Supervised Classification of Array CGH Data With HMM-Based Feature Selection

Abstract

Motivation: For different tumour types, extended knowledge about the molecular mechanisms involved in tumorigenesis is lacking. Looking for copy number variations (CNV) by Comparative Genomic Hybridization (CGH) can help however to determine key elements in this tumorigenesis. As genome-wide array CGH gives the opportunity to evaluate CNV at high resolution, this leads to huge amount of data, necessitating adequate mathematical methods to carefully select and interpret these data. Results: Two groups of patients differing in cancer subtype were defined in two publicly available array CGH data sets as well as in our own data set on ovarian cancer. Chromosomal regions characterizing each group of patients were gathered using recurrent hidden Markov Models (HMM). The differential regions were reduced to a subset of features for classification by integrating different univariate feature selection methods. Weighted Least Squares Support Vector Machines (LS-SVM), a supervised classification method which takes unbalancedness of data sets into account, resulted in leave-one-out or 10-fold cross-validation accuracies ranging from 88 to 95.5%. Conclusion: The combination of recurrent HMMs for the detection of copy number alterations with LS-SVM classifiers offers a novel methodological approach for classification based on copy number alterations. Additionally, this approach limits the chromosomal regions that are necessary to classify patients according to cancer subtype.

Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis

“…Similarly, copy number variation (CNV) is reported to be highly correlated with differential gene expression [90] , and can be ubiquitously detected in cancer cells. As such, CNVs can also be used to train ML models for cancer type classification in lung cancer studies [81] , [91] , [92] . Note that Daemen et al [92] proposed a recurrent hidden Markov model (HMM) for the identification of extended chromosomal regions of altered copy numbers, which offers high accuracy for classification.…”

“…As such, CNVs can also be used to train ML models for cancer type classification in lung cancer studies [81] , [91] , [92] . Note that Daemen et al [92] proposed a recurrent hidden Markov model (HMM) for the identification of extended chromosomal regions of altered copy numbers, which offers high accuracy for classification. More recently, Jurmeister et al [93] used DNA methylation profiles as input features to determine if the detected malignant nodule is primary lung cancer or the metastasis of another cancer.…”

“…Raman et al [81] designed a copy number profile abnormality (CPA) score to reinforce the CNV feature which is more robust and less subject to variable sample quality than directly using CNVs as the input feature. Daemen et al [92] integrated several statistical tests (ordinary fold changes, ordinary t -statistics, SAM-statistics, and moderated t -statistics) to select a robust differential expression gene set. Aside from these single-measured signatures, some studies [81] , [86] , [88] combined the -omics signatures with clinical signatures to achieve better results.…”