TY - GEN
T1 - Optimized prediction of extreme treatment outcomes in ovarian cancer
AU - Misganaw, Burook
AU - Ahsen, Eren
AU - Singh, Nitin
AU - Baggerly, Keith A.
AU - Unruh, Anna
AU - White, Michael A.
AU - Vidyasagar, M.
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/2/8
Y1 - 2015/2/8
N2 - The TCGA ovarian cancer database shows that about 10% of patients respond poorly to platinum-based chemotherapy, with tumors relapsing in seven months or less. At the other extreme, another 10% or so enjoy disease-free survival of three years or more [1]. At present there are more than a dozen prognostic signatures that claim to predict the survival prospects of a patient based on her genetic profile. Yet, according to [2], none of these signatures performs significantly better than pure guessing. Accordingly, in this paper the objective is to propose and validate another gene-based signature. TCGA ovarian cancer data is analyzed using the lone star algorithm [3] that is specifically developed for identifying a small number of highly predictive features from a very large set. Using this algorithm, we are able to identify a biomarker panel of 25 genes (out of 12,000) that can be used to classify patients into one of three groups: super-responders (SR), medium responders (MR), and non-responders (NR). We are also able to determine a discriminant function that can divide patients into two classes, such that there is a clear survival advantage of one group over the other. This signature is developed using the TCGA Agilent platform data, and cross-validated on the TCGA Affymetrix platform data, as well as entirely independent data due to Tothill et al. [4]. The P-value on the training data is below machine zero, while the P-values on cross-validation are well below the widely accepted threshold of 0.05.
AB - The TCGA ovarian cancer database shows that about 10% of patients respond poorly to platinum-based chemotherapy, with tumors relapsing in seven months or less. At the other extreme, another 10% or so enjoy disease-free survival of three years or more [1]. At present there are more than a dozen prognostic signatures that claim to predict the survival prospects of a patient based on her genetic profile. Yet, according to [2], none of these signatures performs significantly better than pure guessing. Accordingly, in this paper the objective is to propose and validate another gene-based signature. TCGA ovarian cancer data is analyzed using the lone star algorithm [3] that is specifically developed for identifying a small number of highly predictive features from a very large set. Using this algorithm, we are able to identify a biomarker panel of 25 genes (out of 12,000) that can be used to classify patients into one of three groups: super-responders (SR), medium responders (MR), and non-responders (NR). We are also able to determine a discriminant function that can divide patients into two classes, such that there is a clear survival advantage of one group over the other. This signature is developed using the TCGA Agilent platform data, and cross-validated on the TCGA Affymetrix platform data, as well as entirely independent data due to Tothill et al. [4]. The P-value on the training data is below machine zero, while the P-values on cross-validation are well below the widely accepted threshold of 0.05.
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U2 - 10.1109/CDC.2015.7402383
DO - 10.1109/CDC.2015.7402383
M3 - Conference contribution
AN - SCOPUS:84961989859
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 1254
EP - 1258
BT - 54rd IEEE Conference on Decision and Control,CDC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 54th IEEE Conference on Decision and Control, CDC 2015
Y2 - 15 December 2015 through 18 December 2015
ER -