Decoding the Tumor Microenvironment with Next-Generation Sequencing

Cancer is increasingly understood as an ecosystem in which tumor cells co-evolve with immune, stromal, and vascular components of the surrounding tumor microenvironment (TME). These interactions affect disease progression, therapeutic response, and the emergence of resistance. Yet, the molecular mechanisms underlying these processes have long been difficult to resolve. Next-generation sequencing (NGS) technologies now enable high-resolution analysis of this dynamic landscape. Whole-genome sequencing (WGS) defines the tumor’s genetic backdrop, while single-cell and spatial sequencing reveal the cellular diversity and organization that define the microenvironment. Together, these complementary approaches are identifying new therapeutic targets, refining precision treatment strategies, and uncovering mechanisms of resistance that arise from tumor-microenvironment crosstalk.

Whole-genome sequencing frames the tumor microenvironment

Although whole-genome sequencing (WGS) does not directly profile immune or stromal cells, it provides critical insight into the genomic context in which tumor–microenvironment interactions unfold. By capturing the full spectrum of somatic mutations, structural variants, and copy-number alterations within cancer cells, WGS reveals tumor-intrinsic features that influence immune recognition, microenvironmental composition, and therapeutic response.

One well-established example is tumor mutational burden (TMB), defined as the number of somatic mutations per megabase of tumor DNA. High TMB increases the likelihood that tumors will generate neoantigens capable of eliciting anti-tumor immune responses. In cutaneous melanoma, ultraviolet light–induced mutagenesis leads to elevated TMB dominated by C-to-T transitions. And numerous studies have demonstrated a positive association between high TMB and response to immune checkpoint inhibitors. These observations illustrate how WGS-derived metrics can serve as indirect indicators of tumor immunogenicity, influencing treatment outcomes.¹

Similar principles apply in colorectal cancer, where genome-wide differences between microsatellite instability–high (MSI-H) and microsatellite-stable tumors correspond to distinct microenvironmental phenotypes. MSI-H tumors arise from hypermutability linked to defects in DNA mismatch repair. The resulting increased neoantigen load often promotes immune infiltration and sensitivity to immunotherapy. However, a substantial fraction of MSI-H colorectal cancers fails to respond to immune checkpoint blockade. Integrative analyses combining WGS with transcriptomic profiling have identified genomic subtypes of MSI-H tumors associated with divergent immune and stromal infiltration patterns, indicating that tumor genomics can condition immune engagement within the TME.²

These findings position WGS as a foundational layer that contextualizes downstream immune and stromal dynamics, setting the stage for higher-resolution approaches such as single-cell and spatial sequencing.

Single-cell and spatial profiling of the TME

While WGS defines the genomic context of tumor–microenvironment interactions, single-cell sequencing directly resolves the cellular populations that comprise the TME. Among these approaches, single-cell RNA sequencing (scRNA-seq) has been widely adopted to dissect immune and stromal heterogeneity. Complementary methods, including single-cell DNA sequencing and chromatin accessibility profiling, are increasingly being integrated to link transcriptional states with tumor evolution and regulatory programs.

Over the last two decades, bulk transcriptomic approaches were widely used to characterize cancer biology. But by averaging signals across many cell types, much critical information was obscured regarding functionally distinct immune and stromal cell states, which can influence cancer progression and therapeutic response.

In contrast, scRNA-seq resolves tumors into individual malignant, immune, and stromal cells, allowing their transcriptional programs to be analyzed independently, revealing rare cell populations and nuanced gene expression differences missed by bulk profiling.³

Applied to solid tumors such as non–small cell lung cancer (NSCLC), scRNA-seq studies have revealed extensive heterogeneity among tumor-infiltrating immune cells, including T cells spanning activated and dysfunctional states, as well as diverse innate immune populations with immunosuppressive features.³ This diversity within a single histologic cancer type helps explain variable responses to immunotherapy.

Single-cell transcriptomic profiling has also uncovered differences between primary and metastatic TMEs. In breast cancer, for example, scRNAseq identified immune and stromal cell states associated with metastatic progression. Further analysis of intercellular communication suggested that metastatic lesions have reduced interactions with immune cells, contributing to a more immunosuppressive environment.⁴

Single-cell sequencing provides rich molecular detail but typically requires tissue dissociation, which disrupts spatial relationships between cells. This limitation has driven the development of spatially resolved sequencing approaches that preserve tissue architecture alongside molecular profiling. Spatial transcriptomics (ST) maps gene expression directly onto histological sections showing which cells are present, where they are located relative to tumor cells, vasculature, and stromal compartments, and how cellular organization and interactions evolve over time.⁵

NGS illuminates mechanisms of therapy resistance in the TME

Resistance to cancer therapy frequently arises from dynamic interactions between malignant cells and the TME, rather than from tumor-intrinsic genetic alterations alone. Increasing evidence indicates that stromal and immune components of the microenvironment, including cancer-associated fibroblasts, tumor-associated macrophages, and other immunosuppressive populations, actively contribute to therapeutic resistance by co-opting metabolic, structural, and immune signaling networks. These processes generate spatially localized resistance niches that support tumor survival under therapeutic pressure.

Integrated, multi-layered profiling approaches are now revealing the systems-level complexity of these resistance mechanisms. By combining genomic and transcriptomic data with proteomic, metabolomic, and epigenomic measurements at single-cell and spatial resolution, researchers can map coordinated tumor–microenvironment interactions that sustain resistance. Key resistance-supporting networks include immune–stromal crosstalk, metabolic symbiosis between tumor and stromal cells, extracellular matrix remodeling, and immune evasion pathways. Such findings help explain why targeting individual resistance pathways in isolation has often yielded limited clinical benefit.⁶

Recent studies integrating single-cell and ST illustrate how TME organization shapes therapeutic response and resistance. In cervical cancer, these approaches revealed that squamous tumors harbor immune-enriched microenvironments, whereas adenocarcinomas display stromal-dominant, immune-poor niches. Such spatially organized differences provide a mechanistic basis for subtype-specific therapeutic responses, potentially guiding treatment targeting both tumor cells and the microenvironment.⁷

New therapeutic strategies increasingly aim to modulate the TME, rather than solely eliminate tumor cells. These include disrupting stromal support functions, targeting hypoxic and nutrient-deprived regions that suppress immune activity, and designing combination therapies that address both tumor-intrinsic vulnerabilities and microenvironmental dependencies. For example, in lung adenocarcinoma, integrative single-cell and spatial analyses identified NOTCH3-driven stromal signaling that promotes collagen deposition and invasion. Targeting this pathway reduced invasion, showing how NGS can inform stromal-directed interventions alongside tumor-focused therapies.⁸

Clinical and translational implications

As NGS technologies advance, incorporating TME–aware profiling into clinical oncology workflows is becoming increasingly feasible. A growing number of clinical trials are now evaluating therapies designed to modulate the TME, either alone or in combination with immune checkpoint inhibitors, reflecting recognition that therapeutic response depends on coordinated interactions among malignant, immune, stromal, and vascular compartments.⁹

Early-phase studies have provided proof-of-concept for targeting immunosuppressive pathways within the TME. For example, the randomized Phase II CITYSCAPE trial evaluated combined inhibition of two immune checkpoint pathways—PD-L1 and TIGIT—using monoclonal antibodies as first-line therapy in PD-L1–positive NSCLC.^10,11 While dual checkpoint blockade improved response rates and progression-free survival compared with PD-L1 inhibition alone, subsequent Phase III evaluation in the SKYSCRAPER-01 trial failed to meet its primary overall survival endpoint, highlighting the difficulty of overcoming layered immune suppression in complex TMEs.¹²

The lack of robust predictive biomarkers remains a central obstacle to the clinical development of TME-modulating therapies. Single-parameter markers often fail to capture the spatial, cellular, and functional complexity that shapes therapeutic response.¹⁰ Integrated molecular profiling approaches offer a path toward more precise patient stratification by resolving immune infiltration patterns, stromal organization, and localized immunosuppressive compartments within individual tumors, and helping translate TME complexity into actionable therapeutic insight.

References

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About the author: Lauren Tanabe has a Ph.D. in pharmacology and molecular signaling from Columbia University. She completed her postdoctoral work at the University of Michigan as a Dystonia Medical Research Foundation Fellow and at Wayne State University as an American Cancer Society Fellow.