Spatial Biomarkers in Latest Drug Discovery and Beyond

By EVOBYTE Your partner in bioinformatics

Introduction

Pathology has always been spatial. We diagnose disease by looking at where cells live, how they interact, and what surrounds them. Spatial biomarkers turn that intuition into quantifiable features you can measure, reproduce, and use to guide decisions. Thanks to spatial omics—methods that retain tissue architecture while profiling RNA or proteins—we can now compute those features at scale and link them to outcomes. That’s changing how we discover drug targets, stratify patients, and design trials.

Spatial biomarkers, defined—with examples

A spatial biomarker is any measurable feature that encodes not just “what” is expressed, but “where”—for example, the distance between CD8+ T cells and tumor cells, the density of PD-L1–high niches, or the presence of tertiary lymphoid structures in the tumor microenvironment (TME). Formally, these arise from quantifying spatial relationships of cells or targets within intact tissue, such as cell–cell proximity, neighborhood composition, or gradients across regions. In immuno-oncology, simple metrics like “T cell–tumor proximity” or “immune-excluded vs infiltrated” patterns already correlate with checkpoint inhibitor response and survival. In other words, position becomes signal.

Here’s a mental picture: two NSCLC biopsies show similar total CD8 counts. In one, CD8 cells sit at the tumor edge; in the other, they intermix with cancer cells. The totals match, but the spatial biomarker differs—and so does therapy response. Quantifying that difference is the essence of spatial biology for precision medicine.

Spatial omics technologies powering discovery

Several platforms make spatial biomarkers measurable. Spatial transcriptomics (ST) maps whole-transcriptome patterns across a tissue section, typically at “multi-cell” spot sizes, while imaging-based assays capture hundreds to thousands of targets at or near single-cell resolution. You’ll often hear terms like Visium (array-based ST), GeoMx DSP (ROI-based profiling that can combine RNA with high-plex protein), and in situ systems like Xenium, MERFISH, or CosMx that image transcripts at subcellular scales. Each trades off gene plex, sensitivity, and resolution; for unbiased discovery of new niches, whole-transcriptome approaches are attractive, while targeted imaging is powerful for validating single-cell neighborhoods.

Practically, samples and study design matter. FFPE tissue dominates clinical research; ROI selection can boost throughput when tissue is limited; and true single-cell resolution may require imaging-based methods. Many teams pair bulk or scRNA-seq for target nomination with spatial assays for validation, then define the biomarker in terms of proximity, neighborhood enrichment, or region-specific expression.

A quick example with Python shows how analysts extract a simple neighborhood biomarker from ST data using Squidpy. It computes whether certain clusters (e.g., “CD8_T”) are enriched next to others (“Tumor”):

import squidpy as sq
adata = sq.datasets.visium_fluo_adata()
sq.gr.spatial_neighbors(adata)
sq.gr.nhood_enrichment(adata, cluster_key="cluster")  # z-scores of co-localization

This converts “cells near other cells” into a z-score—exactly the kind of spatial biomarker you can correlate with outcome.

The state of research and industry relevance

By 2025, spatial biology has moved from niche to necessary. Reviews across oncology highlight spatial transcriptomics for decoding tumor heterogeneity, metastasis, and therapy responsiveness, while methodological papers push better segmentation, deconvolution, and spatial statistics. Notably, spatial analysis now routinely integrates single-cell data to improve domain calling and infer interactions, and it’s spreading beyond cancer into neuroscience and inflammation.

Why does this matter to industry? Because spatial biomarkers frequently out-perform bulk or single-cell-only readouts when tissue architecture drives biology. For target ID, they reveal cell–cell circuits and microenvironmental constraints; for lead optimization, they show whether a mechanism reaches the right neighborhoods; for clinical strategy, they enable patient stratification by spatial phenotypes rather than expression levels alone. End-to-end, the field is converging on best practices for cohort-scale spatial studies—especially with digital spatial profiling (DSP) when tissue is scarce—so programs can scale beyond single slides to reproducible evidence.

A tiny DIY distance metric illustrates another common pattern (average T cell distance to tumor):

import numpy as np
# xy coordinates for tumor and T cells
d = np.sqrt(((T_xy[:,None,:] - Tumor_xy[None,:,:])**2).sum(-1)).min(axis=1)
avg_min_distance = d.mean()  # lower = more infiltration

Turn that number into a decision rule, validate across cohorts, and you have a spatial biomarker.

Summary / Takeaways

Spatial biomarkers quantify where biology happens. With spatial omics—ST, DSP, IMC/mIF, and in situ imaging—you can measure proximity, neighborhoods, and region-specific signals that bulk or dissociated assays miss. These features now inform target discovery, mechanism-of-action studies, and patient selection, especially in the TME. If you’re planning a study, define the clinical question first, pick the platform that fits your sample constraints (FFPE vs fresh, whole-transcriptome vs targeted), and pre-register how you’ll compute the spatial biomarker—distance, neighborhood enrichment, or region score—before you see the outcome data.

Keywords to know and why they matter: spatial biomarker (translatable decision features), spatial omics (platform ecosystem), spatial transcriptomics/ST (RNA maps for unbiased discovery), DSP (high-plex profiling from limited tissue), IMC and mIF (single-cell protein maps), TME (therapy-relevant context), FFPE (clinical sample compatibility), ROI (practical study design), neighborhood enrichment and Ripley’s K/L (core spatial statistics). These terms anchor reproducible analyses and cross-team communication in modern drug discovery.

Further Reading

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