genomic signatures to guide the use of chemotherapeutics authors: anil potti et. al presenter: jong...

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