MicroRNAs (miRNAs) are a class of small noncoding RNAs that control gene expression by targeting mRNAs and triggering either translation repression or RNA degradation. Their aberrant expression may be involved in human diseases, including cancer. Indeed, miRNA aberrant expression has been previously found in human chronic lymphocytic leukemias, where miRNA signatures were associated with specific clinicobiological features. Here, we show that, compared with normal breast tissue, miRNAs are also aberrantly expressed in human breast cancer. The overall miRNA expression could clearly separate normal versus cancer tissues, with the most significantly deregulated miRNAs being mir-125b, mir-145, mir-21, and mir-155. Results were confirmed by microarray and Northern blot analyses. We could identify miRNAs whose expression was correlated with specific breast cancer biopathologic features, such as estrogen and progesterone receptor expression, tumor stage, vascular invasion, or proliferation index. (Cancer Res 2005; 65(16): 7065-70)

Cancer of unknown primary (CUP) represents a common and important clinical problem. There is evidence that most CUPs are metastases of carcinomas whose primary site cannot be recognized. Driven by the hypothesis that the knowledge of primary cancer could improve patient’s prognosis, we investigated microRNA expression profiling as a tool for identifying the tissue of origin of metastases. We assessed microRNA expression from 101 formalin-fixed, paraffin-embedded (FFPE) samples from primary cancers and metastasis samples by using a microarray platform. Forty samples representing ten different cancer types were used for defining a cancer-type-specific microRNA signature, which was used for predicting primary sites of metastatic cancers. A 47-miRNA signature was identified and used to estimate tissue-of-origin probabilities for each sample. Overall, accuracy reached 100% for primary cancers and 78% for metastases in our cohort of samples. When the signature was applied to an independent published dataset of 170 samples, accuracy remained high: correct prediction was found within the first two options in 86% of the metastasis cases (first prediction was correct in 68% of cases). This signature was also applied to predict 16 CUPs. In this group, first predictions exhibited probabilities higher than 90% in most of the cases. These results establish that FFPE samples can be used to reveal the tissue of origin of metastatic cancers by using microRNA expression profiling and suggest that the approach, if applied, could provide strong indications for CUPs, whose correct diagnosis is presently undefined.

Purpose: Early breast cancer presents with a remarkable heterogeneity of outcomes.Undetected, microscopic lymph node tumor deposits may account for a significant fraction of this prognostic diversity. Thus, we systematically evaluated the presence of lymph node tumor cell deposits V0.2 mm in diameter [pN 0(i+) , nanometastases] and analyzed their prognostic effect. Experimental Design: Single-institution, consecutive patients with 8 years of median follow-up (n = 702) were studied. To maximize chances of detecting micrometastases and nanometastases, whole-axilla dissections were analyzed. pN 0 cases (n = 377) were systematically reevaluated by lymph node (n = 6676) step-sectioning and anticytokeratin immunohistochemical analysis. The risk of first adverse events and of distant relapse of bona fide pN 0 patients was compared with that of pN 0(i+) , pN 1mi , and pN 1 cases. Results: Minimal lymph node deposits were revealed in 13% of pN 0 patients.The hazard ratio for all adverse events of pN 0(i+) versus pN 0(iÀ) was 2.51 (P = 0.00019). Hazards of pN 1mi and pN 0(i+) cases were not significantly different. A multivariate Cox model showed a hazard ratio of 2.16 for grouped pN 0(i+) /pN 1mi versus pN 0(iÀ) (P = 0.0005). Crude cumulative incidence curves for metastatic relapse were also significantly different (Gray's test m 2 = 5.54, P = 0.019). Conclusion: Nanometastases are a strong risk factor for disease-free survival and for metastatic relapse. These findings support the inclusion of procedures for nanometastasis detection in tumor-node-metastasis staging.The tumor-node-metastasis (TNM) staging system for breast cancer (1) has proven invaluable in categorizing the extent of neoplastic disease, and as a basis to estimate prognosis and to direct treatment (2). However, this has not lead to the definition of tightly homogeneous prognostic classes, as considerable heterogeneity of outcomes can be observed among disease cases currently categorized as similar. This is particularly evident in the case of small breast tumors (2). We argued that a diverse extent of lymph node dissemination at early stages of disease may account for diverse disease recurrence dynamics. The principle that the macroscopic burden of metastatic cells (e.g., number of invaded lymph nodes) dictates different risks of disease recurrence has been recognized (1,3). This principle might be equally important at the low end of the spectrum, i.e., in the case of microscopic tumor cell deposits (1, 4).Serial sectioning coupled to immunohistochemical analysis has considerably improved the detection of small tumor cell clusters in lymph nodes (5 -10). Occult metastases can indeed be identified in up to 30% of cases previously classified as pN 0 (7 -9), in 14% to 20% of the cases by single lymph node sections (9, 10). Studies based on these procedures have shown that axillary lymph node microinvasion is a prognostic factor for breast cancer patients, and is associated with poorer diseasefree and overall survival (7, 8, 11 -13). As a consequenc...

Purpose: Recent small-sized genomic studies on the identification of breast cancer bioprofiles have led to profoundly dishomogeneous results.Thus, we sought to identify distinct tumor profiles with possible clinical relevance based on clusters of immunohistochemical molecular markers measured on a large, single institution, case series. Experimental Design: Tumor biological profiles were explored on 633 archival tissue samples analyzed by immunohistochemistry. Five validated markers were considered, i.e., estrogen receptors (ER), progesterone receptors (PR), Ki-67/MIB1as a proliferation marker, HER2/NEU, and p53 in their original scale of measurement. The results obtained were analyzed by three different clustering algorithms. Four different indices were then used to select the different profiles (number of clusters). Results: The best classification was obtained creating four clusters. Notably, three clusters were identified according to low, intermediate, and high ER/PR levels. A further subdivision in two biologically distinct subtypes was determined by the presence/absence of HER2/NEU and of p53. As expected, the cluster with high ER/PR levels was characterized by a much better prognosis and response to hormone therapy compared to that with the lowest ER/PR values. Notably, the cluster characterized by high HER2/NEU levels showed intermediate prognosis, but a rather poor response to hormone therapy. Conclusions: Our results show the possibility of profiling breast cancers by means of traditional markers, and have novel clinical implications on the definition of the prognosis of cancer patients. These findings support the existence of a tumor subtype that responds poorly to hormone therapy, characterized by HER2/NEU overexpression.Breast cancer patients with apparently similar clinical and pathologic features can experience rather different disease dynamics or response to adjuvant therapies. This prognostic heterogeneity is considerable, and suggests a corresponding heterogeneity of the underlying biological variables. Hence, it should be possible to identify reliable, novel prognostic/ predictive markers via proteomic or transcriptomic phenotyping, or by genetic analysis. Thus, molecular phenotyping might play an important role as an adjunct to classical clinical/pathologic staging procedures. Recent work focused on the quantitative identification of biological profiles of tumors from transcriptomic data. Transcriptomic profiles were then correlated with clinical behavior, according to the hypothesis that specific profiles could identify breast cancer subtypes (1 -3). Cluster analysis (4) was applied as a statistical method capable of splitting data into subgroups (clusters) based on the relationships between the subjects and the measured variables. In breast cancer studies, cluster analyses have been conducted almost exclusively by hierarchic techniques. However, only a fraction of cases have clearly distinct features; whereas some others show a less clear-cut profile, rendering assignment to one g...

Chromosome 11p15 deletion is frequent in human tumors, suggesting the presence of at least one tumor suppressor gene within this region. While mutation analyses of local genes revealed only rare mutations, we have previously described a mechanism, gain of imprinting, that leads to loss of expression of genes located on the maternal 11p15 chromosome in human hepatocarcinomas. Loss of expression was often associated with loss of maternal-speci®c methylation at the KvDMR1 locus. Here, we show that loss of the maternal KvDMR1 methylation is common, ranging from 30 to 50%, to a variety of adult neoplasms, including liver, breast, cervical and gastric carcinomas. We found that other 11p15.5 loci were concomitantly hypomethylated, indicating that loss of KvDMR1 methylation occurred in the context of a common mechanism a ecting the methylation of a large 11p15 subchromosomal domain. These epigenetic abnormalities were not detected in any normal somatic tissue. Therefore, it seems possible that, contrary to the repression of promoter activity caused by hypermethylation, loss of gene expression at 11p15.5 may result from the activation, by hypomethylation, of one or more negative regulatory elements.