Dataset Information

Stratification of amyotrophic lateral sclerosis patients: a crowdsourcing approach.

ABSTRACT: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease where substantial heterogeneity in clinical presentation urgently requires a better stratification of patients for the development of drug trials and clinical care. In this study we explored stratification through a crowdsourcing approach, the DREAM Prize4Life ALS Stratification Challenge. Using data from >10,000 patients from ALS clinical trials and 1479 patients from community-based patient registers, more than 30 teams developed new approaches for machine learning and clustering, outperforming the best current predictions of disease outcome. We propose a new method to integrate and analyze patient clusters across methods, showing a clear pattern of consistent and clinically relevant sub-groups of patients that also enabled the reliable classification of new patients. Our analyses reveal novel insights in ALS and describe for the first time the potential of a crowdsourcing to uncover hidden patient sub-populations, and to accelerate disease understanding and therapeutic development.

SUBMITTER: Kueffner R

PROVIDER: S-EPMC6345935 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BACKGROUND:It is unclear to what extent pre-clinical studies in genetically homogeneous animal models of amyotrophic lateral sclerosis (ALS), an invariably fatal neurodegenerative disorder, can be informative of human pathology. The disease modifying effects in animal models of most therapeutic compounds have not been reproduced in patients. To advance therapeutics in ALS, we need easily accessible disease biomarkers which can discriminate across the phenotypic variants observed in ALS patients and can bridge animal and human pathology. Peripheral blood mononuclear cells alterations reflect the rate of progression of the disease representing an ideal biological substrate for biomarkers discovery. METHODS:We have applied TMTcalibrator™, a novel tissue-enhanced bio fluid mass spectrometry technique, to study the plasma proteome in ALS, using peripheral blood mononuclear cells as tissue calibrator. We have tested slow and fast progressing SOD1G93A mouse models of ALS at a pre-symptomatic and symptomatic stage in parallel with fast and slow progressing ALS patients at an early and late stage of the disease. Immunoassays were used to retest the expression of relevant protein candidates. RESULTS:The biological features differentiating fast from slow progressing mouse model plasma proteomes were different from those identified in human pathology, with only processes encompassing membrane trafficking with translocation of GLUT4, innate immunity, acute phase response and cytoskeleton organization showing enrichment in both species. Biological processes associated with senescence, RNA processing, cell stress and metabolism, major histocompatibility complex-II linked immune-reactivity and apoptosis (early stage) were enriched specifically in fast progressing ALS patients. Immunodetection confirmed regulation of the immunosenescence markers Galectin-3, Integrin beta 3 and Transforming growth factor beta-1 in plasma from pre-symptomatic and symptomatic transgenic animals while Apolipoprotein E differential plasma expression provided a good separation between fast and slow progressing ALS patients. CONCLUSIONS:These findings implicate immunosenescence and metabolism as novel targets for biomarkers and therapeutic discovery and suggest immunomodulation as an early intervention. The variance observed in the plasma proteomes may depend on different biological patterns of disease progression in human and animal model.

Dataset Information

Stratification of amyotrophic lateral sclerosis patients: a crowdsourcing approach.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets