Our interdisciplinary research

research
Publication keyword map generated from our publications

1. Mapping spatial heterogeneity of the tumour microenvironment

Automated histological image analysis of tumour architecture and cell morphology promises powerful and objective assessment of hundreds of tumours. By drawing on the rich information from histological images, we hope to offer more powerful approaches to study the microenvironment and identify heterogeneous cell populations.

2. Tumour ecology

Cancer and normal cells exhibit co-operative and competitive relationships, analogous to living organisms. Considering the tumour as an ecological habitat, we use a combination of ecological statistics and computing technological advances to analyse the ecological relationships in tumours using data derived from tumour histology. This novel way of studying the tumour microenvironment can expand our knowledge of cancer progression and reveal new clinical prognosticators.

In our recent study in PLOS Medicine (Natrajan et al. 2016), we developed an ecologically inspired measure, the EDI index, to quantify the diversity of tumours as ecosystems. Different types of cells such as cancer cells, immune cells and fibroblasts are analogous to diverse species interacting within a tumour ecosystem. These interactions are critical not only for the functioning of the tumour ecosystem, but also cancer treatment - certain normal cells can protect cancer whilst others may increase treatment efficiency during therapy. We identified a novel, aggressive subtype of breast cancer with high tumour ecosystem diversity. This new knowledge will help us derive a more accurate and cost-efficient predictor of breast cancer outcome beyond what we already know about breast cancer. This study also opens doors to investigating cancer from a novel perspective: ecology, which holds powerful statistical approaches that can propel future cancer research.


Microenvironmental heterogeneity of breast tumors based on histological samples

3. Bioinformatics integration of histology and omics

Through developing new bioinformatics methods to integrate tumour spatial patterns and genomics, we identify novel synergistic interplay between the microenvironment and cancer genomics.



Selected publications in these categories

1. Mapping spatial heterogeneity of the tumour microenvironment

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]

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.

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.

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.

2. Tumour ecology

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.

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.

3. Bioinformatics integration of histology and omics


Breast cancer nuclear integrity regulator

Through bioinformatics integration of morphology and genomics in 1,000 primary breast tumours, we discovered genes acting as regulators of cancer nuclear morphology. Their function as regulators of nuclear integrity has been validated in vitro.
In preparation.

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.

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.

CRImage
Quantitative image analysis of cellular heterogeneity in breast tumors complements genomics

Science Translational Medicine 2012 [PDF; Supplement; R package; Sweave; Data and code for Sweave; Lay-description ]

CRImage provides image analysis tools for automatic segmentation and classification of various cell types in pathological H&E images. We showed that quantitative cellular features can complement genomic and transcriptomic data to construct powerful prognosticators in two independent cohorts of 323 and 241 breast cancer patients.