1. Prediction of Optimal Cancer Drug therapies via SVM
Presented by: Xuwen Zhao

2. Overview Why we need this prediction Algorithms used
SVM (support vector machine)
RFE (recursive feature elimination)
3 different conditions to test for accuracies

3. Why we need this prediction?
Part of Precision medicine
Ideal form of this prediction
E.g. CML (chronic myelogenous leukemia)
Effectiveness in saving money and time
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4. Algorithms Applied Support Vector Machine (SVM) model Predictive model
Built with gene expression and drug sensitivity profiles
Database: NCI-60 panel of human cancer cell lines
Predictive model
Built for seven often used drugs in ovarian cancer treatment
Recursive Feature Elimination (RFE) selection
Optimally distinguish cells’ sensitivity towards drugs
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5. Algorithms Applied SVM-RFE
Works in recursive manner until minimal subset of features is identified
Minimal subset of features that essential to maintain optimal accuracy
The lowest ranked 100 probes will be removed prior to the next round of selection in the RFE process. The process proceeds recursively until 100 features (remain). Thereafter, one feature is removed at each round of selection until a set of optimally predictive features (probes) is established. The process ends when a minimum of 10 optimally predictive features (probes) is identified.

6. A minimal of 10 optimally features obtain
Highest Rank
Highest Rank
100 FEATURES REMIAN
Repeat until
Lowest 100 removed
Lowest Rank Probe
Removed
Lowest Rank
A minimal of 10 optimally features obtain

7. Test for Accuracies under 3 Different Conditions
Condition1: Across Variety of cancer types
Result: Improves predictive accuracy
Condition2: Averaging microarray probe set expression
Result: Reduces predictive accuracy
Condtion3: Pre-filtering learning datasets with current biological understanding

8. Condition1: Across Variety of cancer types
Why?
Development of cancer progression is not only identified by tumor’s origin
Gene expression with particular cancer type may underline other cancer development in other types
Comparison between 2 SVM models
test on Carboplatin
18 Cells lines from NCI60

9. Condition1: Across Variety of cancer types
Randomly selected to be representative of all 9 cancer types
Only lung and melanoma
Test on Carboplatin
75%
Test on Carboplatin 87.5%
Fig1. Comparison of predictive accuracy (ROC curve) for two SVM models

10. Condition2: Averaging microarray probe set expression
Example: Affymetrix
Incorporate multiple probe sets per gene, thereby providing the possibility of monitoring differences in levels of alternative splicing and other post- transcriptional expression variants
Use breast cancer cell lines
Test on drug doxorubicin
Average Affymetrix V.S. original gene expression datasets
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11. Condition2: Averaging microarray probe set expression
Averaging expression value a
Original expression value c
Figure 2 (a, b, c). Comparison of 2 SVM models predictive accuracies towards drug doxorubicin

12. Condtion3: Pre-filtering learning datasets with current biological understanding
Data used: breast cancer cell lines
Filtering data with previously identified genes
Decreases diversity in data pool
297 genes previously implicated in cancer V.S. all significant expressed genes
Figure 3. ROC curves of two SVM models showing reduces predictive accuracy in pre-filtering dataset

13. Model Application on OC patients
Gene expression of 273 OC patients tumor from GEO repository
Predict response to 7 often used drugs
Example: Predictions of 2 individual OC patients a b
Figure 4 (a, b). Predictive response of two individual OC patients.

14. Model Application on OC patients
Drug
Predicted response rate
Observed literature response rate
Carboplatin
75%
Paclitaxel
56%
Docetaxel
36%
28%
Cisplatin
58%
60%
Gefitinib 2% 6%
Doxorubicin 5%
20%
Gemcitabine
47%
Table 1. Comparison between predicted response rates of 273 ovarian cancer patients to 7 chemotherapeutic drugs and response rates as reported in the literature.

15. Data selection SVM-RFE Limitation Diversity Observed data
Some Comments
Data selection
SVM-RFE
Limitation
Diversity
Observed data

16. Reference
Cai Huang, Roman Mezencev, John F. McDonald, Fredrik Vannberg. Open source machine-learning algorithms for the prediction of optimal cancer drug therapies. PLoS One Oct.26