OpinionTransforming the Diagnostic Landscape of Rare Diseases with AI and a Multi-omics...

Transforming the Diagnostic Landscape of Rare Diseases with AI and a Multi-omics Approach

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Rare or orphan diseases refer to health conditions that manifest differently depending on the individual. Despite being individually scarce, the overall prevalence of known rare diseases (RDs) can be as high as 6%, affecting approximately 450 million individuals worldwide. According to studies, approximately 2.8 million individuals in the Middle East are affected by RDs, emphasizing the need for early detection of rare genetic disorders.

Drawbacks of conventional diagnostic methods:

RDs were previously diagnosed using biochemical parameters, molecular genetics, and cytogenetic methods. Conventional diagnostic techniques also relied on heuristic approaches. The limitations of such traditional screening approaches include:

  • Increased chances of misdiagnosis due to overlapping symptoms.
  • Equipment unavailability
  • High cost of case finding (including diagnosis)

Role of Genome Sequencing in RD Testing:

Genome sequencing has transformed the conventional diagnostic process, providing rapid, precise, and cost-effective diagnosis for an array of RDs. Incorporating genomic sequencing into newborn screening programs holds promise for expanding the landscape for early detection of treatable RDs.

Next Generation Sequencing (NGS)

The NGS method has revolutionized the diagnosis of RDs such as Cystic Fibrosis, etc. It is rapid, affordable, and provides high-throughput screening.

Whole Genome Sequencing (WGS)

This method uses blood samples and allows analysis of most of the human genome. This powerful tool that can discover novel genes is being used to diagnose RDs, helping families avoid long diagnostic journeys.

Whole Exome Sequencing (WES)

This technique utilizes blood or saliva samples to investigate protein-coding regions of the genome. It is a cost-effective tool for studying and analyzing genetic diseases.

Laboratory tests for diagnosing RDs:

Immunophenotyping

This technique identifies and characterizes different types of cells in a biological sample based on the markers on their surface. These markers, known as antigens, are specific to different cell types and can be detected using antibodies that bind to them. Immunophenotyping can be utilized to help differentiate Acute Myeloid Leukemia (AML), a common form of blood cancer in older adults, from Acute Lymphoblastic Leukemia (ALL), a cancer of the white blood cells affecting mostly children. It is a rapid and efficient technique that helps determine the prognosis of the disease.

Sanger Sequencing

In rare diseases, such as Birt Hogg Dube syndrome with susceptibility to develop renal cell carcinoma, lung cysts, and spontaneous pneumothorax, Sanger sequencing helped clinicians find the genetic mutation causing the disease. The process involves gathering DNA, reading the sequence, and comparing it to a standard one for any differences responsible for the disease. The process helps understand rare diseases better to develop treatments. Furthermore, it is an accurate, quick, and cost-effective method for analyzing the small target regions of the genome.

Von Willebrand activity and Von Willebrand factor antigen:

Von Willebrand disease is a rare, inherited bleeding disorder that slows down the clotting process. Analysis of the Von Willebrand factor (VWF) helps diagnose or monitor the treatment of Von Willebrand disease through the following tests:

  • VWF: Ag test

This test can be performed with an enzyme-linked immunosorbent assay (ELISA), which measures the levels of clotting factor (VWF) in the blood sample. It is a sensitive, convenient, and reliable test.

  • VWF Activity or Ristocetin Co-factor Test

However, This test determines whether the VWF protein (which helps the blood to clot) in a blood sample is functioning properly. It helps diagnose and monitor the progress of Von Willebrand disease.

Advanced and Cutting-edge Research:

For individuals living with RDs, one of the major hurdles is obtaining a quick and precise diagnosis. A new wave of research has emerged in the field of RD diagnostics, with scientists across the globe working to explore novel ways of detecting these orphan diseases. Breakthroughs in early diagnosis will pave the way for effective treatment of such conditions.

AI in Rare Disease Diagnosis

The use of Artificial Intelligence (AI) can circumvent certain conventional drawbacks in the diagnosis of RDs. Machine learning is a branch of AI that can support clinical decision-making and help with image identification and genetic analysis. For example, a combination of brain function tests and structural imaging data can be employed to determine whether an individual with Huntington’s disease will obtain a clinical diagnosis within five years. Also, there is one deep learning model that creates a biomarker using DNA CpG methylation data to identify Huntington’s disease.

Role of Biomarkers in Diagnosing RDs

Biomarkers will serve as crucial tools for monitoring the progression and prognosis of RDs. For instance, in the case of lysosomal storage disorders like Gaucher disease (GD), glucosyl sphingosine (Lyso-Gb1) serves as a promising biomarker. It has shown sensitivity and specificity in the diagnosis and monitoring of Gaucher disease.

Novel Multi-omics Approach

Infants and children suffering from RDs require equitable access to quick and precise diagnosis for direct clinical management. A research study provides preliminary evidence of integrating multi-omics approaches into current diagnostic practice to completely understand the ability of RD genomic testing in real-time. It suggests that the multi-omics approach has enabled high-powered analysis of biological molecules for diagnosing mitochondrial diseases.

Limitations of Breakthroughs in Rare Disease Diagnosis:

  • The main challenge with RDs is that there is often a lack of sufficient data to train the AI model.
  • For researchers working on developing new biomarkers for detecting RDs like Gaucher disease or Fabry disease, a small sample size is the limiting factor.

Future Research:

Early detection of RDs can halt the diagnostic odyssey. The focus is shifting to developing personalized and precision medicine. With time, healthcare will become patient-centric and individualized. AI can play a pivotal role in this domain. Efforts should be made to develop a feasible molecular diagnostic strategy for RDs.

Also Read: AI NURSES: MY PARTNERS, NOT SUBSTITUTES

Moreover, The landscape of RD diagnosis is witnessing a transformative shift. Researchers have come up with early detection methods to improve the quality of life of patients. Utilizing genomic sequencing for newborn screening tests can play a pivotal role in winning the war against RDs. Advanced methods like immunophenotyping, Sanger sequencing, and NGS are currently employed to diagnose RDs.

Dr. Joowon Oh, M.D., Ph.D., Consultant Physician in Molecular Pathology, Director, Genetic CoE, STMC, PureLab

In addition, innovative research is exploring innovative ways to leverage the potential of AI and a multi-omics approach. Further investigation is required to validate such breakthrough diagnostic methods by using large-scale population studies. The future of RD detection and diagnosis lies in the ongoing integration of personalized and precision medicine into healthcare.

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