Abstract
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.
Original language | English (US) |
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Title of host publication | Proceedings of the IEEE Conference on Decision and Control |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1254-1258 |
Number of pages | 5 |
Volume | 2016-February |
ISBN (Print) | 9781479978861 |
DOIs | |
State | Published - Feb 8 2016 |
Event | 54th IEEE Conference on Decision and Control, CDC 2015 - Osaka, Japan Duration: Dec 15 2015 → Dec 18 2015 |
Other
Other | 54th IEEE Conference on Decision and Control, CDC 2015 |
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Country | Japan |
City | Osaka |
Period | 12/15/15 → 12/18/15 |
ASJC Scopus subject areas
- Control and Systems Engineering
- Modeling and Simulation
- Control and Optimization