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J Pathol Inform. 2016 Nov 29;7:47. eCollection 2016.

Pointwise mutual information quantifies intratumor heterogeneity in tissue sections labeled with multiple fluorescent biomarkers.

Author information

1
Program in Computational Biology, Joint Carnegie Mellon University-University of Pittsburgh, Pittsburgh, Pennsylvania; Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania.
2
Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, Pennsylvania.
3
GE Global Research Center, Diagnostics, Imaging and Biomedical Technologies, Niskayuna, NY, USA.
4
Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania; Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania.
5
GE Global Research Center, Software Science and Analytics Organization, Niskayuna, NY, USA.
6
Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania.
7
Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, Pennsylvania; University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania.
8
Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania; Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania; University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania.
9
Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania.

Abstract

BACKGROUND:

Measures 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.

METHODS:

We 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.

RESULTS:

We 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.

CONCLUSIONS:

This 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.

KEYWORDS:

Computational pathology; multiplexed immunofluorescence; pointwise mutual information; tumor heterogeneity; tumor microenvironment

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