Standardizing Spatial for Clinical Applications

Using automation to standardize tissue biomarker discovery and translation

“The more I learn, the more I realize how much I don’t know.”   --- Albert Einstein

Spatial biology may be considered the technological evolution of immunohistochemistry (IHC). Both techniques examine tissue specimens or biopsies fixed to microscopy slides to spatially identify biomolecules of interest, such as RNA transcripts, proteins, metabolites, or genes. However, contemporary spatial biology techniques enhance the sensitivity and number of simultaneous targets being investigated (multiplexing) by orders of magnitude. 

When it comes to drug development, scientists in the discovery phase can access many tools, while clinicians are mainly confined to more rigorously tested and classical IHC. Once you reach the clinic, it’s almost as if you’d prefer not to know what you don’t know.

But the times they are a-changin'.

Most specimens are preserved using the formalin-fixation and paraffin-embedding (FFPE) technique, which maintains morphology but masks molecular structures. Regardless of the tissue biomarker assay, FFPE samples generally require de-paraffinization (dewaxing) and deconjugating the formalin-mediated crosslinks to expose binding epitopes (antigen retrieval).¹ Fresh or flash-frozen samples circumvent this workflow. Once the tissue is more or less returned to its original state, it can be "stained," whereby fluorescent, hybridizing, or chromogenic-based probes are applied, washed, and fixed. Typically, FFPE processing and staining workflows are accomplished manually, requiring precise techniques and attention on behalf of the researcher.² The same researcher is also expected to analyze, interpret, and report the data.

We previously discussed the benefits of automation in fluid and tissue analyses. Here, we explore how automation supports the translation of discoveries into clinical practice and standardizes tissue biomarker tracking in trials.

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Automated Staining in Tumor Immunology Lab

Colt Egelston runs a tumor immunology laboratory at the City of Hope, which also serves as a spatial biology core lab, providing tissue biomarker services to City of Hope researchers and pharmaceutical companies. “Manual staining is a huge bottleneck,” he says of tissue-based immunology lab work. “An automated system is a great solution.”

Colt speaks highly of Opal-based protein multiplexing (enabled by the Akoya Biosciences PhenoImager platform), which enables his team to screen antibodies and gain more meaningful information by running up to eight antibodies simultaneously. Opal is a sequential staining approach (also known as a cyclic method) where each staining round targets a specific protein. This iterative process enables the detection of numerous biomarkers within a single tissue section, providing valuable insights into cellular heterogeneity and spatial relationships.

“Multiplex assays like Opal can provide a lot of clinical value—useful information on valuable patient samples,” says Egelston. Multiplexing is “particularly meaningful for Phase 1 trials. You can ask, ‘What changed?’ You can explore dose-escalation and subclinical biological changes. Providing that biological insight that will help inform clinical decisions in future cohorts.”

Egelston’s expertise in immuno-oncology therapeutic discovery in breast cancer is evident in his thoughts about how early trials set the stage for future trials. “You can inform potential combination therapies for a later-stage clinical trial,” he says of thoughtful, customized multiplexing experiments on patient tissue samples. “They provide real-time feedback, which shortens the lag time to impact the next exciting clinical trial.”

Accomplish more by automating

Adding a flexible automated system to an immunology lab enables trained staff to accomplish more with their limited time. “It’s like an extra pair of hands in the lab that doesn’t sleep at night,” describes Egelston.
Laboratory efficiency refers to the amount of data generated by one staff member within a specified time frame.³ Egelston is solving the bottlenecks in his lab by addressing laboratory efficiency with the newly installed Parhelia Spatial Station:

•    Less hands-on time spent manually staining slides
•    Increased agility by trying different protocols and molecular probes
•    Creating reporter plates for assays like CODEX (“Almost worth the price”)
•    Flexibility across different assays (e.g., flow cytometry and panel building)
•    More consistent data facilitates data processing

Standardize by automating

The clinical utility of tissue biomarkers has historically been limited to classical histology performed by traditional pathology labs using chromophores that identify only a handful of markers simultaneously.⁴ Modern multiplexed, fluorescent-driven “spatial biology” techniques have struggled to crack the clinical code. In fact, fluorescent microscopy biomarkers as clinical endpoints require validation of the targets by classical pathology approaches, even for time-tested methods like Opal (see webinar from November 2024). 

In Akoya’s context, customers can utilize the highly multiplexed spatial proteomics platform, PhenoCycler (formerly CODEX), to identify disease-specific signatures, such as those found in a biopsy of the tumor microenvironment. A high-throughput, specific multiplex panel is designed with Opal/PhenoImager chemistry and instrumentation, which is then used in the clinic. Validation of the multiplex panel using IHC techniques is required. For example, Akoya partnered with Acrivon to develop the four-panel Oncosignature predictive biomarker test, which identifies likely responders among patients with endometrial carcinoma who are enrolling in clinical trials of prexasertib, a CHK1/2 inhibitor.⁵ 

Intriguingly, both the CODEX and Opal slide processing and staining workflows are fully automated on the Spatial Station platform, providing a flexible, tunable, and consistent pathway to standardizing sample preparation from biomarker discovery to panel generation. A drug development lab can use the same instrument to process and stain preclinical or retrospective tissue samples for large-scale spatial proteomics and high-throughput predictive biomarker measurement, regardless of the chemistry or downstream microscopy methodology. 

Egelston’s lab was instrumental in adopting the Opal methodology to automation on the Spatial Station. “Automating staining should give more consistent staining patterns, which leads to more consistent staining for phenotyping analysis,” he said of his motivation for automating the Opal workflow.

While fluorescent technologies that enable higher sensitivity and multiplexing are the wave of the future, Egelston still appreciates the “relatively cheap approach” of classical IHC methods with FFPE tissues. “There’s a simplicity and beauty to it,” he says. 

Submit to the robots

When adopting modern spatial technologies for drug development, a significant factor to consider is the likelihood that regulatory agencies will accept and approve the resulting biomarker endpoints. Standardized workflows incorporating validated automated systems and single-use kits for routine processing will facilitate IND and first-in-human drug submissions. 

Government agency consortia are already underway to standardize other critical bioanalysis technologies, including the Flow Cytometry Standards Consortium, spearheaded by the National Institute of Standards and Technology (NIST). As spatial techniques advance toward clinical applications, technology and assay developers, clinicians, and regulators must reach a consensus on acceptable validation standards. Like the flow cytometry consortium, a public-private partnership will likely develop, incorporating rigorous testing and expert proceedings.  

Meanwhile, the early adoption of automation in tissue biomarker workflows will continue to facilitate the clinical translation of spatial techniques, benefiting patients and precision medicine.

References:
1.    Ramos-Vara JA. Principles and Methods of Immunohistochemistry. In: Gautier JC, ed. Drug Safety Evaluation, Methods and Protocols. Vol 1641. 2nd ed. Humana Press Inc.; 2017:115-128. doi:10.1007/978-1-4939-7172-5_3
2.    Donovan ML, Jhaveri N, Ma N, et al. Protocol for high-plex, whole-slide imaging of human formalin-fixed paraffin-embedded tissue using PhenoCycler-Fusion. STAR Protoc. 2024;5(3). doi:10.1016/j.xpro.2024.103226
3.    More D, Khan N, Tekade RK, Sengupta P. An Update on Current Trend in Sample Preparation Automation in Bioanalysis: strategies, Challenges and Future Direction. Crit Rev Anal Chem. Published online 2024. doi:10.1080/10408347.2024.2362707
4.    Angerilli V, Galuppini F, Pagni F, Fusco N, Malapelle U, Fassan M. The Role of the Pathologist in the Next-Generation Era of Tumor Molecular Characterization. Diagnostics 2021, Vol 11, Page 339. 2021;11(2):339. doi:10.3390/DIAGNOSTICS11020339
5.    Gouveia MC, Neto FL, Trento M, et al. 744P A phase II study of ACR-368 in patients with ovarian (OvCa) or endometrial carcinoma (EnCa) and prospective validation of OncoSignature patient selection (NCT05548296). Annals of Oncology. 2024;35:S564-S565. doi:10.1016/J.ANNONC.2024.08.805

Geoffrey Feld, Ph.D, Founder & Principal Consultant of Geocyte LLC, is a freelance scientific communicator, writer, marketing and sales strategist, and consultant. His company, Geocyte, specializes in developing tailored science-backed narratives and content for solutions providers in the life science industry, i.e., companies whose products make R&D faster and better. Geocyte's clients span the “multi-omic" universe, from fluid biomarkers to spatial biology lab automation. Geoff received his Ph.D. in biophysical chemistry from the University of California, Berkeley, and conducted postdoctoral research in structural biology at the Lawrence Livermore National Laboratory (Livermore, CA) and the National Institute of Environmental Health Sciences (RTP, NC). He is passionate about personalized medicine and nutrition, which are fueled by molecular and functional biomarkers and a healthy microbiome.