Abstract B128: Spatial proteomics extend tumour subtype and microenvironment classifications across genotype-phenotype axes of pancreatic cancer
Notice bibliographique
Résumé
Abstract Human pancreatic ductal adenocarcinomas (PDAC) are composed of sparse, heterogeneous tumour cell populations embedded within a dense, desmoplastic stroma and are highly resistant to existing therapies. Guided by underlying genomic aberrations, cancer cell states and phenotypes interact with their microenvironment to shape disease trajectory and therapeutic response. To quantify the cellular phenotypic heterogeneity of these complex tumour-microenvironment interactions we designed custom multiplexed histopathology imaging panels against single cell RNA sequencing-defined tumour, immune, and stromal cell compartments. Using imaging mass cytometry (IMC), we profiled the co-localization and organization of 83 cell types and their functional states in 221 resected pancreatic tumours. This identified extensive inter- and intra-patient heterogeneity including the presence of both classical and basal cell types in most patients and a gradient of pancreatic epithelial identity confirmed by scRNAseq. This single cell content defined an expanded spectrum of classifiable intermediate tumour subtypes between classical and basal which have specific associations to neighboring immune and stroma cellular content, ploidy, established broad transcriptional subtypes, heterogeneity, and patient outcome. Eight reproducible cancer microenvironments were quantified, empowering cross-tumour comparisons. Matched whole genome sequencing (WGS) identified aberrations associated with specific tumour phenotypes and microenvironments. These results support asynchronous tumour and microenvironment genotype-phenotype axes which subdivide basal to classical tumour polarization and categorize co-existing microenvironments from stiff, ECM-rich, immune suppressed to immune infiltrated regions alongside fibrovascularized CD105+ stroma. This is based upon spatially heterogeneous co-occurring microenvironment niches and correlated tumour phenotypes which are associated with KRAS or MYC amplification and CDKN2A or LATS2 deletion on one axis, mutation of epigenetic modifiers in intermediate and hybrid subtypes, and RNF43 deletion on the other axis. To deeply profile the signalling pathways and enriched biological processes characteristic of each tissue state, we performed deep proteomics measurements of each microenvironment and tumor phenotype through whole-slide IMC informed laser capture microdissection and mass spectrometry of 273 micro-regions from a subset of patients. Finally, to identify a robust, minimized set of outcome-relevant features, we used a multi-modal technology optimized machine learning model trained to predict overall survival. We compare the predictive potential of WGS and spatial proteomics and show that a combination of genomic and cellular content outperforms clinical features and platform specific models thereby demonstrating the synergistic benefit of integrated multi-modal data. Our findings classify the spatial organization of human PDAC to identify an intertwined tumour/microenvironment axis that is influenced by specific genomic aberrations. Citation Format: Noor Shakfa, Ferris Nowlan, Tiak Ju Tan, Sibyl Drissler, Beth Sunnucks, Jennifer Gorman, Chengxin Yu, Michael Geuenich, Sheng Ben Liang, Barbara Gruenwald, Ayelet Borgida, Cassandra Wong, Brendon Seale, Zhen Yuan Lin, Edward Chen, Golnaz Abazari, Miralem Mrkjonic, Julie Wilson, Kieran Campbell, Anne-Claude Gingras, Rob Grant, Grainne O'Kane, Faiyez Notta, Steve Gallinger, Hartland Jackson. Spatial proteomics extend tumour subtype and microenvironment classifications across genotype-phenotype axes of pancreatic cancer [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 B128.
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) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».