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Multi-omic profiling highlights factors associated with resistance to immuno-chemotherapy in non-small-cell lung cancer

Nature Genetics volume 57, pages 126–139 (2025)Cite this article

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Abstract

Although immune checkpoint blockade (ICB) therapies have shifted the treatment paradigm for non-small-cell lung cancer (NSCLC), many patients remain resistant. Here we characterize the tumor cell states and spatial cellular compositions of the NSCLC tumor microenvironment (TME) by analyzing single-cell transcriptomes of 232,080 cells and spatially resolved transcriptomes of tumors from 19 patients before and after ICB–chemotherapy. We find that tumor cells and secreted phosphoprotein 1-positive macrophages interact with collagen type XI alpha 1 chain-positive cancer-associated fibroblasts to stimulate the deposition and entanglement of collagen fibers at tumor boundaries, obstructing T cell infiltration and leading to poor prognosis. We also reveal distinct states of tertiary lymphoid structures (TLSs) in the TME. Activated TLSs are associated with improved prognosis, whereas a hypoxic microenvironment appears to suppress TLS development and is associated with poor prognosis. Our study provides novel insights into different cellular and molecular components corresponding to NSCLC ICB–chemotherapeutic responsiveness, which will benefit future individualized immuno-chemotherapy.

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Fig. 1: Cellular and molecular characterization of NSCLC tissues before and after ICB–chemotherapy using combined scRNA-seq and spatial transcriptomic analysis.
Fig. 2: Cell states of cancer cells potentially define the response to ICB–chemotherapy.
Fig. 3: Spatial cellular organization of NSCLC following ICB–chemotherapy.
Fig. 4: Spatial location and potential pro-carcinogenic function of COL11A1+ CAFs.
Fig. 5: SPP1+ macrophages colocalize with COL11A1+ CAFs, blocking T cells.
Fig. 6: Characteristics of TLSs at different stages, and clinical significance of TLSs.
Fig. 7: Summary of this study.

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Data availability

Raw scRNA-seq and spatial transcriptomics data have been deposited to the Genome Sequence Archive of the BIG Data Center at the Beijing Institute of Genomics, Chinese Academy of Science, under accession number HRA002509 (accessible at http://bigd.big.ac.cn/gsa-human). Processed bulk RNA-seq, scRNA-seq and spatial transcriptomics data are available from Zenodo (accessible at https://zenodo.org/records/8227624)76. The bulk RNA-seq data of the independent NSCLC cohort6 were acquired from the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo) with the accession GSE207422. The bulk RNA-seq data of the Stand Up To Cancer–Mark Foundation for Cancer Research cohort31 are available from Zenodo (accessible at https://zenodo.org/records/7849582)77. The bulk RNA-seq data of the OAK and POPLAR cohorts30 were acquired from the European Genome-Phenome Archive (https://ega-archive.org) with the accession EGAS00001005013. The bulk RNA-seq data of the melanoma cohort32 were acquired from the Gene Expression Omnibus with the accession GSE78220.

Code availability

The code for identifying the TLSs in spatial transcriptomics slides can be found at https://github.com/wanglabtongji/Scanner. Code for inferring the cell type distribution in spatial transcriptomics data can be found at https://github.com/wanglabtongji/STRIDE.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (82125001 (P.Z.), 62088101 (C.W.), 82201948 (L.Z.), 32170660 (C.W.) and 82030035 (Y.E.S.)), National Key R&D Program of China (grant 2022YFA1106000 (C.W.)), Clinical Research Plan of Shanghai Hospital Development Center (grant SHDC2020CR2020B (P.Z.)), Innovation Program of the Shanghai Municipal Education Commission (grant 2023ZKZD33 (P.Z.)), Shanghai Pulmonary Hospital (FKCX1904 (P.Z.), FKYQ2308 (L.Z.) and FKLY20004 (P.Z.)), Shanghai Pilot Program for Basic Research (C.W.), Shanghai Municipal Science and Technology Major Project (2021SHZDZX0100 (C.W.)), Peak Disciplines (Type IV) of Institutions of Higher Learning in Shanghai (C.W. and Y.E.S.) and Fundamental Research Funds for the Central Universities (22120240435 (C.W.)).

Author information

Author notes

  1. These authors contributed equally: Yilv Yan, Dongqing Sun, Junjie Hu, Yue Chen.

  2. These authors jointly supervised this work: An Zeng, Lele Zhang, Yi Eve Sun, Chenfei Wang, Peng Zhang.

Authors and Affiliations

  1. Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China

    Yilv Yan, Junjie Hu, Liangdong Sun, Huansha Yu, Yicheng Xiong, Zhida Huang, Haoran Xia, Xinsheng Zhu, Dongliang Bian, Fenghuan Sun & Peng Zhang

  2. Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Orthopedics, Tongji Hospital, School of Life Science and Technology, Tongji University, Shanghai, China

    Dongqing Sun & Chenfei Wang

  3. Frontier Science Center for Stem Cell Research, School of Life Sciences and Technology, Tongji University, Shanghai, China

    Dongqing Sun & Chenfei Wang

  4. State Key Laboratory of Cell Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China

    Yue Chen & An Zeng

  5. Department of Pathology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China

    Likun Hou & Chunyan Wu

  6. Stem Cell Translational Research Center, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China

    Orion R. Fan & Yi Eve Sun

  7. Central Laboratory, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China

    Haiyang Hu & Lele Zhang

  8. National Key Laboratory of Autonomous Intelligent Unmanned Systems, Tongji University, Shanghai, China

    Chenfei Wang

  9. Frontier Science Center for Intelligent Autonomous Systems, Tongji University, Shanghai, China

    Chenfei Wang

  10. Department of Thoracic Surgery, The First Affiliated Hospital of Shihezi University Medical College, Shihezi, China

    Peng Zhang

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  1. Yilv Yan
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Contributions

P.Z., C.W., Y.E.S., L.Z. and A.Z. conceived of and designed the study. H.Y., Y.X., Z.H., H.X., X.Z., D.B., F.S., Y.C., L.H., C.W., A.Z. and L.Z. developed and performed the experiments or collected data. Y.Y., D.S., J.H., L.S. and C.W. designed and performed the computation and statistical analyses. Y.Y., D.S., J.H., O.R.F., H.H., L.Z., Y.E.S., C.W. and P.Z. wrote, reviewed and edited the manuscript.

Corresponding authors

Correspondence to An Zeng, Lele Zhang, Yi Eve Sun, Chenfei Wang or Peng Zhang.

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Competing interests

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Nature Genetics thanks Sydney Shaffer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Characterization of immunotherapy-treated NSCLC by combined scRNA-seq and spatial transcriptomic analysis.

a, Dot-heat plot showing marker genes for each major lineage. The color gradient represents the expression level, and the diameter represents the percentage of cells. b, Examples of inferred CNV profiles of malignant cells and normal epithelial cells, with endothelium and CD31− stromal cells as references. Left panel: lung squamous cell carcinoma (LUSC). Right panel: lung adenocarcinoma (LUAD). c, Dot-heat plot showing the markers of adeno, squamous and neuroendocrine tumors in P08 and P17.d, UMAP plot of all epithelial cells colored by subpopulations. e, Boxplot showing the CNV score of each epithelium subpopulation. Box limits denote the first and third quartiles with the median shown in the center and whiskers covering data within 1.5× the interquartile range from the box. f, Dot-heat plot showing the marker genes for each normal epithelial cluster. The color gradient represents the scaled expression level, and the diameter represents the percentage of cells. g, Bar plot showing the percentage of major lineages for each sample. Related to Fig. 1.

Extended Data Fig. 2 Characterization of immunotherapy-treated NSCLC by combined scRNA-seq and spatial transcriptomic analysis.

a, Dot-heat plot showing the marker genes for each T cluster. The color gradient represents the scaled expression level, and the diameter represents the percentage of cells. b, Dot-heat plot showing the marker genes for each B cluster. The color gradient represents the scaled expression level, and the diameter represents the percentage of cells. c, Dot-heat plot showing the marker genes for each Mono/Macro cluster. The color gradient represents the scaled expression level, and the diameter represents the percentage of cells. d, Dot-heat plot showing the marker genes for each CD31− stromal cluster. The color gradient represents the scaled expression level, and the diameter represents the percentage of cells. e, Dot-heat plot showing the marker genes for each DC cluster. The color gradient represents the scaled expression level, and the diameter represents the percentage of cells. f, Dot-heat plot showing the marker genes for each endothelial cluster. The color gradient represents the scaled expression level, and the diameter represents the percentage of cells. Related to Fig. 1.

Extended Data Fig. 3 Spatial mapping of the cellular context in NSCLC after ICB-chemotherapy.

a, Scaled average cell-type compositions within each cell-type context. Related to Fig. 3.

Extended Data Fig. 4 Single-cell and spatial transcriptomic analyses of fibroblast subsets after ICB-chemotherapy.

a, Violin plots showing iCAF and myCAF signature scores in COL11A1+ CAFs (n = 2359) and ADH1B+ CAFs (n = 956). Box limits denote the first and third quartiles with the median shown in the center and whiskers covering data within 1.5× the interquartile range from the box. b, Heatmap showing differentially enriched Hallmark and KEGG metabolic pathways in CD31− stromal subsets. c, Heatmap showing potential ligands driving the phenotype of COL11A1+ CAF cells. d, Boxplots showing the average expression of COL11A1 in COL11A1+ CAF cells from pre-treatment responders (PreR, n = 3), pre-treatment non-responders (PreNR, n = 4), post-treatment responders (PostR, n = 5) and post-treatment non-responders (PostNR, n = 12). The center line indicates the median, and the lower and upper hinges represent the 25th and 75th percentiles, respectively. Whiskers denote 1.5× interquartile range. A two-sided t test was used to determine the statistical significance. e, Kaplan-Meier survival curve of the TCGA-LUAD cohort dichotomized by the expression of COL11A1. The survival curves were compared by log-rank test. f, Spatial distribution of tumor spots, the fractions of COL11A1+ CAFs and ADH1B+ CAFs and the expression of COL11A1 and ADH1B in the slide of isolated tumor from P12. g,h, Scatter plots showing a significantly negative correlation between COL11A1+ CAF abundance and distance to tumors (spot) and a positive correlation between ADH1B+ CAF abundance and distance to tumors (spot) in the slides of isolated tumor from P11 (g) and P12 (h). The expression levels of COL11A1 or ADH1B in each spot are shown by the color gradient. The curves were fitted using a locally weighted regression (loess) model. The two-sided P values were determined by Pearson’s correlation test. i, Spatial distribution of tumor spots, fractions of COL11A1+ CAFs and ADH1B+ CAFs and the expression of COL11A1 and ADH1B in the slides of tumor stromal regions from P02 and P09. j, Scatter plot showing a significant negative correlation between the colocalization levels of COL11A1−DDR1 and the collagen formation signatures and distance to tumors (spot) in the slide from P11. The curves were fitted using linear and locally weighted regression (loess) models, respectively. The two-sided P values were determined by Pearson’s correlation test. Related to Fig.4.

Extended Data Fig. 5 Spatial transcriptomic and scRNA-seq analyses of macrophage subsets after ICB-chemotherapy.

a, Boxplots showing the fractions of Mono/Macro subsets in matched samples from 3 responders and 4 non-responders at baseline and post-treatment phase. b, Boxplots showing the fractions of Mono/Macro subsets from pre-treatment responders (PreR, n = 3), pre-treatment non-responders (PreNR, n = 4), post-treatment responders (PostR, n = 6) and post-treatment non-responders (PostNR, n = 13). c, Scattered plot showing a significant negative correlation between the fraction of SPP1+ macrophages or CXCL9+ macrophages and distance to tumors (spot) in the slide of isolated tumor from P11. The expression level of SPP1 or CXCL9 in each spot is shown by the color gradient. d, Spatial distribution of tumor spots, fractions of SPP1+ macrophages and CXCL9+ macrophages and the expression of SPP1 and CXCL9 in the slide of isolated tumor from P12. e, Scattered plot showing a significant negative correlation between the fractions of SPP1+ macrophages or CXCL9+ macrophages and the distances to tumors (spot) in the slide of isolated tumor from P12. Expression levels of SPP1 or CXCL9 in each spot are shown by the color gradient. f, Scatter plot showing a significant negative correlation between the colocalization levels of CXCL9-CXCR3 and cytotoxic signature and the distances to tumors (spot) in the slide from P11. g, Heatmap showing the averaged module scores of M1 and M2 signatures among macrophage subsets. h, Boxplots showing the fractions of CXCL9+ macrophages in matched samples from 3 responders and 4 non-responders at baseline and post-treatment. i, Box plots of average expression of CXCL9 in CXCL9+ macrophages in matched samples from 3 responders and 4 non-responders at baseline and post-treatment phase. This result indicates that CXCL9 expression in macrophages before but not post-treatment is high in responders. In panels c, e and f, the curves were fitted using locally weighted regression (loess), linear and loess models, respectively. Two-sided P values were determined by Pearson’s correlation test. In panels a, b, h and i, a one-sided t-test was used to determine the statistical significance. For the boxplots, box limits denote the first and third quartiles, with the median shown in the center and whiskers covering data within 1.5× the interquartile range from the box. Related to Fig.5.

Extended Data Fig. 6 Spatial distribution of T and B cells and identification of TLSs.

a, The spatial distribution of T cells in patient 11 (P11). The darker red color represents a higher cell-type proportion. b, The spatial distribution of B cells P11. The darker red color represents a higher cell-type proportion. c, Boxplots showing the differences in T cell subtype proportions in tumor (n = 150), tumor boundary (n = 162) and stromal (n = 3651) regions in patient P11. Statistical significance was determined by two-sided Wilcoxon rank-sum test. ns: P > = 0.05; *: P < 0.05; **: P < 0.01; ***: P < 0.001; ****: P < 0.0001. d, Heat-dot plot showing cell-type colocalizations in patient P11. Pearson correlations of the estimated cell-type proportions were calculated for each pair of cell types across all spots. The color and size of the dot represent the correlation coefficient. A positive correlation coefficient indicates colocalization of the cell-type pair, whereas negative values indicate exclusion of the two cell types from each other. The black rectangle highlights the colocalization of T and B cells. e, Pathological examination of TLSs in patient P11. Scale bar: 1 mm. All Visium slides (n = 17) were examined. f, The automated TLS identification workflow named Space Scanner through combining H&E image and ST data. g, Location of identified TLSs in patient P11. Red and gray colors represent TLSs and surrounding stroma respectively. h, Heatmap showing the averaged expression of 12 chemokines in TLSs and stroma from patient P11. i, Boxplots showing the differences in B (left panels) and T (right panels) cell subtype proportions within TLSs (n = 210) and stroma (n = 294) in patient P11. Statistical significance was determined by two-sided Wilcoxon rank-sum test. ns: P > = 0.05; *: P < 0.05; **: P < 0.01; ***: P < 0.001; ****: P < 0.0001. For the boxplots in c and i, the box limits denote the first and third quartiles, with the median shown in the center and whiskers covering data within 1.5× the interquartile range from the box.

Extended Data Fig. 7 Spatial distributions of T and B cells and identification of TLSs.

a, Spatial distributions of CD4 T cells, Treg and CD8 T cells, relative to tumor spots in patient P11. CD4 and CD8 T cell proportions were measured by the sum of the corresponding subtypes. The darker red color in the left 3 panels represents higher cell-type proportions. The right panel shows the definition of tumor spots, tumor-boundaries and stroma regions in patient P11. b, The spatial distribution of T (top) and B cell proportions (middle) and the location of identified TLSs (bottom) in samples excluding P11. c, Cell-type compositions of the identified TLSs in 14 samples. The average proportions of all TLS spots were calculated to represent the cellular compositions of each TLS. Related to Extended Data Fig.6.

Extended Data Fig. 8 Characteristics of TLSs in different stages.

a, Spatial distributions of LTB expression in all slides with TLSs. b, Boxplots showing the differences in B (left) and T (right) subtype proportions in TLSs at different stages from all samples. For each TLS, the average subtype proportions of covered spots were calculated to represent the cellular subtype proportions of each TLS. lymphoid aggregates, n = 42; Activated TLS, n = 71; Declining TLS, n = 103; Late TLS, n = 67. Statistical significance was determined by two-sided Wilcoxon rank-sum test. ns: P > = 0.05; *: P < 0.05; **: P < 0.01; ***: P < 0.001; ****: P < 0.0001. The box limits denote the first and third quartiles, with the median shown in the center and whiskers covering data within 1.5× the interquartile range from the box. c, Bar plot showing the enriched Hallmark pathways in NMPR, related to Fig. 6h. The color represents the significance. A P value denotes the enrichment P value of the pathway ID determined by Fisher’s exact test. d, PCA visualization of the hypoxia signature score in all TLSs. e, The correlation between the hypoxia signature score and the fraction of Treg (left) and CD4_TCF7 (right) in all TLSs. The blue lines are regression lines. The gray bands are 95% confidence bands for the regression line. Each dot represents an identified TLS. The two-sided P values was measured by Pearson ‘s correlation test. Related to Fig.6.

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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 57, 126–139 (2025). https://doi.org/10.1038/s41588-024-01998-y

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