癌症改变的临床相关性:实体肿瘤分子匹配治疗选择的知识库。

IF 1.1 Q4 MEDICINE, RESEARCH & EXPERIMENTAL Sovremennye Tehnologii v Medicine Pub Date : 2022-01-01 DOI:10.17691/stm2022.14.6.02
A A Lebedeva, A I Kavun, E M Veselovsky, V A Mileyko, M V Ivanov
{"title":"癌症改变的临床相关性:实体肿瘤分子匹配治疗选择的知识库。","authors":"A A Lebedeva,&nbsp;A I Kavun,&nbsp;E M Veselovsky,&nbsp;V A Mileyko,&nbsp;M V Ivanov","doi":"10.17691/stm2022.14.6.02","DOIUrl":null,"url":null,"abstract":"<p><p>Multigene testing using NGS (next-generation sequencing) provides a large amount of information and can detect multiple molecular alterations. Subsequent clinical interpretation is a time-consuming process necessary to select a treatment strategy. Existing databases often contain inconsistent information and are not regularly updated. The use of ESCAT levels of evidence requires a deep understanding of the nature of alterations and does not answer the question of which therapy option to select when multiple biomarkers with the same level of evidence are detected. To address these issues, we created the Clinical Relevance of Alterations in Cancer (CRAC) database on the relevance of detected alterations in specific genes, which are often analyzed as part of NGS panels. The team of oncologists and biologists assigned a CRAC score from 1 to 10 to each biomarker (a type of genomic alteration characteristic of specific genes) for 15 malignancies; an average score was entered into the database. CRAC scores are a numerical reflection of the following factors: therapy availability and the prospects of drug treatment with experimental drugs for patients with a particular type of tumor. A total of 134 genes and 15 of the most common tumor types have been selected for CRAC. The biomarker-nosology associations with CRAC scores in the range of 1-3 are the most frequent (n=2719 out of 3495; 77.8%), the least frequent ones (n=52 out of 3495; 1.5%) are with the highest CRAC scores 9 and 10. To estimate the practical effectiveness of the CRAC database, 208 reports on comprehensive molecular profiling were retrospectively analyzed; the applicability of CRAC was compared with the ESCAT level of evidence system. The highest CRAC scores corresponded to the ESCAT maximum levels of evidence: the range of scores 8-10 corresponded to evidence levels I and II. No biomarker within the same level of evidence was represented by the same CRAC score; the largest range of CRAC scores was observed for biomarkers of levels evidence IIIA and IV - from 2 to 10 and from 1 to 9, respectively. The use of CRAC scores allowed to identify additional 95 alterations with CRAC scores of 1-5 in the studied patients. The developed database is available at: https://crac.oncoatlas.ru/.</p>","PeriodicalId":51886,"journal":{"name":"Sovremennye Tehnologii v Medicine","volume":null,"pages":null},"PeriodicalIF":1.1000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10171055/pdf/","citationCount":"0","resultStr":"{\"title\":\"CRAC (Clinical Relevance of Alterations in Cancer): a Knowledge Base for the Selection of Molecularly Matched Therapy for Solid Tumors.\",\"authors\":\"A A Lebedeva,&nbsp;A I Kavun,&nbsp;E M Veselovsky,&nbsp;V A Mileyko,&nbsp;M V Ivanov\",\"doi\":\"10.17691/stm2022.14.6.02\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Multigene testing using NGS (next-generation sequencing) provides a large amount of information and can detect multiple molecular alterations. Subsequent clinical interpretation is a time-consuming process necessary to select a treatment strategy. Existing databases often contain inconsistent information and are not regularly updated. The use of ESCAT levels of evidence requires a deep understanding of the nature of alterations and does not answer the question of which therapy option to select when multiple biomarkers with the same level of evidence are detected. To address these issues, we created the Clinical Relevance of Alterations in Cancer (CRAC) database on the relevance of detected alterations in specific genes, which are often analyzed as part of NGS panels. The team of oncologists and biologists assigned a CRAC score from 1 to 10 to each biomarker (a type of genomic alteration characteristic of specific genes) for 15 malignancies; an average score was entered into the database. CRAC scores are a numerical reflection of the following factors: therapy availability and the prospects of drug treatment with experimental drugs for patients with a particular type of tumor. A total of 134 genes and 15 of the most common tumor types have been selected for CRAC. The biomarker-nosology associations with CRAC scores in the range of 1-3 are the most frequent (n=2719 out of 3495; 77.8%), the least frequent ones (n=52 out of 3495; 1.5%) are with the highest CRAC scores 9 and 10. To estimate the practical effectiveness of the CRAC database, 208 reports on comprehensive molecular profiling were retrospectively analyzed; the applicability of CRAC was compared with the ESCAT level of evidence system. The highest CRAC scores corresponded to the ESCAT maximum levels of evidence: the range of scores 8-10 corresponded to evidence levels I and II. No biomarker within the same level of evidence was represented by the same CRAC score; the largest range of CRAC scores was observed for biomarkers of levels evidence IIIA and IV - from 2 to 10 and from 1 to 9, respectively. The use of CRAC scores allowed to identify additional 95 alterations with CRAC scores of 1-5 in the studied patients. The developed database is available at: https://crac.oncoatlas.ru/.</p>\",\"PeriodicalId\":51886,\"journal\":{\"name\":\"Sovremennye Tehnologii v Medicine\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10171055/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sovremennye Tehnologii v Medicine\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.17691/stm2022.14.6.02\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"MEDICINE, RESEARCH & EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sovremennye Tehnologii v Medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.17691/stm2022.14.6.02","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0

摘要

使用NGS(下一代测序)的多基因检测提供了大量的信息,可以检测多种分子改变。随后的临床解释是选择治疗策略所必需的一个耗时的过程。现有数据库通常包含不一致的信息,并且不定期更新。使用ESCAT证据水平需要对改变的性质有深刻的理解,并且不能回答当检测到具有相同证据水平的多种生物标志物时选择哪种治疗方案的问题。为了解决这些问题,我们创建了癌症变化的临床相关性(CRAC)数据库,该数据库涉及检测到的特定基因变化的相关性,这些变化通常作为NGS面板的一部分进行分析。肿瘤学家和生物学家团队为15种恶性肿瘤的每个生物标志物(特定基因的一种基因组改变特征)分配了从1到10的CRAC评分;平均分被输入数据库。CRAC评分是以下因素的数值反映:治疗的可用性和对特定类型肿瘤患者使用实验性药物治疗的前景。共有134个基因和15种最常见的肿瘤类型被选择用于CRAC。生物标志物-分类学与CRAC评分在1-3范围内的相关性是最常见的(n=2719 / 3495;77.8%),最不常见的(n=52 / 3495;1.5%)的学生的CRAC得分最高,分别为9分和10分。为了评估CRAC数据库的实际有效性,我们回顾性分析了208份关于综合分子谱分析的报告;比较了crc与ESCAT证据等级制度的适用性。最高的CRAC分数对应于ESCAT的最高证据水平:8-10分的范围对应于证据水平I和II。同一证据水平内的生物标志物不能用相同的CRAC评分来表示;证据IIIA和IV水平的生物标志物的CRAC评分范围最大,分别为2至10和1至9。在研究的患者中,CRAC评分的使用允许识别额外的95个改变,CRAC评分为1-5。开发的数据库可在https://crac.oncoatlas.ru/上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

摘要图片

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
CRAC (Clinical Relevance of Alterations in Cancer): a Knowledge Base for the Selection of Molecularly Matched Therapy for Solid Tumors.

Multigene testing using NGS (next-generation sequencing) provides a large amount of information and can detect multiple molecular alterations. Subsequent clinical interpretation is a time-consuming process necessary to select a treatment strategy. Existing databases often contain inconsistent information and are not regularly updated. The use of ESCAT levels of evidence requires a deep understanding of the nature of alterations and does not answer the question of which therapy option to select when multiple biomarkers with the same level of evidence are detected. To address these issues, we created the Clinical Relevance of Alterations in Cancer (CRAC) database on the relevance of detected alterations in specific genes, which are often analyzed as part of NGS panels. The team of oncologists and biologists assigned a CRAC score from 1 to 10 to each biomarker (a type of genomic alteration characteristic of specific genes) for 15 malignancies; an average score was entered into the database. CRAC scores are a numerical reflection of the following factors: therapy availability and the prospects of drug treatment with experimental drugs for patients with a particular type of tumor. A total of 134 genes and 15 of the most common tumor types have been selected for CRAC. The biomarker-nosology associations with CRAC scores in the range of 1-3 are the most frequent (n=2719 out of 3495; 77.8%), the least frequent ones (n=52 out of 3495; 1.5%) are with the highest CRAC scores 9 and 10. To estimate the practical effectiveness of the CRAC database, 208 reports on comprehensive molecular profiling were retrospectively analyzed; the applicability of CRAC was compared with the ESCAT level of evidence system. The highest CRAC scores corresponded to the ESCAT maximum levels of evidence: the range of scores 8-10 corresponded to evidence levels I and II. No biomarker within the same level of evidence was represented by the same CRAC score; the largest range of CRAC scores was observed for biomarkers of levels evidence IIIA and IV - from 2 to 10 and from 1 to 9, respectively. The use of CRAC scores allowed to identify additional 95 alterations with CRAC scores of 1-5 in the studied patients. The developed database is available at: https://crac.oncoatlas.ru/.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Sovremennye Tehnologii v Medicine
Sovremennye Tehnologii v Medicine MEDICINE, RESEARCH & EXPERIMENTAL-
CiteScore
1.80
自引率
0.00%
发文量
38
期刊最新文献
Sphingomyelins of Local Fat Depots and Blood Serum as Promising Biomarkers of Cardiovascular Diseases Neurogenetics of Brain Connectivity: Current Approaches to the Study (Review) Brain–Computer Interfaces with Intracortical Implants for Motor and Communication Functions Compensation: Review of Recent Developments Evaluation of the Feasibility of Using Commercial Wound Coatings as a Carrier Matrix for Bacteriophages Performance of the Models Predicting In-Hospital Mortality in Patients with ST-Segment Elevation Myocardial Infarction with Predictors in Categorical and Continuous Forms
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1