Holly K. Dressman

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.

Gene expression predictors of breast cancer outcomes

BACKGROUND Correlation of risk factors with genomic data promises to provide specific treatment for individual patients, and needs interpretation of complex, multivariate patterns in gene expression data, as well as assessment of their ability to improve clinical predictions. We aimed to predict nodal metastatic states and relapse for breast cancer patients. METHODS We analysed DNA microarray data from samples of primary breast tumours, using non-linear statistical analyses to assess multiple patterns of interactions of groups of genes that have predictive value for the individual patient, with respect to lymph node metastasis and cancer recurrence. FINDINGS We identified aggregate patterns of gene expression (metagenes) that associate with lymph node status and recurrence, and that are capable of predicting outcomes in individual patients with about 90% accuracy. The metagenes defined distinct groups of genes, suggesting different biological processes underlying these two characteristics of breast cancer. Initial external validation came from similarly accurate predictions of nodal status of a small sample in a distinct population. INTERPRETATION Multiple aggregate measures of profiles of gene expression define valuable predictive associations with lymph node metastasis and disease recurrence for individual patients. Gene expression data have the potential to aid accurate, individualised, prognosis. Importantly, these data are assessed in terms of precise numerical predictions, with ranges of probabilities of outcome. Precise and statistically valid assessments of risks specific for patients, will ultimately be of most value to clinicians faced with treatment decisions.

Gene expression profiling and genetic markers in glioblastoma survival

Despite the strikingly grave prognosis for older patients with glioblastomas, significant variability in patient outcome is experienced. To explore the potential for developing improved prognostic capabilities based on the elucidation of potential biological relationships, we did analyses of genes commonly mutated, amplified, or deleted in glioblastomas and DNA microarray gene expression data from tumors of glioblastoma patients of age >50 for whom survival is known. No prognostic significance was associated with genetic changes in epidermal growth factor receptor (amplified in 17 of 41 patients), TP53 (mutated in 11 of 41 patients), p16INK4A (deleted in 15 of 33 patients), or phosphatase and tensin homologue (mutated in 15 of 41 patients). Statistical analysis of the gene expression data in connection with survival involved exploration of regression models on small subsets of genes, based on computational search over multiple regression models with cross-validation to assess predictive validity. The analysis generated a set of regression models that, when weighted and combined according to posterior probabilities implied by the statistical analysis, identify patterns in expression of a small subset of genes that are associated with survival and have value in assessing survival risks. The dominant genes across such multiple regression models involve three key genes-SPARC (Osteonectin), Doublecortex, and Semaphorin3B-which play key roles in cellular migration processes. Additional analysis, based on statistical graphical association models constructed using similar computational analysis methods, reveals other genes which support the view that multiple mediators of tumor invasion may be important prognostic factor in glioblastomas in older patients.

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.

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.

Gene expression signatures that predict radiation exposure in mice and humans.

Background The capacity to assess environmental inputs to biological phenotypes is limited by methods that can accurately and quantitatively measure these contributions. One such example can be seen in the context of exposure to ionizing radiation. Methods and Findings We have made use of gene expression analysis of peripheral blood (PB) mononuclear cells to develop expression profiles that accurately reflect prior radiation exposure. We demonstrate that expression profiles can be developed that not only predict radiation exposure in mice but also distinguish the level of radiation exposure, ranging from 50 cGy to 1,000 cGy. Likewise, a molecular signature of radiation response developed solely from irradiated human patient samples can predict and distinguish irradiated human PB samples from nonirradiated samples with an accuracy of 90%, sensitivity of 85%, and specificity of 94%. We further demonstrate that a radiation profile developed in the mouse can correctly distinguish PB samples from irradiated and nonirradiated human patients with an accuracy of 77%, sensitivity of 82%, and specificity of 75%. Taken together, these data demonstrate that molecular profiles can be generated that are highly predictive of different levels of radiation exposure in mice and humans. Conclusions We suggest that this approach, with additional refinement, could provide a method to assess the effects of various environmental inputs into biological phenotypes as well as providing a more practical application of a rapid molecular screening test for the diagnosis of radiation exposure.

Gene Expression Phenotypes of Atherosclerosis

Objective—Fulfilling the promise of personalized medicine by developing individualized diagnostic and therapeutic strategies for atherosclerosis will depend on a detailed understanding of the genes and gene variants that contribute to disease susceptibility and progression. To that end, our group has developed a nonbiased approach congruent with the multigenic concept of complex diseases by identifying gene expression patterns highly associated with disease states in human target tissues. Methods and Results—We have analyzed a collection of human aorta samples with varying degrees of atherosclerosis to identify gene expression patterns that predict a disease state or potential susceptibility. We find gene expression signatures that relate to each of these disease measures and are reliable and robust in predicting the classification for new samples with >93% in each analysis. The genes that provide the predictive power include many previously suspected to play a role in atherosclerosis and additional genes without prior association with atherosclerosis. Conclusion—Hence, we are reporting a novel method for generating a molecular phenotype of disease and then identifying genes whose discriminatory capability strongly implicates their potential roles in human atherosclerosis.

Gene Expression Profiles of Multiple Breast Cancer Phenotypes and Response to Neoadjuvant Chemotherapy

Purpose: Breast cancer is a heterogeneous disease, and markers for disease subtypes and therapy response remain poorly defined. For that reason, we employed a prospective neoadjuvant study in locally advanced breast cancer to identify molecular signatures of gene expression correlating with known prognostic clinical phenotypes, such as inflammatory breast cancer or the presence of hypoxia. In addition, we defined molecular signatures that correlate with response to neoadjuvant chemotherapy. Experimental Design: Tissue was collected under ultrasound guidance from patients with stage IIB/III breast cancer before four cycles of neoadjuvant liposomal doxorubicin paclitaxel chemotherapy combined with local whole breast hyperthermia. Gene expression analysis was done using Affymetrix U133 Plus 2.0 GeneChip arrays. Results: Gene expression patterns were identified that defined the phenotypes of inflammatory breast cancer as well as tumor hypoxia. In addition, molecular signatures were identified that predicted the persistence of malignancy in the axillary lymph nodes after neoadjuvant chemotherapy. This persistent lymph node signature significantly correlated with disease-free survival in two separate large populations of breast cancer patients. Conclusions: Gene expression signatures have the capacity to identify clinically significant features of breast cancer and can predict which individual patients are likely to be resistant to neoadjuvant therapy, thus providing the opportunity to guide treatment decisions.

MicroRNAs and their target messenger RNAs associated with endometrial carcinogenesis.

OBJECTIVE Recent advances in gene expression technology have provided insights into global messenger RNA (mRNA) expression changes associated with endometrial cancer development. However, the post-transcriptional events that may also have phenotypic consequences remain to be completely delineated. MicroRNAs (miRNAs) are small non-coding RNA transcripts, that influence cell function via modulation of post-transcriptional activity of multiple target mRNA genes. Although recent reports suggest that miRNAs may influence human cancer development, their role in endometrial carcinogenesis remains to be described. METHODS We measured expression of 335 unique human miRNAs in 61 fresh-frozen endometrial specimens, including 37 endometrial cancers, 20 normal endometrium, and 4 complex atypical hyperplasia samples. In parallel, expression of 22,000 mRNA genes was analyzed using the Affymetrix Human U133A GeneChips in 29 of the endometrial samples, including 20 endometrial carcinomas and 9 normal endometrial samples. Differentially expressed mRNAs, miRNAs, and predicted miRNA-mRNA targets were integrated and evaluated for representation of relevant functional biologic pathways. RESULTS Thirteen miRNAs (p<0.02) and 90 mRNAs (FDR; 0%) were identified to be associated with endometrial cancer development. Twenty-six of the 90 (29%) differentially expressed mRNAs are Sangar-database predicted mRNA targets of the 13 miRNAs. Pathway analysis demonstrates significant involvement of these 26 mRNA genes in processes including cell death, growth, proliferation, and carcinogenesis. CONCLUSION We have identified miRNAs and mRNAs associated with endometrial cancer development. Further, our strategy of integrating miRNA/mRNA data may also aid in the identification of important biologic pathways and additional unique genes that have importance in endometrial pathogenesis.