新型计算方法在儿科肿瘤精准肿瘤学中的真实世界性能分析。

Real-world performance analysis of a novel computational method in the precision oncology of pediatric tumors.

机构信息

Oncompass Medicine Hungary Kft, Retek Str. 34, Budapest, 1024, Hungary.

Division of Pediatric Hematology-Oncology, Department of Pediatrics, Faculty of Medicine, University of Debrecen, Debrecen, Hungary.

出版信息

World J Pediatr. 2023 Oct;19(10):992-1008. doi: 10.1007/s12519-023-00700-2. Epub 2023 Mar 13.

DOI:10.1007/s12519-023-00700-2

PMID:36914906

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10497647/

Abstract

BACKGROUND

The utility of routine extensive molecular profiling of pediatric tumors is a matter of debate due to the high number of genetic alterations of unknown significance or low evidence and the lack of standardized and personalized decision support methods. Digital drug assignment (DDA) is a novel computational method to prioritize treatment options by aggregating numerous evidence-based associations between multiple drivers, targets, and targeted agents. DDA has been validated to improve personalized treatment decisions based on the outcome data of adult patients treated in the SHIVA01 clinical trial. The aim of this study was to evaluate the utility of DDA in pediatric oncology.

METHODS

Between 2017 and 2020, 103 high-risk pediatric cancer patients (< 21 years) were involved in our precision oncology program, and samples from 100 patients were eligible for further analysis. Tissue or blood samples were analyzed by whole-exome (WES) or targeted panel sequencing and other molecular diagnostic modalities and processed by a software system using the DDA algorithm for therapeutic decision support. Finally, a molecular tumor board (MTB) evaluated the results to provide therapy recommendations.

RESULTS

Of the 100 cases with comprehensive molecular diagnostic data, 88 yielded WES and 12 panel sequencing results. DDA identified matching off-label targeted treatment options (actionability) in 72/100 cases (72%), while 57/100 (57%) showed potential drug resistance. Actionability reached 88% (29/33) by 2020 due to the continuous updates of the evidence database. MTB approved the clinical use of a DDA-top-listed treatment in 56 of 72 actionable cases (78%). The approved therapies had significantly higher aggregated evidence levels (AELs) than dismissed therapies. Filtering of WES results for targeted panels missed important mutations affecting therapy selection.

CONCLUSIONS

DDA is a promising approach to overcome challenges associated with the interpretation of extensive molecular profiling in the routine care of high-risk pediatric cancers. Knowledgebase updates enable automatic interpretation of a continuously expanding gene set, a "virtual" panel, filtered out from genome-wide analysis to always maximize the performance of precision treatment planning.

摘要

背景

由于大量遗传改变的意义不明或证据不足，以及缺乏标准化和个性化的决策支持方法，儿科肿瘤常规广泛分子谱分析的实用性存在争议。数字药物分配（DDA）是一种新的计算方法，通过聚合多个驱动因素、靶点和靶向药物之间的大量基于证据的关联，优先考虑治疗方案。DDA 已被验证可基于在 SHIVA01 临床试验中接受治疗的成年患者的结果数据改善个性化治疗决策。本研究旨在评估 DDA 在儿科肿瘤学中的应用。

方法

2017 年至 2020 年，103 名高危儿科癌症患者（<21 岁）参与了我们的精准肿瘤学计划，其中 100 名患者的样本适合进一步分析。组织或血液样本通过全外显子（WES）或靶向面板测序和其他分子诊断方式进行分析，并通过使用 DDA 算法进行治疗决策支持的软件系统进行处理。最后，分子肿瘤委员会（MTB）评估结果并提供治疗建议。

结果

在具有全面分子诊断数据的 100 例病例中，88 例获得 WES 和 12 例面板测序结果。DDA 在 100 例中的 72 例（72%）中确定了匹配的非标签靶向治疗选择（可操作性），而在 100 例中的 57 例（57%）中显示出潜在的药物耐药性。由于证据数据库的不断更新，2020 年可操作性达到 88%（29/33）。MTB 批准了 72 例可操作病例中的 56 例（78%）DDA 列为首选的治疗方案。批准的治疗方案的综合证据水平（AEL）明显高于被驳回的治疗方案。针对靶向面板对 WES 结果进行过滤会遗漏影响治疗选择的重要突变。