Epithelial plasticity shapes intratumoral heterogeneity and cell lineages in early-stage lung cancer

Abstract

Intratumoral heterogeneity (ITH) has been investigated primarily in locally advanced or metastatic cancer; however, much less is known about ITH in early-stage cancer, and the origins of ITH are poorly understood. Through single-cell and spatial transcriptomics of early-stage ground-glass opacity (GGO)-like lung adenocarcinoma (LUAD) (14 patients; 103,375 cells), we systematically define tumor states and demonstrate that pervasive transcriptional ITH exists in early-stage LUAD. Lineage diversification through epithelial plasticity, via a shift to less differentiated states and transdifferentiation, underlies a critical dimension of early ITH in lung cancer. We further reveal that decreased differentiation serves as a pathognomonic feature of malignant transformation and predicts poor prognosis. Notably, we identified a unique transitional state during AT2-to-AT1 transdifferentiation with activated tumor-suppressive pathways/genes. Integrative analysis of scRNA-seq, CUT&Tag, and bulk RNA-seq reveals that KLF4 and JDP2 are key transcription factors that reprogram LUAD into transitional state and inhibit progression. These findings elucidate ITH mechanisms in early-stage cancer and propose epithelial plasticity-targeted therapies.

Fig. 1.. Global analysis of cell populations in IA and MIA LUAD patient samples.

(A) Schematic overview of the experimental design and analysis workflow. (B) UMAP plot depicting the major cell types identified in MIA (n = 6) and IA (n = 6). UMAP, uniform manifold approximation and projection. (C) Dot plot illustrating the expression levels of canonical marker genes across all major cell types. (D) Histogram displaying the cell composition percentage of each patient sample. (E) Quantification of cell cluster frequency representation among tissues from MIA to IA. The median and interquartile range are shown for each patient group, two-sided t test. (F) Heatmap showing large-scale CNVs for individual cells from 12 LUAD patient samples, clustered by the K-means clustering algorithm (rows). Nonmalignant cells were used as references (top), with large-scale CNVs observed in malignant cells (middle). The color indicates the log₂ CNV ratio. Red: amplifications; blue: deletions. (G) Box plot presenting the CNV scores of different groups clustered by the K-means clustering algorithm. (H) Quantification of cell cluster frequency representation among tumor cells from MIA to IA. The median and interquartile range are shown for each patient group, two-sided Wilcoxon test. NK, natural killer; EPCAM, epithelial cell adhesion molecule.

Fig. 2.. The decreased differentiation of epithelial cells marks tumor progression.

(A) UMAP plot depicting the major cell types identified in MIA (n = 6) and IA (n = 6). (B) Hallmark pathway enrichment in tumor cells among patients with MIA and IA, as determined by GSEA. Only pathways with a q value < 0.05 are displayed. (C) Box plots showing the MYC, P53, TNF-α, and WNT pathway scores per cell by AUCell between normal AT2 cells and tumor cells in the MIA, IA, advanced, and metastatic stages. The median and interquartile range are shown for each group, two-sided Wilcoxon test. (D) Inferred tumor cell lineages per cell using the scHCL database, comparing normal AT2 cells, AT1 cells, and tumor cells in the MIA, IA, advanced, and metastatic stages. (E) Distribution of AT1 cell and AT2 cell scores determined by AUCell across normal AT2 cells, AT1 cells, and tumor cells in the MIA, IA, advanced, and metastatic stages. Two-sided Wilcoxon test. (F) Trajectory of cells from normal AT1 cells and AT2 cells to the MIA, IA, advanced, and metastatic stages, constructed by Monocle2. Each point corresponds to a single cell. (G) AT1 cell and AT2 cell scores in the trajectory of cells from normal AT1 cells and AT2 cells to MIA, IA, advanced, and metastatic stages, constructed by Monocle2. Each point corresponds to a single cell. (H) Kaplan-Meier curves of overall survival for AT2 and AT1 scores in two independent LUAD cohorts, the TCGA LUAD cohort and the GSE72094 LUAD cohort (40). P values were calculated using a two-sided log-rank test, with scores dichotomized as high or low. High: samples within top quartile of signature score. Low: samples below the third quartile of signature score.

Fig. 3.. Epithelial plasticity drives early ITH in lung cancer.

(A) UMAP plot showing 9653 tumor cells from MIA (n = 6) and IA (n = 6). (B) Histogram displaying the tumor cell composition percentage of each patient sample. (C) Heatmap showing the top 10 DEGs in the cluster. (D) Spatial transcriptome atlas depicting the spatial regions and sub regions of the cancer region of P13 and P14 patients. (E) Histogram displaying the cell composition percentage of P13 and P14 patient sample. (F) Inferred tumor cell lineages per cell using the scHCL database, comparing normal AT2 cells, AT1 cells, and tumor cells in the C1 to C4 clusters, advanced, and metastatic stages. (G) Distribution of AT1 and AT2 cell scores determined by AUCell across the same groups as in (F). Two-sided Wilcoxon test. (H) Pseudo-time trajectory of normal AT1 cells, AT2 cells, and tumor cells in the C1 to C4 clusters, constructed with Monocle2. (I) Proportion of normal AT1 cells, AT2 cells, and tumor cells on different branches in the trajectory. (J) AT1 cell and AT2 cell scores in the trajectory of normal AT1 cells, AT2 cells, and tumor cells in the C1 to C4 clusters, constructed with Monocle2. (K) Correlation of AT1 and AT2 score with transcriptional diversity expression in genetically engineered mouse LUAD, adapted from data reported by Marjanovic et al. (21). The Pearson’s correlation coefficient is shown. (L) Cell distribution of cells from normal AT1 cells, AT2 cells, and the C2 cluster. Each point corresponds to a single cell. (M) Dot plot illustrating the expression levels of transitional cell marker genes across all tumor clusters. (N) Cell-cell transitions estimated using scvelo, revealing distinct trajectories of C2 cluster from AT2 cells to AT1 cells. Differentiation scores of AT1 cells, AT2 cells, and the C2 cluster were inferred via CytoTRACE. (O) Mechanism of ITH formation.

Fig. 4.. Tumor clusters in early-stage LUAD predict clinical outcome.

(A) UMAP plot of tumor cells, color-coded for the C1 and C3 clusters. (B) Box plots showing the AT1 cell and AT2 cell scores of different tumor clusters. The median and interquartile range are shown for each cluster, two-sided Wilcoxon test. (C) Quantification of tumor cell cluster frequency representation among tissues from MIA to IA. The median and interquartile range are shown for each patient group, two-sided Wilcoxon test. (D) Box plot showing the C1 to C4 scores across early, advanced, and metastatic stages. The median and interquartile range are shown for each cluster, two-sided Wilcoxon test. (E) Violin plot displaying marker gene expression of CRABP2 and LAMP3 in tumor cells. (F) Violin plot displaying the CRABP2⁺ cancer cell and LAMP3⁺ cancer cell marker genes and their signature gene set scores, adapted from LUAD data reported by Zhu et al. (34). (G) Dot plot illustrating different signaling pathways in C1 and C3 clusters using Gene Ontology (GO) enrichment analysis. (H) Number of significant ligand-receptor pairs between any pair of two cell populations in the C1 and C3 clusters. The edge width is proportional to the indicated number of ligand-receptor pairs. The circle size is proportional to the number of cells in each cell group, and the edge width represents the communication probability. (I) Kaplan-Meier curves of overall survival for C1 and C3 scores in two independent LUAD cohorts. P values were calculated using a two-sided log-rank test, with scores dichotomized as high or low. High: samples within top quartile of signature score. Low: samples below the third quartile of signature score.

Fig. 5.. KLF4⁺ cancer cells represent an AT2-to-AT1 transitional state characterized with tumor suppressor pathways/genes activation.

(A) UMAP plot of tumor cells, color-coded for the C2 cluster. (B) Violin plot displaying KLF4 gene expression in C2 cluster. (C) Violin plot displaying the KLF4⁺ cancer cell cluster marker gene and the corresponding signature gene set scores, adapted from LUAD data reported by Zhu et al. (34). (D) Representative images of immunofluorescence staining of LUAD tissue of P03 patient. Green color: EPCAM; yellow color: CLDN4; red color: KLF4; blue color: DAPI. (E) Box plots showing the AT1 scores of different tumor clusters. The median and interquartile range are shown for each cluster, two-sided Wilcoxon test. (F) Dot plot illustrating different signaling pathways in C2 clusters using GO enrichment analysis. (G) Box plot showing the P53 pathway scores of different tumor clusters. The median and interquartile range are shown for each cluster, two-sided Wilcoxon test. (H) Fractions of cells in each cell cycle stage among different tumor clusters. (I) Heatmap showing gene expression of tumor suppressor genes from the TSGene database among the four tumor cell clusters. (J) Dot plot showing TF regulon activity among cell clusters calculated via SCENIC and gene expression of TFs among tumor cell clusters calculated via SCENIC. (K) JDP2 and target genes inferred by RcisTarget from both the KLF4⁺ cancer cluster and the MP2 program. (L) Correlation of JDP2 and KLF4 expression (transcripts per million, TPM) in all LUAD samples from the TCGA dataset. The Pearson’s correlation coefficient is shown.

Fig. 6.. High expression of TF KLF4 restores transitional state features.

(A) Box plots showing KLF4 mRNA expression in the A549 LUAD cell line in the overexpression group and the control group. The median and interquartile range are shown for each cluster, two-sided Wilcoxon test. (B) Cell viability assay showing that overexpression of the KLF4 gene in KLF4^low LUAD A549 cells decreases cell proliferation. (C) Colony formation assays confirming that overexpression of the KLF4 gene decreases cancer cell proliferation in the A549 cells. (D) Transwell assays confirmed that overexpression of the KLF4 gene decreases cell migration in the A549 cells. (E) Soft agar assay showing that overexpression of the KLF4 gene inhibits anchorage-independent growth in the A549 cells. (F and G) Growth curves of xenograft tumors (F) and subcutaneous tumor size (G) of each group showing that the overexpression of the KLF4 gene inhibits tumor growth. (H) CUT&Tag-seq tracks of the gene of transitional marker and C2 cluster–specific genes locus in the indicated cells. KLF4-OE: Flag-KLF4–overexpressing A549 cells (anti-Flag antibody); Ctrl: Flag-overexpressing A549 cells (anti-Flag antibody). (I) Volcano plot showing the transitional marker and C2 cluster–specific genes distribution with respect to KLF4 expression levels. (J and K) GSEA showing that the KLF4⁺ cancer cell (J), AT1 cell, and AT2 cell (K) features were affected by high expression of KLF4.