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Natalia Garcia Martin

DPhil, Oxford-Warwick Statistics Programme (OxWaSP) CDT

Natalia Garcia Martin graduated with a BSc in Mathematics and Statistics from the University of Glasgow in 2018. Following this she joined the Oxford-Warwick Statistics Programme centre for doctoral training where she has begun a project in collaboration with the S:CORT consortium.

What’s your current research and how could it impact patients?

The main objective of the project is to investigate whether chemotherapy response is stratified by tumour evolutionary trajectories in patients with colorectal cancer. The cancer cell fraction (CCF) values can be estimated from copy number and variant calls of the samples. CCF allows us to identify clusters of clonal and subclonal mutations using Bayesian non-parametric methods. Additionally, identification of mutations in cancer driver genes enables further investigation into the chronological order of key mutation events, shedding light on the evolution history of the tumour. Moreover, we apply network community identification approaches, such as stochastic block models, to group patients according to their mutations and tumour subclone analysis. The derived groupings facilitate the exploration of the interaction between chemotherapy treatment and cancer evolutionary trajectory. This is collaboration work with the S:CORT consortium.

Why did you choose your project?

Coming from a mathematical/statistical background, I was interested in finding a project that would allow me to combine statistical methods development and clinical data analytics with the opportunity to broaden my understanding of how tumours develop and evolve.

What does a typical day look like for you?

Having the opportunity to collaborate with both the Statistics Department and CRUK means that I get to spend roughly half of my week at the Big Data Institute and the other half at the Statistics Department. My usual day consists of meeting with my supervisor, reading papers related to the methods I am investigating, coding and analysing clinical data, catching up with my research group during lunch and more coding (which I love!).