CoChat
Sign Up
Cited by 355Growth Hormone and Insulin-like Growth FactorsBreast Cancer Treatment StudiesTGF-β signaling in diseases

The DOI matches its Crossref record; 77 of the 77 references it cites were found in a registry or an index.

IGF-I induced genes in stromal fibroblasts predict the clinical outcome of breast and lung cancer patients

Open access
Rajski, Michal, Zanetti-Dällenbach, Rosanna, Vogel, Brigitte, Herrmann, Richard, Rochlitz, Christoph, Buess, Martin·2010·BMC Medicine
DOI 10.1186/1741-7015-8-1PMC PMC2823652
Open this paper in CoChat — free accountOpen PDF

Abstract

Background: Insulin-like growth factor-1 (IGF-I) signalling is important for cancer initiation and progression. Given the emerging evidence for the role of the stroma in these processes, we aimed to characterize the effects of IGF-I on cancer cells and stromal cells separately. Methods: We used an ex vivo culture model and measured gene expression changes after IGF-I stimulation with cDNA microarrays. In vitro data were correlated with in vivo findings by comparing the results with published expression datasets on human cancer biopsies. Results: Upon stimulation with IGF-I, breast cancer cells and stromal fibroblasts show some common and other distinct response patterns. Among the up-regulated genes in the stromal fibroblasts we observed a significant enrichment in proliferation associated genes. The expression of the IGF-I induced genes was coherent and it provided a basis for the segregation of the patients into two groups. Patients with tumours with highly expressed IGF-I induced genes had a significantly lower survival rate than patients whose tumours showed lower levels of IGF-I induced gene expression (P = 0.029 - Norway/Stanford and P = 7.96e-09 - NKI dataset). Furthermore, based on an IGF-I induced gene expression signature derived from primary lung fibroblasts, a separation of prognostically different lung cancers was possible (P = 0.007 - Bhattacharjee and P = 0.008 - Garber dataset). Conclusion: Expression patterns of genes induced by IGF-I in primary breast and lung fibroblasts accurately predict outcomes in breast and lung cancer patients. Furthermore, these IGF-I induced gene signatures derived from stromal fibroblasts might be promising predictors for the response to IGF-I targeted therapies. See the related commentary by Werner and Bruchim: http://www.biomedcentral.com/1741-7015/8/2

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/1741-7015-8-1

Cites

77 of 77 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 The effects of insulin-like growth factor-1 (IGF-I) on gene expression in CCL-171 fibroblasts and MCF-7 tumour cells. Unsupervised hierarchical clustering of genes deregulated in CCL-171 and MCF-7 cells upon IGF-I stimulation. The gene expression levels were normalized to the non-stimulated specimens as described. Genes are presented in rows and experiments in columns. The red and green colours provide information about up- or down-regulation, respectively. The intensity of the colour renders quantitative information about the change in expression level. IGF-I stimulation induces some common and some distinct effects on the gene expression profiles in different cell types. (A) Genes specifically up-regulated in MCF-7 cells involved in: epidermal growth factor and fibroblast growth factor signalling; protein metabolism and modification; nucleoside, nucleotide and nucleic acid metabolism. (B) Genes specifically up-regulated in CCL-171 cells include transcription factors and transferases, in addition to genes involved in Wnt and TGF-β signalling.
Figure 1 The effects of insulin-like growth factor-1 (IGF-I) on gene expression in CCL-171 fibroblasts and MCF-7 tumour cells. Unsupervised hierarchical clustering of genes deregulated in CCL-171 and MCF-7 cells upon IGF-I stimulation. The gene expression levels were normalized to the non-stimulated specimens as described. Genes are presented in rows and experiments in columns. The red and green colours provide information about up- or down-regulation, respectively. The intensity of the colour renders quantitative information about the change in expression level. IGF-I stimulation induces some common and some distinct effects on the gene expression profiles in different cell types. (A) Genes specifically up-regulated in MCF-7 cells involved in: epidermal growth factor and fibroblast growth factor signalling; protein metabolism and modification; nucleoside, nucleotide and nucleic acid metabolism. (B) Genes specifically up-regulated in CCL-171 cells include transcription factors and transferases, in addition to genes involved in Wnt and TGF-β signalling.CC BY · PubMed Central
Figure 2 Effects of insulin-like growth-1 (IGF-I) stimulation on primary breast fibroblasts and CCL-171 fibroblasts. (A) Unsupervised hierarchical clustering of genes differentially expressed in fibroblasts upon IGF-stimulation. Unsupervised hierarchical clustering of genes differentially expressed between IGF-I stimulated and non-stimulated primary breast fibroblasts as discovered by SAM (genes with a false discovery rate ≤ 0.05% are represented). Grey fields indicate missing expression values. The colour of dendrogram branches renders information about sample stimulation; yellow = not stimulated and blue = stimulated with IGF-I (50 ng/mL). (B) IGF-I induced proliferation of CCL-171 cells. Cell proliferation assay based on absorbance measurement of WST-1. Formazan absorbance correlates to the cell number. Average absolute absorbance of replicates of CCL-171 cells stimulated with 50 ng/mL IGF-I in comparison to non-stimulated cells at different time points. Points represent the average of six replicates per condition and correspond to the cell number. The vertical error bars denote the standard deviation. Stimulation of CCL-171 cells with IGF-I induces significant, constant cell growth after 24, 48 and 72 h. (C) IGF-I induced proliferation of primary breast fibroblasts. Cell proliferation assay based on absorbance measurement of WST-1. Points represent the average absolute absorbance of a minimum of eight replicates of six primary fibroblasts (carcinoma associated fibroblasts and normal fibroblasts) after 24, 48 and 72 h. Error bars correspond to the magnitude of the standard deviation. Stimulation of primary breast fibroblasts with IGF-I induces significant, constant cell growth.
Figure 2 Effects of insulin-like growth-1 (IGF-I) stimulation on primary breast fibroblasts and CCL-171 fibroblasts. (A) Unsupervised hierarchical clustering of genes differentially expressed in fibroblasts upon IGF-stimulation. Unsupervised hierarchical clustering of genes differentially expressed between IGF-I stimulated and non-stimulated primary breast fibroblasts as discovered by SAM (genes with a false discovery rate ≤ 0.05% are represented). Grey fields indicate missing expression values. The colour of dendrogram branches renders information about sample stimulation; yellow = not stimulated and blue = stimulated with IGF-I (50 ng/mL). (B) IGF-I induced proliferation of CCL-171 cells. Cell proliferation assay based on absorbance measurement of WST-1. Formazan absorbance correlates to the cell number. Average absolute absorbance of replicates of CCL-171 cells stimulated with 50 ng/mL IGF-I in comparison to non-stimulated cells at different time points. Points represent the average of six replicates per condition and correspond to the cell number. The vertical error bars denote the standard deviation. Stimulation of CCL-171 cells with IGF-I induces significant, constant cell growth after 24, 48 and 72 h. (C) IGF-I induced proliferation of primary breast fibroblasts. Cell proliferation assay based on absorbance measurement of WST-1. Points represent the average absolute absorbance of a minimum of eight replicates of six primary fibroblasts (carcinoma associated fibroblasts and normal fibroblasts) after 24, 48 and 72 h. Error bars correspond to the magnitude of the standard deviation. Stimulation of primary breast fibroblasts with IGF-I induces significant, constant cell growth.CC BY · PubMed Central
Figure 3 Breast fibroblast derived IGF-I signature in early stage breast cancer. (A) Unsupervised hierarchical clustering of breast fibroblast derived IGF-I signature in Netherlands Cancer institute dataset. The expression values of genes in the breast fibroblast derived IGF-I signature revealed by signficant analysis of microarray were extracted from a published expression study of 295 early stage breast cancers from the Netherlands Cancer Institute (NKI). Genes are presented in rows and experiments in columns. Breast fibroblast derived IGF-I signature stratifies early breast cancer patients (NKI) into two groups with high (blue) or low (yellow) expression levels of genes representing the signature. Horizontal bar below the figure represents positive (purple) or negative (orange) ER status. (B) Relationship of expression level of genes building breast fibroblast derived IGF-I signature with distant metastasis free and overall survival. Kaplan-Meier curves representing the clinical outcomes of tumors exhibiting high (blue curve) and low (yellow curve) expression levels of the IGF-I induced signature. The upper two figures represent all patients and the bottom figure shows only patients with oestrogen receptor positive breast tumours.
Figure 3 Breast fibroblast derived IGF-I signature in early stage breast cancer. (A) Unsupervised hierarchical clustering of breast fibroblast derived IGF-I signature in Netherlands Cancer institute dataset. The expression values of genes in the breast fibroblast derived IGF-I signature revealed by signficant analysis of microarray were extracted from a published expression study of 295 early stage breast cancers from the Netherlands Cancer Institute (NKI). Genes are presented in rows and experiments in columns. Breast fibroblast derived IGF-I signature stratifies early breast cancer patients (NKI) into two groups with high (blue) or low (yellow) expression levels of genes representing the signature. Horizontal bar below the figure represents positive (purple) or negative (orange) ER status. (B) Relationship of expression level of genes building breast fibroblast derived IGF-I signature with distant metastasis free and overall survival. Kaplan-Meier curves representing the clinical outcomes of tumors exhibiting high (blue curve) and low (yellow curve) expression levels of the IGF-I induced signature. The upper two figures represent all patients and the bottom figure shows only patients with oestrogen receptor positive breast tumours.CC BY · PubMed Central
Figure 4 Correlation of the breast fibroblast derived insulin-like growth factor-I (IGF-I) signature with previously reported prognosticators in breast cancer. Correlation of the good-risk 70-genes signature centroid [49], the wound signature centroid [60], the basal type of breast cancer created by Soerlie [46] and the breast fibroblast IGF-I induced signature score in the Netherlands Cancer Institute dataset. Pairwise scatterplot-matrix of four gene signatures. Pearson correlations for the signature are shown in the corners of the plots.
Figure 4 Correlation of the breast fibroblast derived insulin-like growth factor-I (IGF-I) signature with previously reported prognosticators in breast cancer. Correlation of the good-risk 70-genes signature centroid [49], the wound signature centroid [60], the basal type of breast cancer created by Soerlie [46] and the breast fibroblast IGF-I induced signature score in the Netherlands Cancer Institute dataset. Pairwise scatterplot-matrix of four gene signatures. Pearson correlations for the signature are shown in the corners of the plots.CC BY · PubMed Central
Figure 5 Fibroblast derived insulin-like growth factor-I (IGF-I) signature divides lung cancer patients into two groups with significantly different outcome. (A) Unsupervised hierarchical clustering of fibroblast derived IGF-I signature in Garber lung cancer dataset. The expression values of genes in the fibroblast derived IGF-I signature were extracted from a published expression study by Garber [50]. Genes are presented as rows and the experiments are presented as columns. Although some gene expression data are missing, the fibroblast derived IGF-I signature stratifies lung cancer patients into two groups with high (blue) or low (yellow) expression levels of genes representing the signature. (B) Relationship of expression level of genes building fibroblast derived IGF-I signature with overall survival in Garber data. Kaplan-Meier curves denoting the clinical outcomes of the indicated tumours exhibiting high (blue curve) and low (yellow curve) expression levels of the signature. (C) Unsupervised hierarchical clustering of fibroblast derived IGF-I signature in Bhattacharjee lung cancer dataset. The expression values of genes in the fibroblast derived IGF-I signature were extracted from a published expression study by Bhattacharjee [51]. Genes are presented as rows and the experiments are presented as columns. Fibroblast derived IGF-I signature stratifies adenocarcinoma patients into two groups with high (blue) or low (yellow) expression levels of genes representing the signature. (D) Relationship of expression level of genes building fibroblast derived IGF-I signature with overall survival and disease specific survival in Bhattacharjee dataset. Kaplan-Meier curves illustrating the clinical outcomes of the indicated tumours exhibiting high (blue curve) and low (yellow curve) expression levels of the signature.
Figure 5 Fibroblast derived insulin-like growth factor-I (IGF-I) signature divides lung cancer patients into two groups with significantly different outcome. (A) Unsupervised hierarchical clustering of fibroblast derived IGF-I signature in Garber lung cancer dataset. The expression values of genes in the fibroblast derived IGF-I signature were extracted from a published expression study by Garber [50]. Genes are presented as rows and the experiments are presented as columns. Although some gene expression data are missing, the fibroblast derived IGF-I signature stratifies lung cancer patients into two groups with high (blue) or low (yellow) expression levels of genes representing the signature. (B) Relationship of expression level of genes building fibroblast derived IGF-I signature with overall survival in Garber data. Kaplan-Meier curves denoting the clinical outcomes of the indicated tumours exhibiting high (blue curve) and low (yellow curve) expression levels of the signature. (C) Unsupervised hierarchical clustering of fibroblast derived IGF-I signature in Bhattacharjee lung cancer dataset. The expression values of genes in the fibroblast derived IGF-I signature were extracted from a published expression study by Bhattacharjee [51]. Genes are presented as rows and the experiments are presented as columns. Fibroblast derived IGF-I signature stratifies adenocarcinoma patients into two groups with high (blue) or low (yellow) expression levels of genes representing the signature. (D) Relationship of expression level of genes building fibroblast derived IGF-I signature with overall survival and disease specific survival in Bhattacharjee dataset. Kaplan-Meier curves illustrating the clinical outcomes of the indicated tumours exhibiting high (blue curve) and low (yellow curve) expression levels of the signature.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

1 retracted

Cited by

Sorted by citations

See all 355

Related

See all

Metadata

Authors
Rajski, M., Zanetti-Dällenbach, R., Vogel, B., Herrmann, R., Rochlitz, C., & Buess, M.
Affiliations
University of Basel (CH)
Corresponding author
Buess, Martin
ORCID
Venue
BMC Medicine
Publisher
Springer Science and Business Media LLC
Published
2010-01-05
Volume
8
Issue
1
Pages
1-1
ISSN
1741-7015 (ISSN-L)
DOI
10.1186/1741-7015-8-1
PMC
PMC2823652
OpenAlex
W2050096609
Cited by
355
References
76
Citations by year
2026: 5 · 2025: 8 · 2024: 17 · 2023: 2 · 2022: 7 · 2021: 43 · 2020: 21 · 2019: 23 · 2018: 32 · 2017: 65 · 2016: 44 · 2015: 18 · 2014: 15 · 2013: 16 · 2012: 18
FWCI
13.03
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Medicine (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • National Science Foundation
  • Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (112794, 320000)
  • Oncosuisse
Topics
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Growth Hormone and Insulin-like Growth Factors
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › TGF-β signaling in diseases
Keywords
insulin-like growth factor I, tumor stroma, stromal fibroblasts, breast cancer, lung cancer, prognostic gene signature, patient survival prediction, tumor cell proliferation
MeSH terms
Breast Neoplasms, Female, Fibroblasts, Humans, Insulin-Like Growth Factor I, Lung Neoplasms, Prognosis, Stimulation, Chemical, Signal Transduction, Up-Regulation, Gene Expression Regulation, Neoplastic, Cluster Analysis, Stromal Cells, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Databases, Genetic, Cell Line, Tumor, Cell Growth Processes, Kaplan-Meier Estimate

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review