This is the research I carried out in my second postdoc at the Centre for Regenerative Medicine (CRM) in Edinburgh. It was a collaboration between the CRM and Harvard Medical School, in the context of the ΔTissue program. I worked under the supervision of Dr Linus Schumacher (CRM) and Prof. Chris Sander (HMS).
General aims
- Quantify tissue states and transitions for therapeutic impact
- Find patterns in molecular tissue-level data to distinguish
- likely responders from non-responders before treatment
- pre- and post-treatment tissue states,
- Infer opportunities to overcome treatment resistance
- Recognize types and states of cells, expression programs and cell neighbourhoods in the tumour
- Identify differences in pre/post treatment tissues
- Identify predictive patterns among responder and non-responder patients
- Use those changes to nominate therapeutic targets in non-responders
My specific aim
- Putting together a computational pipeline for IMC analysis, optimised for handling heterogeneous patient samples
- Find patterns in molecular tissue-level data in order to predict whether a patient with Triple Negative Breast Cancer will respond to Chemotherapy