Gordon Vansant

Exploring Molecular Pathways of Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype with a high rate of proliferation and metastasis, as well as poor prognosis for advanced-stage disease. Although TNBC was previously classified together with basal-like and BRCA1/2-related breast cancers, genomic profiling now shows that there is incomplete overlap, with important distinctions associated with each subtype. The biology of TNBC is still poorly understood; therefore, to define the relative contributions of major cellular pathways in TNBC, we have studied its molecular signature based on analysis of gene expression. Comparisons were then made with normal breast tissue. Our results suggest the existence of molecular networks in TNBC, characterized by explicit alterations in the cell cycle, DNA repair, nucleotide synthesis, metabolic pathways, NF-κB signaling, inflammatory response, and angiogenesis. Moreover, we also characterized TNBC as a cancer of mixed phenotypes, suggesting that TNBC extends beyond the basal-like molecular signature and may constitute an independent subtype of breast cancer. The data provide a new insight into the biology of TNBC.

Gene expression-signature of belinostat in cell lines is specific for histone deacetylase inhibitor treatment, with a corresponding signature in xenografts

Belinostat is a hydroxamate-type histone deactylase inhibitor (HDACi), which has recently entered phase I and II clinical trials. Microarray-based analysis of belinostat-treated cell lines showed an impact on genes associated with the G2/M phase of the cell cycle and downregulation of the aurora kinase pathway. Expression of 25 dysregulated genes was measured in eight differentially sensitive cell lines using a novel high-throughput assay that combines multiplex reverse transcriptase-PCR and fluorescence capillary electrophoresis. Sensitivity to belinostat and the magnitude of changes in overall gene modulation were significantly correlated. A belinostat-gene profile was specific for HDACi in three cell lines when compared with equipotent concentrations of four mechanistically different chemotherapeutic agents: 5-fluorouracil, cisplatin, paclitaxel, and thiotepa. Belinostat- and trichostatin A (HDACi)-induced gene responses were highly correlated with each other, but not with the limited changes in response to the other non-HDACi agents. Moreover, belinostat treatment of mice bearing human xenografts showed that the preponderance of selected genes were also modulated in vivo, more extensively in a drug-sensitive tumor than a more resistant model. We have demonstrated a gene signature that is selectively regulated by HDACi when compared with other clinical agents allowing us to distinguish HDACi responses from those related to other mechanisms.

Case report: value of gene expression profiling in the diagnosis of atypical neuroblastoma

BackgroundNephroblastoma and neuroblastoma belong to the most common abdominal malignancies in childhood. Similarities in the initial presentation may provide difficulties in distinguishing between these two entities, especially if unusual variations to prevalent patterns of disease manifestation occur. Because of the risk of tumor rupture, European protocols do not require biopsy for diagnosis, which leads to misdiagnosis in some cases.Case presentationWe report on a 4½-year-old girl with a renal tumor displaying radiological and laboratory characteristics supporting the diagnosis of nephroblastoma. Imaging studies showed tumor extension into the inferior vena cava and bilateral lung metastases while urine catecholamines and MIBG-scintigraphy were negative. Preoperative chemotherapy with vincristine, actinomycine D and adriamycin according to the SIOP2001/GPOH protocol for the treatment of nephroblastoma was initiated and followed by surgical tumor resection. Histopathology revealed an undifferentiated tumor with expression of neuronal markers, suggestive of neuroblastoma. MYCN amplification could not be detected. DNA-microarray analysis was performed using Affymetrix genechip human genome U133 plus 2.0 and artificial neural network analysis. Results were confirmed by multiplex RT-PCR.ResultsPrincipal component analysis using 84 genes showed that the patient sample was clearly clustering with neuroblastoma tumors. This was confirmed by hierarchical clustering of the multiplex RT-PCR data. The patient underwent treatment for high-risk neuroblastoma comprising chemotherapy including cisplatin, etoposide, vindesine, dacarbacine, ifosfamide, vincristine, adriamycine and autologous stem cell transplantation followed by maintenance therapy with 13-cis retinoic acid (GPOH NB2004 High Risk Trial Protocol) and is in complete long-term remission.ConclusionThe use of gene expression profiling in an individual patient strongly contributed to clarification in a diagnostic dilemma which finally led to a change of diagnosis from nephroblastoma to neuroblastoma. This case underlines the importance of gene-expression profiling in the correct diagnosis of childhood neoplasms with atypical presentation to ensure that adequate treatment regimens can be applied.

High-throughput toxicological classification of candidate drug compounds using gene expression, evolved neural networks, and a cell-based platform

All new pharmaceutical agents must be screened for potential toxicity in humans. This process includes a series of genotoxic screens in the discovery phase, and in the event the drug is designed for chronic use, a 2-year non-genotoxicity rodent study. Such non-genotoxicity studies are very expensive because of their duration, the amount of compound required, and the number of rodents required. Models capable of predicting genotoxicity during discovery would reduce these costs and increase favorable outcomes for drugs in a pipeline of development by reducing the rate of attrition. To that end, we have used gene expression data and evolved neural networks to classify compounds by their carcinogenicity or genotoxicity. 60 compounds were used for the training and testing of classifiers relative to gene expression from rat liver cells. Genes related to xenobiotic metabolism, proliferation, apoptosis, and DNA damage were identified. Our study demonstrates that evolved neural networks can be used to classify compounds as carcinogenic or genotoxic with reasonable accuracy.

Abstract LB-224: Development of a novel breast cancer segregation panel assay for multiple genetic subtyping based on gene expression modules developed from Oncomine™

Breast cancer is a highly heterogeneous disease as evidenced by comprehensive genetic studies which have revealed multiple subtypes using gene expression profiling and cell lineage classifier analyses. Previous studies have characterized different subtypes including normal breast-like, luminal epithelial A, luminal epithelial B, Her 2 over-expression and basal type carcinoma. However, the genetic variation within breast cancer is far more diverse than these core subtypes, and it is necessary to fully characterize this diversity in order to move beyond simple prognosis and to specifically predict drug sensitivity. In a review of global gene expression and SNP-based cytogenetic data of more than 5,000 breast cancer patients in the Oncomine™ database, we have been able to characterize approximately 30 different genetic variations that are shared by 1% or more of the breast cancer population. These core, independent variables reflect diverse elements of the disease at a molecular level including cell lineage, dysregulated core biological functions, factors of cell growth, and importantly, the tumor microenvironment. Further genetic subtypes are characterized within the various large and focal genomic amplifications, such as Her2 and Myc, as well as focal expression events present subpopulations of patients. In aggregate these genetic variables represent all of the major genetic factors that present within breast cancer. Currently biomarker/diagnostic approaches have tended to be over-tailored to specific clinical questions and therefore have lacked broad applicability, with every diagnostic test requiring a custom gene set and tailored signature and in some cases, requiring separate validated assays using multiple technologies and consequent splitting of clinical samples. To overcome these limitations, we have developed a single, 96-gene qRT-PCR test for rapid breast cancer companion diagnostics development using FFPE tumor tissue. All 30 of the core variables or “modules” are represented by this test which reports on both gene expression and chromosomal amplification events. We demonstrate in this study that this single test, with its multiple modules, can report on standard histopathological parameters, such as ER, PR and Her2, and reproduce existing prognostic and predictive genomic signatures. Data will be presented on prediction of overall survival, neoadjuvant chemotherapy response, and in-vitro sensitivity to MEK and PI3K inhibitors. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr LB-224. doi:10.1158/1538-7445.AM2011-LB-224