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Artificial Nanoscale Erythrocytes from Clinically Relevant Compounds for Enhancing Cancer Immunotherapy.

ABSTRACT: Because of enhanced efficacy and lower side effects, cancer immunotherapies have recently been extensively investigated in clinical trials to overcome the limitations of conventional cancer monotherapies. Although engineering attempts have been made to build nanosystems even including stimulus nanomaterials for the efficient delivery of antigens, adjuvants, or anticancer drugs to improve immunogenic cancer cell death, this requires huge R&D efforts and investment for clinically relevant findings to be approved for translation of the nanosystems. To this end, in this study, an air-liquid two-phase electrospray was developed for stable bubble pressing under a balance between mechanical and electrical parameters of the spray to continuously produce biomimetic nanosystems consisting of only clinically relevant compounds [paclitaxel-loaded fake blood cell Eudragit particle (Eu-FBCP/PTX)] to provide a conceptual leap for the timely development of translatable chemo-immunotherapeutic nanosystems. This was pursued as the efficacy of systems for delivering anticancer agents that has been mainly influenced by nanosystem shape because of its relevance to transporting behavior to organs, blood circulation, and cell-membrane interactions. The resulting Eu-FBCP/PTX nanosystems exhibiting phagocytic and micropinocytic uptake behaviors can confer better efficacy in chemo-immunotherapeutics in the absence and presence of anti-PD-L1 antibodies than similar sized PTX-loaded spherical Eu particles (Eu-s/PTX).

SUBMITTER: Ou W

PROVIDER: S-EPMC7770689 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundPediatric cancers typically have a distinct genomic landscape when compared to adult cancers and frequently carry somatic gene fusion events that alter gene expression and drive tumorigenesis. Sensitive and specific detection of gene fusions through the analysis of next-generation-based RNA sequencing (RNA-Seq) data is computationally challenging and may be confounded by low tumor cellularity or underlying genomic complexity. Furthermore, numerous computational tools are available to identify fusions from supporting RNA-Seq reads, yet each algorithm demonstrates unique variability in sensitivity and precision, and no clearly superior approach currently exists. To overcome these challenges, we have developed an ensemble fusion calling approach to increase the accuracy of identifying fusions.ResultsOur Ensemble Fusion (EnFusion) approach utilizes seven fusion calling algorithms: Arriba, CICERO, FusionMap, FusionCatcher, JAFFA, MapSplice, and STAR-Fusion, which are packaged as a fully automated pipeline using Docker and Amazon Web Services (AWS) serverless technology. This method uses paired end RNA-Seq sequence reads as input, and the output from each algorithm is examined to identify fusions detected by a consensus of at least three algorithms. These consensus fusion results are filtered by comparison to an internal database to remove likely artifactual fusions occurring at high frequencies in our internal cohort, while a "known fusion list" prevents failure to report known pathogenic events. We have employed the EnFusion pipeline on RNA-Seq data from 229 patients with pediatric cancer or blood disorders studied under an IRB-approved protocol. The samples consist of 138 central nervous system tumors, 73 solid tumors, and 18 hematologic malignancies or disorders. The combination of an ensemble fusion-calling pipeline and a knowledge-based filtering strategy identified 67 clinically relevant fusions among our cohort (diagnostic yield of 29.3%), including RBPMS-MET, BCAN-NTRK1, and TRIM22-BRAF fusions. Following clinical confirmation and reporting in the patient's medical record, both known and novel fusions provided medically meaningful information.ConclusionsThe EnFusion pipeline offers a streamlined approach to discover fusions in cancer, at higher levels of sensitivity and accuracy than single algorithm methods. Furthermore, this method accurately identifies driver fusions in pediatric cancer, providing clinical impact by contributing evidence to diagnosis and, when appropriate, indicating targeted therapies.

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Artificial Nanoscale Erythrocytes from Clinically Relevant Compounds for Enhancing Cancer Immunotherapy.

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