Abstract B115: Integrated spatial and multi-omic analysis reveals functional niches in the pancreatic cancer microenvironment
Notice bibliographique
Résumé
Abstract The tumour microenvironment (TME) of pancreatic ductal adenocarcinoma (PDAC) is comprised of heterogenous malignant epithelium and diverse immune infiltrates within a dense, fibrotic stroma. This multi-compartmental TME includes myeloid and lymphoid lineages, cancer-associated fibroblasts (CAFs), mural and endothelial cells, and tumour cells embedded in a rich extracellular matrix (ECM). Efforts to profile and perturb compartments or cellular phenotypes in isolation have often led to unintended adverse effects on neighbouring cells, compromising drug efficacy. This drives the need for an integrated, spatially-resolved understanding of the architecture and molecular features underlying the organization of the TME. To address this, we leveraged single-cell RNA sequencing (scRNAseq) data from eight distinct datasets to design three custom multiplexed imaging mass cytometry (IMC) panels against tumour, immune, and stromal targets. These were applied to three serial sections of a tissue microarray of 221 PDAC patients. Segmentation and clustering of 7.9 million cells revealed 83 distinct cell types and their functional states, including biophysical responses. Advanced image alignment across serial sections enabled the incorporation of TME-wide features and resolved eight spatially recurrent PDAC microenvironments neighbouring phenotypically distinct epithelial ducts. Two microenvironments, ECM-rich and immune-suppressed, were associated with worse overall survival and frequently co-occurred with a third, stiff matrix, characterized by stroma enriched in phospho-myosin light chain 2 expressing CAFs. In contrast, the immune infiltrated stroma microenvironment was linked to improved clinical outcomes and often co-occurred with CD105+ fibrovascular and immune infiltrated microenvironments. Matched whole-genome sequencing (n = 182/221) revealed that patients dominant in ECM-rich regions harboured 12p11.21 (KRAS) and 8q24.21 (MYC and POU5F1B) amplifications, and 17q22 (RNF43) deletions, and stiff matrix were associated with 13q33.3 (LATS2) deletion. To link spatial niches to proteomic signatures, we performed whole-slide IMC-guided laser capture microdissection and mass spectrometry, and further integrated differentially expressed proteins with transcriptomes from an external scRNAseq cohort (n = 163). ECM-rich and immune suppressed regions showed signatures of mechanically induced immune suppression, including NETosis and innate immune surveillance via pattern recognition receptors. Contrastingly, immune infiltrated microenvironments were enriched for cytotoxic activity and immune trafficking programs, while immune infiltrated stroma displayed signatures associated with oxidative stress responses. These findings define unique spatial ecosystems within the PDAC TME that are linked to specific genomic alterations, functional states and clinical outcomes, highlighting their therapeutic relevance and potential as targets to overcome the challenges of treating PDAC. Citation Format: Noor Shakfa, Ferris Nowlan, Tiak Ju Tan, Sibyl Drissler, Elizabeth Sunnucks, Jennifer L. Gorman, Chengxin Yu, Michael J. Geuenich, Sheng-Ben Liang, Barbara Gruenwald, Ayelet Borgida, Cassandra J. Wong, Brendon Seale, Zhen Yuan Lin, Edward L. Chen, Golnaz Abazari, Miralem Mrkonjic, Julie M. Wilson, Kieran R. Campbell, Robert C. Grant, Anne-Claude Gringas, Grainne M. O'Kane, Faiyaz Notta, Steven Gallinger, Hartland W. Jackson. Integrated spatial and multi-omic analysis reveals functional niches in the pancreatic cancer microenvironment [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research—Emerging Science Driving Transformative Solutions; Boston, MA; 2025 Sep 28-Oct 1; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl_3):Abstract nr B115.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».