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Early Detection and Diagnosis
A signature predictive of disease outcome in breast carcinomas, identified by quantitative immunocytochemical assays
Article first published online: 24 NOV 2008
Copyright © 2008 Wiley-Liss, Inc.
International Journal of Cancer
Volume 124, Issue 9, pages 2124–2134, 1 May 2009
How to Cite
Charpin, C., Secq, V., Giusiano, S., Carpentier, S., Andrac, L., Lavaut, M.-N., Allasia, C., Bonnier, P. and Garcia, S. (2009), A signature predictive of disease outcome in breast carcinomas, identified by quantitative immunocytochemical assays. Int. J. Cancer, 124: 2124–2134. doi: 10.1002/ijc.24177
- Issue published online: 24 FEB 2009
- Article first published online: 24 NOV 2008
- Manuscript Accepted: 11 NOV 2008
- Manuscript Received: 5 SEP 2008
- Institut National contre le Cancer/Canceropole Provence Alpes Côte d'Azur—AP-HM
- quantitative immunocytochemistry;
- molecular signature;
- breast cancer;
Quantitative immunocytochemical assays of 1,200 breast carcinomas were assessed after construction of tissue microarrays. A total of 42 markers were evaluated for prognostic significance by univariate log rank test (mean follow-up, 79 months), using quantitative scoring by an image analysis device and specific software. Complete data were obtained for 924 patients, for whom 27 of the 42 markers proved to be significant prognostic indicators. Analysis of these 27 markers by logistic regression showed that 18 (cMet, CD44v6, FAK, moesin, caveolin, c-Kit, CK14, CD10, P21, P27, pMAPK, pSTAT3, STAT1, SHARP2, FYN, ER, PgR and c-erb B2), and 15 when ER, PgR and c-erb B2 were excluded, were 80.52% and 78.9% predictive of disease outcome, respectively. The immunocytochemical assays on 4 micron thick sections of fixed tissue are easy to handle in current practice and are cost-effective. Quantitative densitometric measurement of immunoprecipitates by computer-assisted devices from digitized microscopic images allows standardized high-throughput “in situ” molecular profiling within tumors. It is concluded that this 15 marker immunohistochemical signature is suitable for current practice, since performed on paraffin sections of fixed tumor samples, and can be used to select patients needing more aggressive therapy, since this signature is about 80% predictive of poor clinical outcome. Also, the markers included in the signature may be indicative of tumor responsiveness to current chemotherapy or suggest new targets for specific therapies. © 2008 Wiley-Liss, Inc.
Several studies over the last decade have reported genomic and transcriptional abnormalities in breast carcinomas.1–9 Recently, molecular signatures predictive of prognosis have been documented and recommended for the management of patients with breast carcinomas.3, 5–8 The procedures for identification of such signatures in individual tumors require frozen tissue fragments obtained by trained pathologists using fast appropriate sampling, or frozen tissue prior to fixation in specific fixatives. Moreover, DNA microarrays require expensive laboratory supplies. The resulting high cost of such procedures and the time required for tissue sampling and DNA assay processing make it difficult to recommend these tests for use in everyday current practice at the time of histological diagnosis in individual patients.
In contrast, recent developments in immunohistochemistry show that immunodetection of posttranscriptional protein products of some of the reported prognostic indicator genes within tumor tissue is economically relevant. Such procedures may be suitable for routine practice, since they involve much lower costs, are applicable to formalin-fixed and paraffin-embedded tissue fragments and use documented and commercially available antibodies. Also, they require small amounts of tissue (4 μm per section and per antibody or protein or marker tested) that are easily obtained from remaining paraffin blocks after microscopic examination.
In this respect, recent studies have reported immunohistochemical profiles of breast carcinomas of various types according to the new taxonomic classification based on DNA array profiling, including luminal A and B and Her-2, normal and basal-like carcinoma subtypes.10–23 The latter subtype lacks tailored therapies such as hormone or anti–c-erb B2 therapies.
The main concern in routine practice for early diagnosis of breast carcinomas, also with a close relationship to research aims, is the identification within tumors of molecules which can be potentially blocked by new therapies that specifically target these molecules. In addition, markers of disease outcome are needed, to direct more aggressive treatment specifically to patients with a molecular profile associated with poor prognosis among those categorized in the same subgroup by clinicopathological criteria (namely small node-negative grade 2 tumors).
In view of the high-cost of screening, diagnosis and therapy of breast carcinomas, a simplified cost-effective means of identifying in situ protein signatures, detectable by immunohistochemistry and indicative of poor outcome in patients who then need more aggressive or specific therapies, appears to be a relevant alternative to genomic assays that are more appropriate to basic and academic purposes.
However, the immunocytochemical procedures must be standardized as far as possible before they can be recommended for clinical practice. In particular, quantification of immunoprecipitates with automated computer-assisted devices relying on densitometry allows more objective analysis of results.24–31 Also, quantitative data are more appropriate for statistical analysis and permit more valid studies, although some variation in interpretation of results can still occur.32
Our objective in the present study was to determine immunohistochemical criteria for phenotyping of tumors of poor prognosis, and particularly for metastatic risk that would be economically applicable for individual patients with breast carcinoma, using the latest methodology for standardization and quality control. We have focused on quantitative densitometry of immunoprecipitates on digitized microscopic images to provide accurate numerical data that are compatible with requirements for modern statistical analysis (log rank, logistic regression, unsupervised hierarchical clustering). More precisely, in this study we aimed first to confirm the prognostic value of reported immunocytochemical markers, when evaluated with high-throughput standardized assays using tissue microarrays (TMAs)12–23, 31 in a large retrospective series of breast carcinomas (n = 924); second, to quantify the immunohistochemical precipitates within digitized microscopic TMA images, using an automated computer-assisted device; and third, to correlate the quantified immunohistochemical expression of each marker and of groups of markers with patients' outcome. The overall goal was to identify the best group of markers, in terms of sensitivity and specificity, to predict prognosis within 48 hr on tissue sections that would be suitable in clinical practice for individual patients at the time of diagnosis, simultaneously with pathological reporting.
The selected markers included a set of 42 prognostic markers known to be indicators of tumor cell growth, proliferation and scattering and of angiogenesis, in addition to markers of some signaling pathways (see review22).
Material and methods
The subjects were a consecutive series of 1,200 patients with invasive breast carcinomas who were operated on from 1995 to 2002 (mean follow-up, 79 months) in the same department at the Hôpital Conception, Marseille. Surgery was in all cases the first treatment (PB). For this first step of treatment, patient management was handled by the same group of surgeons and by 3 senior pathologists (CC, SG and LA). Conservative treatment, mastectomy and node resection (complete or sentinel) were applied according to current European recommendations. Likewise, radiotherapy, chemotherapy and hormone therapy were applied according to criteria currently used at that time.
Analysis of the distribution of the series by age, histological type and grade, and nodal status before TMA construction revealed the usual distribution of breast carcinomas and no bias in tumor selection as compared with literature data. Because of technical difficulties in performing immunocytochemical tests on many serial paraffin sections of a TMA to evaluate the 42 different markers, complete data for all markers were finally obtained for only 924 patients of the initial series of 1,200.
The 2005 follow-up data in clinical records showed that 181 of 924 were metastatic, and 32 patients deceased.
Our study focused mainly on correlation of quantitative immunohistochemical data with patients' outcome. Current histoprognostic criteria on H&E staining were not retained for statistical analysis, mainly to limit the burden of data and also to focus the statistical analysis on continuous variables homogeneously obtained by (numerical) densitometric measurements of immunoprecipitates with the image analyzer.
Tissue samples were all taken from surgical specimens after formalin fixation. Attention was paid to optimal consistent tissue-handling procedures, including fast immersion in buffered formalin in an appropriate container by pathologists or by nurses trained in the procedure. Tumor fragments were large and thick enough to allow further TMA construction. Duration of fixation was 24 hr for smaller samples (<5 cm) and 48 hr for larger ones, to improve formalin penetration, before specimen dissection at room temperature. After fixation, paraffin pre-embedding and embedding were performed in currently available automated devices of the same brand.
Paraffin blocks were stored in the same room, where temperature was maintained at 20°C prior to TMA construction.
The procedure for construction of TMAs was as previously described.22, 31 Briefly, cores were punched from the selected 1,200 paraffin blocks (from 1,200 patients), distributed in 6 new blocks including 2 cores for each tumor (200 cases per block, a total of 2,400 cores) of 0.6 mm diameter. To avoid false positive staining that might result from stromal inflammatory cells that could react with the antibodies tested, the tumor areas selected for the TMA 0.6 mm punch were dense carcinomatous areas with minimal stroma including essential vessels and few fibroblasts. For those tumors with low-epithelial structure density and wide stromal component, areas lacking inflammatory reaction, if any, were selected. Also, inflammatory carcinomas were excluded of this series and are specifically under investigation in other TMA (work in preparation).
All the new blocks (TMAs) were stored at 4°C, before that sections (4 μm thick) were prepared for each marker to be examined by immunohistochemistry.
Serial tissue sections were prepared and stored at 4°C, 24 hr before immunohistochemical processing, as previously reported.22, 31 Immunoperoxidase procedure was performed using an automated Ventana Benchmark XT device and Ventana kits.
Markers were detected using commercially documented antibodies (Table I). Dilutions of antibodies were determined by a prescreening on the usual full 4 μm thick sections prior to use on TMA sections.
|9||Caveolin 1||Santa Cruz||Rpab|
|12||PI3 kinase||Cell Signaling||Rpab|
|15||CD 117 (c-Kit)||Dako||Rpab|
|23||P27 Kip1||Cell Signaling||Rpab|
|24||P38 MAP kinase||Cell Signaling||Rpab|
|33||STAT-3||Cell Signaling||Rmab||Tyr 705 D3A7|
|36||Cytokeratins 8 and 18||Zymed||Mmab||Zym5,2(UCD/PR-10,11|
|42||FGFR-1 Flg (C-15)||Santa Cruz||Rpab|
Specificity of signaling molecules was documented by the suppliers. Those antibodies recognizing phosphorylated molecules are identified with “p” sign such as pSTAT3. In contrast, those simply indicated like STAT1, FYN, focal adhesion kinase (FAK), recognize nonphosphorylated molecules.
Automated densitometric measurements of immunoprecipitates in cores were assessed for each marker antibody in each core individually identified after digitization and image cropping of the slides, as previously reported.22, 31 Briefly, TMA analysis with a SAMBA 2050 automated device (SAMBA / TRIBVN, Châtillon 92320, France)24–27 was performed according to the following protocol.
First, an image of the entire slide was built up using low-power magnification (2×). This image was made up of a mosaic of images acquired along a rectangular grid with contiguous fields. Second, the area of the slide containing the TMA cores was automatically delineated and scanned at higher magnification (20×, pixel dimension 7.4 μm). Third, after autofocusing, the images were acquired with an overlap greater than the largest mechanical positioning error. Using the image contents, a matching algorithm determined precisely the relative position of each image with respect to its neighbors. Calculated overlap was removed from images to produce a new set of higher-magnification images, thus covering precisely the cores of interest. A specially developed tool referred to as TMA crop then allowed superposition of the TMA grid onto the reduced image and precise alignment of each node of the grid with the core location within the image. The final step was performed automatically using the core image contents to ensure pixel precision of the match. From the images acquired with 20× magnification, a new set of images was next computed, one for each core. For color analysis of the core images, the SAMBA “immuno” software was applied as previously reported24–31 in usual full tissue sections.
Immunohistochemical expression of each marker was first correlated with patients' disease-free survival using NCSS and Statistica statistical software.
When significant differences in mean expression were identified in patients with disease and without disease, the prognostic significance was determined by log rank tests (Kaplan-Meier curves). The appropriate threshold of prognostic significance for a given marker was determined as previously recommended33 and described.22, 24–31
Logistic regression (with ROC curves) was then used to identify the combination of markers with the best sensitivity and specificity indicative of a proteomic signature of poor prognosis.
Finally, unsupervised hierarchical clustering of significant prognostic indicators in the overall series provided qualitative data to be compared with previously reported research results on the relationship and on the role played by these molecules in the process of cancer metastasis.
Distribution of positive staining
Whatever the positive immunostaining location (nuclear, cytoplasmic or cell membranes), the microscopic images enclosing all spots in individual slide were digitized. After “cropping” of the images, densitometry was assessed by the analyzer in each spot identified by cropping with the SAMBA software specifically developed for immunohistochemically stained sections. The degree of the immunostaining was automatically evaluated and quantitative scores were consequently computed by the software.
Screening of potential markers of prognosis
The 42 markers tested (Tables I and II) were selected on the basis of literature data on breast carcinoma prognostic markers and our experience of immunostaining quality in pretests of commercially available antibodies on frozen tissue and current full paraffin sections, prior to high-throughput immunodetection on TMAs including the series of 1,200 tumors (Fig. 1).
|Tissue markers (immunohistochemistry): Positive tumors||Image analysis (densitometry): Quantitative score of positive immunostaining|
|p||Mean value– disease-free||Mean value– with disease||p||Patient number disease-free||Patient number with disease|
|Tumor progression invasion/cell adhesion|
In the first step of the study, immunoexpression of markers was screened in dedicated TMAs containing tumors from disease-free patients and from patients with metastasis and recurrent disease. The first step in assessing prognostic value consisted in comparison of mean quantitative scores in relation to the number of positive and negative patients in the disease-free and diseased categories (Mann-Whitney and chi-squared tests) (Table II). The positive or negative correlation of the markers expression is indicated in Table II. Quantitative scores in patients with disease-free survival greater than those with metastases, indicate a positive correlation.
Conversely quantitative scores in patients with disease-free survival, smaller than in patients with metastases indicate a negative correlation.
Data for all markers were obtained for only 924 out of the 1,200 patients because of the loss of cores after immunostaining procedures for some antibodies, and tumors lacking data for all 42 markers were not further considered.
Quantitative score and survival
The prognostic significance of markers was further individually evaluated by a univariate log rank test (Kaplan-Meier survival curves).
The threshold of positive staining was first established, determined by the image analysis device as 0 or >0 computed quantitative scores. In positive (>0) cases, optimal thresholds greater than zero, specific for each marker, were determined according to the p value curves from univariate analysis (log rank), as reported by Altman et al.33 and previously used,22, 24–31 as shown in Figure 2 and Table III.
|p (log rank)||Quantitative score threshold||Immunostaining localization|
|1||Caveolin||<0.01||29||Cyto and mb|
|3||CD146||<0.001||2.3||Cyto and mb|
|4||CD 44 v6||<0.001||11.9||Mb|
|9||cKit||<0.001||12.9||Mb and cyto|
|12||FGF-R||<0.01||23||Cyto and mb|
|15||Maspin||<0.001||5.7||Cyto and nuclear|
|16||Moesin||<0.0001||16.4||Cyto and mb|
|17||P16||<0.01||7.5||Nuclear and cyto|
|18||P21||<0.001||2.1||Cyto and nuclear|
|20||P38||<0.001||1.1||Cyto and nuclear|
Table III shows that 27 of the 42 prognostic markers were significant in the univariate analysis and Figure 3 illustrates an unsupervised hierarchical clustering of prognostic significant markers in the log rank test.
Logistic regression (ROC curves)
To determine a proteomic immunocytochemical signature of poor outcome, those markers of prognostic significance in univariate log rank tests were reevaluated by logistic regression (Table IV), which showed that 18 of the 27 markers remained significant predictors of prognosis after a first regression step (p < 0.05). Importantly, with the 27-marker signature, 82.14% of the patients were well classified in either the good or poor prognostic category, with a sensitivity of 85% and specificity of 80.6% (Fig. 4).
|Immunocytochemical marker||p (regression)|
When the signature logistic regression was assessed with exclusion of ER, PgR and cerbB2 (24 markers instead of 27), the sensitivity and specificity remained similar (82.32% and 81.76%, respectively), with 80.73% patients well classified (Table V and Fig. 4). This 15-marker signature includes cMet, CD14v6, FAK, moesin, caveolin, c-Kit, CK14, CD10, P21, P27, pMAPK, pSTAT3, STAT1, SHARP2 and FYN. Markers related to tumor cell motility and spreading were of special interest, since our study aimed at prediction of patients' outcome that is closely linked to development of metastases. Likewise it is not surprising to observe that molecules such as FAK, Erk/PAK-P21, MAPK/P38, STAT1, STAT3 of cMet activation were also found in our signature, like FYN involved in signaling pathways of angiogenesis and skeleton rearrangement.
When a second step of regression was performed with either the 18 or 15 markers, the percentages of well classified patients were very close (80.52% and 78.91%) (Tables VI and VII, Fig. 4) and 17 or 14 markers remained significant (Table VI).
|Immunocytochemical markers||Sensitivity (%)||Specificity (%)||Well classified (%)||Patients well classified (n = 924) with positive signature|
|1st step of regression (n = 27 markers)||85||80.6||82.14||27/181||144/743|
|2nd step of regression (n = 18 markers)||80.35||82.3||80.52||149/181||146/743|
|Without ER, PgR, c-erb B2|
|1st step of regression (n = 24 markers)||82.32||81.76||80.73||148/181||147/743|
|2nd step of regression (n = 15 markers)||81.76||80.2||78.9||149/181||149/743|
Using our quantitative immunohistochemical procedure, we first determined the cut-off points for immunocytochemical expression of each marker having prognostic significance in terms of disease-free survival in a univariate log rank test, in our series of 924 breast carcinomas (Table II). We next determined, by logistic regression, the best association of prognostic indicators (Tables IV and V) and showed that 78.9% prognostic prediction is provided by a 15-marker signature that includes cMet, CD44v6, caveolin, FAK, moesin, c-Kit, CK14, CD10, P21, P27, pMAPK, pSTAT-3, SHARP-2, STAT-1, FYN (Table VI) independently of ER, PgR and c-erb B2 status.
No previously reported study has used this quantitative immunocytochemical approach to tumor profiling in terms of predicting prognosis in breast carcinoma patients. We used an immunohistochemical procedure that is much easier to handle than genomic profiling in routine clinical and pathological practice, requiring very few tissue samples, since it can be applied on tissue remaining within paraffin blocks after diagnosis, is performable in 24 hr in pathological laboratories and costs about 20-fold less than commercially available genomic tests.
The selection of markers was based on a literature review covering principal markers involved in tumor growth and progression, and on our previous experience in their immunocytochemical expression in tissue sections from frozen or fixed tissues and TMAs.24–31
Recent studies have underlined the role of cMet in tumor spreading (see review22).
We and others have shown the relationship with poor prognosis in breast cancers of this marker and also of CD44v6 (see review30). Several recent studies have shown the synergistic role of CD44v6 and cMet in tumor cells of several types34 and the interaction of the EMR (ezrin-moesin-radixin) superfamily and CD44v6 for HGF activation of cMet to promote the ERK signaling cascade, inducing cell migration. Moreover, EMR components act on cell adhesion (integrin β2) and the cytoskeleton.35, 36 FAK is also known to play a pivotal role in the control of integrin-mediated cell functions including cell migration, progression and survival, coacting with cMet and EMR.37 Consistent with these findings, poor outcome and high metastatic risks associated with CD44v6, cMet, EMR and FAK immunoexpression in breast carcinoma have been individually documented in several previous studies.17, 22, 30, 31
It is therefore not surprising that in the present study, overexpression of CD44v6, cMet, moesin and FAK was found to be included in the immunocytochemical signature of poor prognosis as determined by logistic regression for early (79 months mean follow-up of patients in our study) metastatic disease.
Caveolins are membrane proteins involved in membrane trafficking, gene regulation, signal transduction and mediation of intracellular processes, as well as in carcinogenesis, being over-expressed in invasive breast carcinomas.38, 39 Conflicting results on the prognostic significance of caveolin expression in breast carcinomas have been reported.39 More specifically, caveolin 1 has been reported in basal-like and metaplastic breast carcinomas15, 17, 39.Interestingly, our results show that caveolin 1 expression in invasive breast carcinomas is definitely associated with poor prognosis, both in univariate analysis and after logistic regression along with markers reflecting increased motility of tumor cells.
Overexpression of molecules with prognostic significance in cancer results from amplification or mutation of genes, or from epigenetic processes including decreased methylation or increased acetylation. When overexpression is observed, the main interest is to evaluate whether the proteins are activated and involved in cell transduction processes. The results from our first-step regression suggest that transduction pathways are activated with prognosis-significant expression of major signaling proteins such as PI3K, pMAPK, pSTAT3, in addition to more specific signaling pathways, such as SHARP-2, FYN, STAT-1 and FAK (see review22, 31). This may imply that the proteins shown to be expressed are activated. However, only pSTAT-3, STAT-1 and SHARP-2 were still significantly correlated with poor prognosis in the reduced signature (15 markers) after the second step of logistic regression.
Increased tumor vascularization and angiogenesis are associated with poor outcome of patients (see reviews22, 31, 41). We analyzed several markers, such as CD105, Tie2, CD34, VEGF-R1 and -R2, CD146,40–44 in frozen tissue to evaluate angiogenesis in breast cancers associated with poor prognosis. In this study on fixed tissue, only CD146 proved to be of prognostic significance when evaluated on TMAs in univariate analysis, and not in logistic regression.
New approaches to molecular typing of tumors are relevant to prognosis, but also to prediction of response to therapy. Therefore, markers identified as prognostic indicators can also be regarded as indicators of responsiveness to current chemotherapies (like P21) or as targets for tailored therapies. For example, caveolins, moesin and CD44v6 have been shown to be indicators of responsiveness to anthracyclines and paxitaxel.45, 46 Also, specific therapy with agents such as dasatinib (a small molecule orally active as a kinase inhibitor and biologically active in cell lines with elevated caveolins, moesin and yes associated protein 1 expression) can be efficient in breast carcinomas expressing these molecules.47 Dasatinib is also active against PDGF-R and c-Kit and has been shown to be effective in leukemia after failure of imatinib therapy.48 Likewise, sunitinib, that is recommended in gastrointestinal stromal tumors (GIST) expressing c-Kit and in advanced kidney carcinomas, is an inhibitor of angiogenic tyrosine kinase receptors such as PDGR, VEGFR, FLT3 and c-Kit. This suggests that c-Kit in breast carcinomas of poor prognosis could also be targeted by sunitinib. However, response to imatinib and dasatinib or sunitinib in breast carcinomas with c-Kit expression remains to be demonstrated.
Antibodies against cMet and small molecules such as PHA66752 or kerin that target cMet, or the NK4 molecule that blocks HGF binding to cMet have been reported to act as specific cMet inhibitors in breast cancer,22 whereas other tyrosine kinase inhibitors such as Iressa, Tarceva, Herceptin and Genifinib do not inhibit cMet activity (see review49) and breast carcinomas expressing cMet may be responsive to such a tailored therapy.
Activation of cMet results in tumor cell mobility, dissociation, invasion and adhesion to the extracellular matrix via known signaling pathways37 involving PI3K in addition to FAK and ERK/PAK-P21, MAP kinase, STAT-1 and STAT-3, that are responsible for branching morphogenesis (see review32). All these are included in our signature. Small molecules have been reported to act directly against these pivotal kinases and also against Gab-1. In particular, PHA66572 has been reported to block PI3K function in cell lines from small-cell and gastric carcinomas and gliomas, whereas SU11274 inhibits FAK that is responsible for loss of intracellular junctions and increased cell matrix adhesion during mobility and scatter responses in cell culture.49 So cMet downstream transducers and signaling pathways provide a range of potential specific targets.
The CD146 extracellular domain is involved in endothelial–endothelial cell adhesion through tight junctions. The intracellular domain promotes the recruitment of the Src family kinase FYN as well as tyrosine phosphorylation of several intracellular proteins, including FAK (and paxillin), that are present in focal adhesion plaques.37, 50, 51 We observed increased FAK and FYN immunostaining in breast carcinomas of poor outcome compared with tumors from patients with longer survival. This is probably a result of increased cellular signals from cMet and CD146, which both act through FYN signaling pathways upon angiogenesis and rearrangement of the actin skeleton.49–51 Experimental studies have shown that anti-CD146 monoclonal antibodies inhibit proliferation and migration of endothelial cells and angiogenesis, reflected by a reduction in blood vessel density associated with tumor growth inhibition.51 This treatment exhibited no cytotoxic effects in animals, and its efficacy increased when the anti-CD146 monoclonal antibody AA98 was combined with other anticancer agents.51 Our data suggest that inhibition of CD146 and cMet in tumors overexpressing both markers should have a synergistic effect in potentially reducing angiogenesis and cell spreading, but clinical studies are required to demonstrate the efficacy of this strategy for human therapy.
In conclusion, we have used a standardized immunocytochemical procedure that is easy to perform in current practice with fixed tissue, to detect 42 potential prognostic markers. High-throughput quantitative densitometry was then applied to digitized TMA microscopic images. We were thus able to identify a simplified 15-marker signature predictive of disease outcome in 78.9% of a series of 924 patients, whatever their ER, PgR and c-erb B2 status, that could allow selection of those who can benefit from more aggressive therapies. Moreover, the markers identified may also be predictive of response to specific therapies like anthracycline/paxitaxel treatment or to more tailored therapies.
A deeper insight of the immunoprofile of the tumor subgroups included in this 924 series is now under investigation, particularly of those from node negative patients, and of “triple negative” tumor subset, using (i) additional markers and (ii) longer follow-up (up-dating with 2-year longer follow-up), but (iii) the same procedures for immunostaining automated quantification on TMA, and the same methods for statistical analysis. Our goal is now to identify an immunocytochemical signature predictive of poor outcome that would enable to select node negative patients who might benefit from more aggressive therapy and also that could significantly reduce unnecessary treatment in about 70% of node negative patients along with reduced drawbacks and costs of early breast cancer management. Our purpose is also to identify relevant new targets for specific therapy.
We are grateful to ROCHE for supporting Master's and PhD projects (V. Secq and S. Giusiano).