Machine Learning-Based Desmoplastic Signatures Predict Patient Outcomes in Pancreatic Ductal Adenocarcinoma

Abstract

Introduction: Pancreatic ductal adenocarcinoma (PDAC) is characterized by desmoplasia, a fibrotic matrix response with unclear impact on prognosis. We profiled clinical PDAC using two novel technologies: a machine learning algorithm to quantify matrix patterning in 436 patients and a spatial protein/cell phenotyping algorithm using CO-Detection by indEXing (CODEX). We integrated these modalities with patient metadata to define a unified, machine learning-based histologic signature for PDAC prognosis. Methods: Trichrome-stained clinical specimens were analyzed by an unsupervised algorithm for ultrastructural analysis (>13,000 images, 147 local/global fiber features), which were processed by uniform manifold approximation and projection (UMAP) and fitted to a minimum spanning tree-based pseudotime model. A custom 31-plex CODEX panel was used to visualize tumor- and stroma-associated cells by UMAP of protein expression. Cell interactions were quantified based on spatial co-localization (k=20 nearest neighbors). Binary classifiers for Poor/Good survivorship and recurrence (two- and one-year thresholds, respectively) were trained in MATLAB. Results: Matrix patterning comprised of two architectural groups (L,R), with group-R patients experiencing earlier median death and relapse by 461d and 95d, respectively. Poorer outcomes were primarily driven by three survival-negative interaction “nodes”: Type-I stromal fibroblasts, endothelial cells, and proliferating tumor cells, collectively forming two cell-pattern signatures (A,B) in which signature-B patients experienced earlier median death by 858d. A unified machine learning model successfully classified binary survivorship and recurrence with area under the curve of 0.943-0.946. Conclusion: Fibrotic architecture and cell patterning are powerful independent predictors for PDAC outcomes, with specific desmoplasia-associated cell phenotypes largely driving differential prognoses.