The adaptive immune system, especially T cells, plays a central role in cancer evolution. Reawakening the immune system with immunotherapy can be remarkably effective at treating cancer. In this project, we aim to develop computational methods to understand how T cells respond and change through the evolution of colorectal cancer. 

In this interdisciplinary PhD, we will construct mathematical models that describe how populations of T cells change through cancer evolution. We will then construct a Bayesian inference framework (using machine learning/AI approaches) to match models to empirical data from our laboratory.  

We will then apply our computational framework to learn the dynamics of T-cell adaptive immunity through colorectal cancer progression, from early-stage benign tumours, through to metastatic treatment-refractory disease. We have previously generated T-cell repertoire sequencing (TCRseq), and other extensive molecular data, from many patients through the disease course that we hope to better understand by using the new computational method. 

The project is ideal for someone from a maths/physics/computer science background who is interested in complex evolving biological systems. We offer the chance to experience research that spans cancer, immunology, mathematical modelling and machine learning.  The studentship will be based in the Genomics and Evolutionary Dynamics group, within the Centre for Evolution and Cancer, and the Mathematics department at Imperial College London. We are a highly diverse and interdisciplinary collaborative research group of about 20 people, consisting of clinicians, biologists, mathematicians and computational scientists. 

Two key challenges in developing effective treatments for cancer are obtaining potent toxicity against cancer cells while sparing normal tissue, and the evolution of cancer cells leading to resistance and metastasis (the cause of death in many cancers). 

CD73 is associated with a subset of cells that are highly metastatic and resistant to therapy, contributing to tumour heterogeneity.  Small-molecule and antibody therapies inhibiting the function of CD73 are being studied in ongoing clinical trials.  By linking these to other pharmacological agents, we can combine the benefits of CD73 inhibition with the ability to direct other therapeutic agents to metastatic cells. 

Image based profiling is prevalent using clinical imaging modalities such as MRI, CT and PET. Sophisticated AI techniques to aid in diagnostics have arisen owing to the rich high-dimensional data present within images that is difficult or not possible to detect with human perception. It is a less developed and explored area with regards to morphologically profiling primary cells using microscopy. Advanced image analysis can be used to extract cellular features that reflect cell response to treatments such as drug or genetic perturbations. In this project we propose to develop high-resolution image-based profiling to develop early readout assays for CTC response to drugs to overcome challenges with maintaining them ex-vivo.  

Motivating this is the need to rapidly establish CTC reponse to anticancer therapeutics for clinical use. A significant challenge is the limited number of cells extracted from patients and the number required for traditional drug assays. For kinetically fast-acting agents such as cytotoxics, readouts can be made within 24-72 hours. For novel inhibitor class agents or cytostatics such as CDKi’s and PARPi’s these require assays that span 7-10 days or more. Readouts for inhibitor/cytostatic drug assays compare the growth/prolfieration of cells under test to a control set hence why inhibition assays require the time they do to reliably determine whether inhibition has, or hasn’t, occurred. The objective then becomes how to reliably maintain CTCs ex vivo over these timescales. Poor probability of culturing CTCs aside, the time required to expand primary cultures does not support rapid assays that might help inform treatment decisions. We believe new readout methods that circumvent these issues are required. 

As with its more established genomic/transcriptomic/proteomic counterparts, morphological profiling seeks to generate bioactivity profiles (e.g. heat map of transcripts from a PCR array) that describe a perturbed cellular state or condition. We seek here to measure how  morphological profiles arise over the length of a drug assay and determine if, and when, morphological profiling can be used in lieu of proliferation assays when testing cytostatic/inhibitor class drugs. By doing so, there is potential to screen patient-derived CTCs more rapidly and against clinically relevant compounds that are not currently possible with standard techniques.