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

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.

Personalized First-Line Treatment of Metastatic Pancreatic Neuroendocrine Carcinoma Facilitated by Liquid Biopsy and Computational Decision Support.

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.

A computational method for prioritizing targeted therapies in precision oncology: performance analysis in the SHIVA01 trial.

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.

Efficacy of Incremental Next-Generation ALK Inhibitor Treatment in Oncogene-Addicted, ALK-Positive, TP53-Mutant NSCLC.

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.

The PP2C-type phosphatase AP2C1, which negatively regulates MPK4 and MPK6, modulates innate immunity, jasmonic acid, and ethylene levels in Arabidopsis.

Wound signaling pathways in plants are mediated by mitogen-activated protein kinases (MAPKs) and stress hormones, such as ethylene and jasmonates. In Arabidopsis thaliana, the transmission of wound signals by MAPKs has been the subject of detailed investigations; however, the involvement of specific phosphatases in wound signaling is not known. Here, we show that AP2C1, an Arabidopsis Ser/Thr phosphatase of type 2C, is a novel stress signal regulator that inactivates the stress-responsive MAPKs MPK4 and MPK6. Mutant ap2c1 plants produce significantly higher amounts of jasmonate upon wounding and are more resistant to phytophagous mites (Tetranychus urticae). Plants with increased AP2C1 levels display lower wound activation of MAPKs, reduced ethylene production, and compromised innate immunity against the necrotrophic pathogen Botrytis cinerea. Our results demonstrate a key role for the AP2C1 phosphatase in regulating stress hormone levels, defense responses, and MAPK activities in Arabidopsis and provide evidence that the activity of AP2C1 might control the plant's response to B. cinerea.

Conservation of the drought-inducible DS2 genes and divergences from their ASR paralogues in solanaceous species.

The drought-inducible DS2 genes of potatoes are members of the ASR (abscisic acid, stress and ripening) gene family. Previously it was shown that expression of DS2 genes is highly dehydration-specific in potato leaves, is not inducible by cold, heat, salt, hypoxia or oxidative stresses, and is independent of abscisic acid (ABA). Now it is shown that StDS2 does not respond either to sucrose or any plant hormones. Conservation of DS2 genes with this unique mode of regulation was studied in the solanaceous species with different relationships to potatoes. DS2 orthologues were identified by DNA sequence alignment in the closely related Lycopersicon and Capsicum species but not in the more distantly related Nicotiana sp. DNA and RNA gel blot analysis revealed the presence of a gene highly homologous to the potato gene StDS2 in tomato (LeDS2) with the same desiccation-specific expression in leaves and organ-specific expression in flowers and green fruits. The LeDS2 promoter was isolated and found to be almost identical in sequence with the promoter of StDS2, except for a 45-bp insertion in tomato. In contrast, no gene highly similar to StDS2 was detected in Nicotiana species on DNA gel blots. Neither StDS2 nor LeDS2 promoter regions were able to confer expression for the beta-glucuronidase (GUS) reporter gene in transgenic tobacco plants indicating that the trans regulatory factors necessary for DS2 expression are not conserved either in Nicotiana tabacum. These data suggest a narrow species-specificity and late evolution of the DS2-type genes within the family Solanaceae.