Sparse feature selection for classification and prediction of metastasis in endometrial cancer

Abstract

BackgroundMetastasis via pelvic and/or para-aortic lymph nodes is a major risk factor for endometrial cancer. Lymph-node resection ameliorates risk but is associated with significant co-morbidities. Incidence in patients with stage I disease is 4–22% but no mechanism exists to accurately predict it. Therefore, national guidelines for primary staging surgery include pelvic and para-aortic lymph node dissection for all patients whose tumor exceeds 2cm in diameter. We sought to identify a robust molecular signature that can accurately classify risk of lymph node metastasis in endometrial cancer patients. 86 tumors matched for age and race, and evenly distributed between lymph node-positive and lymph node-negative cases, were selected as a training cohort. Genomic micro-RNA expression was profiled for each sample to serve as the predictive feature matrix. An independent set of 28 tumor samples was collected and similarly characterized to serve as a test cohort.ResultsA feature selection algorithm was designed for applications where the number of samples is far smaller than the number of measured features per sample. A predictive miRNA expression signature was developed using this algorithm, which was then used to predict the metastatic status of the independent test cohort. A weighted classifier, using 18 micro-RNAs, achieved 100% accuracy on the training cohort. When applied to the testing cohort, the classifier correctly predicted 90% of node-positive cases, and 80% of node-negative cases (FDR = 6.25%).ConclusionResults indicate that the evaluation of the quantitative sparse-feature classifier proposed here in clinical trials may lead to significant improvement in the prediction of lymphatic metastases in endometrial cancer patients.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-017-3604-y) contains supplementary material, which is available to authorized users.

Breast and Colon Cancer Classification from Gene Expression Profiles Using Data Mining Techniques

“…As an alternative, computer-aided diagnosis (CAD) systems based on machine learning have been continually improving and are employed to support specialists in the determination of diagnosis decisions [3][4][5].Most current CAD systems for medical diagnosis depend on diverse information, such as medical laboratory tests (e.g., blood tests and magnetic resonance imaging (MRI)), medical indicators (finger tremors and lung signs or symptoms), and various types of digital images (such as X-rays and ultrasound images). However, physical medical examinations pose a risk of transmission of infection through tools and other channels, such as scratching of the skin while taking a blood sample [6][7][8]. X-rays are harmful because of the exposure of body cells to radiation.…”

“…Such a method can be used to diagnose cancer in the early stages, unlike other methods that use different kinds of image processing techniques. The challenges that arise in microarray classification are mainly centred on dimensionality and classification accuracy [6,7].Methodologies that depend on gene expression profiles have been able to detect cancer since their inception. In previous work, exhaustive efforts have been made to achieve the best results.…”

“…Such a method can be used to diagnose cancer in the early stages, unlike other methods that use different kinds of image processing techniques. The challenges that arise in microarray classification are mainly centred on dimensionality and classification accuracy [6,7].…”

Sparse Supervised Classification Methods Predict and Characterize Nanomaterial Exposures: Independent Markers of MWCNT Exposures

“…Most of these studies involving "-omics" or highcontent analysis used some variant of the SVM to identify the biomarkers. SVM-based methods are more robust and have the ability to identify a small subset of highly predictive markers (Maulik and Chakraborty 2014;Huang et al 2013;Bevilacqua et al 2012;Duan et al 2005;Guyon et al 2002;Misganaw et al 2015;Ahsen et al 2017). In this article, we selected and identified protein markers of exposed and/or early responses of a class of carbonaceous NPs, that is, multiwalled carbon nanotubes (MWCNTs).…”

“…Analysis of NPs with different physicochemical characteristics studied under various experimental conditions is critical for enabling discovery of convergent/common and divergent markers of exposure to NPs. A sparse classification algorithm that implements a combined l 1 -and l 2 -norm SVM ttest with recursive feature elimination (RFE), in short "lonestar" (Ahsen et al 2012(Ahsen et al , 2017Vidyasagar 2014), was applied to select optimal protein markers that can predict exposure or biological effects of MWCNT ( Figure 1). The sparse classification algorithm lone-star implements various optimization methods to overcome some of the issues inherent to nanotoxicity modeling studies, such as dealing with large number of variables, noisy/inseparable data sets, unequal distribution of sample sizes between classes, and unknown relationships between different experimental conditions.…”