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      Molecular-based tumour subtypes of canine mammary carcinomas assessed by immunohistochemistry

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          Abstract

          Background

          Human breast cancer is classified by gene expression profile into subtypes consisting of two hormone (oestrogen and/or progesterone) receptor-positive types (luminal-like A and luminal-like B) and three hormone receptor-negative types [human epidermal growth factor receptor 2-expressing, basal-like, and unclassified ("normal-like")]. Immunohistochemical surrogate panels are also proposed to potentially identify the molecular-based groups. The present study aimed to apply an immunohistochemical panel (anti-ER, -PR, -ERB-B2, -CK 5/6 and -CK14) in a series of canine malignant mammary tumours to verify the molecular-based classification, its correlation with invasion and grade, and its use as a prognostic aid in veterinary practice.

          Results

          Thirty-five tumours with luminal pattern (ER+ and PR+) were subgrouped into 13 A type and 22 B type, if ERB-B2 positive or negative. Most luminal-like A and basal-like tumours were grade 1 carcinomas, while the percentage of luminal B tumours was higher in grades 2 and 3 (Pearson Chi-square P = 0.009). No difference in the percentage of molecular subtypes was found between simple and complex/mixed carcinomas (Pearson Chi-square P = 0.47). No significant results were obtained by survival analysis, even if basal-like tumours had a more favourable prognosis than luminal-like lesions.

          Conclusion

          The panel of antibodies identified only three tumour groups (luminal-like A and B, and basal-like) in the dog. Even though canine mammary tumours may be a model of human breast cancer, the existence of the same carcinoma molecular subtypes in women awaits confirmation. Canine mammary carcinomas show high molecular heterogeneity, which would benefit from a classification based on molecular differences. Stage and grade showed independent associations with survival in the multivariate regression, while molecular subtype grouping and histological type did not show associations. This suggests that caution should be used when applying this classification to the dog, in which invasion and grade supply the most important prognostic information.

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          Most cited references24

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          Basal-like breast cancer defined by five biomarkers has superior prognostic value than triple-negative phenotype.

          Basal-like breast cancer is associated with high grade, poor prognosis, and younger patient age. Clinically, a triple-negative phenotype definition [estrogen receptor, progesterone receptor, and human epidermal growth factor receptor (HER)-2, all negative] is commonly used to identify such cases. EGFR and cytokeratin 5/6 are readily available positive markers of basal-like breast cancer applicable to standard pathology specimens. This study directly compares the prognostic significance between three- and five-biomarker surrogate panels to define intrinsic breast cancer subtypes, using a large clinically annotated series of breast tumors. Four thousand forty-six invasive breast cancers were assembled into tissue microarrays. All had staging, pathology, treatment, and outcome information; median follow-up was 12.5 years. Cox regression analyses and likelihood ratio tests compared the prognostic significance for breast cancer death-specific survival (BCSS) of the two immunohistochemical panels. Among 3,744 interpretable cases, 17% were basal using the triple-negative definition (10-year BCSS, 6 7%) and 9% were basal using the five-marker method (10-year BCSS, 62%). Likelihood ratio tests of multivariable Cox models including standard clinical variables show that the five-marker panel is significantly more prognostic than the three-marker panel. The poor prognosis of triple-negative phenotype is conferred almost entirely by those tumors positive for basal markers. Among triple-negative patients treated with adjuvant anthracycline-based chemotherapy, the additional positive basal markers identified a cohort of patients with significantly worse outcome. The expanded surrogate immunopanel of estrogen receptor, progesterone receptor, human HER-2, EGFR, and cytokeratin 5/6 provides a more specific definition of basal-like breast cancer that better predicts breast cancer survival.
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            Concordance among gene-expression-based predictors for breast cancer.

            Gene-expression-profiling studies of primary breast tumors performed by different laboratories have resulted in the identification of a number of distinct prognostic profiles, or gene sets, with little overlap in terms of gene identity. To compare the predictions derived from these gene sets for individual samples, we obtained a single data set of 295 samples and applied five gene-expression-based models: intrinsic subtypes, 70-gene profile, wound response, recurrence score, and the two-gene ratio (for patients who had been treated with tamoxifen). We found that most models had high rates of concordance in their outcome predictions for the individual samples. In particular, almost all tumors identified as having an intrinsic subtype of basal-like, HER2-positive and estrogen-receptor-negative, or luminal B (associated with a poor prognosis) were also classified as having a poor 70-gene profile, activated wound response, and high recurrence score. The 70-gene and recurrence-score models, which are beginning to be used in the clinical setting, showed 77 to 81 percent agreement in outcome classification. Even though different gene sets were used for prognostication in patients with breast cancer, four of the five tested showed significant agreement in the outcome predictions for individual patients and are probably tracking a common set of biologic phenotypes. Copyright 2006 Massachusetts Medical Society.
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              Distinct molecular mechanisms underlying clinically relevant subtypes of breast cancer: gene expression analyses across three different platforms

              Background Gene expression profiling has been used to define molecular phenotypes of complex diseases such as breast cancer. The luminal A and basal-like subtypes have been repeatedly identified and validated as the two main subtypes out of a total of five molecular subtypes of breast cancer. These two are associated with distinctly different gene expression patterns and more importantly, a significant difference in clinical outcome. To further validate and more thoroughly characterize these two subtypes at the molecular level in tumors at an early stage, we report a gene expression profiling study using three different DNA microarray platforms. Results Expression data from 20 tumor biopsies of early stage breast carcinomas were generated on three different DNA microarray platforms; Applied Biosystems Human Genome Survey Microarrays, Stanford cDNA Microarrays and Agilent's Whole Human Genome Oligo Microarrays, and the resulting gene expression patterns were analyzed. Both unsupervised and supervised analyses identified the different clinically relevant subtypes of breast tumours, and the results were consistent across all three platforms. Gene classification and biological pathway analyses of the genes differentially expressed between the two main subtypes revealed different molecular mechanisms descriptive of the two expression-based subtypes: Signature genes of the luminal A subtype were over-represented by genes involved in fatty acid metabolism and steroid hormone-mediated signaling pathways, in particular estrogen receptor signaling, while signature genes of the basal-like subtype were over-represented by genes involved in cell proliferation and differentiation, p21-mediated pathway, and G1-S checkpoint of cell cycle-signaling pathways. A minimal set of 54 genes that best discriminated the two subtypes was identified using the combined data sets generated from the three different array platforms. These predictor genes were further verified by TaqMan® Gene Expression assays. Conclusion We have identified and validated the two main previously defined clinically relevant subtypes, luminal A and basal-like, in a small set of early stage breast carcinomas. Signature genes characterizing these two subtypes revealed that distinct molecular mechanisms might have been pre-programmed at an early stage in different subtypes of the disease. Our results provide further evidence that these breast tumor subtypes represent biologically distinct disease entities and may require different therapeutic strategies. Finally, validated by multiple gene expression platforms, including quantitative PCR, the set of 54 predictor genes identified in this study may define potential prognostic molecular markers for breast cancer.
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                Author and article information

                Journal
                BMC Vet Res
                BMC Veterinary Research
                BioMed Central
                1746-6148
                2010
                28 January 2010
                : 6
                : 5
                Affiliations
                [1 ]Department of Veterinary Public Health and Animal Pathology - Division of Veterinary Pathology, Faculty of Veterinary Medicine, University of Bologna, Via Tolara di Sopra, 50 - 40064 - Ozzano dell'Emilia (Bologna), Italy
                [2 ]Department of Veterinary Morphophysiology and Animal Production, Faculty of Veterinary Medicine, University of Bologna, via Tolara di Sopra 50-40064 Ozzano Emilia - Bologna - Italy
                Article
                1746-6148-6-5
                10.1186/1746-6148-6-5
                2837647
                20109214
                5be06b55-294b-4ad1-8ed9-f5d3a95159a7
                Copyright ©2010 Sassi et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 23 October 2008
                : 28 January 2010
                Categories
                Research article

                Veterinary medicine
                Veterinary medicine

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