Download Artificial Neural Network

Classification and diagnostic prediction
of cancers using gene expression
profiling and artificial neural networks
From Nature Medicine 7(6) 2001
By Javed Khan et al.
(Summarized by Marcílio Souto – ICMC/USPSão Carlos)
[email protected]
Abstract
• Small, round blue-cell tumors (SRBCTs)
• Four distinct categories hard to
discriminate
• cDNA microarray and Artificial Neural
Networks (ANNs)
• Tumor diagnosis and the identification of
candidate targets for therapy
2
The Problem
• SRBCTs of childhood
• Neuroblastoma (NB)
• Rhabdomyosarcoma (RMS)
• Non Hodgkin lymphoma (NHL)
• The Ewing family of tumors (EWS)
• All four distinctions have similar appearances in routine
histology
• Accurate diagnosis is essential
• In clinical practice
• Immunohistochemistry: the detection of protein
expression
• Reverse transcription-PCR: tumor-specific
translocation
• EWS-FLI1 in EWS and the PAX3-FKHR in ARMS
3
The Approach
• Gene-expression profiling using cDNA
microarrays
• A simultaneous analysis of multiple markers
• Multiple categorical distinctions
• Artificial neural networks (ANNs)
• Diagnosing myocardial infarcts
• Diagnosing arrhythmias from
electrocardiograms
• Interpreting radiographs
• Interpreting magnetic resonance images
4
The Experiment
• cDNA microarray with 6,567 genes
• 63 training examples
• Tumor biopsy material
• Cell lines
• Filtering for a minimal level of expression
• 2,308 genes
• PCA further reduced the dimensionality.
• 10 dominant PCA components were used. (63% of the
variance in the data matrix)
• Three-fold cross-validation
• 3,750 ANNs were constructed (average vote)
• No overfitting and zero classification error in the
training sample
5
Data Sets
Table for the
train
The number of train
samples for cancer I (EWS)
23
The number of train
samples for cancer II (RMS)
20
The number of train
samples for cancer III (NB)
Table I for the
test
The number of test
samples for cancer I
(EWS)
The number of test
samples for cancer II
(RMS)
6
12
The number of test
samples for cancer III
(NB)
6
The number of train
samples for cancer IV (BL)
8
The number of test
samples for cancer IV
(BL)
3
The number of unlabeled
samples
0
The number of unlabeled
samples (non-SRBCT)
5
Total number of samples for
train and validation
63
Total number of test
samples
5
25
6
The Schematic View of the
Analysis Process
7
Data Analysis
• Initial Cuts
• Principal Components Analysis
• Artificial Neural Network Prediction
• Extraction of Relevant Genes
8
Data Analysis: Initial Cuts and
PCA
• Initial Cuts
• Gene are omitted if for any of the samples the
red intensity (ri) is less than 20
• From 6567 to 2308 genes
• Principal Components Analysis (PCA)
• Reduce the dimensionality of data to 10
components – 2308 genes to 10 inputs inputs
• This number (10) was found by means of preexperiments
9
Data Analysis: Artificial Neural
Network (1/3)
• Architecture and Parameters
• Linear Perceptron (LP)
• 10 inputs representing the PCA components
• 4 output nodes – one for each class of tumor (EWS, BL, NB
and RMS)
• 44 free parameters, including four threshold units
• Calibration (training) was performed using JETNET
•
•
•
•
•
=0.7; momentum=0.3
Learning rate decreased after each epoch (0.99)
Initial weights randomly chosen from [-r,r] – r=0.1/F
Weights updated after every 10 epochs
At most 100 epochs
10
Data Analysis: Artificial Neural
Network (2/3)
• Calibration and Validation
• 3-fold cross-validation
• 63 labeled samples are randomly shuffled and split into
3 equally sized groups
• The network is trained with two of these groups and the
other used to validation
• This procedure is repeated 3 times
• The random shuffling is redone 1250 times
• 3750 networks
• For validation, the average of the result for the 1250
networks as output – committee
• For test samples, the committee is formed with all 3750
networks
• 25 samples in the test set
11
Data Analysis: Artificial Neural
Network (3/3)
• Assessing the quality of classifications
• Each sample is classified as belonging to the cancer type
corresponding to the largest average committee vote
• Rejection of second largest class or samples that do not
belong to any of the class
• Definition of a distance from a sample to the ideal vote for
each cancer type
• Based on the validation set, for each type of cancer an
empirical distribution of its distance is generated
• For a given test sample, the system can reject possible
classification based on these probability distributions
• OBS: the classification as well as the extraction of
important genes converges using less than 100 networks
• The only reason 3750 networks were used is to have
sufficient statistics for these empirical probability
distributions
12
Relevant Gene Extraction
• In order to select relevant genes, the authors proposed a
sensitivity measure (S) of the outputs (o) with respect to any
of the 2308 input variables, summed over the number of
samples and outputs
• All 3750 networks are involved
• They also proposed a measure related for a single output
• Thus, they can rank the genes according to their importance
for the total classification but also according to their
importance for the different disease separately
• They explored for 6, 12, 24, 48, 96, 192, 384, 768 and 1536
genes
• For each choice training (calibration) was redone
13
Summed Square Error Graph
14
Optimizations of Genes Utilized for
Classification
•
•
Using 3,750 trained models, rank all genes according to their
significance for the classification
Determine the classification error rate using increasing number of
these ranked genes
15
Recalibrating the ANNs
•
•
Using only 96 genes, the analysis process was repeated
Zero classification error
16
Diagnostic Classification
•
•
25 test examples (5 non-SRBCTs)
If a sample falls outside the 95th percentile of the probability
distribution of distances between samples and their ideal output,
its diagnosis is rejected
17
Multi-Dimensional Scaling (MDS)
• Using 96 genes
18
Hierarchical Clustering of 96 Genes
- 93 unique genes (3 IGF2 and 2 MYC)
- 13 ESTs
- 41 genes have not been reported as
associated with these diseases.
- Perfect clustering of four categories
19
Expression of FGFR4 on SRBCT
Tissue Array
• At the protein level, Immunohistochemistry on
SRBCT tissue arrays for the expression of
fibroblast growth factor receptor 4 (FGFR4)
• FGFR4
• Expressed during myogenesis (not in adult muscle)
• Potential role in tumor growth
• Prevention of terminal differentiation in muscle
• Strong cytoplasmic immunostaining for FGFR4
was seen in all 26 RMSs tested.
20
Discussion
• Current diagnoses of tumors rely on histology
(morpholgy) and immunohistochemistry
(protein expression)
• Using cDNA microarrays
• Multiple markers (robust)
• Reveal the underlying genetic aberrations or
biological processes
• Tumors and cell lines
• Cell lines for ANN calibration
21
Reference
• J. Khan et al. ”Classification and diagnostic prediction of
cancers using gene expression profiling and artificial
neural networks”, Nature Medicine, Vol. 7, Number 6, June
2001 and the references therein.
• Analysis Methods Supplement for Nature Medicine, Vol. 7,
Number 6, June 2001.
• http://medicine.nature.com
• M. Ringner, C. Peterson and J. Khan ”Analyzing array
data using supervised methods”, Pharmacogenomics,
vol. 3, Number 3, 2003.
• NIH News Release: Gene Chips Accurately Diagnose Four
Complex
Childhood
Cancers
Artificial Intelligence Used With Gene Expression
Microarrays for the First Time.
• http://www.nih.gov/news/pr/may2001/nhgri-30.htm
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