文献检索文档翻译深度研究
Suppr Zotero 插件Zotero 插件
邀请有礼套餐&价格历史记录

新学期,新优惠

限时优惠:9月1日-9月22日

30天高级会员仅需29元

1天体验卡首发特惠仅需5.99元

了解详情
不再提醒
插件&应用
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
高级版
套餐订阅购买积分包
AI 工具
文献检索文档翻译深度研究
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2025

免疫组织化学分析与 92 基因癌症分类器在转移性肿瘤原发灶诊断中的盲法比较研究。

Blinded comparator study of immunohistochemical analysis versus a 92-gene cancer classifier in the diagnosis of the primary site in metastatic tumors.

机构信息

Clarient Inc., Aliso Viejo, California, 92656, USA.

出版信息

J Mol Diagn. 2013 Mar;15(2):263-9. doi: 10.1016/j.jmoldx.2012.10.001. Epub 2012 Dec 31.


DOI:10.1016/j.jmoldx.2012.10.001
PMID:23287002
Abstract

Accurate tumor classification is fundamental to inform predictive biomarker testing and optimize therapy. Gene expression-based tests are proposed as diagnostic aids in cases with uncertain diagnoses. This study directly compared the diagnostic accuracy of IHC analysis versus molecular classification using a 92-gene RT-PCR assay for determination of the primary tumor site. This prospectively defined blinded study of diagnostically challenging cases included 131 high-grade, primarily metastatic tumors. Cases were reviewed and reference diagnoses established through clinical correlation. Blinded FFPE sections were evaluated by either IHC/morphology analysis or the 92-gene assay. The final analysis included 122 cases. The 92-gene assay demonstrated overall accuracy of 79% (95% CI, 71% to 85%) for tumor classification versus 69% (95% CI, 60% to 76%) for IHC/morphology analysis (P = 0.019). Mean IHC use was 7.9 stains per case (median, 8; range, 2 to 15). IHC/morphology analysis accuracy was 79%, 80%, and 46% when 1 to 6 (n = 42), 7 to 9 (n = 41), and >9 (n = 39) IHC stains were used, respectively, versus 81%, 85%, and 69%, respectively, with the 92-gene assay. Results from this blinded series of high-grade metastatic cases demonstrate superior accuracy with the 92-gene assay versus standard-of-care IHC analysis and strongly support the diagnostic utility of molecular classification in difficult-to-diagnose metastatic cancer.

摘要

准确的肿瘤分类对于告知预测性生物标志物检测和优化治疗至关重要。基于基因表达的测试被提议作为不确定诊断病例的诊断辅助工具。本研究直接比较了免疫组织化学分析与使用 92 基因 RT-PCR 测定法进行的分子分类在确定原发肿瘤部位方面的诊断准确性。这项前瞻性定义的、具有挑战性的诊断病例的盲法研究包括 131 例高级别、主要转移性肿瘤。通过临床相关性回顾和参考诊断来确定病例。盲法 FFPE 切片通过免疫组织化学/形态分析或 92 基因测定进行评估。最终分析包括 122 例病例。92 基因测定法对肿瘤分类的总体准确性为 79%(95%置信区间,71%至 85%),而免疫组织化学/形态分析为 69%(95%置信区间,60%至 76%)(P = 0.019)。平均每个病例使用的免疫组织化学染色为 7.9 种(中位数为 8;范围为 2 至 15)。当使用 1 至 6 种(n = 42)、7 至 9 种(n = 41)和> 9 种(n = 39)免疫组织化学染色时,免疫组织化学/形态分析的准确性分别为 79%、80%和 46%,而使用 92 基因测定法的准确性分别为 81%、85%和 69%。这项对高级别转移性病例的盲法系列研究结果表明,92 基因测定法比标准护理免疫组织化学分析具有更高的准确性,并强烈支持在难以诊断的转移性癌症中进行分子分类的诊断效用。

相似文献

[1]
Blinded comparator study of immunohistochemical analysis versus a 92-gene cancer classifier in the diagnosis of the primary site in metastatic tumors.

J Mol Diagn. 2012-12-31

[2]
Multisite validation study to determine performance characteristics of a 92-gene molecular cancer classifier.

Clin Cancer Res. 2012-5-30

[3]
Molecular diagnosis of Ewing sarcoma family of tumors: a comparative analysis of 560 cases with FISH and RT-PCR.

Diagn Mol Pathol. 2009-12

[4]
HER-2 gene amplification, HER-2 and epidermal growth factor receptor mRNA and protein expression, and lapatinib efficacy in women with metastatic breast cancer.

Clin Cancer Res. 2008-12-1

[5]
A multicenter study directly comparing the diagnostic accuracy of gene expression profiling and immunohistochemistry for primary site identification in metastatic tumors.

Am J Surg Pathol. 2013-7

[6]
Molecular profiling diagnosis in unknown primary cancer: accuracy and ability to complement standard pathology.

J Natl Cancer Inst. 2013-5-2

[7]
Molecular classification of cancer with the 92-gene assay in cytology and limited tissue samples.

Oncotarget. 2016-5-10

[8]
Gene expression of estrogen receptor, progesterone receptor and microtubule-associated protein Tau in high-risk early breast cancer: a quest for molecular predictors of treatment benefit in the context of a Hellenic Cooperative Oncology Group trial.

Breast Cancer Res Treat. 2009-7

[9]
Reverse transcriptase-polymerase chain reaction and immunohistochemical studies for detection of prostate stem cell antigen expression in prostate cancer: potential value in molecular staging of prostate cancer.

Int J Urol. 2007-7

[10]
Quantification of CK20 gene and protein expression in colorectal cancer by RT-PCR and immunohistochemistry reveals inter- and intratumour heterogeneity.

J Pathol. 2002-10

引用本文的文献

[1]
Clinical characteristics and survival analysis of cancer of unknown primary.

Oncol Lett. 2025-2-13

[2]
Tracing unknown tumor origins with a biological-pathway-based transformer model.

Cell Rep Methods. 2024-6-17

[3]
Clinical outcomes of patients diagnosed with cancer of unknown primary or malignancy of undefined primary origin who were referred to a regional cancer center.

Int J Clin Oncol. 2023-5

[4]
Predicting Mismatch-Repair Status in Rectal Cancer Using Multiparametric MRI-Based Radiomics Models: A Preliminary Study.

Biomed Res Int. 2022

[5]
Clinical validation of a 90-gene expression test for tumor tissue of origin diagnosis: a large-scale multicenter study of 1417 patients.

J Transl Med. 2022-3-7

[6]
Radiomics and Radiogenomics in Evaluation of Colorectal Cancer Liver Metastasis.

Front Oncol. 2022-1-7

[7]
Biomarker discovery studies for patient stratification using machine learning analysis of omics data: a scoping review.

BMJ Open. 2021-12-6

[8]
90-Gene Expression Profiling for Tissue Origin Diagnosis of Cancer of Unknown Primary.

Front Oncol. 2021-10-7

[9]
The need for validation of MI GPSai in patients with CUP: Comment on: "Machine learning analysis using 77,044 genomic and transcriptomic profiles to accurately predict tumor type" by J Abraham et al.

Transl Oncol. 2021-8

[10]
MicroRNA expression profiling with a droplet digital PCR assay enables molecular diagnosis and prognosis of cancers of unknown primary.

Mol Oncol. 2021-10

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

推荐工具

医学文档翻译智能文献检索