Handler, J. S. | Search bioRxiv

bioRxiv2026

MetScore and classical-basal subtype intersect to define chemotherapy response and tumor microenvironment architecture in pancreatic ductal adenocarcinoma

Background Transcriptomic heterogeneity in pancreatic ductal adenocarcinoma (PDAC) is conventionally modeled along a single classical-basal axis, but this framework incompletely explains clinical behavior. We hypothesized that MetScore, a single-sample measure of metastatic colonization potential, defines a complementary axis. Objective To determine how MetScore and classical-basal identity jointly shape PDAC biology and clinical outcomes. Design We integrated RNA sequencing, DNA sequencing, clinical, and digital pathology data from 512 tumor biopsies from 510 patients in the Pancreatic Cancer Action Network Know Your Tumor real-world cohort. We assessed associations with biopsy site, survival, chemotherapy response, and tumor microenvironment composition; validated metastatic discrimination in an independent cohort; and examined cancer cell-intrinsic drug sensitivity in vitro. Results MetScore distinguished metastatic from primary biopsies, a finding validated externally. High MetScore and basal identity were independently associated with inferior survival. High MetScore and classical identity were each associated with preferential response to 5-fluorouracil (5-FU)-based rather than gemcitabine-based therapy, with the strongest enrichment for 5-FU responses among classical/MetScore-high patients. In vitro drug-sensitivity experiments did not recapitulate the clinical treatment-response pattern, motivating investigation of cancer cell-extrinsic factors. Transcriptomic deconvolution and histopathologic machine learning-based cell annotation demonstrated that high-MetScore and basal states converged on macrophage/monocyte enrichment but diverged in their associations with cancer-associated fibroblasts. Conclusion Integrating MetScore with classical-basal identity establishes a two-axis framework that better explains metastatic behavior, prognosis, treatment response, and microenvironmental composition than the conventional one-axis model and provides a clinically testable strategy for transcriptome-guided patient stratification.

cancer biology↗

bioRxiv2024

Identifying a gene signature of metastatic potential by linking pre-metastatic state to ultimate metastatic fate

Identifying the key molecular pathways that enable metastasis by analyzing the eventual metastatic tumor is challenging because the state of the founder subclone likely changes following metastatic colonization. To address this challenge, we labeled primary mouse pancreatic ductal adenocarcinoma (PDAC) subclones with DNA barcodes to characterize their pre-metastatic state using ATAC-seq and RNA-seq and determine their relative in vivo metastatic potential prospectively. We identified a gene signature separating metastasis-high and metastasis-low subclones orthogonal to the normal-to-PDAC and classical-to-basal axes. The metastasis-high subclones feature activation of IL-1 pathway genes and high NF-{kappa}B and Zeb/Snail family activity and the metastasis-low subclones feature activation of neuroendocrine, motility, and Wnt pathway genes and high CDX2 and HOXA13 activity. In a functional screen, we validated novel mediators of PDAC metastasis in the IL-1 pathway, including the NF-{kappa}B targets Fos and Il23a, and beyond the IL-1 pathway including Myo1b and Tmem40. We scored human PDAC tumors for our signature of metastatic potential from mouse and found that metastases have higher scores than primary tumors. Moreover, primary tumors with higher scores are associated with worse prognosis. We also found that our metastatic potential signature is enriched in other human carcinomas, suggesting that it is conserved across epithelial malignancies. This work establishes a strategy for linking cancer cell state to future behavior, reveals novel functional regulators of PDAC metastasis, and establishes a method for scoring human carcinomas based on metastatic potential.

cancer biology↗