The Challenge of Rare Diseases

Rare Disease Day represents a synchronised global effort advocating for equality in social inclusion, healthcare services, and the availability of diagnosis and treatments for individuals affected by rare diseases. Rare Disease Day is observed every year on the last day of February — the rarest day of the year.

This day epitomises the significant strides we have made in confronting the complexities of serious and intricate illnesses. It highlights pivotal recent studies that enhance our grasp of biomarkers' central role in precision and personalised medicine. We have seen the epochal shift that occurs when biomarkers are fused with bioinformatics, heralding a new age of customised treatments based on each person's genetic identity. This fusion of laboratory expertise and digital technology is key in our pursuit to understand rare diseases. Yet, this journey brings its own set of challenges, as the intricate nature of these diseases calls for sophisticated and ethical bioinformatics approaches to decipher their genetic bases.

While there is much road yet to travel, the trajectory we establish now will shape the future of healthcare – rendering it more accurate, individualised, and meaningful for those confronting the enigmas of rare diseases.

Recent key publications

1) Unsupervised machine learning for risk stratification and identification of relevant subgroups of ascending aorta dimensions using cardiac CT and clinical data. Comput Struct Biotechnol J 2024; 23:287-294. Zanfardino M, Punzo B, Maffei E et al. Link to full article

This key study exploits unsupervised machine learning to dissect the complexities of cardiac CT and clinical data, aiming to stratify risk and extract clinically relevant subgroups within ascending aorta dimensions. Through a random forest-based cluster analysis of data from 1170 individuals, the authors define four distinct patient subgroups, each displaying unique clinical characteristics and aortic dimension ranges. This methodological approach not only advances the precision of population health strategies by enabling tailored prevention and care but also enhances our understanding of aortic pathologies' risk assessment.

2) IntelliGenes: a novel machine learning pipeline for biomarker discovery and predictive analysis using multi-genomic profiles. Bioinformatics 2023; 39. DeGroat W, Mendhe D, Bhusari A et al. Link to full article

This article presents IntelliGenes, an advanced ML pipeline for identifying crucial biomarkers in disease prediction through multi-genomic data analysis. Combining traditional statistical methods with modern ML algorithms, it introduces the I-Gene score to assess biomarkers' significance in complex trait prediction, enabling the creation of I-Gene profiles for deeper insights into ML's role in disease prediction. IntelliGenes offers a user-friendly, portable, cross-platform solution for personalised disease detection and research innovation in ML, promising new directions in personalised treatments.

3) Re-evaluation and re-analysis of 152 research exomes five years after the initial report reveals clinically relevant changes in 18. Eur J Hum Genet 2023; 31:1154-1164. Bartolomaeus T, Hentschel J, Jamra RA, Popp B. Link to full article

This study is an iterative re-analysis of NGS data in rare disease cohorts, focusing on 152 consanguineous families with NDDs. Utilising updated guidelines and the AutoCaSc system, the study re-evaluated variants and GDAs, finding significant changes in 18% of cases. This highlights the importance of re-analysing both solved and unsolved cases to uncover new insights, emphasising the evolving nature of genetic research. The research advocates for a cost-effective screening method to identify new variants efficiently, suggesting a shift in how genetic data is periodically reviewed to enhance diagnostic accuracy and research integrity.

4) Differentiation of MISSLA and Fanconi anaemia by computer-aided image analysis and presentation of two novel MISSLA siblings. Eur J Hum Genet 2019; 27:1827-1835. Danyel M, Cheng Z, Jung C et al. Link to full article

This study explores the diagnostic intricacies of microcephaly, short stature, and limb abnormalities syndrome (MISSLA) vis-à-vis Fanconi anaemia (FA), facilitated by novel findings on a DONSON variant in siblings with MISSLA. The investigation extends to a comprehensive review of MISSLA literature and leverages cutting-edge DeepGestalt technology for a pioneering computer-aided image analysis. This analysis discerns the specific facial gestalt of MISSLA and identifies distinctive facial features in FA patients, underscoring the potential of computer-assisted image analysis to refine diagnostic accuracy for these overlapping clinical spectrums. Our findings not only contribute to a deeper understanding of MISSLA and FA but also highlight the efficacy of modern diagnostic tools in enhancing precision in genetic disorder identification.

5) Episignatures in practice: independent evaluation of published episignatures for the molecular diagnostics of ten neurodevelopmental disorders. Eur J Hum Genet 2024; 32:190-199. Husson T, Lecoquierre F, Nicolas G et al. Link to full article

The authors of this study explore the utility of episignatures as biomarkers to classify variants of uncertain significance (VUS) in the molecular diagnosis of rare Mendelian neurodevelopmental disorders. Through rigorous independent investigation of published episignatures' predictive capabilities using DNA methylation data from a diverse cohort, the work reveals a broad spectrum of specificity and sensitivity across different genes. The findings underscore the variable efficacy of episignatures in a diagnostic context, advocating for cautious application and further validation. This study is a pivotal step toward refining the diagnostic precision of episignatures, emphasising the necessity for extensive validation to ensure their reliable use in clinical settings.

6) Use of Next-Generation Sequencing to Support the Diagnosis of Familial Interstitial Pneumonia. Genes (Basel) 2023; 14. Gigante AR, Tinoco EM, Fonseca A et al. Link to full article

This study delves into the complex landscape of familial interstitial pneumonia (FIP), a form of idiopathic interstitial lung disease (ILD) affecting multiple family members, through a comprehensive analysis of clinical features and genetic variants identified by next-generation sequencing (NGS). Focusing on patients with suspected FIP attending an ILD clinic, this retrospective analysis sheds light on the genetic underpinnings of this condition, highlighting the detection of significant variants associated with telomere maintenance, surfactant homeostasis, and the MUC5B gene. The findings emphasise the importance of genetic diagnosis in understanding and managing FIP, offering invaluable insights for pulmonologists into the hereditary aspects of ILD.

7) Novel lipid biomarkers and ratios as risk predictors for premature coronary artery disease: A retrospective analysis of 2952 patients. J Clin Hypertens (Greenwich) 2023; 25:1172-1184. Chen S, Li Z, Li H et al. Link to full article

In a novel investigation of premature coronary artery disease (PCAD), this study elucidates the relationship between cutting-edge lipid biomarkers – such as small dense LDL-cholesterol (sdLDL-C), lipoprotein(a) [Lp(a)], and free fatty acids (FFA) – and the degree of coronary stenosis, quantified by the Gensini score (GS). Analysing a large cohort through coronary angiography, the research establishes Lp(a) and the triglyceride-glucose (TyG) index as independent predictors for PCAD, marking a significant advance in predicting and diagnosing coronary stenosis. These findings herald a new chapter in cardiovascular diagnostics, leveraging lipid biomarkers to refine risk assessment in PCAD.