Recent studies suggest that thousands of genes may contribute to breast cancer pathophysiologies when deregulated by genomic or epigenomic events. Here, we describe a model "system" to appraise the functional contributions of these genes to breast cancer subsets. In general, the recurrent genomic and transcriptional characteristics of 51 breast cancer cell lines mirror those of 145 primary breast tumors, although some significant differences are documented. The cell lines that comprise the system also exhibit the substantial genomic, transcriptional, and biological heterogeneity found in primary tumors. We show, using Trastuzumab (Herceptin) monotherapy as an example, that the system can be used to identify molecular features that predict or indicate response to targeted therapies or other physiological perturbations.

The genetic alterations identified in melanomas at different sites and with different levels of sun exposure indicate that there are distinct genetic pathways in the development of melanoma and implicate CDK4 and CCND1 as independent oncogenes in melanomas without mutations in BRAF or N-RAS.

A reliable and precise classi cation of tumors is essential for successful diagnosis and treatment of cancer. cDNA microarrays and highdensity oligonucleotide chips are novel biotechnologies increasingly used in cancer research. By allowing the monitoring of expression levels in cells for thousands of genes simultaneously, microarray experiments may lead to a more complete understanding of the molecular variations among tumors and hence to a ner and more informative classi cation. The ability to successfully distinguish between tumor classes (already known or yet to be discovered) using gene expression data is an important aspect of this novel approach to cancer classi cation. This article compares the performance of different discrimination methods for the classi cation of tumors based on gene expression data. The methods include nearest-neighbor classi ers, linear discriminant analysis, and classi cation trees. Recent machine learning approaches, such as bagging and boosting, are also considered. The discrimination methods are applied to datasets from three recently published cancer gene expression studies.

This study explores the roles of genome copy number abnormalities (CNAs) in breast cancer pathophysiology by identifying associations between recurrent CNAs, gene expression, and clinical outcome in a set of aggressively treated early-stage breast tumors. It shows that the recurrent CNAs differ between tumor subtypes defined by expression pattern and that stratification of patients according to outcome can be improved by measuring both expression and copy number, especially high-level amplification. Sixty-six genes deregulated by the high-level amplifications are potential therapeutic targets. Nine of these (FGFR1, IKBKB, ERBB2, PROCC, ADAM9, FNTA, ACACA, PNMT, and NR1D1) are considered druggable. Low-level CNAs appear to contribute to cancer progression by altering RNA and cellular metabolism.

Mutationally activated kinases define a clinically validated class of targets for cancer drug therapy1. However, the efficacy of kinase inhibitors in patients whose tumours harbour such alleles is invariably limited by innate or acquired drug resistance2,3. The identification of resistance mechanisms has revealed a recurrent theme—the engagement of survival signals redundant to those transduced by the targeted kinase4. Cancer cells typically express multiple receptor tyrosine kinases (RTKs) that mediate signals that converge on common critical downstream cell-survival effectors—most notably, phosphatidylinositol-3-OH kinase (PI(3)K) and mitogen-activated protein kinase (MAPK)5. Consequently, an increase in RTK-ligand levels, through autocrine tumour-cell production, paracrine contribution from tumour stroma6 or systemic production, could confer resistance to inhibitors of an oncogenic kinase with a similar signalling output. Here, using a panel of kinase-‘addicted’ human cancer cell lines, we found that most cells can be rescued from drug sensitivity by simply exposing them to one or more RTK ligands. Among the findings with clinical implications was the observation that hepatocyte growth factor (HGF) confers resistance to the BRAF inhibitor PLX4032 (vemurafenib) in BRAF-mutant melanoma cells. These observations highlight the extensive redundancy of RTK-transduced signalling in cancer cells and the potentially broad role of widely expressed RTK ligands in innate and acquired resistance to drugs targeting oncogenic kinases.

Metaplastic breast cancers (MBC) are aggressive, chemoresistant tumors characterized by lineage plasticity. To advance understanding of their pathogenesis and relatedness to other breast cancer subtypes, 28 MBCs were compared with common breast cancers using comparative genomic hybridization, transcriptional profiling, and reverse-phase protein arrays and by sequencing for common breast cancer mutations. MBCs showed unique DNA copy number aberrations compared with common breast cancers. PIK3CA mutations were detected in 9 of 19 MBCs (47.4%) versus 80 of 232 hormone receptor-positive cancers (34.5%; P = 0.32), 17 of 75 HER-2-positive samples (22.7%; P = 0.04), 20 of 240 basal-like cancers (8.3%; P < 0.0001), and 0 of 14 claudin-low tumors (P = 0.004). Of 7 phosphatidylinositol 3-kinase/AKT pathway phosphorylation sites, 6 were more highly phosphorylated in MBCs than in other breast tumor subtypes. The majority of MBCs displayed mRNA profiles different from those of the most common, including basal-like cancers. By transcriptional profiling, MBCs and the recently identified claudin-low breast cancer subset constitute related receptor-negative subgroups characterized by low expression of GATA3-regulated genes and of genes responsible for cell-cell adhesion with enrichment for markers linked to stem cell function and epithelial-tomesenchymal transition (EMT). In contrast to other breast cancers, claudin-low tumors and most MBCs showed a significant similarity to a ''tumorigenic'' signature defined using CD44 + /CD24 À breast tumor-initiating stem cell-like cells. MBCs and claudin-low tumors are thus enriched in EMT and stem cell-like features, and may arise from an earlier, more chemoresistant breast epithelial precursor than basal-like or luminal cancers. PIK3CA mutations, EMT, and stem cell-like characteristics likely contribute to the poor outcomes of MBC and suggest novel therapeutic targets. [Cancer Res 2009;69(10):4116-24]

The RAS/mitogen-activated protein kinase pathway sends external growth-promoting signals to the nucleus. BRAF, a critical serine/threonine kinase in this pathway, is frequently activated by somatic mutation in melanoma. Using a cohort of 115 patients with primary invasive melanomas, we show that BRAF mutations are statistically significantly more common in melanomas occurring on skin subject to intermittent sun exposure than elsewhere (23 of 43 patients; P<.001, two-sided Fisher's exact test). By contrast, BRAF mutations in melanomas on chronically sun-damaged skin (1 of 12 patients) and melanomas on skin relatively or completely unexposed to sun, such as palms, soles, subungual sites (6 of 39 patients), and mucosal membranes (2 of 21 patients) are rare. We found no association of mutation status with clinical outcome or with the presence of an associated melanocytic nevus. The mutated BRAF allele was frequently found at an elevated copy number, implicating BRAF as one of the factors driving selection for the frequent copy number increases of chromosome 7q in melanoma. In summary, the uneven distribution of BRAF mutations strongly suggests distinct genetic pathways leading to melanoma. The high mutation frequency in melanomas arising on intermittently sun-exposed skin suggests a complex causative role of such exposure that mandates further evaluation.

BackgroundIn melanoma, morphology-based classification systems have not been able to provide relevant information for selecting treatments for patients whose tumors have metastasized. The recent identification of causative genetic alterations has revealed mutations in signaling pathways that offer targets for therapy. Identifying morphologic surrogates that can identify patients whose tumors express such alterations (or functionally equivalent alterations) would be clinically useful for therapy stratification and for retrospective analysis of clinical trial data.Methodology/Principal FindingsWe defined and assessed a panel of histomorphologic measures and correlated them with the mutation status of the oncogenes BRAF and NRAS in a cohort of 302 archival tissues of primary cutaneous melanomas from an academic comprehensive cancer center. Melanomas with BRAF mutations showed distinct morphological features such as increased upward migration and nest formation of intraepidermal melanocytes, thickening of the involved epidermis, and sharper demarcation to the surrounding skin; and they had larger, rounder, and more pigmented tumor cells (all p-values below 0.0001). By contrast, melanomas with NRAS mutations could not be distinguished based on these morphological features. Using simple combinations of features, BRAF mutation status could be predicted with up to 90.8% accuracy in the entire cohort as well as within the categories of the current World Health Organization (WHO) classification. Among the variables routinely recorded in cancer registries, we identified age < 55 y as the single most predictive factor of BRAF mutation in our cohort. Using age < 55 y as a surrogate for BRAF mutation in an independent cohort of 4,785 patients of the Southern German Tumor Registry, we found a significant survival benefit (p < 0.0001) for patients who, based on their age, were predicted to have BRAF mutant melanomas in 69% of the cases. This group also showed a different pattern of metastasis, more frequently involving regional lymph nodes, compared to the patients predicted to have no BRAF mutation and who more frequently displayed satellite, in-transit metastasis, and visceral metastasis (p < 0.0001).ConclusionsRefined morphological classification of primary melanomas can be used to improve existing melanoma classifications by forming subgroups that are genetically more homogeneous and likely to differ in important clinical variables such as outcome and pattern of metastasis. We expect this information to improve classification and facilitate stratification for therapy as well as retrospective analysis of existing trial data.