ArticleJournal of pathology informatics2016
Pointwise mutual information quantifies intratumor heterogeneity in tissue sections labeled with multiple fluorescent biomarkers.
Article in Journal of pathology informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Immune landscape in invasive ductal and lobular breast cancer reveals a divergent macrophage-driven microenvironment.Nature cancer · 2023Article
- Article
- Concepts and Applications of Information Theory to Immuno-Oncology.Trends in cancer · 2021Review
- Spatial protein heterogeneity analysis in frozen tissues to evaluate tumor heterogeneity.PloS one · 2021Article
- Spatial domain analysis predicts risk of colorectal cancer recurrence and infers associated tumor microenvironment networks.Nature communications · 2020Article
- Quantification of intrinsic subtype ambiguity in Luminal A breast cancer and its relationship to clinical outcomes.BMC cancer · 2019Article
- Harnessing Human Microphysiology Systems as Key Experimental Models for Quantitative Systems Pharmacology.Handbook of experimental pharmacology · 2019Article
- Opportunities and Challenges in Implementation of Multiparameter Single Cell Analysis Platforms for Clinical Translation.Clinical and translational science · 2018Review
- Single-cell heterogeneity in ductal carcinoma in situ of breast.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2018Article
- Platform for Quantitative Evaluation of Spatial Intratumoral Heterogeneity in Multiplexed Fluorescence Images.Cancer research · 2017Article
- Biologically Relevant Heterogeneity: Metrics and Practical Insights.SLAS discovery : advancing life sciences R & D · 2017Review
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
backgroundMeasures of spatial intratumor heterogeneity are potentially important diagnostic biomarkers for cancer progression, proliferation, and response to therapy. Spatial relationships among cells including cancer and stromal cells in the tumor microenvironment (TME) are key contributors to heterogeneity.
methodsWe demonstrate how to quantify spatial heterogeneity from immunofluorescence pathology samples, using a set of 3 basic breast cancer biomarkers as a test case. We learn a set of dominant biomarker intensity patterns and map the spatial distribution of the biomarker patterns with a network. We then describe the pairwise association statistics for each pattern within the network using pointwise mutual information (PMI) and visually represent heterogeneity with a two-dimensional map.
resultsWe found a salient set of 8 biomarker patterns to describe cellular phenotypes from a tissue microarray cohort containing 4 different breast cancer subtypes. After computing PMI for each pair of biomarker patterns in each patient and tumor replicate, we visualize the interactions that contribute to the resulting association statistics. Then, we demonstrate the potential for using PMI as a diagnostic biomarker, by comparing PMI maps and heterogeneity scores from patients across the 4 different cancer subtypes. Estrogen receptor positive invasive lobular carcinoma patient, AL13-6, exhibited the highest heterogeneity score among those tested, while estrogen receptor negative invasive ductal carcinoma patient, AL13-14, exhibited the lowest heterogeneity score.
conclusionsThis paper presents an approach for describing intratumor heterogeneity, in a quantitative fashion (via PMI), which departs from the purely qualitative approaches currently used in the clinic. PMI is generalizable to highly multiplexed/hyperplexed immunofluorescence images, as well as spatial data from complementary in situ methods including FISSEQ and CyTOF, sampling many different components within the TME. We hypothesize that PMI will uncover key spatial interactions in the TME that contribute to disease proliferation and progression.
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