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采用多模式综合基因组和免疫分析检测策略可实现高检测成功率,检测出临床相关生物标志物,同时优化组织使用。

Utilization of a Multi-modal Comprehensive Genomic and Immune Profiling Testing Strategy Results in a High Rate of Test Success and Detection of Clinically Relevant Biomarkers While Optimizing Tissue Usage.

作者信息

Green Michelle F, Wallen Zachary D, Ko Heidi C, Strickland Kyle C, Dillard Alicia, Conroy Jeffrey M, Dash Durga P, Nesline Mary K, DePietro Paul, Zhang Shengle, Saini Kamal S, Sathyan Pratheesh, Eisenberg Marcia, Caveney Brian, Ramkissoon Shakti, Severson Eric A, Previs Rebecca A

机构信息

Labcorp, Durham, NC, 27560, USA.

Department of Pathology, Duke University Medical Center, Duke Cancer Institute, Durham, NC, 27710, USA.

出版信息

Mol Diagn Ther. 2025 Jun 7. doi: 10.1007/s40291-025-00793-7.

Abstract

BACKGROUND

Molecular profiling is quickly becoming standard for patients with advanced cancer, with an increasing number of biomarker-directed therapies and innovative precision diagnostics available. However, with the expansion of relevant biomarkers, clinicians often face challenges obtaining optimal detection from limited tumor tissue. Here, we present biomarker detection rates from comprehensive genomic and immune profiling (CGIP) performed as a component of routine clinical care using a multi-modal testing strategy.

METHODS

CGIP was performed on 20,645 solid tumor specimens in a CAP/CLIA and NYS CLEP-certified reference laboratory, including DNA- and RNA-based next-generation sequencing (NGS), RNA gene expression profiling, and PD-L1 immunohistochemistry (IHC). RNA and DNA were co-extracted to optimize tissue usage. Clinical significance of detected biomarkers was classified in accordance with the joint consensus recommendations of the Association for Molecular Pathology (AMP), American Society of Clinical Oncology (ASCO), and the College of American Pathologists (CAP).

RESULTS

Adequacy of specimens for analysis with each test component varied from 99.8% (20,612) for PD-L1 IHC to 87.7% (18,113) for RNA-based NGS. DNA-based NGS had a > 96.0% success rate across all result components (short variants, copy number alterations, and genomic signatures), while RNA-based NGS and gene expression profiling were successful for 92.1% (16,689) and 90.2% (17,275) of cases, respectively. Median turnaround time from specimen receipt in the testing laboratory to report delivery was 8 days (range 1-35). Within our cohort of 15,815 cases with complete results available, 61.0% (9650) had at least one tier 1 biomarker with known clinical significance, 88.8% (14,039) had at least one tier 2 biomarker with potential clinical significance, 57.5% (9,090) had both tier 1 and 2 biomarkers, and 7.7% (1216) had no clinically significant biomarkers detected. Biomarker detection rates varied across tumor types, increasing with the addition of testing modalities.

CONCLUSIONS

Utilization of a multi-modal CGIP testing strategy resulted in a high rate of test success and detection of clinically relevant biomarkers while optimizing tissue usage.

摘要

背景

随着越来越多的生物标志物导向疗法和创新的精准诊断方法出现,分子谱分析正迅速成为晚期癌症患者的标准治疗手段。然而,随着相关生物标志物的不断扩展,临床医生常常面临从有限的肿瘤组织中获得最佳检测结果的挑战。在此,我们展示了使用多模态检测策略作为常规临床护理的一部分进行全面基因组和免疫谱分析(CGIP)时的生物标志物检测率。

方法

在一家获得CAP/CLIA和纽约州CLEP认证的参考实验室中,对20,645份实体瘤标本进行了CGIP检测,包括基于DNA和RNA的下一代测序(NGS)、RNA基因表达谱分析以及PD-L1免疫组织化学(IHC)检测。同时提取RNA和DNA以优化组织利用。根据分子病理学协会(AMP)、美国临床肿瘤学会(ASCO)和美国病理学家学会(CAP)的联合共识建议,对检测到的生物标志物的临床意义进行分类。

结果

各检测组件分析标本的充足率有所不同,从PD-L1 IHC的99.8%(20,612份)到基于RNA的NGS的87.7%(18,113份)。基于DNA的NGS在所有结果组件(短变异、拷贝数改变和基因组特征)中的成功率均超过96.0%,而基于RNA的NGS和基因表达谱分析的成功率分别为92.1%(16,689例)和90.2%(17,275例)。从标本送达检测实验室到报告交付的中位周转时间为8天(范围1 - 35天)。在我们15,815例有完整结果的队列中,61.0%(9650例)至少有一个具有已知临床意义的1级生物标志物,88.8%(14,039例)至少有一个具有潜在临床意义的2级生物标志物,57.5%(9,090例)同时具有1级和2级生物标志物,7.7%(1216例)未检测到具有临床意义的生物标志物。生物标志物检测率因肿瘤类型而异,并随着检测方式的增加而提高。

结论

采用多模态CGIP检测策略可实现较高的检测成功率,并能检测到临床相关生物标志物,同时优化组织利用。

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