Dr Olwyn Mahon on how she uses 3D models to understand tumour behaviour in urological cancers and why translating lab work to the real world can be difficult.
Dr Olwyn Mahon has always been drawn to science. After earning her bachelor’s degree in neuroscience, she earned a master’s degree in immunology followed by a PhD in immunology and tissue engineering, all of which she achieved at Trinity College Dublin.
During her studies, she developed an interest in interdisciplinary research, particularly where immunology meets biomedical engineering, and during her PhD, she studied immune responses to orthopaedic implant materials and developed immunomodulatory scaffolds for bone regeneration.
She was awarded an Irish Research Council postdoctoral fellowship at the Health Research Institute at University of Limerick (UL), where she developed 3D cancer models and applied spatial multiomics to study extracellular matrix and immune interactions.
She went on to become a senior research fellow at UL and a Fulbright visiting research scholar at Dana-Farber Cancer Institute, working on biomimetic 3D models of urological cancers for CRISPR-Cas gene editing.
Through a recently awarded Marie Skłodowska-Curie global fellowship, she will extend this work by investigating cancer metastasis using multi-organ-on-a-chip systems at Columbia University.
“I was drawn to this research by a desire to better understand complex diseases through interdisciplinary, systems-based approaches,” Mahon tells SiliconRepublic.com.
“Bladder and other urological cancers stood out to me as particularly challenging, marked by high recurrence rates, metastatic potential and limited treatment options. I saw an opportunity to combine my interdisciplinary background in immunology and tissue engineering to develop more representative models of these diseases.”
Mahon started developing 3D cancer models to more accurately represent urological cancers, such as bladder cancer.
“Unlike traditional 2D cell cultures (cells in a flat dish) our 3D models allow cancer cells to grow and interact in a way that closely mimics how tumours develop and behave in the body,” Mahon says.
She explains that the 3D models help them understand how tumours behave and how they interact with their surroundings. As an added bonus, because the models are built from patient-specific cells, they offer a “more personalised and clinically relevant” platform for testing therapies and studying tumour behaviour.
“We also use CRISPR-Cas gene editing to investigate genetic drivers of cancer growth and survival, helping to uncover new targets for treatment.”
Tricky research
With the use of such advanced techniques for studying these cancers, one has to wonder about why these urological cancers are so difficult to study and treat.
Mahon says that with metastatic urological cancers, it can be tricky due to the complexity of the disease, the diversity of metastatic sites and the limitations of current therapies.
“When cancer spreads to distant organs like the lungs, bones or liver, it becomes much harder to control,” she says.
“Each site has a unique tumour microenvironment that influences how cancer cells grow and respond to treatment. These tumours can also alter their characteristics, making them harder to target or hide from the immune system.”
Complicating it even further, according to Mahon, is the high degree of patient variability, where additional genetic changes can occur in cells and make them treatment-resistant, which differs significantly between individuals.
“This complexity makes it difficult to predict how metastatic tumours will respond to therapies for every patient. Moreover, there are few therapies specifically designed to target these resistant populations,” she says. “Our understanding of metastasis and resistance remains limited and therefore is a critical area of research.”
Recently, Mahon started a new position as Marie Skłodowska-Curie Research Fellow at Columbia University Irving Medical Centre in New York, where she will extend her research by investigating cancer metastasis using advanced organ-on-a-chip systems.
She intends to use these systems to model metastatic bladder cancer, with a specific emphasis on bone metastasis. She says that these microengineered platforms are designed to “recapitulate the dynamic interactions between bladder cancer cells and bone tissue within a physiologically relevant microenvironment”.
“This approach allows for real-time investigation of the mechanisms underlying cancer cell migration, invasion and colonisation of bone, a site of metastasis in bladder cancer,” she explains. “The complexity of this model lies in its ability to simultaneously mimic the unique biological and mechanical properties of both the primary tumour site and the metastatic niche.”
Furthermore, Mahon will be incorporating a sex-specific dimension into the model due to the fact that bladder cancer progression and treatment response “differs significantly” between men and women.
“Hormonal influences and sex-linked molecular pathways can alter tumour behaviour and therapeutic efficacy, making it essential to consider sex as a biological variable when designing more accurate and personalised treatment strategies.”
Translation issues
With something as important and life-altering as cancer research, the transition from the lab to the real world can often be difficult. Mahon says that one of the biggest challenges in translating lab-based research into real-world clinical applications is bridging the gap between academic research, hospitals and patients.
“While lab studies may produce promising results, implementing these innovations into clinical practice requires establishing connections between researchers, healthcare providers and access to large, diverse patient populations for testing. Without these links, it’s difficult to validate new treatments and models in real-world conditions,” she says. “This network establishment requires large, concerted efforts, ensuring alignment with hospital protocols and ensuring secure, ethical access to large, diverse patient cohorts.
“In practice, this involves dealing with fragmented data systems, inconsistent infrastructure, varying consent processes and regulatory hurdles that can significantly delay or limit progress.”
She explains that in Ireland, there are ongoing efforts to address this challenge through initiatives aimed at integrating health data from all the different data points, which include developing interoperable systems to improve data sharing and creating frameworks that allow for “meaningful collaboration” between researchers and clinical teams.
However, this is no small task, as linking datasets requires considerations such as patient privacy and maintaining data quality and security.
“These initiatives are crucial for overcoming the barriers to translating research into clinical practice,” she says. “Better data connectivity and collaboration will facilitate more efficient clinical trial recruitment and tracking of long-term outcomes, which are often major bottlenecks in translational research. Without this kind of infrastructure and collaboration between research and clinicians, even the most promising scientific advances can remain stuck in the lab.”
Discipline convergence
As different concepts and disciplines – such as engineering and data science – become ever more integrated in cancer diagnostics and treatment, we ask Mahon what she envisions for the future of cancer research.
“Advances in engineering is allowing us to develop innovative 3D tissue models and microfluidic devices that can more accurately represent the tumour microenvironment. In parallel, breakthroughs in molecular and cellular biology are deepening our understanding of tumour heterogeneity, treatment resistance and the tumour microenvironment, all of which are crucial for designing more targeted interventions,” she adds.
“In the future, we can certainly expect increasingly integrated platforms that combine patient-derived biological data with real-time clinical inputs to provide adaptive, data-driven treatment plans.”
Further to this, Mahon foresees artificial intelligence (AI) having an “increasingly transformative” role in health research.
AI’s ability to analyse large, complex datasets – including medical imaging, electronic health records, genomic sequencing and real-time patient monitoring – goes beyond “traditional” analysis methods, according to Mahon.
“In oncology, where the field is rapidly shifting toward data-driven, individualised treatment strategies, AI is emerging as a critical tool in precision medicine,” she says. “Machine learning models may be able to stratify patients by molecular features, indicate likelihood of therapeutic efficacy and even predict resistance mechanisms before they appear. This not only enables more accurate, personalised treatment planning but also accelerates the development of targeted therapies.
“As the complexity and volume of patient data continue to grow, AI will be key to translating these insights into meaningful clinical outcomes.”
However, an important clarification that Mahon adds is that AI “should be viewed as a powerful tool that complements human expertise, not replaces it”.
“The real impact will come from integrating AI thoughtfully into multidisciplinary teams, where it can support, enhance, and speed up scientific and clinical decision-making, while researchers and clinicians provide the critical interpretation and context.”
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