Research themes

Machine learning for digital pathology

We apply technological innovations in machine learning and computer vision to large-scale applications in digital pathlogy. These applications enable us to map the spatial distribution of heterogeneous cell types within tumour section images at single-cell resolution.

Cancer as evolving ecosystems

Our scientific focus is on understanding tumours as evolving ecosystems. This is an emerging concept, that tumours are complex, evolving ecosystems with dynamic crosstalk among multiple cell types. Such crosstalk could ultimately dictate the evolutionary trajectory of cancer, thereby influencing cancer progression and therapeutic response.

Image-Omics data integration

Understanding how genetically diverse cancer cells adapt to their microenvironment is key to appreciating how they develop and evolve. We combine image-based cell spatial mapping with bioinformatics and ecological statistics to studies how different cancers evolve and spread amidst healthy tissue at high spatial resolution.

Latest developments

Deep learning for DCIS detection

Integration of precision segmentation of Ductal Carcinoma in situ (DCIS) components, single cell classification and spatial analysis in routine H&E histology images enabled by a bespoke deep learning system, UNMaSk.

Open positions

Postdoc position in Computer Vision for lung cancer


An exciting opportunity to develop new computer vision programs for the study of lung cancer within the TRACERx program. In collaboration with Charles Swanton's team at the Francis Crick Institute, we are integrating histology images with next-generation sequencing data generated from the same tumour regions to understand how lung cancer evolves immune evasion and treatment resistance. Apply here!

Selected publications

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)

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

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.

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.