title: An ecological measure of immune-cancer colocalization as a prognostic factor for breast cancer.
abstract: BACKGROUND
  Abundance of immune cells has been shown to have prognostic and predictive significance in many tumor types. Beyond abundance, the spatial organization of immune cells in relation to cancer cells may also have significant functional and clinical implications. However there is a lack of systematic methods to quantify spatial associations between immune and cancer cells.
  
  
  METHODS
  We applied ecological measures of species interactions to digital pathology images for investigating the spatial associations of immune and cancer cells in breast cancer. We used the Morisita-Horn similarity index, an ecological measure of community structure and predator-prey interactions, to quantify the extent to which cancer cells and immune cells colocalize in whole-tumor histology sections. We related this index to disease-specific survival of 486 women with breast cancer and validated our findings in a set of 516 patients from different hospitals.
  
  
  RESULTS
  Colocalization of immune cells with cancer cells was significantly associated with a disease-specific survival benefit for all breast cancers combined. In HER2-positive subtypes, the prognostic value of immune-cancer cell colocalization was highly significant and exceeded those of known clinical variables. Furthermore, colocalization was a significant predictive factor for long-term outcome following chemotherapy and radiotherapy in HER2 and Luminal A subtypes, independent of and stronger than all known clinical variables.
  
  
  CONCLUSIONS
  Our study demonstrates how ecological methods applied to the tumor microenvironment using routine histology can provide reproducible, quantitative biomarkers for identifying high-risk breast cancer patients. We found that the clinical value of immune-cancer interaction patterns is highly subtype-specific but substantial and independent to known clinicopathologic variables that mostly focused on cancer itself. Our approach can be developed into computer-assisted prediction based on histology samples that are already routinely collected.
authors.affiliation: Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK. Maley@asu.edu.|Center for Evolution and Cancer, University of California San Francisco, San Francisco, CA, USA. Maley@asu.edu.|Biodesign Institute, School of Life Sciences, Arizona State University, Tempe, AZ, USA. Maley@asu.edu.
authors.forename: Carlo C
authors.initials: CC
authors.lastname: Maley
authors.affiliation: Division of Breast Cancer, The Institute of Cancer Research, London, UK. Konrad.Koelble@icr.ac.uk.|Department of Histopathology, The Royal Marsden Hospital, London, UK. Konrad.Koelble@icr.ac.uk.
authors.forename: Konrad
authors.initials: K
authors.lastname: Koelble
authors.affiliation: Division of Breast Cancer, The Institute of Cancer Research, London, UK. Rachael.Natrajan@icr.ac.uk.|Division of Molecular Pathology, The Institute of Cancer Research, London, UK. Rachael.Natrajan@icr.ac.uk.
authors.forename: Rachael
authors.initials: R
authors.lastname: Natrajan
authors.affiliation: Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK. Aktipis@asu.edu.|Center for Evolution and Cancer, University of California San Francisco, San Francisco, CA, USA. Aktipis@asu.edu.|Department of Psychology, Arizona State University, Tempe, AZ, USA. Aktipis@asu.edu.|Center for Evolution and Medicine, Biodesign Institute, Arizona State University, AZ, USA. Aktipis@asu.edu.
authors.forename: Athena
authors.initials: A
authors.lastname: Aktipis
authors.affiliation: Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK. Yinyin.Yuan@icr.ac.uk.|Division of Molecular Pathology, The Institute of Cancer Research, London, UK. Yinyin.Yuan@icr.ac.uk.|Centre for Molecular Pathology, The Royal Marsden Hospital, London, UK. Yinyin.Yuan@icr.ac.uk.
authors.forename: Yinyin
authors.initials: Y
authors.lastname: Yuan
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001943:Breast Neoplasms / Q000276:immunology* / Q000401:mortality; D017753:Ecosystem; D005260:Female; D006801:Humans; D053208:Kaplan-Meier Estimate; D016246:Lymphocytes, Tumor-Infiltrating / Q000276:immunology*; D008875:Middle Aged; D008954:Models, Biological; D015999:Multivariate Analysis; D011379:Prognosis; D016016:Proportional Hazards Models; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000378:metabolism; D055815:Young Adult
chemical_list: D018719:Erb-b2 Receptor Tyrosine Kinases; C508053:ERBB2 protein, human