Cracking the code of itchy skin: A new way to diagnose and treat skin problems

a: The ridgeline plots show the activity scores of different immune response types (Th1, Th2, Th17, type I interferon, neutrophil-related, macrophage-related, and eosinophil-related responses) in skin samples from different inflammatory skin diseases (Psoriasis, Atopic Dermatitis, Lichen Planus, Cutaneous Lupus Erythematosus, Neutrophilic Dermatosis, and Wells Syndrome). The solid lines mark the point where each response type is considered active. b: Box plots compare the immune response scores in skin samples across all these diseases for the "sentinel" (reference) biopsies. Each dot is one sample. The number of samples for each condition is listed: Psoriasis (25), Atopic Dermatitis (16), etc., along with healthy controls (8). c: Box plots show the immune response scores for "test" samples (a different set of biopsies). Sample numbers for each disease are also listed, like Psoriasis (8) and Atopic Dermatitis (15). b–c: In the box plots, the middle line shows the median score, the box edges show the middle range of scores (25th to 75th percentile), and the "whiskers" extend to scores that are within 1.5 times this range. d: A UMAP plot (a type of visualization) groups test samples with reference biopsies that match their disease. Black rings are test samples, and grey rings are the reference biopsies. 

Image credit Nat Communications (2024). https://doi.org/10.1038/s41467-024-54559-6

 

Have you ever struggled with a skin condition that doctors found difficult to diagnose? Or perhaps you've been treated for a skin problem, but the treatment didn't work? Well, there’s some exciting news from a group of scientists who have been digging deep into the world of inflammatory skin diseases. They've come up with a clever new way to understand and treat these conditions, and it all comes down to something they call "immune modules".

What are Immune Modules?

Think of your immune system as a complex network of pathways, each with its own job. These pathways can become overactive and lead to inflammatory skin conditions like psoriasis, eczema (atopic dermatitis), and lupus. The researchers have identified seven key 'immune modules', each representing a different pathway:

• Th17: Often seen in psoriasis.
• Th2: Common in eczema.
• Th1: Involved in conditions like lichen planus.
• Type I IFNs: Related to lupus.
• Neutrophilic: Seen in conditions with lots of neutrophils.
• Macrophagic: Related to macrophages.
• Eosinophilic: Linked to eosinophils.

The scientists found that by looking at the activity of these modules in a skin biopsy, they could better understand what was happening in an individual patient's skin.

A molecular map for skin diseases

The team created a "molecular map" of inflammatory skin diseases, using these modules as their guide. They took skin samples (biopsies) from patients with different conditions and measured the activity of genes related to each module. They discovered that each disease had its own distinct pattern of module activity. For instance, psoriasis showed high activity in the Th17 module, while eczema showed high activity in the Th2 module. This was like creating a fingerprint for each disease, based on the activity of these modules.

This approach proved to be very accurate in diagnosing various skin diseases, even those that are usually tricky to identify. It was even better at classifying diseases than using all the genes they measured or other standard methods. This molecular map also worked well for diagnosing unusual cases like erythroderma (widespread skin redness) and rashes where the cause was unknown.

Personalized treatment

The researchers didn't stop at diagnosis. They also wanted to see if these immune modules could help guide treatment decisions. Because each module corresponds to a specific immune pathway that can be targeted with medication, they explored the possibility of matching the dominant module in a patient's skin biopsy to the right treatment.

They looked at 80 patients who were being treated for psoriasis, eczema or lichen planus, and they found that patients who responded well to treatment had a module profile that matched the treatment they were receiving. For example, if a patient with psoriasis had a dominant Th17 module, they were more likely to respond to anti-Th17 medications. However, some patients were not responding because their module profile did not match the treatment they were given. And of those who were not responding to treatment, many had mismatched profiles, which were changed according to the new model to achieve success.

Furthermore, they discovered that in some patients, the dominant module in their skin changed under treatment, causing them to stop responding. By identifying the new dominant module, they were able to switch to a more effective treatment. This module-based approach not only improved treatment responses but also helped to understand why some treatments fail.

What does this mean for you?

This research is a big step forward for people with inflammatory skin diseases. It suggests that:

• More accurate diagnoses are possible: Using module-based profiling, doctors can diagnose conditions more accurately, even when symptoms are confusing.
• Treatments can be tailored: By matching a patient's dominant immune module to a targeted therapy, doctors can improve treatment success rates.
• Treatment failures can be understood: Module profiling can help doctors understand why treatments fail and switch to more effective ones.

The future of skin care

The scientists hope that this approach will be used in routine clinical practice in the future. It’s a move towards a more personalised way of treating skin conditions, where decisions are based on a deeper understanding of the individual patient’s unique biology. This could mean quicker diagnoses and better outcomes for people struggling with these conditions.

Additional information: Immune modules to guide diagnosis and personalized treatment of inflammatory skin diseases. Nat Communications (2024). https://doi.org/10.1038/s41467-024-54559-6

Journal information: https://www.nature.com/ncomms/