Detection of Local DNA Copy Number Changes in Lung Cancer Population Analyses Using A Multi-Scale Approach

Discriminant Analysis of Lung Cancer Using Nonlinear Clustering of Copy Numbers

Background: Patient survival is not optimal for non-small cell lung cancer (NSCLC) patients, recurrence rate is high, and hence, early detection is crucial to increase the patient's survival. Gene-cancer mapping intends to discover associated genes with cancers and due to advances in high-throughput genotyping, screening for disease loci on a genome-wide scale is now possible. DNA copy numbers can potentially be used to identify cancer from normal cells in early detection of cancer.Methods: We use a nonlinear clustering method, so-called kernel K-means to separate cancer from normal samples. Kernel K-means is applied to the copy numbers obtained for each chromosome to cluster 63 paired cancer-blood samples (total of 126 samples) into two groups. Clustering performance is evaluated using true and false-positive rates, true and false-negative rates, and a nonlinear criterion, normalized mutual information (NMI).Results: Copy numbers of paired cancer-blood samples for 63 NSCLC patients are used in this study. Kernel K-means was applied to cluster 126 samples in two groups using copy numbers on each chromosome separately. The clustering results for 22 chromosomes are evaluated and discriminant power of them in identifying cancer is computed. We identified the top five and bottom five chromosomes based on their discriminant power.Conclusions: The results reveal high discriminant power of chromosomes 8, 5, 1, 3, and 19 for identifying cancer with the highest sensitivity of 75% yielded by chromosome 5. Bottom 5 chromosomes 9, 6, 4, 13, and 21 show low discriminant power with the accuracy of below 54% where true cancer and normal samples are grouped into substantially overlapping groups using copy numbers. This indicates the similarities of copy numbers obtained for cancer and normal samples on these chromosomes.

Identifying Similarities and Disparities Between DNA Copy Number Changes in Cancer and Matched Blood Samples

Background: Non-small cell lung cancer (NSCLC) is the first cause of cancer-related mortality for men and women in the United States. In spite of curative resection in early-stage, patient survival is not optimal and recurrence rate is high. Consequently, early detection and staging is essential to increase the patient's survival.Methods: Copy number (CN) changes in cancer populations have been broadly investigated to identify CN gains and deletions associated with cancer. In contrast, in this research, we quantify the similarities and disparities between cancer and paired peripheral blood samples using maximal information coefficient (MIC). We then detect the spatial locations with substantially high and the spatial locations with very low MICs in each chromosome. These locations can potentially help with early diagnosis, treatment, and prevention of cancer by identifying the similarities and disparities between cancer and healthy tissues.Results: Lung cancer data used in this project contains CN pairs for cancer and blood (non-involved) samples for 63 subjects. MIC was obtained to quantify the relation (linear or nonlinear) between cancer-blood pair samples for 63 subjects at each location for each chromosome. MIC values above a high threshold and MIC values below a low threshold were located. Among them top five (with lowest MIC's and with highest MIC's) were identified for each chromosome. For these identified locations, a high MIC score indicates high similarity between blood (non-involved) and cancer samples, while a low MIC score shows lack of similarity between the two samples.Conclusions: The results showed that a few chromosomes have a large number of MICs exceeding a high threshold. These locations can potentially be used to identify early indicators of NSCLC. In contrast, second group of chromosomes have several locations with small MICs which are potential candidates to develop biomarkers for discriminating cancer from the matched blood sample. Moreover, there is a third group of chromosomes with a large number of MICs exceeding a high threshold and a large set of MICs below a low threshold. These locations can help with both finding early indicators of cancer and developing biomarkers for discriminating cancer from non-involved tissue.