About the Project
PhD studentship: Discovery of rational therapeutic biomarkers in breast cancer by systems pathology and deep learning
Reference: SW50984
Supervisor: Dr Hamid Raza Ali
Department/location: Cancer Research UK Cambridge Institute
Deadline for application: 16th October 2026
Course start date: 1st October 2027
Overview
The Ali Lab wishes to recruit a student to work on the project entitled: “Discovery of rational therapeutic biomarkers in breast cancer by systems pathology and deep learning”.
For further information about the research group, including their most recent publications, please visit their website at www.ali-lab.co.uk/.
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. AI has become integrated into all areas of research, as well as standalone, to generate new pathways for innovation and collaboration. 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.
If you are interested in finding out more about our groundbreaking scientific research, please visit our website at www.cruk.cam.ac.uk/.
Project details
The therapeutic landscape for breast cancer patients is rapidly evolving with novel therapies regularly receiving regulatory approval. Yet directing these treatments to patients likely to benefit while sparing those unlikely to respond from their toxicities remains a major challenge. Many modern therapies, like immunotherapy and ADCs, rely on tissue architecture to be effective. Intercellular relationships in breast cancer tissues also determine cellular activation states and expression profiles, rendering some cells susceptible and others resistant to new treatments.
The aim of this project is to use modern multiomic spatial methods (in which our group has extensive expertise1–4) together with deep learning (for efficient representation and cross-modal learning) to discover the potential therapeutic landscape for novel therapies in breast cancer, and to propose rational multidimensional biomarkers for combinatorial therapy. We are generating multimodal spatial datasets in cohorts of breast cancer patients (the largest of their kind; making extensive use of imaging mass cytometry and spatial transcriptomics) that span observational studies and clinical trials. We must precisely define the landscape of novel target expression, quantify its heterogeneity, and the contribution of tissue architecture as a determinant of expression profiles. This project will involve large scale data processing and analysis in a setting with ample expertise and infrastructure. This is a rare opportunity to develop expertise in quantitative pathology in the burgeoning field of spatial cancer biology.
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.
References/further reading
- Wang, X. Q. et al. Spatial predictors of immunotherapy response in triple-negative breast cancer. Nature 621, 868–876 (2023).
- Danenberg, E. et al. Breast tumor microenvironment structures are associated with genomic features and clinical outcome. Nat Genet 54, 660–669 (2022).
- Ali, H. R. et al. Imaging mass cytometry and multiplatform genomics define the phenogenomic landscape of breast cancer. Nat Cancer 1, 163–175 (2020).
- Gupta, P. et al. Single-cell spatial atlas of the aging human breast. Nat Aging https://doi.org/10.1038/s43587-026-01104-3 (2026) doi:10.1038/s43587-026-01104-3.
Preferred skills/knowledge
Applications are invited from graduates in quantitative disciplines such as computer science, AI, and mathematics, but we also encourage applications from biologists and clinicians 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, with an index-linked stipend starting at £22,894pa for four years in 2027-28.
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.
Applicants with relevant research experience, gained through Master’s study or while working in a laboratory, are strongly encouraged to apply.
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 October 2027.
Additional information
To complete your online application, you will need to answer/provide the following:
Choice of project and supervisor
Please ensure that you name the project (with reference code) and supervisor, where indicated. You are permitted to apply for up to three projects.
Course-specific questions
- You will be asked to give details of your Research Experience (up to 2,500 characters)
- Your Statement of Interest (up to 2,500 characters) should explain why you wish to be considered for the studentship and what qualities and experience you will bring to the role.
Supporting documents
Applicants will be asked to provide:
- Academic transcripts
- Evidence of competence in English (if appropriate)
- Details of two academic referees
- CV/resume
References
We would appreciate it if you could ask your referees to submit their references as soon as possible upon request, despite the longer University deadline for references. They will receive a request once you have completed the References section of your application.
Deadline
The closing date for applications is 16th October 2026, with interviews expected to take place in the week beginning 4th January 2027.
Funding Notes
This four-year studentship is funded by Cancer Research UK Cambridge Institute and includes full funding for University fees, with an index-linked stipend starting at £22,894pa for four years in 2027-28.
References
Wang, X. Q. et al. Spatial predictors of immunotherapy response in triple-negative breast cancer. Nature 621, 868–876 (2023).
Danenberg, E. et al. Breast tumor microenvironment structures are associated with genomic features and clinical outcome. Nat Genet 54, 660–669 (2022).
Ali, H. R. et al. Imaging mass cytometry and multiplatform genomics define the phenogenomic landscape of breast cancer. Nat Cancer 1, 163–175 (2020).
Gupta, P. et al. Single-cell spatial atlas of the aging human breast. Nat Aging https://doi.org/10.1038/s43587-026-01104-3 (2026) doi:10.1038/s43587-026-01104-3.
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