Insulin-like growth factor-I activates gene transcription programs strongly associated with poor breast cancer prognosis

Abstract

Purpose: Substantial evidence implicates insulin-like growth factor-I (IGF-I) signaling in the development and progression of breast cancer. To more clearly elucidate the role of IGF in human breast cancer, we identified and then examined gene expression patterns of IGF-I-treated breast cancer cells.

Methods: MCF-7 cells were stimulated with IGF-I for 3 or 24 hours and were profiled for greater than 22,000 RNA transcripts. We defined an IGF-I signature pattern of more than 800 genes that were up- or downregulated at both time points. The gene signature was examined in clinical breast tumors and in experimental models that represented other oncogenic pathways. The signature was correlated with clinical and pathologic variables and with patient outcome.

Results: IGF-I caused temporal changes in gene expression that were strongly associated with cell proliferation, metabolism, and DNA repair. Genes with early and sustained regulation by IGF-I were highly enriched for transcriptional targets of the estrogen receptor (ER), Ras/extracellular signal-related kinase 1/2, and phosphatidylinositol 3-kinase/Akt/mammalian target of rapamycin pathways. In three large, independent data sets of profiled human breast tumors, the IGF-I signature was manifested in the majority of ER-negative breast tumors and in a subset (approximately 25%) of ER-positive breast tumors. Patients who had tumors that manifested the IGF-I signature (including patients who did not receive adjuvant therapy) had a shorter time to a poor outcome event. The IGF gene signature was highly correlated with numerous poor prognostic factors and was one of the strongest indicators of disease outcome.

Conclusion: Transcriptional targets of IGF-I represent pathways of increased aggressiveness and possibly hormone independence in clinical breast cancers.

Fig A1.

Hierarchical clustering of genes regulated by IGF-I in MCF-7 cells at either 3 or 24 hours, or both (P < .01, fold change > 1.5).

Fig A2.

Ingenuity Pathways Analysis (IPA) of biologic functions and/or disease enriched in the IGFI-gene expression data set and ranked according to significance after (A) 3 hours or (B) 24 hours of insulin-like growth factor I (IGF-I). IPA of biologic functions and/or diseases that were most significantly associated with IGF-I gene regulation. Fisher's exact test was used to calculate a P value determining the probability that each biologic function and/or disease assigned to that data set is due to chance alone.

Fig A3.

Ingenuity Pathways Analysis (IPA) of canonical pathways significantly enriched in the gene expression data set ranked according to data at (A) 3 hours or (B) 24 hours of insulin-like growth factor I (IGF-I). IPA canonical pathways analysis identified the pathways from the Ingenuity Pathways Analysis library of canonical pathways that were most significant to the IGF-I data set. Genes from the data set that met the fold change cutoff of 1.85 and were associated with a canonical pathway in the Ingenuity Pathways Knowledge Base were considered for the analysis. The data is represented as a ratio of the number of genes from the data set that map to the pathway divided by the total number of genes that map to the canonical pathway.

Fig A4.

Ingenuity Pathways Analysis (IPA) network map highlighting regulation of a G2/M DNA damage checkpoint after 24 hours insulin-like growth factor I (IGF-I) stimulation. Network graphical representation of the molecular relationships between genes/gene products in a G2M/DNA damage checkpoint. Genes or gene products are represented as nodes, and the biologic relationship between two nodes is represented as an edge (line). All edges are supported by at least one reference from the literature, from a textbook, or from canonical information stored in the Ingenuity Pathways Knowledge Base. Human, mouse, and rat orthologs of a gene are stored as separate objects in the Ingenuity Pathways Knowledge Base, but are represented as a single node in the network. The intensity of the node color indicates the degree of upregulation (red) or downregulation (green). Nodes are displayed using various shapes that represent the functional class of the gene product (see www.ingenuity.com). Edges are displayed with various labels that describe the nature of the relationship between the nodes (eg, P, phosphorylation; T, transcription).

Fig A5.

Comparison of the IGF gene signature with gene signatures of EGFR and ERBB2. Heat map representation of genes in the insulin-like growth factor (IGF) signature of Figure 2B (with cell cycle–associated genes removed). Alongside the IGF treatment profile data set (left panel) are the corresponding expression patterns in the data set by (Creighton CJ, Hilger AM, Murthy S, et al: Cancer Res 66:3903-3911, 2006) of MCF-7 cell lines activated with various oncogenes including EGFR and ERBB2 (middle panel) and in the Wang estrogen receptor (ER) –negative human tumor profiles (right panel; genes centered on median of ER-positive tumors, not shown).

Fig A6.

The IGF gene signature is not a generic proliferation signature. A data set of in-triplicate NCI60 gene expression data generated by Novartis on the Affymetrix U95v2 array platform (http://dtp.nci.nih.gov/mtargets/madownload.html) was considered. Cell cultures that showed high expression of the cell cycle signature of Whitfield et al (Whitfield ML, Sherlock G, Saldanha AJ, et al: Mol Biol Cell 13:1977-2000, 2002) were first identified (left panel). These same highly proliferative cell cultures did not manifest the IGF gene signature patterns (right panel).

Fig A7.

Kaplan-Meier analysis of the insulin-like growth factor (IGF) signature in the subset of 165 patients in the van de Vijver data set that did not receive hormone or chemotherapy. P values by log-rank statistic.

Fig A8.

Comparison of the prognostic ability of the IGF gene signature with that of previously published prognostic signatures. In addition to the IGF gene signature, an activation score was derived for each of four previously published prognostic gene signatures (taking the Pearson's correlation between the upregulated and downregulated pattern of the genes in the signature and the expression of these genes within each tumor). (A) For the van de Vijver data set, hierarchical clustering of tumors (across) versus activation scores (down) for five prognostic signatures. Grade, estrogen receptor (ER) status, and metastases events for the tumors are also indicated. (B) For the van de Vijver data set, Kaplan-Meier analysis of the five prognostic signatures (including IGF) in all tumors (left panels) and ER-positive tumors only (right panels). (continued on next page).

Fig A8.

Comparison of the prognostic ability of the IGF gene signature with that of previously published prognostic signatures. In addition to the IGF gene signature, an activation score was derived for each of four previously published prognostic gene signatures (taking the Pearson's correlation between the upregulated and downregulated pattern of the genes in the signature and the expression of these genes within each tumor). (A) For the van de Vijver data set, hierarchical clustering of tumors (across) versus activation scores (down) for five prognostic signatures. Grade, estrogen receptor (ER) status, and metastases events for the tumors are also indicated. (B) For the van de Vijver data set, Kaplan-Meier analysis of the five prognostic signatures (including IGF) in all tumors (left panels) and ER-positive tumors only (right panels). (continued on next page).

Fig A9.

Kaplan-Meier analysis after removing PI3K-associated genes from insulin-like growth factor (IGF) signature. P values by log-rank statistic.

Fig 1.

Global gene transcription patterns initiated by insulin-like growth factor (IGF) treatment in vitro. Affymetrix profiles were taken of MCF-7 cells in serum-free medium with or without the presence of IGF-I (8 nmol/L) at 3 and 24 hours. (A) Supervised clustering of 2,154 RNA transcripts that showed up- or downregulation by IGF at 3 or 24 hours (P < .01, fold change > 1.5). For indicated clusters, representative enriched Gene Ontology annotation terms (P < .0005) are given (including the number of unique genes in the cluster v the number represented on the Affymetrix array). (B) Genes with early and sustained regulation by IGF from 3 through 24 hours. Alongside the IGF treatment profile data set are the corresponding expression patterns in the profile data sets from Bild et al (Duke Oncogene Activation) of breast cells with activation of indicated oncogenes and from Lamb et al (Broad Connectivity Map) of cultured cells treated by 184 different small-molecule inhibitors. (Indicated profiles are highlighted). (C) An IGF gene signature of genes from (B) with proliferation-associated genes removed. Corresponding patterns of genes in estrogen-stimulated and in oncogene-activated University of Michigan (MI Oncogene activation10) MCF-7 cells are shown.

Fig 2.

An insulin-like growth factor (IGF) gene signature in clinical breast tumors is associated with estrogen receptor (ER)–negative status and basal/luminal “B” tumor subtypes. (A) Genes in the IGF gene signature shown in Figure 1C were examined in three published profile data sets of clinical breast tumors from van de Vijver, Wang, and Miller. Patterns for genes that were upregulated in the IGF gene signature are separate from the patterns for genes that were downregulated. Within ER-positive and ER-negative subtypes, tumors are ordered from those that have low similarity with the IGF patterns to those with high similarity; tumors with significant positive correlations (P < .01) are bracketed. Red bars along the bottom indicate poor outcome events in patients (gray; data not available). (B) As in (A), for the Sorlie tumor data set.

Fig 3.

Gene expression patterns associated with insulin-like growth factor (IGF) signaling are correlated with poor prognosis. Kaplan-Meier analysis of three tumor profile data sets (A and D from Van de Vijver et al; B and E from Wang et al; and C and F from Miller et al) comparing the differences in risk among three groups of patients when (A to C) all tumors were considered or when (D to F) only those classified as ER-positive were considered. Yellow line, tumors that have high similarity (ie, correlation; P < .01) to the pattern of up- and downregulation by IGF observed in vitro in Figure 1C; blue line, tumors that have low similarity (ie, anticorrelation; P < .01) to the IGF pattern; and gray line, tumors neither similar nor dissimilar to the IGF pattern (P > .01). The log-rank test evaluated whether there are significant differences among any of the three groups. The univariate Cox test evaluated the association of the IGF activation score with patient outcome when the coefficient was treated as a continuous variable.