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BackgroundThe 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.MethodsBetween 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.ResultsOf 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.ConclusionsDDA 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.

Precision oncology is currently based on pairing molecularly targeted agents (MTA) to predefined single driver genes or biomarkers. Each tumor harbors a combination of a large number of potential genetic alterations of multiple driver genes in a complex system that limits the potential of this approach. We have developed an artificial intelligence (AI)-assisted computational method, the digital drug-assignment (DDA) system, to prioritize potential MTAs for each cancer patient based on the complex individual molecular profile of their tumor. We analyzed the clinical benefit of the DDA system on the molecular and clinical outcome data of patients treated in the SHIVA01 precision oncology clinical trial with MTAs matched to individual genetic alterations or biomarkers of their tumor. We found that the DDA score assigned to MTAs was significantly higher in patients experiencing disease control than in patients with progressive disease (1523 versus 580, P  = 0.037). The median PFS was also significantly longer in patients receiving MTAs with high (1000+ <) than with low (<0) DDA scores (3.95 versus 1.95 months, P  = 0.044). Our results indicate that AI-based systems, like DDA, are promising new tools for oncologists to improve the clinical benefit of precision oncology.

Tumors harbor multiple genetic alterations, yet treatment decisions are commonly based on single biomarkers, leading to underutilization of genomic information by comprehensive molecular tests, uncertainty in clinical practice, and frequent treatment failures. Although molecular tumor boards can assist personalized treatments, this process is not scalable or standardized, resulting in highly discordant recommendations. Validated digital solutions for personalized decision support are highly needed. The Digital Drug Assignment (DDA) system is a computational reasoning model that scores treatment options based on the full tumor genomic data. We retrospectively analyzed data of 111 lung cancer patients and found that high-score MTAs (1000≦DDA score) provided significant clinical benefit over other treatments, in terms of ORR, PFS, and OS. These results demonstrate that the DDA system is predictive of relative benefit of the various agents used in lung cancer care. Digital drug assignment can potentially address challenges with complex molecular profiles in routine clinical settings.

Background: We present the case of a 50-year-old female whose metastatic pancreatic neuroendocrine tumor (pNET) diagnosis was delayed by the COVID-19 pandemic. The patient was in critical condition at the time of diagnosis due to the extensive tumor burden and failing liver functions. The clinical dilemma was to choose between two registered first-line molecularly-targeted agents (MTAs), sunitinib or everolimus, or to use chemotherapy to quickly reduce tumor burden. Methods: Cell-free DNA (cfDNA) from liquid biopsy was analyzed by next generation sequencing (NGS) using a comprehensive 591-gene panel. Next, a computational method, digital drug-assignment (DDA) was deployed for rapid clinical decision support. Results: NGS analysis identified 38 genetic alterations. DDA identified 6 potential drivers, 24 targets, and 79 MTAs. Everolimus was chosen for first-line therapy based on supporting molecular evidence and the highest DDA ranking among therapies registered in this tumor type. The patient’s general condition and liver functions rapidly improved, and CT control revealed partial response in the lymph nodes and stable disease elsewhere. Conclusion: Deployment of precision oncology using liquid biopsy, comprehensive molecular profiling, and DDA make personalized first-line therapy of advanced pNET feasible in clinical settings.

Background: The anaplastic lymphoma kinase (ALK) gene fusion rearrangement is a potent oncogene, accounting for 2–7% of lung adenocarcinomas, with higher incidence (17–20%) in non-smokers. ALK-positive tumors are sensitive to ALK tyrosine kinase inhibitors (TKIs), thus ALK-positive non-small-cell lung cancer (NSCLC) is currently spearheading precision medicine in thoracic oncology, with three generations of approved ALK inhibitors in clinical practice. However, these treatments are eventually met with resistance. At the molecular level, ALK-positive NSCLC is of the lowest tumor mutational burden, which possibly accounts for the high initial response to TKIs. Nevertheless, TP53 co-mutations are relatively frequent and are associated with adverse outcome of crizotinib treatment, whereas utility of next-generation ALK inhibitors in TP53-mutant tumors is still unknown. Methods: We report the case of an ALK-positive, TP53-mutant NSCLC patient with about five years survival on ALK TKIs with continued next-generation regimens upon progression. Results: The tumor showed progression on crizotinib, but long tumor control was achieved following the incremental administration of next-generation ALK inhibitors, despite lack of evident resistance mechanisms. Conclusion: TP53 status should be taken into consideration when selecting ALK-inhibitor treatment for personalized therapies. In TP53-mutant tumors, switching TKI generations may overcome treatment exhaustion even in the absence of ALK-dependent resistance mechanisms.