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Mass spectrometry imaging

Cancer Grand Challenges: Rosetta Project

Overcoming the biggest challenges in a global effort to beat cancer sooner

Funded through Cancer Research UK's Grand Challenge scheme, NPL's Professor Josephine Bunch leads a group of international and multidisciplinary chemists, physicists and biologists from the UK to develop reproducible, standardised methods to fully map tumours with extraordinary precision. This ground-breaking project has progressed with impressive speed and achieved some remarkable results. The advances achieved through the Rosetta project are set to transform our understanding of cancer and open the door to new and better ways to diagnose and treat the disease.

The information unlocked by the Rosetta pipeline provides a much deeper understanding of cancer which could offer a radical change in clinical decision making at every point of the patient journey.

Josephine Bunch - Principal Investigator on the Rosetta project and Fellow of NPL

Key statistics

  • 22 peer-reviewed scientific papers

 

  • 95 people tackling the challenge, directly or in collaborationwiththe team since its inception in 2017

  • MSI pipeline integrated into around 85% of AstraZeneca's drug development projects
     

  • 90 talks and presentations at scientific conferences around the world

The 'Rosetta Stone' of cancer cell metabolism

In the same way cartographers build maps of cities and countries to help people get around, scientists build maps of tumours to better understand their inner workings. But despite significant advances in technology and our understanding of cancer, our tumour maps remain incomplete. What's missing is our ability to see down to the very core of cancer cells and understand how changes in their metabolism can impact their overall state and function within a tumour. No one has ever mapped tumours to this level of detail – until now.

The 'Rosetta' team are focusing on breast, bowel and pancreatic tumours, as well as investigating an aggressive type of brain tumour. They are working to capture changes to metabolites as tumours develop or respond to treatments, while simultaneously recording information about the cell's exact location within the tumour.

Superimposing this data with maps revealing information about the underlying genetics of these cells will produce the world's first 'Rosetta Stone' of cancer cell metabolism – a high-resolution metabolic map that offers unprecedented insight into a cell's biochemical state. This cutting-edge approach is generating vast amounts of data, which will be made freely available to the research community, providing a Google Earth-type view of a tumour, on a scale that we have never known before.

                         

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Where are we now?

The Rosetta team’s journey is ‘a story of two halves’. Initially it was focused on developing and validating, optimising and combining independent components of their mass spectrometry imaging (MSI) pipeline. These technological breakthroughs raised MSI from a promising research tool to a vital and fundamentally new way of analysing cancer through the spatial resolution of metabolism. Already, this is transforming our understanding of the disease and the biology underpinning its heterogeneity – the diversity of cancer cells within an individual tumour and from patient to patient.

Now at the four-year juncture, the team is largely finishing preclinical studies and is turning their collective attention towards studies with greater clinical impact. In particular, they have been focusing on colorectal, pancreatic, breast and glioblastoma brain tumours – cancer types which present different challenges in the clinic – and using their pipeline to map their altered metabolism.

What next?

Over the next year the team aims to make the first comprehensive atlas of tumour immunometabolism. The preliminary studies, including DESI MSI (desorption electrospray ionisation mass spectrometry imaging) of tumour samples, tentatively show how key metabolites regulate the immune status of a tumour, and that this is influenced by treatment. Conventional immunometabolomics analysis does not show how metabolites align spatially with the immune microenvironment – filling this gap could provide important information about why some people’s cancers respond differently to treatment. The focus will initially be on pancreatic cancer, which has very poor survival rates. With the suite of techniques developed as part of our pipeline, the team is in a fantastic position to better understand the immunometabolism underpinning tumour heterogeneity in this hard-to-treat disease.

The Rosetta Project

A global collaboration - the work has featured in many recent publications:

Bunch J, Steven R, Taylor A, Thomas S, Race A, Dexter A, ... Takats Z. (2018). Abstract 5661: A multi modal mass spectrometry imaging strategy to profile the metabolic hallmarks of colorectal cancer. doi: 10.1158/1538-7445.AM2018-5661

Colclough N, Chen K, Johnström P, Strittmatter N, Yan Y, Wrigley GL, ... Cross DAE. (2020). Preclinical Comparison of the Blood-brain barrier Permeability of Osimertinib with Other EGFR TKIs. Clinical cancer research : an official journal of the American Association for Cancer Research, doi: 10.1158/1078-0432.CCR-19-1871

Dannhorn A, Kazanc E, Ling S, Nikula C, Karali E, Serra MP, ... Takats Z. (2020). Universal Sample Preparation Unlocking Multimodal Molecular Tissue Imaging. Analytical chemistry, 92(16), pp. 11080-11088. doi: 10.1021/acs.analchem.0c00826

Dexter A, Thomas S, Steven R, Robinson K, Taylor A, Elia E, ... Bunch J. (2020). Training a neural network to learn other dimensionality reduction removes data size restrictions in bioinformatics and provides a new route to exploring data representations.

Elia EA, Niehaus M, Steven RT, Wolf JC, Bunch J. (2020). Atmospheric Pressure MALDI Mass Spectrometry Imaging Using In-Line Plasma Induced Postionization. Analytical chemistry, 92(23), pp. 15285-15290. doi: 10.1021/acs.analchem.0c03524

Fala M, Somai V, Dannhorn A, Hamm G, Gibson K, Couturier DL, ... Goodwin RJA & Brindle K. (2020). Comparison of 13C magnetic resonance images of hyperpolarized [1-13C]pyruvate and lactate with the corresponding mass spectrometry images in a murine lymphoma model. Magnetic Resonance in Medicine,

Flint LE, Hamm G, Ready JD, Ling S, Duckett CJ, Cross NA, ... Clench MR. (2020). Characterization of an Aggregated Three-Dimensional Cell Culture Model by Multimodal Mass Spectrometry Imaging. Analytical chemistry, 92(18), pp. 12538-12547. doi: 10.1021/acs.analchem.0c02389

Goodwin RJA, Takats Z, Bunch J. (2020). A Critical and Concise Review of Mass Spectrometry Applied to Imaging in Drug Discovery. SLAS discovery : advancing life sciences R & D, 25(9), pp. 963-976. doi: 10.1177/2472555220941843

Hulme H, Meikle LM, Strittmatter N, van der Hooft JJJ, Swales J, Bragg RA, ... Wall DM. (2020). Microbiome-derived carnitine mimics as previously unknown mediators of gut-brain axis communication. Science advances, 6(11), pp. eaax6328. doi: 10.1126/sciadv.aax6328

Koundouros N, Karali E, Tripp A, Valle A, Inglese P, Perry NJS, ... Poulogiannis G. (2020). Metabolic Fingerprinting Links Oncogenic PIK3CA with Enhanced Arachidonic Acid-Derived Eicosanoids. Cell, 181(7), pp. 1596-1611.e27. doi: 10.1016/j.cell.2020.05.053

Koundouros N, Poulogiannis G. (2020). Reprogramming of fatty acid metabolism in cancer. British journal of cancer, 122(1), pp. 4-22. doi: 10.1038/s41416-019-0650-z

Koundouros N,, Tripp A, Karali E, Valle A, Inglese P, Anjomani-Virmouni S, ... Poulogiannis G. (2019). Near real-time stratification of PIK3CA mutant breast cancers using the iKnife. 211th Meeting of the Pathological-Society-of-Great-Britain-and-Ireland.

Kreuzaler P, Panina Y, Segal J, Yuneva M. (2020). Adapt and conquer: Metabolic flexibility in cancer growth, invasion and evasion. Molecular Metabolism, doi: 10.1016/j.molmet.2019.08.021

Mair R, Wright AJ, Ros S, Hu DE, Booth T, Kreis F, ... Brindle KM. (2018). Metabolic Imaging Detects Low Levels of Glycolytic Activity That Vary with Levels of c-Myc Expression in Patient-Derived Xenograft Models of Glioblastoma. Cancer research, 78(18), pp. 5408-5418. doi: 10.1158/0008-5472.CAN-18-0759

Moss J, Barjat H, Emmas S, Strittmatter N, Maynard J, Goodwin R, ... Barry S. (2020). High-resolution 3D visualization of nanomedicine distribution in tumors. Theranostics, (2), doi: 10.7150/thno.37178

Najumudeen AK, Ceteci F, Fey SK, Hamm G, Steven RT, Hall H, ... Sansom OJ. (2020). The amino acid transporter SLC7A5 is required for efficient growth of KRAS-mutant colorectal cancer. Nature Genetics,

Niehaus M, Robinson KN, Murta T, Elia EA, Race AM, Yan B, ... Bunch J. (2020). MALDI-2 at Atmospheric Pressure-Parameter Optimization and First Imaging Experiments. Journal of the American Society for Mass Spectrometry, 31(11), pp. 2287-2295. doi: 10.1021/jasms.0c00237

Ros S, Wright AJ, D'Santos P, Hu DE, Hesketh RL, Lubling Y, ... Brindle KM. (2020). Metabolic Imaging Detects Resistance to PI3Kα Inhibition Mediated by Persistent FOXM1 Expression in ER Breast Cancer. Cancer cell, 38(4), pp. 516-533.e9. doi: 10.1016/j.ccell.2020.08.016

Schuijs M, Png S, Richard A, Tsyben A, Hamm G, Stockis J, ... Halim T. (2020). ILC2-driven innate immune checkpoint mechanism antagonizes NK cell antimetastatic function in the lung. Nature Immunology, (9), doi: 10.1038/s41590-020-0745-y

Seth Nanda C, Venkateswaran SV, Patani N, Yuneva M. (2020). Defining a metabolic landscape of tumours: genome meets metabolism. British journal of cancer, 122(2), pp. 136-149. doi: 10.1038/s41416-019-0663-7

Smith AL, Whitehall JC, Bradshaw C, Gay D, Robertson F, Blain AP, ... Greaves LC. (2020). Age-associated mitochondrial DNA mutations cause metabolic remodelling that contributes to accelerated intestinal tumorigenesis. Nature cancer, 1(10), pp. 976-989. doi: 10.1038/s43018-020-00112-5

Tzafetas M, Mitra A, Paraskevaidi M, Bodai Z, Kalliala I, Bowden S, ... Kyrgiou M. (2020). The intelligent knife (iKnife) and its intraoperative diagnostic advantage for the treatment of cervical disease. Proceedings of the National Academy of Sciences, (13), doi: 10.1073/pnas.1916960117

Upendra Rao J, Gibson K, Hamm G, Wright A, Fala M, Mair R, ... Brindle K. (2019). CBMT-33. VISUALIZING THE METABOLISM OF GLIOBLASTOMA PATIENT-DERIVED ORTHOTOPIC XENOGRAFTS BY MASS SPECTROMETRY IMAGING. Neuro-Oncology, (Supplement_6), doi: 10.1093/neuonc/noz175.155

Yan B, Murta T, Elia EA, Steven RT, Bunch J. (2020). Direct Tissue Mass Spectrometry Imaging by Atmospheric Pressure UV-Laser Desorption Plasma Postionization. Journal of the American Society for Mass Spectrometry, doi: 10.1021/jasms.0c00315

Meet the team

Grand Challenge team

I'm very excited about the progress we have made. We now have data to suggest that the major mutation that occur in colorectal and pancreatic cancer drives metabolic signature that can be easily identified using image based mass spec approaches. This shows the power of this methodology to stratify cancers. Going forward I'm looking forward to seeing if we can see if we can use these approaches to predict therapeutic responses in these cancers.

Professor Owen Sansom - Beatson Institute