Grimshaw, S. | Search bioRxiv

bioRxiv2026

Integrated analysis of stemness-associated immune modulatory circuits in squamous cell carcinomas

Emerging evidence indicates that a subset of cancer cells enriched for stemness-related gene signatures possess distinct immunomodulatory capacities, enabling these tumor-initiating stem cells (tSCs) to more effectively evade or resist anti-tumor immunity. Despite these advances, the tSC-specific molecular circuits orchestrating their specialized immune privilege program are not well defined. Here, in squamous cell carcinomas of the skin and oral cavity, we comprehensively delineate the unique immune-evasive properties of tSCs and dissect the transcriptional regulation shaping their immunomodulatory programs. By integrating transcriptome profiling, chromatin landscape mapping, genetic perturbation, and single-cell RNA sequencing, we found that the tSC-specific immune program is broadly governed by SOX2, a stemness-associated transcription factor. We demonstrate that SOX2 enables tSCs to sustain immature tumor-associated neutrophils (TANs) and subsequently trigger these myeloid cells to foster the development of tumor-associated macrophages (TAMs). This SOX2-directed tSC-TAN-TAM axis establishes a localized immunosuppressive niche for protecting tSC. SIGNIFICANCEHere, we uncover SOX2 as a master regulator that orchestrates conserved immune modulatory circuits in tSCs to sustain pro-tumor myeloid cell states. These findings place tSCs at the apex of immune landscape remodeling, asserting a central role of stemness-associated program in organizing the immunosuppressive tumor microenvironment.

cancer biology↗

bioRxiv2020

Explainable AI reveals key changes in skin microbiome associated with menopause, smoking, aging and skin hydration

Alterations in the human microbiome have been observed in a variety of conditions such has asthma, gingivitis, dermatitis and cancer, and much remains to be learned about the links between the microbiome and human health. The fusion of artificial intelligence with rich microbiome datasets can offer an improved understanding of the microbiomes role in our health. To gain actionable insights it is essential to consider both the predictive power and the transparency of the models by providing explanations for the predictions. We combine the effort of collecting a corpus of leg skin microbiome samples of two healthy cohorts of women with the development of an explainable artificial intelligence (EAI) approach that provides accurate predictions of phenotypes and explanations. The explanations are expressed in terms of variations in the abundance of key microbes that drive the predictions. We predict skin hydration, subjects age, pre/post-menopausal status and smoking status from the leg skin microbiome. The key changes in microbial composition linked to skin hydration can accelerate the development of personalised treatments for healthy skin, while those associated with age may offer insights into the skin aging process. The leg microbiome signatures associated with smoking and menopausal status are consistent with previous findings from oral/respiratory tract microbiomes and vaginal microbiomes respectively. This suggests that easily accessible microbiome samples could be used to investigate health-related phenotypes, offering potential for non-invasive diagnosis and condition monitoring. Our EAI approach sets the stage for new work focused on understanding the complex relationships between microbial communities and phenotypes. Our approach can be applied to predict any conditions from microbiome samples and has the potential to accelerate the development of microbiome-based personalised therapeutics and non-invasive diagnostics.

bioinformatics↗