We develop technologies for doctors to make better decisions faster
We tackle some of the most deadly cancers with genomes that look like a chaotic mess of missing and redundant parts – like ovarian, oesophageal and pancreatic cancer.
We work on (1) understanding the mechanisms behind this genomic chaos; (2) how to target cancer weaknesses with therapies; (3) how to overcome resistance to treatment; and (4) how to spot cancers as early as possible.
The methods we use combine machine learning and AI with experimental techniques ranging from single cell sequencing to tissue imaging. Some highlights of our research:
The 17 deadly CINs
We have developed a compendium of 17 copy number signatures characterising different types of chromosomal instability (CIN) and continuously work on extending and refining it. Current efforts are to further validate the signatures in large data cohorts and to extend them to single cell data, which will allow us to measure currently active mutational processes and evaluate how well they predict treatment response. This work is funded by an ERC (now UKRI) grant and the foundation of Tailor Bio, a genomics start up.
Read more about copy number signatures: Drews et al (2022), Macintyre et al (2018); and their applications: Vias, Morrill et al (2023), Cheng et al (2022), Jiménez-Sánchez et al (2020).
Spotting cancer early
We are creating AI approaches to analyse data from the Cytosponge, a minimally invasive device to detect a precursor of oesophageal cancer. Our work automates labour-intensive tasks and refines pathology biomarkers to identify the patients at the highest risk of developing cancer.
Read more about it: Gehrung et al (2021), Berman et al (2022)
Come and shine!
Our supportive and interdisciplinary lab is a great starting point for your career, wherever it takes you.
A pan-cancer compendium of chromosomal instability.E-pub date: 1 Jun 2022Journal name: Nature
Triage-driven diagnosis of Barrett’s esophagus for early detection of esophageal adenocarcinoma using deep learning.E-pub date: 1 May 2021Journal name: Nat Med
Scabsolute: Measuring Single-Cell Ploidy and Replication StatusE-pub date: 16 Nov 2022Journal name:
Clinically Interpretable Radiomics-Based Prediction of Histopathologic Response to Neoadjuvant Chemotherapy in High-Grade Serous Ovarian CarcinomaE-pub date: 1 Aug 2022Journal name: Front Oncol