bioRxivCancer BiologyPreprint
Integrated spatial proteomics of human PDAC uncovers an expanded tumour-immune-stroma spectrum with genomic associations
Noor E. ShakfaiD, Ferris NowlaniD, Sibyl DrissleriD, Tiak Ju TaniD, Elizabeth Sunnucks, E. CheniD, Cassandra J. WongiD, Brendon SealeiD, Zhen‐Yuan Lin, Michelle Chan‐Seng‐YueiD, Amy Xin Wei ZhangiD, Sabiq Chaudhary, Chengxin YuiD, Golnaz Abazari, Michael J. GeuenichiD, Matthew Watson, Jiaxi PengiD, Somaieh Afiuni‐ZadehiD, Ayelet BorgidaiD, Ricardo Gonzalez, Sheng‐Ben LiangiD, Klaudia M. NowakiD, Miralem MrkonjiciD, Anna DoddiD, Julie M. WilsoniD, , Hartland Warren JacksoniD
Abstract
Distinctively, pancreatic ductal adenocarcinoma (PDAC) consists of sparse tumour lesions intertwined with extensive desmoplastic stroma. The complexity of tumour-microenvironment interactions within this desmoplasia poses a challenge for accurate tumour profiling and patient stratification, and characterizes a profoundly chemoresistant tumour. Here we mapped the spatial relationships between tumour, stroma, and immune cell compartments delineating tumour and microenvironment types that expand the classical to basal spectrum of human PDAC. We used imaging mass cytometry to profile the in situ multi-cellular organization of 81 cell types in resected cases with paired whole genome sequencing. Cell types, functions, and pathway activation were distributed as highly reproducible environments in discrete locations throughout these tumours, which we deep-profiled using laser-capture mass spectrometry. We show that the connections between tumour phenotypes, vascularization, immune response, and stromal biophysical state are reinforced by genomic aberrations, altered by treatment, and associated with patient outcome. Predictive machine-learning models showed that spatial single cell data outperformed genomic or clinical features but integrated multi-omics models provide the best prediction of patient survival with compressed models requiring only 10 non-redundant robust molecular measures associated with the phenotypic spectrum of PDAC. Together, these findings define a phenotypic and molecular framework of PDAC that captures tumour-microenvironment co-dependencies and offers a refined basis for patient stratification and therapeutic targeting.
- organism: human
- assay: proteomics
- assay: spatial transcriptomics
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: deep learning
- Citations
- 3
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