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New hydrogel technology has promise in breast cancer modelling

In science, a model is used as a representation of something in the real world, so that ideas and concepts may be tested out. Models have a variety uses, but in cancer biology they are often popular as they can help to mimic the complex environment seen in human   disease. Models are used to explore the effects of new drugs, understand genetic or cellular pathways on tumour development or predict the potential response of a patients cancer.

It’s in a researcher’s best interest to create a model that is as faithful to the real world as possible, so that the outcomes are accurate and can translate successfully into humans. However, the go-to models to recapitulate human cells in a lab use, a protein matrix extracted from mouse tumours, which is used to resemble the extracellular environment found human tumours. But the extent to which mouse matrix can be used is limited by its fixed extracellular matrix components, which are often not representative of the human tissue, and the inability to add or remove the individual extracellular components to explore the influence these on tumour growth.

Dr Gillian Farnie, Nuffield Department of Orthopaedics, Rheumatology and Musculorskeletal Sciences, has focused her work on developing new models that allow human breast cancer cells to be grown and researched, whilst overcoming these limitations.

A recent publication in Matrix Biology, funded by the NC3Rs, outlines a new peptide hydrogel developed by the Farnie group in collaboration with Prof Merry (University of Nottingham).  This new peptide hydrogel offers the added benefit of being customisable, by incorporating or removing specific extracellular matrix components that researchers want to test, to better understand their influence on cancer cells. It therefore allows full control over the biochemical and physical properties of the model, providing researchers with the opportunity to more accurately adapt the model to the real-life environment of human breast tumour.

The new technology’s applications are incredibly widespread and promising. For example, certain extracellular matrix proteins, when found in high quantities in a tumour, can often be associated with a poorer prognosis for a patient. Researchers may want to understand if this is a simple correlation, or if the proteins are assisting the cancer in some way, such as promoting treatment resistance. The ability to remove these proteins from a cancer model and test the response, whilst remaining faithful and accurate to human cells, is incredibly useful and can allow us to discover therapeutic targets.

Dr Gillian Farnie is currently working with the breast cancer research community to apply this new technology in multiple breast tumour research projects. The hydrogel’s applications are not limited to just matrix biology, but also in investigating areas such as the biological significance of blood vessel supply to tumours or even other cancer types outside the breast.

This new hydrogel provides an opportunity to better understand the individual influences of the extracellular matrix, mechanical properties and cell-cell interactions on breast cancer and other disease. It is an open and reproducible model that Dr Farnie is currently publishing a detailed methodology in JOVE, so that more cancer researchers can have access to the new technology.

About this research

Dr Gillian Farnie is based in the Botnar Research Centre, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences.  Her research focuses on the development of patient derived pre-clinical breast cancer models that are used to examine mechanisms of inherent and induced therapy resistance, interrogating both intra-tumour heterogeneity (cancer stem cells) and the tumour microenvironment (ECM, Stroma, Immune cells).

New AI technology to help research into cancer metastasis

Cell migration is the process of cells moving around the body, such as immune cells moving through the body’s tissues to fight off disease, or the cells that move to fill the gap where a tissue has been injured. Whilst cell migration is an important process for regeneration and growth, it is also the process that allows cancer cells to invade and spread across the body.

Therefore understanding the factors that regulate and instruct cells to move is an important part of understanding how we can prevent the metastasis of many cancers. One method of doing this is through scratch assays, which as the title suggests, involves inflicting a wound or ‘scratch’ on cells grown in a petri-dish and analysing how the surrounding cells react and migrate to ‘heal’ the scratch under a microscope.

Although cell migration is intensively studied, we still do not have efficient therapies to target it in the context of cancer metastasis. Observing cancer cell behaviour to artificial wounding and how this can be altered in response to pharmacological drug treatment or gene editing is important to fully understand the factors that drive this process in tumours and provide insights on the processes that drive such behaviours. Whilst current microscopic analysis methods of wound healing data are hindered by the limited image resolution in these assays. Therefore, there is a need to develop new methods that overcome current challenges and help to answer these questions.

Dr Heba Sailem a Research Fellow from the Department of Engineering, has led a study to develop a new deep learning technology known as DeepScratch. DeepScratch can detect cells from heterogenous image data with a limited resolution, allowing researchers to better characterise changes in tissue arrangement in response to wounding and how this affect cell migration.

Tests using the technology have found that DeepScratch can accurately detect cells in both membrane and nuclei images under different treatment conditions that affected cell shape or adhesion, with over 95% accuracy. This out-performs traditional analysis methods, and can also be used when the scratch assays in question are applied to genetically mutated cells or under the influence of pharmaceutical drugs – which makes this technology applicable to cancer cell research too.

Dr Heba Sailem says;

“Scratch assays are prevalent tool in biomedical studies, however only the wound area is typically measured in these assays. The change in wound area does not reflect the cellular mechanisms that are affected by genetic or pharmacological treatments.

“By analysing the patterns formed by single cells during healing process, we can learn much more on the biological mechanisms influenced by certain genetic or drug treatments than what we can learn from the change in wound area alone.”

Using this technology, the team have already observed that cells respond to wounds by changing their spatial organisation, whereby cells that are more distant from the wound have higher local cell density and are less spread out. Such reorganisation is affected differently when perturbing different cellular mechanisms. This approach can be useful for identifying more specific therapeutic targets and advance our understanding of mechanisms driving cancer invasion.

The team predicts that DeepScratch will prove useful in cancer research that studies changes in cell structures during migration and improve the understanding of various disease processes and engineering regenerative medicine therapies. You can read more about DeepScratch and its applications in a recent study published in Computational and Structural Biotechnology.

About Heba

Dr Heba Sailem is a Sir Henry Wellcome Research Fellow at the Big Data Institute and Institute of Biomedical Engineering at the University of Oxford. Her research is focused on developing intelligent systems that help further biological discoveries in the field of cancer.

Therapeutic potential for breast cancer found in the matrix

Work currently underway in the laboratory of Prof Kim Midwood is investigating the therapeutic anti-cancer potential of tenascin-C, a molecule found in the extracellular matrix of breast cancer

Previously unknown ‘genetic vulnerability’ in breast cancer found

New Nature paper reveals discovery of a genetic vulnerability in nearly 10% of breast cancer tumours and how this can be targeted to selectively kill cancer cells.

The relationship between iRhom2 and KRAS proteins

Development Fund winners from the Freeman group are investigating newly discovered proteins and their potential as targets for new cancer treatments

Breast cancer awareness month

Metabolic profiles of breast cancer linked to response to metformin treatment

 

(B)(extract): Static PET-CT images in coronal plane pre- and post-metformin are from an individual with an

increase in KFDG-2cpt following metformin;note increased uptake in axillary lymph nodes (circled).

 

 

An international collaborative team of medical oncologists, radiologists, cell biologists and bioinformaticians led from Oxford by Simon Lord and Adrian Harris, have identified different metabolic response to metformin in breast tumours that link to change in a transcriptomic proliferation signature.

Published last week in Cell Metabolism, the team integrated tumour metabolomic and transcriptomic signatures with dynamic FDG PET imaging to profile the bioactivity of metformin in primary breast cancer.

Simon Lord stated: “This study shows how the integrated study of dynamic response to a short window of treatment can inform our understanding of drug bioactivity including mechanism of action and resistance. Further work will look to identify how mutations in mitochondria may define the metabolic response of tumours.”

The group demonstrated that metformin reduces the levels of several mitochondrial metabolites, activates multiple mitochondrial metabolic pathways, and increases 18-FDG flux in tumours.

The paper “Integrated pharmacodynamic analysis identifies two metabolic adaption pathways to metformin in breast cancer” defines two distinct metabolic signatures after metformin treatment, linked to mitochondrial metabolism. These differential metabolic signatures apparent in tumour biopsy samples, did not link to changes in systemic metabolic blood markers including insulin and glucose, suggesting metformin has a predominant direct effect on tumour cells. Analysis of the dynamic FDG-PET-CT data showed that this novel imaging technique may have potential to identify early response to treatment that is not apparent using standard static clinical FDG-PET-CT.

 

 

Key point summary of the study:

  • There is great interest in repurposing metformin, a diabetes drug, as a cancer treatment
  • Two distinct metabolic responses to metformin seen in primary breast cancer
  • Increased 18-FDG flux, a surrogate marker of glucose uptake, observed in primary breast tumours following metformin treatment
  • Multiple pathways associated with mitochondrial metabolism activated at the transcriptomic level
  • Further evidence that metformin’s predominant effects in breast cancer are driven by direct interaction with tumour mitochondria rather than its effects on ‘host’ glucose/insulin metabolism

 

The study has been funded by Cancer Research UK, the Engineering and Physical Sciences Research Council, the Medical Research Council, and the Breast Cancer Research Foundation.

 

The published paper can be found at:

https://www.cell.com/cell-metabolism/home

 

Content adapted from the original paper by Simon Lord et al.