Dr. Mihnea-Paul Dragomir Dr. Fabian Coscia
Charité – Universitätsmedizin Berlin Max Delbruck Center (MDC)
The following interview discusses how spatial proteomics and antibody-based target validation advance early cancer detection.
Q: Your study used spatial proteomics to investigate early changes in high-grade serous ovarian cancer. What was the main biological question?
Mihnea-Paul Dragomir: From a proteomic perspective, the precursors of high-grade serous ovarian cancer, termed serous tubal intraepithelial carcinoma or STIC, had not been explored comprehensively at cohort-scale. We wanted to understand how the proteomic makeup of STIC compares to normal fallopian tube and to high-grade serous ovarian cancer. Importantly, we did not limit the analysis to the epithelial compartment alone, but also analyzed the stroma.
Q: Fabian, your lab develops and applies mass spectrometry-based proteomics workflows for spatially resolved systems biology. How does this ovarian cancer precursor study fit into the broader vision of your group?
Fabian Coscia: I have worked on ovarian cancer throughout my career, starting with bulk tissue proteomics together with the Lengyel laboratory at the University of Chicago, where we could identify drivers of chemotherapy response at the level of whole tissue samples. Over time, it became clear to me that to truly understand this disease, we need to know not only which proteins are altered, but where these changes occur, in which cell populations, and how they relate to the surrounding tissue architecture. That transition from bulk tissue proteomics to spatial proteomics is now at the heart of my current group. Ovarian cancer precursor lesions are an especially important setting for this approach, because early disease-related changes may be highly localized and easily missed when tissue is analyzed in bulk. By studying these lesions with spatially resolved proteomics, we can begin to identify molecular programs that emerge during the earliest steps of tumor development and place them directly within their histological and cellular context.
Q: What did spatial proteomics allow you to see that would have been difficult to capture otherwise?
Mihnea-Paul Dragomir: Spatial proteomics allowed the team to study protein changes in the tissue context of the disease. Instead of looking only at bulk tissue, the approach made it possible to compare different compartments and disease stages, helping to identify early dysregulated pathways and potential therapeutic targets.
Q: One finding was the upregulation of cholesterol biosynthesis enzymes, including DHCR7. Why was antibody-based validation important?
Mihnea-Paul Dragomir: After discovering by mass spectrometry that DHCR7 was upregulated in STIC and high-grade serous ovarian cancer, we wanted to validate the target orthogonally in tissue samples. In this case, we expected the protein to be overexpressed in the cytoplasm and to be homogeneously expressed. More broadly, when moving from discovery proteomics to biological interpretation, immunohistochemistry provides subcellular resolution, especially in the hands of a pathologist.
Q: What do reliable, well-characterized antibodies add in translational cancer research?
Mihnea-Paul Dragomir: Reliable antibodies add confidence when researchers move from candidate discovery to biological interpretation. Knowing which antibody is most suitable, what staining pattern to expect, whether expression is homogeneous or heterogeneous, and what the subcellular localization should be saves time. It allows researchers to focus on making biological discoveries rather than establishing IHC protocols from scratch.
Q: Do you use the Human Protein Atlas as a resource in your work?
Mihnea-Paul Dragomir: Yes. We always use the Human Protein Atlas to check the expression of a target of interest in different tissues, cells and diseases; to find the best antibody when planning IHC; and to check expression in cancer cell lines in order to select the best models for in vitro studies.
Q: How does Deep Visual Proteomics change what researchers can ask in tumor biology?
Fabian Coscia: Deep Visual Proteomics allows us to study tumors in a spatially and cell-type-specific way. Instead of averaging signals across a whole tissue sample, we can now ask what is happening in very precise regions of a tumor, and even in specific cell populations within their native tissue context. This is important because tumors are highly heterogeneous: neighboring cells can behave very differently, respond differently to therapy, or interact with the surrounding microenvironment in distinct ways. By combining imaging, AI-guided cell recognition and highly sensitive proteomics, the approach makes it possible to connect what we see under the microscope with the underlying protein programs that drive tumor behavior.
Q: Why is it important to study proteins in their spatial tissue context?
Fabian Coscia: Proteins do not act in isolation. Their function often depends on where they are located, which cells they are in, and which neighboring cells they interact with. In tumor biology, this spatial context is especially important because cancer tissue is highly heterogeneous: tumor cells, immune cells, stromal cells and blood vessels can all occupy distinct niches and influence each other locally. Studying proteins directly in their spatial tissue context helps bridge this gap. It allows us to connect molecular changes to the architecture of the tumor and to understand how local microenvironments may shape disease progression, immune responses or therapy resistance.
Q: Looking ahead, where is spatial proteomics heading?
Mihnea-Paul Dragomir: The most exciting question is the analysis of big data with AI: using these tools to discover patterns we could not see before. From a pathology perspective, the future is to integrate this type of tool into routine practice to improve diagnosis, prognosis and quantification of predictive markers.
Fabian Coscia: Spatial proteomics is moving toward a much more integrated view of tissue biology. Clinically, the long-term goal is to turn this spatial information into better biomarkers and more precise treatment strategies—identifying which tumor niches drive progression, immune escape, or therapy resistance, and which patients are most likely to benefit from a particular treatment.
Featured Target Validation Product
Anti-DHCR7 Primary Antibody (HPA044280)
Validate spatial proteomics candidate targets with Triple A Polyclonals™ developed through the Human Protein Atlas project.
Target Protein: 7-Dehydrocholesterol Reductase (DHCR7)
Tested Applications: IHC, IF/ICC
Host / Reactivity: Rabbit / Human
Validation Profile: Characterized through the Human Protein Atlas project across normal human tissues and independently validated for IHC in fallopian tube precursor lesions (STIC) and high-grade serous ovarian cancer (HGSOC).
