentiation, frequent bone metastases, poor response to chemotherapy, and shorter time to progression to metastatic disease.
ABSTRACT
SUMMARY: Single amino acid variations (SAVs) are a primary contributor to variations in the human genome. Identifying pathogenic SAVs can provide insights to the genetic architecture of complex diseases. Most approaches for predicting the functional effects or pathogenicity of SAVs rely on either sequence or structural information. This study presents ãLai Yang Rubenstein Uzun Sarkarã (LYRUS), a machine learning method that uses an XGBoost classifier to predict the pathogenicity of SAVs. LYRUS incorporates five sequence-based, six structure-based and four dynamics-based features. Uniquely, LYRUS includes a newly proposed sequence co-evolution feature called the variation number. LYRUS was trained using a dataset that contains 4363 protein structures corresponding to 22 639 SAVs from the ClinVar database, and tested using the VariBench testing dataset. Performance analysis showed that LYRUS achieved comparable performance to current variant effect predictors. LYRUS's performance was also benchmarked against six Deep Mutational Scanning datasets for PTEN and TP53. AVAILABILITY AND IMPLEMENTATION: LYRUS is freely available and the source code can be found at https://github.com/jiaying2508/LYRUS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Advances online.
ABSTRACT
Christianson syndrome (CS), an X-linked neurological disorder characterized by postnatal attenuation of brain growth (postnatal microcephaly), is caused by mutations in SLC9A6, the gene encoding endosomal Na+/H+ exchanger 6 (NHE6). To hasten treatment development, we established induced pluripotent stem cell (iPSC) lines from patients with CS representing a mutational spectrum, as well as biologically related and isogenic control lines. We demonstrated that pathogenic mutations lead to loss of protein function by a variety of mechanisms: The majority of mutations caused loss of mRNA due to nonsense-mediated mRNA decay; however, a recurrent, missense mutation (the G383D mutation) had both loss-of-function and dominant-negative activities. Regardless of mutation, all patient-derived neurons demonstrated reduced neurite growth and arborization, likely underlying diminished postnatal brain growth in patients. Phenotype rescue strategies showed mutation-specific responses: A gene transfer strategy was effective in nonsense mutations, but not in the G383D mutation, wherein residual protein appeared to interfere with rescue. In contrast, application of exogenous trophic factors (BDNF or IGF-1) rescued arborization phenotypes across all mutations. These results may guide treatment development in CS, including gene therapy strategies wherein our data suggest that response to treatment may be dictated by the class of mutation.
Subject(s)
Induced Pluripotent Stem Cells , Microcephaly , Ataxia , Epilepsy , Genetic Diseases, X-Linked , Humans , Intellectual Disability , Microcephaly/genetics , Mutation/genetics , Neurons , Ocular Motility DisordersABSTRACT
Advances in tumor genome sequencing created an urgent need for bioinformatics tools to support the interpretation of the clinical significance of the variants detected. VarStack is a web tool which is a base to retrieve somatic variant data relating to cancer from existing databases. VarStack incorporates data from several publicly available databases and presents them with an easy-to-navigate user interface. It currently supports data from the Catalogue of Somatic Mutations in Cancer, gnomAD, cBioPortal, ClinVar, OncoKB, CiViC and UCSC Genome Browser. It retrieves the data from these databases and returns them back to the user in a fraction of the time it would take to manually navigate each site independently. Users submit a variant with a gene symbol, peptide change and coding sequence change. They may select a variety of tumor-specific studies in cBioPortal to search through in addition to their original query. The results from the databases are presented in tabs. Users can export the results as an Excel file. VarStack also has the batch search feature in which the user can submit a list of variants and download an Excel file with the data from the databases. With the batch search and data download options, users can easily incorporate VarStack into their workflow or tools. VarStack saves time by providing somatic variant information to the user from multiple databases in an easy-to-export and interpretable format. VarStack is freely available under https://varstack.brown.edu.
Subject(s)
Neoplasms , User-Computer Interface , Computational Biology , Databases, Genetic , Humans , Information Storage and Retrieval , Internet , Neoplasms/genetics , SoftwareABSTRACT
The objective of this study was to establish a large, densely sampled, U.S. population-based cohort of people with autism spectrum disorder (ASD). The Rhode Island Consortium for Autism Research and Treatment (RI-CART) represents a unique public-private-academic collaboration involving all major points of service for families in Rhode Island affected by ASD. Diagnosis was based on direct behavioral observation via the Autism Diagnostic Observation Schedule, Second Edition. For the first 1,000 participants, ages ranged from 21 months to 64 years. Using Geographic Information System and published prevalence rates, the overall cohort is estimated to represent between 20% and 49% of pediatric age persons in Rhode Island with ASD, with demographics representative of U.S. Census. We observed a high rate of co-occurring medical and psychiatric conditions in affected individuals. Among the most prominent findings of immediate clinical importance, we found that females received a first diagnosis of ASD at a later age than males, potentially due to more advanced language abilities in females with ASD. In summary, this is the first analysis of a large, population-based U.S. cohort with ASD. Given the depth of sampling, the RI-CART study reflects an important new resource for studying ASD in a representative U.S. population. Psychiatric and medical comorbidities in ASD constitute a substantial burden and warrant adequate attention as part of overall treatment. Our study also suggests that new strategies for earlier diagnosis of ASD in females may be warranted. Autism Res 2020, 13: 474-488. © 2020 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: The Rhode Island Consortium for Autism Research and Treatment (RI-CART) represents a unique public-private-academic collaboration involving all major points of service for families in Rhode Island affected by autism spectrum disorder (ASD). In this article, we provide results from the first 1,000 participants, estimated to represent >20% of affected families in the state. Importantly, we find a later age at first diagnosis of ASD in females, which potentially calls attention to the need for improved early diagnosis in girls. Also, we report a high rate of co-occurring medical and psychiatric conditions in affected individuals.
Subject(s)
Autism Spectrum Disorder/epidemiology , Autism Spectrum Disorder/psychology , Adolescent , Adult , Autism Spectrum Disorder/physiopathology , Child , Child, Preschool , Cohort Studies , Comorbidity , Female , Humans , Infant , Male , Middle Aged , Prevalence , Registries , Rhode Island/epidemiology , Social Behavior , Young AdultABSTRACT
Christianson syndrome (CS) is an X-linked disorder resulting from loss-of-function mutations in SLC9A6, which encodes the endosomal Na+/H+ exchanger 6 (NHE6). Symptoms include early developmental delay, seizures, intellectual disability, nonverbal status, autistic features, postnatal microcephaly, and progressive ataxia. Neuronal development is impaired in CS, involving defects in neuronal arborization and synaptogenesis, likely underlying diminished brain growth postnatally. In addition to neurodevelopmental defects, some reports have supported neurodegenerative pathology in CS with age. The objective of this study was to determine the nature of progressive changes in the postnatal brain in Nhe6-null mice. We examined the trajectories of brain growth and atrophy in mutant mice from birth until very old age (2 yr). We report trajectories of volume changes in the mutant that likely reflect both brain undergrowth as well as tissue loss. Reductions in volume are first apparent at 2 mo, particularly in the cerebellum, which demonstrates progressive loss of Purkinje cells (PCs). We report PC loss in two distinct Nhe6-null mouse models. More widespread reductions in tissue volumes, namely, in the hippocampus, striatum, and cortex, become apparent after 2 mo, largely reflecting delays in growth with more limited tissue losses with aging. Also, we identify pronounced glial responses, particularly in major fiber tracts such as the corpus callosum, where the density of activated astrocytes and microglia are substantially increased. The prominence of the glial response in axonal tracts suggests a primary axonopathy. Importantly, therefore, our data support both neurodevelopmental and degenerative mechanisms in the pathobiology of CS.