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Vol 25, No. 08, August 2021   |   Issue PDF view/purchase
FEATURES
The Spatial and Temporal Complexity of Glioblastoma
A multi-disciplinary team comprising researchers from the National Neuroscience Institute, National University of Singapore and the Duke-NUS Medical School has recently been awarded the Large Collaborative Grant from the National Medical Research Council of Singapore. This effort addresses critical issues underlying the difficulty in treating glioblastoma (GBM) brain tumours.

Primary malignant brain tumours, although rare compared to other cancers, almost always end in fatal outcomes for the patient. Even with the standard of care chemotherapy, temozolomide (TMZ) and radiotherapy, survival is extended by at best three months.1,2 The main reason driving this highly aggressive and recurrent disease is the cellular and molecular heterogeneity of glioblastoma (GBM) tissue.3,4 This means that the tumour tissue represents an ecosystem between the cancerous cells, as well as its surrounding environment, often described as the tumour microenvironment (TME). Thus, with the advent of major international efforts at deep profiling the molecular content of several cancers, GBM is well-poised as an example for precision oncology-based therapeutic approaches.

The brain is a unique organ where repeat surgeries are often not possible due to tumour tissue located at eloquent areas of the brain. As the disease progresses, the tumour cells invade surrounding areas of the brain, thus, in most cases, the development of appropriate therapies becomes imperative. TMZ has been the standard of care for the past decade with little success at prolonging survival and improving the quality of life for the patient. Under such circumstances, it is clear that novel, minimally invasive diagnostic tools must be developed that utilise fluid biopsies such as blood, and that can accurately detect molecular changes as the disease progresses.

The National Neuroscience Institute (NNI) of Singapore was recently awarded the Large Collaborative Grant (LCG) which is supported by the National Research Foundation Singapore and administered by the Singapore Ministry of Health’s National Medical Research Council. This program represents the nation’s highest level of research funding that was accorded to an institute with a leadership role in the clinical management and research of the disease. The LCG program is helmed by several academic and industry experts spanning different disciplines such as biology, bioengineering, systems biology, and clinical trial physicians. Briefly, the following advances will be studied, tapping into NNI’s recently formalized brain tumour tissue bank, called Glioportal, which will provide crucial preclinical evidence.5,6,7,8

Identification of Drugs Targeting the Evolving State of a Tumour

In the past years, most precision oncology-driven treatment options target bulk tumour mutation profiles; however, little has been borne out to date. This has largely been attributed to the presence of different mutations during the evolution of the disease, further compounded by changes upon recurrence.9

As with all neurological disorders, the brain is a relatively inaccessible organ as with each surgery, the patient’s quality of life is compromised. Our studies show that another layer of complexity exists (i.e. heterogeneity within a patient’s tumour tissue). Thus, the tumour is really one of a dynamic state of events captured by snapshots of changing molecular and cellular profiles.

These changes will be biologically validated in our lab models established using clinical material, and congruence with clinical information will provide more robustness of its relevance to the disease. We previously identified a key event regulating tumour cell transition upon recurrence to a more aggressive state, STAT3 activation.8 We developed a gene signature profile capturing this transition and biologically validated its efficacy at stratifying patient tumour cells. This gene signature, besides characterising the transition, also reveals predicted kinase targets for therapeutic intervention. Kinases form about 70 per cent of all major pharmaceutical drug targets in the discovery pipeline, thus the availability of existing drugs that could be re-positioned proffers a speedier evaluation process for the patient.

To this end, we are collaborating with an industry partner with expertise in artificial intelligence-driven drug discovery. BenevolentAI in the UK will guide us in the prioritisation of small molecules most likely to succeed in brain tumours where blood-brain barrier penetrance is a key consideration.

Systems Biology Approach Categorises Interactions Between Cells of the Tumour and TME

In this thematic aim, we seek to elucidate molecular hallmarks describing the tumour’s ability to transit to a more aggressive state. We will approach this aim with consideration for tumour features alternating between that of its initial diagnosis, and the recurrent state. We will also explore the spatial distribution of such features within a patient’s brain tumour. Although this approach represents early-stage data acquisition, recent technological advancements in untapped areas such as RNA biology are being explored by many international biotech start-ups.

RNA biology is an important conceptual advancement to enable studies on tumour features as most of the genome constitutes untranslated RNA, in contrast to protein-coding portions (defined by mRNA). Single-cell technologies have also enabled research aiming to distinguish between molecular signalling arising from tumour intrinsic cells, and/or the TME.10,11,12 Such tumour-stroma crosstalk has substantial implications on brain tumour development and immunotherapeutic approaches.

Diagnostic Nanosensor of the Future

Our bioengineer co-investigator, Asst Prof Shao Huilin from NUS has designed and fabricated a nanosensor platform. Although many diagnostic devices have been established by the scientific community over the past decade, most lack the ability to detect complex molecular signatures defined in tumours in recent years.

Asst Prof Shao’s team successfully demonstrated that her unique nanosensor amplifies and accurately detects nucleic acid biomarkers co-established with the biology team at NNI. As such, due to the invasive nature of GBM tumours and the unavailability of tissue at subsequent recurrence (repeat surgeries affect a patient’s quality of life, unique to brain tumours), this diagnostic platform proffers a minimally invasive tool to serially monitor a disease as it progresses, by evaluating fluid biopsies such as blood and cerebrospinal fluid.

Asst Prof Shao’s technologies have since been validated in notable publications including COVID-19 detection, and neurological diseases such as GBM, Alzheimer’s’ Disease, and cancer.13,14,15,16,17,18,19,20

Adaptive Clinical Trial and Global Participation Hastens Evaluation of Patient Responses

The LCG team will be inducted into the GBM Adaptive Global Innovative Learning Environment (GBM AGILE) trial consortium where it will participate directly in a Phase II trial of WP1066 drug, using Asst Prof Shao’s optimised nanosensor platform.21 This trial concept is run on a master protocol, where data analysed is dynamic, to reduce failure numbers of treatment arms that are not statistically viable. As such, the global consortium facilitates data sharing where it is expected that the control and placebo arms share common patient cohorts; thus reducing significant side effects, emotional toll and financial costs to the individual and caregivers. The efficacy and speediness of adaptive trials are well described in breast cancer such as ISPY, and most recently, Solidarity I/II in COVID-19 drug testing and serologic responses.

Conclusion

Collectively, the LCG describes a multi-disciplinary approach to tackling a disease that has morbid outcomes for the patient. Our goal is to establish a long-lasting and curative treatment strategy that uses the ever-changing tumour tissue landscape to guide its diagnosis and hence therapeutic decisions.

References

  1. Louis, D. N., Perry, A., Reifenberger, G., Von Deimling, A., Figarella-Branger, D., Cavenee, W. K., ... & Ellison, D. W. (2016). The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta neuropathologica, 131(6), 803-820.
  2. Louis, D. N., Perry, A., Wesseling, P., Brat, D. J., Cree, I. A., Figarella-Branger, D., ... & Ellison, D. W. (2021). The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro-oncology.
  3. Cancer Genome Atlas (TCGA) Research Network. (2008). Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature, 455(7216), 1061.
  4. Wang, Q., Hu, B., Hu, X., Kim, H., Squatrito, M., Scarpace, L., ... & Verhaak, R. G. (2018). Erratum: Tumor Evolution of Glioma-Intrinsic Gene Expression Subtypes Associates with Immunological Changes in the Microenvironment (Cancer Cell (2017) 32 (1)(42–56. e6)(S1535610817302532)(10.1016/j. ccell. 2017.06. 003)). Cancer cell, 33(1), 152.
  5. Chong, Y. K., Sandanaraj, E., Koh, L. W., Thangaveloo, M., Tan, M. S., Koh, G. R., ... & Ang, B. T. (2016). ST3GAL1-associated transcriptomic program in glioblastoma tumor growth, invasion, and prognosis. JNCI: Journal of the National Cancer Institute, 108(2).
  6. Chong, Y. K., Toh, T. B., Zaiden, N., Poonepalli, A., Leong, S. H., Ong, C. E. L., ... & Tang, C. (2009). Cryopreservation of neurospheres derived from human glioblastoma multiforme. Stem cells, 27(1), 29-39.
  7. Ng, F. S. L., Toh, T. B., Ting, E. H. L., Koh, G. R. H., Sandanaraj, E., Phong, M., ... & Ang, B. T. (2012). Progenitor-like traits contribute to patient survival and prognosis in oligodendroglial tumors. Clinical Cancer Research, 18(15), 4122-4135.
  8. Tan, M. S. Y., Sandanaraj, E., Chong, Y. K., Lim, S. W., Koh, L. W. H., Ng, W. H., ... & Tang, C. (2019). A STAT3-based gene signature stratifies glioma patients for targeted therapy. Nature communications, 10(1), 1-15.
  9. Lee, J. K., Wang, J., Sa, J. K., Ladewig, E., Lee, H. O., Lee, I. H., ... & Nam, D. H. (2017). Spatiotemporal genomic architecture informs precision oncology in glioblastoma. Nature genetics, 49(4), 594-599.
  10. Bhat, K. P., Balasubramaniyan, V., Vaillant, B., Ezhilarasan, R., Hummelink, K., Hollingsworth, F., ... & Aldape, K. (2013). Mesenchymal differentiation mediated by NF-κB promotes radiation resistance in glioblastoma. Cancer cell, 24(3), 331-346.
  11. Ghoshdastider, U., Rohatgi, N., Naeini, M. M., Baruah, P., Revkov, E., Guo, Y. A., ... & Skanderup, A. J. (2021). Pan-Cancer Analysis of Ligand–Receptor Cross-talk in the Tumor Microenvironment. Cancer Research, 81(7), 1802-1812.
  12. Hara, T., Chanoch-Myers, R., Mathewson, N. D., Myskiw, C., Atta, L., Bussema, L., . . . Tirosh, I. (2021). Interactions between cancer cells and immune cells drive transitions to mesenchymal-like states in glioblastoma. Cancer Cell, 39(6), 779-792.e711. doi:https://doi.org/10.1016/j.ccell.2021.05.002
  13. Lim, C. Z., Zhang, Y., Chen, Y., Zhao, H., Stephenson, M. C., Ho, N. R., ... & Shao, H. (2019). Subtyping of circulating exosome-bound amyloid β reflects brain plaque deposition. Nature communications, 10(1), 1-11.
  14. Pan, S., Zhang, Y., Natalia, A., Lim, C. Z., Ho, N. R., Chowbay, B., ... & Shao, H. (2021). Extracellular vesicle drug occupancy enables real-time monitoring of targeted cancer therapy. Nature nanotechnology, 16(6), 734-742.
  15. Shao, H., Chung, J., Lee, K., Balaj, L., Min, C., Carter, B. S., ... & Weissleder, R. (2015). Chip-based analysis of exosomal mRNA mediating drug resistance in glioblastoma. Nature communications, 6(1), 1-9.
  16. Sundah, N. R., Ho, N. R., Lim, G. S., Natalia, A., Ding, X., Liu, Y., ... & Shao, H. (2019). Barcoded DNA nanostructures for the multiplexed profiling of subcellular protein distribution. Nature biomedical engineering, 3(9), 684-694.
  17. Sundah, N. R., Natalia, A., Liu, Y., Ho, N. R., Zhao, H., Chen, Y., ... & Shao, H. (2021). Catalytic amplification by transition-state molecular switches for direct and sensitive detection of SARS-CoV-2. Science Advances, 7(12), eabe5940.
  18. Wang, Z., Sun, X., Natalia, A., Tang, C. S. L., Ang, C. B. T., Ong, C. A. J., ... & Shao, H. (2020). Dual-selective magnetic analysis of extracellular vesicle glycans. Matter, 2(1), 150-166.
  19. Wang, Z., Zhao, H., Zhang, Y., Natalia, A., Ong, C. A. J., Teo, M. C., ... & Shao, H. (2021). Surfactant-guided spatial assembly of nano-architectures for molecular profiling of extracellular vesicles. Nature Communications, 12(1), 1-12.
  20. Wu, X., Zhao, H., Natalia, A., Lim, C. Z., Ho, N. R., Ong, C. A. J., ... & Shao, H. (2020). Exosome-templated nanoplasmonics for multiparametric molecular profiling. Science advances, 6(19), eaba2556.
  21. Alexander, B. M., Ba, S., Berger, M. S., Berry, D. A., Cavenee, W. K., Chang, S. M., ... & Barker, A. D. (2018). Adaptive global innovative learning environment for glioblastoma: GBM AGILE. Clinical Cancer Research, 24(4), 737-743.

About the Authors

A/Prof Ang Beng Ti Christopher

Head and Senior Consultant, Department of Neurosurgery, National Neuroscience Institute @ Singapore General Hospital campus.

A/Prof (Adjunct) Carol Tang

Principal Investigator (I), Department of Research, National Neuroscience Institute.

Prof Patrick Tan

Programme in Cancer and Stem Cell Biology, Duke-NUS Medical School.

Asst Prof Shao Huilin

Department of Biomedical Engineering, and Institute for Health Innovation & Technology (iHealthtech), National University of Singapore.

Dr Lin Xuling

Senior Consultant, Department of Neurology, National Neuroscience Institute.

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