ArticleJournal of the Royal Society, Interface2015
Modelling the spatial heterogeneity and molecular correlates of lymphocytic infiltration in triple-negative breast cancer.
Article in Journal of the Royal Society, Interface, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.
What it found
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Who cites it
59 citing papers in PubMed, 129 citations in OpenAlex.
- Artificial intelligence-based tumour infiltrating lymphocyte quantification in patients with triple-negative breast cancer: an independent validation study.The Lancet. Oncology · 2026Article
- AI-powered and manual assessment of tumor-infiltrating lymphocytes in early HER2-positive breast cancer in NSABP B-41.NPJ breast cancer · 2026Article
- Epigenetically driven and early immune evasion in colorectal cancer evolution.Nature genetics · 2025Article
- Article
- Mapping molecular landscapes in triple-negative breast cancer: insights from spatial transcriptomics.Naunyn-Schmiedeberg's archives of pharmacology · 2025Review
- High tumor expression of CTLA4 identifies lymph node-negative basal-like breast cancer patients with excellent prognosis.Communications medicine · 2025Article
- Augmented reality microscopy to bridge trust between AI and pathologists.NPJ precision oncology · 2025Article
- Computational pathology applied to clinical colorectal cancer cohorts identifies immune and endothelial cell spatial patterns predictive of outcome.The Journal of pathology · 2025Article
- Prediction of post-treatment recurrence in early-stage breast cancer using deep-learning with mid-infrared chemical histopathological imaging.NPJ precision oncology · 2025Article
- The analytical and clinical validity of AI algorithms to score TILs in TNBC: can we use different machine learning models interchangeably?EClinicalMedicine · 2024Article
- Deciphering cell to cell spatial relationship for pathology images using SpatialQPFs.Scientific reports · 2024Article
- Article
- Integrating mechanism-based T cell phenotypes into a model of tumor-immune cell interactions.APL bioengineering · 2024Article
- Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology.Briefings in bioinformatics · 2024Review
- Spatially resolved tissue imaging to analyze the tumor immune microenvironment: beyond cell-type densities.Journal for immunotherapy of cancer · 2024Review
- VOLTA: an enVironment-aware cOntrastive ceLl represenTation leArning for histopathology.Nature communications · 2024Article
- Epigenome and early selection determine the tumour-immune evolutionary trajectory of colorectal cancer.bioRxiv : the preprint server for biology · 2024Article
- Inflamed immune phenotype predicts favorable clinical outcomes of immune checkpoint inhibitor therapy across multiple cancer types.Journal for immunotherapy of cancer · 2024Article
- Computational staining of CD3/CD20 positive lymphocytes in human tissues with experimental confirmation in a genetically engineered mouse model.Frontiers in immunology · 2024Article
- A Review of AI-Based Radiomics and Computational Pathology Approaches in Triple-Negative Breast Cancer: Current Applications and Perspectives.Clinical breast cancer · 2023Review
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
Lymphocytic infiltration is associated with a favourable prognosis and predicts response to chemotherapy in many cancer types, including the aggressive triple-negative breast cancer (TNBC). However, it is not well understood owing to the high levels of spatial heterogeneity within tumours, which is difficult to analyse by traditional pathological assessment. This paper describes an unbiased methodology to statistically model the spatial distribution of lymphocytes among tumour cells based on automated analysis of haematoxylin-and-eosin-stained whole-tumour section images, which is applied to two independent TNBC cohorts of 181 patients with matched microarray gene expression data. The novelty of the proposed methodology is the fusion of image analysis and statistical modelling for an integrative understanding of intratumour heterogeneity of lymphocytic infiltration. Using this methodology, a quantitative measure of intratumour lymphocyte ratio is developed and found to be significantly associated with disease-specific survival in both TNBC cohorts independent to standard clinical parameters. The proposed image-based measure compares favourably to a number of gene expression signatures of immune infiltration. In addition, heterogeneous immune infiltration at the morphological level is reflected at the molecular scale and correlated with increased expression of CTLA4, the target of ipilimumab. Taken together, these results support the fusion of high-throughput image analysis and statistical modelling to offer reproducible and robust biomarkers for the objective identification of patients with poor prognosis and treatment options.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.