title: Pointwise mutual information quantifies intratumor heterogeneity in tissue sections labeled with multiple fluorescent biomarkers.
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.
authors.affiliation: Program in Computational Biology, Joint Carnegie Mellon University-University of Pittsburgh, Pittsburgh, Pennsylvania; Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania.
authors.forename: Daniel M
authors.initials: DM
authors.lastname: Spagnolo
authors.affiliation: Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, Pennsylvania.
authors.forename: Rekha
authors.initials: R
authors.lastname: Gyanchandani
authors.affiliation: GE Global Research Center, Diagnostics, Imaging and Biomedical Technologies, Niskayuna, NY, USA.
authors.forename: Yousef
authors.initials: Y
authors.lastname: Al-Kofahi
authors.affiliation: Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania; Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania.
authors.forename: Andrew M
authors.initials: AM
authors.lastname: Stern
authors.affiliation: Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania; Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania.
authors.forename: Timothy R
authors.initials: TR
authors.lastname: Lezon
authors.affiliation: Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania; Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania.
authors.forename: Albert
authors.initials: A
authors.lastname: Gough
authors.affiliation: GE Global Research Center, Diagnostics, Imaging and Biomedical Technologies, Niskayuna, NY, USA.
authors.forename: Dan E
authors.initials: DE
authors.lastname: Meyer
authors.affiliation: GE Global Research Center, Diagnostics, Imaging and Biomedical Technologies, Niskayuna, NY, USA.
authors.forename: Fiona
authors.initials: F
authors.lastname: Ginty
authors.affiliation: GE Global Research Center, Software Science and Analytics Organization, Niskayuna, NY, USA.
authors.forename: Brion
authors.initials: B
authors.lastname: Sarachan
authors.affiliation: Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania.
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Fine
authors.affiliation: Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, Pennsylvania; University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania.
authors.forename: Adrian V
authors.initials: AV
authors.lastname: Lee
authors.affiliation: 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.
authors.forename: D Lansing
authors.initials: DL
authors.lastname: Taylor
authors.affiliation: Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania.
authors.forename: S Chakra
authors.initials: SC
authors.lastname: Chennubhotla
keywords: Computational pathology; multiplexed immunofluorescence; pointwise mutual information; tumor heterogeneity; tumor microenvironment