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Updated: Aug 7, 2025
Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts
Published on: July 25, 2020
Real-world performance analysis of a novel computational method in the precision oncology of pediatric tumors
Barbara Vodicska1, Júlia Déri1, Dóra Tihanyi1
1Oncompass Medicine Hungary Kft, Retek Str. 34, Budapest, 1024, Hungary.
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.
Insights
Digital drug assignment (DDA) effectively prioritizes targeted treatments for pediatric cancers by analyzing molecular profiles. This computational method aids clinical decisions, improving precision oncology for high-risk children.
Area of Science:
- Oncology
- Genomics
- Computational Biology
Background:
- Extensive molecular profiling in pediatric tumors presents challenges due to numerous genetic alterations of unknown significance.
- Lack of standardized decision support hinders personalized treatment strategies for pediatric cancer patients.
- Digital drug assignment (DDA) offers a novel computational approach to prioritize treatments by linking genetic drivers, targets, and therapies.
Purpose of the Study:
- To evaluate the utility and effectiveness of the Digital Drug Assignment (DDA) algorithm in pediatric oncology.
- To assess DDA's ability to support personalized treatment decisions for high-risk pediatric cancer patients.
Main Methods:
- 103 high-risk pediatric cancer patients (age < 21) were enrolled in a precision oncology program.
- Whole-exome sequencing (WES) or targeted panel sequencing was performed on 100 patient samples.
- A software system utilizing the DDA algorithm processed molecular data for therapeutic decision support, followed by Molecular Tumor Board (MTB) review.
Main Results:
- DDA identified actionable targeted treatment options in 72% of cases, with potential drug resistance noted in 57%.
- Actionability increased to 88% by 2020 due to continuous knowledgebase updates.
- MTBs approved DDA-recommended treatments in 78% of actionable cases, with approved therapies showing higher aggregated evidence levels.
Conclusions:
- DDA shows promise in addressing challenges of interpreting extensive molecular profiling for pediatric cancers.
- Continuous knowledgebase updates allow DDA to function as a 'virtual' panel, maximizing precision treatment planning.
- DDA facilitates automated interpretation of complex genomic data, enhancing routine care for high-risk pediatric cancers.
