Jeffrey R. Marks

Oncogenic pathway signatures in human cancers as a guide to targeted therapies

The development of an oncogenic state is a complex process involving the accumulation of multiple independent mutations that lead to deregulation of cell signalling pathways central to the control of cell growth and cell fate. The ability to define cancer subtypes, recurrence of disease and response to specific therapies using DNA microarray-based gene expression signatures has been demonstrated in multiple studies. Various studies have also demonstrated the potential for using gene expression profiles for the analysis of oncogenic pathways. Here we show that gene expression signatures can be identified that reflect the activation status of several oncogenic pathways. When evaluated in several large collections of human cancers, these gene expression signatures identify patterns of pathway deregulation in tumours and clinically relevant associations with disease outcomes. Combining signature-based predictions across several pathways identifies coordinated patterns of pathway deregulation that distinguish between specific cancers and tumour subtypes. Clustering tumours based on pathway signatures further defines prognosis in respective patient subsets, demonstrating that patterns of oncogenic pathway deregulation underlie the development of the oncogenic phenotype and reflect the biology and outcome of specific cancers. Predictions of pathway deregulation in cancer cell lines are also shown to predict the sensitivity to therapeutic agents that target components of the pathway. Linking pathway deregulation with sensitivity to therapeutics that target components of the pathway provides an opportunity to make use of these oncogenic pathway signatures to guide the use of targeted therapeutics.

Predicting the clinical status of human breast cancer by using gene expression profiles

Prognostic and predictive factors are indispensable tools in the treatment of patients with neoplastic disease. For the most part, such factors rely on a few specific cell surface, histological, or gross pathologic features. Gene expression assays have the potential to supplement what were previously a few distinct features with many thousands of features. We have developed Bayesian regression models that provide predictive capability based on gene expression data derived from DNA microarray analysis of a series of primary breast cancer samples. These patterns have the capacity to discriminate breast tumors on the basis of estrogen receptor status and also on the categorized lymph node status. Importantly, we assess the utility and validity of such models in predicting the status of tumors in crossvalidation determinations. The practical value of such approaches relies on the ability not only to assess relative probabilities of clinical outcomes for future samples but also to provide an honest assessment of the uncertainties associated with such predictive classifications on the basis of the selection of gene subsets for each validation analysis. This latter point is of critical importance in the ability to apply these methodologies to clinical assessment of tumor phenotype.

Young Age at Diagnosis Correlates With Worse Prognosis and Defines a Subset of Breast Cancers With Shared Patterns of Gene Expression

PURPOSE Breast cancer arising in young women is correlated with inferior survival and higher incidence of negative clinicopathologic features. The biology driving this aggressive disease has yet to be defined. PATIENTS AND METHODS Clinically annotated, microarray data from 784 early-stage breast cancers were identified, and prospectively defined, age-specific cohorts (young: </= 45 years, n = 200; older: >/= 65 years, n = 211) were compared by prognosis, clinicopathologic variables, mRNA expression values, single-gene analysis, and gene set enrichment analysis (GSEA). Univariate and multivariate analyses were performed. RESULTS Using clinicopathologic variables, young women illustrated lower estrogen receptor (ER) positivity (immunohistochemistry [IHC], P = .027), larger tumors (P = .012), higher human epidermal growth factor receptor 2 (HER-2) overexpression (IHC, P = .075), lymph node positivity (P = .008), higher grade tumors (P < .0001), and trends toward inferior disease-free survival (DFS; hazard ratio = 1.32; P = .094). Using genomic expression analysis, tumors arising in young women had significantly lower ERalpha mRNA (P < .0001), ERbeta (P = .02), and progesterone receptor (PR) expression (P < .0001), but higher HER-2 (P < .0001) and epidermal growth factor receptor (EGFR) expression (P < .0001). Exploratory analysis (GSEA) revealed 367 biologically relevant gene sets significantly distinguishing breast tumors arising in young women. Combining clinicopathologic and genomic variables among tumors arising in young women demonstrated that younger age and lower ERbeta and higher EGFR mRNA expression were significant predictors of inferior DFS. CONCLUSION This large-scale genomic analysis illustrates that breast cancer arising in young women is a unique biologic entity driven by unifying oncogenic signaling pathways, is characterized by less hormone sensitivity and higher HER-2/EGFR expression, and warrants further study to offer this poor-prognosis group of women better preventative and therapeutic options.

Compromised HOXA5 function can limit p53 expression in human breast tumours

Expression of the p53 gene protects cells against malignant transformation. Whereas control of p53 degradation has been a subject of intense scrutiny, little is known about the factors that regulate p53 synthesis. Here we show that p53 messenger RNA levels are low in a large proportion of breast tumours. Seeking potential regulators of p53 transcription, we found consensus HOX binding sites in the p53 promoter. Transient transfection of Hox/HOXA5 activated the p53 promoter. Expression of HOXA5 in epithelial cancer cells expressing wild-type p53, but not in isogenic variants lacking the p53 gene, led to apoptotic cell death. Moreover, breast cancer cell lines and patient tumours display a coordinate loss of p53 and HOXA5 mRNA and protein expression. The HOXA5 promoter region was methylated in 16 out of 20 p53-negative breast tumour specimens. We conclude that loss of expression of p53 in human breast cancer may be primarily due to lack of expression of HOXA5.

Epigenetic regulation of CD133 and tumorigenicity of CD133+ ovarian cancer cells

The cancer stem cell hypothesis posits that malignant growth arises from a rare population of progenitor cells within a tumor that provide it with unlimited regenerative capacity. Such cells also possess increased resistance to chemotherapeutic agents. Resurgence of chemoresistant disease after primary therapy typifies epithelial ovarian cancer and may be attributable to residual cancer stem cells, or cancer-initiating cells, that survive initial treatment. As the cell surface marker CD133 identifies cancer-initiating cells in a number of other malignancies, we sought to determine the potential role of CD133+ cells in epithelial ovarian cancer. We detected CD133 on ovarian cancer cell lines, in primary cancers and on purified epithelial cells from ascitic fluid of ovarian cancer patients. We found CD133+ ovarian cancer cells generate both CD133+ and CD133− daughter cells, whereas CD133− cells divide symmetrically. CD133+ cells exhibit enhanced resistance to platinum-based therapy, drugs commonly used as first-line agents for the treatment of ovarian cancer. Sorted CD133+ ovarian cancer cells also form more aggressive tumor xenografts at a lower inoculum than their CD133− progeny. Epigenetic changes may be integral to the behavior of cancer progenitor cells and their progeny. In this regard, we found that CD133 transcription is controlled by both histone modifications and promoter methylation. Sorted CD133− ovarian cancer cells treated with DNA methyltransferase and histone deacetylase inhibitors show a synergistic increase in cell surface CD133 expression. Moreover, DNA methylation at the ovarian tissue active P2 promoter is inversely correlated with CD133 transcription. We also found that promoter methylation increases in CD133− progeny of CD133+ cells, with CD133+ cells retaining a less methylated or unmethylated state. Taken together, our results show that CD133 expression in ovarian cancer is directly regulated by epigenetic modifications and support the idea that CD133 demarcates an ovarian cancer-initiating cell population. The activity of these cells may be epigenetically detected and such cells might serve as pertinent chemotherapeutic targets for reducing disease recurrence.

Hypermethylation of 14-3-3 σ (stratifin) is an early event in breast cancer

We have identified 14-3-3 σ (σ) as a gene whose expression is lost in breast carcinomas, primarily by methylation-mediated silencing. In this report, we investigated the timing of loss of σ gene expression during breast tumorigenesis in vivo. We analysed the methylation status of σ in breast cancer precursor lesions using microdissection for selective tissue sampling. We found hypermethylation of σ in 24 of 25 carcinomas (96%), 15 of 18 (83%) of ductal carcinoma in situ, and three of eight (38%) of atypical hyperplasias. None of the five hyperplasias without atypia showed σ-hypermethylation. Unexpectedly, patients with breast cancer showed σ hypermethylation in adjacent histologically normal breast epithelium, while this was never observed in individuals without evidence of breast cancer. Also, samples of periductal stromal breast tissue were consistently hypermethylated, underscoring the importance of selective tissue sampling for accurate assessment of 14-3-3-σ methylation in breast epithelium. These results suggest that hypermethylation of 14-3-3-σ occurs at an early stage in the progression to invasive breast cancer, and may occur in apparently normal epithelium adjacent to breast cancer. These results provide evidence that loss of expression of σ is an early event in neoplastic transformation.

An Integrated Genomic-Based Approach to Individualized Treatment of Patients With Advanced-Stage Ovarian Cancer

PURPOSE The purpose of this study was to develop an integrated genomic-based approach to personalized treatment of patients with advanced-stage ovarian cancer. We have used gene expression profiles to identify patients likely to be resistant to primary platinum-based chemotherapy and also to identify alternate targeted therapeutic options for patients with de novo platinum-resistant disease. PATIENTS AND METHODS A gene expression model that predicts response to platinum-based therapy was developed using a training set of 83 advanced-stage serous ovarian cancers and tested on a 36-sample external validation set. In parallel, expression signatures that define the status of oncogenic signaling pathways were evaluated in 119 primary ovarian cancers and 12 ovarian cancer cell lines. In an effort to increase chemotherapy sensitivity, pathways shown to be activated in platinum-resistant cancers were subject to targeted therapy in ovarian cancer cell lines. RESULTS Gene expression profiles identified patients with ovarian cancer likely to be resistant to primary platinum-based chemotherapy with greater than 80% accuracy. In patients with platinum-resistant disease, we identified expression signatures consistent with activation of Src and Rb/E2F pathways, components of which were successfully targeted to increase response in ovarian cancer cell lines. CONCLUSION We have defined a strategy for treatment of patients with advanced-stage ovarian cancer that uses therapeutic stratification based on predictions of response to chemotherapy, coupled with prediction of oncogenic pathway deregulation, as a method to direct the use of targeted agents.

Acquired Expression of Periostin by Human Breast Cancers Promotes Tumor Angiogenesis through Up-Regulation of Vascular Endothelial Growth Factor Receptor 2 Expression

ABSTRACT The late stages of human breast cancer development are poorly understood complex processes associated with the expression of genes by cancers that promote specific tumorigenic activities, such as angiogenesis. Here, we describe the identification of periostin as a mesenchyme-specific gene whose acquired expression by human breast cancers leads to a significant enhancement in tumor progression and angiogenesis. Undetectable in normal human breast tissues, periostin was found to be overexpressed by the vast majority of human primary breast cancers examined. Tumor cell lines engineered to overexpress periostin showed a phenotype of accelerated growth and angiogenesis as xenografts in immunocompromised animals. The underlying mechanism of periostin-mediated induction of angiogenesis was found to derive in part from the up-regulation of the vascular endothelial growth factor receptor Flk-1/KDR by endothelial cells through an integrin αvβ3-focal adhesion kinase-mediated signaling pathway. These findings demonstrate the presence of a novel mechanism by which tumor angiogenesis is acquired with the expression of a mesenchyme-specific gene as a crucial step in late stages of tumorigenesis.

Patterns of Gene Expression That Characterize Long-term Survival in Advanced Stage Serous Ovarian Cancers

Purpose: A better understanding of the underlying biology of invasive serous ovarian cancer is critical for the development of early detection strategies and new therapeutics. The objective of this study was to define gene expression patterns associated with favorable survival. Experimental Design: RNA from 65 serous ovarian cancers was analyzed using Affymetrix U133A microarrays. This included 54 stage III/IV cases (30 short-term survivors who lived <3 years and 24 long-term survivors who lived >7 years) and 11 stage I/II cases. Genes were screened on the basis of their level of and variability in expression, leaving 7,821 for use in developing a predictive model for survival. A composite predictive model was developed that combines Bayesian classification tree and multivariate discriminant models. Leave-one-out cross-validation was used to select and evaluate models. Results: Patterns of genes were identified that distinguish short-term and long-term ovarian cancer survivors. The expression model developed for advanced stage disease classified all 11 early-stage ovarian cancers as long-term survivors. The MAL gene, which has been shown to confer resistance to cancer therapy, was most highly overexpressed in short-term survivors (3-fold compared with long-term survivors, and 29-fold compared with early-stage cases). These results suggest that gene expression patterns underlie differences in outcome, and an examination of the genes that provide this discrimination reveals that many are implicated in processes that define the malignant phenotype. Conclusions: Differences in survival of advanced ovarian cancers are reflected by distinct patterns of gene expression. This biological distinction is further emphasized by the finding that early-stage cancers share expression patterns with the advanced stage long-term survivors, suggesting a shared favorable biology.

Development of a Multimarker Assay for Early Detection of Ovarian Cancer

PURPOSE Early detection of ovarian cancer has great promise to improve clinical outcome. PATIENTS AND METHODS Ninety-six serum biomarkers were analyzed in sera from healthy women and from patients with ovarian cancer, benign pelvic tumors, and breast, colorectal, and lung cancers, using multiplex xMAP bead-based immunoassays. A Metropolis algorithm with Monte Carlo simulation (MMC) was used for analysis of the data. RESULTS A training set, including sera from 139 patients with early-stage ovarian cancer, 149 patients with late-stage ovarian cancer, and 1,102 healthy women, was analyzed with MMC algorithm and cross validation to identify an optimal biomarker panel discriminating early-stage cancer from healthy controls. The four-biomarker panel providing the highest diagnostic power of 86% sensitivity (SN) for early-stage and 93% SN for late-stage ovarian cancer at 98% specificity (SP) was comprised of CA-125, HE4, CEA, and VCAM-1. This model was applied to an independent blinded validation set consisting of sera from 44 patients with early-stage ovarian cancer, 124 patients with late-stage ovarian cancer, and 929 healthy women, providing unbiased estimates of 86% SN for stage I and II and 95% SN for stage III and IV disease at 98% SP. This panel was selective for ovarian cancer showing SN of 33% for benign pelvic disease, SN of 6% for breast cancer, SN of 0% for colorectal cancer, and SN of 36% for lung cancer. CONCLUSION A panel of CA-125, HE4, CEA, and VCAM-1, after additional validation, could serve as an initial stage in a screening strategy for epithelial ovarian cancer.