Abstract
Emerging targeted therapies have shown benefits such as less toxicity and higher effectiveness in specific types of cancer treatment; however, the accessibility of these advantages may rely on correct identification of suitable patients, which remains highly immature. We assume that copy number profiles, being accessible genomic data via microarray techniques, can provide useful information regarding drug response and shed light on personalized therapy. Based on the mechanism of action (MOA) of trastuzumab in the HER2 signaling pathway, a Bayesian network model in which copy number alterations (CNAs) serve as latent parents modifying signal transduction is applied. Two model parameters M-score and R -value which stand for the qualitative and quantitative effects of CNAs on drug effectiveness and are functions of conditional probabilities (CPs), are defined. An expectation-maximization (EM) algorithm is developed for estimating CPs, M-scores, and R-values from continuous measures, such as microarray data. We show through simulations that the EM algorithm can outperform classical threshold-based methods in the estimation of CPs and thereby provide improved performance for the detection of unfavorable CNAs. Several candidates of unfavorable CNAs to the trastuzumab therapy in breast cancer are provided in a real data example.
| Original language | English (US) |
|---|---|
| Article number | 6524018 |
| Pages (from-to) | 2933-2942 |
| Number of pages | 10 |
| Journal | IEEE Transactions on Biomedical Engineering |
| Volume | 60 |
| Issue number | 10 |
| DOIs | |
| State | Published - 2013 |
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Keywords
- Bayesian network
- copy number
- drug response
- expectation-maximization algorithm
- gene expression
ASJC Scopus subject areas
- Biomedical Engineering
Cite this
Estimating conditional probabilities for the detection of unfavorable copy number alterations in a targeted therapy. / Hsu, Fang Han; Dougherty, Edward R.; Chen, Yidong; Serpedin, Erchin.
In: IEEE Transactions on Biomedical Engineering, Vol. 60, No. 10, 6524018, 2013, p. 2933-2942.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Estimating conditional probabilities for the detection of unfavorable copy number alterations in a targeted therapy
AU - Hsu, Fang Han
AU - Dougherty, Edward R.
AU - Chen, Yidong
AU - Serpedin, Erchin
PY - 2013
Y1 - 2013
N2 - Emerging targeted therapies have shown benefits such as less toxicity and higher effectiveness in specific types of cancer treatment; however, the accessibility of these advantages may rely on correct identification of suitable patients, which remains highly immature. We assume that copy number profiles, being accessible genomic data via microarray techniques, can provide useful information regarding drug response and shed light on personalized therapy. Based on the mechanism of action (MOA) of trastuzumab in the HER2 signaling pathway, a Bayesian network model in which copy number alterations (CNAs) serve as latent parents modifying signal transduction is applied. Two model parameters M-score and R -value which stand for the qualitative and quantitative effects of CNAs on drug effectiveness and are functions of conditional probabilities (CPs), are defined. An expectation-maximization (EM) algorithm is developed for estimating CPs, M-scores, and R-values from continuous measures, such as microarray data. We show through simulations that the EM algorithm can outperform classical threshold-based methods in the estimation of CPs and thereby provide improved performance for the detection of unfavorable CNAs. Several candidates of unfavorable CNAs to the trastuzumab therapy in breast cancer are provided in a real data example.
AB - Emerging targeted therapies have shown benefits such as less toxicity and higher effectiveness in specific types of cancer treatment; however, the accessibility of these advantages may rely on correct identification of suitable patients, which remains highly immature. We assume that copy number profiles, being accessible genomic data via microarray techniques, can provide useful information regarding drug response and shed light on personalized therapy. Based on the mechanism of action (MOA) of trastuzumab in the HER2 signaling pathway, a Bayesian network model in which copy number alterations (CNAs) serve as latent parents modifying signal transduction is applied. Two model parameters M-score and R -value which stand for the qualitative and quantitative effects of CNAs on drug effectiveness and are functions of conditional probabilities (CPs), are defined. An expectation-maximization (EM) algorithm is developed for estimating CPs, M-scores, and R-values from continuous measures, such as microarray data. We show through simulations that the EM algorithm can outperform classical threshold-based methods in the estimation of CPs and thereby provide improved performance for the detection of unfavorable CNAs. Several candidates of unfavorable CNAs to the trastuzumab therapy in breast cancer are provided in a real data example.
KW - Bayesian network
KW - copy number
KW - drug response
KW - expectation-maximization algorithm
KW - gene expression
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UR - http://www.scopus.com/inward/citedby.url?scp=84884544802&partnerID=8YFLogxK
U2 - 10.1109/TBME.2013.2266356
DO - 10.1109/TBME.2013.2266356
M3 - Article
C2 - 23744663
AN - SCOPUS:84884544802
VL - 60
SP - 2933
EP - 2942
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
SN - 0018-9294
IS - 10
M1 - 6524018
ER -