Explore the vast range of titles available.

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.

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.

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.

Issue Title: Special Issue: Birt-Hogg-Dubé syndrome.

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.

One of the cruelest ironies in the recent brutal and reprehensible acts of terrorism inside Israel, for which El Fatah, a member of the executive committee of the Palestine Liberation Organization, has taken credit, is that the PLO alone among the leading actors in the Middle East will benefit...

Activation of the DNA-sensing STING axis by RNA viruses plays a role in antiviral response through mechanisms that remain poorly understood. Here, we show that the STING pathway regulates Nipah virus (NiV) replication in vivo in mice. Moreover, we demonstrate that following both NiV and measles virus (MeV) infection, IFNγ-inducible protein 16 (IFI16), an alternative DNA sensor in addition to cGAS, induces the activation of STING, leading to the phosphorylation of NF-κB p65 and the production of IFNβ and interleukin 6. Finally, we found that paramyxovirus-induced syncytia formation is responsible for loss of mitochondrial membrane potential and leakage of mitochondrial DNA in the cytoplasm, the latter of which is further detected by both cGAS and IFI16. These results contribute to improve our understanding about NiV and MeV immunopathogenesis and provide potential paths for alternative therapeutic strategies.

Activation of the DNA-sensing STING axis by RNA viruses plays a role in antiviral response through mechanisms that remain poorly understood. Here, we show that the STING pathway regulates Nipah virus (NiV) replication in vivo in mice. Moreover, we demonstrate that following both NiV and measles virus (MeV) infection, IFNγ-inducible protein 16 (IFI16), an alternative DNA sensor in addition to cGAS, induces the activation of STING, leading to the phosphorylation of NF-κB p65 and the production of IFNβ and interleukin 6. Finally, we found that paramyxovirus-induced syncytia formation is responsible for loss of mitochondrial membrane potential and leakage of mitochondrial DNA in the cytoplasm, the latter of which is further detected by both cGAS and IFI16. These results contribute to improve our understanding about NiV and MeV immunopathogenesis and provide potential paths for alternative therapeutic strategies.