Deciphering Tumour Microenvironement

A pathologic-genomic approach

Pathological analysis of tumour architecture and cell morphology dates back to the 19th century. Now, morden image-analysis tools promises objective and quantitative insights into hundreds of tumours. 

By drawing on the rich information from pathological images and combining it with molecular data from next generation sequencing, we hope to offer more powerful approaches to study the microenvironment and its implications to cancer progression.

Building an automated classifier for histopathological images, enables us to identify heterogeneous cell populations in patient tumours and quantify their spatial proximity to each other.

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Quantitative scores of cell spatial patterns

The cell spatial patterns can be summarised using methods from spatial statistics and pattern recognition techniques to provide quantitative scores.

These quantitative scores of cell patterns can reveal various metrics that would be impossible to conclude by eye, for example, the degree of clustering and the spatial interactions between different cells.

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Discovery of functional roles of normal cells

One of the central dogma in cancer biology is the functional roles of heterogenous types of cells in cancer progression. 

With quantitative readout from the images, we can correlate the abundance and spatial patterns of cells with patient outcome. For example, by summarising the abundance of lymphocytes in the ER-negative breast cancer, we are able to stratify patients into different outcome groups, where the patients with high numbers of lymphoctyes have significantly better prognosis than patients with few lymphocytes.

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Integrating images with molecular signal

Combining molecular signal from the same samples, the image-based quantitative scores can either complement molecular data, or offer completely new observations from this entirely different angle.

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