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Glioblastoma Multiforme (GBM) – Subtype Analysis

Glioblastoma Multiforme (GBM) – Subtype Analysis. Lance Parsons. Introduction. Clinicians (meat readers) determine histological categorization: Astrocytoma, Oligodendrocytoma, Mixed, or Glioblastoma multiforme (GBM) GBM patients have poor prognosis, but some surive unexpectly long.

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Glioblastoma Multiforme (GBM) – Subtype Analysis

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  1. Glioblastoma Multiforme (GBM) – Subtype Analysis Lance Parsons

  2. Introduction • Clinicians (meat readers) determine histological categorization: Astrocytoma, Oligodendrocytoma, Mixed, or Glioblastoma multiforme (GBM) • GBM patients have poor prognosis, but some surive unexpectly long. • Molecularly and clinically distinct subtypes of GBMs

  3. Tumor Type Age Astro Mixed Oligo GBM Young Medium Old Gene Subset Expression X Y X Glioma Subtypes A B C Survival Short Long Incorporating Biological Knowledge • “Tiers” of classification can assist with discovery of downstream groups • Glioma Classification • Histological Level • Clinical Level (Survival, Age, etc) • Transcriptiome (Gene Expression Level) • Gene Classification • GO Hierarchy • Pathway Databases • Expression Level (Microarray Data)

  4. Age and Survival • Young patients show greater variability in survival • Use this level of the “hierarchy” to assist in downstream analysis. • Very simple method is to use only the Young samples and find the groups within that set of samples.

  5. Normalization • Making the numbers comparable • Log Transform – Equalize variance, lineraize data • Median Center Arrays – Correct for differences between arrays • Standardize to unit variance?

  6. Noise Filter • Removing noise from the dataset • Affymetrix software does some of this with Present/Absent calls • Fold-change filter? • Other methods?

  7. Feature (Gene) Selection • Find genes highly correlated with patient survival, within young sample group. • Cox Proportional Hazards model • Regression model that accounts for “censored” data • Permutation test can improve robustness • Simple Cox selects 39 genes (permutation pending)

  8. Exploration of Results • The genes we select are statistically significant (as dictated by our Cox testing methodology), but they may not be biologically or clinically significant. • Initial exploration through hierarchical clustering.

  9. Clinical Validation • Kaplan-Meier curves fit the two groups to a survival model

  10. Biological Validation • file:///C:/Documents%20and%20Settings/Lance/My%20Documents/Research/projects/HenryFord/HFAnalysis-GBM-Young_annotations.html

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