Personalized cancer vaccines from bacteria-derived outer membrane vesicles with antibody-mediated persistent uptake by dendritic cells

Nanocarriers with intrinsic immune adjuvant properties can activate the innate immune system while delivering tumor antigen,thus efficiently facilitating antitumor adaptive immunity.Bacteria-derived outer membrane vesicles(OMVs)are an excellent candidate due to their abundance of pathogen associated molecular patterns.However,during the uptake of OMVs by dendritic cells(DCs),the interaction between lipopolysaccharide and toll-like receptor 4 induces rapid DC maturation and uptake blockage,a phenomenon we refer to as“maturation-induced uptake obstruction"(MUO).Herein we decorated OMV with the DC-targeting aDEC205 antibody(OMV-DEC),which endowed the nanovaccine with an uptake mechanism termed as 4<not restricted to maturation via antibody modifying”(Normandy),thereby overcoming the MUO phenomenon.We also proved the applicability of this nanovaccine in identifying the human tumor neoantigens through rapid antigen display.In summary,this engineered OMV represents a powerful nanocarrier for personalized cancer vaccines,and this antibody modification strategy provides a reference to remodel the DC uptake pattern in nanocarrier design.

Applications of probability and statistics in cancer genomics

Background:The past decade has witnessed a rapid progress in our understanding of the genetics of cancer and its progression.Probabilistic and statistical modeling played a pivotal role in the discovery of general patterns from cancer genomics datasets and continue to be of central importance for personalized medicine.Results:In this review we introduce cancer genomics from a probabilistic and statistical perspective.We start from(1)functional classification of genes into oncogenes and tumor suppressor genes,then(2)demonstrate the importance of comprehensive analysis of different mutation types for individual cancer genomes,followed by(3)tumor purity analysis,which in turn leads to(4)the concept of ploidy and clonality,that is next connected to(5)tumor evolution under treatment pressure,which yields insights into cancer drug resistance.We also discuss future challenges including the non-coding genomic regions,integrative analysis of genomics and epigenomics,as well as early cancer detection.Conclusion:We believe probabilistic and statistical modeling will continue to play important roles for novel discoveries in the field o f cancer genomics and personalized medicine.