Single‑Cell Genomics in Oncology: From MOA to In Vivo Studies

By EVOBYTE Your partner in bioinformatics

Introduction

Every solid tumor is a crowded city. Bulk assays average the noise from millions of residents; single‑cell genomics hands you the keys to each apartment. That shift—seeing individual cell states rather than population averages—has changed how oncology programs discover targets, design therapies, and validate mechanism of action (MOA). It is not just a better microscope. It is a different playbook for picking winners earlier and rescuing promising assets before resistance takes hold.

In this post, we’ll unpack how single‑cell RNA sequencing (scRNA‑seq) and single‑cell multiomics help you map the tumor microenvironment (TME), delineate immune components, identify druggable cell populations, and follow drug phenotypes in vivo. Along the way, we’ll look at examples from checkpoint therapy, targeted therapy, and cell therapies, and we’ll finish with a practical way to weave single‑cell readouts into your next preclinical study.

Why “cell states” matter more than averages in tumors

Oncology trials often fail not because the target is absent, but because the target is present in the wrong cells—or present in the right cells at the wrong state. “Cell state” here means the transient program a cell is running: activated, quiescent, stressed, proliferative, or exhausted. Single‑cell genomics reads those programs directly. When you zoom in, it becomes clear why two patients with the same driver mutation diverge: one harbors drug‑tolerant persisters; the other hosts immunologically “hot” niches where therapy can take root.

A flagship example comes from checkpoint immunotherapy. Single‑cell profiling of melanoma tumors showed that a memory‑like CD8 T‑cell state marked by the transcription factor TCF7 predicted response to PD‑1 blockade, while other exhausted states correlated with progression. That one state, detectable before treatment, helped explain why seemingly similar tumors behave differently under the same drug. Such cell‑state biomarkers now anchor many translational plans for immunotherapy.

Mapping the tumor–immune ecosystem to find druggable opportunities

If cell states are the scripts, the TME is the stage. scRNA‑seq can separate malignant cells from cancer‑associated fibroblasts, endothelial cells, myeloid subtypes, and diverse T‑ and B‑cell lineages, then reveal how they talk to each other. That matters because resistance is rarely a solo act. Macrophages can suppress T cells; fibroblasts can create physical and cytokine barriers; tumor cells can upregulate alternative survival pathways.

Clinical case work has already shown how single‑cell readouts can redirect therapy. In a chemorefractory bladder cancer patient, single‑cell profiling of the tumor and matched patient‑derived xenografts before and after an HRAS‑targeting agent revealed PD‑L1 upregulation in the surviving tumor cells and expansion of immunosuppressive subsets. Based on those cell‑type‑specific changes, the team added a PD‑L1 inhibitor, and the patient responded. That is target identification and rational combination design—grounded in cell‑resolved data rather than bulk averages.

As methods matured, reviews synthesizing dozens of datasets have mapped the tumor immune microenvironment (TIME) in depth, documenting reproducible states of exhausted, progenitor‑exhausted, and cytotoxic T cells; immunosuppressive myeloid programs; and tertiary lymphoid structures that predict response. These atlases double as shopping lists for drug discovery: they enumerate which levers exist in which cells and suggest where a therapy should act first.

A short, practical pattern many translational teams use is simple but powerful: quantify where a candidate’s target actually lives. With scRNA‑seq and protein tagging (for example, CITE‑seq, which couples transcriptomes with surface proteins), you can confirm antigen density, co‑expression of escape markers, and proximity to effector cells—before you invest in expensive in vivo work.

Here’s a tiny example using Scanpy to estimate target expression across annotated TME cell types and flag co‑expression of potential resistance markers.

import scanpy as sc
import pandas as pd

adata = sc.read_h5ad("tumor_scRNAseq.h5ad")       # pre-annotated cell types in adata.obs['cell_type']
targets = ["PDL1", "EGFR"]                         # gene symbols to inspect
resistance = ["MCL1", "AXL"]

sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)

by_type = (adata.to_df()[targets + resistance]
           .join(adata.obs["cell_type"])
           .groupby("cell_type").mean().round(2))

print(by_type.loc[["Malignant", "T cell", "Macrophage"]])

In a few lines, you learn which compartments truly express your target and whether they already co‑express survival pathways that could blunt your drug.

From target to patient: selecting the right cell population and biomarkers

Single‑cell genomics also clarifies “who” your drug should hit and “how” you’ll know it did. For small molecules and biologics, you can quantify on‑target engagement by the transcriptional programs that collapse (or ignite) in the intended cells. For cell therapies, you can characterize engineered products at baseline and after antigen exposure. Multiomic single‑cell readouts—combining mRNA, surface proteins, TCR clonotypes, and even chromatin accessibility (scATAC‑seq)—help you confirm that the right cells are being activated and expanded.

For CAR‑T, single‑cell multiomics has characterized basal and antigen‑stimulated states, revealing tonic signaling, activation heterogeneity, and differences between autologous and allogeneic products. These profiles help teams pick manufacturing protocols, select co‑stimulatory domains, and define release criteria that better predict in‑patient performance.

And beyond the product itself, longitudinal single‑cell atlases built from patient samples have charted how endogenous immune cells and residual tumor evolve after CAR‑T infusion, pointing to stress response pathways in relapsed disease and exhaustion programs in T cells that presage failure. Those are not just retrospective curiosities; they are starting points for the next combination or the next construct.

Tracking mechanism of action and resistance in vivo

Once a program enters efficacy studies, the most valuable question becomes simple: did the drug do what we said it would, in the cells we cared about, inside a living tumor? Single‑cell genomics, now frequently paired with spatial transcriptomics, turns that into a measurable endpoint.

In non‑small cell lung cancer, paired single‑cell and spatial analyses after neoadjuvant chemo‑immunotherapy have shown how the TME remodels, including formation of tertiary lymphoid structures and re‑wiring of myeloid states. These maps connect a treatment to structural changes in the tissue, not just changes in bulk gene signatures, which helps bridge from preclinical models to human biology.

On the preclinical side, time‑resolved single‑cell methods have pushed pharmacodynamics into the domain of hours and days. For example, a metabolic labeling approach captured nascent RNA in vivo and revealed chemotherapy‑induced dynamics inside tumor‑infiltrating lymphocytes that standard methods missed, including rapid changes in costimulatory and exhaustion genes. When a candidate claims to “re‑energize” T cells, these are the kinds of kinetic fingerprints that can validate—or falsify—the hypothesis.

Here is a minimal recipe to quantify whether a therapy decreased T‑cell exhaustion and increased cytotoxicity in a mouse model. It scores cells against literature‑based gene sets, then compares pre‑ and post‑treatment distributions in the T‑cell compartment.

from collections import defaultdict
import scanpy as sc
import numpy as np

adata = sc.read_h5ad("pd_study.h5ad")              # obs['condition'] in {"pre","post"}; obs['cell_type'] has "T cell"
exhaustion = ["PDCD1","LAG3","HAVCR2","CTLA4","TIGIT"]
cytotoxic = ["GZMB","PRF1","NKG7","GNLY"]

t = adata[adata.obs["cell_type"]=="T cell"].copy()
for name, genes in {"exhaustion":exhaustion,"cytotoxic":cytotoxic}.items():
    sc.tl.score_genes(t, gene_list=genes, score_name=f"{name}_score")

summary = t.obs.groupby("condition")[["exhaustion_score","cytotoxic_score"]].mean().round(3)
print(summary)

With a few dozen animals and serial sampling, this kind of readout becomes a robust PD endpoint, aligned to MOA and informative about resistance.

A quick example: reading a cell therapy’s footprint with single‑cell data

Consider a BCMA‑targeted CAR‑T program for multiple myeloma. Longitudinal single‑cell profiling across marrow and blood has shown that, even after initial responses, relapsing tumor cells upregulate stress and unfolded protein response pathways, while endogenous T‑cell compartments drift toward exhaustion. If your next‑gen CAR aims to counteract this drift—say, by armored cytokine support or a transcriptional re‑wiring module—then single‑cell assays become your best early detector. You can demonstrate that the engineered product expands into the right activation state, that niches formerly dominated by suppressive myeloid cells recede, and that antigen‑low clones don’t immediately fill the vacuum. That’s a direct, cellular line from MOA to outcome.

The same logic extends beyond cell therapy. For a small‑molecule HRAS inhibitor, a single‑cell comparison of pre‑ and on‑treatment samples can reveal whether the drug is suppressing the intended oncogenic program in malignant cells, which myeloid states emerge in response, and whether immune checkpoint ligands surge in the remnants. As the bladder cancer case illustrates, that information can justify an immediate combination move instead of waiting for relapse.

Summary / Takeaways

Single‑cell genomics doesn’t replace the rest of your platform; it connects the dots. It turns bulk‑level hypotheses into cell‑level evidence and exposes the micro‑ecology that makes or breaks response. Practically, the advantages show up in four places.

First, target discovery gets sharper. You can test whether a target is enriched in malignant cells versus essential normal counterparts, and whether co‑expression patterns forecast resistance. That helps avoid toxicity cul‑de‑sacs and pick druggable biology with fewer surprises. Reviews of the tumor immune landscape and cell‑state atlases make it easier than ever to benchmark your findings against large, curated cohorts.

Second, you can delineate the immune components that matter for your asset. Instead of treating “myeloid cells” as a monolith, you can split them into states that will either amplify or blunt your mechanism. That clarity informs dosing, scheduling, and rational combinations.

Third, you can identify the precise cell population your drug should hit and build biomarkers that travel from mouse to man. For biologics and small molecules, transcriptional collapse of the targeted pathway inside the intended cells is the most direct PD you can ask for. For cell therapies, single‑cell multiomics reveals activation, exhaustion, and tonic signaling that tie to product design and clinical durability.

Finally, you can monitor MOA and resistance in vivo with timelines that match real biology. If your claim is rapid re‑programming of T cells or disruption of immunosuppressive niches, time‑resolved single‑cell and spatial assays can prove it in days, not months, and can tell you what new obstacles emerge as the tumor adapts.

If you’re planning a study this quarter, start small and intentional. Pick one or two decision‑making questions—where is the target, and did the intended cells change state?—and embed a single‑cell module in your in‑vivo design. The first dataset will teach you enough to refine sampling, cell numbers, and signatures for the next iteration. What cell‑level question, if answered early, would most de‑risk your program?

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