PhD studentship: Multi-modal spatial data integration to predict breast cancer treatment response

Supervisor: Dr Raza Ali

Deadline for application: 31st October 2024

Course start date: 1st October 2025

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".

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.

There are around 100 postgraduate students at the Cambridge Institute, who play a vital and pivotal role in its continuing success. We are committed to providing an inclusive and supportive working environment that fosters intellectual curiosity and scientific excellence.

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.

References/further reading

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.

Eligibility

We welcome applications from both UK and overseas students.

Applications are invited from recent graduates or final-year undergraduates who hold or expect to gain a First/Upper Second Class degree (or equivalent) in a relevant subject from any recognised university worldwide.

You should select to commence study in Michaelmas Term 2025 (October 2025).

Additional information

To complete your online application, you will need to answer/provide the following:

- Choice of project and supervisor

- Course-specific questions

- Supporting documents

Applicants will be asked to provide:

Deadline

The closing date for applications is 31st October 2024 with interviews expected to take place in January 2025.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

The University has a responsibility to ensure that all employees are eligible to live and work in the UK.