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NGS-based RNA expression profiling in action

Translational applications in autoimmune (AI) and immuno-oncology (IO) research

Patient stratification

  • Biomarkers differentiate AI patients from healthy controls1
  • RNA signatures reveal distinct subsets of AI patients2
  • Patients with high vs low disease activity cluster according to biomarker expression3


Treatment responses

  • Certain biomarkers correlate with improved response to AI treatment4
  • Patients with IO-induced AI symptoms have RNA signatures related to AI patients5
  • RNA signatures can potentially be used to predict IO and AI treatment response6,7

Mechanism of action (MOA)

  • Biomarkers could help discover the MOA in patients with adverse responses to AI or IO therapy
  • Unique biomarker signatures in patients that do not respond to treatments could be further investigated to identify new drug targets


See autoimmune gene expression profiling in action.

Download the case studies

The healthy immune system is characterized by carefully balanced control of immune activity. Too little and diseases like cancer are allowed to proliferate. Too much and the immune system can begin attacking the body's own organs, tissues, and cells.

Autoimmune (AI) diseases encompass more than 80 conditions such as Type 1 diabetes, rheumatoid arthritis, systemic lupus erythematosus and inflammatory bowel disease. Despite the prevalence of AI disorders, they remain difficult to diagnose and treatment response and disease severity are highly patient-specific.

Conversely, immuno-oncology (IO) is emerging as a promising avenue of cancer treatment. Immunotherapy drugs such as immune checkpoint inhibitors and CAR-T therapy take advantage of and promote the immune system’s natural defenses. Unfortunately, these treatments are not effective for all patients and can cause adverse effects, sometimes even inducing AI disease.

Autoimmune diseases and immuno-oncology can both greatly benefit from discovery and monitoring of biomarkers that correlate with disease diagnosis, prognosis, severity, and treatment response.

The HTG EdgeSeq Autoimmune Panel provides a comprehensive view of biomarker expression for applications that span the entire research and development landscape for AI and IO from biomarker discovery to development of potential companion diagnostics.

Horizontal bar chart demonstrating different autoimmune disease states and the number of drugs at each phase of clinical trial in process.

Please note to the following disclaimers and reference:



  1. Bader, L. et al. Candidate Markers for Stratification and Classification in Rheumatoid Arthritis. Front. Immunol. 10, (2019).
  2. Rai, R., Chauhan, S. K., Singh, V. V., Rai, M. & Rai, G. RNA-seq Analysis Reveals Unique Transcriptome Signatures in Systemic Lupus Erythematosus Patients with Distinct Autoantibody Specificities. PLOS ONE 11, e0166312 (2016).
  3. Zollars, E. et al. Clinical Application of a Modular Genomics Technique in Systemic Lupus Erythematosus: Progress towards Precision Medicine. Int. J. Genomics 2016, (2016).
  4. Wright, H. L., Thomas, H. B., Moots, R. J. & Edwards, S. W. Interferon gene expression signature in rheumatoid arthritis neutrophils correlates with a good response to TNFi therapy. Rheumatology 54, 188–193 (2015).
  5. Balko, J. M. et al. Molecular characterization of immune-related severe adverse events (irSAE). J. Clin. Oncol. 35, 3076–3076 (2017).
  6. Darvin, P., Toor, S. M., Sasidharan Nair, V. & Elkord, E. Immune checkpoint inhibitors: recent progress and potential biomarkers. Exp. Mol. Med. 50, (2018).
  7. Nagafuchi, Y., Shoda, H. & Fujio, K. Immune Profiling and Precision Medicine in Systemic Lupus Erythematosus. Cells 8, (2019).
  8. Johnson, D. B. et al. Fulminant Myocarditis with Combination Immune Checkpoint Blockade. N. Engl. J. Med. 375, 1749–1755 (2016).
  9. Parikh, S. V. et al. Characterising the immune profile of the kidney biopsy at lupus nephritis flare differentiates early treatment responders from non-responders. Lupus Sci. Med. 2, (2015).