Selected publications

Deconvolving convolutional neural network for cell detection
Raza SEA, AbdulJabbar K, Jamal-Hanjani M, Veeriah S, Le Quesne J, Swanton C, Yuan Y
ISBI: IEEE International Symposium on Biomedical Imaging (2019)

We present an alternate approach to conventional local maxima detection for cell nucleus identification in a histology image. A convolutional neural network (CNN) is modified to convolve it's output with the same mapping filter and is trained for the mapped labels. Compared with conventional deep learning methods, our method detects cells with comparatively high precision and F1-score.

Microenvironmental niche divergence shapes BRCA1-dysregulated ovarian cancer morphological plasticity
Heindl A, Khan AM, Rodrigues DN, Eason K, Sadanandam A, Orbegoso C, Punta M, Sottoriva A, Lise S, Banerjee S, Yuan Y
Nature Communications (2018) [Online Sweave]

Understanding morphological diversity in the spatial context of ecological environment was fundamental to the discovery of Darwinian evolution. Here we provide empirical evidence supporting the selective advantage of cancer cell subpopulation with morphological diversification in locally immunosuppressive microenvironment, signifying morphological diversification as an ecological process of cancer with profound clinical implications.

Capturing global spatial context for accurate cell classification in skin cancer histology
Zormpas-Petridis K, Failmezger H, Roxanis I, Blackledge M.D., Jamin Y, Yuan Y

We propose a hierarchical framework for computational pathology, which mirrors the way pathologists perceive tumour architecture and define tumour heterogeneity. By using the global spatial context, we can accurately characterise the tumour microenvironment and extend significantly beyond cell classification methods that rely solely on cell nuclei morphology.

DeepSDCS: Dissecting cancer proliferation heterogeneity in Ki67 digital whole slide images
Narayanan, P, Dodson A, Gusterson B, Dowsett M, Yuan Y
Medical Imaging with Deep Learning (2018)

Simultaneous Detection and Cell Segmentation (DeepSDCS) performs automated cell segmentation and detection in Ki67-stained immunohistochemistry images. This network combines VGG16 with hypercolumn descriptors to form the vector of activation from specific layers to capture features at different granularity. Features from these layers were propagated using a stochastic gradient descent optimizer to yield accurate detection of cell nuclei and the final cell segmentations.

Interfaces of Malignant and Immunologic Clonal Dynamics in Ovarian Cancer
Zhang AW et al.
Cell (2018)

In a large international collaboration to profile cancer-immune interface, we apply digital pathology to map the microscopic spatial relationships between cancer cells and TILs. ES-TIL (tumors with substaintial levels of epithelial and stromal TILs), identified by multi-omics approaches, were enriched with cancer and lymphocyte hotspots, i.e. regions of local lymphoctye aggregation relative to epithelial cell density.

Relevance of spatial heterogeneity of immune infiltration for predicting risk of recurrence after endocrine therapy of ER+ breast cancer.
Heindl A, Sestak I, Naidoo R, Cuzick J, Dowsett M, Yuan Y*
JNCI (2018) [Additional ppt]

We provide a missing link between tumour immunity and disease outcome in ER+ breast cancer subtype by examining tumour spatial architecture. The association between immune spatial scores and late recurrence suggests a lasting memory of protumour immunity that may impact disease progression and evolution of endocrine treatment resistance, which may be exploited for therapeutic advances.

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, we quantify the spatial clustering of cancer and immune cells in breast tumours. This allows us to discover that unexpectedly high levels of co-clustering of cancer and immune cells is indicative of favourable long-term prognosis in ER-negative breast cancer, an aggressive breast cancer subtype. Our study highlight the importance of studying spatial variation of immune response beyond cell abundance 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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