Mountain Lakes High School

Abstract: Background DNA repair is a critical process to maintain DNA integrity. It is conducted by distinct pathways of genes, many of whose alterations are thought to result in genomic instability and hypermutability, ultimately contributing to tumorigenesis. Tumor Mutation Burden (TMB) and Microsatellite Instability (MSI) are considered as efficacy biomarkers for immunotherapy.1 2 However, there has been little characterization of the association between DNA repair genes and TMB/MSI in cancer. This study aims to further understand DNA repair genes and evaluate the contribution of their alteration to TMB and MSI. Methods We systematically analyzed 282 DNA repair genes involved in 20 DNA repair pathways. These genes were evaluated for mutations based on 274 sequenced colorectal tumor samples from the TCGA database. The functional impacts of these mutations were analyzed, and only damaging mutations were used for the subsequent analysis. The most frequently mutated genes were identified. The association between the damaging mutations and TMB/MSI status was calculated for each gene, and the significant genes were subject to further pathway enrichment analysis. We also compared the gene expression between TMB high and low as well as between MSI-H and MSI-L/MSS for each gene based on their RNAseq data. The potential associations with TMB/MSI high phenotypes were evaluated. Results 94 genes were identified to be significantly mutated in TMB high, including all of the 26 genes that were significant in MSI-H . The genes are enriched in multiple pathways, including Fanconi anemia, Base excision repair, and Mismatch repair. At the expression level, 28 genes are significantly downregulated in TMB high samples, while 35 genes in MSI-H, suggesting that the inactivation of these genes might be mediated by epigenetic abnormalities (figure 1). 10 genes, including POLE, were identified that are significantly mutated in TMB high samples as compared to MSI-H samples (table 1). Loss of function of these genes may result in an ultra-mutated phenotype. Contradicting the notion that POLE mutation is predominantly associated with MSS tumors and are mutually exclusive with the complete loss of MMR,4–6 we found about half of POLE-mutant samples (8/16) were MSI high, five of which had MMR mutations (figure 2). Conclusions The study investigated the association of DNA repair genes with TMB and MSI. We compared genes significantly altered in TMB high and MSI-H samples and identified genes pointing to a potential mechanism that induces ultra-mutation in a subset of cancer patients with intact MMR, which can serve as potential biomarkers for immunotherapy efficacy linked with high TMB. References Chan TA, Yarchoan M, Jaffee E, et al. Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic. Ann Oncol 2019; 30(1):44–56. Chang L, Chang M, Chang HM, Chang F. Microsatellite Instability: A Predictive Biomarker for Cancer Immunotherapy. Appl Immunohistochem Mol Morphol 2018;26(2):e15–e21. Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature 2012;487:330–337. Albertson TM, Ogawa M, Bugni JM, et al. DNA polymerase epsilon and delta proofreading suppress discrete mutator and cancer phenotypes in mice. Proc Natl Acad Sci U S A 2009;106:17101-17104. Church DN, Briggs SE, Palles C, et al. DNA polymerase epsilon and delta exonuclease domain mutations in endometrial cancer. Hum Mol Genet 2013;22:2820–2828. Valle L, Hernandez-Illan E, Bellido F, et al. New insights into POLE and POLD1 germline mutations in familial colorectal cancer and polyposis. Hum Mol Genet 2014;23:3506–3512.

Abstract: (1993). Graceful Configurations in the Plane. Mathematics Magazine: Vol. 66, No. 2, pp. 96-104.

Abstract: An imbalanced class problem occurs within abundant real-world applications, e.g., fraud detection, text classification, and cancer diagnosis. Beside balancing the imbalanced data distribution to deal with imbalanced data problems, another significant way to solve the bias-to-majority problem is via proper feature selection. This work is intended to use a feature selection method that can choose a subset of features and make ROC AUC and F-measure results in order to achieve high performance on a minority class. In this paper, a weighted Gini index(WGI) feature selection method is proposed. In order to evaluate the proposed method, a comparison result among Chi-square, F-statistic and Gini index feature selection is shown, and Xgboost is the classifier that is used to test the performance of the subset of features. Experimental results indicate that F-statistic contains the best performance when a few features are selected. However, when the number of selected features increases, WGI feature selection achieves the best results. A comparison between the average results from ROC AUC and F-measure are also presented. It shows that ROC AUC always contains a good performance, even if only a few features are selected, and only changes slightly as the subset of features expands. However, the performance of F-measure achieves a good performance after 60% of features are chosen. The results are helpful for practitioners to select a proper feature selection method when facing a practical problem.