Recent advances in breast cancer biology

MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays

Purpose To investigate relationships between computer-extracted breast magnetic resonance (MR) imaging phenotypes with multigene assays of MammaPrint, Oncotype DX, and PAM50 to assess the role of radiomics in evaluating the risk of breast cancer recurrence. Materials and Methods Analysis was conducted on an institutional review board-approved retrospective data set of 84 deidentified, multi-institutional breast MR examinations from the National Cancer Institute Cancer Imaging Archive, along with clinical, histopathologic, and genomic data from The Cancer Genome Atlas. The data set of biopsy-proven invasive breast cancers included 74 (88%) ductal, eight (10%) lobular, and two (2%) mixed cancers. Of these, 73 (87%) were estrogen receptor positive, 67 (80%) were progesterone receptor positive, and 19 (23%) were human epidermal growth factor receptor 2 positive. For each case, computerized radiomics of the MR images yielded computer-extracted tumor phenotypes of size, shape, margin morphology, enhancement texture, and kinetic assessment. Regression and receiver operating characteristic analysis were conducted to assess the predictive ability of the MR radiomics features relative to the multigene assay classifications. Results Multiple linear regression analyses demonstrated significant associations (R² = 0.25-0.32, r = 0.5-0.56, P < .0001) between radiomics signatures and multigene assay recurrence scores. Important radiomics features included tumor size and enhancement texture, which indicated tumor heterogeneity. Use of radiomics in the task of distinguishing between good and poor prognosis yielded area under the receiver operating characteristic curve values of 0.88 (standard error, 0.05), 0.76 (standard error, 0.06), 0.68 (standard error, 0.08), and 0.55 (standard error, 0.09) for MammaPrint, Oncotype DX, PAM50 risk of relapse based on subtype, and PAM50 risk of relapse based on subtype and proliferation, respectively, with all but the latter showing statistical difference from chance. Conclusion Quantitative breast MR imaging radiomics shows promise for image-based phenotyping in assessing the risk of breast cancer recurrence. ^© RSNA, 2016 Online supplemental material is available for this article.

Gene expression profiling of NMU-induced rat mammary tumors: cross species comparison with human breast cancer

Breast cancer is a complex genetic disease characterized by the accumulation of multiple molecular alterations. The NMU breast cancer model induced in the rat is used for the study of mammary carcinogenesis because it closely mimics human breast disease. To assess the validity of this model from a more global molecular perspective, and also to devise a general technique to compare animal profiles with human microarray studies, we have characterized 25 NMU-induced mammary tumors and 11 normal glands using a combination of immunohistochemical and microarray analyses. The rat mammary carcinomas were classified as non-invasive, ER-positive ductal carcinomas with a composition of differentiated epithelial and myoepithelial cell lineages. Gene expression profiles generated using rat Affymetrix arrays containing 15,866 genes demonstrated that the rat mammary tumors are homogeneous and that H-ras mutations did not confer a unique molecular signature. We compared the resulting rat profiles with those obtained from a human dataset by merging the raw microarray data, using an approach that involves a combination of cross-species and cross-platform analysis. Using this novel strategy, we demonstrate the ability of 2305 rat orthologs to recapitulate the classification of human tumors derived from human Affymetrix arrays. The gene expression profiles of the NMU-induced primary tumors were most similar to ER-positive, low to intermediate grade breast cancer. Our technique provides a means to correlate gene expression data from animal models of cancer to human cancer and disease states.

Molecular epidemiology of sporadic breast cancer. The role of polymorphic genes involved in oestrogen biosynthesis and metabolism

The major known risk factors for female breast cancer are associated with prolonged exposure to increased levels of oestrogen. The predominant theory relates to effects of oestrogen on cell growth. Enhanced cell proliferation, induced either by endogenous or exogenous oestrogens, increases the number of cell divisions and thereby the possibility for mutation. However, current evidence also supports a role for oxidative metabolites, in particular catechol oestrogens, in the initiation of breast cancer. As observed in drug and chemical metabolism, there is considerable interindividual variability (polymorphism) in the conjugation pathways of both oestrogen and catechol oestrogens. These person-to-person differences, which are attributed to polymorphisms in the genes encoding for the respective enzymes, might define subpopulations of women with higher lifetime exposure to hormone-dependent growth promotion, or to cellular damage from particular oestrogens and/or oestrogen metabolites. Such variation could explain a portion of the cancer susceptibility associated with reproductive effects and hormone exposure. In this paper the potential role of polymorphic genes encoding for enzymes involved in oestrogen biosynthesis (CYP17, CYP19, and 17beta-HSD) and conversion of the oestrogen metabolites and their by-products (COMT, CYP1A1, CYP1B1, GSTM1, GSTM3, GSTP1, GSTT1 and MnSOD) in modulating individual susceptibility to breast cancer are reviewed. Although some of these low-penetrance genes appeared as good candidates for risk factors in the etiology of sporadic breast cancer, better designed and considerably larger studies than the majority of the studies conducted so far are evidently needed before any firm conclusions can be drawn.