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Brain Tumor Update William A. Freije, M.D.
 

Brain Tumor Update William A. Freije, M.D.

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    Brain Tumor Update William A. Freije, M.D. Brain Tumor Update William A. Freije, M.D. Presentation Transcript

    • Brain Tumor Update William A. Freije, M.D.
    • How to summarize 140,140,533 individual measurements in twenty five slides William A. Freije, M.D.
    • Math
      • 22,283 probesets in 133A chip
      • 22,645 probesets in 133B chip
      • 44,928 probesets total
      • 20 pairs for each probeset
      • 151 samples
      • 140,140,533 data points total
    • Tumor types examined to date
      • Glial tumors
      • Astrocytoma
      • Pilocytic/infantile 2/2
      • Grade II 2
      • Grade III 9
      • Oligodendroglioma
      • Grade II 8
      • Grade III 11
      • Mixed oligo/astrocytoma (Grade III) 9
      • Glioblastoma multiforme 63
      • Gliosarcoma 3
      • Ependymoma 3
      • Poorly differentiated embryonal tumors
      • Medulloblastoma 5
      • Menigothelial tumors
      • Meningioma 17
      • Nerve sheath tumors
      • Schwannoma 3
      • Metastatic tumors 4 TOTAL 141
    • Summary of completed arrays
      • 143 samples from 129 patients
        • 141 tumor samples
        • 2 normal brain specimens from GBM pts
      • 8 normal brain controls
        • From autopsy patients
      • 151 samples completed on 133A and 133B Affymetrix oligonucleotide arrays
    • Patient samples- replicates, multiple sampling, progression, and autopsy
      • Replicates: same tumor, same biopsy
        • Mixed oligo/astro (2,1) proceeding to GBM (1)
        • Oligo III (2) proceeding to GBM (1)
      • Multiple sampling with progression analysis
        • GBM tumor (2) 2 nd resection of GBM (2)
      • Progression
        • Oligo III (1) 2 pts proceeding to GBM (1) 2 pts
        • GBM (1) 2 pts proceeding to 2 nd cranio GBM (1) 2 pts
      • Autopsy
        • GBM (1) 2 pts with normal contralateral brain (1) 2 pts
    • Objectives
      • Global microarray classifier to discriminate the major types of primary brain tumors: medulloblastoma, glioma, and meningothelial.
        • Complement analysis with normal tissue data base.
      • Dissect gene expression profiles of glial tumors :
        • Replace histologic classification scheme with expressed gene profile classification
        • Prognosis- identify high and low risk disease
        • Identify tumors with susceptibility to chemotherapeutic agents
        • Tumor subclass identification
        • Define molecular changes associated with disease progression
    • Objectives
      • Explore the different mathematical models and software used to summarize microarray data.
        • Dchip
        • Genespring
        • Statistical analysis
          • t test
          • Multi-dimensional scaling
          • Random forest prediction
          • Fisher linear discriminant analysis
          • Log rank and Wilcoxon nonparametric tests
          • Kaplan-Meier survival plots
    • Global microarray classifier
    • Global microarray classifier
      • Gene expression profiles of the three major brain tumor types- medulloblastoma, meningioma, and glial- were examined with:
        • Multidimensional scaling
        • Random forest prediction- all gene analysis
        • Fisher linear discriminant analysis- univariate
    • MDS plot with 127 samples(normal not included) 12021 genes
    • Top 30 genes separating medulloblastoma, meningioma, and gliomas: Random Forest
    • Boxplots of probesets identified by Random Forest Prediction which separate medulloblastoma, meningioma, and glioma
    • Probeset 213033_s_at: medulloblastoma specific gene Normal tissue project Brain tumor data: complementary
    • Top 30 genes separating medulloblastoma, meningioma, and gliomas: Fisher Linear Discriminant Assay
    • GBM analysis
    • GBM samples: de novo vs progressive samples
      • 63 samples total from 57 patients
      • 48 de novo samples from 46 patients
      • 15 progressive samples from 11 patients
      • 3 de novo samples are autopsy specimens
      • 1 progressive sample is from autopsy
      • De novo
        • Treated
        • Untreated
        • Total 48
      • Progressive
        • Treated
        • Untreated
        • Total 15
      GBM samples: de novo vs progressive samples
    • Clinical parameters
      • TTP- time to progression
        • Time in days from diagnosis or intervention (at UCLA) to the first evidence of disease progression
      • TTS- time to survival
        • Time in days from diagnosis or intervention (at UCLA) to the current day if alive or to the day of death
      • Survival time
        • Time in days from initial diagnosis to the current day if alive or to the day of death
    • Birth Clinical identification Disease 1 st Rx DEATH 2 nd Rx Progression TTS 1st TTP 1st TTS 2nd TTP 2nd TTP and TTS are related to time of treatment Unknown Survival
      • TTP (Days)
        • Range 7-1031
        • Mean 188
        • Median 115
        • SD 226
      • TTP Alive Dead
        • Median 219 79
      GBM clinical parameters- TTP
      • TTS (Days)
        • Range 7-1247
        • Mean 367
        • Median 236
        • SD 328
      • TTS Alive Dead
        • Median 927 187
      GBM clinical parameters- TTS
      • Survival (Days)
        • Range 56-8330
        • Mean 850
        • Median 541
        • SD 1191
      • Survival Alive Dead
        • Median 936 520
      GBM clinical parameters- Survival
    • GBM clinical parameters- outliers
      • Long term survivors: all patients with TTS> 723 days. This group of patients all have TTP that are > 612 days (TTP mean + 2 SD). 10 patients in this group
      • Short term survivors: all patients with TTP < 60 days. This group of patients all had a TTS < 150 days. 13 patients in this group.
      • All short term survivors are being reviewed for neurodecline secondary to other causes.
    • GBM living patients
      • 13/57 GBM patients are alive
      • 4/13 alive GBM patients have a TTP>300 (range 659-1311). All of these patients were identified in the outlier group.
    • GBM survival analysis statistical methods employed
      • R square of LDA to determine number of groups
      • Best split measures
      • Wilcoxon test statistics
      • Logrank test statistics
      • Kaplan-Meier survival plots
    • Determine Number of Clusters by Separating the 63 samples with gene expression ( with top 2000 brain tissue genes by LDA)
    • Association between gene expression and survival time in GBM samples: top 36 genes
    • Kaplan-Meier survival curves for specific probesets
    • Future
      • Turn this information into clinical difference
      • Develop a model through insight developed from the microarray screen
      • Confirm RT-PCR
      • Develop specific assays for genes thought to be important in GBM survival (Ab)
      • Cell line models
      • Invasion assay
      • Mouse models
      • Example: IGF2 highly expressed in poor survivors:
        • Global epigenetic imprint altered in GBM poor survival?
    • IGF2 expression in GBM long and short survivors Graph courtesy of E. Castro