Maher Albitar MD, Andre Goy MD, Andrew Pecora MD, Deena Graham MD, Donna McNamara MD, Ahmad Charifa MD, Andrew IP MD, Wanlong Ma MS, Stanley Waintraub MD
{"title":"利用转录组数据开发乳腺癌生物标记物","authors":"Maher Albitar MD, Andre Goy MD, Andrew Pecora MD, Deena Graham MD, Donna McNamara MD, Ahmad Charifa MD, Andrew IP MD, Wanlong Ma MS, Stanley Waintraub MD","doi":"10.1002/imed.1051","DOIUrl":null,"url":null,"abstract":"<p>HER2 and hormone receptors are biomarkers for selecting breast cancer therapy and predicting outcomes. In the era of antibody-drug conjugates (ADC), a relatively low HER2 expression level is adequate for targeting tumor cells. We explored the potential of RNA profiling, determined by next generation sequencing (NGS), to provide more flexible clinical biomarkers as compared with immunohistochemistry (IHC) or fluorescent in situ hybridization (FISH). Data from 57 breast cancers was used to study biomarker levels as detected by routine clinical transcriptomic tests. HER2 (ERBB2), estrogen receptor alpha (ESR1), and androgen receptor (AR) mRNA levels were compared with IHC and FISH results. There was a significant overlap in the levels of ERBB2 mRNA between cases scored by IHC as zero, 1+, and 2+. This variation correlated with progression-free survival (PFS). Similarly, the ESR1 RNA accurately reflected estrogen receptor (ER) status. Patients with high AR mRNA had better PFS (<i>p</i> = 0.05). Patients expressing high ER and AR levels had better PFS than those expressing low ESR1 and AR (<i>p</i> = 0.03). These findings suggest that RNA analysis can be an alternative to IHC and FISH and provides continuous data that can better determine cut-off points for predicting response to ADC.</p>","PeriodicalId":73348,"journal":{"name":"Immunomedicine","volume":"4 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/imed.1051","citationCount":"0","resultStr":"{\"title\":\"The use of transcriptomic data in developing biomarkers in breast cancer\",\"authors\":\"Maher Albitar MD, Andre Goy MD, Andrew Pecora MD, Deena Graham MD, Donna McNamara MD, Ahmad Charifa MD, Andrew IP MD, Wanlong Ma MS, Stanley Waintraub MD\",\"doi\":\"10.1002/imed.1051\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>HER2 and hormone receptors are biomarkers for selecting breast cancer therapy and predicting outcomes. In the era of antibody-drug conjugates (ADC), a relatively low HER2 expression level is adequate for targeting tumor cells. We explored the potential of RNA profiling, determined by next generation sequencing (NGS), to provide more flexible clinical biomarkers as compared with immunohistochemistry (IHC) or fluorescent in situ hybridization (FISH). Data from 57 breast cancers was used to study biomarker levels as detected by routine clinical transcriptomic tests. HER2 (ERBB2), estrogen receptor alpha (ESR1), and androgen receptor (AR) mRNA levels were compared with IHC and FISH results. There was a significant overlap in the levels of ERBB2 mRNA between cases scored by IHC as zero, 1+, and 2+. This variation correlated with progression-free survival (PFS). Similarly, the ESR1 RNA accurately reflected estrogen receptor (ER) status. Patients with high AR mRNA had better PFS (<i>p</i> = 0.05). Patients expressing high ER and AR levels had better PFS than those expressing low ESR1 and AR (<i>p</i> = 0.03). These findings suggest that RNA analysis can be an alternative to IHC and FISH and provides continuous data that can better determine cut-off points for predicting response to ADC.</p>\",\"PeriodicalId\":73348,\"journal\":{\"name\":\"Immunomedicine\",\"volume\":\"4 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-12-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/imed.1051\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Immunomedicine\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/imed.1051\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Immunomedicine","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/imed.1051","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The use of transcriptomic data in developing biomarkers in breast cancer
HER2 and hormone receptors are biomarkers for selecting breast cancer therapy and predicting outcomes. In the era of antibody-drug conjugates (ADC), a relatively low HER2 expression level is adequate for targeting tumor cells. We explored the potential of RNA profiling, determined by next generation sequencing (NGS), to provide more flexible clinical biomarkers as compared with immunohistochemistry (IHC) or fluorescent in situ hybridization (FISH). Data from 57 breast cancers was used to study biomarker levels as detected by routine clinical transcriptomic tests. HER2 (ERBB2), estrogen receptor alpha (ESR1), and androgen receptor (AR) mRNA levels were compared with IHC and FISH results. There was a significant overlap in the levels of ERBB2 mRNA between cases scored by IHC as zero, 1+, and 2+. This variation correlated with progression-free survival (PFS). Similarly, the ESR1 RNA accurately reflected estrogen receptor (ER) status. Patients with high AR mRNA had better PFS (p = 0.05). Patients expressing high ER and AR levels had better PFS than those expressing low ESR1 and AR (p = 0.03). These findings suggest that RNA analysis can be an alternative to IHC and FISH and provides continuous data that can better determine cut-off points for predicting response to ADC.