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Multiomic analysis of cervical squamous cell carcinoma identifies cellular ecosystems with biological and clinical relevance

Nature Genetics volume 55, pages 2175–2188 (2023)Cite this article

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Abstract

Cervical squamous cell carcinoma (CSCC) exhibits a limited response to immune-checkpoint blockade. Here we conducted a multiomic analysis encompassing single-cell RNA sequencing, spatial transcriptomics and spatial proteomics, combined with genetic and pharmacological perturbations to systematically develop a high-resolution and spatially resolved map of intratumoral expression heterogeneity in CSCC. Three tumor states (epithelial-cytokeratin, epithelial-immune (Epi-Imm) and epithelial senescence), recapitulating different stages of squamous differentiation, showed distinct tumor immune microenvironments. Bidirectional interactions between epithelial-cytokeratin malignant cells and immunosuppressive cancer-associated fibroblasts form an immune exclusionary microenvironment through transforming growth factor β pathway signaling mediated by FABP5. In Epi-Imm tumors, malignant cells interact with natural killer and T cells through interferon signaling. Preliminary analysis of samples from a cervical cancer clinical trial (NCT04516616) demonstrated neoadjuvant chemotherapy induces a state transition to Epi-Imm, which correlates with pathological complete remission following treatment with immune-checkpoint blockade. These findings deepen the understanding of cellular state diversity in CSCC.

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Fig. 1: Cell atlas revealed by scRNA-seq offered a fundamental perspective to interrogate CSCC heterogeneity.
Fig. 2: MP7 is associated with abundant tumor-infiltrating T cells skewed toward a cytotoxic/exhaustion state.
Fig. 3: Spatial transcriptomics demonstrate inverse associations of MP6 and MP7 with immune cell infiltration.
Fig. 4: Multimodal analyses of tumor states demonstrate TGFβ signaling in MP6 tumors.
Fig. 5: Surrounding immunosuppressive CAF contribute to the immune exclusionary microenvironment at the periphery of MP6 tumor.
Fig. 6: The inverse relationship of MP6 tumor cell phenotype and immune infiltration is present in multiple CSCC datasets.
Fig. 7: FABP5 is critical for MP6 state maintenance and contributed to activated TGFβ and the interaction with immunosuppressive CAF.

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Data availability

Raw sequencing data generated in this study are deposited in Genome Sequence Archive for Human (https://ngdc.cncb.ac.cn/gsa-human/) with accession number HRA004971. Processed sequencing data are available at Science Data Bank (https://doi.org/10.57760/sciencedb.11624). Existing RNA sequencing datasets of NSCLC (OAK and POPLAR) and TCGA CESC cohorts are downloaded from European Genome-phenome Archive (EGA; EGAS00001005013) and UCSC Xena (https://xenabrowser.net/datapages/?cohort=GDC%20TCGA%20Cervical%20Cancer%20(CESC)). Reference genome files for alignment of single-cell data can be downloaded from https://support.10xgenomics.com/single-cell-gene-expression/software/release-notes/build. Human protein sequences from Swiss-Prot for searching of MS/MS data can be downloaded from https://ftp.uniprot.org/pub/databases/uniprot/previous_releases/release-2020_06/knowledgebase/uniprot_sprot-only2020_06.tar.gz. Source data are provided with this paper.

Code availability

Code required to reproduce the analyses in this paper is available through GitHub (https://github.com/FlyPythons/CSCC_heterogeneity).

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (grant no. 82141106 to D.M., grant no. 82073259 to G.C. and grant no. 81974408 to C.S.), the Key R&D Program of Hubei Province (grant no. 2023BCB023 to C.S.) and Chen Xiao-ping Foundation for the Development of Science and Technology of Hubei Province (grant no. CXPJJH122006-1006 to B.Y.).

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Author notes

  1. These authors contributed equally: Junpeng Fan, Funian Lu, Tianyu Qin, Wenju Peng.

Authors and Affiliations

  1. Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

    Junpeng Fan, Funian Lu, Tianyu Qin, Wenju Peng, Xucui Zhuang, Yinuo Li, Xin Hou, Zixuan Fang, Yunyi Yang, Ensong Guo, Bin Yang, Xi Li, Yu Fu, Xiaoyan Kang, Zimeng Wu, Xiangyi Ma, Kezhen Li, Ding Ma, Gang Chen & Chaoyang Sun

  2. National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

    Junpeng Fan, Funian Lu, Tianyu Qin, Wenju Peng, Xucui Zhuang, Yinuo Li, Xin Hou, Zixuan Fang, Yunyi Yang, Ensong Guo, Bin Yang, Xi Li, Yu Fu, Xiaoyan Kang, Zimeng Wu, Xiangyi Ma, Kezhen Li, Peng Wu, Ding Ma, Gang Chen & Chaoyang Sun

  3. Department of Gynecology, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, China

    Lili Han

  4. Division of Oncological Sciences, Oregon Health and Sciences University, Portland, OR, USA

    Gordon B. Mills

  5. Knight Cancer Institute, Portland, OR, USA

    Gordon B. Mills

  6. Department of Gynecological Oncology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

    Peng Wu

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Contributions

C.S. and J.F. conceived the study idea. X.Z., E.G. and B.Y. collected the samples and performed the IHC staining. J.F. and W.P. performed the bioinformatics analyses. F.L., T.Q., Y.L. and X.H. performed the experiments. C.S., J.F., W.P., T.Q. and F.L. wrote the manuscript. Z.F., Y.Y., E.G., B.Y., X.L., Y.F., X.K. and Z.W. collected the patients’ clinical information and provided support with the analysis. X.M., K.L., P.W., L.H. and G.B.M. provided expertise and feedback. C.S., G.C., D.M., P.W., K.L. and X.M. supervised the project and provided valuable critical discussion.

Corresponding authors

Correspondence to Xiangyi Ma, Kezhen Li, Peng Wu, Ding Ma, Gang Chen or Chaoyang Sun.

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Nature Genetics thanks Ajit Johnson Nirmal 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 The cell atlas revealed by scRNA-seq offered a fundamental perspective to interrogate CSCC heterogeneity.

(a) Distributions of count (top) and mitochondrial fraction (bottom) per cell in each sample. The inner boxplots are represented with the middle dot indicating the median, while the upper and lower hinges indicate the 25% and 75% quartiles, respectively. The upper or lower whiskers indicate the extremum values no larger or smaller than 1.5 × interquartile range. (b) Expression of selected cell markers in UMAP space. (c) Preference of each stromal cell types in CSCC and HD estimated by the ratio of observed to expected cell numbers (Ro/e). (d) Heatmap of CNV inferred from scRNA-seq and WES. The bottom panel displays the short (grey) and long (black) arm of each chromosome. For each tumor, CNV from scRNA-seq is inferred by inferCNV using normal epithelial cells as control and expressed as median CNV score of tumor cells, while CNV from WES is inferred by CNVkit using paired PBMC as control. (e) Profile of somatic mutations of each tumor ordered by gene. Gene mutation are colored by mutational type.

Extended Data Fig. 2 The robustness of meta-programs and comparison to pan-cancer and HNSCC programs.

(a) The NMF programs were clustered into MPs using the algorithm described by Gavish. Heatmap showing the clustering of MPs and Jaccard similarity among NMF programs. (b) Heatmap showing Jaccard similarity between MPs identified in (a) with the clustering method proposed by Gavish et al. and MPs identified through UPGMA method (Methods). mp7 (RP high) featured by ribosomal protein genes from Gavish et al. was excluded from analysis. (c) Scatter plot shows Jaccard similarity (y axis) and Pearson correlation of single-cell scores (x axis) between MPs and previously identified HNSCC and pan-cancer programs. MP1 was excluded from downstream analysis due to enrichment of mitochondrial genes.

Extended Data Fig. 3 Correlations between MPs and tumor immune microenvironment cells and differences between MP cells and normal epithelial cells.

(a) Dot plot displays the expression of squamous and glandular epithelial markers. (b) Heatmap displays the Pearson correlation coefficients calculated among signature scores of distinct tumor MPs of this study. (c) Scatter plot shows the correlation between MP7 signature scores of tumor cells and the fractions of CAF and CD8_Tex-GZMK cells in each patient. The MP6 signature score for each sample is displayed as the median score of tumor cells. P values are derived from the spearmanr function in scipy. (d) UMAP plots of 2,942 epithelial cells colored by epithelial subtype and the expression of epithelial markers in HDs. (e) Expression of representative MP6, MP7 signature genes and glandular marker genes in selected cell types. (f) Box plot shows the expression of selected MP6 signature genes between selected cell types (CSCC-MP6 (n = 3666), CSCC-MP7 (n = 2491), CSCC-MP8 (n = 1285), HD-Basal (n = 500), HD-Squamous (n = 173)). Data are represented as boxplots with the middle line indicating the median, while the upper and lower hinges indicate the 25% and 75% quartiles, respectively. The upper or lower whiskers indicate the extremum values no larger or smaller than 1.5 × interquartile range. P values are from two-sided Wilcoxon rank-sum tests.

Extended Data Fig. 4 MP7 associated tumor infiltrated phenotypes with abundant T cells skewed toward a cytotoxic or exhaustion state.

(a) Representative IHC of CD4 at stromal (str) and epithelial (epi) regions in CSCCs with infiltrated or excluded/desert TIPs. (b) Heatmap showing IHC score of CD4 or CD8 positive cells in tumor epithelial regions (epiCD4 or epiCD8). (c) UMAP plot of cells colored by TIP in CSCC. (d) Lollipop plots of p values based on comparing each cell cluster between infiltrated and excluded/desert TIPs. The lollipops are colored by enrichment in TIPs (red for infiltrated and blue for excluded/desert TIP, no significant (P > = 0.05) cell types are colored by grey). P values are calculated by two-sided t-tests.

Extended Data Fig. 5 Identification of MP6 and MP7 tumor spots on spatial transcriptomics.

(a) Workflow for identifying MP6 and MP7 tumor spots by integrating scRNA-Seq and spatial transcriptomic data. (b) Scatterplot of MP6 and MP7 scores in tumor spots of 15 slides. (c) Scatterplot of MP6 and MP7 scores in tumor spots of TJH34. Spots were defined as MP6 or MP7 spots by hierarchical clustering. Red and blue dots indicate MP6 and MP7 tumor spots, respectively. (d) Spatial distribution of tumor MP6 (blue) and MP7 (red) spots among 15 slides.

Extended Data Fig. 6 Spatial transcriptomics demonstrate inverse associations of MP6 and MP7 with immune cell infiltration.

(a) Composition of MP6 and MP7 spots in tumor spots of 15 slides. (b) Dot plot of average cell type score in MP6 and MP7 spots of TJH34. (c) scRNA-seq data mapped to the Stereo-seq profile by CytoSPACE, with annotated cell types from the scRNA-seq atlas distinguished by color. MP6 and MP7 region are circled by solid and dashed lines respectively. (d) Heat maps depicting CytoSPACE performance for aligning scRNA-seq data to spatial locations in Stereo-seq. CD4 T cells and CD8 T cells with distinct spatial distribution are shown. Bar plots show the proportion of CD4 and CD8 T cells per spatial spot type. (e) T cell exhaustion signatures enrichment in CD4 and CD8 T cell within MP7 tumor core when compared to T cells in tumor stroma. P values are derived from clusterProfiler package and adjusted with Benjamini-Hochberg procedure. NES: normalized enrichment score. (f) Spatial distribution of pan-cancer immune score in slide TJH34. (g) Spatial distribution of MP6 score, MP7 score, T cell inflamed GEP and Hot tumor-related genes score in slide TJH34. GEP: gene expression profile. (h) Representative area of MP6, MP7 tumor showing spatial distribution of MP6, MP7 tumor spots and stromal spots as well as spatial expression of MP7 score, T cell inflamed GEP and HOT tumor-related genes score.

Extended Data Fig. 7 Multimodal analyses of tumor states demonstrate TGFβ signaling in MP6 tumors.

(a) Bar plot shows enriched pathways from GO BP, REACTOME and HALLMARK using DEGs of tumor MP6, MP7, MP3 and MP4/5 cells profiled by scRNA-seq. FDR q values are derived from clusterProfiler package. (b) Dot plot shows the expression of immune inhibitory molecules in MP6 and MP7 tumor cells profiled by scRNA-seq. (c) Bar plot displays enriched pathways from GO BP using DEGs of immune-rich (left) and immune-poor (right) ROIs profiled by spatial proteomics. FDR q values are derived from clusterProfiler package.

Extended Data Fig. 8 The inverse relationship of MP6 tumor cell phenotype and immune infiltration is present in multiple CSCC datasets.

(a) Heatmap showing the significantly associated proteins or phosphorylation sites with MP6/7 scores in our in-house CSCC cohort. Proteins or phosphorylation sites are marked if they are MP6 or MP7 related genes. (b) Multiplex immunofluorescence of tumor (pCK), CD8T (CD8), Treg (FOXP3), Macrophage (CD68), fibroblasts (alpha-SMA) and nucleus (DAPI) in representative samples with high and low MP6/7 scores. (c) Heatmap of scaled values of genes in immune activation, immune evasion signaling and the cell composition inferred with xCell within CSCC samples in TCGA CESC datasets. Samples are ranked by increasing MP6/7 score. The top panel shows MP6, MP7 scores, and information about clinical stage, grade, age, histopathology and iCluster. (d, e) Kaplan-Meier curves comparing the probability of survival according to MP6/7 score in non-squamous cancers patients of OAK (d) and POPLAR (e) cohorts. Patients are dichotomized as high or low score using the median MP6/7 score as cutoff. P values were calculated with log-rank test.

Extended Data Fig. 9 MP6/7 signature provides an additional and complementary signal independent of immune-hot dichotomy.

(a–b). Kaplan-Meier curves comparing the survival probability based on MP6/7 score, T cell inflamed GEP and hot tumor-related genes score in OAK squamous cancer patients with immunotherapy (a) or chemotherapy (b). Patients are dichotomized as high/low subgroup using the median value cutoff. Hazard ratios (HR) with 95% CI were derived from a cox proportional model fit. (c–d) Scatter plot comparing T cell inflamed GEP signature (c) and hot tumor-related genes (d) with MP6/7 score in OAK squamous cancer patients with immunotherapy. Dashed vertical and horizontal lines represent the optimal cut points using maximally selected rank statistics. MP6/7hi and MP6/7lo groups are defined by cutoffs greater than or equal to and less than 0.05. GEPhi and GEPlo groups are defined by cutoffs greater than or equal to and less than 0.42. HOThi and HOTlo groups are defined by cutoffs greater than or equal to and less than 0.5. GEP: T cell inflamed GEP score. HOT: hot tumor-related genes score. (e–f) Kaplan-Meier curves showing relationship of T cell inflamed GEP (e), hot tumor-related genes score (f) and MP6/7 score with OS in OAK squamous immunotherapy cohort as treated per GEP cutoff (e), HOT cutoff (f) and MP6/7 cutoff. HR with 95% CI were derived from a cox proportional model fit.

Extended Data Fig. 10 FABP5 is critical for MP6 state maintenance and contributed to the activated TGFβ and interaction with immunosuppressive CAF.

(a) Heatmap shows relative expression of MP6 signatures after knockdown of each MP6 signature gene by siRNA for 48 hours in HeLa and C33A cells. The relative expression of MP6 signatures as calculated by the mean value of MP6 genes (except the knockdown gene) relative to negative control (NC) and shown as log2 transformed. (b) Quantification of MP6 genes by qPCR in SCC25 and CAL27 cells after si-FABP5 for 48 hours. (c) Western blot of expression of MP6 proteins in SCC25 and CAL27 cells after si-FABP5 for 72 hours. (d) Morphological photography and HE staining of patient-derived organoids (PDO) P36 after treatment with or without 10 μM FABP5-IN-1 for 6 days. (e) Immunofluorescence of indicated proteins in PDO P36 after treatment with or without 10 μM FABP5-IN-1 for 6 days. (f) Quantifications of CSCC PDOs’ diameters after treatment with 10 μM FABP5-IN-1 for 6 days. (g) Quantifications of fluorescence intensity of S100A8 and SERPINB3 in PDOs after treatment with or without 10 μM FABP5-IN-1 for 6 days. (h) qPCR of genes from TGFβ pathway in HeLa, SCC25, CAL27 and A431 cells after si-FABP5. (i) Western blot of proteins from TGFβ pathway in C33A cells after treatment with increasing concentration of FABP5-IN-1 for 72 hours. (j) The weight curves of C57BL/6 mice during treatment with vehicle or FABP5-IN-1 (n = 6; two independent experiments). (k) The work flow procedure of flow cytometry analysis in TC-1 tumors at treatment termination. (l) Tumor growth curves (left) and tumor sizes (right) of parental WT and FABP5 knockout (ko-FABP5 sg#A13) tumors in C57BL/6 mice (n = 6). The circles on mouse indicate the location of tumor and are colored by source. (m) Representative images and quantification of CD4+ and CD8+ cells in WT and FABP5 knockout tumors in B1 mice. For b,f-h,j,l,m, data represent the mean ± s.e.m. with three independent replicates if not indicated otherwise. P values are calculated by two-side t-tests. n.s.: not significant.

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Fan, J., Lu, F., Qin, T. et al. Multiomic analysis of cervical squamous cell carcinoma identifies cellular ecosystems with biological and clinical relevance. Nat Genet 55, 2175–2188 (2023). https://doi.org/10.1038/s41588-023-01570-0

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