PO.CL09.01 · 临床研究

精准肿瘤学实践:全面基因组分析在756例临床实体瘤中识别可干预改变并指导治疗选择

Precision oncology in practice: Comprehensive genomic profiling identifies actionable alterations and guides therapeutic selection in 756 clinical solid tumors

海报缩略图:精准肿瘤学实践:全面基因组分析在756例临床实体瘤中识别可干预改变并指导治疗选择
编号 5359 展板 27 时间 4/21 09:00–12:00 区域 Section 46 主讲 Sarabjot Pabla
分会场 Precision Oncology and Real World Data
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作者与单位 Authors & Affiliations

Sarabjot Pabla1, Sushant Khadgi2, Anjana N. Bhattacharya2, Asia Chang3, Mark Gardner2

1Quest Diagnostics, Baltimore, MD,2Quest Diagnostics, Secaucus, NJ,3Quest Diagnostics, San Franscisco, CA

摘要 Abstract

中文摘要
背景:实体瘤的全面基因组分析(CGP)正越来越多地用于指导靶向和免疫导向治疗;需要真实世界的性能数据以指导进一步推广应用。本研究报告了在756例临床FFPE样本(涵盖不同肿瘤类型)上运行TSO500(TruSight Oncology 500)实体瘤检测的多实验室经验,并评估了影响治疗决策的变异分层和生物标志物分布。 方法:在756例常规临床标本上进行TSO500检测,从FFPE标本中分离DNA/RNA。在两个中心使用NovaSeq600进行测序。测序后,使用Illumina TSO500分析流程分析NGS数据。从DNA中评估SNV、INDEL和拷贝数改变,而基因融合则使用肿瘤RNA进行评估和报告。变异按照AMP/ASCO/CAP分层进行分类。还报告了包括肿瘤突变负荷(TMB)和微卫星不稳定性(MSI)在内的其他生物标志物。通过变异分层分类评估临床决策影响。 结果:在756例临床报告的样本中,主要疾病类型包括肺癌(63.3%)、结直肠癌(11.51%)、实体肿瘤(9.26%)、胰腺癌(5.16%)、黑色素瘤(1.72%)和乳腺癌(1.19%)。在所有样本中报告的12,461个变异中,19.3%(2407/12461)经AMP变异分类归类。这些变异中50%(1216/2407)为可干预变异(AMP I/II级)。意义未明的III级变异占29.3%(705/2407),IV级/良性变异占20.2%(486/2407)。检测到的临床相关生物标志物包括32.8%的病例为高TMB(≥10 mut/Mb),3.3%的病例为MSI-high;9.8%(74/756)检测到基因融合。CGP结果指导了治疗选择(具有强临床意义的变异(如具有FDA批准疗法或纳入专业指南的变异)占30%的病例,具有潜在临床意义的变异(如有临床前数据、病例报告或正在进行的临床试验支持的变异)占所检测标本总数的58%。此外,60%的病例报告了意义未明的III级变异。 结论:在这项大型真实世界研究中,我们的CGP检测在涵盖多种实体瘤的近一半病例中识别出可干预的基因组改变。这些结果表明该高通量CGP检测可提供可扩展的分子改变检测以支持精准肿瘤学,实现每周数百个样本的报告和治疗选择。
查看英文原文 English abstract
Background: Comprehensive genomic profiling (CGP) of solid tumors is increasingly used to inform targeted and immune-directed therapies; real-world performance data are needed to guide further adoption. This study reports multi‑laboratory experience running the TSO500 (TruSight Oncology 500) solid tumor assay on 756 clinical FFPE samples across different tumor types and evaluates, variant tier and biomarker distribution that impacts treatment decisions. Methods: TSO500 was performed on 756 routine clinical specimens, where DNA/RNA were isolated from FFPE specimens. Sequencing was performed at two sites using NovaSeq600. Post sequencing, NGS data was analyzed using Illumina TSO500 analysis pipeline. SNVs, INDELs, Copy Number Alterations were estimated from DNA whereas gene fusions were estimated and reported using tumor RNA. Variants were classified as per AMP/ASCO/CAP tiers. Other biomarkers including tumor mutational burden (TMB) and microsatellite instability (MSI) were reported. Clinical decision impact was assessed by variant Tier classification. Results: Of the 756 clinically reported samples, major disease types included lung cancer (63.3%), colorectal cancer (11.51%), solid neoplasm (9.26%), pancreatic cancer (5.16%), melanoma (1.72%) and breast cancer (1.19%). of the 12,461 reported variants across all samples, 19.3% (2407/12461) were classified by AMP variant classification. 50% (1216/2407) of these variants were actionable variants (AMP Tier I/II). Tier III variants of unknown significance comprised 29.3% (705/2407) and Tier IV/benign 20.2% (486/2407). Clinically relevant biomarkers detected included high TMB (≥10 mut/Mb) in 32.8% and MSI‑high in 3.3% of cases; gene fusions were detected in 9.8% (74/756). CGP results informed treatment selection (Variants with strong clinical significance (e.g., those with FDA-approved therapies or included in professional guidelines) in 30% of cases and Variants with potential clinical significance (e.g., supported by preclinical data, case reports, or ongoing clinical trials in 58% of overall specimens tested. Additionally, Tier 3 variants of unknown significance were reported in 60% of cases. Conclusions: In this large real‑world study, our CGP assay delivered identified actionable genomic alterations in nearly half of cases across diverse solid tumors. These results demonstrate the high‑throughput CGP assay, provide scalable detection of molecular alterations to support precision oncology, enabling reporting and therapy selection of hundreds of samples per week.
利益披露 Disclosure
S. Pabla, Labcorp Employment. Quest Diagnostics Employment. S. Khadgi, Quest Diagnostics Employment. A. N. Bhattacharya, Quest Diagnostics Employment. Biofidelity Employment. A. Chang, Quest Diagnostics Employment. Freenome Employment. M. Gardner, Quest Diagnostics Employment.

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