PhD Studentship: Multi-Modal Spatial Data Integration to Predict Breast Cancer Treatment Response

Overview

Dr Raza Ali wishes to recruit a student to work on the project entitled: "Multi-modal spatial data integration to predict breast cancer treatment response".

For further information about the research group, including their most recent publications, please visit our website at https://www.cruk.cam.ac.uk/research-groups/ali-group/

This is a unique opportunity for PhD study in the world-leading Cancer Research UK Cambridge Institute (CRUK CI), to start a research career in an environment committed to training outstanding cancer research scientists of the future.

The Institute's particular strengths are in genomics, computational biology and imaging; and significant research effort is currently devoted to cancers arising in the breast, pancreas, brain, and colon. Our Core Facilities provide researchers with access to state-of-the-art equipment, in-house expertise and training. Scientists at CRUK CI aim to understand the fundamental biology of cancer and translate these findings into the clinic to benefit patients.

If you are interested in finding out more about our groundbreaking scientific research, please visit our website at https://www.cruk.cam.ac.uk/

Project details

Breast cancer patients show highly variable responses to different treatments. Some respond durably, while others start by responding but eventually relapse and a subset show little evidence of response at all. The biological basis of these differences remains obscure but the spatial architecture of tumours is likely a major contributor. Novel technologies for multiplexed molecular measurements of tumour tissues that preserve spatial relationships offer the opportunity to precisely dissect the contribution of the spatially resolved multicellular tumour ecosystem as a response driver. To take full advantage of multiplexed spatial measurements, however, we must devise efficient computational tools to parse and integrate these data across assays.

We are collating a large and unique collection of multi-modal spatial data from hundreds of breast cancer patients enrolled in neoadjuvant immunotherapy trials. In our recent landmark paper1, we used machine-learning to robustly identify cellular drivers of response. We are extending these data across assays and modalities to better understand the contributions of the wider tumour microenvironment and the potential utility of digital pathology. You will be responsible for collating these diverse data, placing them in a shared coordinate space, and unpicking the critical correlations that underpin treatment effect. This project offers the opportunity to build multi-modal predictive models that we will test in independent datasets with the real potential to surpass by far the current state-of-the-art.

Ours is a diverse and collaborative group that spans clinicians, pathologists, computational and cancer biologists. You will receive extensive training in cancer pathology, highly multiplexed imaging, and predictive modelling. Applications are invited from graduates from quantitative disciplines such as computer science and mathematics, but we also encourage applications from biologists already experienced in computational methods.

Funding

This four-year studentship is funded by Cancer Research UK Cambridge Institute and includes full funding for University fees and, in addition, a stipend currently of £21,000 per annum for four years. 

How to apply Please apply via the University Applicant Portal. For further information about the course and to access the Applicant Portal, visit: https://www.postgraduate.study.cam.ac.uk/courses/directory/cvcrpdmsc You should select to commence study in Michaelmas Term 2025 (October 2025).

More information can be found by clicking the 'Apply' button above. 

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