# Cancer Grand Challenges Rosetta

# About

Rosetta was a Cancer Research UK-funded project led by Professor Josephine Bunch at the National Physical Laboratory (NPL, UK). The initiative began in 2017 as part of the 3D Tumour Mapping Challenge, bringing together over 70 experts from physics, biology, chemistry, biochemistry, and technology innovation to revolutionize the way cancer metabolism was visualized.

The challenge was to develop mass spectrometry imaging (MSI) techniques capable of creating Google Earth-like maps of tumours at an unprecedented level of detail. By integrating multiple MSI modalities, the team aimed to measure and spatially map metabolites, proteins, lipids, amino acids, and carbohydrates across tumour samples, providing a holistic view of cancer metabolism in both experimental models and patient tissues.

Over its seven-year span, Rosetta pioneered multimodal-multiscale MSI pipelines and computational tools that linked in vitro findings with ex vivo and in vivo measurements. These innovations led to critical discoveries, such as:

Identifying arachidonic acid as a metabolic vulnerability in PIK3CA-driven breast cancers, with potential dietary intervention strategies.
Demonstrating how pantothenic acid (Vitamin B5) was linked to MYC-driven tumour metabolism.
Highlighting new therapeutic targets, including SLC7A5 in KRAS-driven colorectal cancer and AHCY in APC-deficient tumours.

Collaborating with AstraZeneca to integrate MSI into drug development, providing insights into compound distribution and resistance mechanisms.

# My Role

Within Rosetta, I contributed to data analysis, computational integration, and spatial biology applications. My focus was on:

Collecting MALDI and DESI MSI datasets to map tumour metabolism in MYC-driven breast cancer models
Integrating MSI with histology and immunofluorescence (IF) images to enhance spatial correlations between molecular and morphological features
Visiting scientist at the Francis Crick Institute, expanding expertise in MSI and collaborative research on tumour metabolism.

Through this work, I helped refine spatial metabolomics workflows, ensuring they were reproducible, scalable, and interpretable for downstream applications in cancer research and potential clinical translation.