Immune cell profiles and patient clustering in complex cases of interstitial lung disease

Interstitial lung disease comprises numerous clinical entities posing significant challenges towards a prompt and accurate diagnosis. Amongst the contributing factors are intricate pathophysiological mechanisms, an overlap between conditions, and interobserver disagreement. We developed a model for patient clustering offering an additional approach to such complex clinical cases. The model is based on surface phenotyping of over 40 markers on immune cells isolated from bronchoalveolar lavage in combination with clinical data. Based on the marker expression pattern we constructed an individual immune cell profile, then merged these to create a global profile encompassing various pathologies. The contribution of each participant to the global profile was assessed through dimensionality reduction tools and the ensuing similarity between samples was calculated. Our model enables two approaches. First, assessing the immune cell population landscape similarity between patients within a diagnostic group allows rapid identification of divergent profiles, which is particularly helpful for cases with uncertain diagnoses. Second, sample clustering is based exclusively on the calculated similarity of the immune cell profiles, thereby removing physician bias and relying on cellular nearest neighbors.

Automated summary

Interstitial lung disease poses challenges for diagnosis due to complex pathophysiological mechanisms, overlapping conditions, and interobserver disagreement. We developed a patient clustering model based on surface phenotyping of immune cells and clinical data to offer an additional approach in complex cases. Our model allows for rapid identification of divergent profiles within a diagnostic group and removes physician bias by relying on cellular nearest neighbors for sample clustering.