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.

Absence of the Tks4 Scaffold Protein Induces Epithelial-Mesenchymal Transition-Like Changes in Human Colon Cancer Cells

Epithelial to mesenchymal transition (EMT) is a multipurpose process involved in wound healing, development, and certain pathological processes, such as metastasis formation. The Tks4 scaffold protein has been implicated in cancer progression; however, its role in oncogenesis is not well defined. In this study, the function of Tks4 was investigated in HCT116 colon cancer cells by knocking the protein out using the CRISPR/Cas9 system. Surprisingly, the absence of Tks4 induced significant changes in cell morphology, motility, adhesion and expression, and localization of E-cadherin, which are all considered as hallmarks of EMT. In agreement with these findings, the marked appearance of fibronectin, a marker of the mesenchymal phenotype, was also observed in Tks4-KO cells. Analysis of the expression of well-known EMT transcription factors revealed that Snail2 was strongly overexpressed in cells lacking Tks4. Tks4-KO cells showed increased motility and decreased cell–cell attachment. Collagen matrix invasion assays demonstrated the abundance of invasive solitary cells. Finally, the reintroduction of Tks4 protein in the Tks4-KO cells restored the expression levels of relevant key transcription factors, suggesting that the Tks4 scaffold protein has a specific and novel role in EMT regulation and cancer progression.

Soluble VEGFR1 signaling guides vascular patterns into dense branching morphologies

Vascular patterning is a key process during development and disease. The diffusive decoy receptor sVEGFR1 (sFlt1) is a known regulator of endothelial cell behavior, yet the mechanism by which it controls vascular structure is little understood. We propose computational models to shed light on how vascular patterning is guided by self-organized gradients of the VEGF/sVEGFR1 factors. We demonstrate that a diffusive inhibitor can generate structures with a dense branching morphology in models where the activator elicits directed growth. Inadequate presence of the inhibitor leads to compact growth, while excessive production of the inhibitor blocks expansion and stabilizes existing structures. Model predictions were compared with time-resolved experimental data obtained from endothelial sprout kinetics in fibrin gels. In the presence of inhibitory antibodies against VEGFR1 vascular sprout density increases while the speed of sprout expansion remains unchanged. Thus, the rate of secretion and stability of extracellular sVEGFR1 can modulate vascular sprout density.