Review Article | Published:

Moving towards a molecular taxonomy of autoimmune rheumatic diseases

Nature Reviews Rheumatology volume 14, pages 7593 (2018) | Download Citation

  • An Erratum to this article was published on 21 February 2018

This article has been updated

Abstract

Autoimmune rheumatic diseases pose many problems that have, in general, already been solved in the field of cancer. The heterogeneity of each disease, the clinical similarities and differences between different autoimmune rheumatic diseases and the large number of patients that remain without a diagnosis underline the need to reclassify these diseases via new approaches. Knowledge about the molecular basis of systemic autoimmune diseases, along with the availability of bioinformatics tools capable of handling and integrating large volumes of various types of molecular data at once, offer the possibility of reclassifying these diseases. A new taxonomy could lead to the discovery of new biomarkers for patient stratification and prognosis. Most importantly, this taxonomy might enable important changes in clinical trial design to reach the expected outcomes or the design of molecularly targeted therapies. In this Review, we discuss the basis for a new molecular taxonomy for autoimmune rheumatic diseases. We highlight the evidence surrounding the idea that these diseases share molecular features related to their pathogenesis and development and discuss previous attempts to classify these diseases. We evaluate the tools available to analyse and combine different types of molecular data. Finally, we introduce PRECISESADS, a project aimed at reclassifying the systemic autoimmune diseases.

Key points

  • Current systemic autoimmune disease classification criteria are commonly used for diagnosing diseases and tend to be ambiguous, generic or difficult to assess, which might lead to misdiagnosis or leave patients undiagnosed

  • Ample evidence demonstrates that rheumatic autoimmune diseases share molecular disease pathways

  • Discriminating between diseases by comparing molecular profiles is a feasible approach that could be useful in clinical practice, if adequately implemented

  • Disease stratification has previously been based on supervised methods and predefined clinical diagnoses

  • Integration of multi-layered data with unsupervised clustering should provide valuable information about disease mechanisms and treatment responses by simultaneously considering multiple types of information

  • Precision medicine of systemic autoimmune diseases should start with a new robust molecular classification of autoimmune disorders and the identification of biomarkers for use in routine clinical practice

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Change history

  • 21 February 2018

    In the original version of this article, concatenation and non-concatenation were incorrectly referred to as catenation and non-catenation in the subheadings in Table 2 and in a subheading on page 87 in the main text. These errors have now been corrected in the PDF and HTML versions of the article.

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Acknowledgements

The authors' work is supported in part by a EU/EFPIA/ Innovative Medicines Initiative Joint Undertaking PRECISESADS grant (no. 115565). G.B. is supported by financing from Genzyme/Sanofi in the context of the PRECISESADS project.

Author information

Affiliations

  1. GENYO Centre for Genomics and Oncological Research: Pfizer - University of Granada - Andalusian Government, Avenida de la Ilustración 114, P.T.S., 18016 Granada, Spain.

    • Guillermo Barturen
    •  & Marta E. Alarcón-Riquelme
  2. Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, University of Milan, Via Francesco Sforza 35, Milan 20122, Italy.

    • Lorenzo Beretta
  3. Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Villarroel 170, 08036 Barcelona, Spain.

    • Ricard Cervera
  4. Amsterdam Rheumatology and Immunology Center, PO Box 22660, 1100 DD Amsterdam, Netherlands.

    • Ronald Van Vollenhoven
  5. Unit of Inflammatory Chronic Diseases, Institute of Environmental Medicine, Karolinska Institutet, 177 77 Solna, Sweden.

    • Marta E. Alarcón-Riquelme

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Contributions

All authors researched the data for the article, provided substantial contributions to discussions of its content, wrote the article and reviewed and/or edited the manuscript before submission.

Competing interests

The authors declare no competing interests. G.B. and M.E.A-R are public employees of the Andalusian Government.

Corresponding author

Correspondence to Marta E. Alarcón-Riquelme.

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  1. 1.

    Supplementary information S1 (figure)

    Timeline of the classification criteria for systemic lupus erythematosus (blue), primary antiphospholipid syndrome (orange, APS), rheumatoid arthritis (RA, grey), mixed connective tissue disease (yellow), systemic sclerosis (SSc, green), and Sjögren syndrome (red), showing the criteria for each disease and the criteria that changed (included or excluded), as well as the time these criteria were published.

Glossary

Next-generation sequencing

A group of massive parallel technologies that enable the sequencing of millions of DNA or RNA fragments in a short period of time. Also known as high-throughput sequencing.

Type I interferon signature

An increased expression of type I interferon regulated or inducible genes, which have a major role in the activation of both the innate and adaptive immune systems.

Gene expression modules

Sets of genes with highly correlated expressed patterns (co-expressed).

Umbrella trial

A trial intended to study multiple targeted therapies in the context of a single disease or multiple diseases.

Quantitative trait loci

Loci whose allelic variation is associated with the variation of a quantitative feature, such as gene expression (expression quantitative trait loci (eQTL)) or methylation (methylation QTL (mQTL)).

Integrative clustering

A statistical method in which heterogeneous datasets are combined (data integration) and samples are grouped by similarity (clusters).

Supervised analysis

Comparative analysis where prior biological and/or classification knowledge is required.

Unsupervised analysis

In contrast to supervised analysis, no prior biological information and/or classification is required; the goal is to obtain new information based only on molecular information.

Concatenation algorithms

A type of data integration algorithm where the different layers of information are analysed together before any data modelling or transformation is performed.

Feature selection

A type of dimensionality reduction technique, which consists of selecting a subset of the most relevant features (for example, genes).

Overfitting

When a statistical model fits random error or noise, instead of the real features of the datasets; overfitting can occur if the number of variables in the model is much higher than the number of observations.

Dimensionality reduction

The process of reducing the number of variables (features) of a dataset; it can be divided in feature selection and feature extraction.

Model-based algorithms

A type of data integration algorithm that fits data to a statistical model in order to infer or predict some features from the general population.

Bayesian statistics

A mathematical method for calculating posterior probabilities (Bayesian probability) based on prior and current information.

Parametric methods

A group of statistical techniques that assume that the datasets come from populations that follow a probability distribution defined by fixed parameters.

Longitudinal datasets

Sets of repeated observations of the same variables at multiple points in time.

Transformation-based algorithms

A type of data integration algorithm, which applies a mathematical function to each point of the datasets, transforming all the different types of data into a common feature space.

Feature space

A set of values which summarize any kind of information (for example gene expression values for transcriptomic information); different data types can share a common feature space if transformed to have the same dimensions and the same range of values

Network theory

The study of graphs as the representation of the relationship between the features of multiple observations; for example, networks could be defined as graphs with the nodes representing individuals and the lines between nodes (the edges) representing connections in gene expression profiles.

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Publication history

Published

DOI

https://doi.org/10.1038/nrrheum.2017.220

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