| Literature DB >> 23113900 |
Antonia Patsialou, Yarong Wang, Juan Lin, Kathleen Whitney, Sumanta Goswami, Paraic A Kenny, John S Condeelis.
Abstract
INTRODUCTION: Metastasis of breast cancer is the main cause of death in patients. Previous genome-wide studies have identified gene-expression patterns correlated with cancer patient outcome. However, these were derived mostly from whole tissue without respect to cell heterogeneity. In reality, only a small subpopulation of invasive cells inside the primary tumor is responsible for escaping and initiating dissemination and metastasis. When whole tissue is used for molecular profiling, the expression pattern of these cells is masked by the majority of the noninvasive tumor cells. Therefore, little information is available about the crucial early steps of the metastatic cascade: migration, invasion, and entry of tumor cells into the systemic circulation.Entities:
Mesh:
Year: 2012 PMID: 23113900 PMCID: PMC4053118 DOI: 10.1186/bcr3344
Source DB: PubMed Journal: Breast Cancer Res ISSN: 1465-5411 Impact factor: 6.466
Figure 1Study design for the derivation of the human invasion signature (HIS).Schematic of experimental procedures for the selective profiling of migratory tumor cells in vivo. Migratory/invasive tumor cells were isolated from live primary tumors based on their chemotactic and highly motile properties toward known chemoattractants. The whole or "average" primary tumor cells were isolated with fluorescence-activated cell sorting (FACS) based on the tumor cells stably expressing green fluorescent protein (GFP). Both cell populations analyzed were >95% pure tumor cells (detailed discussion in Additional File 1). Comparative gene-expression analysis with microarrays was then used to derive a signature specific to in vivo migration and invasion of breast tumor cells. Methods and technical controls are discussed in more detail in Methods and in Additional File 1.
Significant upregulated and downregulated functional gene networks of the migratory breast tumor cells
| Rank | Score | Function network | Genes regulated in function network |
|---|---|---|---|
| 1 | 48 | DNA replication and repair | |
| 2 | 36 | Embryonic and tissue development | |
| 3 | 33 | Cellular movement and development | |
| 4 | 33 | Cell-to-cell signaling and interaction | |
| 5 | 27 | Cellular assembly and organization | |
| 1 | 44 | Nervous system development and function | |
| 2 | 31 | Cell death and cell cycle | |
| 3 | 22 | Hematologic disease | |
| 4 | 19 | Protein synthesis and cell morphology | |
| 5 | 18 | Drug and nucleic acid metabolism | |
The human invasion signature (HIS) was analyzed for significant regulated functions by using Ingenuity Pathway Analysis. The genes associated with each function network shown in the last column are significantly regulated in the HIS. Score: negative exponent of p value calculated by a right-tailed Fisher Exact test (calculates the likelihood that the Network Eligible Molecules that are part of a network are found therein by random chance alone).
Figure 2Validation of specific genes upregulated in the migratory breast tumor cells. mRNA expression of genes from the top three significant upregulated function networks in Table 1 was assessed with real-time polymerase chain reaction (PCR) in independent biological repeats of migratory tumor cells versus average primary tumor cells from MDA-MB-231 breast tumors. Genes are grouped by function, as determined by Ingenuity Pathways Knowledge Base (IPA) and Gene Ontology annotations. Bars, relative average mRNA expression of migratory tumor cells compared with average primary tumor, log2-transformed scale for ease of display. The linear fold-upregulation for every gene is shown at the end of every bar. Error bars: SEM, n = 6, P < 0.05 for all data shown in this graph (Student t test).
Development of patient-derived breast tumor xenografts in SCID mice
| Total | ER+ | ER- | Triple negative | |
|---|---|---|---|---|
| Patients samples received | 29 | 17 | 12 | 7 |
| Samples that grew tumors in mice after first implantation | 8 | 4 | 4 | 4 |
| Take rate | 27.59% | 23.53% | 33.33% | 57.14% |
| Samples that established a stable and propagatable tumor in mice (successful growth in subsequent passages) | 6 | 2 | 4 | 4 |
| Stable take rate | 20.69% | 11.76% | 33.33% | 57.14% |
Numbers of patient samples implanted in the mammary fat pad of SCID mice and take rates for successful growth in the mice. For some of the samples, a tumor grew only on the first implantation. We call stable take-rate the percentage of samples that established tumors in mice that were capable of growing tumors in subsequent passages.
Figure 3Histologic and metastatic properties of the patient-derived orthotopic breast tumor xenografts. (A) For the HT17 and HT39 patient tumors, representative images are shown here of (from left to right) primary tumor from the patient of origin (H&E), primary tumor in the xenograft (H&E), and staining of the xenograft tumor with a human-specific anti-cytokeratin antibody (immunohistochemistry, brown). Magnification ×40. (B) In vivo invasion assay for HT17 and HT39 xenograft tumors to an EGF gradient, passages 1 through 4 (P1-P4). Invasion to a gradient of serum (FBS) showed similar results. The number of migratory cells remains similar over passages (P = 0.47 for HT17, P = 0.82 for HT39, by one-way ANOVA). Results are plotted as average number of cells per microneedle. Error bars: SEM, n ≥ 5 mice. (C) Representative images of spontaneous lung metastasis of the xenograft orthotopic tumors (H&E). Magnification ×40.
Figure 4Functional validation of specific targets from the HIS in human breast tumors . (A) Migratory and average primary tumor cells were isolated in vivo from MDA-MB-231 as well as the patient-derived HT17 and HT39 tumors. Cells were fixed immediately after collection and immunostained for total Smad2/3 complex, with DAPI used as a nuclear counterstain. A representative image for a cell with cytoplasmic Smad2/3 staining from the primary tumor samples and a cell with nuclear accumulation of Smad2/3 from the migratory cell samples is shown. Quantification of total results is shown in the graph, for which the average percentage of cells with nuclear Smad2/3 accumulation over total number of cells (by DAPI count) was calculated for each xenograft. Error bars: SEM, *P < 0.05 (Student t test), n = 10 to 50 cells per sample; samples from at least three different mice. (B) In vivo invasion and intravasation were measured in mice bearing either orthotopic MDA-MB-231-GFP tumors (MDA231) or patient-derived HT17 and HT19 tumors, shortly after treatment with specific inhibitors or blocking antibodies. In vivo invasion is plotted as average number of migratory cells collected per microneedle. Intravasation is plotted as average number of circulating tumor cells per milliliter of blood. Results are shown for mice that received treatment with either vehicle control or specific inhibitor: neutralizing antibody specific to human IL8, PTPN11 specific inhibitor NSC87877, TGF-β receptor-specific inhibitor SB431542, NPM1-specific inhibitor NSC34884, or MYC-specific inhibitor 10058-F4 (negative control). Bars, average number of cells; error bars: SEM, *P < 0.05; **P < 0.01; ***P < 0.001; ns, not significant (Student t test for each condition relative to its vehicle control); n ≥ 6 microneedles from at least four mice for the in vivo invasion assay; n ≥ 6 mice for the intravasation assay. (C) mRNA expression of MDA-MB-231 cells transfected with siRNA for genes SMAD2, IL8, PTPN11, NPM1. Shown is expression for each target gene by its respective siRNA relative to the nontargeting control siRNA (si-control). Error bars: SEM, *P < 0.05; **P < 0.01; ***P < 0.001 (Student t test); n = 3 separate experiments for each siRNA. (D) In vitro invasion over Matrigel-coated transwells was measured for MDA-MB-231 cells, either transfected with siRNA to the genes indicated or with specific inhibitors or blocking antibodies. Shown is the relative invasion for each condition toward the appropriate control. Error bars: SEM, *P < 0.05; **P < 0.01; ***P < 0.001 (Student t test); n = three separate experiments for each condition with duplicate transwells per experiment.
Figure 5The human invasion signature (HIS) is prognostic of clinical outcome in breast cancer patient cohorts. (A) Metastasis-free survival Kaplan-Meier analysis on cases identified as high and low risk by the HIS in the NKI295 cohort. Hazard ratio, 3.10; 95% CI, 1.98 to 4.84; P = 3.99e-07 (log-rank test). Also shown is the recurrence-free survival Kaplan-Meier analysis on cases identified as high and low risk by the HIS in the UNC232 cohort. Hazard ratio, 2.84; 95% CI, 1.60 to 5.00; P = 2.15e-05 (log-rank test). (B) One thousand signatures of equal size to the HIS were generated by picking random genes from the genome, and their association to distant metastasis in the NKI295 cohort was calculated. In the scatterplot shown here, each dot represents the P value calculated for each of the random signatures. Blue line, P value of 0.05; red line, P value cutoff for the best 5% random signatures (P = 2.41e-05); green line, P value for the HIS (P = 3.99e-07). (C) Multivariate Cox-Proportional Hazard Regression Analysis of the HIS in the NKI295 and UNC232 cohorts, incorporating established clinical parameters. HR, hazard ratio; CI, confidence interval.
Figure 6Comparative analysis of the human invasion signature (HIS) with the NKI-70-signature. (A) Metastasis-free survival Kaplan-Meier analysis on cases identified as high and low risk by the HIS in the NKI295 cohort (P < 0.0001). The graph is repeated here from Figure 5A for ease of comparison. Also shown is the metastasis-free survival Kaplan-Meier analysis on cases identified as high and low risk by the NKI-70 gene signature in the NKI295 cohort (P < 0.0001). (B) Multivariate Cox proportional hazard regression analysis was performed to evaluate the relation between the HIS and distant metastasis in the NKI295 cohort, incorporating relevant clinical variables as well as the NKI-70 signature (HR, hazard ratio; CI, confidence interval). The NKI-70 signature is a strong predictor, which is expected, because this signature was derived from this same cohort. However, the HIS is significant even in the presence of the NKI-70 signature, indicating that it contains additional prognostic information for this cohort beyond that captured by the NKI-70 signature.
Figure 7The prognostic significance of the human invasion signature (HIS) is not confined to basal-like breast tumors. (A) The HIS remains prognostic of outcome in patient cohorts after exclusion of basal-like tumor patients. Cox proportional hazards model analysis was repeated for the NKI295 and the UNC232 cohorts, excluding the patients with the basal-like breast cancer subtype. P = 0.00147 for NKI and P = 0.000345 for UNC (log-rank test). (B) A Pearson correlation R value was calculated to assess the relation between the HIS gene-expression pattern and the gene expression of each tumor in the UNC232 database. In the plot shown, R values for all patients are clustered by breast cancer subtype. R values above the dotted line are significant at P < 0.05. Patients with a gene-expression pattern positively correlated to the HIS appear in multiple breast cancer subtypes.