Opendata, web and dolomites

Report

Teaser, summary, work performed and final results

Periodic Reporting for period 1 - OPN-Can (Investigating the role of OPN-CD44 in mechanosensitive tumour invasion using biomimetic models)

Teaser

New fields are rapidly emerging that provide exciting and unforeseen opportunities to target disease. This is especially relevant for difficult-to-treat tumours where, despite decades of promising research, successful clinical translation of therapeutic options remains...

Summary

New fields are rapidly emerging that provide exciting and unforeseen opportunities to target disease. This is especially relevant for difficult-to-treat tumours where, despite decades of promising research, successful clinical translation of therapeutic options remains intractable. The interrelated fields of mechanobiology and biomimetic engineered disease models have come to the fore in the last decade and provide a range of opportunities to investigate therapeutic modalities. These fields have implications for cancer research in three major aspects:
1) understanding how tumour cells interact with their immediate surroundings (microenvironment)
2) development of biomimetic platforms that enhance both the relevance (accuracy) of experimental models and our understanding of tumour biology,
3) creation of analytical tools to quantify how tumour cells sense and interact with their surroundings.

In the outgoing phase of the MSCA Global Fellowship, I undertook a formative advanced research training period at one of the world’s leading institutions of cell and molecular biology and bioengineering (University of California, Berkeley) to learn and apply state-of-the-art biomimetic platforms for a novel tumour biology research project building on their expertise in glioblastoma invasion.

Glioblastoma (GBM), the highest grade glioma, is a leading cause of cancer-related mortality. In Europe, glioblastoma represents 49% of all malignant brain tumours, with the worst 5-year relative survival rate at only 6%. Even with aggressive treatment involving radiation, chemotherapy and surgery according to the latest treatment guidelines, median survival is only 15 months . The disease is largely intractable, as complete surgical excision is virtually impossible, the tumour spreads aggressively and diffusely into the brain, and treatments are rendered largely ineffective in the long term due to high rates of recurrence and resistance to therapeutic drugs. Strategies targeting existing biological targets in the tumour such as the formation of new blood vessels have delivered little benefit and seem to possibly even make the tumours more invasive. As such, targeting GBM invasion is considered a crucial new research strategy to develop additional therapies that enhance current treatment options.

A barrier towards understanding GBM biology has been the inability of traditional laboratory experimental systems to recapitulate the distinct biochemical and physical environment of the tumour cells. To address this, the Sanjay Kumar Lab at Berkeley has developed numerous technologies, including brain ‘matrix-mimetic’ HA hydrogels of tunable ligand density and stiffness that enhances our ability to mimic and investigate tumour properties such as invasion. Embedding within this lab to perform the outgoing phase of this MSCA provided a rich and stimulating opportunity, ideal for the investigating GBM biology and learning about emerging developments in researching tumour biology.

In this project the main objective involved studying two interacting proteins known to have a role in worsening glioblastoma outcomes, Osteopontin and CD44, investigating for the first time how their relationship may impact specifically how this tumour invades the neighbouring healthy brain tissue. Additionally, these investigations were considered in the context of hyaluronan (hyaluronic acid), the main matrix component of brain tissue, which undergoes fundamental mechanical alterations in glioblastoma, especially becoming physically stiffer, which encourages the tumour invasion. In doing so, this work also had the objective of developing a greater understanding of the emerging biomimetic and mechanobiological modelling approaches.

Work performed

This project found significant associations between OPN and CD44 within the genetic signature subtypes of GBM tumour-initiating cells (glioma stem cells) which may contribute to their aggressiveness and invasiveness. Genetic manipulations to reduce the levels of OPN in tumour cells induced altered morphology characterised by increased cell size and acquisition of a cell-cell adherent phenotype. This was accompanied by changes in cell-adhesion molecules consistent with mesenchymal-epithelial transition, as well as a limited capacity for migration. Using 2D biomimetic HA matrix substrates, direct binding of OPN-CD44 on the surface of GBM cells was observed for the first time, which was greatly enhanced on soft HA substrates. To progress these lab bench findings into a more natural context, using a mouse model of GBM involving implantation of patient-derived GBM stem cells into mice resulted in longer survival when the cells were OPN-deficient.

Final results

The results obtained may imply a role for OPN in mediating CD44-dependent GBM invasion. This could position invasion as a potential process that would benefit from targeting the OPN-CD44 relationship, potentially in addition to targeting tumour cell survival mechanisms. This helps to increment our understanding of options to develop new therapeutic approaches to glioblastoma, a disease which continues to defy our current treatment approaches.
As a researcher, this experience has provided insight and experience in emerging strategies to target tumor invasion which can hopefully be used to continue towards developing new therapeutic options and potentially to apply beyond GBM to other tumour types in the future.

Website & more info

More info: https://www.researchgate.net/project/OPN-Can-Investigating-the-role-of-OPN-CD44-in-mechanosensitive-tumour-invasion-using-biomimetic-models.