Stratification of amyotrophic lateral sclerosis patients: A crowdsourcing approach

The ALS Stratification Consortium

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.

Original languageEnglish
Article number690
JournalScientific reports
Volume9
Issue number1
DOIs
Publication statusPublished - 2019 Jan 1

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Crowdsourcing
Amyotrophic Lateral Sclerosis
Cluster Analysis
Clinical Trials
Neurodegenerative Diseases

All Science Journal Classification (ASJC) codes

  • General

Cite this

@article{12e41d0bcf5a4c47a2017e981db6b909,
title = "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.",
author = "{The ALS Stratification Consortium} and Robert Kueffner and Neta Zach and Maya Bronfeld and Raquel Norel and Nazem Atassi and Venkat Balagurusamy and {Di Camillo}, Barbara and Adriano Chio and Merit Cudkowicz and Donna Dillenberger and Javier Garcia-Garcia and Orla Hardiman and Bruce Hoff and Joshua Knight and Leitner, {Melanie L.} and Guang Li and Lara Mangravite and Thea Norman and Liuxia Wang and Jinfeng Xiao and Fang, {Wen Chieh} and Jian Peng and Chen Yang and Chang, {Huan Jui} and Gustavo Stolovitzky and Rached Alkallas and Catalina Anghel and Jeanne Avril and Jaume Bacardit and Barbara Balser and John Balser and Yoav Bar-Sinai and Noa Ben-David and Eyal Ben-Zion and Robin Bliss and Jialu Cai and Anatoly Chernyshev and Chiang, {Jung Hsien} and Davide Chicco and Corriveau, {Bhavna Ahuja Nicole} and Junqiang Dai and Yash Deshpande and Eve Desplats and Durgin, {Joseph S.} and Espiritu, {Shadrielle Melijah G.} and Fan Fan and Philippe Fevrier and Fridley, {Brooke L.} and Adam Godzik and Agnieszka Golińska",
year = "2019",
month = "1",
day = "1",
doi = "10.1038/s41598-018-36873-4",
language = "English",
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journal = "Scientific Reports",
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Stratification of amyotrophic lateral sclerosis patients : A crowdsourcing approach. / The ALS Stratification Consortium.

In: Scientific reports, Vol. 9, No. 1, 690, 01.01.2019.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Stratification of amyotrophic lateral sclerosis patients

T2 - A crowdsourcing approach

AU - The ALS Stratification Consortium

AU - Kueffner, Robert

AU - Zach, Neta

AU - Bronfeld, Maya

AU - Norel, Raquel

AU - Atassi, Nazem

AU - Balagurusamy, Venkat

AU - Di Camillo, Barbara

AU - Chio, Adriano

AU - Cudkowicz, Merit

AU - Dillenberger, Donna

AU - Garcia-Garcia, Javier

AU - Hardiman, Orla

AU - Hoff, Bruce

AU - Knight, Joshua

AU - Leitner, Melanie L.

AU - Li, Guang

AU - Mangravite, Lara

AU - Norman, Thea

AU - Wang, Liuxia

AU - Xiao, Jinfeng

AU - Fang, Wen Chieh

AU - Peng, Jian

AU - Yang, Chen

AU - Chang, Huan Jui

AU - Stolovitzky, Gustavo

AU - Alkallas, Rached

AU - Anghel, Catalina

AU - Avril, Jeanne

AU - Bacardit, Jaume

AU - Balser, Barbara

AU - Balser, John

AU - Bar-Sinai, Yoav

AU - Ben-David, Noa

AU - Ben-Zion, Eyal

AU - Bliss, Robin

AU - Cai, Jialu

AU - Chernyshev, Anatoly

AU - Chiang, Jung Hsien

AU - Chicco, Davide

AU - Corriveau, Bhavna Ahuja Nicole

AU - Dai, Junqiang

AU - Deshpande, Yash

AU - Desplats, Eve

AU - Durgin, Joseph S.

AU - Espiritu, Shadrielle Melijah G.

AU - Fan, Fan

AU - Fevrier, Philippe

AU - Fridley, Brooke L.

AU - Godzik, Adam

AU - Golińska, Agnieszka

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

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U2 - 10.1038/s41598-018-36873-4

DO - 10.1038/s41598-018-36873-4

M3 - Article

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VL - 9

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JF - Scientific Reports

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