Gene expression profiling identifies molecular subtypes of gliomas

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Gene expression profiling identifies molecular
subtypes of gliomas

• Ruty Shai, Tao Shi, Thomas J Kremen, Steve
  Horvath, Linda M Liau, Timothy F Cloughesy, Paul
  S Mischel and Stanley F Nelson
• Presented by Stephanie Tsung

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Outline

• Descriptions of Data
• Statistical Methods
• Multidimensional Scaling Plot
• Hierarchical Clustering
• K-means Clustering
• Gene Filtering/Selection
• Predictor Comparison
• Conclusion/ Future works

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Background

• Brain tumors can be classified by tumor origins,
  cell type origin or the tumor site etc
• Tumor classification has been critical in
  treatment selection and outcome prediction.
  However, current classification methods are still
  far from perfect
• As a new technology, DNA microarray has been
  introduced to cancer classification on the basis
  of gene expression levels.

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Background Cancer Classification

• Cancer classification can be divided into two
  challenges class discovery and class prediction.
  
•  Class discovery refers to defining previously
  unrecognized tumor subtypes.
•  Class prediction refers to the assignment of
  particular tumor samples to already-defined
  classes.

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Objectives

• To test whether gene expression measurements can
  be used to classify different brain tumors
• To determine sets of significant genes to
• distinguish brain tumor of different
  pathological types, grades and survival times
• To validate the selected informative genes in
  brain tumor classification and prediction.

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Data and Pre-Processing

• Affymetrix HG-U95Av2 chips
• 12,555 Genes and total 42 samples
• Tumor Types ()
• N(7) O(3) D(18) A(2) AA(3) P(9)
• Data pre-processing
• Each tumor was examined by a neuropathologist and
  dissected into two portions tissue diagnosis and
  RNA extraction.
• Normalization and Model-Based Expression indices
  in dChip.

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Q. Are the global transcriptional signatures of
the different pathologic subtypes of gliomas
molecularly distinct?
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Multidimensional Scaling Plot (MDS Plot)

• To uncover the hidden structure of data.
• D(N) -gt D(2)
• Dimension reduction technique
• 12,555 dimensional space to low dimensional
  Euclidean space
• Explain observed similarities and dissimilarity
  between objects such as correlation, euclidean
  distance etc.
• R cmd1 lt- cmdscale(dist(dat1,130),k2,eigT)

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MDS Plot
Figure 1. (a)Multidimensional scaling plot of all
42 tissue samples plotted in two-dimensional
space using expression values from all 12 555
probesets.
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Hierarchical Clustering

• Evaluate all pair wise distance between objects
• Look for a pair with shortest distance
• Construct new obj by avg. of two obj.
• Evaluate distance from new obj to all other
  objects and Go to Step 2
• R h1 lt- hclust(dist(x), methodaverage)

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Hierarchical Clustering
I
II
Figure 1. (b) The same 42 tissue samples were
grouped into hierarchical clusters. Tissue
samples are color-coded.
I II P0.00006, Fishers exact test III IV
P0.00001
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Fishers Exact Test
Sample w/ charat. w/o charat. Total
1 A B AB
2 C D CD
Total AC BD N
Ho Whether proportion of interest differs
between two groups.
The two-tailed probability .326 .007 .093
.163 .019 .608
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Q. Can we uncover these subtypes without prior
knowledge?

• i.e. How many categories of gliomas are suggested
  by the gene expression data?

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K-means Clustering

• To find a K-partition of the observations that
  minimizes the within sum of squares (WSS) for
  each clusters
• The number of clusters, k, needs to be
  pre-specified.
• Tibshirani prediction strength can be used to
  determine the optimal k.
• R cl1lt- kmeans (x, 3)

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Figure 2 Grouping of tumors. All tumor samples
were plotted using multidimensional scaling using
all 12 555 probesets. We performed
nonhierarchical Kmeans clustering (Kaufmann and
Rousseeu, 1990).
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Gene Filtering/Selection

• To find the interesting genes which differently
  expressed in 6 two groups comparisons
• Using top 30 genes based on T-test
• 170 most differentially expressed genes using
  T-test

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Predictor Comparison

• Compare the performance of predictors
• Gene Vote
• Leave-one-out crossvalidation error rates were
  calculated.
• For a given method and sample size, n, a
  classifier is generated using
• (n - l) cases and tested on the single remaining
  case. This is repeated n times, each time
  designing a classifier by leaving-one-out. Thus,
  each case in the sample is used as a test case,
  and each time nearly all the cases are used to
  design a classifier

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Table 1.
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Using 170 filtered genes based on t-test
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Table 2.
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Conclusion

• Performed MDS plots and K-means clustering
  analysis and found evidence for three clusters
  glioblastomas, lower grade astrocytomas, and
  oligodendrogilmas (plt0.00001).
• A relatively small number of genes can be used
  to distinguish between molecular subtypes.
• Subsets of gliomas can be potentially used for
  patient stratification and potential targets for
  treatment.

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Future Directions

• Construct predictors using different gene
  selection methods.
• Validate the selected genes with new tumor
  samples.

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K3 gave us the best prediction power
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Statistical problems in response-basedclassificat
ion

• Identification of new or unknown
  classes--unsupervised learning
• Classification into known classes supervised
  learning
• Identification of best predictor
  variablesvariable selection, e.g. marker genes
  in microarray data (gene voting, hierarchical
  clustering)