A comparison of Multiple Biomarker Selection Algorithms for Early Screening of Ovarian Cancer

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1 A comparison of Multiple Biomarker Selection Algorithms for Early Screening of Ovarian Cancer Yu-Seop Kim 1,3, Jong-Dae Kim 1,3, Min-Ki Jang 2,3, Chan-Young Park 1,3, and Hye-Jung Song 1,3 1 Dept. of Ubiquitous Computing, Hallym University, 1 Hallymdaehak-gil, Chuncheon, Gangwon-do, Korea { yskim01, kimjd cypark, hjsong}@hallym.ac.kr 2 Dept of Computer Engineering, Hallym University, 1 Hallymdaehak-gil, Chuncheon, Gangwon-do, Korea percussive@gmail.com 3 Bio-IT Research Center, Hallym University, 1 Hallymdaehak-gil, Chuncheon, Gangwon-do, Korea Abstract. This paper demonstrates and compares the classification performance from cancer to normal under Luminex exposed environment of the optimal biomarker combinations that can diagnose ovarian cancer. The optimal combinations having high resolutions were determined from T-Test, Genetic Algorithm, and Random Forest. Each selected combinations sensitivity, specificity, and accuracy were compared by Linear Discriminant Analysis (LDA), k-nearest Neighbor (k-nn), and Logistic Regression. The 21 biomarker data used in this experiment was obtained through Luminex-PRA from the serum of 65 patients (caner 27, normal 38) of two hospitals. In this study, the results showed the selecting 2 to 3 marker with Genetic Algorithm and categorizing themed with LDA, it shows the closet sensitivity, specificity, and accuracy similar to that of the results obtained through complete enumeration of the combination of 2-4 markers. Keywords: Biomarker, Ovarian Cancer, Marker, T-Test, Genetic Algorithm, Random Forest, LDA, Logistic Regression 1 Introduction Ovarian cancer is a malignant tumor frequently arising in the age between Early diagnosis is associated with a 92% 5-year survival rate, yet only 19% of ovarian cancers are detected early [1]. Therefore, early detection of ovarian cancer has greate promise to improve clinical outcome. It is evident that the development of a biomarker for early detection of the ovarian cancer has become paramount [2]. Biomarker consists of molecular information based on the pattern of a single or multiple molecules originating from DNA, metabolite, or protein. Biomarkers are indicators that can detect the physical change of an organism due to the genetic after required genetic change. Along with the completion of the genome project, various biomarkers are being developed, providing critical clues for cancers and senile disorders. 162

2 Fig. 1. Changes in gynecologic cancer causes in Korea ( ) The early stages of research focused on a single biomarker for cancer diagnosis. Recent researches focus on combining multiple biomarkers to diagnose cancer more efficiently. Researches tend to focus especially on improving the sensitivity and specificity in order to increase the accuracy of the diagnosis, and the commercialization of multi-biomarkers seems to be close at hand. However, a new technology to find the right biomarker combinations is required, since the sensitivity and quantity has not yet reached a satisfactory level [3]. In this research, the mass value of the biomarkers was obtained using Luminex [4]. Luminex follows the panel reactive antibody (PRA), a solid phase-based method of Luminex corp. Luminex-PRA reacts the human leucocyte antigen (HLA) marker attached Luminex-beads to the HLA antibodies in the serum. Then detects the florescence of the antibodies from the beads by utilizing a exclusive equipment and program [5]. This paper determines the optimal marker combinations for ovarian cancer diagnosis from the combinations selected with T-Test [6], Genetic Algorithm [7], and Random Forest[8] from all the possible combinations of the 21 markers, based on their the florescence data measured. Linear Discriminant Analysis[6], k-nearest Neighbor[7], and Logistic Regression[6] was used to evaluate the sensitivity, specificity, and classification accuracy of the optimal combinations. The research aims to determine the optimal marker combination and categorization algorithm by comparing the experimental results with all the possible combinations. Methods for the collection of data are illustrated in chapter 2, and the experimental details are demonstrated in chapter 3. The results of the marker combinations and its classification performance are discussed in chapter 4, and chapter 5 presents the conclusion and possible future researches. 163

3 2. Data collection For the experiment, the serum of 65 Koreans (27 Cancer, 38 Normal) provided from two hospitals was used. These samples were reacted with Luminex-beads attached with 21 biomarkers, and the florescence from the antibodies on the beads was measured. In order to equalize the range of the biomarker florescence, the florescence values of each biomarker were normalized to 0-1 based on their maximum and minimum values. The selected 21 biomarkers are the most commonly discussed markers in the modern cancer research [5], [6], [9]. These biomarkers are usually provided by UPCI Luminex Cor Facility [10]. 3. Methods This paper conducts two experiments: (1) determination of biomarkers with T-Test, Genetic Algorithm(GA), and Random Forest(RF), and (2) performance comparison of the selected markers using Linear Discriminant Analysis(LDA), k-nearest Neighbor(k-NN), and Logistic Regression(LR). The random tree creation for the RF was 50, the k for k-nn was 3, and the score threshold for the classification of LR was 0.5. The combination of the biomarkers consisted of 2-4 markers, and leave-one-out cross validation was conducted for the evaluation. The algorithms for the marker combination and combinations of evaluation algorithms used in the experiment are shown in fig. 2. As a control, the optimal marker combination gained through a complete enumeration survey of 2-4 markers was used. Fig. 2. The algorithms for the marker combination and combinations of evaluation algorithms 164

4 4. Results The experiment compares the difference in performance of the selected 2-4 multibiomarkers by T-Test, GA, and RF to that of the optimal combination amongst the total possible combinations of the markers. The sensitivity, specificity, and accuracy of each optimal combination for both cancer and benign group was measured and compared with LDA, k-nn, and LR. The markers that ought to be combined was limited to four, because of the high cost to combine of more than 4 markers will make it difficult to be realize and commercialize the use of multi-biomarkers. Also to avoid the infringement of patent, the names of the markers are concealed. 4.1 Optimum combination results according to classification algorithm Table 1 shows the optimal marker combination and their performance when applying LDA, k-nn, and LR to the combine the 2-4 markers. The best accuracy of 90.8% was seen in the 4-marker combinations when applying k-nn. Table 1. Performance test of the classification algorithm through all the possible marker combinations of 2-4 markers and the formation of the optimal combination Classification Algorithm Marker 1 Marker 2 Marker 3 Marker 4 Sensitivity Specificity Accuracy LDA k-nn LR M7 M M1 M10 M M1 M5 M10 M M1 M10 M14 M M7 M M11 M M15 M M7 M14 M M6 M7 M14 M M7 M M17 M M4 M15 M M7 M15 M M9 M10 M M6 M10 M15 M M6 M10 M15 M M8 M9 M10 M M9 M10 M15 M M10 M11 M15 M

5 Table 2 compares accuracy of the selected markers through T-Test with all the combinations shown in Table 1. The 3-marker combination derived by Genetic Algorithm showed identical results with the optimal marker combination shown in Table 1. By comparing the rank with the optimum marker combination, all the algorithms except for LDA showed a similar level to that of the experiment in Table 2. This indicates that the most frequent marker combination is not sufficient enough to reproduce the maximum categorization accuracy. It also shows 13.9% point difference for the categorization accuracy. The four markers shown in Table 4 were selected with Random Forest, which includes the first, second, and fourth frequent markers shown in Table 2. Also the marker combinations differed greatly from the optimal marker combinations. The accuracy was up to 9.3% point low. Table 2. Classification performance comparison of the marker combinations obtained through T-Test Rank Accuracy Of Difference Classification Marker Marker Marker Marker Algorithm Sens. Spec. Accu. Optimum with Marker the Optimum Combination marker Combination M9 M LDA M9 M15 M M9 M15 M16 M M9 M k-nn M9 M15 M M9 M15 M16 M M9 M LR M9 M15 M M9 M15 M16 M Table 3. Classification performance comparison of the marker combinations obtained through Genetic Algorithm Rank Accuracy Of Difference Classification Marker Marker Marker Marker Algorithm Sens. Spec. Accu. Optimum with Marker the Optimum Combination marker Combination M10 M LDA M10 M1 M M15 M16 M13 M M10 M k-nn M10 M1 M M15 M16 M13 M

6 LR M10 M M10 M1 M M15 M16 M13 M Table 4. Classification performance comparison of the marker combinations obtained through Random Forest Rank Accuracy of Difference Classification Marker Marker Marker Marker Algorithm Sens. Spec. Accu. Optimum with the Marker Optimum Combination marker Combination M11 M LDA k-nn LR M7 M19 M M9 M19 M15 M M11 M M7 M19 M M9 M19 M15 M M11 M M7 M19 M M9 M19 M15 M Dot Plot and ROC curve for optimum marker combination As illustrated in Fig. 3, the cancer and benign are not distinguishable in one dimension. The results were easier to analyze when it was projected in two dimensions using a marker combination. The ROC curve of Fig. 4 demonstrates the aforementioned statement. Fig. 3. Dot Plot of individual markers in optimum marker combination 167

7 Fig. 4. ROC curve of individual markers. 5. Conclusion This paper searches for the biomarker combination that can easily distinguish the cancer to benign. Comparing the classification performance of ovarian cancer, selecting 2 or 3 markers through GA and classifying with LDA shows the most similar sensitivity, specificity, and accuracy to that of the marker combinations of 2-4 markers derived from the complete enumeration survey. However, except for the LDA 3-marker combination of GA, the marker combination of 2-4 markers obtained with the marker selection algorithm showed significantly low accuracy compared to the marker combinations obtained from the complete enumeration survey. In the future, we plan to develop an algorithm with better performance by optimizing the various parameters, fitness function, and generation function of the Genetic Algorithm. Also, by applying more various algorithms such as SVM(Support Vertor Machine) or ANN(Artificial Neural Network), we anticipate to discover an algorithm that will find a more similar marker combination of 2-4 markers to the combinations derived with complete enumeration survey. In addition, we will experiment on more various types of classification algorithms References 1. American Cancer Society, (2012) 168

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