Robust diagnosis DLBCL from gene expression data from different laboratories

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Robust diagnosis DLBCL from gene expression data
from different laboratories
Gyan Bhanot, IBM Research
Dimacs Workshop, June 22, 2005
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• Collaborators
• Gabriela Alexe1
• Arnold Levine2,3
• Gustavo Stolovitzky1

1 IBM 2 IAS 3 UMDNJ
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Overview

• Motivation
• Pattern-based meta-classifiers
• Case study compare data from two labs for DLBCL
  vs FL diagnosis

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Motivation Cancer is a genetic/proteomic disease

• Genetic mutations/viruss/radiation modify
  pathways to create survival advantage for damaged
  cell
• Gene Arrays are a way to study the variation of
  mRNA levels between diseased and healthy cells.
• This allows diagnosis and inference of pathways
  that cause disease

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Cancer diagnosis

• Input
• Training (biomedical / proteomic, microarray)
  data
• k ? 2 classes (m samples) described by N gtgt
  features
• Output
• Collection of robust biomarkers, models
• Robust, accurate classifier / tested on
  out-of-sample data

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Strategy of present paper

• 1. Transform original data to pattern space
• 2. Find robust sets of biomarkers with
  significant collective discriminatory power
• 3. Use many machine learning tools on original
  and pattern data
• ANN, SVM, kNN, Weighted voting, Classification
  trees
• 4. Validate the results on data from a different
  lab

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Patterns
Observed dataset
System response
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Pattern basics
Positive patterns
Negative patterns
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Individual classifiers used

• SVM, ANN, WV, KNN, CART, LR
• Trained / calibrated (leave-one-out)
• raw data
• pattern data

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Application Progression of Follicular Lymphoma
(FL) to Diffuse Large B Cell Lymphoma (DLBCL)
Gene Array data
from different laboratories
Shipp et al. (2002) Nature Med. 8(1), 68-74.
(Whitehead Lab) Stolovitzky G. (2005) In
Deisboeck et al Complex Systems Science in
BioMedicine (in press) (preprint
http//www.wkap.nl/prod/a/Stolovitzky.pdf).
(DellaFavera Lab) Alexe et al (2005) Artificial
Intelligence in Medicine (in press)
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Non-Hodgkin lymphomas

• FL low grade non-Hodgkin lymphoma
• t(1418) translocation over-expression of
  anti-apoptotic bcl2
• 25-60 FL cases evolve to DLBCL
• DLBCL high grade non-Hodgkin lymphoma
• lt 2 years survival if untreated
• Biomarkers FL transformation to DLBCL
• p53/MDM2 (Moller et al., 1999)
• p16 (Pyniol, 1998)
• p38MAPK (Elenitoba-Johnson et al., 2003)
• c-myc (Lossos et al., 2002)

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Lymphoma datasets

• Data WI (Shipp et al., 2002) Affy HuGeneFL
• CU (DallaFavera Lab, Stolovitzky, 2005) Affy
  Hu95Av2
• Samples
• WI 58 DLBCL 19 FL
• CU 14 DLBCL 7 FL
• Genes
• WI 6817
• CU 12581

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Data Preprocessing

• 50 P calls, UL 16000, LL 20
• 2/1 stratify WI data to train/test. CU data test
• Compute SD per gene across samples
• Normalize data to mean 0, SD 1 per gene
• Generate 500 data sets using noise k fold
  stratified sampling jacknife
• Find genes with high correlation to phenotype
  using t-test or SNR. Keep genes that are in gt
  450/501 of datasets

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Choosing Support Sets

• Create good patterns using small subsets of
  genes, validate using weighted voting with 10
  fold cross validation
• Sort genes by their appearance in good patterns
• Select top genes to cover each sample by at least
  10 patterns

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The 30 genes that best distinguish FL from DLBCL
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Examples of FL and DLBCL patterns
WI training data Each DLBCL case satisfies at
least one of the patterns P1 and P2 Each FL case
satisfies the pattern N1 (and none of the
patterns P1 and P2)
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Pattern data
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Meta-classifier performance
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Error distribution raw and pattern data
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Biology Based Methods
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p53 related genes identified by filtering
procedure
FL ? DLBCL progression
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p53 pattern data
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Examples of p53 responsive genes patterns
WI data Each DLBCL case satisfies one of the
patterns P1, P2, P3 Each FL case satisfies one of
the patterns N1, N2, N3
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p53 combinatorial biomarker
77 FL 21 DLBCL cases (3.7 fold) at most one
gene over-expressed 79 DLBCL 23 FL cases
(3.4 fold) at least two genes over-expressed
Each individual gene over- expressed in about
40-70 DLBCL 20-40 FL (specificity 50-60,
sensitivity 60-70)
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What are these genes?

• Plk1 (stpk13) polo-like kinase serine threonine
  protein kinase 13, M-phase specific
• cell transformation, neoplastic, drives quiescent
  cells into mitosis
• over-expressed in various human tumors
• Takai et al., Oncogene, 2005 plk1 potential
  target for cancer therapy, new prognostic marker
  for cancer
• Mito et al, Leuk Lymph, 2005 plk1 biomarker for
  DLBCL
• Cdk2 (p33) cyclin -dependent kinase G2/M
  transition of mitotic cell cycle, interacts with
  cyclins A, B3, D, E
• P53 tumor suppressor gene (Levine 1982)

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Conclusions

• Pattern-based meta-classifier is robust against
  noise
• Good prediction of FL ? DLBCL
• Biology Based Analysis also possible
• Yields useful Biomarker
• Should Study Biologically motivated sets of genes
  ? build pathways

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ltgt

• Thank you for your attention !

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Artificial neural networks
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Support vector machines
Find a maximum margin hyperplane in pattern space
(Vapnik)
(P)
(D)
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k-Nearest neighbors

• Training data samples in normalized peptide
  space
• Prediction for test data The dominant class of
  the k-nearest neighbors in Euclidean metric

Positive
Negative
New case Negative
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Weighted voting

• Pattern data
• each pattern P is a voter
• weight fraction of correctly classified cases
  by the pattern
• each test case compute sum of weights of
  triggered positive patterns and negative patterns
• classify by highest weight

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Logistic regression

• Dataset of two phenotypes (e.g., cancer vs.
  non-cancer)
• Transform into logit space y-gtln(p/1-p)
• Find phenotype predictor as a linear combination
  of data values in logit space

Insightful Miner
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Decision trees / forests

• Find rules in training data
• find root feature which best classifies samples
  by phenotype
• iterate on each branch to find two new features
  which best split each branch by phenotype
• if necessary prune weak support nodes
• CART Classification and Regression Trees
  (Breiman)
• Many trees forest