Biology subjects

Loughrey, M. B.

Publications and source records attributed to Loughrey, M. B..

6 recordsLinked to original sources

bioRxiv2022

Molecular phenotyping of colorectal neoplasia shows dynamic and adaptive cancer stem cell population admixture

Intestinal homeostasis is underpinned by LGR5+ve crypt-base columnar stem cells (CBCs), but following injury, dedifferentiation results in the emergence of LGR5-ve regenerative stem cell populations (RSCs), characterised by fetal transcriptional profiles. Neoplasia hijacks regenerative signalling, so we assessed the distribution of CBCs and RSCs in mouse and human intestinal tumors. Using combined molecular-morphological analysis we demonstrate variable expression of stem cell markers across a range of lesions. The degree of CBC-RSC admixture was associated with both epithelial mutation and microenvironmental signalling disruption, and could be mapped across disease molecular subtypes. The CBC-RSC equilibrium was adaptive, with a dynamic response to acute selective pressure, and adaptability was associated with chemoresistance. We propose a fitness landscape model where individual tumors have equilibrated stem cell population distributions along a CBC-RSC phenotypic axis. Cellular plasticity is represented by position shift along this axis, and is influenced by cell-intrinsic, extrinsic and therapeutic selective pressures.

cancer biology↗

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

Development of a semi-automated method for tumor budding assessment in colorectal cancer and comparison with manual methods

Tumor budding is an established prognostic feature in multiple cancers but routine assessment has not yet been incorporated into clinical pathology practice. Recent efforts to standardize and automate assessment have shifted away from haematoxylin and eosin (H&E)-stained images towards cytokeratin (CK) immunohistochemistry. In this study, we compare established manual H&E and cytokeratin budding assessment methods with a new, semi-automated approach built within the QuPath open-source software. We applied our method to tissue cores from the advancing tumor edge in a cohort of stage II/III colon cancers (n=186). The total number of buds detected by each method, over the 186 TMA cores, were as follows; manual H&E (n=503), manual CK (n=2290) and semi-automated (n=5138). More than four times the number of buds were detected using CK compared to H&E. A total of 1734 individual buds were identified both using manual assessment and semi-automated detection on CK images, representing 75.7% of the total buds identified manually (n=2290) and 33.7% of the total buds detected using our proposed semi-automated method (n=5138). Higher bud scores by the semi-automated method were due to any discrete area of CK immunopositivity within an accepted area range being identified as a bud, regardless of shape or crispness of definition, and to inclusion of tumor cell clusters within glandular lumina ("luminal pseudobuds"). Although absolute numbers differed, semi-automated and manual bud counts were strongly correlated across cores ({rho}=0.81, p<0.0001). Despite the random, rather than "hotspot", nature of tumor core sampling, all methods of budding assessment demonstrated poorer survival associated with higher budding scores. In conclusion, we present a new QuPath-based approach to tumor budding assessment, which compares favorably to current established methods and offers a freely-available, rapid and transparent tool that is also applicable to whole slide images.

pathology↗

bioRxiv2021

Stratification Of Chemotherapy-Treated Stage III Colorectal Cancer Patients Using Multiplexed Imaging And Single Cell Analysis Of T Cell Populations

Colorectal cancer (CRC) has one of the highest cancer incidences and mortality rates. In stage III, postoperative chemotherapy benefits <20% of patients, while more than 50% will develop distant metastases. Predictive biomarkers for identification of patients with increased risk for disease recurrence are currently lacking, with progress in biomarker discovery hindered by the diseases inherent heterogeneity. The immune profile of colorectal tumors has previously been found to have prognostic value. The aims of this study were to evaluate immune signatures in the tumor microenvironment (TME) using an in situ multiplexed immunofluorescence imaging and single cell analysis technology (Cell DIVE). Tissue microarrays (TMAs) with up to three 1mm diameter cores per patient were prepared from 117 stage III CRC patients treated with adjuvant fluoropyrimidine/oxaliplatin chemotherapy. Single sections underwent multilplexed immunofluorescence with Cy3- and Cy5-conjugated antibodies for immune cell markers (CD45, CD3, CD4, CD8, FOXP3, PD1) and cell segmentation markers (DAPI, pan-cytokeratin, AE1, NaKATPase and S6). We applied a probabilistic multi-class, multi-label classification algorithm based on multi-parametric models to build statistical models of protein expression to classify immune cells. Expert annotations of immune cell markers were made on a range of images, and Support Vector Machines (SVM) were used to derive a statistical model for cell classification. Images were also manually scored independently by a Pathologist as high, moderate or low, for stromal and total immune cell content. Excellent agreement was found between manual and total automated scores (p<0.0001). Higher levels of multi-marker classified regulatory T cells (CD3+CD4+FOXP3+PD1-) were significantly associated with disease-free survival (DFS) and overall-survival (OS) (p=0.049 and 0.032), compared to FOXP3 alone. Our results also showed that PD1- Tregs rather than PD1+ Tregs were associated with improved survival. Overall, compared to single markers, multi-marker classification provided more accurate quantitation of immune cells with greater potential for predicting patient outcomes.

cancer biology↗

bioRxiv2017

Bcl-xL as a poor prognostic biomarker and predictor of response to adjuvant chemotherapy specifically in BRAF-mutant stage II and III colon cancer

BackgroundBRAF mutation occurs in 8-15% of colon cancers (CC), and is associated with poor prognosis in metastatic disease. Compared to wild-type BRAF (BRAFWT) disease, stage II/III CC patients with BRAF mutant (BRAFMT) tumors have shorter overall survival after relapse; however, time-to-relapse is not significantly different. The aim of this investigation was to identify, and validate, novel predictors of relapse of stage II/III BRAFMT CC.\n\nPatients and methodsWe used gene expression data from a cohort of 460 patients (GSE39582) to perform a supervised classification analysis based on risk-of-relapse within BRAFMT stage II/III CC, to identify transcriptomic biomarkers associated with prognosis within this genotype. These findings were validated using immunohistochemistry in an independent population-based cohort of Stage II/III CC (n=691), applying Cox proportional hazards analysis to determine associations with survival.\n\nResultsHigh gene expression levels of Bcl-xL, a key regulator of apoptosis, were associated with increased risk of relapse, specifically in BRAFMT tumors (HR=8.3, 95% CI 1.7-41.7), but not KRASMT/BRAFWT or KRASWT/BRAFWT tumors. High Bcl-xL protein expression in BRAFMT, untreated, stage II/III CC was confirmed to be associated with an increased risk of death in an independent cohort (HR=12.13, 95% CI 2.49-59.13). Additionally, BRAFMT tumors with high levels of Bcl-xL protein expression appeared to benefit from adjuvant chemotherapy (P for interaction =0.006), indicating the potential predictive value of Bcl-xL expression in this setting.\n\nConclusionsThese findings provide evidence that Bcl-xL gene and/or protein expression identifies a poor prognostic subgroup of BRAFMT stage II/III CC patients, who may benefit from adjuvant chemotherapy.\n\nKey MessageO_LIUsing a combination of computational biology discovery and immunohistochemistry validation in independent patient cohorts, we show that high expression of the apoptosis regulator Bcl-xL is associated with disease relapse specifically within BRAF mutant stage II/III colon cancer.\nC_LIO_LIThis data could enable tailored disease management to reduce relapse rates in the most aggressive subtype.\nC_LI

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↗