Showing papers by "Jane Fridlyand published in 2010"

Reversing HOXA9 Oncogene Activation by PI3K Inhibition: Epigenetic Mechanism and Prognostic Significance in Human Glioblastoma

Abstract: HOXA genes encode critical transcriptional regulators of embryonic development that have been implicated in cancer. In this study, we documented functional relevance and mechanism of activation of HOXA9 in glioblastoma (GBM), the most common malignant brain tumor. Expression of HOXA genes was investigated using reverse transcription-PCR in primary gliomas and glioblastoma cell lines and was validated in two sets of expression array data. In a subset of GBM, HOXA genes are aberrently activated within confined chromosomal domains. Transcriptional activation of the HOXA cluster was reversible by a phosphoinostide 3-kinase (PI3K) inhibitor through an epigenetic mechanism involving histone H3K27 trimethylation. Functional studies of HOXA9 showed its capacity to decrease apoptosis and increase cellular proliferation along with tumor necrosis factor-related apoptosis-including ligand resistance. Notably, aberrant expression of HOXA9 was independently predictive of shorter overall and progression-free survival in two GBM patient sets and improved survival prediction by MGMT promoter methylation. Thus, HOXA9 activation is a novel, independent, and negative prognostic marker in GBM that is reversible through a PI3K-associated epigenetic mechanism. Our findings suggest a transcriptional pathway through which PI3K activates oncogenic HOXA expression with implications for mTOR or PI3K targeted therapies.

Statistical techniques to construct assays for identifying likely responders to a treatment under evaluation from cell line genomic data

Abstract: Developing the right drugs for the right patients has become a mantra of drug development. In practice, it is very difficult to identify subsets of patients who will respond to a drug under evaluation. Most of the time, no single diagnostic will be available, and more complex decision rules will be required to define a sensitive population, using, for instance, mRNA expression, protein expression or DNA copy number. Moreover, diagnostic development will often begin with in-vitro cell-line data and a high-dimensional exploratory platform, only later to be transferred to a diagnostic assay for use with patient samples. In this manuscript, we present a novel approach to developing robust genomic predictors that are not only capable of generalizing from in-vitro to patient, but are also amenable to clinically validated assays such as qRT-PCR. Using our approach, we constructed a predictor of sensitivity to dacetuzumab, an investigational drug for CD40-expressing malignancies such as lymphoma using genomic measurements of cell lines treated with dacetuzumab. Additionally, we evaluated several state-of-the-art prediction methods by independently pairing the feature selection and classification components of the predictor. In this way, we constructed several predictors that we validated on an independent DLBCL patient dataset. Similar analyses were performed on genomic measurements of breast cancer cell lines and patients to construct a predictor of estrogen receptor (ER) status. The best dacetuzumab sensitivity predictors involved ten or fewer genes and accurately classified lymphoma patients by their survival and known prognostic subtypes. The best ER status classifiers involved one or two genes and led to accurate ER status predictions more than 85% of the time. The novel method we proposed performed as well or better than other methods evaluated. We demonstrated the feasibility of combining feature selection techniques with classification methods to develop assays using cell line genomic measurements that performed well in patient data. In both case studies, we constructed parsimonious models that generalized well from cell lines to patients.