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.

Combined dabrafenib and trametinib treatment in a case of chemotherapy-refractory extrahepatic BRAF V600E mutant cholangiocarcinoma: dramatic clinical and radiological response with a confusing synchronic new liver lesion.

Since the prognosis of advanced cholangiocarcinoma (CCA) remains poor with traditional chemotherapy, attention has shifted to molecularly targeted agents. Results of available clinical studies reveal little or no benefit of using targeted agents in advanced CCA. Limitations of these trials could be the lack of comprehensive molecular and genetic characterization of CCA samples in order to identify potential drug targets. Here we report a case of a 59-year-old female with chemotherapy-refractor, metastatic extrahepatic cholangiocarcinoma (EHCCA). After failure of first-line chemotherapy with cisplatin plus gemcitabine, next generation sequencing (NGS) based tumor molecular profiling was performed on aspiration cytological sample, that revealed BRAF V600E mutation. Multidisciplinary team decided on the initiation of combined treatment with BRAF and MEK inhibitors. Dabrafenib was started orally 150 mg twice a day, adding trametinib 2 mg once a day. Right from the initiation of targeted therapy, significant clinical improvement had been observed. Even though the first restaging computed tomography (CT) scan at 8 weeks revealed spectacular decrease in all metastatic sites, a new hepatic mass of 67 mm × 40 mm was identified and interpreted as new metastatic lesion. As the clinical and radiological response was contradictory, CT-guided biopsy was taken from the hepatic lesion while the therapy was continued on. Histopathologic evaluation excluded the hepatic lesion from being a metastasis, instead described it as a fibrotic, inflammatory lesion. At 12 week, PET CT confirmed further tumor regression with complete regression of the multiple cerebral metastases. The therapy has been extremely well tolerated by the patient. According to our knowledge, this is the first reported case on a successful treatment of EHCCA with the combination of dabrafenib and trametinib. Our case highlights the importance of molecular profiling in CCA, in order to find potential actionable driver mutations for personalised treatment.

Closing the loop: an interactive action-research conference format for delivering updated medical information while eliciting Latina patient/family experiences and psychosocial needs post-genetic cancer risk assessment.

A patient/family-centered conference was conducted at an underserved community hospital to address Latinas' post-genetic cancer risk assessment (GCRA) medical information and psychosocial support needs, and determine the utility of the action research format. Latinas seen for GCRA were recruited to a half-day conference conducted in Spanish. Content was partly determined from follow-up survey feedback. Written surveys, interactive discussions, and Audience Response System (ARS) queries facilitated the participant-healthcare professional action research process. Analyses included descriptive statistics and thematic analysis. The 71 attendees (41 patients and 27 relatives/friends) were primarily non-US born Spanish-speaking females, mean age 43 years. Among patients, 73 % had a breast cancer history; 85 % had BRCA testing (49 % BRCA+). Nearly all (96 %) attendees completed the conference surveys and ARS queries; ≥48 % participated in interactive discussions. Most (95 %) agreed that the format met their personal interests and expectations and provided useful information and resources. Gaps/challenges identified in the GCRA process included pre-consult anxiety, uncertainty about reason for referral and expected outcomes, and psychosocial needs post-GCRA, such as absorbing and disseminating risk information to relatives and concurrently coping with a recent cancer diagnosis. The combined action research and educational conference format was innovative and effective for responding to continued patient information needs and addressing an important data gap about support needs of Latina patients and family members following genetic cancer risk assessment. Findings informed GCRA process improvements and provide a basis for theory-driven cancer control research.