How multimodal biomarker innovation is driving precision oncology

Cancer research is increasingly moving beyond single technologies toward integrated, multimodal approaches that better reflect the complexity of the disease. While innovations such as spatial biology and ultra-sensitive biomarker detection continue to advance individually, their convergence is opening new opportunities to connect discovery with clinical impact.

In a recent conversation, Ritu Mihani and Jorge Marques-Signes from Quanterix shared how combining complementary technologies is helping researchers build a more complete picture of cancer biology, and why this shift is key to improving patient outcomes.

Moving beyond single-modality analysis

Cancer cannot be fully understood through a single lens. As Ritu explains, context plays a critical role in determining how cells behave: “It’s the same cell type, but the behavior of the cell will change depending on the neighborhood of cells it’s surrounded by.”

This is where spatial biology has emerged as a powerful tool, enabling researchers to study protein expression directly within tissue architecture. By preserving the spatial context of cells, these approaches reveal insights into tumor heterogeneity and microenvironmental interactions-factors that are central to treatment response, particularly for immunotherapies and antibody–drug conjugates (ADCs).

Importantly, this evolution is also shaping how assays are designed. Rather than focusing solely on technical performance metrics such as plex or sensitivity, there is a growing emphasis on biological relevance. At Quanterix, this includes aligning biomarker panels with established frameworks such as the hallmarks of cancer, ensuring that generated data is not only high-quality, but meaningful for downstream interpretation and clinical translation.

Adding sensitivity through blood-based biomarkers

While tissue-based insights are essential, they provide only part of the picture. Complementing spatial analysis with ultra-sensitive blood-based detection introduces another critical dimension.

“Simoa is a high-sensitivity, ultra-high-sensitivity digital ELISA platform,” says Jorge. “We are able to measure molecule by molecule(…)at very low levels of proteins in the bloodstream.”

This level of sensitivity enables the detection of biomarkers that may otherwise go unnoticed, supporting earlier insights into disease progression and treatment response.

Connecting tissue and blood for a fuller picture

Bringing these modalities together represents a fundamental shift toward more holistic cancer research. By linking tissue-based spatial data with biomarkers detected in biofluids, researchers can better understand how molecular changes within tumors relate to signals circulating throughout the body.

This multimodal strategy also supports longitudinal monitoring, offering the potential to track how a patient responds to treatment over time rather than relying on single-timepoint measurements. As Jorge explains, integrating these approaches “opens huge possibilities” for both research and diagnostics.

Looking ahead, this convergence could help bridge discovery and clinical care, enabling more dynamic and personalized treatment strategies.

Bridging discovery and clinical application

Despite rapid technological progress, translating research findings into clinical practice remains a significant challenge. Quanterix is addressing this through its Accelerator laboratory, which focuses on validating biomarker-driven assays in regulated environments and supporting their use in clinical trials.

“We take innovation from the biomarker space and bring this innovation into a validated regulatory environment(…)to enter into the clinical,” says Jorge.

A key part of this process is adopting a fit-for-purpose approach. While discovery platforms often prioritize depth and multiplexing, clinical diagnostics require simplicity, robustness, and reproducibility. As a result, researchers are increasingly working to identify focused biomarker signatures that can be translated into scalable and clinically viable tests.

As Ritu notes, “in research, more is better(…)but in clinic it’s about getting the right information.”

Advancing spatial biology toward clinical use

Although spatial biology continues to evolve rapidly, adoption in clinical settings presents challenges. Traditional pathology workflows remain heavily reliant on immunohistochemistry (IHC), and transitioning to multiplex immunofluorescence requires both technical validation and increased familiarity among clinicians.

“There’s a need to show that multi-immunofluorescence could be a good substitution for immunohistochemistry,” Jorge explains.

Progress is underway, with emerging applications demonstrating how spatial and multiomics approaches can support diagnostic and prognostic decisions. For example, combining spatial imaging with RNA-based analysis is already being explored for early cancer detection, highlighting the growing clinical potential of integrated technologies.

The importance of collaboration and community

Advances in cancer research do not happen in isolation. Collaboration between researchers, clinicians, industry, and patients remains essential for driving meaningful progress.

Ritu emphasizes the importance of engaging with the broader scientific community to both share knowledge and learn from ongoing research: “It’s not just about presenting solutions, it’s about building partnerships.”

Patient advocates are also playing a crucial role. By sharing their perspectives and experiences, they help shape research priorities and ensure that innovation remains focused on real-world impact, including improving access to diagnostics and enabling earlier detection.

Looking ahead: early detection and AI-driven insights

Looking to the future, both Ritu and Jorge highlight early detection as one of the most transformative opportunities in cancer research. The ability to identify disease before symptoms appear, potentially through a simple blood test, could fundamentally change patient outcomes.

“If you can bring spatial technology and ultra-sensitive technology to the same patient, you can really map the journey,” Ritu says.

Artificial intelligence is expected to play a central role in enabling this shift. As datasets become increasingly complex, AI-driven models will be essential for integrating multimodal data and uncovering patterns that would be difficult to detect using traditional approaches.

From predicting treatment response to modeling disease progression through concepts such as digital twins, AI has the potential to accelerate the move toward truly personalized medicine.

As Jorge puts it: “We are moving towards an approach where each patient is going to be a data point itself.”

A connected future for cancer research

The convergence of spatial biology, ultra-sensitive detection, and advanced data analysis is redefining how cancer is studied and treated. While challenges remain, the shift toward integrated, biologically driven approaches is clear.

By connecting insights across technologies, and across the research and clinical continuum, the field is moving closer to a future where every patient can be understood as a unique biological system, and where precision medicine becomes a practical reality.

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