Pre-operative management of Pleomorphic and florid lobular carcinoma in situ of the breast: Report of a large multi-institutional series and review of the literature

Background: Pleomorphic and Florid Lobular carcinoma in situ (P/F LCIS) are rare variants of LCIS, the exact nature of which is still debated. Aim: To collect a large series of P/F LCIS diagnosed on preoperative biopsies and evaluate their association with invasive carcinoma and high grade duct carcinoma in situ (DCIS). Data obtained were compared with those reported in the literature. Methods: A multi-institutional series of P/F LCIS was retrieved. All cases were diagnosed on pre-operative biopsies, which was followed by an open surgical excision. Data on post-operative histopathology were available. A literature review was performed. Results: A total of 117 cases were collected; invasive carcinoma and/or DCIS was present in 78/117 cases (66.7%). Seventy cases of P/F LCIS were pure on biopsy and 31 of these showed pathological upgrade in post-surgical specimens. Pre-operative biopsy accuracy was 47/78 (60.3%); pre-operative biopsy underestimation of cancer was 31/78 (39,7.%). In the literature review papers, invasive carcinoma or DCIS was associated with 274 of 418 (65.5%) cases of P/F LCIS. Pre-operative biopsy accuracy was 66% (181/274) whereas pre-operative biopsy underestimation of cancer was 33.9% (93/274). Conclusions: The data presented here indicate that P/F LCIS is frequently associated with invasive carcinoma or high grade DCIS and that pre-operative biopsy is associated with an underestimation of malignancy. Open surgery is indicated when P/F LCIS is diagnosed pre-operatively

Intra-tumour heterogeneity is one of the main sources of inter-observer variation in scoring stromal tumour infiltrating lymphocytes in triple negative breast cancer

Stromal tumour infiltrating lymphocytes (sTILs) are a strong prognostic marker in triple negative breast cancer (TNBC). Consistency scoring sTILs is good and was excellent when an internet-based scoring aid developed by the TIL-WG was used to score cases in a reproducibility study. This study aimed to evaluate the reproducibility of sTILs assessment using this scoring aid in cases from routine practice and to explore the potential of the tool to overcome variability in scoring. Twenty-three breast pathologists scored sTILs in digitized slides of 49 TNBC biopsies using the scoring aid. Subsequently, fields of view (FOV) from each case were selected by one pathologist and scored by the group using the tool. Inter-observer agreement was good for absolute sTILs (ICC 0.634, 95% CI 0.539–0.735, p < 0.001) but was poor to fair using binary cutpoints. sTILs heterogeneity was the main contributor to disagreement. When pathologists scored the same FOV from each case, inter-observer agreement was excellent for absolute sTILs (ICC 0.798, 95% CI 0.727–0.864, p < 0.001) and good for the 20% (ICC 0.657, 95% CI 0.561–0.756, p < 0.001) and 40% (ICC 0.644, 95% CI 0.546–0.745, p < 0.001) cutpoints. However, there was a wide range of scores for many cases. Reproducibility scoring sTILs is good when the scoring aid is used. Heterogeneity is the main contributor to variance and will need to be overcome for analytic validity to be achieved

Association of GATA3, P53, Ki67 status and vascular peritumoral invasion are strongly prognostic in luminal breast cancer

International audienceIntroduction: Breast cancers are traditionally divided into hormone-receptor positive and negative cases. This classification helps to guide patient management. However, a subgroup of hormone-receptor positive patients relapse irrespective of hormonal therapy. Gene expression profiling has classified breast tumours into five major subtypes with significant different outcome. The two luminal subtypes, A and B, show high expression of ESR1, GATA3 and FOXA1 genes. Prognostic biomarkers for oestrogen receptor (ER)-positive cases include progesterone receptor (PR) and androgen receptor (AR), and proteins related to proliferation or apoptotic resistance. The aim of this study was to identify the best predictors of success of hormonal therapy.Methods: By immunohistochemistry we studied 10 markers in a consecutive series of 832 cases of breast carcinoma treated at the Paoli-Calmettes Institute from 1990 to 2002 and deposited onto tissue microarrays (TMA). These markers were luminal-related markers ER, PR, AR, FOXA1 and GATA3 transcription factors, proliferation-related Ki67 and CCND1, ERBB2, anti-apoptotic BCL2 and P53. We also measured vascular peritumoural invasion (VPI), size, grade and lymph node involvement. For 143 cases, gene expression profiles were available. Adjuvant chemotherapy and hormonal therapy were given to high- and low-risk patients, respectively. The 162 events observed and taken into account were metastases.Results: Molecular expression of the 10 parameters and subtype with ER status were strongly correlated. Of the 67 luminal A cases of this series, 63 were ER-positive. Multivariate analyses showed the highly significant prognostic value of VPI (hazard ratio (HR) = 2.47), Ki67 (HR = 2.9), P53 (HR = 2.9) and GATA3 (HR = 0.5) for the 240 patients who received hormonal therapy.Conclusions: A panel of three antibodies (Ki67, P53 and GATA3) associated with VPI can significantly improve the traditional prognosticators in predicting outcome for ER-positive breast cancer patients receiving hormonal therapy

Pathological non-response to chemotherapy in a neoadjuvant setting of breast cancer: an inter-institutional study

To identify markers of non-response to neoadjuvant chemotherapy (NAC) that could be used in the adjuvant setting. Sixteen pathologists of the European Working Group for Breast Screening Pathology reviewed the core biopsies of breast cancers treated with NAC and recorded the clinico-pathological findings (histological type and grade; estrogen, progesterone receptors, and HER2 status; Ki67; mitotic count; tumor-infiltrating lymphocytes; necrosis) and data regarding the pathological response in corresponding surgical resection specimens. Analyses were carried out in a cohort of 490 cases by comparing the groups of patients showing pathological complete response (pCR) and partial response (pPR) with the group of non-responders (pathological non-response: pNR). Among other parameters, the lobular histotype and the absence of inflammation were significantly more common in pNR (p < 0.001). By ROC curve analyses, cut-off values of 9 mitosis/2 mm(2) and 18 % of Ki67-positive cells best discriminated the pNR and pCR + pPR categories (p = 0.018 and < 0.001, respectively). By multivariable analysis, only the cut-off value of 9 mitosis discriminated the different response categories (p = 0.036) in the entire cohort. In the Luminal B/HER2- subgroup, a mitotic count < 9, although not statistically significant, showed an OR of 2.7 of pNR. A lobular histotype and the absence of inflammation were independent predictors of pNR (p = 0.024 and < 0.001, respectively). Classical morphological parameters, such as lobular histotype and inflammation, confirmed their predictive value in response to NAC, particularly in the Luminal B/HER2- subgroup, which is a challenging breast cancer subtype from a therapeutic point of view. Mitotic count could represent an additional marker but has a poor positive predictive value

A new molecular breast cancer subclass defined from a large scale real-time quantitative RT-PCR study

BACKGROUND: Current histo-pathological prognostic factors are not very helpful in predicting the clinical outcome of breast cancer due to the disease's heterogeneity. Molecular profiling using a large panel of genes could help to classify breast tumours and to define signatures which are predictive of their clinical behaviour. METHODS: To this aim, quantitative RT-PCR amplification was used to study the RNA expression levels of 47 genes in 199 primary breast tumours and 6 normal breast tissues. Genes were selected on the basis of their potential implication in hormonal sensitivity of breast tumours. Normalized RT-PCR data were analysed in an unsupervised manner by pairwise hierarchical clustering, and the statistical relevance of the defined subclasses was assessed by Chi2 analysis. The robustness of the selected subgroups was evaluated by classifying an external and independent set of tumours using these Chi2-defined molecular signatures. RESULTS: Hierarchical clustering of gene expression data allowed us to define a series of tumour subgroups that were either reminiscent of previously reported classifications, or represented putative new subtypes. The Chi2 analysis of these subgroups allowed us to define specific molecular signatures for some of them whose reliability was further demonstrated by using the validation data set. A new breast cancer subclass, called subgroup 7, that we defined in that way, was particularly interesting as it gathered tumours with specific bioclinical features including a low rate of recurrence during a 5 year follow-up. CONCLUSION: The analysis of the expression of 47 genes in 199 primary breast tumours allowed classifying them into a series of molecular subgroups. The subgroup 7, which has been highlighted by our study, was remarkable as it gathered tumours with specific bioclinical features including a low rate of recurrence. Although this finding should be confirmed by using a larger tumour cohort, it suggests that gene expression profiling using a minimal set of genes may allow the discovery of new subclasses of breast cancer that are characterized by specific molecular signatures and exhibit specific bioclinical features

Integration of gene expression data with prior knowledge for network analysis and validation

<p>Abstract</p> <p>Background</p> <p>Reconstruction of protein-protein interaction or metabolic networks based on expression data often involves in silico predictions, while on the other hand, there are unspecific networks of in vivo interactions derived from knowledge bases.</p> <p>We analyze networks designed to come as close as possible to data measured in vivo, both with respect to the set of nodes which were taken to be expressed in experiment as well as with respect to the interactions between them which were taken from manually curated databases</p> <p>Results</p> <p>A signaling network derived from the TRANSPATH database and a metabolic network derived from KEGG LIGAND are each filtered onto expression data from breast cancer (SAGE) considering different levels of restrictiveness in edge and vertex selection.</p> <p>We perform several validation steps, in particular we define pathway over-representation tests based on refined null models to recover functional modules. The prominent role of the spindle checkpoint-related pathways in breast cancer is exhibited. High-ranking key nodes cluster in functional groups retrieved from literature. Results are consistent between several functional and topological analyses and between signaling and metabolic aspects.</p> <p>Conclusions</p> <p>This construction involved as a crucial step the passage to a mammalian protein identifier format as well as to a reaction-based semantics of metabolism. This yielded good connectivity but also led to the need to perform benchmark tests to exclude loss of essential information. Such validation, albeit tedious due to limitations of existing methods, turned out to be informative, and in particular provided biological insights as well as information on the degrees of coherence of the networks despite fragmentation of experimental data.</p> <p>Key node analysis exploited the networks for potentially interesting proteins in view of drug target prediction.</p

Molecular differences between ductal carcinoma in situ and adjacent invasive breast carcinoma: a multiplex ligation-dependent probe amplification study

Ductal carcinoma in situ (DCIS) accounts for approximately 20% of mammographically detected breast cancers. Although DCIS is generally highly curable, some women with DCIS will develop life-threatening invasive breast cancer, but the determinants of progression to infiltrating ductal cancer (IDC) are largely unknown. In the current study, we used multiplex ligation-dependent probe amplification (MLPA), a multiplex PCR-based test, to compare copy numbers of 21 breast cancer related genes between laser-microdissected DCIS and adjacent IDC lesions in 39 patients. Genes included in this study were ESR1, EGFR, FGFR1, ADAM9, IKBKB, PRDM14, MTDH, MYC, CCND1, EMSY, CDH1, TRAF4, CPD, MED1, HER2, CDC6, TOP2A, MAPT, BIRC5, CCNE1 and AURKA

Individualized markers optimize class prediction of microarray data

BACKGROUND: Identification of molecular markers for the classification of microarray data is a challenging task. Despite the evident dissimilarity in various characteristics of biological samples belonging to the same category, most of the marker – selection and classification methods do not consider this variability. In general, feature selection methods aim at identifying a common set of genes whose combined expression profiles can accurately predict the category of all samples. Here, we argue that this simplified approach is often unable to capture the complexity of a disease phenotype and we propose an alternative method that takes into account the individuality of each patient-sample. RESULTS: Instead of using the same features for the classification of all samples, the proposed technique starts by creating a pool of informative gene-features. For each sample, the method selects a subset of these features whose expression profiles are most likely to accurately predict the sample's category. Different subsets are utilized for different samples and the outcomes are combined in a hierarchical framework for the classification of all samples. Moreover, this approach can innately identify subgroups of samples within a given class which share common feature sets thus highlighting the effect of individuality on gene expression. CONCLUSION: In addition to high classification accuracy, the proposed method offers a more individualized approach for the identification of biological markers, which may help in better understanding the molecular background of a disease and emphasize the need for more flexible medical interventions

Prognostic significance of bcl-2 expression in stage III breast cancer patients who had received doxorubicin and cyclophosphamide followed by paclitaxel as adjuvant chemotherapy

BACKGROUND: Bcl-2 is positively regulated by hormonal receptor pathways in breast cancer. A study was conducted to assess the prognostic significances of clinico-pathologic variables and of ER, PR, p53, c-erbB2, bcl-2, or Ki-67 as markers of relapse in breast cancer patients who had received the identical adjuvant therapy at a single institution. METHODS: A cohort of 151 curatively resected stage III breast cancer patients (M:F = 3:148, median age 46 years) who had 4 or more positive lymph nodes and received doxorubicin and cyclophosphamide followed by paclitaxel (AC/T) as adjuvant chemotherapy was analyzed for clinico-pathologic characteristics including disease-free survival (DFS) and overall survival (OS). Patients with positive ER and/or PR expression received 5 years of tamoxifen following AC/T. The protein expressions of biomarkers were assessed immunohistochemically. RESULTS: The median follow-up duration was 36 months, and 37 patients (24.5%) experienced a recurrence. Univariate analyses indicated that the tumor size (P = 0.038) and the number of involved lymph nodes (P < 0.001) significantly affected the recurrences. However, the type of surgery, the histology, histologic grade, the presence of endolymphatic emboli, and a close resection margin did not. Moreover, ER positivity (P = 0.013), bcl-2 positivity (P = 0.002) and low p53 expression (P = 0.032) were found to be significantly associated with a prolonged DFS. Furthermore, multivariate analysis identified 10 or more involved lymph nodes (HR 7.366; P < 0.001), negative bcl-2 expression (HR 2.895; P = 0.030), and c-erbB2 over-expression (HR 3.535; P = 0.001) as independent indicators of poorer DFS. In addition, bcl-2 expression was found to be significantly correlated with the expressions of ER and PR, and inversely correlated with the expressions of p53, c-erbB2 and Ki-67. Patients with bcl-2 expression had a significantly longer DFS than those without, even in the ER (+) subgroup. Moreover, OS was significantly affected by ER, bcl-2 and c-erbB2. CONCLUSION: Bcl-2 is an independent prognostic factor of DFS in curatively resected stage III breast cancer patients and appears to be a useful prognostic factor in combination with c-erbB2 and the number of involved lymph nodes