Bio-collections in autism research

Reilly, Jamie; Gallagher, Louise; Chen, June L.; Leader, Geraldine; Shen, Sanbing

doi:10.1186/s13229-017-0154-8

Cited by 19 publications

(11 citation statements)

References 365 publications

(485 reference statements)

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“…Finally, we recognize some important limitations. First, despite the efforts of stablishing bio-collections aimed to uncover the genetic bases of ASD [92], the number of available datasets including gene expression data derived from autistic patient brains is scarce as it is the number of included samples in each dataset. This fact has two main consequences.…”

Section: Discussionmentioning

confidence: 99%

Transcriptomic metaanalyses of autistic brains reveals shared gene expression and biological pathway abnormalities with cancer

et al. 2019

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Background Epidemiological and clinical evidence points to cancer as a comorbidity in people with autism spectrum disorders (ASD). A significant overlap of genes and biological processes between both diseases has also been reported. Methods Here, for the first time, we compared the gene expression profiles of ASD frontal cortex tissues and 22 cancer types obtained by differential expression meta-analysis and report gene, pathway, and drug set-based overlaps between them. Results Four cancer types (brain, thyroid, kidney, and pancreatic cancers) presented a significant overlap in gene expression deregulations in the same direction as ASD whereas two cancer types (lung and prostate cancers) showed differential expression profiles significantly deregulated in the opposite direction from ASD. Functional enrichment and LINCS L1000 based drug set enrichment analyses revealed the implication of several biological processes and pathways that were affected jointly in both diseases, including impairments of the immune system, and impairments in oxidative phosphorylation and ATP synthesis among others. Our data also suggest that brain and kidney cancer have patterns of transcriptomic dysregulation in the PI3K/AKT/MTOR axis that are similar to those found in ASD. Conclusions Comparisons of ASD and cancer differential gene expression meta-analysis results suggest that brain, kidney, thyroid, and pancreatic cancers are candidates for direct comorbid associations with ASD. On the other hand, lung and prostate cancers are candidates for inverse comorbid associations with ASD. Joint perturbations in a set of specific biological processes underlie these associations which include several pathways previously implicated in both cancer and ASD encompassing immune system alterations, impairments of energy metabolism, cell cycle, and signaling through PI3K and G protein-coupled receptors among others. These findings could help to explain epidemiological observations pointing towards direct and inverse comorbid associations between ASD and specific cancer types and depict a complex scenario regarding the molecular patterns of association between ASD and cancer. Electronic supplementary material The online version of this article (10.1186/s13229-019-0262-8) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Transcriptomic metaanalyses of autistic brains reveals shared gene expression and biological pathway abnormalities with cancer

et al. 2019

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“…There are some current resources that are immediately available to meet such needs (e.g., the ABIDE datasets [78], the National Database for Autism Research (NDAR) [79], the Simons Simplex Collection [80], SPARK [81], the Healthy Brain Network [82], and see refs. [83,84]) and we would expect much more in the coming years. As we get better at detecting what are the relevant dimensions and/or subtypes explaining important heterogeneity in autism, we may be better able to design high-powered targeted studies where the requirements for massive sample size may be reduced substantially.…”

Section: Autism Populationmentioning

confidence: 93%

Big data approaches to decomposing heterogeneity across the autism spectrum

2019

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Autism is a diagnostic label based on behavior. While the diagnostic criteria attempt to maximize clinical consensus, it also masks a wide degree of heterogeneity between and within individuals at multiple levels of analysis. Understanding this multi-level heterogeneity is of high clinical and translational importance. Here we present organizing principles to frame research examining multi-level heterogeneity in autism. Theoretical concepts such as 'spectrum' or 'autisms' reflect nonmutually exclusive explanations regarding continuous/dimensional or categorical/qualitative variation between and within individuals. However, common practices of small sample size studies and case-control models are suboptimal for tackling heterogeneity. Big data are an important ingredient for furthering our understanding of heterogeneity in autism. In addition to being 'feature-rich', big data should be both 'broad' (i.e., large sample size) and 'deep' (i.e., multiple levels of data collected on the same individuals). These characteristics increase the likelihood that the study results are more generalizable and facilitate evaluation of the utility of different models of heterogeneity. A model's utility can be measured by its ability to explain clinically or mechanistically important phenomena, and also by explaining how variability manifests across different levels of analysis. The directionality for explaining variability across levels can be bottom-up or top-down, and should include the importance of development for characterizing changes within individuals. While progress can be made with 'supervised' models built upon a priori or theoretically predicted distinctions or dimensions of importance, it will become increasingly important to complement such work with unsupervised data-driven discoveries that leverage unknown and multivariate distinctions within big data. A better understanding of how to model heterogeneity between autistic people will facilitate progress towards precision medicine for symptoms that cause suffering, and person-centered support.

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“…As data sharing and open data initiatives become more prevalent, we should see more investigations on heterogeneity that meet this big data requirement. There are some current resources that are immediately available to meet such needs (e.g., the ABIDE datasets 76 , the National Database for Autism Research (NDAR) 77 , the Simons Simplex Collection 78 , SPARK 79 , the Healthy Brain Network 80 , and see 81,82 ) and we would expect much more in the coming years. As we get better at detecting what are the relevant dimensions and/or subtypes explaining important heterogeneity in autism, we may be better able to design high-powered targeted studies where the requirements for massive n may be reduced substantially.…”

Section: Essential Big Data Characteristics For Studying Heterogeneitymentioning

confidence: 99%

Big data approaches to decomposing heterogeneity across the autism spectrum

Lombardo

Lai

Baron‐Cohen

2018

Preprint

120

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Autism is a diagnostic label based on behavior. While the diagnostic criteria attempts to maximize clinical consensus, it also masks a wide degree of heterogeneity between and within individuals at multiple levels of analysis. Understanding this multi-level heterogeneity is of high clinical and translational importance. Here we present organizing principles to frame the work examining multi-level heterogeneity in autism. Theoretical concepts such as 'spectrum' or 'autisms' reflect non-mutually exclusive explanations regarding continuous/dimensional or categorical/qualitative variation between and within individuals. However, common practices of small sample size studies and case-control models are suboptimal for tackling heterogeneity. Big data is an important ingredient for furthering our understanding heterogeneity in autism. In addition to being 'feature-rich', big data should be both 'broad' (i.e. large sample size) and 'deep' (i.e. multiple levels of data collected on the same individuals). These characteristics help ensure the results from a population are more generalizable and facilitate evaluation of the utility of different models of heterogeneity. A model's utility can be shown by its ability to explain clinically or mechanistically important phenomena, but also by explaining how variability manifests across different levels of analysis. The directionality for explaining variability across levels can be bottom-up or top-down, and should include the importance of development for characterizing change within individuals. While progress can be made with 'supervised' models built upon a priori or theoretically predicted distinctions or dimensions of importance, it will become increasingly important to complement such work with unsupervised data-driven discoveries that leverage unknown and multivariate distinctions within big data. Without a better understanding of how to model heterogeneity between autistic people, progress towards the goal of precision medicine may be limited.. CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/278788 doi: bioRxiv preprint first posted online Mar. 9, 2018; 3 Autism occurs in approximately 1-2% of the population 1 and autistic individuals'wellbeing is a major public health issue. In economic terms, the lifetime individual cost of autism is estimated at $2.4 (£1.5) million in the USA and UK and annual population costs are around $268 billion in the USA 2,3 . While interest in and science on autism has been growing rapidly, progress towards translating scientific knowledge into high-impact clinical practice has been less rapid. We are still far from delivering more effective intervention and support, more precise and earlier diagnosis, better understanding and prediction of prognosis and development, and personalization of support and intervention. All of these points are within the scope of stratified psychiatry 4 and precision me...

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Bio-collections in autism research

Cited by 19 publications

References 365 publications

Transcriptomic metaanalyses of autistic brains reveals shared gene expression and biological pathway abnormalities with cancer

Transcriptomic metaanalyses of autistic brains reveals shared gene expression and biological pathway abnormalities with cancer

Big data approaches to decomposing heterogeneity across the autism spectrum

Big data approaches to decomposing heterogeneity across the autism spectrum

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