Analysis of Gene Expression Metadata In Studies of HNSCC and LSCC Louise Showe Molecular and Cellula

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Analysis of Gene Expression Metadata In Studies
of HNSCC and LSCCLouise ShoweMolecular and
Cellular Oncogenesis Program
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• Cancer Functional Genomics
• Gene Expression Microarrays
• Classification, Diagnosis, Prognosis, Response to
  Treatment
• Who will analyze the data?

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Molecular Biology Computational Biology
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(No Transcript)
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Data Pre-processing
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Classification Class Discovery
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Biomarker Selection

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(RFE) Recursive Feature Elimination Iteratively
Discards Genes That Contribute Least
Multivariate Wrapper Approach
Univariate Filter Approach
Data
Data
Univariate Feature selection
Learning Machine
Learning machine
Multivariate Feature Selection
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Resampling Methods

• Goals
• estimate the value and uncertainty of model
  parameters
• identify the best classification model for the
  dataset
• boost the probability of classification of
  hard samples

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Biomedical Problem

• Patients with previous HNSCC are at risk for both
  lung metastases and new primary lung cancers.
• Distinction between a primary LSCC and a HNSCC
  lung metastasis can be very difficult.
• Clinical dilemma Depending on the origin,
  patients have drastically different treatment
  options and prognoses.

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Can We Distinguish Primary Lung SCC Tumors From
Head Neck Derived SCC Metastasis?
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Study Schema
18 HNSCC 10 LSCC (Training Set For Gene
Selection on U133A)
Identify Best Diagnostic Genes
Validate Genes On Independent Data Sets Data
for 122 Samples 4 Sources-2 different Affy
chips
Test On Lung Nodules From Patients With Previous
History Of HNSCC (MSKCC)
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Top genes by PDA-RFE
Top PDA genes have distinct profiles Top genes
by selected by t-test alone have common profile
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Penn Data Set U133A
10 Genes Can Accurately Classify Penn Samples
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Summary

• Excellent classification accuracy on small data
  set.
• Could it be validated on new samples?

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External Validation Datasets

• U133A
• 40 HNSCC Samples from Minnesota (MN)
• U95Av2
• 21 LSCC Samples from Dana-Farber (DF)
• 11 LSCC Samples from Columbia (CU)
• Note Only the 9530 probe sets common between
  U95Av2 and U133A were considered. Raw data
• re-analyzed (RMA).

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Unsupervised Clustering Shows Strong Systematic
Bias Due To Chip Type And Institution
LC.DF
LC.CU
HN.MN
HN.UP
LC.UP
U95Av2
U133A
Penn
Minnesota
CU
Dana-Farber
Head Neck
Lung
Lung
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DWD Visualization
courtesy of S. Marron, UNC
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DWD Correction

• Correct Bias Due To Different Hybridization
  Sites
• 1. Merge Dana Farber Set with Columbia Set
• (same chip U95, same phenotype LSCC)
• 2. Merge Minnesota Set with Penn Head Neck Set
• (same chip U133, same phenotype HNSCC)
• Correct Bias Due To Affy Chip Type And Cancer
  Type
• 3. Merge Penn LSCC Set with combined Dana Farber
  Columbia Sets
• (different chips U133 vs. U95, same phenotype)

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DWD Corrected For Systematic Bias
Head Neck
Lung
Samples cluster by cancer type and not By chip
type or institution
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Reduction in systematic bias improved global
correlation
Before Correction
After Correction
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Reduction in systematic bias regularized
classification
AFTER DWD
BEFORE DWD
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10 GeneClassification of Independent Test Set
Head Neck
Lung
Dana-Farber
CU
Minnesota
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Study at MSKCC

• Training Set
• 52 subjects
• 31 HNSCC
• 18 LSCC
• Test Set
• 12 lung nodules in patients with prior HNSCC

Talbot et al, Cancer Res (2005) 65 (8)
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Methods used

• Gene Selection by t-test
• Classification by Support Vector Machines (SVM)
• Accuracy by Leave-one-out cross-validation (LOOCV)

Main Conclusions

• Minimum set of 500 genes is needed for robust
  classification

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10-gene Classification of Talbot et al Samples
Head Neck
Lung
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10 genes on MKSCC Samples
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PDA Classification of 50 New Lung 72 new HN
Samples is 96 Accurate With 10 Genes
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Additional Test Set Lung Nodules from patients
with prior HNSCC

• 13 samples from patients with prior HNSCC
• 11 samples clinically classified as primary lung
  cancer (U01-U11)
• 2 samples (lung nodule and pancreatic nodule from
  the same patient) classified as metastases (U12
  U13)

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10 gene classifier predictions agree with the
clinical assessment
Based on Data from Talbot et al Cancer Res. 2005
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10 genes separate HNSCC adjacent tissue
HNSCC
Adjacent Tissue (donor matched)
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Validation by QRT-PCR
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Expression Ratios From QPCR For Selected Genes
Pairs Correctly Classify New HNSCC LSCC Samples
9 7
Vachani ,Nebozhyn et al. Submitted to Cancer
Research
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Summary

• Microarray analysis enables diagnostics for HNSCC
  - LSCC with just few genes
• Using different analysis methods can make a big
  difference in the results 10 genes vs. 500 for
  HNSCC
• Selection of biomarkers by RFE is much superior
  to t-test
• When combining data from separate experiments,
  observed batch effect needs to be addressed and
  alleviated
• Resampling and validation on independent data
  sets and experimental platforms are crucial for
  assessing the reliability and reproducibility of
  the results
• Close clinical, biological and mathematical
  collaboration is essential for proper design and
  analysis of experiments

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• Wistar
• Michael Showe
• Michael Nebozhyn
• Malik Yousef
• Wen-Hwai Horng
• Linda Alila
• UNC
• Steve Marron
• Everett Zhou
• Xuxin Liu

• Univ of Pennsylvania
• Steve Albelda
• Anil Vachani
• Charles Powell (Columbia)
• Patrick Gaffney (U. of Minn)
• Bhuvanesh Singh (MSKCC)
• Matt Myerson (Harvard)
• Ruth Muschel (Penn)