From Patient Tumors to Treatment Decisions: How 3D Cell Models and AI Are Driving Functional Precision Oncology
Breakthroughs in molecular and immune-based therapies have reshaped cancer treatment, yet many patients still face poor outcomes. A research team led by Professor Helen Rizos at Macquarie University’s ACRF Centre for Advanced Cancer Modelling is working to change that by integrating functional precision oncology into clinical care.
Their approach? Growing patient-derived mini-tumors in the lab using Inventia Life Science’s RASTRUM™ 3D cell culture platform, then testing them against clinically approved drugs to identify the most effective treatment for each individual. Combined with high-resolution imaging and AI-driven analytics, this strategy aims to bridge the gap between tumor biology and real-time treatment decision-making.
This research is part of a broader collaboration between Macquarie University, the Chris O’Brien Lifehouse (focused on head and neck squamous cell carcinoma), and the Melanoma Institute Australia.
Reimagining Cancer Care—One Mini-Tumor at a Time
The vision behind this work is both bold and deeply human: to tailor cancer treatment not just to a diagnosis, but to the unique biology of each patient’s tumor.
“Despite the remarkable activity of molecular and immune-based cancer therapies, many patients still die of cancer,” the team shared. “To improve patient outcomes, we’re working to bring real-time functional precision oncology into routine clinical care using each patient’s tumour to guide treatment decisions.”
At the core of this approach is a simple but powerful idea: let the tumor tell its story. Using RASTRUM, the team creates 3D bioprinted models directly from patient tissue collected after surgery. These mini-tumors are then exposed to therapies already used in the clinic, and over several days, the team observes how the cells respond.
“Artificial intelligence helps us analyse how the cells behave by tracking growth, survival, and cell interactions,” they explained. “This approach will help us identify the most effective treatments for each patient and supports the discovery of better drug combinations.”
This fusion of 3D bioprinting, real patient biology, and computational power isn’t just novel, it’s redefining how cancer research can serve patients today.
Why 3D Cell Culture Matters for Personalized Medicine
To make personalized medicine truly personal, researchers need models that reflect the real-world complexity of each patient’s tumor. Traditional 2D culture systems, while useful, are fundamentally limited. Flattening cells into artificial environments that miss the spatial, cellular, and biochemical cues that shape how tumors grow and respond to treatment.
For the team at Macquarie University, that limitation wasn’t acceptable.
“Tumour response is shaped by complex interactions between a variety of cell types, including non-adherent immune cells, and by the spatial and metabolic features of the tumour microenvironment,” they explained. “Cell–cell and cell–matrix interactions are also critical drivers of treatment response and cannot be accurately replicated in traditional 2D culture approaches.”
RASTRUM provided a solution: a scalable, high-throughput platform that enables the team to recreate miniaturized, biologically faithful tumor environments. These models retain the complexity of cell–cell interactions, support co-cultures with immune cells, and reproduce the gradients that influence tumor evolution, all while maintaining reproducibility across experiments.
This balance, between biological accuracy and practical usability, is what makes 3D culture a critical step forward in building truly translational research models.
From Cell Lines to Patients: Building a Platform That Can Keep Up with Cancer
Modeling cancer’s complexity isn’t just about creating realistic biology—it’s about building an approach that works consistently, across patients and over time. That’s why the team began with well-characterized cancer cell lines, laying a foundation for a reproducible workflow before scaling to patient-derived samples.
Cancer isn’t static. It evolves, resists, adapts. To keep pace with these dynamics, the team combines their 3D models with powerful analytic tools to capture how tumors respond in real time.
“We are at the early stages of 3D cancer modelling and are focused on establishing a consistent and reproducible approach to assess treatment response in real time,” they shared. “This includes integrating AI-based analyses and tracking cancer cell-lineage expression markers to monitor dynamic changes.”
Shaping the Future of Cancer Care with 3D Models
It’s not hard to imagine a future where patients benefit from lab-tested insights into how their tumor responds to therapy—helping doctors tailor treatments with even greater precision.
This is the future the team at Macquarie University is helping to build.
“3D models will increasingly underpin precision medicine by more faithfully recapitulating tumour architecture, microenvironmental gradients, and multicellular interactions than 2D cultures,” they noted. “They enable functional drug screening, reveal dynamic biomarkers of treatment response, and support patient-specific treatment planning in near real time.”
For that future to become a reality, these models need to deliver more than biological complexity. They must be reproducible. Scalable. Cost-effective. And they must generate data that’s not only scientifically valid, but clinically meaningful.
That’s where the RASTRUM platform plays a key role, delivering complex yet consistent models that meet the rigor of both research and translational medicine.
Used to uncover biomarkers, test drug combinations, and gain individualized insights, these 3D systems offer a valuable extension to today’s precision oncology toolkit. Rather than replacing existing diagnostic techniques, they complement them, bringing functional, patient-specific response data into the treatment planning process. By bridging the gap between molecular profiling and how a tumor actually behaves, 3D models open new opportunities to refine therapy choices in a clinically meaningful way.
Advice from the Bench: Start Small, Think Big
Shifting to 3D cell culture can feel daunting. But for the team at Macquarie University, the transition was both achievable and transformative, thanks to the right tools, a clear scientific purpose, and a supportive community.
“Start small with a few well-characterised cancer cell lines and a user-friendly printer (we love RASTRUM!) and the helpful team (Inventia’s team has been very supportive),” they advised.
The key? Start with models you understand. Lean on a platform that’s intuitive and scalable. And don’t be afraid to experiment.
“Dive into the community—chat with fellow researchers, swap assay ideas, and adopt the try, tweak and repeat approach to master your 3D workflow.”
This mindset, curious, iterative, collaborative, is at the heart of every scientific leap. And as more labs begin to adopt 3D cell culture for precision medicine applications, shared knowledge and best practices will accelerate progress for everyone.
For those ready to move beyond the limitations of 2D, the message is clear: the tools are here, the path is proven, and the future is already taking shape—one mini-tumor at a time.
Many thanks to Helen Rizos, PhD, Bernadette Pedersen, Mal Irvine, and Angelo Audish for sharing their insights.
Explore 3D Cell Models and Precision Oncology Research
The future of personalized cancer care is being shaped by researchers who are willing to rethink what's possible, and by technologies that make complexity scalable. If you're interested in exploring how 3D bioprinting can advance your own research, or want to see how functional precision oncology is moving from bench to bedside, here’s where to go next:
- Explore Inventia’s 3D cell culture platform and see how it’s helping researchers build more biologically relevant models, faster
- Learn more about the groundbreaking work at the Rizos Lab
- Read our latest application note to see how RASTRUM Allegro can supercharge your precision oncology workflow