Transforming Biomarker Discovery with Spatial Biology

Biomarker discovery in cancer is challenging due to data complexity, inherent tumor heterogeneity, and technological limitations. Spatial biology offers a transformative solution by allowing researchers to study cancer biology in situ, identify biomarkers, and evaluate treatment responses with greater precision. This article will review various spatial biology approaches that are advancing the discovery of biomarkers predicting responses to cancer immunotherapy.

Current challenges

Biomarker discovery in cancer immunotherapy faces significant challenges, including the complexity of the human proteome and variable protein expression. One of the key challenges is ensuring that potential biomarkers identified at the transcript level translate into functional proteins, as most current immunotherapies target proteins. Tumor heterogeneity further complicates biomarker discovery, as varying cell phenotypes and functions within the tumor microenvironment can impact disease progression and therapy response. Logistical challenges, such as limited patient samples, high costs of large-scale validation, and the need for scalable technologies, also play a significant role.

Spatial biology, with its ability to capture cell locations and interactions within the tissue microenvironment, can better inform our understanding of disease pathogenesis and drug-resistance mechanisms. Below, we explore various spatial biology approaches that are transforming biomarker discovery by overcoming these barriers.

Visualizing up to 40+ markers at once

Identifying quantifiable, reliable biomarkers correlated with disease outcomes requires fast, sensitive, and targeted data generation. Imaging Mass Cytometry™ (IMC™) by Standard BioTools™ overcomes challenges related to low-parameter spatial proteomics, such as cycling, fluorescent overlap, and limited marker coverage, increasing the likelihood of identifying relevant biomarkers. “IMC is the only high-parameter spatial proteomics technology that can measure up to 40+ protein markers simultaneously,” explains Jennifer Ellis, Director of Scientific Content at Standard BioTools. “This enables visualization of all markers at once, without cyclic detection, helping to better identify meaningful signatures, unlock predictive responses, and define mechanisms in complex immunotherapies. This high-coverage approach is critical when analyzing tumor reduction, immune cell infiltration, and the absence of checkpoints.”

IMC avoids autofluorescence issues by using metal tags conjugated to antibodies that are then detected via time-of-flight mass spectrometry. This approach also provides the quantitative analysis needed for targeted biomarker discovery from mass detection, which improves the ability to associate biomarkers with outcomes, to more in-depth statistical analysis to investigate on- and off-target effects. “We are advancing the field for quantitative biomarkers that can clarify mechanisms and patient stratification, bringing us closer to true human in vivo biology versus genomics,” adds Ellis.

Thanks to high sensitivity and lack of background issues, IMC can detect low-abundance proteins that might be missed with fluorescence-based systems. Additionally, the latest generation IMC system, Hyperion XTi, offers rapid whole-slide imaging flexibility and integrated automation of up to 40 slides within 24 hours.

“These advantages allow researchers to identify predictive biomarkers by analyzing a broader range of proteins and visualizing more markers in context, facilitating more accurate biomarker prediction,” says Ellis. “IMC also improves efficiency by detecting all markers simultaneously and uncovering unique content using different imaging modes depending on study goals.”

Spatial proteomics with IMC, designated for research use only (RUO), has been used in several clinical trials to characterize checkpoint therapy in melanoma¹ and breast cancer,² as well as to identify predictors of immunotherapy response in triple-negative breast cancer.³

Maximizing the available imaging area

Canopy’s CellScape Precise Spatial Multiplexing platform aids biomarker discovery by providing in situ detection of 60+ proteins on the same sample with higher dynamic range and subcellular resolution compared to immunohistochemistry. This allows researchers to identify cell types, their functional states, and cell-to-cell interactions, along with their broader spatial relationships within the tissue biopsy.

“CellScape maximizes the available imaging area on standard microscope slides using our Whole-Slide Imaging Chamber (WSIC) technology”, says Benton Berigan, Ph.D., Product Manager for Spatial Biology at Canopy Biosciences. “This technology creates a microfluidic chamber with minimal volume and uses a safe, long-term sample storage buffer to preserve slides after staining and imaging. To facilitate multiplex fluorescence imaging, CellScape uses filtered photobleaching to eliminate the fluorescence signal while protecting the tissue and epitopes from ultraviolet and infrared exposure, thus allowing the sample to be used for downstream workflows that provide additional insights.”

Importantly, CellScape allows researchers to collect initial results from staining and imaging, then analyze the data to generate new hypotheses and re-stain original samples for additional biomarkers. “This ability to store and re-stain samples over time is crucial when you have precious samples and need more time to analyze data, collect input from collaborators, or wait for new antibodies,” highlights Berigan.

In a 2024 study, researchers at Magee-Womens Research Institute and the University of Pittsburgh School of Medicine used CellScape to identify a rare population of antibody-secreting B cells residing within the tumor-infiltrating lymphocyte population in epithelial ovarian cancer.⁴ This novel cell population provides unique insights and opportunities to study intratumor B cell biology and new target antigen recognition.

Seamless integration from discovery to clinical applications

Akoya Biosciences’ spatial biology solutions support the entire biomarker development continuum, from early discovery to translation and clinical applications. “At the discovery stage, researchers can start with a defined set of content, such as an immune core panel to phenotype the major immune cell subsets in your tissue,” says Clemens Duerrschmid, Ph.D., Technical Applications Scientist at Akoya Biosciences.

“The flexibility of Akoya’s PhenoCycler^®-Fusion platform allows scientists to seamlessly integrate newly identified targets from prior unbiased screens. Its high-plexing capability, enabling the simultaneous imaging of over 100 protein markers, allows researchers to rigorously assess and validate the most critical targets. This approach provides relevant, meaningful, and actionable information for downstream targeted studies.”

As studies progress into translational and clinical research, where the objective is to test and validate discoveries across numerous samples, cost and scalability become important considerations. “A novel biomarker may appear in a screen of a small cohort of patients or samples, but for clinical application, it needs to be validated over a large group of samples—tens to hundreds or even more patients,” explains Duerrschmid. “Our PhenoImager^® HT platform facilitates a cost-effective and rapid way to simultaneously measure the most critical protein biomarkers across hundreds of tissue samples for large-scale translational and clinical research studies. This seamless integration from discovery to clinical applications ensures consistent and reliable results across various research phases, providing a unique advantage in the scalability of the workflow.”

Using Akoya’s PhenoImager^® HT platform, Antoine Italiano’s group at Institut Bergonié, Bordeaux, France, together with Explicyte Immuno-Oncology conducted a large-scale retrospective study that found mature tertiary lymphoid structures (TLS) in tumor samples to be predictive of positive outcomes in cancer patients treated with immune checkpoint inhibitors.⁵ This study highlights the potential of TLS as a predictive biomarker for identifying patients likely to respond to immune checkpoint therapy across various solid tumors, including non-small cell lung cancer, soft tissue sarcoma, bladder cancer, colorectal cancer, head/neck carcinoma, and renal carcinoma.

“Spatial biology brings together not only the ‘what’ or the ‘who’, but also the crucial ‘where’, providing the critical aspect of spatial context,” emphasizes Duerrschmid. “This is significant because very often, biomarkers in immuno-oncology are not just isolated to a ‘yes’ or ‘no’ answer—like cell density or the presence of a different cell type or different protein—but include information on how these elements are interconnected.”

References

1. Martinez-Morilla, S., Villarroel-Espindola, F., Wong, P. F., Toki, M. I., Aung, T. N., Pelekanou, V., Bourke-Martin, B., Schalper, K. A., Kluger, H. M., & Rimm, D. L. (2021). Biomarker Discovery in Patients with Immunotherapy-Treated Melanoma with Imaging Mass Cytometry. Clin Cancer Research, 27(7), 1987–1996.

2. Olea J, Dadrastoussi H, Hernandez EF, et al. (2023). Analysis of the HR+/HER2- breast cancer tumor microenvironment following immune priming with pelareorep and atezolizumab using imaging mass cytometry. Results from the AWARE-1 trial. J ImmunoTher Cancer 11: 

3. Wang, X.Q., Danenberg, E., Huang, CS. et al. (2023). Spatial predictors of immunotherapy response in triple-negative breast cancer. Nature 621, 868–876. 

4. Zhang, L., Strange, M., Elishaev, E., Zaidi, S., Modugno, F., Radolec, M., Edwards, R. P., Finn, O. J., & Vlad, A. M. (2024). Characterization of latently infected EBV+ antibody-secreting B cells isolated from ovarian tumors and malignant ascites. Frontiers in Immunology, 15, 1379175. 

5. Vanhersecke L et al. (2021). Mature tertiary lymphoid structures predict immune checkpoint inhibitor efficacy in solid tumors independently of PD-L1 expression. Nat Cancer. 2(8):794-802.