Selected publications

Biopsy variability of lymphocytic infiltration in breast cancer subtypes and the ImmunoSkew score.
Khan AM, Yuan Y*
Nature Scientific Reports (2016)

We systematically investigate biopsy variability for the lymphocytic infiltrate in 998 breast tumours using a novel virtual biopsy method. Interestingly, biopsy variability of lymphocytic infiltrate differs considerably among breast cancer subtypes, with the HER2+ subtype having the highest variability.

Similarity and diversity of the tumor microenvironment in multiple metastases: critical implications for overall and progression-free survival of serous ovarian cancer.
Heindl A, Lan C, Rodrigues DN, Koelble K, Yuan Y*
Oncotarget (2016) [Sweave; Digital slides]

Our study has revealed that diverse cell populations at the sites of cancer spread are a clinically important feature of particularly aggressive ovarian cancers. Based on this, we have developed a new test to assess the diversity of metastatic sites, and use it to predict a woman's chances of surviving their diseases.

Spatial Heterogeneity in the Tumor Microenvironment.
Yuan Y*
Cold Spring Harb Perspect Med. (2016) [Limited reprints]

There is a desperate need to understand influence of the tumor microenvironment on cancer development and evolution. Applying principles and quantitative methods from ecology can suggest novel solutions to fulfil this need. We discuss spatial heterogeneity as a fundamental biological feature of the microenvironment, which has been largely ignored.

Microenvironmental heterogeneity parallels breast cancer progression: A histology-genomics integration analysis
Natrajan R, Sailem H, Mardakheh FM, Arias MG, Dowsett M, Bakal C, Yuan Y*
PLOS Medicine (2016) [Sweave; R package beta version]

We propose a clinically relevant role of tumour microenvironmental diversity for advanced breast tumors and highlight that ecological statistics can be translated into medical advances for identifying new biomarkers and for understanding the synergistic interplay between the microenvironment and cancer genomics.

Computational pathology: Exploring the spatial dimension of tumor ecology
Nawaz S, Yuan Y*
Cancer Letters (2015)

Cancer and normal cells exhibit both co-operative and competitive relationships, analogous to living organisms. Considering the tumour as an ecological habitat, we present current and potential applications of methods developed for analysing ecological relationships in nature to data derived from tumour histology. We discuss how this novel way of studying the tumour microenvironment can expand our knowledge of cancer progression and reveal new clinical prognosticators.

Quantitative histology analysis of the ovarian tumour microenvironment
Lan C^, Heindl A^, Huang X, Xi S, Banerjee S, Liu J*, Yuan Y*
Nature Scientific Reports (2015) [Sweave; data; Digital slides]

By developing automated histology analysis as a cost-efficient subtyping technology for ovarian cancer, we reveal a strong effect of the tumour microenvironment on ovarian cancer progression and highlight the potential of therapeutic interventions that target the stromal compartment.

An ecological measure of immune-cancer colocalization as a prognostic factor for breast cancer
Maley CC, Koelble K, Natrajan R, Aktipis A, Yuan Y*
Breast Cancer Research (2015) [Sweave; data]

We demonstrate how ecological methods applied to the study of the tumour microenvironment using routine histology samples can provide reproducible, quantitative biomarkers for identifying high-risk breast cancer patients.

Beyond immune density: critical role of spatial heterogeneity in estrogen receptor-negative breast cancer
Nawaz S, Heindl A, Koelble K, Yuan Y*
Modern Pathology (2015) [Sweave; data]

Using a statistical method commonly applied in crime mapping to quantify the spatial distribution of cancer and immune cells in breast tumours, we uncover that tumours presented with unexpectedly high levels of clustering between these cells is indicative of favourable long-term prognosis in ER-negative breast cancer, an aggressive subtype of the disease. Our study demonstrates the additional prognostic value of quantifying not only the abundance of lymphocytes but also their spatial variation in the tumour specimen, for which statistical methods from other disciplines can be successfully applied. Read about our study in The Times, Science Daily, and The Telegraph.

Modelling the spatial heterogeneity and molecular correlates of lymphocytic infiltration in triple-negative breast cancer
Yuan Y*
Journal of the Royal Society Interface (2015) [Sweave; data and functions]

This paper reports the first fully automated, computer-assisted method for scoring intratumour lymphocytes in breast cancer samples with the fusion of high-throughput image processing and robust statistical analysis. This method facilitates the discovery of a previously unidentified type of aggressive breast cancer and the identification of a potential target for immunotherapy through the fusion of histological image analysis and transcriptomics.

Mapping spatial heterogeneity in the tumor microenvironment: a new era for digital pathology
Heindl A, Nawaz S, Yuan Y*
Laboratory Investigation (2015)

We review the existing methods for analysing spatial heterogeneity in the tumour microenvironment and how they may be integrated with molecular profiling to help further our understanding of the complex relationship between cancer cells and the surrounding healthy tissue that can play a pivotal role in tumour progression.



FISHalyseR is a highthroughput tool for quantitative analysis of molecular heterogeneity at 
single-cell resolution in FISH images with an unlimited number of probes. [Download from Bioconductor]

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CRImage provides image analysis tools for segmentation, classification, and downstream analysis of H&E images. One application is for cellularity scoring of tumours by counting the number of cancer cells and other cells. The package also comes with a novel algorithm for copy-number data correction for SNP microarray data using estimates of tumours cellularity from pathological image analysis. [Download from Bioconductor]

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