What ASCO Taught Me About the Future of Pathology – Part 2

by Rajendra Singh MD
Professor of Pathology, University of Pennsylvania
Co-Founder, PathPresenter

TROP2, Tissue, and the Missing Spatial Dimension

Last week, I wrote that the future of pathology would not be defined by any single technology—not AI, not molecular testing, and not digital pathology alone. Rather, the future belongs to those who can read tissue morphology, imaging, molecular biology, and clinical outcomes as a single integrated story rather than four separate chapters.

The discussions that followed were illuminating. Conversations with radiologists, oncologists, data scientists, and colleagues across the pharmaceutical industry reinforced something I had already begun to suspect: many of the most important questions in precision medicine no longer sit comfortably within the traditional boundaries of a single specialty.

As I reflected on the clinical data presented at ASCO this year, one story stood out because it illustrates exactly where this convergence may be heading. That story is TROP2.

Why TROP2 Matters: The Architecture of an ADC

For those not immersed in oncology drug development, TROP2 (trophoblast cell-surface antigen 2) is a transmembrane glycoprotein expressed across a wide range of epithelial malignancies, including lung, breast, bladder, and endometrial carcinomas. It has become a major target in oncology because it serves as an address for a rapidly expanding class of therapies known as antibody-drug conjugates (ADCs).

The concept is elegant. An antibody binds a target on the cancer cell, carries a highly potent cytotoxic payload, and delivers that payload into the cell following binding and internalization. In practice, many ADCs—including TROP2-directed therapies—also exert a “bystander effect,” where payload can diffuse into neighboring cells, making therapeutic activity less dependent on perfectly uniform target expression.

At ASCO, clinical data across multiple programs continued to reinforce the potential of this strategy. Early-phase and emerging late-phase results suggest meaningful clinical activity for TROP2-directed ADCs, including in combination with immunotherapy in selected settings. With multiple global trials underway across tumor types, this represents one of the most active areas of drug development in oncology today.

There is clear biology here—and real benefit for patients who respond. Yet as these data accumulate, a fundamental question becomes harder to ignore: how do we determine which patients will benefit most? The answer may expose a broader limitation in how we currently define biomarkers.

What the H-Score Misses

Today, our approach to TROP2 remains centered on expression. Pathologists stain tissue, evaluate intensity and extent, and report a score. Computational approaches have improved on this by quantifying expression more reproducibly and, in some cases, distinguishing subcellular localization.

These are meaningful advances. But they are still asking the same core question: What is the biology of TROP2?

At the microscope, the question often feels different: What is the biology surrounding TROP2?

A biomarker is never seen in isolation. Tumor cells exist within a structured environment: immune cells attempting to infiltrate, fibroblasts shaping stromal architecture, and physical barriers organizing the tumor into compartments. The tumor–stroma interface—the boundary between malignant epithelium and host response—is often where critical biology resides.

Experienced pathologists recognize these patterns intuitively. When I am teaching residents, I remind them that the tissue is often smarter than the pathologist. If the stroma is walling something off, that reaction is telling you something. The interface between tumor and host is rarely silent. Yet much of this contextual information is lost when complex tissue architecture is reduced to a single expression score.

That raises an important possibility: What if response to TROP2-directed therapy depends not only on target expression, but also on the biological context in which that target exists?

The Missing Spatial Dimension: An Ecosystem Approach

Consider the immune microenvironment.

A tumor with high TROP2 expression but minimal immune infiltration may behave very differently from one with the same expression profile embedded within an organized immune response. The molecular target may be identical, but the surrounding ecosystem is not.

Preclinical and early clinical observations suggest that ADC activity may interact with the tumor microenvironment in ways that extend beyond direct cytotoxicity. If those interactions contribute to clinical response, then baseline tissue architecture—including immune organization—becomes an important variable that current biomarker strategies only partially capture.

This concept is not new. In colorectal cancer, the Immunoscore demonstrated that the spatial distribution of immune cells—particularly at the invasive margin—has strong prognostic value and has influenced how we think about tumor–immune interactions more broadly.

Yet we have only begun to ask whether similar spatial features influence response to ADCs such as those targeting TROP2.

Even at a purely physical level, tissue architecture may matter. A TROP2-high tumor with dense stromal compartmentalization could experience very different drug delivery and payload diffusion dynamics than a tumor with a more permissive structure. These are measurable features—but they are rarely incorporated into current biomarker frameworks.

This points toward a broader realization: we may have spent years searching for answers in individual molecules when the more informative signal, in some settings, lies in the relationships between them—their spatial arrangement, biological context, and interaction within a tissue ecosystem.

Beyond the Slide

The second observation that stayed with me after ASCO is that biology does not operate at a single scale.

Tumors do not live on glass slides. They exist within tissues, tissues within organs, and organs within patients. It follows that response to therapy is shaped across all of these levels simultaneously.

Some relevant features are visible histologically. Others emerge through imaging, molecular profiling, or longitudinal clinical data. Increasingly, computational approaches are making it possible to integrate these layers.

The central question for precision oncology is no longer which modality is most informative in isolation. It is whether these modalities are capturing different dimensions of the same biological system—and whether we are building the infrastructure to interpret them together.

For decades, medicine has organized these data into separate workflows, databases, and specialties. Biology never made those distinctions.

The Bigger Lesson

TROP2 is not the story. It is a case study.

For the past two decades, precision medicine has focused on identifying the right molecule—and that approach has transformed cancer care. But in some contexts, the molecule alone may not be sufficient to explain therapeutic response.

Tumors exist within tissues. Tissues exist within organs. Organs exist within patients. Every level contains information. Every level influences outcome.

This is why pathology is becoming more important—not less—in the era of AI and advanced molecular diagnostics. Not because it replaces other disciplines, but because it is inherently grounded in biological context.

The role of the pathologist is evolving accordingly. It is no longer limited to interpreting a slide or assigning an expression score. It increasingly involves connecting tissue architecture, molecular data, imaging, and clinical outcomes into a more complete model of disease.

That integration is beginning to take shape. The groups that build this connective infrastructure first will be positioned to answer some of the most important biomarker questions in oncology.

The tissue has been telling us this story for years. Imaging has been telling the same story from a different vantage point. Molecular data and clinical outcomes add additional layers. For most of modern medicine, we have read these stories separately.

We are finally learning how to read them together.

About the Author

Dr. Rajendra Singh is a Professor of Pathology at the University of Pennsylvania and co-founder of PathPresenter. He serves as a member of the Digital and Computational Pathology Committee of the CAP, Editorial Board of the WHO for Classification of tumors, 5th Edition and the Board of Digital Pathology Association.