OUR RESEARCH
MASS SPECTROMETRY IMAGING
Matrix assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) is a molecular histology technique, which ionizes endogenous compounds directly from the surface of a sample. By scanning across the sample surface and obtaining discrete mass spectrum for each ionization event, a multiplexed representation of each ion distribution can be reconstructed.
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We have leveraged this capability to visualize tissue inhomogeneity at the tumour-boundary interface of hepatocellular carcinoma (HCC) tissue samples and the penetration of paclitaxel in pressurized intraperitoneal aerosol chemotherapy (PIPAC).
Our access to state-of-the-art MSI technology allows for extremely rapid, high spatial resolution imaging of tissue samples. Our goal is to develop the current platform to sample tissue obtained from the patient, even during surgery and use the information to guide clinical or surgical decisions. Surgeons will be able to improve malignant tissue resection, reducing the post-op burden of patients. Clinician will also have access to another dimension of data to tailor treatment regiments to their patients.
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References:
Tan, H. L., Kim, G., Charles, C. J., Li, R. R., Jang, C. J., Shabbir, A., Chue, K. M., Tai, C. H., Sundar, R., Goh, B. C., Bonney, G. K., Looi, W. D., Cheow, E. S., So, J. B., Wang, L., & Yong, W. P. (2021). Safety, pharmacokinetics and tissue penetration of PIPAC paclitaxel in a swine model. European Journal of Surgical Oncology, 47(5), 1124–1131. https://doi.org/10.1016/j.ejso.2020.06.031
Andersen, M. K., Høiem, T. S., Claes, B. S. R., Balluff, B., Martin-Lorenzo, M., Richardsen, E., Krossa, S., Bertilsson, H., Heeren, R. M. A., Rye, M. B., Giskeødegård, G. F., Bathen, T. F., & Tessem, M.-B. (2021). Spatial differentiation of metabolism in prostate cancer tissue by MALDI-TOF MSI. Cancer & Metabolism, 9(1), 1–13. https://doi.org/10.1186/s40170-021-00242-z
Buchberger, A. R., DeLaney, K., Johnson, J., & Li, L. (2018). Mass Spectrometry Imaging: A Review of Emerging Advancements and Future Insights. In Analytical Chemistry (Vol. 90, Issue 1, pp. 240–265). American Chemical Society. https://doi.org/10.1021/acs.analchem.7b04733
PERSONALIZED THERAPY
Using an adapted protocol from Hans Clevers (1), we have been able to culture malignant tissue obtained directly from patients through biopsies and resections to generate primary patient-derived organoids (PDOs) of PDAC. By screening these PDOs with different chemotherapeutic drug combinations, we aim to be able to identify the most effective drug combination for each patient potentially greatly improving individual response with minimal toxicity. By using the quadratic phenotypic optimisation platform (QPOP), developed and pioneered by Assoc/Prof Edward Chow's laboratory (2) for identification and ranking of optimised drug-dose combinations, we have managed to identify anovel drug combination that ranked higher than the FOLFIRINOX regimn, the current fist line treatment option for PDAC.
In addition to drug screening, we are able to analyze the proteomic profiles of the PDOs, giving us a patient specific profile. Gene ontological analysis of the proteomic profiles derived from the PDOs have shown a large proportion of unique proteins identified were extracellular exosome and membrane protein. these proteins are excellent candidates for biomarker discovery and by correlating these profiles with our drug screening data, we hope to discover novel biomarkers able to advise chemotherapy treatment for patients.
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References:
Driehuis, E., Van Hoeck, A., Moore, K., Kolders, S., Francies, H. E., Gulersonmez, M. C., Stigter, E. C. A., Burgering, B., Geurts, V., Gracanin, A., Bounova, G., Morsink, F. H., Vries, R., Boj, S., Van Es, J., Offerhaus, G. J. A., Kranenburg, O., Garnett, M. J., Wessels, L., … Clevers, H. (2019). Pancreatic cancer organoids recapitulate disease and allow personalized drug screening. Proceedings of the National Academy of Sciences of the United States of America, 116(52), 26580–26590. https://doi.org/10.1073/pnas.1911273116
Rashid, M. B. M. A., Toh, T. B., Hooi, L., Silva, A., Zhang, Y., Tan, P. F., Teh, A. L., Karnani, N., Jha, S., Ho, C. M., Chng, W. J., Ho, D., & Kai-Hua Chow, E. (2018). Optimizing drug combinations against multiple myeloma using a quadratic phenotypic optimization platform (QPOP). Science Translational Medicine, 10(453). https://doi.org/10.1126/scitranslmed.aan0941
CANCER VACCINES
Similar to how normal vaccines work, in cancer vaccines, the aim is to prime the patient’s adaptive immune system to specifically target the tumour cells while ignoring healthy cells. To achieve this, tumour specific antigens, or neoantigens, need to be identified and presented to the patient’s T-cells. These neoantigens that can be specifically recognized by T cells typically come in the form of short peptides of 8–12 amino acids, on human leukocyte antigen (HLA) molecules at the cancer cell surface (1). While non-patient specific vaccines have been developed for some cancers, such as cervical cancer, in Pancreatic Ductal Adenocarcinoma (PDAC) and many other cancers, the mutational profile differs so greatly from patient to patient that it becomes nearly impossible to identify a universal neoantigen effective in majority of patients. As such majority of cancer vaccine development takes a personalized approach to Neoantigen discovery.
While the high mutational burden of PDAC presents a challenge in developing a universal vaccine and leads to a poor clinical diagnosis, it also potentially results in tumour cells which are more highly differentiated from normal healthy tissue. This would allow for a strong immune response, suggesting that PDAC may benefit from immunotherapies such as neoantigen-targeted cancer vaccines. Most efforts in the discovery of neoantigen peptides have been through indirect means such as genomic and transcriptomic sequencing to identify mutations, followed by in-sillico computational analysis to predict MHC class I-binding affinity of the resulting peptides. However, this approach has had mixed results as it is not able to predict which neoantigens will be presented endogenously or are able to induce an immune response (2). Recently, liquid chromatography-mass spectrometry (LC-MS) based approaches have been developed which are now considered the gold standard of neoantigen identification. However, such methods have not been widely employed due to the large number of cells required for such procedures and the high barrier of entry due to limited access to MS technology (3)
Using our patient derived organoid (PDO) protocol, (see personalised therapy section above) we have the ability to grow and expand patient derived PDAC cells and organoids in large quantities. This along with our expertise and access to LC-MS technology puts us in a prime position for the discovery of patient specific neoantigens for the development of cancer vaccines. By expanding PDO lines verified to express HLA class I through immunofluorescence (IF) staining, we generated enough cells to purify through immunoprecipitation sufficient MHC class 1 molecules for analyses. Any peptides bound to the purified MHC class I molecules will be isolated and identified using LC-MS to provide us with potential neoantigens.
References:
Rist, M. J., Theodossis, A., Croft, N. P., Neller, M. A., Welland, A., Chen, Z., Sullivan, L. C., Burrows, J. M., Miles, J. J., Brennan, R. M., Gras, S., Khanna, R., Brooks, A. G., McCluskey, J., Purcell, A. W., Rossjohn, J., & Burrows, S. R. (2013). HLA Peptide Length Preferences Control CD8 + T Cell Responses. The Journal of Immunology, 191(2), 561–571. https://doi.org/10.4049/jimmunol.1300292
Blass, E., & Ott, P. A. (2021). Advances in the development of personalized neoantigen-based therapeutic cancer vaccines. In Nature Reviews Clinical Oncology (Vol. 18, Issue 4, pp. 215–229). Nature Publishing Group. https://doi.org/10.1038/s41571-020-00460-2
Kote, S., Pirog, A., Bedran, G., Alfaro, J., & Dapic, I. (2020). Mass spectrometry-based identification of MHC-associated peptides. In Cancers (Vol. 12, Issue 3). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/cancers12030535