Brian Tom

Contact

Brian Tom – brian.tom@mrc-bsu.cam.ac.uk

Potential PhD Project

Causality and Multi-State Processes in Psoriatic Arthritis

For over ten years, I have been interested in understanding disease progression in Psoriatic Arthritis (PsA); both in terms of patterns of damage and the relationship between disease activity (characterised by joint tenderness and swelling) and joint damage. We have performed a number of investigations at the individual joint level – O’Keeffe, Tom and Farewell (2011), O’Keeffe, Tom and Farewell (2013), Yiu, Tom and Farewell (2016) and Yiu, Farewell and Tom (2018) – to better elucidate the mechanisms involved. We have adopted a stochastic view point to more explicitly reflect the importance of time, to better capture the dynamics of the damage and disease activity processes at the joint level, and the influences that these processes may have on each other. For these analysis, we have not taken the various treatments for PsA into account and therefore have interpreted our findings in the context of the standard treatment patterns being employed in the Toronto PsA clinic from where the data comes from. In this PhD project, we propose to investigate the impact of the various treatments for PsA on disease progression. As treatments are influenced by current and past disease activity and are developed to target inflammation; in addition can be systemic or more local to the joint inflamed and can be given in combination, issues such as time-varying confounding, interference (where the treatment received for treating a particular joint may affect the outcome for other joints) and causal attribution need to be addressed.

Precision Medicine

I am also open to developing a PhD project in the area of Precision Medicine. Areas of interest are Outcome-guided Clustering, Latent Trajectory Modelling and Optimal Treatment Regimes either with clinical trials data or observational data.

The molecular relationships between child and adult arthritis

This project is jointly supervised with Chris Wallace.

Arthritis is an immune-mediated disease characterised by inflammation of the synovial tissues around joints that leads to chronic pain and progressive disability. Childhood arthritis is the most common chronic rheumatic disease of unknown aetiology in childhood and considered a collection of seven subtypes, with several considered paralogs of better studied adult arthritis types. For example, genetic data suggest rheumatoid factor positive childhood arthritis is most like adult rheumatoid arthritis (Hinks et al). However, the division into subtypes has been controversial. One important but unresolved question is whether adult rheumatoid arthritis and (some subtypes of) childhood arthritis are the same disease at different ages?

Chris Wallace and Brian Tom have led data analysis workstreams for stratified medicine consortia in childhood (CLUSTER) and adult (RA-MAP) arthritis consortia, which have collected a wealth of molecular data characterising the immune system in hundreds of child and adult patients early in their disease course.  We propose that a systematic comparison of these high-dimensional molecular data can be used to address this central question. Further important questions include:

• If the diseases are not the same, what are the similarities/differences in terms of immune profile?
• If they are “close enough”, then what information from the larger adult studies can we use to better understand the childhood disease (and vice versa)?
• Can the molecular data be used to better align childhood arthritis subtypes with adult rheumatoid arthritis?
• Given the known age-dependent differences in the immune system, what are the differences in how the same treatment given to adults and children affects immune gene activity?

There are interesting statistical challenges in this project, not least in dealing with age (because the immune system changes with age, even in healthy individuals), inevitable “batch effects” between adult and child samples which were collected and processed differently, and the dimensionality of the covariate space. We also need to consider statistical definitions of what “the same” means, and what would it look like in this kind of data. There are always multiple possible approaches, but we envisage that the use of clustering and latent variable modelling techniques will be relevant, as may other dimension reduction techniques.

How to apply

For details of the MRC BSU application process please see How to apply

To be considered for funding applications need to be submitted to the University of Cambridge application system by 23:59 (GMT) on January 5th 2023