Identifying Key Genes that cause Aggressive Brain Cancer Grant

abstract

  • Each year, roughly 18,000 Americans will be found to have malignant glioma brain tumors and approximately 13,000 will die. Despite advances in surgery, radiation therapy, and chemotherapy, median survival for the most aggressive forms of the disease such as glioblastoma have remained at 12 months over the past decade. Faced with growing evidence that these traditional therapies have failed to improve the clinical course of this lethal disease, researchers are now turning to novel approaches including the identification of genes that facilitate tumor invasiveness and mobility. By targeting these genes that lead to aberrant activation of mitogenic signaling and cell cycle control, it is hoped that ne treatments can be developed to address these devastating diseases. Our previous research from 13 independent microarray studies has identified 180 genes (92 up regulated and 88 down regulated) that are significantly altered in glioblastoma patients. By utilizing a Bayesian network learning method, we were able to identify the requisite state (whether each of the genes is expressed high or low) of the following genes that is required to identify whether subjects have glioblastoma: DPYSL3, NUP205, C1S, MEF2C, LDOC1, FOXO4, and SPOCK3. Based on these data, we hypothesize that a minimum of six to eight key novel genes are involved in the causation of aggressive brain cancer (in this proposal we refer 'aggressive brain cancer' as malignant grades of gliomas, i.e., grade II astrocytoma, grade III astrocytoma, and glioblastoma (grade IV astrocytoma)). The pay-off will be substantial if we are able to identify key genes in the progression of malignant grades of gliomas because this will help health professionals to provide a better treatment for patients with aggressive brain cancer and improve their prognosis. We have identified the following specific aims to test our hypothesis: Aim 1: Identify key genes and their overall pathway and transcription factors in the progression of aggressive brain cancer. Aim 2: Identify better treatments for patients with aggressive brain cancer and new gene targets for therapy of aggressive brain cancer using a causal Bayesian statistical model. We hypothesize that the causal learning system will discover novel causal relationships among genes, treatment, and survival. If so, the system will provide a better treatment for patients withaggressive brain cancer and improve their prognosis. PUBLIC HEALTH RELEVANCE: The goal of this proposal is to identify genes that act as key control switches in the progression of the aggressive form of brain cancer, grades II and III astrocytomas and glioblastoma. We propose to learn interactions among the genes and environment factors. We will test the model using simulated data. The pay-off of this proposal will be substantial if we are able to identify key genes in the progression of aggressive brain cancer because this will help health professionals to provide a better treatment for patients with aggressive brain cancer and improve their prognosis

date/time interval

  • September 1, 2012 - October 31, 2018

sponsor award ID

  • 1SC3GM096948-01A1

local award ID

  • AWD000000001685

contributor

keywords

  • Address
  • American
  • Astrocytoma
  • Bayesian Method
  • Brain Neoplasms
  • Cell Cycle Regulation
  • Clinical
  • Complement component C1s
  • Data
  • Development
  • Disease
  • Environment
  • Etiology
  • Gene Expression
  • Gene Targeting
  • Genes
  • Genomics
  • Glioblastoma
  • Glioma
  • Goals
  • Health Professional
  • Knowledge
  • Lead
  • Learning
  • MLLT7 gene
  • Malignant - descriptor
  • Malignant Glioma
  • Malignant neoplasm of brain
  • Methods
  • Metric
  • Modeling
  • Operative Surgical Procedures
  • Pathway interactions
  • Patients
  • Process
  • Radiation therapy
  • Research
  • Research Personnel
  • Signal Transduction
  • Simulate
  • Statistical Models
  • System
  • Testing
  • aggressive therapy
  • base
  • chemotherapy
  • computer based statistical methods
  • improved
  • novel
  • novel strategies
  • outcome forecast
  • therapeutic target
  • traditional therapy
  • transcription factor
  • tumor