The intercalated PhD (iPhD) Programme is an integral aspect of the Cancer Research UK Clinical Academic Training Programme, generously funded through the collaboration between Imperial College London and The Institute of Cancer Research, London.


Empowering Clinical Academics Through Multidisciplinary Training


The iPhD programme is designed to equip clinical academics with the expertise to tackle complex challenges by integrating cancer research with engineering and physical sciences. Tailored for exceptional undergraduate students enrolled in the MBBS/BSc degree course, this programme presents the unique opportunity to pursue an intercalated PhD alongside their studies.


The PhD journey involves three years of intensive research, following the successful completion of the intercalated BSc (iBSc) during the fourth year of the MBBS degree. Upon finishing the PhD training, trainees will transition back into their undergraduate medical education for the fifth year. This dynamic approach ensures a comprehensive learning experience that bridges medical knowledge with cutting-edge research and innovation.

Timeline showing where phd fits in medical degree


What is available?

The iPhD is fully funded, inclusive of a generous tax-free fixed stipend, tuition fees for UK students and the cost for undertaking research. Overseas students are also eligible; however, they should discuss other options to support the difference in international fees with prospective supervisors. The offer doesn’t end there, Cancer Research UK is also committed to underwriting the undergraduate tuition fees for years 1-4 of UK students who successfully apply to this programme. The NHS will cover tuition fees for years 5 and 6.

Are you the right candidate?

If You're a Year 4 Medical Student at Imperial, This Opportunity is for You!

We invite Year 4 medical students registered at Imperial to consider this unique opportunity. We are open to students who are interested in any of the iBSc pathways. However, this specific research opportunity in cancer research will be available to those applying for the Cancer Frontiers pathway and the Biomedical Engineering pathway with a focus on convergence science. While you might also find some cancer-related projects in other iBSc pathways, all pathways provide a valuable experience that offers insight into the world of research.. 


What are the benefits of undertaking the iPhD in cancer?


World class training

You will be trained by world leading experts in cancer biology, engineering and physical sciences at Imperial and the ICR. This will enable you to acquire a broad skill set and learn the language of multiple disciplines.


High-quality research outputs

With a range of outputs spanning research publications, presentations at relevant conferences and translation of your research for patient benefit, the iPhD will provide the grounding for your future success as a clinical academic.


Dedicated mentorship

You will receive tailored mentorship from clinically qualified cancer researchers who will provide guidance on successfully navigating the PhD years, becoming a clinical academic and establishing a successful career in oncology.



We asked a selection of our supervisors their thoughts on convergence science, their philosophy to mentoring, what attributes they are looking for in a convergence science student, and what key factors prospective students should consider when choosing a PhD. We also asked a selection of our current students what attracted them to this field, their experience of the programme so far, and what their aspirations are for the future. Click here to read perspectives from our current supervisors and students.

Research projects 2024 (now closed)


Enclosed is the list of projects which were available during our last call. For any inquiries, please do not hesitate to contact us via email.





Professor Chris Lord (ICR)

Dr Sam Au (Imperial - Bioeng)

Professor Andrew Tutt (ICR)


Project summary

Therapies such as platinum salts and PARP inhibitors (PARPi) have transformed the treatment of homologous recombination defective (HRD) cancers. Although these treatments are often effective, resistance to treatment is a growing phenotype which is poorly understood. We propose to use an analysis of circulating tumour cell clusters (CTC clusters), viable multicellular aggregates of circulating tumour cells that can be isolated from routinely taken blood samples, to better understand the genomics, transcriptomics and DNA repair biology of treatment resistance. Using a convergence of microfluidic engineering technologies alongside microscopy, genomics and a clinical biopsy study, we aim to understand how resistance to treatment occurs and how the tumour cell population evolves in response to the selective pressure of treatment. In doing so, we aim to generate optimised technologies for assessing DNA repair biology in humans and to inform the design of new treatment paradigms that either prevent, delay or target resistant disease. 








Dr Iain Dunlop (Imperial - Materials)

Professor Anastasios Karadimitris (Imperial - ImmInf)



Proposal summary

Multiple myeloma is an incurable malignancy of the bone marrow, and the second most common blood cancer. New treatment approaches, including mAb and CAR-based immunotherapies, are being developed, creating a need for model systems to evaluate their effectiveness and mechanisms of treatment resistance. A key candidate technology is organoids: model tissues and tumours that are grown in vitro from patient tissue. This interdisciplinary iPhD project will develop an on-chip bioengineered 3D-organoid model for multiple myeloma. The system will comprise 3 connected bioengineered niches, modelling not only the primary tumour growth in the bone marrow, but also the invasion/destruction of nearby osseous bone, and the metastatic invasion of distant soft tissue sites. This will enable the evaluation of new therapies in terms of their impact on all these key aspects of multiple myeloma pathology. Technologically, the concept is founded on developing new hydrogel biomaterials that mimic the distinct biophysical and biochemical properties of these 3 niches. The developed system will be applicable to both conventional chemotherapy drugs and also to new cellular immunotherapies such as CAR-T approaches. The project is a collaboration between Prof. Karadimitris, Professor of Haematology and Director of Imperial’s Langmuir Centre for Myeloma Research, and Dr Iain Dunlop, Reader in Biomaterials and Cell Engineering in Imperial’s Faculty of Engineering (Dept. Materials).








Dr Gregory Scott (Imperial - BrainSci)

Dr Matthew Williams (Imperial - S&C)

Professor Payam Barnaghi (Imperial - BrainSci)



Proposal summary

Brain tumours cause a range of difficulties for patients, including cognitive, neurological, and behavioural problems. In some patients, changes in these features over time may relate to tumour recurrence. Others make be linked to side effects of treatments. Importantly, the ability to automatically detect subtle changes within individual patients – even before a patient or caregiver has detected them – could improve long-term outcomes if early detection leads to earlier, more effective, treatment interventions. Recently, we have begun using long-term multi-modal wearable sensing and mobile app technology to gather longitudinal data in a cohort of more than 100 people living with brain tumours. These data include symptoms scores, quality of life measures, and detailed recordings of movement, speech, and cognition. It may be that, when combined with clinical and outcome data, we can develop highly sensitive measures of tumour recurrence. A potentially important missing piece of the puzzle is a more direct readout of brain function. Clinical electroencephalography (EEG) safely records the brain’s electrical activity from the scalp. Whilst traditional clinical EEG uses ~20 “wet” electrodes attached with conductive gel by a healthcare scientist, with advances in electronics there are now simpler ~2-8 “dry” electrode EEG headsets that can be easily attached by anyone. For our patient cohort, this development means we can add in regular (i.e., daily) self-initiated EEG recordings into our multi-modal data collection. The challenge, then, is to derive from these EEG signals objective metrics that can provide the most clinically informative information, e.g., that may be able to detect tumour recurrence.


In this project, we will introduce wearable EEG headsets into a subset of our cohort, and use the methods of signal processing and machine learning to: (1) evaluate the signal:noise and recording characteristics of the wearable EEG devices; (2) examine the relationships between EEG signals and other data collected, e.g., measures of cognition and behaviour; (3) develop machine learning classifiers that can predict tumour recurrence, exploring which combination of data modalities (e.g., EEG + movement + cognition, EEG + movement, EEG alone, movement alone) yields the most accurate and practicable solution. Supported by a multi-disciplinary team of experts, the student will receive training in signal processing, neurophysiology, cognitive neuroscience, and machine learning, and gain practical experience in clinical trials of novel technologies. This is an exciting opportunity to use a convergence science approach to find the best ways to use new technology to improve outcomes for people living with brain tumours.



Professor Hector Keun (Imperial - S&C)

Professor Iain McNeish (Imperial - S&C)


Proposal summary

High grade serous carcinoma (HGSC) is the most common type of epithelial ovarian cancer and accounts for 70-80% of all ovarian cancer deaths. The recent inclusion of PARP inhibitors as maintenance therapy in HGSC has demonstrated significant clinical benefit. However, intrinsic and acquired resistance are both frequent, and recurrence rates remain extremely high. There is therefore an urgent need to develop novel strategies to improve responsiveness to PARPi for HGSC patients.


Our preliminary data provide strong evidence for substantial therapeutic benefit when combining a metabolic therapy (arginine deprivation via ADI-PEG20) with PARP inhibitors to treat ASS1 negative ovarian cancer. The activity of the enzyme ASS1 has been shown to affect response to ADI-PEG20. However, a key question for the design of any future clinical trial is the necessity of ASS1 negative status for a combination benefit, i.e. can we improve PARP inhibitor response in the larger ASS1 positive patient population by co-treating with ADI-PEG20?


To address this question, in this project our main aim is to re-express ASS1 in our human and mouse ASS1 negative HGSC models and repeat our initial experiments to determine the impact on cellular/overall survival, metabolism and DNA repair/damage biomarkers. You will learn a range of multidisciplinary skills within a convergence science approach specifically: molecular and cellular biology; metabolomics using mass spectrometry and computational metabolic flux analysis; in vivo techniques



Professor Trevor Graham (ICR)

Dr Barbara Bravi (Imperial - Mathematics)

Dr Annie Baker (ICR)

Dr Kevin Monahan (Imperial - S&C)


Proposal summary

Lynch syndrome (LS) is the most common hereditary colorectal cancer, with approximately 1 in 300 people carrying a LS germline mutation in a DNA mismatch repair protein. In these LS carriers there is a significant potential for cancer prevention, yet it is underdiagnosed and more than 90% aren’t aware that they are carriers. There is an urgent clinical need for reliable and low-cost tools firstly to identify LS carriers before they get cancer, and secondly to monitor diagnosed LS patients for (pre)-cancers without the need for invasive colonoscopy. In this computational project we aim to derive blood-based biomarkers for LS diagnosis and for detection of LS malignancies. We will perform mutational signature analysis of DNA from the blood of people with LS and without LS, and determine if LS carriers have a unique pattern of mutations that can be detected in their blood. Secondly we will analyse existing T-cell receptor (TCR) sequencing data using machine learning methods to search for unique patterns in the TCRs that could reliable identify LS. Then we will compare the mutational signatures and TCR repertoires of LS patients both with and without cancer, and determine if there is a specific statistical signal associated with progression to cancer. Finally to investigate the biological mechanisms underlying our biomarker we will re-analyse publicly available sequencing of mismatch repair deficient (MMRd) cancers, determining the neoantigen burden and immune recognition potential in each case and stratifying by the germline or somatic MMR mutation. This project combines the bioinformatic analysis of sequencing data (led by co-supervisor Trevor Graham at the Institute of Cancer Research) with the application of powerful machine learning methods (led by co-supervisor Barbara Bravi at Imperial College). Taken together this work will generate new insight into mutagenesis, neoantigen generation and T cell response in LS carriers. By comparing to non-LS individuals we aim to identify new bloodbased biomarkers for LS diagnosis, and by comparing LS carriers with cancer to those without cancer we hope to derive further biomarkers for early cancer detection in LS.







Professor Louis Chesler (ICR)

Professor Eric Aboagye (Imperial - S&C)

Dr Elizabeth Tucker (ICR)


Proposal summary

The paediatric malignancy, neuroblastoma, has a survival outcome of only 50% in high-risk cases.  Recent changes to induction regimes allows children diagnosed with Anaplastic Lymphoma Kinase (ALK) mutant neuroblastoma to receive targeted inhibition of ALK alongside multi-agent chemotherapy.  Whether this addition will provide enhanced protection against metastatic disease progression is the primary question of the randomised SIOPEN High-Risk Neuroblastoma Trial.  Preclinical evaluation of ALK inhibitors provided the evidence for use of the third-generation ALK inhibitor, Lorlatinib, into clinical trials for children with neuroblastoma.  Now, preclinical studies which model multi-drug resistant disease relapse will again be pivotal in guiding optimal treatment strategies in children.  In this project, we propose to use the immunocompetent genetically-engineered mouse model (GEMM), Th-ALKF1174L/MYCN, to study metastatic relapse in neuroblastoma.  Specifically, this project will investigate the sensitivity of the novel radio-tracer 18F against the Somatostatin receptor (SSTR2), to monitor the development of micrometastatic neuroblastoma in this GEMM via Positron Emission Tomography (PET) imaging.


SSTR2, which is highly expressed on neuroendocrine tumours and other cancers, has already been characterised as a bona fide target in clinical neuroblastoma tumours.  Preliminary data demonstrates that the Th-ALKF1174L/MYCN GEMM tumour cells also express high levels of SSTR2, making this an ideal model to study the expression of SSTR2 in a drug resistance setting. 18F-SSTR2 analogs are an alternative to 68Ga analogs, and the first-in-human biodistribution, dosimetry, and safety study of this radioligand has been confirmed in patients with neuroendocrine tumours.  The 18F radioligand also demonstrates good in vivo imaging properties, including fast clearance from non-target organs to allow for imaging shortly after injection, and high target-to-background ratio for efficient detection of metastatic lesions. This Phd project will be an opportunity to assess the sensitivity of 18F-SSTR2 in a unique preclinical model of neuroblastoma utilising clinically-relevant treatment strategies to induce metastatic disease relapse.


Ultimately this project proposes to evaluate a novel imaging approach for metastatic neuroblastoma whilst also providing key preclinical analysis of synergy using the chemotherapy-lorlatinib combination.  Together these aims will contribute to the rational development of better treatments and improved monitoring of neuroblastoma patients



Professor Mona El-Bahrawy (Imperial - MDR)

Dr Bernhard Kainz (Imperial - Computing)



Proposal summary

Endometrial biopsies represent the largest proportion of biopsies handled by gynaecological pathologists.  The principal reason for doing endometrial biopsies is to check for the presence of premalignant or malignant lesions, which are detected in less than 5% of the examined biopsies.  While these lesions need to be detected as soon as possible to initiate the due management, sometimes due to the huge work load there is delay in the diagnosis of these specimens, unless these are highlighted by the clinicians as likely to be malignant based on imaging or hystrosopic findings. 


The aim of this study is to use machine learning to develop an algorithm to classify whole slide images of endometrial biopsies into benign, suspicious for malignancy, malignant and insufficient.  This would enable stratification of the specimens and accordingly setting a prioritisation work flow, so cases that are malignant, suspicious for malignancy and insufficient, which will represent the smaller proportion of cases be prioritised for review by pathologists so further management and / or rebiopsy are initiated without delay.


Study design & Case selection:


The cases will include endometrial biopsies diagnosed at the Histopathology Laboratory at Imperial College Healthcare NHS Trust. The biopsies will include cases of normal endometrium, benign endometrial lesions, premalignant / malignant lesions and insufficient for diagnosis.  Whole slide scans of the Haematoxylin and eosin stained sections will be acquired and relevant clinical information including patient age and clinical presentation / diagnosis will be collected.

Using computing infrastructure and expert input from the Department of Computing the project will be investigated in three stages:

  1. Image quality control and rough outline of the endometrium: A selection of whole slide images will be sampled in small patches and a subset will be annotated in a browser-based interface for identification of the presence of endometrial tissue.  The student will learn how to build a state-of-the-art image classification network to distinguish patches containing endometrium and those that do not. This classifier will serve as quality control to label whole slide images without endometrium as ‘insufficient’ and to provide a rough outline for the relevant image information in biopsies that show endometrium.
  2. Classification of the relevant endometrium image content into benign/malignant after further labelling of a relevant subset of image patches containing endometrium. Calibration techniques and algorithmic uncertainty calibration and communication will be explored to provide efficient means for patient triage.
Integration into a demo-platform similar to and evaluation of the effects of a triage meachanism on the pathologist workforce and importance-based patient turn around time in a pilot study.  



Professor Long R Jiao (Imperial - S&C, RMH)

Professor David Cunningham (RMH, ICR [Hon])

Dr Anguraj Sadanandam (ICR)



Proposal summary

We are planning to study high risk biomarker and early detection (GREAT) in 3 epithelial hepatobiliary and cancers using circulating tumour cells (CTCs), plasma DNA and tumour markers as an estimation of the lead interval in patients at extremely high risk of relapse following treatment in: i) pancreatic cancer ii) cholangiocarcinoma, iii) hepatocellular carcinoma to determine a prognostic indicator of relapse and estimate the lead interval: the time from the development of a high prognostic score to the development of overt new metastatic disease and to compare them with patients with benign diseases.  If this time window is sufficient to allow for new treatment paradigms, this study ultimately has the potential to prevent metastatic disease as future studies can focus on this therapeutic window and also to detect cancer at early stage for intervention.





Dr Ali Salehi-Reyhani (Imperial - S&C)

Dr Rachael Natrajan (ICR)


Proposal summary

Metastasis is the most lethal feature of cancer. Recent reports suggest the prevalence of mitotic circulating tumour cells (CTCs) in aggressive late-stage breast cancer is a more accurate method of stratifying highly aggressive breast carcinomas and correlates with shortened overall survival. The development of CTC isolation methods has stalled over the last two decades and, its limitations notwithstanding, ctDNA profiling has become a more oft used method to track the progress of disease and its response to treatment. While multi-omic profiling of a patient’s tumour and metastatic cells promises a future of personalised treatment, the spread of disease (i.e. stage) and its aggressiveness (i.e. grade) currently remain the two most clinically important factors in a patient’s survival and treatment.

Our hypothesis is that gaining insight into CTC cell cycle / mitotic indexing might better stratify patients into prognostic groups and identify more aggressive cancer targets using a blood-based biopsy. Based on this we aim to address the following questions:

  1. Can we develop facile imaging methods to determine the cell cycle stage of patient derived primary CTCs?
  2. Can we use this as a readout of CTC therapy response in real time in patients with metastatic triple negative breast cancer (TNBC)?

A primary reason for limited clinical use of CTCs is the inability to translate prognostic applications into mainstream clinical treatment, invariably down to complexity of lab assays leading to significant costs. We also believe this is due to the current inability to accurately distinguish highly aggressive from less aggressive CTCs. This project will develop relatively cost-effective microscopy methods along with in-vitro/vivo modelling to identify more aggressive CTCs. CTC therapy response will be evaluated on the microfluidic chip in real-time using a panel of agents clinically approved for metastatic TNBC. This project will pave the way for accurate prognostic and therapeutic stratification of highly aggressive TNBC patients.


The team brings together a mix of well-established medical and molecular research oncologists as well as multidisciplinary scientists from Imperial College and the ICR. The Salehi-Reyhani lab develops microfluidic systems capable of isolating CTCs and determining on-chip drug efficacy with single cell resolution for translation into the clinic. The Natrajan lab focusses on understanding and targeting drivers of epigenetic and transcriptional plasticity in treatment resistant breast cancers using in vitro and in vivo models to develop new biomarkers and therapeutic targets. The project brings to bear molecular biology, microscopy, and machine learning on an unmet need in breast cancer research and treatment. This is clearly a task that is not possible without a team-based convergence science approach suited to clinicians with an interest in academic research.





Dr David Pinato (Imperial - S&C)

Dr Wenjia Bai (Imperial - BrainSci)

Dr Francesco Mauri (Imperial - S&C)


Proposal summary

Hepatocellular carcinoma (HCC) is the most common form of liver cancer which can occur in people with pre-existing liver scarring (cirrhosis). Immunotherapy like atezolizumab plus bevacizumab (A+B) is a new way to kill cancer for those patients in whom liver cancer cannot be cured with surgery. Immunotherapy works by training the patients’ own immune system to detect and kill cancer. Immunotherapy is very effective but not perfect and only shrinks liver tumours effectively in up to 30% of patients. This leaves up to 70% of patients who are currently offered this treatment on the NHS without a clue as to whether immunotherapy with A+B will truly work by allowing them to live longer and with good quality of their life. With this research we plan to improve the way we use immunotherapy in patients with HCC, aiming to help clinicians understand and predict who can respond well to immunotherapy and live longer, spared others from unnecessary and negative side effects.

We have collected hundreds of tumour biopsy samples taken prior to patients received A+B immunotherapy. Samples will be tested in the laboratory to determine whether they have certain features and indicators which may help us understand why a specific patient either responded or not to A+B. By understanding who responds best to immunotherapy, we will be able to switch those patients who are unlikely to benefit from immunotherapy to other treatments, sparing them from unnecessary treatment and helping NHS funds to be spent more efficiently.





Mr Stefan Antonowicz (Imperial - S&C)

Professor Daniele Dini (Imperial - MechEng) 


Proposal summary

Clinical need: Better treatments for oesophageal cancer patients are needed urgently, as only 1 in 8 survive long-term. The additive nature of chemotherapy trials has led to a toxicity ceiling being reached, despite a relatively ineffective standard-of-care. New strategies are vitally needed.


Metabolic heterogeneity as a source of treatment resistance: We have discovered that oesophageal cancer chemicals vary between different tumour areas. Biologically these areas behave differently despite looking identical, and respond differently to anti-cancer drugs. This might explain why standard chemotherapy for oesophageal adenocarcinoma is often ineffective, and may provide an opportunity for refined treatment selection. Our leading hypothesis is that tumour structure influences this metabolic and therapeutic variation.


Objective and aims: This project’s overarching goal is to understand how a cancer’s physical structure contributes to this metabolic variation, and how we can be exploit this therapeutically. To address this, we have established a new collaboration between the Departments of Surgery and Cancer and Mechanical Engineering, with a convergence science approach which marries spatial biology and biomechanics. Four complementary aims are planned:

(i) Provide validated fluid dynamics models to describe drug flow in the oesophageal cancer microenvironment

(ii) Assess how tumour flow physics is correlated with metabolic variation

(iii Establish spatially-defined drug candidates

(iv) Develop direct tissue injection as a novel strategy for enhancing systemic anti-cancer treatment.


Methods: In Aim 1, in silico models of oesophageal adenocarcinoma tissue geometry will be developed using three-dimensional reconstructions of serial microscopic images and micro-Ct based mesoscopic images, to solve governing equations for particle flow through porous media. These physical predictions will be validated using robust, complementary ex vivo and in vivo models, including (i) explant slice cultures in a bespoke microfluidics-based fluid delivery (“iSlice”), and (ii) murine subcutaneous xenografts. In Aim 2, these physical parameters will be compared between different IMH zones, defined using mass spectrometry imaging. In Aim 3, spatial transcriptomics will be used to predict drug candidates in these metabolic zones (pharmacogenomics). Finally, the physical and therapeutic parameters from Aim 1 and 3 will be used to optimise a direct injection method for augmenting chemotherapy response, using murine xenografts and ex vivo whole-tumour specimens.


Patient perspective: Our patients believe tailored cures with less side effects are their research priority. Our approach is designed to meet this need and can later be developed to deliver personalised treatment plans after surgery, in oesophageal and other solid tumours.


Dr Kirill Veselkov (Imperial - S&C)

Dr Olivier Pardo (Imperial - S&C)


Proposal summary

Gastric cancer remains a global health challenge, ranking as the fifth most common and third deadliest cancer worldwide. Despite affecting nearly a million individuals annually and resulting in over 780,000 deaths, effective strategies for improving patient prognosis are lacking, particularly in advanced stages where survival plummets to an average of 12 months.


The insidious nature of gastric cancer, often precipitated by chronic H. pylori infection, underscores the need for preventive measures and early detection. As chronic inflammation progresses to atrophy and intestinal metaplasia, the window for intervention narrows. While current treatments like surgery and chemotherapy provide some recourse, they fall short in halting disease progression. This proposal envisions a transformative approach, harnessing AI to preempt the onset of gastric cancer. Through network-driven analysis of risk factors and preemptive screening, our initiative seeks to shift the paradigm from reactive to proactive management. We aim to enhance computational tools, formulating an AI-centric strategy to manage H. pylori infections and incorporating transparent AI methods within endoscopic and pathology assessments. Our initiative focuses on integrating a variety of data sources, including patient medical histories, diagnostic images, and omics profiles, to better identify individuals at increased risk of gastric cancer.


The expected outcomes are to increase diagnostic precision, reduce reliance on invasive procedures, and customize patient survelleince strategies. This initiative provides a platform not only for education but also for pioneering the future of oncological care with AI at its core, nurturing a new generation of medical researchers ready to lead in AI innovation.




Dr Christopher Rowlands (Imperial - Bioeng)

Dr Nelofer Syed (Imperial - BrainSci)

Dr Elizabeth Want (Imperial - MDR) 

Proposal summary

Brain tumours are one of the most challenging cancers to diagnose and treat. Amongst these challenges are the fact that any surgical intervention (or even just a biopsy) carries significant risk of permanent cognitative damage, that surgical margins must be as small as possible to preserve brain tissue, that chemotherapy has limited effect due to the blood-brain barrier, and that radiotherapy carries significant risk of neurological trauma. Any technique which can recover more information from a biopsy, minimize surgical margins or diagnose tissue in situ would therefore be worth developing, particularly if it allows improved treatment outcomes without requiring poorly-tolerated therapeutic interventions.

Raman microspectroscopy is just such a technology. It is based on the inelastic scattering of light, i.e. light scattering in which the light changes colour. It turns out this colour change tells you about the molecules that the light scattered from, which in turn helps you infer the status of the sample. Unfortunately, biomedical Raman microscopy is not easy to interpret, requiring advanced algorithms to recover the relevant diagnostic information. In addition it can be slow. Part of the student’s work may include developing new algorithms to better recover relevant information from the sample, guided by Dr Rowlands who has expertise in optical instrumentation, software engineering, algorithm development and hardware automation.

Of course, there must be a use for the gathered Raman spectra, and in this case the goal is to help classify the tumour so as to determine its sensitivity to metabolic restriction. This technique exploits the fact that tumours can rely on very different metabolic pathways to normal; it should therefore be possible to deny them critical metabolites while minimising the effect on normal cells. The Syed lab is a pioneer of this kind of therapy, developing tumour xenografts from which to determine which forms of metabolic restriction the tumour might be sensitive to.

The iPhD student on this project will be able to tackle a range of different tasks, according to their interests and aptitude. This might include the development of new xenograft models, automation and refinement of the Raman spectrometer, AI techniques to provide virtual histopathological staining, or even to perform a (non-clinical) diagnosis themselves. As this encompasses a wide range of skills, the supervisory team will help train the student in the necessary methods and techniques.


Dr Burak Temelkuran (Imperial - MDR)

Dr Anguraj Sadanandam (ICR)

Dr Salzitsa Anastasova (Imperial - MechEng)

Dr Ali Yetisen (Imperial - ChemEng)


Proposal summary

Due to the lack of early symptoms and appropriate methods to detect pancreatic carcinoma at an early stage and its aggressive progression, the disease is often quite advanced by the time a definite diagnosis is established. There has been intense research ongoing to identify biomarkers of response to chemotherapy. Here, we propose the development of a novel sensor for potential biomarker detection including different detection methods such as electrochemical sensors using enzyme-linked immunosorbent assay or aptamers as recognition elements. We will initially focus on detection of a novel biomarker uridine, along with well-established and developed optical and electrochemical glucose sensors, to monitor therapy response in PDA.

In this proposed project, the candidate will work with international experts in translational optical and electrochemical sensing, biomedical photonics, analytical biochemistry, computational biology, personalised therapy, pancreatic cancer minimally invasive surgery experts to continue the development of a miniaturised multisensing surgical tip on a fibre that can sense continuously changes of the cancer related biomarkers.

In parallel the candidate will contribute towards developing metabolomic profiling using techniques for advanced clinical cancer research with the Phenome Centre under the clinical guidance of the cancer specialised surgeons. In addition, the candidate will fabricate a microprobe integrating real-time sensors. Preliminary work and analysis on the sensor targets are very promising but more work will provide validation of the working principle, accuracy, repeatable, consistent performance in a clinical environment.

Preliminary experiments suggest that continuous sensing of analytes such as glucose, dissolved oxygen and acid level, temperature will give initial navigation towards the tumor but real-time sensing of cancer related biomarker such as uridine alongside glucose will provide insights of clinically unmet need such as the challenges of therapy response and therapy resistance.

The candidate will work with the Hamlyn Centre for Global Health Innovation and Department of Chemical Engineering to develop functional eletrochemical and optical multisensors with high specificity and sensitivity. A probe softening mechanism at body temperature will be designed and fabricated. Experts at the Institute of Cancer Research will provide experimental (pre-clinical and in vivo models) and clinical (accessing clinical samples) environment to test the developed probes.

Over the course of the project, the candidate will receive appropriate training. A combination of formal courses and apprenticeship-type approach, they will develop skills in probe manufacturing, optical and electrochemical sensing, material design, metabolomics, understand the mass spectrometry data acquisition and analysis, clinical biology, and work under the guidance of a clinical team towards the deployment of this new medical device.



Professor Chris Bakal (ICR)

Professor Chris Dunsby (Imperial - Physics)

Professor Manuel Salto-Tellez (ICR)

Proposal summary

The primary cause of cancer patient mortality are alterations in cell and tissue morphology, which impairs the proper functioning of organs. Indeed, for nearly two centuries cancer diagnosis has relied on morphological variances between cancerous and healthy tissues as observed in biopsy sections (Hajdu, 2012). Recent developments in Artificial Intelligence (AI) have revolutionized pathology through the generation of models which diagnose cancer using digitized images of biopsy sections (Zarella et al., 2019, Aeffner et al., 2019). However, while tumour images contain a wealth of diagnostic information, essentially all cancer pathology in the clinic or laboratory is done on micron thin slices. When digitized, these sections are done so as 2D images. Thus, many three-dimensional (3D) attributes of tumour tissues are hidden from both pathologists and AI models, and there little understanding of 3D cancer biology in patients. This oversight can be attributed, in part, to the technical challenges associated with imaging biopsy sections in 3D. Consequently, both traditional and AI-based digital pathology have predominantly focused on two-dimensional (2D) representations of cancer. This limitation has profound implications, leading to overworked pathologists, misdiagnoses, an inability to distinguish between different cancer subtypes, and hampers early detection. In response to these challenges, we hypothesize there is untapped diagnostic information within the 3D morphology and organization of cancer cells and tissues in patient biopsy sections. We envision a research project led by an intercalated Ph.D. student who will pioneer novel methods for imaging patient sections, and quantifying cell and tissue morphology, in 3D. This work will be carried out under the supervision of: Professor Chris Bakal (Institute of Cancer Research, ICR), Professor Chris Dunsby (Imperial College, IC), and Professor Manuel Salto-Tellez (ICR). This collaboration will combine state-of-the-art light-sheet microscopy technology and optical physics from the Dunsby lab (IC), advanced Artificial Intelligence (AI)-based image analysis techniques developed by the Bakal group (ICR), and the practical clinical pathology expertise of the Salto-Tellez team (ICR).





Professor Hector keun (Imperial - S&C)

Professor Louis Chesler (ICR)

Dr Anke Nijhuis (Imperial - S&C)


Project summary

Neuroblastoma is a highly lethal paediatric cancer that arises in the sympathetic nervous system, with a poor prognosis for many children. Immunotherapy has been demonstrated to be safe and tolerated in early trials, however early CAR-T cell approaches lack strong clinical activity. Therefore, rational engineering of CAR-T production to enhance tumour-specificity is required. Our research has shown that neuroblastoma are sensitive to compounds that interfere with RNA splicing (aryl sulfonamide indisulam), predominantly Myc-N driven cancers. Further consequences of the aberrant spliced transcripts are the production of immunogenic neopeptides which have been previously shown to provoke T-cell mediated anti-tumour responses.


This project aims to understand the functional consequences of interference of RNA splicing on immune cell-mediated anti-tumour responses in neuroblastoma. We will combine innovative longread sequencing methodology, immunopeptidomics and immune cell-based assays to characterise indisulam-derived immunogenic peptides. Neo-antigen-specific genetic T-cell engineering approaches will be used to test the effectiveness of this combination therapy in vivo.


Dr Piers Boshier (Imperial - S&C)

Dr Jorge Bernardino de la Serna (Imperial - NHLI)


Project summary


Gastric cancer is the fifth most common cancer worldwide, affecting over 1 million people each year. Factors that result in the growth and spread of gastric cancer in some people and not others remain poorly understood. New research has suggested that specific microbes (bacteria) and immune cells play an important role in the development of gastric cancers.


Our previous work

In our previous work we showed that cancers of the stomach are linked to specific bacteria. We also found that immune cells in these cancers were also associated with tumour size and spread. We developed multiple advanced imaging techniques to study the micro-structure of tissues as well as an organ on-a-chip model to study cell-cell interactions in the gut. 


Aims of this PhD

This iPhD will use advance imaging techniques to map the microbial and immune microenvironment of gastric cancers resected from patients. We then plan to adapt the organ on-a-chip model to recapitulate this environment in-vitro. Specifics aims:

[1] To characterise and map the immune and microbial environment of gastric adenocarcinoma

[2] To study the influence of immune and microbial factors on tumour growth and spread


Experimental plan

Student will used the new Cell dive (Leica) microscope, to visualise the distribution of microbial and immune cells within gastric adenocarcinoma specimens. Using this microscope, we will perform a digital pathology analysis of the tissue samples employing multiparametric and multiplexing imaging technologies.


The second aim of the PhD will replicate the tumour microenvironment employing microfluidics and organ on-a-chip approached (micro-physiological models). These will be integrated onto fluorescence microscopes so we can understand how microbial and immune cells identified in Aim 1 influence tumour cell growth and invasive characteristics.



The student will be based primarily at Hammersmith Hospital Campus under the supervision of Piers Boshier (Surgeon/Clinical Lecture) and Jorge Bernardino de la Serna (Senior lecturer). This will afford a unique opportunity to work both within clinical and laboratory environments. The student will be part of a vibrant research environment working alongside a large number of scientists, clinicians and clinical research fellows. The student will be supported to achieve the aims of this PhD as well as their broader development as a future a clinician scientist.


Dr Richard Lee (ICR &RMH)

Professor Zoltan Takats (Imperial - MDR)


Project summary

Lung cancer is responsible for the highest number of cancer-related deaths globally. Conducting chest CT scans on high-risk populations can lead to early cancer detection, increasing the likelihood of successful treatment. However, these screening CT scans often reveal anomalies known as "lung nodules." While the majority of lung nodules are benign and result from lung scarring or infection, some may indicate early-stage lung cancer. Detecting lung nodules on a screening CT scan necessitates further testing, occasionally requiring an invasive procedure called a "lung biopsy."

Our research aims to extend upon the lung cancer screening biomarker field  that can differentiate between patients with lung cancer and those without, without the need for invasive tests by integrating novel quantitative and spatial modalities of metabolomic and imaging radiomic analysis testing these biomarkers using easily accessible bodily fluids. Biomarkers used in conjunction with CT scans, in this way could help prioritize higher-risk patients and minimize unnecessary tests for those at lower risk.

Existing ctDNA studies on biomarkers for early lung cancer diagnosis have shown promising results, but none have led to significant changes in clinical practice. Our unique screening sample set will allow us to validate candidate biomarkers in a high risk population with suspicious nodules and differentiate from cancerous nodules. With further samples collection of a subpopulation we can investigate the identifies cancer-related metabolite changes in blood, urine, exhaled breath and sputum.

The final step will involve creating multi-modal risk assessment model based on the identified cancer-related biomarkers, aiming to enhance the management of lung abnormalities detected through screening.

Our collaborative multi-disciplinary team that incorporates scientists with expertise in metabolomic analysis in the national phenome centre, imaging scientists who have already developed radiomics approaches in lung nodules, and a clinician leading lung cancer screening nationally and has already collected the main sample sets in existing clinical studies, is well placed to advance the field in this area and provide the candidate with training to become one of the future clinician scientist leaders of the lung screening biomarker domain.

The application and recruitment process


Stage 1: Exploring Project Opportunities

Imperial and ICR academics present summaries of their research projects. This is a chance to showcase the available research prospects for prospective students. The list of project summaries is available on this page every October.


Stage 2: Selecting Projects

As the iBSc year begins, students review these project summaries and choose three projects they're interested in pursuing for their potential PhD. Once the review is done, students need to send their top three preferences to the Programme Manager. Project supervisors are then informed of students' interest in their projects, leading to meetings to discuss the opportunities.


Candidate Preference Submission Form


Stage 3: Developing Proposals

After these meetings, students rank the three preferred projects from 1 to 3 and share the rankings. The chosen supervisor is informed, and if they agree, they collaborate with the student from January to April. During this partnership, a comprehensive PhD project proposal is developed. A panel of experts reviews this proposal, assessing its scientific quality and its fit for a PhD.


In Case of Unsuccessful Selection

If a student doesn't secure their top project choice, their second choice becomes available for consideration.


Stage 4: Conducting Interviews

Next, shortlisted students, along with their supervisory team, participate in interviews. These interviews serve to assess a) the project's quality, suitability, and feasibility for a PhD, b) the support the supervisory team offers, and c) the student's motivations for pursuing a PhD.


When should I make a decision about pursuing a PhD? 

Our iPhD Programme is designed to be flexible, understanding that not all students may immediately consider research. Therefore, this opportunity is also open to students who develop a keen interest in scientific research during their 15-week iBSc research project, which typically starts in January.


At this point, students have two options. They can either propose a research project in the field of cancer research, possibly extending their iBSc research, and collaborate with their iBSc project supervisor. Alternatively, they can choose from a list of available projects. Following this, students would proceed with the ranking process described in stage 3 and collaborate with supervisors to create a comprehensive PhD proposal. This proposal would be submitted by April.


Further information

For more information on this opportunity send an email to


Time lapse image of an aggressive breast cancer cell sensing its environment through focal adhesions
Training the next

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