Dario Ghersi

Validation of HOXA10-specific antibody

CIBERSORT analysis for HOXA10 and its driven signature

GSEA gene sets enriched in STS PDAC patients

CIBERSORT analysis for T cell exhaustion markers

Human Protein Atlas pathology for the 5-gene prognostic signature in PDAC

Purpose:Despite the significant association of molecular subtypes with poor prognosis in patients with pancreatic ductal adenocarcinoma (PDAC), few efforts have been made to identify the underlying pathway(s) responsible for this prognosis. Identifying a clinically relevant prognosis-based gene signature may be the key to improving patient outcomes.Experimental Design:We analyzed the transcriptomic profiles of treatment-naïve surgically resected short-term survivor (STS) and long-term survivor (LTS) tumors (GSE62452) for expression and survival, followed by validation in several datasets. These results were corroborated by IHC analysis of PDAC-resected STS and LTS tumors. The mechanism of this differential survival was investigated using CIBERSORT and pathway analyses.Results:We identified a short-surviving prognostic subtype of PDAC with a high degree of significance (P = 0.018). One hundred thirty genes in this novel subtype were found to be regulated by a master regulator, homeobox gene HOXA10, and a 5-gene signature derived from these genes, including BANF1, EIF4G1, MRPS10, PDIA4, and TYMS, exhibited differential expression in STSs and a strong association with poor survival. This signature was further associated with the proportion of T cells and macrophages found in STSs and LTSs, demonstrating a potential role in PDAC immunosuppression. Pathway analyses corroborated these findings, revealing that this HOXA10-driven prognostic signature is associated with immune suppression and enhanced tumorigenesis.Conclusions:Overall, these findings reveal the presence of a HOXA10-associated prognostic subtype that can be used to differentiate between STS and LTS patients of PDAC and inform on the molecular interactions that play a role in this poor prognosis.

Pancreatic ductal adenocarcinoma (PDAC) exhibits a distinctive propensity to invade nearby organs and infiltrate large blood vessels, even in the absence of distant metastasis. While the genetic and transcriptomic drivers of PDAC progression have been well studied, the mechanisms by which these molecular changes translate into functional, invasive behavior remain largely unknown. Here, we uncover a striking level of tissue organization, characterized by previously unrecognized spatial and geometric properties within and among tumor structures. Leveraging the first large-scale, AI-assisted, human-curated PDAC atlas from hematoxylin and eosin (H&E) images, we annotated, classified, and characterized 144,474 malignant and normal structures from treatment-naive (n=118) and neoadjuvant-treated PDAC patients (n=50). Additionally, we developed a new computational tool, SHAPE, to investigate PDAC aggressiveness through a comprehensive geometrization of cancer progression. Using traditional H&E-stained slides and three-dimensional (3D) tissue reconstruction experiments, we observed that invading tumor structures display an eccentric morphology with pronounced local angular coherence. These geometric and spatial properties revealed coherent architectural patterns, with invasive structures closely tracking vessels and nerves as they infiltrate surrounding tissue. Mechanistically, integration of morphological features from 39,045annotated tumor structures with whole-genome and RNA sequencing data revealed that PDACs with numerous eccentric structures exhibit increased copy number alterations (CNAs), loss of heterozygosity (LoH) on the p-arm of chromosome 17, and a quasi-mesenchymal/basal-like molecular subtype. Spatial transcriptomic analysis of 1,650 tumor structures from six additional PDAC patients further confirmed upregulation of invasive cellular programs within highly eccentric structures, such as epithelial-to-mesenchymal transition (EMT), angiogenesis, coagulation, and complement pathways, underscoring their infiltrative nature. Finally, cross-validation of our AI-based method enabled a fully automated, highly interpretable computational approach to assist pathologists and clinicians in evaluating neoadjuvant chemotherapy response, predicting patient survival, and guiding chemotherapy in adjuvant settings. Collectively, these findings deepen our understanding of PDAC progression, identify a new hallmark of tumor architecture, and pave the way for full integration of AI-driven morphology-based approaches into clinical workflows to improve the management of PDAC patients.