Abstract LB548: Genomic analysis of antineoplastic drug-related genetic variations based on large-scale population sequencing

Abstract

Abstract Purpose: Cancer has become the second leading cause of death in the world. In 2020, there were 19.29 million new cancer patients and a total of 10 million deaths. Drug are the most popular ways to control the development of cancer, but the effectiveness of anti-tumor drugs is only about 25%. Drug efficacy is affected by many factors, among which the most critical is genetic variants. Genomic analysis based on large-scale population sequencing was performed to calculate the mutation frequency of antineoplastic drug-related genetic variations and systematically analyze general descriptions and population differences. Methods: Information of antineoplastic drugs is available from FDA and CFDA, and gene list comes from DrugBank, PharmGKB, FDA biomarker list and clinical guidelines. The maximum likelihood method was used to estimate the population mutation frequency of a single locus, and the calculation process was completed on Ali Cloud platform. GATK, ANNOVAR, PROVEAN, SIFT and other software were used for quality control, annotation and function prediction of mutations. Perl, Python, R and other computer languages were used for statistical analysis. Results: we collect 206,604 samples from mainland China for analysis, and identified 104 antineoplastic drugs and 517 drug-related genes at the same time. There were 201,774 genetic variations, most of which were intron mutations, exon mutations account for about 1% of all mutations. We found 41,955 novel mutations, which unrecorded in dbSNP, accounting for about 20.8%. Most of these mutations were rare mutations with frequencies less than 1%, subsequent prediction of mutation function also indicated that a large proportion of these mutations were deleterious. These results suggest that rare mutations may also play an important role in the use of antineoplastic drugs, which has previously been underestimated. The mutation frequency distribution of different types of genes suggests that transporters have a greater impact on personalized medicine during the use of antineoplastic drugs in China. By analyzing the genetic variation of important metabolic enzymes, we found that mutations such as UGT1A1*6, DPYD*9A, CYP2D6*2 and *10 have a high frequency and high detection probability in the Chinese population. Therefore, when patients use drugs metabolized by related metabolic enzymes, genetic testing should be done in advance to prevent the occurrence of adverse reactions. Regional analysis revealed that the overall differences among provinces in China were small, but the differences were large compared with other ethnic groups. Conclusion: Results of large-scale population sequencing reveal distribution and discipline of genetic variation of antineoplastic drug-related genes in Chinese population, further confirm the importance of precision medicine in cancer, and illustrate that the use of antineoplastic drugs can also be affected by ethnic differences. Citation Format: Yan Zhan, Ji-Ye Yin. Genomic analysis of antineoplastic drug-related genetic variations based on large-scale population sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB548.