Biology subjects

Piyadasa, H.

Publications and source records attributed to Piyadasa, H..

5 recordsLinked to original sources

bioRxiv2026

Spatially Resolved Epithelial States Delineate Immunosuppressive Niches of Impaired Myeloid Antigen Presentation in PDAC

Bulk transcriptomic classifiers stratify pancreatic ductal adenocarcinoma (PDAC) into classical and basal-like subtypes with prognostic and therapeutic relevance, yet increasing evidence indicates that these epithelial programs frequently coexist within individual tumors. How these intermediate states affect the local tumor microenvironment remains poorly defined. Here, we integrate multiplexed ion beam imaging (MIBI) with bulk RNA sequencing to resolve epithelial subtype identity at the level of spatially contiguous cancer nests and quantify their associated microenvironments. Across 47 primary tumor samples from 34 patients, we identified classical, intermediate, and basal cancer cell states at single-cell resolution and delineated discrete cancer nests with mixed or dominant subtype compositions. Distance-resolved spatial analysis reveals that basal-rich cancer nests are surrounded by locally immunosuppressive microenvironments characterized by reduced expression of MHC class II and co-stimulatory molecules in proximal myeloid cells, independent of myeloid abundance. These regions are enriched in fibroblast-dominated neighborhoods and distinct cell-cell interaction architectures. Using EcoTyper analysis of two independent bulk RNA-seq cohorts, including an OHSU discovery cohort (N = 277 patients) and TCGA as a validation cohort (N = 147 patients), we identified poor-prognosis tumor ecotypes enriched for basal epithelial states that similarly exhibited depleted myeloid antigen presentation signatures, linking spatial niche phenotypes to transcriptional ecotypes and patient outcomes. Together, these findings demonstrate that epithelial subtype programs in PDAC are organized at the level of spatially defined cancer nests and that basal cancer programs reside within localized niches of myeloid antigen presentation dysfunction, linking intratumoral architecture to immune suppression and clinical prognosis.

cancer biology↗

bioRxiv2025

Multi-omic landscape of human gliomas from diagnosis to treatment and recurrence

Gliomas are among the most lethal cancers, with limited treatment options. To uncover hallmarks of therapeutic escape and tumor microenvironment (TME) evolution, we applied spatial proteomics, transcriptomics, and glycomics to 670 lesions from 310 adult and pediatric patients. Single-cell analysis shows high B7H3+ tumor cell prevalence in glioblastoma (GBM) and pleomorphic xanthoastrocytoma (PXA), while most gliomas, including pediatric cases, express targetable tumor antigens in less than 50% of tumor cells, potentially explaining trial failures. Longitudinal samples of isocitrate dehydrogenase (IDH)-mutant gliomas reveal recurrence driven by tumor-immune spatial reorganization, shifting from T-cell and vasculature-associated myeloid cell-enriched niches to microglia and CD206+ macrophage-dominated tumors. Multi-omic integration identified N-glycosylation as the best classifier of grade, while the immune transcriptome best predicted GBM survival. Provided as a community resource, this study opens new avenues for glioma targeting, classification, outcome prediction, and a baseline of TME composition across all stages.

cancer biology↗

bioRxiv2025

Temporal and spatial composition of the tumor microenvironment predicts response to immune checkpoint inhibition

Immune checkpoint inhibition (ICI) has fundamentally changed cancer treatment. However, only a minority of patients with metastatic triple negative breast cancer (TNBC) benefit from ICI, and the determinants of response remain largely unknown. To better understand the factors influencing patient outcome, we assembled a longitudinal cohort with tissue from multiple timepoints, including primary tumor, pre-treatment metastatic tumor, and on-treatment metastatic tumor from 117 patients treated with ICI (nivolumab) in the phase II TONIC trial. We used highly multiplexed imaging to quantify the subcellular localization of 37 proteins in each tumor. To extract meaningful information from the imaging data, we developed SpaceCat, a computational pipeline that quantifies features from imaging data such as cell density, cell diversity, spatial structure, and functional marker expression. We applied SpaceCat to 678 images from 294 tumors, generating more than 800 distinct features per tumor. Spatial features were more predictive of patient outcome, including features like the degree of mixing between cancer and immune cells, the diversity of the neighboring immune cells surrounding cancer cells, and the degree of T cell infiltration at the tumor border. Non-spatial features, including the ratio between T cell subsets and cancer cells and PD-L1 levels on myeloid cells, were also associated with patient outcome. Surprisingly, we did not identify robust predictors of response in the primary tumors. In contrast, the metastatic tumors had numerous features which predicted response. Some of these features, such as the cellular diversity at the tumor border, were shared across timepoints, but many of the features, such as T cell infiltration at the tumor border, were predictive of response at only a single timepoint. We trained multivariate models on all of the features in the dataset, finding that we could accurately predict patient outcome from the pre-treatment metastatic tumors, with improved performance using the on-treatment tumors. We validated our findings in matched bulk RNA-seq data, finding the most informative features from the on-treatment samples. Our study highlights the importance of profiling sequential tumor biopsies to understand the evolution of the tumor microenvironment, elucidating the temporal and spatial dynamics underlying patient responses and underscoring the need for further research on the prognostic role of metastatic tissue and its utility in stratifying patients for ICI.

cancer biology↗

bioRxiv2024

Automated classification of cellular expression in multiplexed imaging data with Nimbus

Multiplexed imaging offers a powerful approach to characterize the spatial topography of tissues in both health and disease. To analyze such data, the specific combination of markers that are present in each cell must be enumerated to enable accurate phenotyping, a process that often relies on unsupervised clustering. We constructed the Pan-Multiplex (Pan-M) dataset containing 197 million distinct annotations of marker expression across 15 different cell types. We used Pan-M to create Nimbus, a deep learning model to predict marker positivity from multiplexed image data. Nimbus is a pre-trained model that uses the underlying images to classify marker expression across distinct cell types, from different tissues, acquired using different microscope platforms, without requiring any retraining. We demonstrate that Nimbus predictions capture the underlying staining patterns of the full diversity of markers present in Pan-M. We then show how Nimbus predictions can be integrated with downstream clustering algorithms to robustly identify cell subtypes in image data. We have open-sourced Nimbus and Pan-M to enable community use at https://github.com/angelolab/Nimbus-Inference.

bioinformatics↗

bioRxiv2022

Proteomic Profiling of Hypoplastic Lungs Suggests an Underlying Inflammatory Response in the Pathogenesis of Abnormal Lung Development in Congenital Diaphragmatic Hernia

The pathogenesis of lung hypoplasia in congenital diaphragmatic hernia (CDH), a common birth defect, is poorly understood. The diaphragmatic defect can be repaired surgically, but the abnormal lung development contributes to a high mortality in these patients. To better understand the underlying pathobiology, we used the nitrofen rat model of CDH and characterized the proteome of hypoplastic CDH lungs at the alveolar stage (E21). Amongst the 218 significantly altered proteins between CDH and control lungs were Tenascin C, CREBBP, LYN and STAT3. We showed that Tenascin C was decreased around the distal airway branches in nitrofen and human fetal CDH lungs. In contrast, STAT3 was significantly increased in the airway epithelium of nitrofen lungs at E21. STAT3 inhibition after direct nitrofen exposure to fetal rat lung explants (E14.5) partially reversed the hypoplastic lung phenotype ex vivo by increasing peripheral lung budding. Moreover, we demonstrated that several STAT3 associated cytokines (IL-15, IL-9, IL-2) are increased in fetal tracheal aspirates of CDH survivors compared to non-survivors after fetoscopic tracheal occlusion. Using pathway analysis for significantly altered proteins in our proteomic analysis, we observed an enrichment in inflammatory response associated with Epstein Barr Virus and cytokine signaling in nitrofen CDH lungs. However, we were unable to detect EBV mRNA via in-situ Hybridization in human CDH lungs. With our unbiased proteomics approach, we show for the first time that inflammatory processes are likely underlying the pathogenesis of abnormal lung development in CDH.

developmental biology↗