1. 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 Kyu-Baek Hwang)

2. 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

3. 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

4. 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

5. 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

6. The Schematic View of the Analysis Process

7. Summed Square Error Graph

8. 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.

9. Recalibrating the ANNs Using only 96 genes, the analysis process was repeated. Zero classification error

10. Multi-Dimensional Scaling (MDS) Using 96 genes

11. Hierarchical Clustering of 96 Genes - 93 unique genes (3 IGF2 and two MYC) - 13 ESTs - 41 genes have not been reported as associated with these diseases. - Perfect clustering of four categories

12. Diagnostic Classification 25 test examples (5 non-SRBCTs) If a sample falls outside the 95 th percentile of the probability distribution of distances between samples and their ideal output, its diagnosis is rejected.

13. 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.

14. 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