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Kreis J, Aybey B, Geist F, Brors B, Staub E. Stromal Signals Dominate Gene Expression Signature Scores That Aim to Describe Cancer Cell-intrinsic Stemness or Mesenchymality Characteristics. CANCER RESEARCH COMMUNICATIONS 2024; 4:516-529. [PMID: 38349551 PMCID: PMC10885853 DOI: 10.1158/2767-9764.crc-23-0383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 12/14/2023] [Accepted: 02/09/2024] [Indexed: 02/24/2024]
Abstract
Epithelial-to-mesenchymal transition (EMT) in cancer cells confers migratory abilities, a crucial aspect in the metastasis of tumors that frequently leads to death. In multiple studies, authors proposed gene expression signatures for EMT, stemness, or mesenchymality of tumors based on bulk tumor expression profiling. However, recent studies suggested that noncancerous cells from the microenvironment or macroenvironment heavily influence such signature profiles. Here, we strengthen these findings by investigating 11 published and frequently referenced gene expression signatures that were proposed to describe EMT-related (EMT, mesenchymal, or stemness) characteristics in various cancer types. By analyses of bulk, single-cell, and pseudobulk expression data, we show that the cell type composition of a tumor sample frequently dominates scores of these EMT-related signatures. A comprehensive, integrated analysis of bulk RNA sequencing (RNA-seq) and single-cell RNA-seq data shows that stromal cells, most often fibroblasts, are the main drivers of EMT-related signature scores. We call attention to the risk of false conclusions about tumor properties when interpreting EMT-related signatures, especially in a clinical setting: high patient scores of EMT-related signatures or calls of "stemness subtypes" often result from low cancer cell content in tumor biopsies rather than cancer cell-specific stemness or mesenchymal/EMT characteristics. SIGNIFICANCE Cancer self-renewal and migratory abilities are often characterized via gene module expression profiles, also called EMT or stemness gene expression signatures. Using published clinical tumor samples, cancer cell lines, and single cancer cells, we highlight the dominating influence of noncancer cells in low cancer cell content biopsies on their scores. We caution on their application for low cancer cell content clinical cancer samples with the intent to assign such characteristics or subtypes.
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Affiliation(s)
- Julian Kreis
- The healthcare business of Merck KGaA, Darmstadt, Germany
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Bogac Aybey
- The healthcare business of Merck KGaA, Darmstadt, Germany
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Felix Geist
- The healthcare business of Merck KGaA, Darmstadt, Germany
| | - Benedikt Brors
- Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg University, Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg University, Heidelberg, Germany
- Medical Faculty Heidelberg and Faculty of Biosciences, Heidelberg University, and National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Eike Staub
- The healthcare business of Merck KGaA, Darmstadt, Germany
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Braune EB, Geist F, Tang X, Kalari K, Boughey J, Wang L, Leon-Ferre RA, D'Assoro AB, Ingle JN, Goetz MP, Kreis J, Wang K, Foukakis T, Seshire A, Wienke D, Lendahl U. Identification of a Notch transcriptomic signature for breast cancer. Breast Cancer Res 2024; 26:4. [PMID: 38172915 PMCID: PMC10765899 DOI: 10.1186/s13058-023-01757-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 12/19/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Dysregulated Notch signalling contributes to breast cancer development and progression, but validated tools to measure the level of Notch signalling in breast cancer subtypes and in response to systemic therapy are largely lacking. A transcriptomic signature of Notch signalling would be warranted, for example to monitor the effects of future Notch-targeting therapies and to learn whether altered Notch signalling is an off-target effect of current breast cancer therapies. In this report, we have established such a classifier. METHODS To generate the signature, we first identified Notch-regulated genes from six basal-like breast cancer cell lines subjected to elevated or reduced Notch signalling by culturing on immobilized Notch ligand Jagged1 or blockade of Notch by γ-secretase inhibitors, respectively. From this cadre of Notch-regulated genes, we developed candidate transcriptomic signatures that were trained on a breast cancer patient dataset (the TCGA-BRCA cohort) and a broader breast cancer cell line cohort and sought to validate in independent datasets. RESULTS An optimal 20-gene transcriptomic signature was selected. We validated the signature on two independent patient datasets (METABRIC and Oslo2), and it showed an improved coherence score and tumour specificity compared with previously published signatures. Furthermore, the signature score was particularly high for basal-like breast cancer, indicating an enhanced level of Notch signalling in this subtype. The signature score was increased after neoadjuvant treatment in the PROMIX and BEAUTY patient cohorts, and a lower signature score generally correlated with better clinical outcome. CONCLUSIONS The 20-gene transcriptional signature will be a valuable tool to evaluate the response of future Notch-targeting therapies for breast cancer, to learn about potential effects on Notch signalling from conventional breast cancer therapies and to better stratify patients for therapy considerations.
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Affiliation(s)
- Eike-Benjamin Braune
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | | | - Xiaojia Tang
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Krishna Kalari
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Judy Boughey
- Department of Surgery, Mayo Clinic, Rochester, MN, USA
| | - Liewei Wang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | | | | | - James N Ingle
- Department of Oncology, Mayo Clinic, Rochester, MN, USA
| | - Matthew P Goetz
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
- Department of Oncology, Mayo Clinic, Rochester, MN, USA
| | | | - Kang Wang
- Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Theodoros Foukakis
- Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden
| | | | | | - Urban Lendahl
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden.
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Xia WT, Qiu WR, Yu WK, Xu ZC, Zhang SH. Identifying TME signatures for cervical cancer prognosis based on GEO and TCGA databases. Heliyon 2023; 9:e15096. [PMID: 37095983 PMCID: PMC10121839 DOI: 10.1016/j.heliyon.2023.e15096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 03/24/2023] [Accepted: 03/27/2023] [Indexed: 04/26/2023] Open
Abstract
The mortality rate from cervical cancer (CESC), a malignant tumor that affects women, has increased significantly globally in recent years. The discovery of biomarkers points to a direction for the diagnosis of cervical cancer with the advancement of bioinformatics technology. The goal of this study was to look for potential biomarkers for the diagnosis and prognosis of CESC using the GEO and TCGA databases. Because of the high dimension and small sample size of the omic data, or the use of biomarkers generated from a single omic data, the diagnosis of cervical cancer may be inaccurate and unreliable. The purpose of this study was to search the GEO and TCGA databases for potential biomarkers for the diagnosis and prognosis of CESC. We begin by downloading CESC (GSE30760) DNA methylation data from GEO, then perform differential analysis on the downloaded methylation data and screen out the differential genes. Then, using estimation algorithms, we score immune cells and stromal cells in the tumor microenvironment and perform survival analysis on the gene expression profile data and the most recent clinical data of CESC from TCGA. Then, using the 'limma' package and Venn plot in R language to perform differential analysis of genes and screen out overlapping genes, these overlapping genes were then subjected to GO and KEGG functional enrichment analysis. The differential genes screened by the GEO methylation data and the differential genes screened by the TCGA gene expression data were intersected to screen out the common differential genes. A protein-protein interaction (PPI) network of gene expression data was then created in order to discover important genes. The PPI network's key genes were crossed with previously identified common differential genes to further validate them. The Kaplan-Meier curve was then used to determine the prognostic importance of the key genes. Survival analysis has shown that CD3E and CD80 are important for the identification of cervical cancer and can be considered as potential biomarkers for cervical cancer.
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Affiliation(s)
- Wen-Tao Xia
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, China
| | - Wang-Ren Qiu
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, China
- Corresponding author.
| | - Wang-Ke Yu
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, China
| | - Zhao-Chun Xu
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, China
| | - Shou-Hua Zhang
- Department of General Surgery, Jiangxi Provincial Children's Hospital, Nanchang, China
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