Research focus

Studying tumour ecology

The main focus of the Yuan lab is to study tumours as evolving ecosystems. Tumours consist of not only cancer cells, but also normal cells such as immune cells that can be critical in eliminating cancer cells. These different types of cells co-exist in different parts of the same tumour with profound clinical implications. Just like in ecology where spatial organisation of animals, their predators and habitats is central for understanding the ecosystem and making predictions, it is becoming increasingly evident that we need to use a similar spatial approach to evaluate tumour heterogeneity.

We develop machine learning and deep learning algorithms for high-throughput pathological image analysis and bionformatics integration with cancer genomics. Understanding how genetically diverse cancer cells adapt to their microenvironment is key to appreciating how they develop and evolve. Thus far, our work has changed the way we think of the microenvironment and its critical roles in different cancer types.

This novel concept of tumours as evolving ecosystems could help us understand why cancer is so difficult to treat, and direct new therapeutic interventions - akin to draining the swamps to get rid of mosquitoes as one way of eradicating malaria. Through building highly interdisciplinary programmes, our goals are to deliver scientific advances and clinical innovations by dissecting the spatial and molecular heterogeneity of tumour microenvironment, to foster new developments in machine learning for applications in oncology and pathology, and to develop objective methodologies for directing cancer therapeutic strategies.


Spatial statistics to understand breast cancer recurrence

Our recent study on 1178 breast cancer patients underscored the importance of examining spatial heterogeneity of the tumour. We studied how immune cells are spatially arranged within the tumours, and detected the so-called immune hotspots, which are tumour regions that contain spatial clustering of immune cells. This uses a spatial statistical method called Getis-Ord Hotspot analysis, which is commonly used for detecting crime hotspots in cities. High amount of immune hotspots, but not the amount of immune cells, correlates with high probability of cancer recurrence. This study provides a new way to predict patient prognosis, and open the door to new therapeutic opportunities using immunotherapy for breast cancers.

Computational pathology to explore tumour spatial dimensions

Cancer and normal cells exhibit both co-operative and competitive relationships, analogous to living organisms. Considering the tumour as an ecological habitat, we use a combination of ecological statistics and medical 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. Read our review paper Computational pathology: Exploring the spatial dimension of tumour ecology
Nawaz S, Yuan Y* Cancer Letters (2015)


Centre for Evolution and Cancer & Division for Molecular Pathology,
The Institute of Cancer Research, London
The Royal Marsden Hospital

Open positions

Postdoc positions and fully funded PhD studentship available. Please enquire by emailing (yinyin.yuan (a) with your CV.

The PhD studentship is funded by Horizon 2020 ITN 'CONTRA' to develop advanced machine vision and statistical modeling methods for studying the tumour microenvironment. Apply through (ESR13). This position is part of a large training network for cancer evolution, and will include short term visits to University of Cambridge, University of Vigo, and Karolinska Institute.

Selected publications

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+ disease 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.

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 tumours 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.