{"title":"健康信念对乳房x光筛查行为采用的影响:路径分析模型","authors":"M. Gordin, H. Philip","doi":"10.37421/JIO.2020.S1.014","DOIUrl":null,"url":null,"abstract":"To study the T-cell repertoire during tumor progression, we followed 10 female mice of a transgenic mouse strain that expresses the un-activated rat neu (Erbb2) oncogene, along with 5 control mice. These mice develop mammary tumors spontaneously over 5-8 months. To quantify the peripheral T cell repertoire, we extracted T cells from blood, every month, over the period of 9 months. Cells from these samples were sorted and later processed through a cDNA TCR С and С library preparation protocol using single-molecule barcoding and then NGS sequenced. We were able to use the repertoire to classify tumor and non-tumor mice, using their immunological repertoire. Using feature selection algorithms, we were able to provide superior classification using a small subset (3 to 6 clones) of the T cell repertoire. Thus, machine learning and feature selection allowed us to reduce the hundreds of thousands of TCR alpha and beta sequences obtained during repertoire sequencing, to a set of six clones, with which we can identify the source of a blood sample as tumor or control. We can further stratify older transgenic mice (older than 5 months) and those of older control mice, using the same small T cell clones’ subset. This latter classification has been obtained with as little as three T cell clones.","PeriodicalId":16252,"journal":{"name":"Journal of Integrative Oncology","volume":"67 1","pages":"14-14"},"PeriodicalIF":0.0000,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Health Beliefs on the Behavioral Adoption of Mammography Screening: A Path Analytic Model\",\"authors\":\"M. Gordin, H. Philip\",\"doi\":\"10.37421/JIO.2020.S1.014\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To study the T-cell repertoire during tumor progression, we followed 10 female mice of a transgenic mouse strain that expresses the un-activated rat neu (Erbb2) oncogene, along with 5 control mice. These mice develop mammary tumors spontaneously over 5-8 months. To quantify the peripheral T cell repertoire, we extracted T cells from blood, every month, over the period of 9 months. Cells from these samples were sorted and later processed through a cDNA TCR С and С library preparation protocol using single-molecule barcoding and then NGS sequenced. We were able to use the repertoire to classify tumor and non-tumor mice, using their immunological repertoire. Using feature selection algorithms, we were able to provide superior classification using a small subset (3 to 6 clones) of the T cell repertoire. Thus, machine learning and feature selection allowed us to reduce the hundreds of thousands of TCR alpha and beta sequences obtained during repertoire sequencing, to a set of six clones, with which we can identify the source of a blood sample as tumor or control. We can further stratify older transgenic mice (older than 5 months) and those of older control mice, using the same small T cell clones’ subset. This latter classification has been obtained with as little as three T cell clones.\",\"PeriodicalId\":16252,\"journal\":{\"name\":\"Journal of Integrative Oncology\",\"volume\":\"67 1\",\"pages\":\"14-14\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Integrative Oncology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.37421/JIO.2020.S1.014\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Integrative Oncology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37421/JIO.2020.S1.014","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Health Beliefs on the Behavioral Adoption of Mammography Screening: A Path Analytic Model
To study the T-cell repertoire during tumor progression, we followed 10 female mice of a transgenic mouse strain that expresses the un-activated rat neu (Erbb2) oncogene, along with 5 control mice. These mice develop mammary tumors spontaneously over 5-8 months. To quantify the peripheral T cell repertoire, we extracted T cells from blood, every month, over the period of 9 months. Cells from these samples were sorted and later processed through a cDNA TCR С and С library preparation protocol using single-molecule barcoding and then NGS sequenced. We were able to use the repertoire to classify tumor and non-tumor mice, using their immunological repertoire. Using feature selection algorithms, we were able to provide superior classification using a small subset (3 to 6 clones) of the T cell repertoire. Thus, machine learning and feature selection allowed us to reduce the hundreds of thousands of TCR alpha and beta sequences obtained during repertoire sequencing, to a set of six clones, with which we can identify the source of a blood sample as tumor or control. We can further stratify older transgenic mice (older than 5 months) and those of older control mice, using the same small T cell clones’ subset. This latter classification has been obtained with as little as three T cell clones.