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Noninvasive early detection of breast cancer through the use of biomarkers is urgently needed since the risk of recurrence, morbidity, and mortality is closely related to disease stage at the time of primary surgery. A crucial issue in this approach is the availability of relevant markers and corresponding monoclonal antibodies suitable for the development of effective immunodiagnostic modalities. The identification of such markers from human pathological lesions and the isolation of specific antibodies using conventional approaches remain major challenges. Camelids produce functional antibodies devoid of light chains in which the single N-terminal domain of the heavy chain is fully capable of antigen binding. When produced as an independent domain, these so-called single-domain antibody fragments (sdAbs) or nanobodies have several advantages for biotechnological applications owing to their unique properties of size (13 kDa), stability, solubility, and expression yield. In this work, we have generated phage display libraries from animals immunized with breast cancer biopsies. These libraries were used to isolate sdAbs against known and relevant antigens such as HER2, or several cancer-specific sdAbs against unknown targets. We describe the identification of one these targets, cytokeratin 19, using affinity purification in combination with mass spectrometry. Some of these sdAbs were used in several straightforward diagnostic applications such as immunohistochemical analysis of tumor samples, multiplexed cytometric bead array analysis of crude samples, or an immune enrichment procedure of rare cells. Here, we demonstrate that phage display-based selection of single-domain antibodies is an efficient and high-throughput compatible approach to generate binders with excellent characteristics for the fast development of diagnostic and prognostic modalities.

We recently reported that standardized quantitative immunohistochemical (IHC) assays allowed prediction of an adverse outcome among 572 node negative (N-) patients with breast carcinoma (BrCa). To further validate our prior findings, we repeated the IHC stains including a second series of BrCa diagnosed at Yale University. Tissue microarrays (TMAs) of two cohorts of patients with BrCa (418 Marseille University and 303 Yale University) were respectively investigated for IHC expression of 15 markers (HIF-1α, PI3K, pAKT, pmTOR, moesin, P21, 4(E) BP-1, P27, Ker5-6, pMAPKAPK-2, SHARP2, claudin-1, ALDH, AF6 and CD24). Quantitative measurements of immunoprecipitates densitometry assessed with an image analyzer were correlated with 8-year patients' outcome and compared in the two cohorts. The best predictive signature consisted of a combination of five markers that included HIF-1α, PI3K, claudin-1, AF6 and pAKT in N- BrCa. This combination permitted an accurate prediction of outcome in 92.34% (386/418) of N- patients in the first set (Marseille) and 89.8% (158/176) in the second set (Yale). The close results in both cohorts confirmed the validity of this original IHC signature predictive of prognosis in node negative BrCa. This validation suggests that in clinical practice, it would be possible with standardized kits (i) to identify patients with poor prognosis at diagnosis time, particularly in the N- BrCa subset, who would require more aggressive adjuvant therapy and (ii) to avoid useless expensive therapies and their side effects in N- patients with favorable prognosis., (Copyright © 2011 UICC.)

Background: CK2α is a signalling molecule that participates in major events in solid tumour progression. The aim of this study was to evaluate the prognostic significance of the immunohistochemical expression of CK2α in breast carcinomas., Methods: Quantitative measurements of immunohistochemical expression of 33 biomarkers using high-throughput densitometry, assessed on digitised microscopic tissue micro-array images were correlated with clinical outcome in 1000 breast carcinomas using univariate and multivariate analyses., Results: In univariate analysis, CK2α was a significant prognostic indicator (p<0.001). Moreover, a multivariable model allowed the selection of the best combination of the 33 biomarkers to predict patients' outcome through logistic regression. A nine-marker signature highly predictive of metastatic risk, associating SHARP-2, STAT1, eIF4E, pmapKAPk-2, pAKT, caveolin, VEGF, FGF-1 and CK2α permitted to classify well 82.32% of patients (specificity 81.59%, sensitivity 92.55%, area under ROC curve 0.939). Importantly, in a node negative subset of patients an even more (86%) clinically relevant association of eleven markers was found predictive of poor outcome., Conclusion: A strong quantitative CK2α immunohistochemical expression in breast carcinomas is individually a significant indicator of poor prognosis. Moreover, an immunohistochemical signature of 11 markers including CK2α accurately (86%) well classifies node negative patients in good and poor outcome subsets. Our results suggest that CK2α evaluation together with key downstream CK2 targets might be a useful tool to identify patients at high risk of distant metastases and that CK2 can be considered as a relevant target for potential specific therapy., (Copyright © 2010 Elsevier Ltd. All rights reserved.)

We examined a series of 667 patients with node-negative breast carcinomas in order to identify prognostic immunohistochemical molecular signatures for the prediction of early metastasis, and potential new therapeutic targets. We used a standardized quantitative immunocytochemical approach with 37 antibodies, based on high-throughput tissue microarrays and image analysis, and analyzed the results with respect to metastatic status after a mean follow-up of 86 months. Complete data were obtained for 586 patients. The predictive value of the markers was first analyzed individually by univariate analysis (log rank test) in 586 node-negative tumors, according to metastatic status during follow-up. Twenty-seven markers had significant prognostic value. ROC curve analysis (logistic regression) was then used to determine the marker combination that best classified the patients with and without metastases. A 15-marker signature (Bcl2, P16, P21, P27, P53, CD34, CA IX, c-kit, FGF-R1, P38, JAK, pSTAT3, CK9, STAT1 and ER) correctly classified 84.8% of the patients (sensitivity 85.5%, specificity 84.6%). When ER, PR and c-erb B2 were excluded from the analysis, a very similar signature, in which CK8-18 replaced ER, correctly classified 83.6% of the patients (sensitivity 84.5%, specificity 83.4%). These results show that quantitative immunoprofiling, independently of ER, PR and c-erb B2 status, can provide a basal-like signature, can properly predict the metastatic risk of node-negative breast carcinomas at diagnostic time. This may be helpful for selecting patients who do not need aggressive adjuvant chemotherapy, and for developing tailored therapies.

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., ((c) 2008 Wiley-Liss, Inc.)

We aimed in this study at identifying prognostic immunohistochemical molecular signatures indicative of disease outcome, also relevant for development of new specific therapies, in triple-negative (ER, PR, c-erbB2- negative) breast carcinoma subtypes. We evaluated 42 markers in tissue micro-arrays from a series of 924 breast carcinomas including 184 triple-negative tumors using standardized quantitative immunocytochemical assays and correlated the data with patients' outcome (mean follow-up of 79 months). When 27/42 markers including basal-like markers first found to be individually significant for prognosis in a univariate analysis (log-rank test) in 924 tumors, were secondly evaluated in the triple-negative tumor subtype (184/924), eleven including maspin, P21, P27, PTEN, caveolin, EGFR, FAK, P38, pMAPK, STAT1 and CD10 were 89.2% predictive of disease outcome in logistic regression. When markers reported in the literature as expressed in basal-like subtype were evaluated in the 924 series, only eight (EGFR, CK14, moesin, caveolin, cMet, ckit, CD44v6, C10) were prognosis predictive in univariate analysis (log-rank test) and in logistic regression were predictive of disease outcome in 66.3% independently of ER, PR and c-erbB2 expression and in 72% in triple-negative tumor subset. The results suggest that the category of 'triple-negative' breast carcinomas does not exactly overlap the basal-like subtype, and that immunoprofiling of triple-negative tumors (not similar to that of basal-like tumors) may be helpful to select patients for more aggressive treatment and provides a basis for development of tailored therapy.