Clinical clusters in hidradenitis suppurativa: Interesting observations but incomplete answers

Among the numerous challenges faced by clinicians managing hidradenitis suppurativa (HS), two stand out: the marked clinical heterogeneity of the disease and the relatively low response rates to existing treatments. In their recent publication, Passera et al.¹ attempt to address both issues by leveraging data from SUNSHINE and SUNRISE—the two pivotal Phase III trials that led to the approval of secukinumab for moderate-to-severe HS.²

First, the classification of HS into clinically meaningful phenotypes has been the subject of more than 15 attempts over the past decade. The work by Canoui-Poitrine et al.³ was among the first to propose a tripartite division based on clinical features. However, no consensus has yet emerged: inter-rater reliability remains modest, proposed phenotypes often overlap, and their clinical relevance continues to be debated.⁴

Second, therapeutic response in HS remains disappointingly low compared with other chronic inflammatory dermatoses. This observation still persists despite extensive translational and clinical research efforts, including the development of biologics specifically targeting key cytokine pathways.

The authors applied unsupervised clustering to a large cohort of 1084 patients enrolled in the SUNSHINE and SUNRISE trials, using baseline clinical characteristics to stratify the population. This approach led to the identification of three distinct subgroups. Cluster 1 includes 54% of the patients, primarily obese women with moderate disease severity. Cluster 2 comprises 18% of the patients, characterized mainly by less obese, non-smoking, non-white men with earlier disease onset. Cluster 3 accounts for 28% of the patients and is associated with more severe and extensive disease, with a balanced gender distribution. These subgroups likely reflect real-world diversity and underscore the complexity of HS beyond conventional Hurley staging.

The authors conclude that secukinumab demonstrated efficacy versus placebo in the three clusters. However, a closer inspection of the primary endpoint—HiSCR50 at Week 16—reveals a lower response in Cluster 3 (37.5% and 34.7%) compared with Cluster 1 (50.3% and 48.9%). In addition, a higher proportion of lost-to-follow-up patients and persistently flat response curves in the group of patients treated by placebo for the first 16 weeks within Cluster 3 raise concerns about more refractory disease, as might be expected.

While the clustering approach is methodologically sound and clinically intuitive, the study also highlights key limitations in the current understanding of HS. Notably, the analysis does not incorporate any biological markers or molecular signatures—elements that would be essential to define mechanistic endotypes and eventually support precision medicine. Furthermore, the identified clinical clusters do not predict treatment response, limiting their utility in guiding therapeutic decisions.

This limitation is echoed in recent transcriptomic efforts. Wang et al.⁵ published an elegant clustering analysis based on publicly available bulk skin RNA-seq datasets from HS patients. They identified two major molecular clusters, with Cluster 1 showing enrichment in metabolic pathways and a higher likelihood of response to adalimumab. However, this molecular stratification was not cross-referenced with clinical phenotype data, thereby limiting translational applicability. These complementary efforts, clinical and molecular, highlight the urgent need to integrate multidimensional datasets.

In summary, this study represents a valuable effort to dissect the clinical heterogeneity of HS using modern data-driven methods. However, the absence of biomarker integration and the lack of association with treatment outcomes underline the need for deeper, multidimensional profiling. The future of HS management likely resides at the intersection of phenotyping, molecular endotyping, and predictive modelling of therapeutic response.

Dr. Charles Cassius has the following relationships with industry over the past 36 months: grants or contracts from any entity: Almirall SAS, AstraZeneca, AbbVie, Bristol-Myers Squibb, Exafield, Janssen-Cilag, Leo Pharma, Lilly France SAS, Novartis Pharma SAS and Sanofi Aventis France; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events: Almirall SAS, AbbVie, Bristol-Myers Squibb, Leo Pharma, Lilly France SAS and Novartis Pharma SAS; and support for attending meetings and/or travel: Almirall SAS, Janssen-Cilag, Leo Pharma, Novartis Pharma SAS, Pierre Fabre Medicament and Sanofi Aventis France. Dr. Bénédicte Oulès has the following relationships with industry over the past 36 months: Support for attending meetings and/or travel: UCB Pharma, Novartis Pharma and Pierre Fabre.