Deciphering the clinical and molecular determinants of tertiary lymphoid structure heterogeneity in pancreatic cancer by integrating multimodal spatial transcriptomics, proteomics, and histopathology imaging
MJ Geuenich1,2; C Zhang1,3; Y Zhu1,3; E Tsang4; M Hopkins5; M Spears5; S Gallinger5; H Jackson1,2,5; K Nowak6; KR Campbell1,2,5,7,8,9
Introduction: Pancreatic ductal adenocarcinoma (PDAC) is a malignant neoplasm of the pancreas characterized by late-stage detection, with few treatment options and prognostic biomarkers. Tertiary lymphoid structures (TLS) have been found to be predictive of survival in PDAC, however, their phenotypic heterogeneity, functional significance, and relationship to genomic and transcriptomic subtypes remain poorly understood.
Methodology: Using TLS histopathology detection methods we identified TLS across a cohort of over 600 patients and linked their presence to matched genomics and clinical metadata. We generated GeoMx spatial transcriptomics data for 13 PDAC patients with over 200 regions of interest (ROIs) focused on TLS and tumour. We stained and digitized matched whole-slide hematoxylin and eosin (H&E) images and single-cell spatial proteomic data using Imaging Mass Cytometry (IMC) on serial sections of the same ROIs profiled with GeoMx. Using unsupervised clustering and differential expression analyses we characterized TLS into subgroups.
Results: We show that we can automate TLS detection in H&E from primary tumour resections and liver metastases biopsies. We quantified the percentage of PDAC samples with TLS, the number of TLS per patient and linked their presence to PDAC transcriptomic subtypes, genomic aberrations and patient metadata. We identified three distinct TLS subgroups based on whole-transcriptome TLS expression profiles, and find that TLS subgroup identity is determined by tumour proximity, and specific pathway activation within the adjacent tumour. Finally, we use IMC to deconvolve the single cell content of each subgroup.
Conclusion: TLS are a known prognostic factor in PDAC, however, they have never been thoroughly characterized or linked to genomic and transcriptomic PDAC subtypes. We find previously unappreciated heterogeneity in TLS phenotypes and link these to tumour phenotypes in what is the most comprehensive characterization of TLS to date.
Funding: This work was supported by CIHR, the Princess Margaret Foundation, the University of Toronto Data Science Institute and the Terry Fox Research Institute
MJ Geuenich1,2; C Zhang1,3; Y Zhu1,3; E Tsang4; M Hopkins5; M Spears5; S Gallinger5; H Jackson1,2,5; K Nowak6; KR Campbell1,2,5,7,8,9
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Canada
- Department of Cell & Systems Biology, University of Toronto, Canada
- Department of Medical Oncology, Princess Margaret Cancer Centre, Toronto, Canada
- Ontario Institute of Cancer Research, Toronto, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
- Department of Statistical Sciences, University of Toronto, Toronto, Canada
- Department of Computer Science, University of Toronto, Toronto, Canada
- Vector Institute, Toronto, Canada
Introduction: Pancreatic ductal adenocarcinoma (PDAC) is a malignant neoplasm of the pancreas characterized by late-stage detection, with few treatment options and prognostic biomarkers. Tertiary lymphoid structures (TLS) have been found to be predictive of survival in PDAC, however, their phenotypic heterogeneity, functional significance, and relationship to genomic and transcriptomic subtypes remain poorly understood.
Methodology: Using TLS histopathology detection methods we identified TLS across a cohort of over 600 patients and linked their presence to matched genomics and clinical metadata. We generated GeoMx spatial transcriptomics data for 13 PDAC patients with over 200 regions of interest (ROIs) focused on TLS and tumour. We stained and digitized matched whole-slide hematoxylin and eosin (H&E) images and single-cell spatial proteomic data using Imaging Mass Cytometry (IMC) on serial sections of the same ROIs profiled with GeoMx. Using unsupervised clustering and differential expression analyses we characterized TLS into subgroups.
Results: We show that we can automate TLS detection in H&E from primary tumour resections and liver metastases biopsies. We quantified the percentage of PDAC samples with TLS, the number of TLS per patient and linked their presence to PDAC transcriptomic subtypes, genomic aberrations and patient metadata. We identified three distinct TLS subgroups based on whole-transcriptome TLS expression profiles, and find that TLS subgroup identity is determined by tumour proximity, and specific pathway activation within the adjacent tumour. Finally, we use IMC to deconvolve the single cell content of each subgroup.
Conclusion: TLS are a known prognostic factor in PDAC, however, they have never been thoroughly characterized or linked to genomic and transcriptomic PDAC subtypes. We find previously unappreciated heterogeneity in TLS phenotypes and link these to tumour phenotypes in what is the most comprehensive characterization of TLS to date.
Funding: This work was supported by CIHR, the Princess Margaret Foundation, the University of Toronto Data Science Institute and the Terry Fox Research Institute