Ozbilgic, R. | Search bioRxiv

bioRxiv2026

Integrated Collagen Architecture and Composition Improve Risk Stratification in Triple-Negative Breast Cancer

PurposeTriple-negative breast cancer (TNBC) exhibits substantial clinical heterogeneity, with some patients experiencing early recurrence and poor survival despite similar clinicopathologic features. We sought to determine whether quantitative measures of intratumoral collagen architecture and composition derived from standard histopathologic specimens can identify patients at risk of recurrence and adverse survival outcomes. Experimental DesignWe analyzed a retrospective cohort of 79 TNBC tumors assembled into a tissue microarray using a multimodal computational pathology framework integrating Massons Trichrome staining with COL1 and COL3 immunohistochemistry. Collagen architecture was quantified using fiber-based image analysis and unsupervised clustering, while collagen composition was assessed using a normalized COL3:COL1 ratio. Associations with recurrence-free interval (RFI) and overall survival (OS) were evaluated using Kaplan-Meier analysis, restricted mean survival time (RMST), and Cox proportional hazards modeling. ResultsUnsupervised analysis identified four distinct collagen architectural states, which were consolidated into low-risk and high-risk groups based on recurrence patterns. High-risk collagen architecture was associated with significantly worse long-term RFI (log-rank p=0.025; RMST difference 10.1 months). Independently, a higher COL3:COL1 ratio was associated with improved OS (log-rank p=0.042; RMST difference 9.4 months). Integration of architectural and compositional biomarkers further refined risk stratification, identifying a subgroup with high-risk architecture and low COL3:COL1 ratio that exhibited the poorest survival outcomes. Notably, collagen-based stratification identified patients with divergent outcomes not readily predicted from tumor stage alone. ConclusionsQuantitative assessment of intratumoral collagen architecture and composition provides clinically meaningful prognostic information in TNBC and enables stratification of recurrence and survival risk. These findings support extracellular matrix phenotyping as a practical and scalable computational pathology approach for refining risk assessment in TNBC. Translational RelevanceTriple-negative breast cancer (TNBC) remains clinically challenging due to heterogeneous outcomes that are not fully captured by standard clinicopathologic variables. In this study, we demonstrate that quantitative features of intratumoral collagen architecture and composition, derived from routine pathology specimens, provide clinically meaningful prognostic information. Collagen-based biomarkers, including distinct collagen architectural phenotypes and the COL3:COL1 ratio, identify patient subgroups with distinct recurrence and survival outcomes, particularly among individuals whose risk is not adequately predicted by conventional staging. Importantly, these features can be extracted from widely available histological stains and immunohistochemistry, supporting the potential integration into existing pathology workflows. These findings support the tumor microenvironment as an underutilized source of biomarkers and suggest that extracellular matrix-based phenotyping may improve risk stratification and inform clinical decision-making in TNBC.

cancer biology↗

bioRxiv2025

Temporal dynamics of macrophages transcriptional profiles during zebrafish wound healing

Macrophages participate to wound healing by contributing to host defense and orchestrating inflammation and repair. They adapt dynamically to the wound microenvironment by adopting diverse polarization states, which are determinant of wound outcome, influencing whether healing is successful or becomes chronic. The zebrafish embryo, widely used for live imaging of immune responses, is a powerful model to study macrophage behavior after injury. However, the transcriptional landscape of polarized macrophages in this model during wound healing remains insufficiently characterized. Here, we employ bulk RNA sequencing to characterize macrophage transcriptional programs following tail fin wounding a robust model for studying sterile inflammation. Our findings reveal that zebrafish macrophages undergo large transcriptomic changes along different wound healing phases, particularly between 2 and 5 hours post-amputation, suggesting a fast reprogramming leading to different functional states. We further show that, at 2h, macrophages acquire a pro-inflammatory profile with a gene signature closed to M1 signature. At 5h, macrophages express genes involved in immunoregulation and healing associated with shutoff of pro-inflammatory pathways and the activation of glucose and glycogen metabolism. Finally, we show that macrophage reprogramming becomes deeply attenuated by 29h. Our findings provide a foundation for understanding macrophage polarization in zebrafish, revealing underpinning molecular mechanisms, including both specific and evolutionarily conserved pathways with a potential impact on translational medicine.

immunology↗