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Genomic signatures to guide the use of chemotherapeutics
Authors: Anil Potti et. al
Presenter: Jong Cheol Jeong
Motivation
What will be happened if ineffective chemotherapy is used?
Increasing the probability of side effects
Decreasing the quality of life
Purpose
Developing gene expression signatures which predict responses to various cytotoxic chemotherapeutic drugs.
Giving us the direction for using cytotoxic agents which best matches the characteristics of the individual.
Outline
Method
Results
Conclusion
Method
NCI-60: composed with 60 cell line and the sensitivity to 5084 compounds
Sensitivity: exposing each cell line to each compound for 48hours, assessing the growth inhibition by sulforhodamine B
Method
Using the cell line in the NCI-60 Panel- 60 cancer cell line: sensitivity of 5084 compounds
1) Identifying cell line: most resistant or sensitive to docetaxel
2) Identifying genes: their expression correlated most highly with drug sensitivity
3) Bayesian binary regression analysis with LOOCV
Results
50GI 50ICor The concentration of compound requiring 50% growth inhibition
50LC The concentration of compound requiring 50% cytotoxic
Cell lines from NCI-60
Red : highest expressionBlue: lowest expression
Results
Validation of docetaxel response prediction model
30 lung and ovarian cancer cell lines
Significant correlation between predicted probability of docetaxel sensitive and IC50
29 lung cancer cell lines
Results
showing the capacity of the
predictor
Applying a Mann-Whitney
U-test
Results
Results(developing series of expression profile from NCI-60)
Results(developing series of expression profile from NCI-60)
Results(developing series of expression profile from NCI-60)
Results(developing series of expression profile from NCI-60)
Result (predicting response of combinations of drugs)
4 cytotoxic agents: paclitaxel, 5-FU, adriamycin, and cyclophosphamide
51 cell lines: 13 responders, 38 nonresponders
Individual chemosensitivity predictions
Result (predicting response of combinations of drugs)
Statistically significant distinction between the responders and nonresponders
Result (predicting response of combinations of drugs)
Breast cancer with 45 cell lines
38 responders11 nonresponders
Result (predicting response of combinations of drugs)
PPV: Positive Predicted ValueNPV: Negative Predicted Value
Blue: sensitiveRed: resistant
FAC adjuvant chemotherapy
Kaplan-Meier survival analysis
Result (patterns of predicted chemotherapy response)
Respond to 5-FU are resistant to Adriamycin and Docetaxel:suggesting possibility of alternate treatments
Step1. Chemotherapy response predictors calculates the likelihood of sensitivity to the seven agents in a large collection of samplesEx) breast, lung, and ovarian tumor
Step2. Clustering the samples according to patterns of predicted sensitivity to the various chemotherapeutics and plotted a heatmap
Red: high probability of sensitivity of responseBlue: low probability of resistance
Result (linking chemotherapy sensitivity to oncogenic pathway status)
Someone who initially responds to a given agent is likely to eventually suffer a relapse; therefore the development of gene expression signatures that reflect the activation of several oncogenic pathways are needed
Step1: stratifying the NCI cell lines based on predicted docetaxel response
Step2: examining the patterns of pathway deregulation associated with docetaxel sensitivity or resistance
Result (linking chemotherapy sensitivity to oncogenic pathway status)
Significant relationship between phosphatidylinositol 3-OH (PI3)-kinase pathway deregulation and docetaxel resistance. - Giving an opportunity to use a PI3-kinase inhibitor in this group
Red: high probability of sensitivity of response or activationBlue: low probability of resistance or deregulation
17 lung cancer cell lines
Conclusion
The signature of chemosensitivity generated from the NCI-60 panel have the capacity to predict therapeutic response in individuals receiving either single agent or combination chemotherapy
References
Staunton, et. Al. “Chemosensitivity prediction by transcriptional profiling”, PNAS, 98-19, 10787-10792, 2001
Potti, A. “Genomic signatures to guide the use of chemotherapeutics”, Nature Medicine, 12-11, 2006