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Renal angiomyolipoma (AML) is a benign neoplasm of the kidney arising sporadically in an idiopathic manner, or syndromically as a component of tuberous sclerosis complex. Although the classic AML has no malignant potential, and is the most common mesenchymal tumor of the kidney, variant AML cases with epithelioid morphology have demonstrated aggressive or invasive behavior. Classic AML, on the other hand, can occasionally display focal histology concerning for sarcomatous transformation, but in the absence of invasive features, it is easy to distinguish from a malignancy. In this article, we describe a remarkable case of classic AML that harbored areas histologically mimicking liposarcoma and invaded into the renal vein and extended up to inferior vena cava, thereby presenting a unique diagnostic conundrum. However, the tumor is negative for a CPM gene amplification, arguing against a liposarcomatous transformation. In addition, the patient does not have any sign of recurrence and metastasis clinically after 2 years of follow-up, also favoring a benign diagnosis of this tumor. [ABSTRACT FROM AUTHOR]

Background: Genetics and genomics have radically altered our understanding of breast cancer progression. However, the genomic basis of various histopathologic features of breast cancer is not yet well-defined. Materials and Methods: The Cancer Genome Atlas (TCGA) is an international database containing a large collection of human cancer genome sequencing data. cBioPortal is a web tool developed for mining these sequencing data. We performed mining of TCGA sequencing data in an attempt to characterize the genomic features correlated with breast cancer histopathology. We first assessed the quality of the TCGA data using a group of genes with known alterations in various cancers. Both genome-wide gene mutation and copy number changes as well as a group of genes with a high frequency of genetic changes were then correlated with various histopathologic features of invasive breast cancer. Results: Validation of TCGA data using a group of genes with known alterations in breast cancer suggests that the TCGA has accurately documented the genomic abnormalities of multiple malignancies. Further analysis of TCGA breast cancer sequencing data shows that accumulation of specific genomic defects is associated with higher tumor grade, larger tumor size and receptor negativity. Distinct groups of genomic changes were found to be associated with the different grades of invasive ductal carcinoma. The mutator role of the TP53 gene was validated by genomic sequencing data of invasive breast cancer and TP53 mutation was found to play a critical role in defining high tumor grade. Conclusions: Data mining of the TCGA genome sequencing data is an innovative and reliable method to help characterize the genomic abnormalities associated with histopathologic features of invasive breast cancer. [ABSTRACT FROM AUTHOR]

Introduction: Previous research in the Nurses' Health Study (NHS) and the NHSII observed that, among women diagnosed with benign breast disease (BBD), those with predominant type 1/no type 3 lobules (a marker of complete involution) versus other lobule types were at lower risk of subsequent breast cancer. Studies in animal models suggest that insulin-like growth factor-1 (IGF-1) may inhibit involution of lobules in the breast; however, this has not been studied in humans. Methods: We conducted a cross-sectional study among 472 women in the NHSII who were diagnosed with biopsy-confirmed proliferative BBD between 1991 and 2002 and provided blood samples between 1996 and 1999. A pathologist, blinded to exposure status, classified lobule type in normal adjacent tissue on available biopsy slides according to the number of acini per lobule. For each participant, the pathologist determined the predominant lobule type (that is, type 1, type 2, or type 3) and whether any type 1 or any type 3 lobules were present. Lobule type was then classified as: predominant type 1/no type 3 lobules, which is suggestive of complete involution; or other lobule types. Multivariate logistic models were used to assess the associations between plasma IGF-1, insulin-like growth factor binding protein-3 (IGFBP-3), and the ratio of IGF-1:IGFBP-3 levels with lobule type. Results: In univariate analyses, greater age, higher body mass index, postmenopausal status, nulliparity, and lower IGF-1 levels were associated with predominant type 1/no type 3 lobules (P < 0.05). In multivariate models adjusting for age and assay batch, higher IGF-1 levels were associated with decreased odds of predominant type 1/no type 3 lobules (odds ratio quartile 4 vs. quartile 1 = 0.37, 95% confidence interval = 0.15 to 0.89). Greater ratios of IGF-1:IGFBP-3 levels were also associated with decreased odds of predominant type 1/no type 3 lobules (odds ratio quartile 4 vs. quartile 1 = 0.26, 95% confidence interval = 0.11 to 0.64). These results were slightly attenuated after adjustment for other potential predictors of lobule type. Conclusions: Higher IGF-1 levels and a greater IGF-1:IGFBP-3 ratio were associated with decreased odds of having predominant type 1 lobules/no type 3 lobules among women with proliferative BBD in the NHSII. This study provides further evidence for the role of insulin-like growth factors in the structure of breast lobules and lobular involution.

The progression from preinvasive lesion to invasive carcinoma is a critical step contributing to breast cancer lethality. We identified downregulation of milk fat globule-EGF factor 8 (MFG-E8) as a contributor to breast cancer progression using microarray analysis of laser capture microdissected (LCM) tissues. We first identified MFG-E8 downregulation in invasive lesions in transgenic mammary tumor models, which were confirmed in LCM-isolated human invasive ductal carcinomas compared with patient-matched normal tissues. In situ analyses of MFG-E8 expression in estrogen receptor (ER) positive cases confirmed its downregulation during breast cancer progression and small inhibitory MFG-E8 RNAs accelerated ER(+) breast cancer cell proliferation. MFG-E8 also decreased in erbB2(+) human cancers and erbB2 transgenic mice lacking MFG-E8 showed accelerated tumor formation. In contrast, MFG-E8 expression was present at high levels in triple-negative (ER(-), PgR(-), erbB2(-)) breast cancers, cell lines, and patient sera. Knockdown, chromatin immunoprecipitation, and reporter assays all showed that p63 regulates MFG-E8 expression, and MFG-E8 knockdowns sensitized triple-negative breast cancers to cisplatin treatment. Taken together, our results show that MFG-E8 is expressed in triple-negative breast cancers as a target gene of the p63 pathway, but may serve a suppressive function in ER(+) and erbB2(+) breast cancers. Its potential use as a serum biomarker that contributes to the pathogenesis of triple-negative breast cancers urges continued evaluation of its differential functions.

Chemotherapy is often used for breast cancer treatment, but individual outcome varies widely. We hypothesized that tumor proteomic profiles obtained prior to chemotherapy may predict the individual tumor response to treatment. The goal of our study was to explore feasibility of using proteomic profiling to preselect patients for an effective chemotherapeutic regimen. Tumors from 52 patients with T2-T4 breast cancer were prospectively collected before neoadjuvant chemotherapy, and were analyzed using surface-enhanced laser desorption ionization/time of flight (SELDI) mass spectrometry. Mass spectral profiles were obtained from tumors with various sensitivities to chemotherapy. Both non-supervised hierarchical clustering and supervised neural network-based classification approaches were employed to compare the profiles. The first two thirds of the enrolled cases (35) were allocated to a training set to select peaks characteristic of resistant tumors. The candidate peaks were used to develop a predicting rule to evaluate the remaining 17 specimens in the validation set. In the training set, the most prominent differences were found between drug resistant and drug susceptible tumors by non-supervised hierarchical clustering. In the validation set, the supervised classification with the K nearest neighbor (KNN) model correctly classified most tumor responses with an accuracy rate of 92.3% [100% of resistant tumors (4/4), and 84.6% of the tumors with favorable response (11/13)]. In the entire group, a single peak at m/z 16,906 correctly separated 88.9% of the tumors with pathologically complete response, and 91.7% of the resistant tumors. The data suggest that breast cancer protein biomarkers may be used to pre-select patients for optimal chemotherapeutic treatment.

To test the efficacy of combined high-throughput analyses (HTA) in target gene identification, screening criteria were set using >fivefold difference by microarray and statistically significant changes (p<0.01) in SAGE and EST. Microarray analysis of two normal and seven breast cancer samples found 129 genes with >fivefold changes. Further SAGE and EST analyses of these genes identified four qualified genes, ERBB2, GATA3, AGR2, and ANXA1. Their expression pattern was validated by RT-PCR in both breast cell lines and tissue samples. Loss of ANXA1 in breast cancer was further confirmed at mRNA level by Human Breast Cancer Tissue Profiling Array and at protein level by immunohistochemical staining. This study demonstrated that combined HTA effectively narrowed the number of genes for further study, while retaining the sensitivity in identifying biologically important genes such as ERBB2 and ANXA1. A distinctive loss of ANXA1 in breast cancer suggests its involvement in maintaining normal breast biology.