Biology subjects

Salto-Tellez, M.

Publications and source records attributed to Salto-Tellez, M..

5 recordsLinked to original sources

bioRxiv2026

Development of a multiplex immunofluorescence panel to study heterogenous cancer-associated fibroblast subtypes with spatial resolution

BackgroundSingle cell RNA sequencing provides a wealth of information to explore the complexities of the tumour microenvironment, but crucially the spatial topology of the tumour is lost and studying cellular interactions is limited. Spatial transcriptomics aims to address this however the technique remains cost prohibitive for the generation of data from meaningfully-sized clinical cohorts. In contrast, spatial proteomic profiling with multiplex immunofluorescence, preserves spatial interactions, is relatively cost accessible, and is scalable for large clinical cohorts to address powerful translational questions. Whilst multiplex approaches have advanced in recent years, we note that cancer-associated fibroblasts (CAFs) have been explored in less detail, potentially due to difficulties associated with CAF heterogeneity and the diversity of markers used to define them. MethodsWe designed, optimised, and validated a multiplex immunofluorescence panel that combines four frequently used CAF markers; alpha smooth muscle actin (SMA), fibroblast activation protein (FAP), podoplanin (PDPN) and platelet-derived growth factor receptor alpha (PDGFR) with CD8 and pan-cytokeratin. Here we share our methodology and the practical considerations taken to inform the final panel design. We also highlight the benefits of robust optimisation experiments.

pathology↗

bioRxiv2021

Swarm learning for decentralized artificial intelligence in cancer histopathology

Artificial Intelligence (AI) can extract clinically actionable information from medical image data. In cancer histopathology, AI can be used to predict the presence of molecular alterations directly from routine histopathology slides. However, training robust AI systems requires large datasets whose collection faces practical, ethical and legal obstacles. These obstacles could be overcome with swarm learning (SL) where partners jointly train AI models, while avoiding data transfer and monopolistic data governance. Here, for the first time, we demonstrate the successful use of SL in large, multicentric datasets of gigapixel histopathology images comprising over 5000 patients. We show that AI models trained using Swarm Learning can predict BRAF mutational status and microsatellite instability (MSI) directly from hematoxylin and eosin (H&E)-stained pathology slides of colorectal cancer (CRC). We trained AI models on three patient cohorts from Northern Ireland, Germany and the United States of America and validated the prediction performance in two independent datasets from the United Kingdom using SL-based AI models. Our data show that SL enables us to train AI models which outperform most locally trained models and perform on par with models which are centrally trained on the merged datasets. In addition, we show that SL-based AI models are data efficient and maintain a robust performance even if only subsets of local datasets are used for training. In the future, SL can be used to train distributed AI models for any histopathology image analysis tasks, overcoming the need for data transfer and without requiring institutions to give up control of the final AI model.

bioinformatics↗

bioRxiv2021

HistoClean: Open-source Software for Histological Image Pre-processing and Augmentation to Improve Development of Robust Convolutional Neural Networks

The growth of digital pathology over the past decade has opened new research pathways and insights in cancer prediction and prognosis. In particular, there has been a surge in deep learning and computer vision techniques to analyse digital images. Common practice in this area is to use image pre-processing and augmentation to prevent bias and overfitting, creating a more robust deep learning model. Herein we introduce HistoClean; user-friendly, graphical user interface that brings together multiple image processing modules into one easy to use toolkit. In this study, we utilise HistoClean to pre-process images for a simple convolutional neural network used to detect stromal maturity, improving the accuracy of the model at a tile, region of interest, and patient level. HistoClean is free and open-source and can be downloaded from the Github repository here: https://github.com/HistoCleanQUB/HistoClean.

cancer biology↗

bioRxiv2020

The clinical and molecular significance associated with STING signaling in estrogen receptor-positive early breast cancer

STING signaling in cancer is a crucial component of response to immunotherapy and other anti-cancer treatments. Conversely, STING signaling can promote tumor invasion and metastasis. Currently, there is no robust method of measuring STING activation in cancer. Here, we describe an immunohistochemistry-based assay with digital pathology assessment of STING in tumor cells. Using this novel approach, we identify perinuclear-localized expression of STING (pnSTING) in estrogen receptor-positive (ER+) breast cancer as an independent predictor of good prognosis, associated with immune cell infiltration and upregulation of immune checkpoints. Tumors with low pnSTING are immunosuppressed with increased infiltration of "M2" -polarised macrophages. In ER-disease, pnSTING does not have a significant prognostic role, and STING appears to be uncoupled from interferon responses. Importantly, a gene signature defining low pnSTING expression in ER+ disease is predictive of poor prognosis in independent datasets. Low pnSTING is associated with chromosomal instability, MYC amplification and mTOR signaling, suggesting novel therapeutic approaches for this subgroup.

cancer biology↗

bioRxiv2017

QuPath: Open source software for digital pathology image analysis

QuPath is new bioimage analysis software designed to meet the growing need for a user-friendly, extensible, open-source solution for digital pathology and whole slide image analysis. In addition to offering a comprehensive panel of tumor identification and high-throughput biomarker evaluation tools, QuPath provides researchers with powerful batch-processing and scripting functionality, and an extensible platform with which to develop and share new algorithms to analyze complex tissue images. Furthermore, QuPaths flexible design make it suitable for a wide range of additional image analysis applications across biomedical research.

pathology↗