Prediction Model of Amyotrophic Lateral Sclerosis by Deep Learning with Patient Induced Pluripotent Stem Cells.
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ABSTRACT:
SUBMITTER: Imamura K
PROVIDER: S-EPMC8247989 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Amyotrophic lateral sclerosis (ALS) is a complex progressive neurodegenerative disorder with an estimated prevalence of about 5 per 100,000 people in the United States. In this study, the ALS disease progression is measured by the change of Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS) score over time. The study aims to provide clinical decision support for timely forecasting of the ALS trajectory as well as accurate and reproducible computable phenotypic clustering of participants. Patient data are extracted from DREAM-Phil Bowen ALS Prediction Prize4Life Challenge data, most of which are from the Pooled Resource Open-Access ALS Clinical Trials Database (PRO-ACT) archive. We employed model-based and model-free machine-learning methods to predict the change of the ALSFRS score over time. Using training and testing data we quantified and compared the performance of different techniques. We also used unsupervised machine learning methods to cluster the patients into separate computable phenotypes and interpret the derived subcohorts. Direct prediction of univariate clinical outcomes based on model-based (linear models) or model-free (machine learning based techniques - random forest and Bayesian adaptive regression trees) was only moderately successful. The correlation coefficients between clinically observed changes in ALSFRS scores relative to the model-based/model-free predicted counterparts were 0.427 (random forest) and 0.545(BART). The reliability of these results were assessed using internal statistical cross validation and well as external data validation. Unsupervised clustering generated very reliable and consistent partitions of the patient cohort into four computable phenotypic subgroups. These clusters were explicated by identifying specific salient clinical features included in the PRO-ACT archive that discriminate between the derived subcohorts. There are differences between alternative analytical methods in forecasting specific clinical phenotypes. Although predicting univariate clinical outcomes may be challenging, our results suggest that modern data science strategies are useful in clustering patients and generating evidence-based ALS hypotheses about complex interactions of multivariate factors. Predicting univariate clinical outcomes using the PRO-ACT data yields only marginal accuracy (about 70%). However, unsupervised clustering of participants into sub-groups generates stable, reliable and consistent (exceeding 95%) computable phenotypes whose explication requires interpretation of multivariate sets of features. HIGHLIGHTS: • Used a large ALS data archive of 8,000 patients consisting of 3 million records, including 200 clinical features tracked over 12 months. • Employed model-based and model-free methods to predict ALSFRS changes over time, cluster patients into cohorts, and derive computable phenotypes. • Research findings include stable, reliable, and consistent (95%) patient stratification into computable phenotypes. However, clinical explication of the results requires interpretation of multivariate information. Graphical Abstract ᅟ.
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Project description:Amyotrophic lateral sclerosis (ALS) is a highly complex and heterogeneous neurodegenerative disease that affects motor neurons. Since life expectancy is relatively low, it is essential to promptly understand the course of the disease to better target the patient's treatment. Predictive models for disease progression are thus of great interest. One of the most extensive and well-studied open-access data resources for ALS is the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) repository. In 2015, the DREAM-Phil Bowen ALS Prediction Prize4Life Challenge was held on PRO-ACT data, where competitors were asked to develop machine learning algorithms to predict disease progression measured through the slope of the ALSFRS score between 3 and 12 months. However, although it has already been successfully applied in several studies on ALS patients, to the best of our knowledge deep learning approaches still remain unexplored on the ALSFRS slope prediction in PRO-ACT cohort. Here, we investigate how deep learning models perform in predicting ALS progression using the PRO-ACT data. We developed three models based on different architectures that showed comparable or better performance with respect to the state-of-the-art models, thus representing a valid alternative to predict ALS disease progression.
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Project description:Induced pluripotent stem cells (iPSCs), which are generated through reprogramming adult somatic cells by expressing specific transcription factors, can differentiate into derivatives of the three embryonic germ layers and accelerate rapid advances in stem cell research. Neurological diseases such as amyotrophic lateral sclerosis (ALS) have benefited enormously from iPSC technology. This approach can be particularly important for creating iPSCs from patients with familial or sporadic forms of ALS. Motor neurons differentiated from the ALS-patient-derived iPSC can help to determine the relationship between cellular phenotype and genotype. Patient-derived iPSCs facilitate the development of new drugs and/or drug screening for ALS treatment and allow the exploration of the possible mechanism of ALS disease. In this article, we reviewed ALS-patient-specific iPSCs with various genetic mutations, progress in drug development for ALS disease, functional assays showing the differentiation of iPSCs into mature motor neurons, and promising biomarkers in ALS patients for the evaluation of drug candidates.
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Project description:Identification of familial amyotrophic lateral sclerosis (fALS) related genes. Material from three hSOD1(G93A) transgenic mice was compared to material from three non-transgenic control mice using an alternating loop design on two-colour cDNA microarrays. Statistical data management and analysis: postgreSQL relational database (www.postgresql.org), Perl, and R (www.r-project.org); pin-wise lowess-regression based normalisation (Yang et al., 2002 [PMID: 11842121]); mixed ANOVA-model. Keywords = amyotrophic lateral sclerosis, ALS, SOD1 mouse model
2010-06-05 | E-GEOD-2400 | biostudies-arrayexpress
Project description:Amyotrophic lateral sclerosis (ALS) is a late-onset motor neuron disorder. Although its neuropathology is well understood, the cellular and molecular mechanisms are yet to be elucidated due to limitations in the currently available human genetic data. In this study, we generated induced pluripotent stem cells (iPSC) from two familial ALS (FALS) patients with a missense mutation in the fused-in sarcoma (FUS) gene carrying the heterozygous FUS H517D mutation, and isogenic iPSCs with the homozygous FUS H517D mutation by genome editing technology. These cell-derived motor neurons mimicked several neurodegenerative phenotypes including mis-localization of FUS into cytosolic and stress granules under stress conditions, and cellular vulnerability. Moreover, exon array analysis using motor neuron precursor cells (MPCs) combined with CLIP-seq datasets revealed aberrant gene expression and/or splicing pattern in FALS MPCs. These results suggest that iPSC-derived motor neurons are a useful tool for analyzing the pathogenesis of human motor neuron disorders.
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Project description:Identification of familial amyotrophic lateral sclerosis (fALS) related genes. Material from three hSOD1(G93A) transgenic mice was compared to material from three non-transgenic control mice using an alternating loop design on two-colour cDNA microarrays. Statistical data management and analysis: postgreSQL relational database (www.postgresql.org), Perl, and R (www.r-project.org); pin-wise lowess-regression based normalisation (Yang et al., 2002 [PMID: 11842121]); mixed ANOVA-model. Keywords = amyotrophic lateral sclerosis, ALS, SOD1 mouse model Keywords: other
2005-12-31 | GSE2400 | GEO
Project description:Amyotrophic lateral sclerosis (ALS) is a progressive neuromuscular disease, with large variation in survival between patients. Currently, it remains rather difficult to predict survival based on clinical parameters alone. Here, we set out to use clinical characteristics in combination with MRI data to predict survival of ALS patients using deep learning, a machine learning technique highly effective in a broad range of big-data analyses. A group of 135 ALS patients was included from whom high-resolution diffusion-weighted and T1-weighted images were acquired at the first visit to the outpatient clinic. Next, each of the patients was monitored carefully and survival time to death was recorded. Patients were labeled as short, medium or long survivors, based on their recorded time to death as measured from the time of disease onset. In the deep learning procedure, the total group of 135 patients was split into a training set for deep learning (n = 83 patients), a validation set (n = 20) and an independent evaluation set (n = 32) to evaluate the performance of the obtained deep learning networks. Deep learning based on clinical characteristics predicted survival category correctly in 68.8% of the cases. Deep learning based on MRI predicted 62.5% correctly using structural connectivity and 62.5% using brain morphology data. Notably, when we combined the three sources of information, deep learning prediction accuracy increased to 84.4%. Taken together, our findings show the added value of MRI with respect to predicting survival in ALS, demonstrating the advantage of deep learning in disease prognostication.
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Project description:Amyotrophic lateral sclerosis is a progressive disease characterized by the loss of upper and lower motor neurons, leading to paralysis of voluntary muscles. About 10% of all ALS cases are familial (fALS), among which 15-20% are linked to Cu/Zn superoxide dismutase (SOD1) mutations, usually inherited in an autosomal dominant manner. To date only one FDA approved drug is available which increases survival moderately. Our understanding of ALS disease mechanisms is largely derived from rodent model studies, however due to the differences between rodents and humans, it is necessary to have humanized models for studies of disease pathogenesis as well as drug development. Therefore, we generated a comprehensive library of a total 22 of fALS patient-specific induced pluripotent stem cell (iPSC) lines. These cells were thoroughly characterized before being deposited into the library. The library of cells includes a variety of C9orf72 mutations, sod1 mutations, FUS, ANG and FIG4 mutations. Certain mutations are represented with more than one line, which allows for studies of variable genetic backgrounds. In addition, these iPSCs can be successfully differentiated to astroglia, a cell type known to play a critical role in ALS disease progression. This library represents a comprehensive resource that can be used for ALS disease modeling and the development of novel therapeutics.
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Project description:This SuperSeries is composed of the following subset Series: GSE39642: NanoString nCounter immune-related gene expression in blood sorted CD14+CD16- monocytes from sALS, fALS and HC subjects GSE39643: NanoString miRNA profiling of peripheral blood sorted CD14+CD16- monocytes from amyotrophic lateral sclerosis, multiple sclerosis and healthy control subjects Refer to individual Series
2012-07-25 | E-GEOD-39644 | biostudies-arrayexpress
Project description:Amyotrophic Lateral Sclerosis (ALS) is a degenerative disorder of motor neurons which leads to complete loss of movement in patients. The only FDA approved drug Riluzole provides only symptomatic relief to patients. Early Diagnosis of the disease warrants the importance of diagnostic and prognostic models for predicting disease and disease progression respectively. In the present study we represent the predictive statistical model for ALS using plasma and CSF biomarkers. Forward stepwise (Binary likelihood) Logistic regression model is developed for prediction of ALS. The model has been shown to have excellent validity (94%) with good sensitivity (98%) and specificity (93%). The area under the ROC curve is 99.3%. Along with age and BMI, VEGF (Vascular Endothelial Growth Factor), VEGFR2 (Vascular Endothelial Growth Factor Receptor 2) and TDP43 (TAR DNA Binding Protein 43) in CSF and VEGFR2 and OPTN (Optineurin) in plasma are good predictors of ALS.
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