Deciphering lung cancer at high resolution: a narrative review of applications of single-cell and spatial transcriptomics sequencing

Introduction

According to GLOBOCAN, lung cancer was the most commonly diagnosed cancer, responsible for almost 2.5 million new cases and remaining the leading cause of cancer-related death worldwide (1,2). Lung cancer exhibits high genomic complexity and intratumoral heterogeneity, with different mutations and cellular states distributed across tumor regions. Although targeted therapies and immunotherapy have improved outcomes, many patients still experience immune evasion, therapeutic resistance, or recurrence (3,4). To contextualize the biological complexity of lung cancer, Figure 1 summarizes the multistep progression from precancerous lesions to malignant subtypes, as well as the molecular and evolutionary processes that contribute to tumor heterogeneity and therapeutic failure (5).

Figure 1 The progression, molecular mechanisms, and evolutionary dynamics of lung cancer. The figure is created via BioRender with credit. (A) The stepwise progression from precancerous lesions to cancerous lesions in lung cancer. Precancerous lesions, including AAH and SD, evolve into carcinoma in situ such as AIS and squamous carcinoma in situ. These lesions further progress into IAC, MIA, or other cancer subtypes, such as squamous cell carcinoma, large cell carcinoma, adenosquamous carcinoma, sarcomatoid carcinoma, neuroendocrine carcinoma, and carcinoma of the salivary glands. CT scans and histopathological images illustrate key stages of lesion development. (B) Molecular mechanisms driving lung cancer progression. Key processes include mutational burdens (e.g., EGFR mutations: L858R, T790M, G719C), copy number variations (e.g., MET and ALK amplification), epigenetic modifications, chromosomal instability, and cellular plasticity characterized by dedifferentiation of normal lung cells into malignant cells. (C) Evolutionary dynamics and driver mutations in lung cancer. Tumor progression is shaped by positive, neutral, and negative selection pressures, leading to the emergence of distinct clonal populations. Squamous carcinoma and adenocarcinoma exhibit unique mutational profiles, with alterations in genes such as EGFR, MET, ALK, and others. Truncal alterations occur early in tumor evolution, followed by branching evolution, which results in tumor heterogeneity. Tyrosine kinase inhibitors can influence clonal selection and tumor progression. AAH, atypical adenomatous hyperplasia; AIS, adenocarcinoma in situ; CT, computed tomography; IAC, invasive adenocarcinoma; MIA, minimally invasive adenocarcinoma; pGGN, pure ground-glass nodule; SD, squamous dysplasia.

Single-cell sequencing technologies have emerged as a valuable tool for resolving tumor heterogeneity at cellular resolution (6-8). Among these technologies, single-cell RNA sequencing (scRNA-seq) measures messenger RNA (mRNA) expression at the individual-cell level and enables the analysis of gene expression programs, differentiation trajectories, and cell-cell communication networks (9). However, scRNA-seq requires tissue dissociation and therefore loses the spatial localization of cell populations, which is increasingly recognized as essential for understanding tumor architecture, local cellular interactions, and pathological progression (10,11).

Spatial transcriptomics (ST) addresses this limitation by preserving tissue microarchitecture while mapping gene expression profiles within their native spatial context. This is particularly relevant in lung cancer, where spatial heterogeneity can result in region-specific mutations, transcriptional programs, and microenvironmental niches (12). ST has advanced research across multiple fields, including oncology, by supporting both hypothesis-driven and exploratory analyses and by improving the interpretation of transcriptomic signals within tissue structures (13,14). Integrating scRNA-seq with ST therefore provides complementary information: scRNA-seq resolves cellular states, whereas ST places these states within their anatomical and pathological context (15,16).

In this review, we discuss how scRNA-seq and ST have been applied to decode lung cancer biology. We focus on their contributions to tumor cell characterization, tumor microenvironment (TME) organization, tumor biological behaviors, predictive and therapeutic markers, and treatment resistance. By critically evaluating key findings and current limitations, we aim to provide a framework for future research and clinical translation of single-cell and spatial technologies in lung cancer. We present this article in accordance with the Narrative Review reporting checklist (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-0317/rc).

Methods

A literature search was conducted in PubMed/MEDLINE for articles published between January 2020 and 2025. The search terms used included (“Lung Neoplasms” OR “lung cancer” OR “lung carcinoma” OR “SCLC” OR “NSCLC” OR “lung adenocarcinoma”) AND (“single-cell analysis” OR “single-cell sequencing” OR “scRNA-seq” OR “spatial transcriptomics” OR “spatially resolved transcriptomics”). The selection focused on English-language original studies that used single-cell sequencing, single-nucleus sequencing, spatial transcriptomics, or integrated single-cell/spatial approaches in lung cancer. Articles were excluded if lung cancer was not the main research focus, if they did not involve single-cell or spatial transcriptomic analysis, or if they were purely technical, editorial, or commentary articles without direct relevance to the review topic. The initial search retrieved 617 records, and 37 representative studies were finally selected for detailed evidence mapping. The search strategy is summarized in Table 1.

Technical advances in single-cell and spatial multi-omics sequencing

Traditional transcriptomic profiling approaches, such as bulk RNA sequencing, measure gene expression as an average across heterogeneous populations of cells (5,17). These methods obscure cellular diversity and fail to resolve the spatial organization of gene activity, which is particularly crucial in complex tissues such as lung tumors. As depicted in Figure 2, scRNA-seq sequencing workflow encompasses sample collection, single-cell suspension preparation, cell sorting, library construction and sequencing (Figure 2A). The standardized analytical workflow initiates with raw data processing using pipelines like Cell Ranger to generate quality-filtered UMI count matrices. Subsequent analyses typically employ platforms such as Scanpy or Seurat to perform: (I) data normalization and highly variable gene (HVG) selection, (II) principal component analysis for dimensionality reduction, and (III) batch effect correction using methods like Harmony or BBKNN to account for technical variability. Following these preprocessing steps, graph-based clustering algorithms (e.g., Leiden or Louvain) are applied to identify distinct cellular populations, which are then annotated through marker gene analysis. This integrated approach combines multiple analytical modules including cell clustering, state annotation, pseudotime trajectory reconstruction, and cell-cell communication analysis, to robustly identify cell states and their functional relationships within the lung cancer ecosystem (Figure 2B). Additionally, emerging multi-omics approaches—such as cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) for simultaneous transcriptome and surface protein profiling, as well as the assay for transposase-accessible chromatin using sequencing (ATAC-seq) for assessing chromatin accessibility—have been integrated with single-cell platforms to systematically dissect the molecular mechanisms underlying lung cancer evolution and decipher the regulatory networks within the TME (Figure 2B-2D).

Figure 2 Comprehensive workflow and analysis framework for single-cell sequencing and application in tumor studies. The figure is created via BioRender with credit. (A) Single-cell preparation and sequencing workflow. Tissue samples are processed to obtain single-cell suspensions, followed by tagging with microbeads for library preparation and sequencing. (B) a: cell clustering identifies rare and major cell populations. b: heatmap visualization of cell states across identified genes. c: developmental trajectory analysis traces cell differentiation. d: cell cross-talk networks. e: marker protein and mRNA analysis via CITE-seq. f: using ATAC-seq to identify open chromatin regions. (C) Tumor evolution studies. Schematic representation of tumor development, highlighting oncogenic drivers (e.g., KRAS, EGFR) and cellular transitions leading to diverse lung cancer types, including squamous and small cell lung cancers. Key genes and co-expression patterns (e.g., ITGA3, SRC) are involved in the plasticity states of cancer cells. (D) Tumor microenvironment analysis. Interaction of tumor cells with surrounding components, including collagen deposition, CAFs, and immune cells. Strategies to modulate the TME, such as targeting CAFs and reinvigorating exhausted T cells, are illustrated. ATAC-seq, assay for transposase-accessible chromatin using sequencing; CAFs, cancer-associated fibroblasts; CITE-seq, cellular indexing of transcriptomes and epitopes by sequencing; TME, tumor microenvironment.

While scRNA-seq has greatly advanced our understanding of lung cancer heterogeneity, it falls short in capturing the spatial context of cell populations and local cellular interactions within the TME. In addition, tissues used for RNA extraction may also include variable amounts of adjacent nonmalignant lung tissue, leading to confusion in cell data interpretation. ST analysis addresses these limitations by mapping gene expression patterns back to their precise locations within tissue sections, providing a critical layer of anatomical and pathological context often absent in single-cell transcriptomics (14,18). Several ST techniques are now available, each with its own resolution, throughput, and methodological strengths. Region of interest (ROI)-based methods, such as GeoMx, profile selected regions of interest by using ultra violet (UV)-cleavable oligonucleotide tags, followed by oligonucleotide collection and quantitative analysis (19) (Figure 3A). Specifically, Visium HD achieves higher resolution through densely arrayed barcoded squares (20), whereas Slide-seq and High-definition spatial transcriptomics (HDST) use bead-based barcoding approaches to achieve high spatial resolution (21,22) (Figure 3B). Alternatively, imaging-based in situ hybridization platforms, such as seqFISH+, MERFISH, and 10x Genomics Xenium, enable targeted RNA detection in individual cells with subcellular resolution (23-25) (Figure 3C). The Deterministic Barcoding in Tissue for Spatial Omics Sequencing (DBiT-seq) method leverages a microfluidic-based approach to integrate spatial transcriptomics with multi-omics analysis on FFPE slides, offering high-resolution insights into multiple molecular layers (26) (Figure 3B). ZipSeq introduces a novel approach to label live cells within intact tissues using photocaged oligonucleotide “zipcodes” that can be activated with unique illumination patterns. The labeled tissues are lysed into individual cells for RNA sequencing, expanding the applicability of spatial transcriptomics to dynamic and live-cell systems (27). These spatially resolved profiles can be cross-validated by scRNA-seq, immunofluorescence imaging, or imaging mass cytometry, reinforcing the biological relevance of key findings (28-30). However, these methods differ in coverage and technical demands: imaging-based platforms offer superior resolution but often require complex probe design and have limited gene panels; barcoding-based approaches provide broader transcriptomic coverage but at lower resolution. To facilitate comparison across platforms, we added a summary table comparing major spatial transcriptomic technologies (Table 2).

Figure 3 Overview of spatial transcriptomics methods. The figure is created via BioRender with credit. The diagram illustrates the workflow of various spatial transcriptomics technologies. (A) ROI-based methods use UV-cleavable fluorescent antibodies to mark and isolate ROIs for subsequent oligonucleotide collection and quantitative analysis. (B) Spatially barcoded capture methods use spatial barcoding and sequencing for transcript localization at varying resolutions. (C) Hybridization-based methods employ in situ sequencing or fluorescence-based hybridization to detect RNA targets in spatial contexts. Data from these workflows are processed via NGS sequencing and image reading to generate spatial transcriptomic plots. NGS, next-generation sequencing; ROI, region of interest; UV, ultra violet.

The lack of spatial transcriptomics methods yielding depth and coverage comparable with scRNA-seq underscores the need to integrate data from both techniques. The combination uncovers dynamic changes in cellular arrangements and their implications for development, disease pathogenesis, and therapeutic targets (31-33). The integration of scRNA-seq and ST holds transformative potential for lung cancer research. These complementary methods enable both high-resolution transcriptional profiling and context-aware mapping, uncovering novel cell populations, tissue niches, and predictive biomarkers for therapy response. Together, these techniques allow robust identification of functionally relevant cell types and their spatial niches, enabling mechanistic insights into tumor progression and therapeutic response (34,35). Figure 4A provides an overview of how spatial transcriptomics and scRNA-seq can be integrated via deconvolution and mapping methods to resolve the tumor microenvironment and uncover biologically relevant spatial features. In this context, deconvolution refers to the computational inference of the cellular composition of each spatial spot using scRNA-seq profiles as a reference (36), as illustrated in Figure 4B. By contrast, mapping denotes the alignment of scRNA-seq-defined cell states onto spatial coordinates to reconstruct their distribution across the tissue (37), as shown in Figure 4C.

Figure 4 Overview of combining ST and scRNA-seq for TME analysis and insights into tumor biology. The figure is created via BioRender with credit. (A) Integration of ST and scRNA-seq data using deconvolution and mapping methods. The deconvolution method estimates the proportion of multiple cell types and aids in TME component analysis, while the mapping method offers finer cell type resolution with higher single-cell precision. (B) Commonly used computational tools for these methods are listed. (C) Applications in tumor biology research, including predicting unknown transcripts, detecting cell-cell interactions, and defining invasive tumor margins with specific cell types such as CD4⁺ T cells, macrophages, and fibroblasts. Combining ST with other omics data enhances insights into tumor biology, facilitating multi-omic integration and discovery of key molecular features. scRNA-seq, single-cell RNA sequencing; ST, spatial transcriptomics; TME, tumor microenvironment.

Mapping the tumor microenvironment: cellular composition, functional states, and spatial organization

The TME comprises malignant cells, immune and stromal populations, and extracellular matrix (38). scRNA-seq has expanded TME analysis from broad cell-type identification to fine-grained characterization of cellular states. Spatial transcriptomics further extends this framework by revealing specific tissue structures, such as immune-excluded regions and invasive-front interfaces, and by clarifying how these structures influence tumor progression and therapeutic response. Accordingly, this section discusses how stromal and immune compartments are organized at both cellular and spatial levels and how they collectively shape lung cancer biology.

Stromal cells

Stromal cells, including fibroblasts, endothelial cells, and pericytes, are essential components of the TME and play pivotal roles in tumor progression, immune modulation, and therapeutic resistance (39). Among them, cancer-associated fibroblasts (CAFs) are one of the most abundant and functionally diverse populations in solid tumors (40). High CAF-related signatures are often associated with immune evasion and poor response to immunotherapy (41).

scRNA-seq and spatial profiling have refined our understanding of CAFs heterogeneity in lung cancer. Although CAFs are commonly defined by markers such as fibroblast activation protein (FAP), α-smooth muscle actin (αSMA), and fibroblast-specific protein 1 (FSP1), these markers are not uniformly expressed across all CAFs and may instead define subsets with distinct functions and spatial distributions (42-45). In lung cancer, an increased myofibroblast-to-fibroblast ratio has been associated with advanced disease (46,47). CAF programs related to collagen production, extracellular matrix remodeling, and inflammatory signaling are enriched in specific tumor stages (48). For example, ADH1B⁺ CAFs were enriched in stage I lung adenocarcinoma (LUAD), whereas FAP⁺ CAFs and MYH11⁺αSMA⁺ CAFs were more associated with advanced disease (49). FAP⁺ CAFs also upregulate matrix-related genes such as COL1A1 and COL3A1, suggesting a role in increasing tissue stiffness and limiting T-cell access to tumor cells

Some CAF-derived programs may also provide prognostic information for postoperative recurrence in early-stage LUAD. THBS2, secreted via exosomes by a specific subset of CAFs within lung tumors, is identified as a promising molecular biomarker to stratify high-risk early-stage LUAD patients (50). Integrated multi-omics analyses suggest that THBS2 is mainly derived from specific CAF subsets distinct from those defined by canonical CAF markers such as FAP, FSP1, and αSMA, and its association with prognosis has been supported by TCGA-based analyses (51).

Endothelial cells and pericytes also contribute to stromal remodeling. Tumor-associated endothelial cells can form disorganized and leaky vessels, promoting hypoxia, limiting immune-cell infiltration, and supporting immune checkpoint upregulation (52,53). Pericytes may further stabilize dysfunctional vasculature, sustain angiogenesis, and interact with endothelial cells and CAFs to reinforce fibrotic and immunosuppressive niches (54).

In summary, the stromal compartment, particularly CAFs, plays a multifaceted role in shaping the TME and influencing prognosis. From a translational perspective, some stromal markers, such as FAP, have emerged as prominent targeting candidates due to their membranous expression, and their druggability and therapeutic efficacy have been validated in some studies (55). Other stromal markers, including COL1A1, MYH11, and THBS2, although associated with advanced disease stages and poor prognosis, should still be regarded as early translational candidates that warrant further investigation.

Immune cells

Immune cells constitute a major component of the TME, mediating both anti-tumor immunity and immune evasion. scRNA-seq has revealed extensive heterogeneity among tumor-infiltrating immune populations, including T cells, B cells, natural killer (NK) cells, and myeloid cells, as well as dynamic changes in their abundance and functional states during tumor progression.

Among these populations, T cells are the most extensively characterized. Guo et al. provided one of the first global single-cell maps of T cells in non-small cell lung cancer (NSCLC), demonstrating that tumor-infiltrating T cells comprise diverse CD4⁺ and CD8⁺ states, including effector, tissue-resident memory, regulatory, and exhausted subsets (56). CD8⁺ T cells are particularly relevant because they often show stronger clonal expansion than CD4⁺ T cells and represent a major source of antitumor cytotoxicity (57). Subsequent single-cell studies further indicate that CD8⁺ T cells should not be interpreted as a uniform cytotoxic population, but rather as a continuum ranging from memory-like or progenitor-like states to cytotoxic and terminally exhausted states (56,58-60). The precursor CD8⁺ T cells are reprogrammed by cytokine-receptor responses and differentiated into cytotoxic CD8⁺ T cells expressing higher levels of effector markers (61,62). Exhausted CD8⁺ T cells are characterized by inhibitory receptors such as PD-1, TIGIT, CTLA4, TIM-3, and LAG3, but exhaustion markers alone do not necessarily define tumor specificity or therapeutic responsiveness (59,60). Therefore, T-cell mapping in lung cancer should focus not only on CD8⁺ T-cell abundance but also on clonal expansion, differentiation state, tissue residency, and exhaustion trajectory.

In addition to CD8⁺ T cells, CD4⁺ T cell subsets contribute to both immune activation and suppression in NSCLC. Th1-like cells can support antitumor immunity, whereas regulatory T cells (Tregs) and some Th2-like programs promote immunosuppression. scRNA-seq has also revealed transitional Th17-like states that may shift toward Treg-like programs under specific TME conditions, highlighting the plasticity of CD4⁺ T cell states in lung cancer (46). NK cells represent another important lymphoid compartment. Although NK cells can mediate antitumor cytotoxicity, tumor-infiltrating NK cells in lung carcinoma frequently show impaired function, including reduced IFN-γ production and cytotoxic activity (56).

Myeloid cells constitute a major immune compartment in the lung cancer TME, with tumor-associated macrophages (TAMs) being the most extensively studied population. TAMs can originate from both tissue-resident macrophages and recruited monocyte-derived macrophages, and these populations differ in origin, spatial distribution, and functional programs during tumor progression (63,64). In developing NSCLC lesions, tissue-resident macrophages can initially accumulate near tumor cells and promote epithelial-mesenchymal transition and tumor cell invasiveness, whereas monocyte-derived macrophages become increasingly dominant as the TME evolves (65).

Functionally, macrophage states are closely linked to tumor aggressiveness and therapeutic response. Although the M1/M2 framework remains useful as a simplified model, scRNA-seq and spatial studies show that TAMs occupy more diverse and plastic states than this binary classification suggests. Enrichment of CD163⁺ M2-like macrophages has been associated with poor clinical outcomes, whereas patients responding to PD-1/PD-L1 blockade show increased expression of M1-like features, including STAT1, CD44, IFNGR1, and HLA-E, in TAMs (66,67).

Together, single-cell immune profiling reveals that the lung cancer TME contains both antitumor and immunosuppressive immune states. However, the spatial relationships among these cellular populations, and the ways in which they interact to drive tumor progression and therapeutic resistance, cannot be fully captured by single-cell technologies.

Spatial organization of the TME revealed by ST

The 3D spatial organization of tissues is crucial for understanding tumor progression, metastasis, and therapeutic responses (68). Spatial transcriptomics extends TME mapping from cell-type identification to tissue-level organization, allowing stromal and immune populations to be interpreted within their anatomical niches. This spatial perspective is particularly informative at invasive fronts, where malignant cells directly interact with immune and stromal compartments and where local organization may influence immune surveillance, extracellular matrix remodeling, and tumor progression.

In lung cancer, the invasive margin has also been identified as a critical tumor-TME interface. Zheng et al. reported that M2-like TAMs were more abundant than M1-like TAMs at the invasive margin, were located closer to tumor cells, and were associated with poor survival (69). These findings highlight the value of spatial analysis for evaluating not only TAM abundance and phenotype, but also their localization and proximity to malignant cells.

Beyond immune cells, stromal remodeling at the invasive edge also contributes to tumor progression. Bouchard et al. found that tumor-adjacent fibroblasts exhibited metabolic reprogramming, including enhanced glycan biosynthesis and O-glycosylation, at the invasive edge (70). These changes may facilitate tumor-stroma interactions, promote cancer cell proliferation, and support mesenchymal features. Targeting stromal glycosylation programs may therefore represent a potential strategy to disrupt invasion-associated tumor-stroma crosstalk.

Together, these studies suggest that the invasive front is not merely a histological boundary, but a spatially organized TME niche shaped by tumor cells, macrophages, and fibroblasts. Further investigations are needed to fully elucidate the comprehensive biological landscape of the invasive front and its role in tumor progression and therapy resistance, especially in lung cancer.

Biological behaviors revealed by scRNA-seq and spatial transcriptomics

scRNA-seq and ST provide complementary perspectives for understanding lung cancer progression. scRNA-seq resolves malignant, immune, and stromal cell states at single-cell resolution, whereas ST links these states to anatomical location, histological architecture, and local cell-cell interactions. In this section, we discuss how these technologies have refined our understanding of four key biological processes: invasive-front remodeling, intratumoral heterogeneity, early tumor evolution, and metastatic dissemination.

Tumor heterogeneity

Tumor heterogeneity in lung cancer arises from both genetic diversification and non-genetic cellular plasticity. Multiregion sequencing studies have established that localized NSCLC can contain substantial intratumoral genetic heterogeneity, with distinct subclonal architectures and evolutionary trajectories across different tumor regions (71,72). However, genomic heterogeneity alone does not fully explain the phenotypic diversity. Increasing evidence from scRNA-seq indicates that malignant cells with similar genomic backgrounds can occupy distinct transcriptional states, suggesting that epigenetic regulation and microenvironmental cues also contribute to tumor evolution (73-75).

Single-cell analyses have therefore shifted the interpretation of heterogeneity from a purely clonal model toward a combined clonal and cell-state model. In genetically engineered mouse models of lung cancer, Marjanovic et al. showed that transcriptional diversity increases during progression from pre-neoplastic lesions to adenocarcinoma, and that reproducible high-plasticity cell states can emerge across tumors (74). Similarly, single-cell epigenomic profiling in mouse LUAD demonstrated that tumor progression is accompanied by chromatin-state transitions, supporting the idea that epigenetic plasticity contributes to malignant evolution (75). These findings suggest that at least part of lung cancer heterogeneity follows structured and recurrent state transitions rather than random accumulation of unrelated cell states.

Recurrent transcriptional programs have also been observed beyond individual tumors. Kinker et al. analyzed scRNA-seq profiles across more than 100 cancer cell lines and identified recurrent heterogeneity programs, including cell-cycle, epithelial-mesenchymal transition (EMT)-like, stress-response, and senescence-like states, several of which were also detected in patient tumors (76). Among them, the epithelial senescence-associated (EpiSen) program in LUAD cells exhibited features of partial senescence with a strong secretory phenotype and was inducible by hypoxia and oxidative stress. Intriguingly, EpiSen-high and -low subpopulations showed differential drug sensitivities, e.g., to ferroptosis inducers and EGFR/AKT inhibitors, highlighting potential clinical relevance. In lung cancer, such programs may help explain why phenotypically distinct malignant states can arise even without clearly distinct genomic subclones.

Spatial approaches add another layer by showing how heterogeneous cell states are organized within tissue architecture. For example, DSP-based profiling of LUAD showed that solid tumor regions exhibited spatial segregation between cancer and non-cancer cells: immune cells and macrophages were mainly localized at the tumor periphery, whereas the tumor core was relatively immune-excluded and enriched for immunosuppressive markers, including FOXP3, CD25, TIM3, CTLA4, and VISTA (77).

In summary, current evidence suggests that lung cancer heterogeneity cannot be explained by a single mechanism. Genetic subclones provide one layer of diversity, whereas recurrent transcriptional programs and spatially restricted cell states provide another. However, several issues remain unresolved. Many transcriptional states identified by scRNA-seq may represent context-dependent programs rather than stable biological lineages. In addition, computational clustering, dissociation bias, sampling region, and platform resolution can influence how heterogeneity is detected and interpreted. Although EMT-like, stress-response, senescence-like, and high-plasticity programs may have translational relevance, most remain exploratory rather than clinically validated biomarkers.

Origins and early evolution of lung cancer

Understanding the origins and early evolution of lung cancer requires distinguishing the cell of origin from the cellular states that emerge during malignant progression. In LUAD, both club cells and alveolar type II (AT2) cells have been proposed as potential cells of origin, supporting a dual-origin model in which different epithelial lineages may undergo malignant transformation under distinct oncogenic contexts (78,79).

Lineage tracing combined with scRNA-seq has provided important evidence that early lung tumor evolution is not simply a linear accumulation of mutations. In genetically engineered mouse models of LUAD, Yang et al. showed that tumors derived from a single AT2 cell can diverge along distinct evolutionary trajectories, including gastric/endoderm-like and lung-mixed paths that eventually converge toward more aggressive mesenchymal-like states (80). These findings suggest that structured lineage plasticity can precede detectable subclonal expansion or metastasis (80). Thus, early evolution should be interpreted as a combination of genetic perturbation and state plasticity.

Human LUAD progression studies further support this stepwise but heterogeneous model. By integrating scRNA-seq and ST across matched AIS, MIA, IAC, and adjacent normal lung tissues, Zhu et al. delineated the dynamic transition from preneoplastic lesions to invasive LUAD (79). The proportions of TM4SF1⁺ and UBE2C⁺ malignant cell subpopulations increased during progression, with UBE2C⁺ cells particularly enriched in IAC. These cells also showed activation of proliferative and oncogenic transcriptional changes. Mechanistically, UBE2C has also been implicated in lung cancer cell growth through regulation of the DEPTOR axis (81). This UBE2C may be a candidate gene for the pathological identification of preneoplasia of LUAD.

Radiological progression provides another window into early LUAD evolution. Single-cell studies of ground-glass opacity (GGO)-associated LUAD have linked imaging patterns to distinct epithelial and immune microenvironmental states (82,83). GGO lesions are enriched for epithelial populations expressing AT2 and club cell markers, such as SFTPC, SFTPD, and SCGB3A1, whereas solid components show increased inflammatory, glycolytic, and differentiation-related programs, including CXCL14, GAPDH, and S100A9 (82). Progression toward solid nodules has also been associated with increased immune exhaustion, immune-stromal interactions, and an immunosuppressive TME (83). These observations suggest that radiologic changes may reflect underlying cellular-state transitions

Spatial transcriptomics extends this framework by showing where early progression-associated programs arise within tissue architecture. In the Zhu et al. study, TGF-β-related genes were enriched in the central region of AIS samples, whereas EMT, MYC target, and oxidative phosphorylation programs were more active in the peripheral regions of IAC samples (79). Thus, the oncogenic feature acquisition is highly likely originated from the peripheral region of tumor. Geospatial studies of LUAD further show that tumor-stromal organization can influence immune function: AbdulJabbar et al. used spatial metrics, including fractal dimension, to demonstrate that cancer-stromal interactions were associated with antigen-presentation dysfunction and immune exclusion (84).

Taken together, malignant progression of early LUAD appears to emerge from the interaction between epithelial lineage origin, oncogenic perturbation, transcriptional and epigenomic plasticity, and microenvironmental remodeling. Cell-of-origin studies define the epithelial states that are susceptible to transformation, whereas lineage tracing and single-cell profiling show how transformed cells diversify into distinct evolutionary paths. ST further adds spatial context by showing that these programs may arise in specific tumor regions rather than uniformly across the lesion.

Tumor metastasis

Recent advances in scRNA-seq and ST have provided new tools to dissect the cellular and spatial programs underlying lung cancer metastasis. Metastasis is governed by both tumor-intrinsic features and the permissiveness of distant organ microenvironments, consistent with the classical “seed-and-soil” concept (85,86). In this framework, single-cell and spatial approaches are particularly useful because they can link metastatic potential to specific tumor subclones, transcriptional states, and organ-specific niches.

Lineage tracing combined with scRNA-seq has enabled direct investigation of how metastatic lesions arise from primary tumors. In the KP-Tracer model, clustered regularly interspaced short palindromic repeats (CRISPR)-based lineage barcoding and scRNA-seq were used to connect the phylogenetic origin of tumor cells with their transcriptional states (80). This study revealed that distant metastases are seeded by spatially localized subclones in the primary tumor that exhibit enhanced fitness and distinct programs such as EMT. Metastatic subclones often retained transcriptional similarity to their parental clones but could also acquire adaptive features upon colonizing new microenvironments. These findings suggest that metastatic dissemination reflects both clonal selection and cell-state plasticity, rather than a simple linear expansion of the most abundant primary-tumor clone.

Brain metastasis provides an important example of organ-specific metastatic colonization in lung cancer. Compared with extracranial tumors, the brain microenvironment is shaped by unique features such as the blood-brain barrier, resident microglia, and a distinct immunosuppressive landscape (87,88). Spatial transcriptomic profiling of NSCLC brain metastasis has shown that metastatic lesions contain region-specific tumor and microenvironmental programs, with compartmental differences between the tumor core, tumor immune microenvironment, and adjacent tumor brain microenvironment (89). These findings suggest that metastasis is driven by both tumor-cell migration and invasion programs, such as partial EMT, and interactions with local stromal and immune components and brain-resident myeloid cells.

Single-cell analyses further indicate that immune composition changes substantially after metastatic colonization. In LUAD brain metastases, primary tumors and metastatic lesions contain distinct immune landscapes: T cells are more prominent in primary lung lesions, whereas microglia and macrophage-like populations become dominant in brain metastatic sites (90). These myeloid populations are not adequately defined by a simple M1/M2 framework. Instead, microglial and macrophage states show heterogeneous inflammatory, antigen-presentation, and lipid-metabolism programs. Ligand-receptor analyses have suggested potential interactions such as the DLL4-NOTCH4 axis.

Beyond brain metastases, recent multi-omic studies have begun to identify predictors of organ-specific metastases in LUAD. By integrating scRNA-seq with serum proteomics, Zhang et al. identified dynamic gene modules associated with pre-metastatic states across different organs (91). Serum amyloid A1 (SAA1) emerged as a strong predictor of bone metastasis, validated across bulk RNA-seq, immunohistochemistry (IHC), and in vivo models (90). Prospective clinical validation of such markers is expected before they can guide treatment decisions.

Taken together, current evidence suggests that metastatic potential is shaped by the interaction between selected tumor subclones, plastic transcriptional programs such as EMT, and organ-specific microenvironments. Key unresolved issues include whether metastatic cell states are stable lineages or transient adaptive programs and how sampling bias affects detection of rare metastatic precursors.

Therapeutic implications

The biological insights described above provide a basis for therapeutic translation. scRNA-seq and spatial transcriptomics have been increasingly used to identify biological traits associated with prognosis, treatment response, and therapeutic resistance. As summarized in Figure 5, these applications can be organized into: (I) response stratification by identifying transcriptomic and spatial markers (Figure 5A), (II) revealing mechanisms of treatment resistance, thereby generating hypotheses for therapy adjustment and candidate drug development (Figure 5B). To organize the translational evidence, we summarized representative scRNA-seq- and ST-derived biomarkers in Table 3. This table was intended to evaluate the evidence maturity according to clinical significance, predictive value, therapeutic potential, and level of validation.

Figure 5 Applications of spatial and single-cell transcriptomics in lung cancer prognosis and therapy. The figure is created via BioRender with credit. (A) Insights on lung cancer prognosis: spatial distance between TAM-enriched spots and tumor-enriched spots provides insights into patient prognosis. Integration of transcriptomics identifies response-related modules, predicting anti-PD-(L)1 therapy responses. To conclude it, biomarkers derived from transcriptomic features allow stratification of patients into high- and low-risk groups for prognosis prediction. (B) Insights on treatment failure and recurrence: spatial and single-cell transcriptomics identify mechanisms underlying resistance to immunotherapy and recurrence after TKI treatment. Features like COL11A1⁺ CAFs and CTLA4 pathways offer insights into drug resistance. CAFs, cancer-associated fibroblasts; CTLA4, cytotoxic T-lymphocyte associated protein 4; PD-1, programmed death-1; PD-L1, programmed death-ligand 1; TAM, tumor-associated macrophage; TKI, tyrosine kinase inhibitor.

Decoding T cells to advance immunotherapeutic strategies

T cells are central mediators of antitumor immunity and an important compartment in immunotherapy. Single-cell profiling has shown that immune checkpoint blockade (ICB) response is also determined simply by presence of specific tumor-reactive, progenitor-like, cytotoxic, and exhausted T-cell states (126,127). Studies of anti-PD-1-treated lung cancers have characterized transcriptional programs of neoantigen-specific TILs, supporting the importance of tumor-reactive T-cell states in therapeutic response (128). CD39 expression also enriches for tumor-reactive CD8⁺ T cells in lung cancer and has been associated with ICB benefit (129). Similarly, Liu et al. showed that the expression of CXCL13 identifies both precursor-like and terminally differentiated tumor-reactive CD8⁺ T cells in lung cancer (130). Sakai et al. also identified significant increase of CXCL13⁺ CD8⁺ T cells in post-treatment TME and directly impact cancer cells, suggesting their potential as biomarkers (131). Other T-cell states have also been linked to immunotherapy response. TOX has been associated with T-cell exhaustion and anti-PD-1 response-related states (132). Xue et al. further identified tumor-infiltrating T-cell populations, including CXCR5⁺TCF1⁺CD8⁺ T cells and LAYN⁺CD8⁺ T cells, that were associated with durable responses to ICB therapy (133). Together, these findings suggest that response prediction should consider tumor reactivity and differentiation state.

Paired scRNA-seq and T-cell receptor sequencing (TCR-seq) provide a distinct framework by linking transcriptional state with clonal dynamics. In NSCLC, temporal paired scRNA/TCR-seq has been used to track T-cell clonotypes across pretreatment and post-treatment tumor biopsies during PD-1-based therapy (134). Regional scRNA/TCR-seq studies further showed that tumor-specific exhausted CD8⁺ T cells can be clonally linked to TCF7⁺SELL⁺ progenitor-like T cells in tumor-draining lymph nodes, and that tumor-specific T-cell clones may persist for years after ICB therapy (57). These findings support the value of TCR sequencing for distinguishing tumor-reactive clonal lineages from non-tumor-specific T-cell states.

Beyond response prediction, several T-cell-associated regulatory mechanisms have emerged as potential therapeutic targets. Zhong et al. reported ATPIF1 as a regulator of CD8⁺ T-cell antitumor immunity: ATPIF1 deficiency promoted T-cell exhaustion and impaired antitumor activity, whereas ATPIF1 overexpression enhanced T-cell function and CAR-T efficacy in experimental settings (135).

In addition to T-cell-intrinsic mechanisms, dendritic-cell support may influence the maintenance of therapeutically relevant T-cell populations. In a LUAD mouse model, Schenkel et al. showed that cDC1 in tumor-draining lymph nodes maintained a reservoir of proliferative tumor-antigen-specific TCF-1⁺CD8⁺ T cells. As tumors progressed, intratumoral TCF-1⁺CD8⁺ T cells acquired dysfunctional features, whereas Flt3L plus anti-CD40 treatment restored SlamF6⁺TCF-1⁺CD8⁺ T-cell frequencies and reduced tumor burden (123). This finding suggests a potential strategy for enhancing antitumor immunity by supporting dendritic-cell-dependent T-cell reservoirs.

T-cell-focused single-cell studies consistently show that antitumor immunity is shaped collectively by clonal expansion, tumor reactivity and differentiation state. However, most proposed T-cell markers, including CD39, TOX, ATPIF1, CXCR5⁺TCF1⁺CD8⁺ T cells, and LAYN⁺CD8⁺ T cells, remain exploratory. Moreover, adoptive T-cell therapies have shown clinical activity in selected non-lung cancer settings (136-139), supporting the feasibility of T-cell-based therapeutic strategies. However, in lung cancer, the key challenge is to determine which TCR clonotypes are truly tumor-specific, persistent after therapy, and functionally capable of mediating durable antitumor responses.

Predictive biomarkers for response stratification in immunotherapy

Building on the T-cell-centered markers discussed above, response stratification for ICB also requires a broader view of the immune contexture. ICB treatments can be highly effective but benefit only a subset of patients, with some experiencing no response or even adverse effects. Therefore, response stratification remains a major clinical challenge. Currently, PD-L1 expression and tumor mutational burden (TMB) are the only clinically established predictive biomarkers for immunotherapy. PD-L1 expression is clinically used but imperfect, and TMB correlates with response rates across cancer types but does not fully capture the immune state of individual tumors (140-142). Single-cell approaches can complement these markers by resolving the cellular states that underlie therapeutic sensitivity or resistance.

Beyond T-cell markers, single-cell studies have identified several non-T-cell immune programs associated with immunotherapy response. B-cell and plasma-cell programs represent one important layer. Patil et al. used transcriptomic signatures of B-cell subsets and showed that intratumoral plasma cells were associated with improved outcomes to PD-L1 blockade in NSCLC (143). Moreover, B cells and plasma cells often localize within or near tertiary lymphoid structures (TLSs), where they may support antigen presentation and sustained T-cell responses (144,145). Other single-cell-derived immune markers listed in Table 2, such as VISTA⁺ B cells, DC-related gene signatures, FABP4⁺C1q⁺ macrophages, TREM2⁺ TAMs, and AQP3-related macrophage polarization, further suggest that response stratification may depend on coordinated immune-cell states rather than on T-cell abundance alone.

Beyond individual cell types, composite immune modules may provide more robust stratification. Leader et al. identified a Lung Cancer Activation Module (LCAM) in NSCLC, with LCAM-high tumors showing stronger adaptive immune activation, including activated T cells, IgG⁺ plasma cells, and myeloid populations, whereas LCAM-low tumors showed a more suppressed immune profile (121). LCAM was also associated with TMB and cancer-testis antigen expression, underscoring the potential of the LCAM score as a more accurate marker for immune responses than TMB alone.

Emerging multi-omics scores may further expand the biomarker landscape, but their translational status remains preliminary. For example, R-loop-related scores derived from single-cell and bulk transcriptomic analyses have been associated with immune evasion, metabolic reprogramming, prognosis, and resistance to chemotherapy and immunotherapy (146).

To enhance the immunotherapy response prediction accuracy, future improvements could include integration of multi-dimensional biomarkers with the assistance of multi-omics approaches. These advancements will enable fully personalized therapies and improve outcomes for cancer patients.

Spatial profiling of tumor immunity and therapeutic response

Spatial profiling adds a tissue-level dimension to immunotherapy research by showing where immune, stromal, and malignant populations are located and how they interact within the TME (147). This is important because treatment response is not determined only by the presence of immune cells, but also by whether these cells can access tumor regions, stromal compartments or immune-excluded niches.

Recent spatial multi-omics studies in NSCLC support this compartment-based view of immunotherapy response. In NSCLC, spatial multi-omics has shown stromal-region signatures sometimes may outperform tumor-region signatures for predicting immunotherapy response (148). Consistently, Aung et al. used spatial proteomics and compartment-based transcriptomics, and identified spatially defined response and resistance niches (149). Poor outcomes were associated with resistance programs involving proliferating tumor cells, granulocytes, and vascular compartments, whereas favorable outcomes were associated with immune response programs involving macrophage and CD4⁺ T-cell compartments. These findings indicate that immunotherapy stratification may require assessment of tissue organization, not merely measurement of individual immune markers. Myeloid and stromal organization prove to be highly related to treatment response. GeoMx DSP studies have shown that proximity of TAM-enriched regions to tumor regions exerts great predictive impact (150). Fan et al. further showed that CD14⁺APOE⁺ myeloid cells co-localize with MMP7⁺ tumor cells in immunosuppressive regions of NSCLC, and that this spatial interaction is associated with worse immunotherapy response (103). Similarly, multi-omics profiling has implicated interactions between SPP1⁺ macrophages and COL11A1⁺ CAFs at tumor boundaries in collagen deposition, impaired T-cell infiltration, and resistance to immunochemotherapy (96). Cui et al. reported that CD4⁺ Tregs and mCAFs were associated with an immunosuppressive TME, whereas CD4⁺ Th17 cells, inflammatory CAFs (iCAFs), and SELENOP⁺ macrophage-related spatial programs were linked to treatment sensitivity (151). These findings underscore the need to examine coordinated remodeling of immune and stromal niches to better understand variability in immunotherapy outcomes.

Spatial profiling also refines the interpretation of immune checkpoint expression. Rather than treating PD-L1 expression as a uniform tumor-level marker, spatial analyses suggest that its compartmental distribution may matter. Stromal enrichment of PD-L1 has been associated with treatment response, whereas tumor-region enrichment has been linked to progressive disease in spatially profiled NSCLC cohorts (148). These findings suggest that spatial localization of checkpoint signals may provide information beyond bulk RNA-seq or conventional IHC.

TLSs provide a contrasting example of spatial organization associated with favorable antitumor immunity. TLSs, ectopic lymphoid aggregates formed in tissues under chronic inflammatory conditions (152), have been associated with improved response to ICB, independently of PD-L1 expression and CD8⁺ T-cell density (153). In lung cancer, computerized TLS density on hematoxylin and eosin (H&E) images has also been reported as a predictive biomarker in resectable LUAD (154). However, TLS assessment remains technically variable, because studies differ in how they define TLS maturity, and relationship to nearby tumor or stromal compartments.

Taken together, ST provides therapeutic insight by redefining how existing and emerging biomarkers should be interpreted within tissue architecture. In this sense, ST helps explain why single-marker assessments based on bulk RNA-seq or conventional IHC may be insufficient. Spatial studies therefore generate clinically relevant hypotheses, such as targeting myeloid-stromal exclusion programs, enhancing TLS-associated immune activation, or refining PD-L1 interpretation. However, the evidence level remains uneven. Several signatures remain association-based, bioinformatics-based, or lack functional validation. Key technical barriers include sampling bias, region selection, dissociation effects, spatial resolution limits, cell-type deconvolution uncertainty, and inconsistent clinical endpoint definitions.

scRNA-seq and ST in targeted therapies

In addition to immunotherapy, scRNA-seq and spatial profiling have increasingly been used to investigate mechanisms of resistance to targeted therapies, particularly EGFR tyrosine kinase inhibitors (TKIs) in NSCLC. These approaches suggest that resistance is not driven solely by tumor-cell-intrinsic alterations, but may also involve immune and stromal remodeling within the TME.

EGFR-mutant LUAD is often characterized by an immune-suppressive microenvironment, with reduced immune-cell recruitment and altered checkpoint expression compared with EGFR wild-type tumors (155-157). Furthermore, an integrative study showed that EGFR-TKI-resistant tumors were characterized by an immunosuppressive microenvironment with increased infiltration of CXCR1⁺ neutrophils (158). In resistant tumors, CXCR1⁺ neutrophils were redistributed from the tumor periphery into the tumor core, with upregulation of TNF-α/NF-κB and EMT-related pathways. Clinically, baseline CXCR1⁺ neutrophil infiltration was associated with shorter PFS and OS in patients treated with third-generation EGFR-TKIs, highlighting its potential as a resistance biomarker and therapeutic target.

Tumor-cell-intrinsic drug-tolerant states also contribute to EGFR-TKI resistance. Izumi et al. integrated scRNA-seq and ST analyses in EGFR-mutated lung cancer and showed that BCL2L1, encoding the anti-apoptotic protein BCL-XL, was preferentially expressed in tumor cells during drug-tolerant and acquired resistant states (159). Targeting BCL-XL in combination with EGFR-TKI treatment delayed or overcame resistance in experimental models.

Stromal remodeling represents another mechanism of EGFR-TKI resistance. In EGFR-mutant NSCLC, scRNA-seq identified a subset of CTHRC1⁺ CAFs enriched in drug-resistant tumors (160). These CAFs promoted EGFR-TKI resistance by activating TGF-β/Smad3 signaling in cancer cells and enhancing glycolytic activity through HK2 upregulation.

Conclusions

scRNA-seq and spatial transcriptomics have expanded lung cancer research from bulk molecular profiling to cellular and spatial ecosystem analysis. scRNA-seq has revealed heterogeneous malignant epithelial states, immune and stromal populations, tumor evolution trajectories, and therapy-associated cell states, whereas ST has placed these findings back into tissue architecture by identifying organized immune and stromal compartments. Together, these technologies have shown that lung cancer progression and treatment response are shaped not only by genetic alterations, but also by transcriptional plasticity, spatial organization, and dynamic tumor-microenvironment interactions.

Despite these advances, several limitations remain. High cost, limited access to high-quality clinical specimens, especially from patients with metastatic disease, and variability in sample processing continue to restrict large-scale application. Many current ST platforms do not fully achieve true single-cell resolution across large tissue areas, and spot- or pixel-based measurements may still contain mixed cellular signals (161). Imaging-based methods such as MERFISH, seqFISH+, and Xenium provide high spatial resolution but may be constrained by inherent technical limitations. Overlapping fluorescent signals can constrain mRNA transcripts detected and resolved per cell, particularly in highly transcriptionally active or spatially crowded tumor regions. In addition, data integration, cell-type deconvolution, spatial mapping, quality control, and cross-platform standardization remain major analytical challenges (162).

For clinical translation, an important issue is that single-cell and spatial studies often identify cell states, trajectories, and spatial niches rather than directly druggable genetic alterations. Therefore, many proposed biomarkers and therapeutic candidates need further support by functional validation and clinical outcome data, especially candidates derived from computational clustering, ligand-receptor inference, or spatial co-localization, which may generate useful hypotheses but do not by themselves prove biological causality.

Future research should move toward larger treatment-annotated cohorts, longitudinal sampling, functional perturbation experiments, and integration of multi-facet data. Another important direction is the transition from two-dimensional to three-dimensional spatial profiling. Three-dimensional ST may provide more accurate information about cell neighborhoods, subcellular localization, and spatially contiguous tumor structures that cannot be fully captured in conventional two-dimensional sections (163). In addition, subclonal evolution, a key driver of therapeutic resistance, has been extensively investigated in other cancer types but remains less fully characterized in lung cancer using integrated single-cell and spatial approaches (92,164).

Overall, scRNA-seq and ST are currently valuable as tools for mechanistic discovery, biomarker nomination, and hypothesis generation. With improved standardization and clinical validation, these technologies may help refine patient stratification, reveal mechanisms of resistance, and guide more precise therapeutic strategies in lung cancer.

Acknowledgments

We sincerely thank all colleagues who provided helpful support during the preparation of this review but did not meet the criteria for authorship. We also acknowledge the infrastructure and academic resources provided by West China Hospital, Sichuan University. All figures were created with BioRender under an appropriate license.

Footnote

Reporting Checklist: The authors have completed the Narrative Review reporting checklist. Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-0317/rc

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-0317/prf

Funding: This work was supported by the Noncommunicable Chronic Diseases-National Science and Technology Major Project (Nos. 2023ZD0506102/2023ZD0506100), the National Natural Science Foundation of China (No. 82470109 and 32300544), the 1.3.5 Project of State Key Laboratory of Respiratory Health and Multimorbidity (No. RHM24208), the 1.3.5 Project of Disciplines Excellence (No. ZYYC23027), the Science and Technology Project of Sichuan (No. 2026NSFSCZY0142 and 2025JDDJ0005), the Fundamental Research Funds for the Central Universities (No. SCU2026D010), and the State Key Laboratory of Respiratory Health and Multimorbidity, State Key Laboratory Special Fund (No. 2060204).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-0317/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Siegel RL, Kratzer TB, Wagle NS, et al. Cancer statistics, 2026. CA Cancer J Clin 2026;76:e70043. [Crossref] [PubMed]
2. Sung H, Filho AM, Laversanne M, et al. Global cancer statistics 2024: GLOBOCAN estimates of incidence and mortality worldwide for 34 cancers in 186 countries. CA Cancer J Clin 2026;76:e70090. [Crossref] [PubMed]
3. Wang M, Herbst RS, Boshoff C. Toward personalized treatment approaches for non-small-cell lung cancer. Nat Med 2021;27:1345-56. [Crossref] [PubMed]
4. Blaquier JB, Ortiz-Cuaran S, Ricciuti B, et al. Tackling osimertinib resistance in EGFR-mutant non-small cell lung cancer. Clin Cancer Res 2023;29:3579-91. [Crossref] [PubMed]
5. Tang F, Barbacioru C, Wang Y, et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods 2009;6:377-82. [Crossref] [PubMed]
6. Kolodziejczyk AA, Kim JK, Svensson V, et al. The technology and biology of single-cell RNA sequencing. Mol Cell 2015;58:610-20. [Crossref] [PubMed]
7. Navin N, Kendall J, Troge J, et al. Tumour evolution inferred by single-cell sequencing. Nature 2011;472:90-4. [Crossref] [PubMed]
8. Zhao L, Tang C, Jin X, et al. Unraveling tumoral heterogeneity and angiogenesis-associated mechanisms of PD-1 and LAG-3 dual inhibition in lung cancers by single-cell RNA sequencing. Chin Med J Pulm Crit Care Med 2025;3:41-9. [Crossref] [PubMed]
9. Heumos L, Schaar AC, Lance C, et al. Best practices for single-cell analysis across modalities. Nat Rev Genet 2023;24:550-72. [Crossref] [PubMed]
10. Longo SK, Guo MG, Ji AL, et al. Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics. Nat Rev Genet 2021;22:627-44. [Crossref] [PubMed]
11. Chen S, Zhu G, Yang Y, et al. Single-cell analysis reveals transcriptomic remodellings in distinct cell types that contribute to human prostate cancer progression. Nat Cell Biol 2021;23:87-98. [Crossref] [PubMed]
12. Wu HJ, Temko D, Maliga Z, et al. Spatial intra-tumor heterogeneity is associated with survival of lung adenocarcinoma patients. Cell Genom 2022;2:100165. [Crossref] [PubMed]
13. Zhu M, Peng J, Wang M, et al. Transcriptomic and spatial GABAergic neuron subtypes in zona incerta mediate distinct innate behaviors. Nat Commun 2025;16:3107. [Crossref] [PubMed]
14. Cheng M, Jiang Y, Xu J, et al. Spatially resolved transcriptomics: a comprehensive review of their technological advances, applications, and challenges. J Genet Genomics 2023;50:625-40. [Crossref] [PubMed]
15. Kleshchevnikov V, Shmatko A, Dann E, et al. Cell2location maps fine-grained cell types in spatial transcriptomics. Nat Biotechnol 2022;40:661-71. [Crossref] [PubMed]
16. Elosua-Bayes M, Nieto P, Mereu E, et al. SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes. Nucleic Acids Res 2021;49:e50. [Crossref] [PubMed]
17. Stuart T, Butler A, Hoffman P, et al. Comprehensive integration of single-cell data. Cell 2019;177:1888-902.
18. Maniatis S, Äijö T, Vickovic S, et al. Spatiotemporal dynamics of molecular pathology in amyotrophic lateral sclerosis. Science 2019;364:89-93. [Crossref] [PubMed]
19. Merritt CR, Ong GT, Church SE, et al. Multiplex digital spatial profiling of proteins and RNA in fixed tissue. Nat Biotechnol 2020;38:586-99. [Crossref] [PubMed]
20. Oliveira MF, Romero JP, Chung M, et al. High-definition spatial transcriptomic profiling of immune cell populations in colorectal cancer. Nat Genet 2025;57:1512-23. [Crossref] [PubMed]
21. Vickovic S, Eraslan G, Salmén F, et al. High-definition spatial transcriptomics for in situ tissue profiling. Nat Methods 2019;16:987-90. [Crossref] [PubMed]
22. Rodriques SG, Stickels RR, Goeva A, et al. Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution. Science 2019;363:1463-7. [Crossref] [PubMed]
23. Eng CL, Lawson M, Zhu Q, et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature 2019;568:235-9. [Crossref] [PubMed]
24. Chen KH, Boettiger AN, Moffitt JR, et al. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 2015;348:aaa6090. [Crossref] [PubMed]
25. Marco Salas S, Kuemmerle LB, Mattsson-Langseth C, et al. Optimizing Xenium In Situ data utility by quality assessment and best-practice analysis workflows. Nat Methods 2025;22:813-23. [Crossref] [PubMed]
26. Liu Y, Yang M, Deng Y, et al. High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue. Cell 2020;183:1665-81.
27. Hu KH, Eichorst JP, McGinnis CS, et al. ZipSeq: barcoding for real-time mapping of single cell transcriptomes. Nat Methods 2020;17:833-43. [Crossref] [PubMed]
28. Gut G, Herrmann MD, Pelkmans L. Multiplexed protein maps link subcellular organization to cellular states. Science 2018;361:eaar7042. [Crossref] [PubMed]
29. Lin JR, Fallahi-Sichani M, Sorger PK. Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method. Nat Commun 2015;6:8390. [Crossref] [PubMed]
30. Goltsev Y, Samusik N, Kennedy-Darling J, et al. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 2018;174:968-81.
31. Tumeh PC, Harview CL, Yearley JH, et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 2014;515:568-71. [Crossref] [PubMed]
32. Pauken KE, Sammons MA, Odorizzi PM, et al. Epigenetic stability of exhausted T cells limits durability of reinvigoration by PD-1 blockade. Science 2016;354:1160-5. [Crossref] [PubMed]
33. Chen J, Tan Y, Sun F, et al. Single-cell transcriptome and antigen-immunoglobin analysis reveals the diversity of B cells in non-small cell lung cancer. Genome Biol 2020;21:152. [Crossref] [PubMed]
34. Wang J, Ye F, Chai H, et al. Advances and applications in single-cell and spatial genomics. Sci China Life Sci 2025;68:1226-82. [Crossref] [PubMed]
35. Liao J, Lu X, Shao X, et al. Uncovering an organ's molecular architecture at single-cell resolution by spatially resolved transcriptomics. Trends Biotechnol 2021;39:43-58. [Crossref] [PubMed]
36. Dong R, Yuan GC. SpatialDWLS: accurate deconvolution of spatial transcriptomic data. Genome Biol 2021;22:145. [Crossref] [PubMed]
37. Biancalani T, Scalia G, Buffoni L, et al. Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram. Nat Methods 2021;18:1352-62. [Crossref] [PubMed]
38. Wu T, Dai Y. Tumor microenvironment and therapeutic response. Cancer Lett 2017;387:61-8. [Crossref] [PubMed]
39. Kalluri R. The biology and function of fibroblasts in cancer. Nat Rev Cancer 2016;16:582-98. [Crossref] [PubMed]
40. Lavie D, Ben-Shmuel A, Erez N, et al. Cancer-associated fibroblasts in the single-cell era. Nat Cancer 2022;3:793-807. [Crossref] [PubMed]
41. Hanley CJ, Waise S, Ellis MJ, et al. Single-cell analysis reveals prognostic fibroblast subpopulations linked to molecular and immunological subtypes of lung cancer. Nat Commun 2023;14:387. [Crossref] [PubMed]
42. Koliaraki V, Prados A, Armaka M, et al. The mesenchymal context in inflammation, immunity and cancer. Nat Immunol 2020;21:974-82. [Crossref] [PubMed]
43. Chen C, Guo Q, Liu Y, et al. Single-cell and spatial transcriptomics reveal POSTN(+) cancer-associated fibroblasts correlated with immune suppression and tumour progression in non-small cell lung cancer. Clin Transl Med 2023;13:e1515. [Crossref] [PubMed]
44. Costa A, Kieffer Y, Scholer-Dahirel A, et al. Fibroblast heterogeneity and immunosuppressive environment in human breast cancer. Cancer Cell 2018;33:463-79.
45. Hu H, Piotrowska Z, Hare PJ, et al. Three subtypes of lung cancer fibroblasts define distinct therapeutic paradigms. Cancer Cell 2021;39:1531-47.
46. Wu F, Fan J, He Y, et al. Single-cell profiling of tumor heterogeneity and the microenvironment in advanced non-small cell lung cancer. Nat Commun 2021;12:2540. [Crossref] [PubMed]
47. Nam YH, Lee SK, Sammut D, et al. Preliminary study of the cellular characteristics of primary bronchial fibroblasts in patients with asthma: expression of alpha-smooth muscle actin, fibronectin containing extra type III domain A, and smoothelin. J Investig Allergol Clin Immunol 2012;22:20-7.
48. Ou Z, Lin S, Qiu J, et al. Single-nucleus RNA sequencing and spatial transcriptomics reveal the immunological microenvironment of cervical squamous cell carcinoma. Adv Sci (Weinh) 2022;9:e2203040. [Crossref] [PubMed]
49. Grout JA, Sirven P, Leader AM, et al. Spatial positioning and matrix programs of cancer-associated fibroblasts promote T-cell exclusion in human lung tumors. Cancer Discov 2022;12:2606-25. [Crossref] [PubMed]
50. Hoshino A, Kim HS, Bojmar L, et al. Extracellular vesicle and particle biomarkers define multiple cancers. Cell 2020;182:1044-61.
51. Yang H, Sun B, Fan L, et al. Multi-scale integrative analyses identify THBS2(+) cancer-associated fibroblasts as a key orchestrator promoting aggressiveness in early-stage lung adenocarcinoma. Theranostics 2022;12:3104-30. [Crossref] [PubMed]
52. Pan X, Li X, Dong L, et al. Tumour vasculature at single-cell resolution. Nature 2024;632:429-36. [Crossref] [PubMed]
53. Ye D, Jin Y, Weng Y, et al. High endothelial venules predict response to PD-1 inhibitors combined with anti-angiogenesis therapy in NSCLC. Sci Rep 2023;13:16468. [Crossref] [PubMed]
54. Xiang H, Pan Y, Sze MA, et al. Single-cell analysis identifies NOTCH3-mediated interactions between stromal cells that promote microenvironment remodeling and invasion in lung adenocarcinoma. Cancer Res 2024;84:1410-25. [Crossref] [PubMed]
55. Xie Y, Ma J, Tang W, et al. Efficacy and safety evaluation of 177Lu-FAP-2286 in the treatment of advanced lung cancer. Clin Nucl Med 2024;49:830-7. [Crossref] [PubMed]
56. Guo X, Zhang Y, Zheng L, et al. Global characterization of T cells in non-small-cell lung cancer by single-cell sequencing. Nat Med 2018;24:978-85. [Crossref] [PubMed]
57. Pai JA, Hellmann MD, Sauter JL, et al. Lineage tracing reveals clonal progenitors and long-term persistence of tumor specific T cells during immune checkpoint blockade. Cancer Cell 2023;41:776-90.
58. Chen Z, Ji Z, Ngiow SF, et al. TCF-1-centered transcriptional network drives an effector versus exhausted CD8 T cell-fate decision. Immunity 2019;51:840-55.
59. Blank CU, Haining WN, Held W, et al. Defining 'T cell exhaustion'. Nat Rev Immunol 2019;19:665-74. [Crossref] [PubMed]
60. Zheng L, Qin S, Si W, et al. Pan-cancer single-cell landscape of tumor-infiltrating T cells. Science 2021;374:abe6474. [Crossref] [PubMed]
61. Paley MA, Kroy DC, Odorizzi PM, et al. Progenitor and terminal subsets of CD8+ T cells cooperate to contain chronic viral infection. Science 2012;338:1220-5. [Crossref] [PubMed]
62. Utzschneider DT, Charmoy M, Chennupati V, et al. T cell factor 1-expressing memory-like CD8(+) T cells sustain the immune response to chronic viral infections. Immunity 2016;45:415-27. [Crossref] [PubMed]
63. Hashimoto D, Chow A, Noizat C, et al. Tissue-resident macrophages self-maintain locally throughout adult life with minimal contribution from circulating monocytes. Immunity 2013;38:792-804. [Crossref] [PubMed]
64. Mulder K, Patel AA, Kong WT, et al. Cross-tissue single-cell landscape of human monocytes and macrophages in health and disease. Immunity 2021;54:1883-1900.
65. Casanova-Acebes M, Dalla E, Leader AM, et al. Tissue-resident macrophages provide a pro-tumorigenic niche to early NSCLC cells. Nature 2021;595:578-84. [Crossref] [PubMed]
66. Engblom C, Pfirschke C, Pittet MJ. The role of myeloid cells in cancer therapies. Nat Rev Cancer 2016;16:447-62. [Crossref] [PubMed]
67. Larroquette M, Guegan JP, Besse B, et al. Spatial transcriptomics of macrophage infiltration in non-small cell lung cancer reveals determinants of sensitivity and resistance to anti-PD1/PD-L1 antibodies. J Immunother Cancer 2022;10:e003890. [Crossref] [PubMed]
68. Almagro J, Messal HA, Elosegui-Artola A, et al. Tissue architecture in tumor initiation and progression. Trends Cancer 2022;8:494-505. [Crossref] [PubMed]
69. Zheng X, Weigert A, Reu S, et al. Spatial density and distribution of tumor-associated macrophages predict survival in non-small cell lung carcinoma. Cancer Res 2020;80:4414-25. [Crossref] [PubMed]
70. Bouchard G, Garcia-Marques FJ, Karacosta LG, et al. Multiomics analysis of spatially distinct stromal cells reveals tumor-induced O-glycosylation of the CDK4- pRB axis in fibroblasts at the invasive tumor edge. Cancer Res 2022;82:648-64. [Crossref] [PubMed]
71. Zhang J, Fujimoto J, Zhang J, et al. Intratumor heterogeneity in localized lung adenocarcinomas delineated by multiregion sequencing. Science 2014;346:256-9. [Crossref] [PubMed]
72. Jamal-Hanjani M, Wilson GA, McGranahan N, et al. Tracking the evolution of non-small-cell lung cancer. N Engl J Med 2017;376:2109-21. [Crossref] [PubMed]
73. Sharma A, Merritt E, Hu X, et al. Non-genetic intra-tumor heterogeneity is a major predictor of phenotypic heterogeneity and ongoing evolutionary dynamics in lung tumors. Cell Rep 2019;29:2164-74.
74. Marjanovic ND, Hofree M, Chan JE, et al. Emergence of a high-plasticity cell state during lung cancer evolution. Cancer Cell 2020;38:229-46.
75. LaFave LM, Kartha VK, Ma S, et al. Epigenomic state transitions characterize tumor progression in mouse lung adenocarcinoma. Cancer Cell 2020;38:212-28.
76. Kinker GS, Greenwald AC, Tal R, et al. Pan-cancer single-cell RNA-seq identifies recurring programs of cellular heterogeneity. Nat Genet 2020;52:1208-18. [Crossref] [PubMed]
77. Tavernari D, Battistello E, Dheilly E, et al. Nongenetic evolution drives lung adenocarcinoma spatial heterogeneity and progression. Cancer Discov 2021;11:1490-507. [Crossref] [PubMed]
78. Mainardi S, Mijimolle N, Francoz S, et al. Identification of cancer initiating cells in K-Ras driven lung adenocarcinoma. Proc Natl Acad Sci U S A 2014;111:255-60. [Crossref] [PubMed]
79. Zhu J, Fan Y, Xiong Y, et al. Delineating the dynamic evolution from preneoplasia to invasive lung adenocarcinoma by integrating single-cell RNA sequencing and spatial transcriptomics. Exp Mol Med 2022;54:2060-76. [Crossref] [PubMed]
80. Yang D, Jones MG, Naranjo S, et al. Lineage tracing reveals the phylodynamics, plasticity, and paths of tumor evolution. Cell 2022;185:1905-23.
81. Zhang S, You X, Zheng Y, et al. The UBE2C/CDH1/DEPTOR axis is an oncogene and tumor suppressor cascade in lung cancer cells. J Clin Invest 2023;133:e162434. [Crossref] [PubMed]
82. Deng Y, Xia L, Zhang J, et al. Multicellular ecotypes shape progression of lung adenocarcinoma from ground-glass opacity toward advanced stages. Cell Rep Med 2024;5:101489. [Crossref] [PubMed]
83. Lu T, Yang X, Shi Y, et al. Single-cell transcriptome atlas of lung adenocarcinoma featured with ground glass nodules. Cell Discov 2020;6:69. [Crossref] [PubMed]
84. AbdulJabbar K. Geospatial immune variability illuminates differential evolution of lung adenocarcinoma. Nat Med 2020;26:1054-62. [Crossref] [PubMed]
85. Akhtar M, Haider A, Rashid S, et al. Paget's “seed and soil” theory of cancer metastasis: an idea whose time has come. Adv Anat Pathol 2019;26:69-74. [Crossref] [PubMed]
86. Micalizzi DS, Maheswaran S, Haber DA. A conduit to metastasis: circulating tumor cell biology. Genes Dev 2017;31:1827-40. [Crossref] [PubMed]
87. Gonzalez H, Mei W, Robles I, et al. Cellular architecture of human brain metastases. Cell 2022;185:729-45.
88. Srinivasan ES, Deshpande K, Neman J, et al. The microenvironment of brain metastases from solid tumors. Neurooncol Adv 2021;3:v121-32. [Crossref] [PubMed]
89. Zhang Q, Abdo R, Iosef C, et al. The spatial transcriptomic landscape of non-small cell lung cancer brain metastasis. Nat Commun 2022;13:5983. [Crossref] [PubMed]
90. Souza VGP, Telkar N, Lam WL, et al. Comprehensive analysis of lung adenocarcinoma and brain metastasis through integrated single-cell transcriptomics. Int J Mol Sci 2024;25:3779. [Crossref] [PubMed]
91. Zhang X, Xiao K, Wen Y, et al. Multi-omics with dynamic network biomarker algorithm prefigures organ-specific metastasis of lung adenocarcinoma. Nat Commun 2024;15:9855. [Crossref] [PubMed]
92. Tang J, Tu K, Lu K, et al. Single-cell exome sequencing reveals multiple subclones in metastatic colorectal carcinoma. Genome Med 2021;13:148. [Crossref] [PubMed]
93. Sun Z, Hu M, Huang X, et al. Predictive value of dendritic cell-related genes for prognosis and immunotherapy response in lung adenocarcinoma. Cancer Cell Int 2025;25:13. [Crossref] [PubMed]
94. Huang Y, Wu G, Bi G, et al. Unveiling chemotherapy-induced immune landscape remodeling and metabolic reprogramming in lung adenocarcinoma by scRNA-sequencing. Elife 2024;13:RP95988. [Crossref] [PubMed]
95. Qian L, Wu L, Miao X, et al. The role of TIGIT-CD226-PVR axis in mediating T cell exhaustion and apoptosis in NSCLC. Apoptosis 2025;30:784-804. [Crossref] [PubMed]
96. Yan Y, Sun D, Hu J, et al. Multi-omic profiling highlights factors associated with resistance to immuno-chemotherapy in non-small-cell lung cancer. Nat Genet 2025;57:126-39. [Crossref] [PubMed]
97. Zhao Y, Gao J, Wang J, et al. Genomic and immune heterogeneity of multiple synchronous lung adenocarcinoma at different developmental stages. Nat Commun 2024;15:7928. [Crossref] [PubMed]
98. Yang C, Qu J, Wu J, et al. Single-cell dissection reveals immunosuppressive F13A1+ macrophage as a hallmark for multiple primary lung cancers. Clin Transl Med 2024;14:e70091. [Crossref] [PubMed]
99. Ren YF, Ma Q, Zeng X, et al. Single-cell RNA sequencing reveals immune microenvironment niche transitions during the invasive and metastatic processes of ground-glass nodules and part-solid nodules in lung adenocarcinoma. Mol Cancer 2024;23:263. [Crossref] [PubMed]
100. Molina-Alejandre M, Perea F, Calvo V, et al. Perioperative chemoimmunotherapy induces strong immune responses and long-term survival in patients with HLA class I-deficient non-small cell lung cancer. J Immunother Cancer 2024;12:e009762. [Crossref] [PubMed]
101. Li Y, Xie T, Wang S, et al. Mechanism exploration and model construction for small cell transformation in EGFR-mutant lung adenocarcinomas. Signal Transduct Target Ther 2024;9:261. [Crossref] [PubMed]
102. Zhang D, Wang M, Liu G, et al. Novel FABP4(+)C1q(+) macrophages enhance antitumor immunity and associated with response to neoadjuvant pembrolizumab and chemotherapy in NSCLC via AMPK/JAK/STAT axis. Cell Death Dis 2024;15:717. [Crossref] [PubMed]
103. Fan G, Xie T, Tang L, et al. The co-location of CD14+APOE+ cells and MMP7+ tumour cells contributed to worse immunotherapy response in non-small cell lung cancer. Clin Transl Med 2024;14:e70009. [Crossref] [PubMed]
104. Lin G, Lin L, Chen X, et al. PPAR-γ/NF-kB/AQP3 axis in M2 macrophage orchestrates lung adenocarcinoma progression by upregulating IL-6. Cell Death Dis 2024;15:532. [Crossref] [PubMed]
105. Zhang Z, Sun X, Liu Y, et al. Spatial transcriptome-wide profiling of small cell lung cancer reveals intra-tumoral molecular and subtype heterogeneity. Adv Sci (Weinh) 2024;11:e2402716. [Crossref] [PubMed]
106. De Zuani M, Xue H, Park JS, et al. Single-cell and spatial transcriptomics analysis of non-small cell lung cancer. Nat Commun 2024;15:4388. [Crossref] [PubMed]
107. Hu Z, Sui Q, Jin X, et al. IL6-STAT3-C/EBPβ-IL6 positive feedback loop in tumor-associated macrophages promotes the EMT and metastasis of lung adenocarcinoma. J Exp Clin Cancer Res 2024;43:63. [Crossref] [PubMed]
108. Li Y, Jiang M, Aye L, et al. UPP1 promotes lung adenocarcinoma progression through the induction of an immunosuppressive microenvironment. Nat Commun 2024;15:1200. [Crossref] [PubMed]
109. Qin Z, Yue M, Tang S, et al. EML4-ALK fusions drive lung adeno-to-squamous transition through JAK-STAT activation. J Exp Med 2024;221:e20232028. [Crossref] [PubMed]
110. Ao YQ, Gao J, Jin C, et al. ASCC3 promotes the immunosuppression and progression of non-small cell lung cancer by impairing the type I interferon response via CAND1-mediated ubiquitination inhibition of STAT3. J Immunother Cancer 2023;11:e007766. [Crossref] [PubMed]
111. Chen M, Ma P, Zhang Y, et al. Divergent tumor and immune cell reprogramming underlying immunotherapy response and immune-related adverse events in lung squamous cell carcinoma. J Immunother Cancer 2023;11:e007305. [Crossref] [PubMed]
112. Wang Y, Liu B, Min Q, et al. Spatial transcriptomics delineates molecular features and cellular plasticity in lung adenocarcinoma progression. Cell Discov 2023;9:96. [Crossref] [PubMed]
113. Dykema AG, Zhang J, Cheung LS, et al. Lung tumor-infiltrating T(reg) have divergent transcriptional profiles and function linked to checkpoint blockade response. Sci Immunol 2023;8:eadg1487. [Crossref] [PubMed]
114. Fan F, Gao J, Zhao Y, et al. Elevated mast cell abundance is associated with enrichment of CCR2+ cytotoxic T cells and favorable prognosis in lung adenocarcinoma. Cancer Res 2023;83:2690-703. [Crossref] [PubMed]
115. He P, Lim K, Sun D, et al. A human fetal lung cell atlas uncovers proximal-distal gradients of differentiation and key regulators of epithelial fates. Cell 2022;185:4841-60.
116. Salcher S, Sturm G, Horvath L, et al. High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer. Cancer Cell 2022;40:1503-20.
117. Tian Y, Li Q, Yang Z, et al. Single-cell transcriptomic profiling reveals the tumor heterogeneity of small-cell lung cancer. Signal Transduct Target Ther 2022;7:346. [Crossref] [PubMed]
118. Wang C, Yu Q, Song T, et al. The heterogeneous immune landscape between lung adenocarcinoma and squamous carcinoma revealed by single-cell RNA sequencing. Signal Transduct Target Ther 2022;7:289. [Crossref] [PubMed]
119. Zhang H, Liu Z, Wen H, et al. Immunosuppressive TREM2(+) macrophages are associated with undesirable prognosis and responses to anti-PD-1 immunotherapy in non-small cell lung cancer. Cancer Immunol Immunother 2022;71:2511-22. [Crossref] [PubMed]
120. Zhang L, Zhang Y, Wang C, et al. Integrated single-cell RNA sequencing analysis reveals distinct cellular and transcriptional modules associated with survival in lung cancer. Signal Transduct Target Ther 2022;7:9. [Crossref] [PubMed]
121. Leader AM, Grout JA, Maier BB, et al. Single-cell analysis of human non-small cell lung cancer lesions refines tumor classification and patient stratification. Cancer Cell 2021;39:1594-1609.
122. Chan JM, Quintanal-Villalonga Á, Gao VR, et al. Signatures of plasticity, metastasis, and immunosuppression in an atlas of human small cell lung cancer. Cancer Cell 2021;39:1479-96.
123. Schenkel JM, Herbst RH, Canner D, et al. Conventional type I dendritic cells maintain a reservoir of proliferative tumor-antigen specific TCF-1+ CD8+ T cells in tumor-draining lymph nodes. Immunity 2021;54:2338-53.
124. Ireland AS, Micinski AM, Kastner DW, et al. MYC drives temporal evolution of small cell lung cancer subtypes by reprogramming neuroendocrine fate. Cancer Cell 2020;38:60-78.
125. Kim N, Kim HK, Lee K, et al. Single-cell RNA sequencing demonstrates the molecular and cellular reprogramming of metastatic lung adenocarcinoma. Nat Commun 2020;11:2285. [Crossref] [PubMed]
126. Forde PM, Kelly RJ, Brahmer JR. New strategies in lung cancer: translating immunotherapy into clinical practice. Clin Cancer Res 2014;20:1067-73. [Crossref] [PubMed]
127. Spitzer MH, Carmi Y, Reticker-Flynn NE, et al. Systemic immunity is required for effective cancer immunotherapy. Cell 2017;168:487-502.
128. Caushi JX, Zhang J, Ji Z, et al. Transcriptional programs of neoantigen-specific TIL in anti-PD-1-treated lung cancers. Nature 2021;596:126-32. [Crossref] [PubMed]
129. Chow A, Uddin FZ, Liu M, et al. The ectonucleotidase CD39 identifies tumor-reactive CD8(+) T cells predictive of immune checkpoint blockade efficacy in human lung cancer. Immunity 2023;56:93-106.
130. Liu B, Zhang Y, Wang D, et al. Single-cell meta-analyses reveal responses of tumor-reactive CXCL13(+) T cells to immune-checkpoint blockade. Nat Cancer 2022;3:1123-36. [Crossref] [PubMed]
131. Sakai SA, Oyoshi H, Nakamura M, et al. Single-cell spatial analysis with Xenium reveals anti-tumour responses of CXCL13 + T and CXCL9+ cells after radiotherapy combined with anti-PD-L1 therapy. Br J Cancer 2025;133:795-808. [Crossref] [PubMed]
132. Kim K, Park S, Park SY, et al. Single-cell transcriptome analysis reveals TOX as a promoting factor for T cell exhaustion and a predictor for anti-PD-1 responses in human cancer. Genome Med 2020;12:22. [Crossref] [PubMed]
133. Xue Q, Peng W, Zhang S, et al. Promising immunotherapeutic targets in lung cancer based on single-cell RNA sequencing. Front Immunol 2023;14:1148061. [Crossref] [PubMed]
134. Liu B, Hu X, Feng K, et al. Temporal single-cell tracing reveals clonal revival and expansion of precursor exhausted T cells during anti-PD-1 therapy in lung cancer. Nat Cancer 2022;3:108-21. [Crossref] [PubMed]
135. Zhong G, Wang Q, Wang Y, et al. scRNA-seq reveals ATPIF1 activity in control of T cell antitumor activity. Oncoimmunology 2022;11:2114740. [Crossref] [PubMed]
136. Rapoport AP, Stadtmauer EA, Binder-Scholl GK, et al. NY-ESO-1-specific TCR-engineered T cells mediate sustained antigen-specific antitumor effects in myeloma. Nat Med 2015;21:914-21. [Crossref] [PubMed]
137. Kageyama S, Ikeda H, Miyahara Y, et al. Adoptive transfer of MAGE-A4 T-cell receptor gene-transduced lymphocytes in patients with recurrent esophageal cancer. Clin Cancer Res 2015;21:2268-77. [Crossref] [PubMed]
138. Andersen R, Donia M, Ellebaek E, et al. Long-lasting complete responses in patients with metastatic melanoma after adoptive cell therapy with tumor-infiltrating lymphocytes and an attenuated IL2 regimen. Clin Cancer Res 2016;22:3734-45. [Crossref] [PubMed]
139. Chandran SS, Somerville RPT, Yang JC, et al. Treatment of metastatic uveal melanoma with adoptive transfer of tumour-infiltrating lymphocytes: a single-centre, two-stage, single-arm, phase 2 study. Lancet Oncol 2017;18:792-802. [Crossref] [PubMed]
140. Yarchoan M, Hopkins A, Jaffee EM. Tumor mutational burden and response rate to PD-1 inhibition. N Engl J Med 2017;377:2500-1. [Crossref] [PubMed]
141. Garassino MC, Cho BC, Kim JH, et al. Durvalumab as third-line or later treatment for advanced non-small-cell lung cancer (ATLANTIC): an open-label, single-arm, phase 2 study. Lancet Oncol 2018;19:521-36. [Crossref] [PubMed]
142. Sunshine J, Taube JM. PD-1/PD-L1 inhibitors. Curr Opin Pharmacol 2015;23:32-8. [Crossref] [PubMed]
143. Patil NS, Nabet BY, Müller S, et al. Intratumoral plasma cells predict outcomes to PD-L1 blockade in non-small cell lung cancer. Cancer Cell 2022;40:289-300.
144. Sharonov GV, Serebrovskaya EO, Yuzhakova DV, et al. B cells, plasma cells and antibody repertoires in the tumour microenvironment. Nat Rev Immunol 2020;20:294-307. [Crossref] [PubMed]
145. Bruno TC, Ebner PJ, Moore BL, et al. Antigen-presenting intratumoral B cells affect CD4(+) TIL phenotypes in non-small cell lung cancer patients. Cancer Immunol Res 2017;5:898-907. [Crossref] [PubMed]
146. Zhang S, Liu Y, Sun Y, et al. Aberrant R-loop-mediated immune evasion, cellular communication, and metabolic reprogramming affect cancer progression: a single-cell analysis. Mol Cancer 2024;23:11. [Crossref] [PubMed]
147. Schmelz K, Toedling J, Huska M, et al. Spatial and temporal intratumour heterogeneity has potential consequences for single biopsy-based neuroblastoma treatment decisions. Nat Commun 2021;12:6804. [Crossref] [PubMed]
148. Song X, Xiong A, Wu F, et al. Spatial multi-omics revealed the impact of tumor ecosystem heterogeneity on immunotherapy efficacy in patients with advanced non-small cell lung cancer treated with bispecific antibody. J Immunother Cancer 2023;11:e006234. [Crossref] [PubMed]
149. Aung TN, Monkman J, Warrell J, et al. Spatial signatures for predicting immunotherapy outcomes using multi-omics in non-small cell lung cancer. Nat Genet 2025;57:2482-93. [Crossref] [PubMed]
150. Monkman J, Kim H, Mayer A, et al. Multi-omic and spatial dissection of immunotherapy response groups in non-small cell lung cancer. Immunology 2023;169:487-502. [Crossref] [PubMed]
151. Cui X, Liu S, Song H, et al. Single-cell and spatial transcriptomic analyses revealing tumor microenvironment remodeling after neoadjuvant chemoimmunotherapy in non-small cell lung cancer. Mol Cancer 2025;24:111. [Crossref] [PubMed]
152. Schumacher TN, Thommen DS. Tertiary lymphoid structures in cancer. Science 2022;375:eabf9419. [Crossref] [PubMed]
153. Vanhersecke L, Brunet M, Guégan JP, et al. Mature tertiary lymphoid structures predict immune checkpoint inhibitor efficacy in solid tumors independently of PD-L1 expression. Nat Cancer 2021;2:794-802. [Crossref] [PubMed]
154. Wang Y, Lin H, Yao N, et al. Computerized tertiary lymphoid structures density on H&E-images is a prognostic biomarker in resectable lung adenocarcinoma. iScience 2023;26:107635. [Crossref] [PubMed]
155. Li J, Gu J. PD-L1 expression and EGFR status in advanced non-small-cell lung cancer patients receiving PD-1/PD-L1 inhibitors: a meta-analysis. Future Oncol 2019;15:1667-78. [Crossref] [PubMed]
156. Datar I, Sanmamed MF, Wang J, et al. Expression analysis and significance of PD-1, LAG-3, and TIM-3 in human non-small cell lung cancer using spatially resolved and multiparametric single-cell analysis. Clin Cancer Res 2019;25:4663-73. [Crossref] [PubMed]
157. Yang L, He YT, Dong S, et al. Single-cell transcriptome analysis revealed a suppressive tumor immune microenvironment in EGFR mutant lung adenocarcinoma. J Immunother Cancer 2022;10:e003534. [Crossref] [PubMed]
158. Wang H, Xiong A, Chen X, et al. CXCR1(+) neutrophil infiltration orchestrates response to third-generation EGFR-TKI in EGFR mutant non-small-cell lung cancer. Signal Transduct Target Ther 2024;9:342. [Crossref] [PubMed]
159. Izumi M, Fujii M, Kobayashi IS, et al. Integrative single-cell RNA-seq and spatial transcriptomics analyses reveal diverse apoptosis-related gene expression profiles in EGFR-mutated lung cancer. Cell Death Dis 2024;15:580. [Crossref] [PubMed]
160. Zhang C, Zhou W, Xu H, et al. Cancer-associated fibroblasts promote EGFR-TKI resistance via the CTHRC1/glycolysis/H3K18la positive feedback loop. Oncogene 2025;44:1400-14. [Crossref] [PubMed]
161. Xu Y, Su GH, Ma D, et al. Technological advances in cancer immunity: from immunogenomics to single-cell analysis and artificial intelligence. Signal Transduct Target Ther 2021;6:312. [Crossref] [PubMed]
162. Jain S, Eadon MT. Spatial transcriptomics in health and disease. Nat Rev Nephrol 2024;20:659-71. [Crossref] [PubMed]
163. Schott M, León-Periñán D, Splendiani E, et al. Open ST: High-resolution spatial transcriptomics in 3D. Cell 2024;187:3953-72.
164. Chen HN, Shu Y, Liao F, et al. Genomic evolution and diverse models of systemic metastases in colorectal cancer. Gut 2022;71:322-32. [Crossref] [PubMed]