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Molecular classification of hormone receptor-positive HER2-negative breast cancer

Nature Genetics volume 55, pages 1696–1708 (2023)Cite this article

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Abstract

Hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2−) breast cancer is the most prevalent type of breast cancer, in which endocrine therapy resistance and distant relapse remain unmet challenges. Accurate molecular classification is urgently required for guiding precision treatment. We established a large-scale multi-omics cohort of 579 patients with HR+/HER2− breast cancer and identified the following four molecular subtypes: canonical luminal, immunogenic, proliferative and receptor tyrosine kinase (RTK)-driven. Tumors of these four subtypes showed distinct biological and clinical features, suggesting subtype-specific therapeutic strategies. The RTK-driven subtype was characterized by the activation of the RTK pathways and associated with poor outcomes. The immunogenic subtype had enriched immune cells and could benefit from immune checkpoint therapy. In addition, we developed convolutional neural network models to discriminate these subtypes based on digital pathology for potential clinical translation. The molecular classification provides insights into molecular heterogeneity and highlights the potential for precision treatment of HR+/HER2− breast cancer.

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Fig. 1: Schematic overview of the study.
Fig. 2: Integrated landscape of HR+/HER2− breast cancer.
Fig. 3: Distinct clinical characteristics and pathological patterns of the four SNF subtypes.
Fig. 4: Proteogenomic analysis reveals cell cycle signaling as the target of the SNF3 subtype.
Fig. 5: Microenvironment landscape of HR+/HER2− breast cancers.
Fig. 6: RTK-driven (SNF4) subtype-derived CAFs could enhance tumor growth and were vulnerable to sorafenib.

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Data availability

The WES data, CNA data, RNA sequencing data and metabolome data for this study have been deposited into the Genome Sequence Archive (GSA) database under accession codes PRJCA017539 (https://ngdc.cncb.ac.cn/bioproject/browse/PRJCA017539). TMT-based mass spectrometry (MS)-quantified protein data have been submitted into iProX (https://www.iprox.cn) under accession codes IPX0006535000. Human Primary Cell Atlas data are obtained from the celldex package (v1.11; https://github.com/LTLA/celldex). The TCGA, METABRIC and CPTAC data were downloaded from the cBioPortal website (https://www.cbioportal.org/). Source data are provided with this paper.

Code availability

All data were analyzed and processed using published software packages whose details are provided and cited either in the Methods section or Supplementary Note. The CNN models and code from this manuscript are available at GitHub (https://github.com/yifanzhou330/SNF) and Zenodo (https://doi.org/10.5281/zenodo.8022438)87.

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Acknowledgements

We are grateful to the patients and their families who contributed to this study. This work was supported by grants from the National Key Research and Development Project of China (2021YFF1201300), the National Natural Science Foundation of China (82341003, 91959207, 92159301, 82272822, 82272704 and 82103039), the Shanghai Key Laboratory of Breast Cancer (12DZ2260100), the Shanghai Hospital Development Center (SHDC) Municipal Project for Developing Emerging and Frontier Technology in Shanghai Hospitals (SHDC12021103), the Program of Shanghai Academic/Technology Research Leader (20XD1421100), the Natural Science Foundation of Shanghai (22ZR1479200 and 23ZR1411800), Youth Talent Program of Shanghai Health Commission (2022YQ012), China Postdoctoral Science Foundation (2022M720790), Shanghai Sailing Program (20YF1408700) and Youth Medical Talents of Shanghai (WJWRC202014). The funders had no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Xi Jin, Yi-Fan Zhou, Ding Ma, Shen Zhao, Cai-Jin Lin, Yi-Zhou Jiang.

  2. These authors jointly supervised this work: Yi-Zhou Jiang, Zhi-Ming Shao.

Authors and Affiliations

  1. Key Laboratory of Breast Cancer, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China

    Xi Jin, Yi-Fan Zhou, Ding Ma, Shen Zhao, Cai-Jin Lin, Yi Xiao, Tong Fu, Cheng-Lin Liu, Yi-Yu Chen, Wen-Xuan Xiao, Yi-Zhou Jiang & Zhi-Ming Shao

  2. State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China

    Ya-Qing Liu, Qing-Wang Chen, Ying Yu & Le-Ming Shi

  3. International Human Phenome Institutes (Shanghai), Shanghai, China

    Le-Ming Shi

  4. Shanghai-MOST Key Laboratory of Health and Disease Genomics, Shanghai Institute for Biomedical and Pharmaceutical Technologies (SIBPT), Shanghai, China

    Jin-Xiu Shi & Wei Huang

  5. University of Nottingham, Royal Derby Hospital, Derby, UK

    John F. R. Robertson

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  1. Xi Jin
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  2. Yi-Fan Zhou
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Contributions

X.J., Y.Z.J. and Z.M.S. conceived and designed the study. Y.F.Z., D.M., C.J.L. and C.L.L. performed the proteomics and contributed to the data processing and analyses. X.J. and Y.Z.J. wrote the first draft and organized the figures. S.Z. reviewed the pathological sections and performed the deep-learning-based digital pathology. Y.X. and W.X.X. performed the metabolomics. T.F. performed scRNA-seq. Y.Y.C. carried out IHC experiments and PDO assays. Y.Q.L., Q.W.C., Y.Y., J.X.S., L.M.S. and W.H. performed the WES, OncoScan and RNA sequencing. J.F.R. and Z.M.S. supervised all aspects of the study.

Corresponding authors

Correspondence to Yi-Zhou Jiang or Zhi-Ming Shao.

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Extended data

Extended Data Fig. 1 Landscape of FUSCC HR+/HER2- breast cancer cohort, related to Fig. 1.

(a, b) Schematic overview of multi-omics data acquired for this cohort. (c) The proportion of PAM50 subtypes. (d) Determination of optimal cluster number. (e) Silhouette plot of SNF clustering. (f) Summary of adjusted p-value for differences in each multi-omic features among subtypes under different clustering strategies. NS: not significant. Mut: mutation. Amp: amplification. Met: metabolite. HRD: homologous recombination deficiency. Bold font indicates the clustering strategy we used. (g) Distribution of PAM50 subtypes among SNF subtypes. P = 5e-04. P values were from the two-sided Fisher’s exact test.

Source data

Extended Data Fig. 2 SNF subtype-specific metabolomic features, related to Fig. 2.

(a) Global differences in metabolic gene expression between tumors and normal tissues in the luminal cohort. The distribution distances (r.m.s.d) were calculated between tumors and corresponding normal tissues (red), different samples of tumor tissues (yellow), and different samples of normal tissues (blue). The inset shows the average distances between pairs of tissues as a percentage of the average distance between tumors and normal tissues. Tumor samples n = 351, normal samples n = 11. P(T VS N - T VS T) < 2.2e-16, P(T VS N - N VS N) < 2.2e-16, P(T VS T - N VS N) < 2.2e-16. P values were from the two-sided Wilcox rank-sum test and Kruskal-Wallis test. (b) Heatmap illustrating subtype-specific metabolic genes. P values were from the two-sided Kruskal-Wallis test. (c) Illustration of subtype-specific polar metabolite subclasses. P(Amino acid) = 4.952e-05, P(Carbohydrates) = 0.4084, P(Lipid) = 0.02426, P(Nucleotide) = 0.3861, P(Other) = 0.6101, P(Peptide) = 0.0002545, P(Vitamins and Cofactors) = 0.8432, P(Xenobiotics) = 0.7095. P values were from the two-sided Kruskal-Wallis test. SNF1 subtype n = 86 biologically independent samples, SNF2 subtype n = 89 biologically independent samples, SNF3 subtype n = 118 biologically independent samples, SNF4 subtype n = 58 biologically independent samples, normal samples n = 11. (d) Illustration of subtype-specific lipid subclasses. P(FA) = 0.0005, P(GL) = 0.008, P(GP) = 9.113e-14, P(SP) = 0.0005, P(ST) = 0.0005. P values were from the two-sided Kruskal-Wallis test. SNF1 subtype n = 86 biologically independent samples, SNF2 subtype n = 89 biologically independent samples, SNF3 subtype n = 118 biologically independent samples, SNF4 subtype n = 58 biologically independent samples, normal samples n = 11. FA: Fatty acyls. GL: Glycerolipids. GP: Glycerophospholipids. SP: Sphingolipids. ST: Sterol Lipids. In all boxplots, the center lines represent median values; the bounds of the boxplot represent the interquartile ranges; the whiskers show the range of the data. All P values were adjusted using the Benjamini‒Hochberg procedure. ***FDR < 0.001; **0.001 ≤ FDR < 0.01; *0.01 ≤ FDR < 0.05; ns, FDR ≥ 0.05.

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Extended Data Fig. 3 The associations of polar metabolites and lipids with genomic features, related to Fig. 2.

(a) Heatmap showing the associations between the abundances of metabolites and the presence of mutations within the indicated genes. The mutations include high frequency somatic mutations (mutated in at least 6% of the cases in at least one SNF subtype) within cancer-related genes and high frequency germline mutations in BRCA1 and BRCA2. T statistics were calculated by a linear regression model that adjusted the cofounding factors. (b) Correlations between TP53 mutations and deoxyinosine (top panel) and OxPG levels (bottom panel). All samples were ordered based on the abundance (y-axis) of deoxyinosine (top panel) or OxPG (bottom panel), and those with TP53 mutations were highlighted in red and indicated by the corresponding lines displayed on the x-axis. Two-sided T statistics were calculated. (c) Heatmap showing the associations between the abundances of metabolites and copy number values of SCNA peaks. T statistics were calculated by a linear regression model that adjusted the cofounding factors. (d) Top panel: correlations between the copy number values of 8q23.3 and the abundances of deoxyinosine, ribothymidine, uracil and some amino acids. Bottom panel: correlations between the copy number values of 1q32.1 and the abundances of uridine and D-pantethine. SCNA-related metabolites were shown as lines, and samples were ordered by increasing copy number values. The abundances of the metabolites were illustrated in colors. (e) Heatmap showing the correlations between the mRNA expression of cell cycle-related genes (y-axis) and the abundances of metabolites (x-axis). T statistics were calculated by a linear regression model that adjusted the cofounding factors. (f) The correlation of deoxyinosine and dUMP abundance with AURKA mRNA expression. P(AURKA-Deoxyinosine) < 2.2e-16, P(CCND3-dUMP) = 3.2e-4. P values were from two-sided Pearson’s correlation analysis. ***FDR < 0.001; **0.001 ≤ FDR < 0.01; *0.01 ≤ FDR < 0.05.

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Extended Data Fig. 4 Extended analysis of clinical status of four subtypes, related to Fig. 3.

(a) Association of the SNF types with different clinical statuses. P values were from the two-sided Fisher’s exact test. (b) Association of the SNF subtypes with relapse-free survival (RFS). P = 9.3e-04. (c) Association of the SNF subtypes with metastasis-free survival (MFS) in PAM50 Luminal B patients. (d) Forest plot of univariate cox regression analysis for MFS adjusting for tumor size, lymph node status, SNF subtypes, chemotherapy, histological grade. The included patients all received endocrine therapy (n = 296). The hazard ratios (HR) were shown with 95% confidence intervals (CI). Error bar center indicates HR. SNF2vsSNF1: HR = 1.14[0.61–2.11], P = 0.687. SNF3vsSNF1: HR = 1.32[0.75–2.32], P = 0.330. SNF4vsSNF1: HR = 2.25[1.23–4.12], P = 0.008. Lymph node met: HR = 1.08[1.06–1.10], P = 1.02e-15. Chemotherapy: HR = 1.41[0.77–2.59], P = 0.262. Tumor size: HR = 1.59[1.37–1.85], P = 2.17e-09. Grade: HR = 1.31[0.86–1.99], P = 0.213. Bold font indicates statistical significance. met: metastasis.

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Extended Data Fig. 5 Prediction of SNF subtypes based on the transcriptomics data.

(a) Workflow for the prediction of SNF subtypes based on the transcriptomics data. (b) ROC curves for using the random forest classifier to identify the SNF subtypes. Molecular features of inferred SNF subtypes in (c) CPTAC, (d) METABRIC and (e) TCGA cohort. ***FDR < 0.001; **0.001 ≤ FDR < 0.01; *0.01 ≤ FDR < 0.05; ns: not significant. P values were from the two-sided Kruskal-Wallis test.

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Extended Data Fig. 6 Extended analysis of SNF3 subtype, related to Fig. 4.

(a) Representative gene set enrichment analysis plot showing upregulated cell cycle pathway in SNF3 subtype in FUSCC and TCGA cohorts. (b) The CNA, mRNA abundance, and protein abundance of CCND1, CDK2, CDK1; the mRNA expression of E2F1, E2F2, and E2F target genes among different subtypes. Copy number amplification was defined as copy number value > log2(4/2). P values were from the two-sided ANOVA or Fisher’s exact test. MGPS: multi-gene proliferation scores. (c) The CNV alteration, and mRNA abundance of CCND1, CDK1, and CDK2 among different subtypes in TCGA cohort. Copy number amplification was defined as copy number value > log2(4/2). P values were from the two-sided ANOVA or Fisher’s exact test. (d) Heatmap showing the mRNA expression of E2F1, E2F2, and E2F target genes in TCGA cohort. P values were from the two-sided ANOVA test. (e) The alteration of two key G2/M cell-cycle regulators (MDM2 and ATM) at the copy number level and mRNA level compared between SNF3 (n = 233) and the other subtypes (n = 259) in TCGA cohort. P(MDM2 CNA) = 7.1e-07, P(ATM CNA) = 1.2e-09, P(MDM2 RNA) = 2e-11, P(ATM RNA) = 5.3e-12. P values were from the two-sided Wilcoxon or Fisher’s exact test. Center line indicates the median, and bounds of box indicate the 25th and 75th percentiles. Whiskers were plotted at 1.5xIQR and the data points outside the whisker were outliers. ***FDR < 0.001; **0.001 ≤ FDR < 0.01; *0.01 ≤ FDR < 0.05; NS, FDR ≥ 0.05.

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Extended Data Fig. 7 Extended analysis of SNF2 subtype, related to Fig. 5.

(a) Heatmap showing the estimated abundance of 24 microenvironment cell types among four SNF subtypes in TCGA cohort. P values were from the two-sided ANOVA test. (b) Representative gene set enrichment analysis plot showing upregulated T cell activation and adaptive immune response in SNF2 subtype in FUSCC and TCGA cohorts. (c) Expression of PDCD1 mRNA expression between SNF2 subtype (n = 80) and other subtypes (n = 412) in TCGA cohort. P = 5.6e-05. P values were from the two-sided Wilcoxon test. Center line indicates the median, and bounds of box indicate the 25th and 75th percentiles. Whiskers were plotted at 1.5xIQR and the data points outside the whisker were outliers. (d) The mRNA expression of CD8A, GZMA, PRF1, and IDO1 between SNF2 subtype and other subtypes in TCGA cohort. P values were from the two-sided ANOVA test. ***FDR < 0.001; **0.001 ≤ FDR < 0.01; *0.01 ≤ FDR < 0.05; NS, FDR ≥ 0.05.

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Extended Data Fig. 8 Cell types detected based on scRNA-seq, related to Fig. 5.

(a) Heatmap showing the expression of marker genes in the indicated cell types. (b) Heatmap showing copy number variations for individual cells (rows) in different genomic segments (column). Sampled immune cells were used as references. (c) Distribution of each cell subtype in each SNF subtype. (d) GSEA on differentially expressed genes in CD8 + T cell from SNF2 versus non-SNF2 patients for REACTOME, GO and hallmark gene sets. Top 10 pathways enriched in CD8 + T cell from SNF2 samples were shown. (e, f) Violin plot comparing cytotoxic/dysfunction score (E) or cytotoxic-related gene (F), GNLY and GZMK, between CD8 + T cell (n = 6827 cells) from SNF2 (n = 3) versus non-SNF2 (n = 6) patients. P values were obtained by two-sided Wilcoxon test. Center line indicates the median, and bounds of box indicate the 25th and 75th percentiles. Whiskers were plotted at 1.5xIQR and the data points outside the whisker were outliers.

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Extended Data Fig. 9 Extended analysis of SNF4 subtype, related to Fig. 6.

(a) Cleveland plot showing the top 15 statistically significant pathways (p-value < 0.05, q-value < 0.25) with the highest NES value in FUSCC cohort. Pathways with gene sizes between 50 and 200 have been included. All pathways included were statistically significant. Gene Ontology Molecular Function (GOMF) gene sets were used for GSEA analysis. Receptor tyrosine kinase related pathways were highlighted in red. P-values were calculated by two-sided nonparametric permutation test and adjusted using the Benjamini‒Hochberg procedure (q-value). (b) Cleveland plot showing the top 15 statistically significant pathways (p-value < 0.05, q-value < 0.25) with the highest NES value in TCGA cohort. Pathways with gene sizes between 50 and 200 have been included. All pathways included were statistically significant. Receptor tyrosine kinase related pathways were highlighted in red. P-values were calculated by two-sided nonparametric permutation test and adjusted by the false discovery rate (q-value). (c, d) The expression of EGFR, PDGFRA, KIT, and MET mRNA level between SNF4 (n = 34) and other subtypes (n = 458) in TCGA cohort. Center line indicates the median, and bounds of box indicate the 25th and 75th percentiles. Whiskers were plotted at 1.5xIQR and the data points outside the whisker were outliers. P(EGFR) = 9.7e-15, P(PDGFRA) = 3.8e-11, P(KIT) = 9.4e-15, P(MET) = 1e-12. P values were from the two-sided Wilcoxon test.

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Extended Data Fig. 10 Extended analysis of SNF4 subtype, related to Fig. 6.

(A) Heatmaps showing phosphosite abundance of EGFR and their downstream substrates. P values were from the two-sided Kruskal-Wallis test without multiple test corrections. (B) Heatmaps showing phosphosite abundance of PDGFRA and their downstream substrates. P values were from the two-sided Kruskal-Wallis test without multiple test corrections. (C) Schematic diagram of PDGFRA/EGFR and their downstream MAPK signaling pathway. (D) Immunohistochemical detection of Phospho-ERK 1/2 and the immunohistochemical staining score quantification among the SNF1 (n = 45), SNF2 (n = 47), SNF3 (n = 55), and SNF4 (n = 27) subtypes. P values were from the two-sided Kruskal-Wallis test without multiple test corrections. Scale bar: 100μm. Center line indicates the median, and bounds of box indicate the 25th and 75th percentiles. Whiskers were plotted at 1.5xIQR and the data points outside the whisker were outliers. ***P < 0.001; **0.001 ≤ P < 0.01; *0.01 ≤ P < 0.05; NS, P ≥ 0.05.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–3 and Supplementary Note.

Reporting Summary

Supplementary Tables

Supplementary Table 1: Cohort information. Supplementary Table 2: Correlation between metabolites and oncogenic somatic mutations in HR+/HER2− breast cancer. Supplementary Table 3: Correlation between metabolites and oncogenic SCNA in HR+/HER2− breast cancer. Supplementary Table 4: Correlation between metabolites and mRNA expression of cell-cycle-related genes in HR+/HER2− breast cancer. Supplementary Table 5: Comparisons among SNF1–SNF4. Supplementary Table 6: List of SNF-specific features in transcriptomics data-based model for SNF subtype classification. Supplementary Table 7: Patient-derived organoids information.

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Jin, X., Zhou, YF., Ma, D. et al. Molecular classification of hormone receptor-positive HER2-negative breast cancer. Nat Genet 55, 1696–1708 (2023). https://doi.org/10.1038/s41588-023-01507-7

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