PURPOSE: To overcome the limitations of cancer gene therapy using replication-incom- petent adenovirus, we generated E1B 55 kD-deleted adenovirus (YKL-1) by polymerase chain reaction (PCR) and homologous recombination. We then investigated tumor-specific virus replication and cytotoxicity of YKL-1 in vitro and in vivo. MATERIALS AND METHODS: YKL-1 was constructed by reintroducting E1A and E1B 19 kD into pTG-CMV El/E3-deficient adenoviral vector and inducing homologous recombination in E. coli. The recombinant vector pYKL-1 was transfected into 293 cells to generate YKL-1. The properties of newly constructed YKL-1 was defined by PCR and immuno- blotting analysis. Virus replication was examined by infecting human normal and cancer cells on 6-wells at multiplicity of infection (MOI) of 10 for 3 days. Virus was then recovered and titered. Cytopathic effect was analyzed by infecting human normal and cancer cells on 24-wells at MOIs of 10, 1 or 0.1 for 7 to 10 days and staining them with crystal violet solution. Inhibition of tumor growth was examined in human cancer cell xenografts in nu/nu mice by intratumoral injection of YKL-l. RESULTS: PCR and immunoblotting analysis confirmed that YKL-1 contained E1A and E1B 19 kD but not E1B 55 kD. In human normal cells, virus replication and subsequent cytopathic effect of E1B 55 kD-deleted adenovirus YKL-1 was markedly attenuated by larger than 2 to 3 log in magnitude, compared to that of wild-type ad-XJ. In contrast, YKL-1 was capable of replicating and inducing cytotoxicity i.n most human cancer cells. C33A and Hep3B containing p53 mutation were much more sensitive, whereas HeLa and H460 with wild type p53 were relatively resistant to YKL-1. Finally, the tumor growth was dramatically retarded by intratumoral injection of YKL-1 in C33A cervical cancer xenograft and the histology showed significant necrosis by intratumoral injection of YKL-1. CONCLUSION: The results here demonstrated the ability of preferential virus replication and cytotoxicity of ElB 55 kD-deleted adenovirus YKL-1 in human cancer cells. Therefore, these indicated a promising potential of YKL-1 as an antitumoral virus agent and a selective replication-competent virus vector.
Adenoviridae* ; Animals ; Genes, Neoplasm ; Genetic Therapy ; Gentian Violet ; Heterografts ; Homologous Recombination ; Humans ; Immunoblotting ; Mice ; Necrosis ; Polymerase Chain Reaction ; Uterine Cervical Neoplasms ; Virus Replication*

Background: Bladder cancer is characterized by frequent mutations, which provide potential therapeutic targets for most patients. The effectiveness of emerging personalized therapies depends on an accurate molecular diagnosis, for which the accurate estimation of the neoplastic cell percentage (NCP) is a crucial initial step. However, the established method for determining the NCP, manual counting by a pathologist, is time-consuming and not easily executable. Methods: To address this, artificial intelligence (AI) models were developed to estimate the NCP using nine convolutional neural networks and the scanned images of 39 cases of urinary tract cancer. The performance of the AI models was compared to that of six pathologists for 119 cases in the validation cohort. The ground truth value was obtained through multiplexed immunofluorescence. The AI model was then applied to 41 cases in the application cohort that underwent next-generation sequencing testing, and its impact on the copy number variation (CNV) was analyzed. Results: Each AI model demonstrated high reliability, with intraclass correlation coefficients (ICCs) ranging from 0.82 to 0.88. These values were comparable or better to those of pathologists, whose ICCs ranged from 0.78 to 0.91 in urothelial carcinoma cases, both with and without divergent differentiation/ subtypes. After applying AI-driven NCP, 190 CNV (24.2%) were reclassified with 66 (8.4%) and 78 (9.9%) moved to amplification and loss, respectively, from neutral/minor CNV. The neutral/minor CNV proportion decreased by 6%. Conclusions These results suggest that AI models could assist human pathologists in repetitive and cumbersome NCP calculations.

Background: Bladder cancer is characterized by frequent mutations, which provide potential therapeutic targets for most patients. The effectiveness of emerging personalized therapies depends on an accurate molecular diagnosis, for which the accurate estimation of the neoplastic cell percentage (NCP) is a crucial initial step. However, the established method for determining the NCP, manual counting by a pathologist, is time-consuming and not easily executable. Methods: To address this, artificial intelligence (AI) models were developed to estimate the NCP using nine convolutional neural networks and the scanned images of 39 cases of urinary tract cancer. The performance of the AI models was compared to that of six pathologists for 119 cases in the validation cohort. The ground truth value was obtained through multiplexed immunofluorescence. The AI model was then applied to 41 cases in the application cohort that underwent next-generation sequencing testing, and its impact on the copy number variation (CNV) was analyzed. Results: Each AI model demonstrated high reliability, with intraclass correlation coefficients (ICCs) ranging from 0.82 to 0.88. These values were comparable or better to those of pathologists, whose ICCs ranged from 0.78 to 0.91 in urothelial carcinoma cases, both with and without divergent differentiation/ subtypes. After applying AI-driven NCP, 190 CNV (24.2%) were reclassified with 66 (8.4%) and 78 (9.9%) moved to amplification and loss, respectively, from neutral/minor CNV. The neutral/minor CNV proportion decreased by 6%. Conclusions These results suggest that AI models could assist human pathologists in repetitive and cumbersome NCP calculations.

Background: Bladder cancer is characterized by frequent mutations, which provide potential therapeutic targets for most patients. The effectiveness of emerging personalized therapies depends on an accurate molecular diagnosis, for which the accurate estimation of the neoplastic cell percentage (NCP) is a crucial initial step. However, the established method for determining the NCP, manual counting by a pathologist, is time-consuming and not easily executable. Methods: To address this, artificial intelligence (AI) models were developed to estimate the NCP using nine convolutional neural networks and the scanned images of 39 cases of urinary tract cancer. The performance of the AI models was compared to that of six pathologists for 119 cases in the validation cohort. The ground truth value was obtained through multiplexed immunofluorescence. The AI model was then applied to 41 cases in the application cohort that underwent next-generation sequencing testing, and its impact on the copy number variation (CNV) was analyzed. Results: Each AI model demonstrated high reliability, with intraclass correlation coefficients (ICCs) ranging from 0.82 to 0.88. These values were comparable or better to those of pathologists, whose ICCs ranged from 0.78 to 0.91 in urothelial carcinoma cases, both with and without divergent differentiation/ subtypes. After applying AI-driven NCP, 190 CNV (24.2%) were reclassified with 66 (8.4%) and 78 (9.9%) moved to amplification and loss, respectively, from neutral/minor CNV. The neutral/minor CNV proportion decreased by 6%. Conclusions These results suggest that AI models could assist human pathologists in repetitive and cumbersome NCP calculations.

Background: Bladder cancer is characterized by frequent mutations, which provide potential therapeutic targets for most patients. The effectiveness of emerging personalized therapies depends on an accurate molecular diagnosis, for which the accurate estimation of the neoplastic cell percentage (NCP) is a crucial initial step. However, the established method for determining the NCP, manual counting by a pathologist, is time-consuming and not easily executable. Methods: To address this, artificial intelligence (AI) models were developed to estimate the NCP using nine convolutional neural networks and the scanned images of 39 cases of urinary tract cancer. The performance of the AI models was compared to that of six pathologists for 119 cases in the validation cohort. The ground truth value was obtained through multiplexed immunofluorescence. The AI model was then applied to 41 cases in the application cohort that underwent next-generation sequencing testing, and its impact on the copy number variation (CNV) was analyzed. Results: Each AI model demonstrated high reliability, with intraclass correlation coefficients (ICCs) ranging from 0.82 to 0.88. These values were comparable or better to those of pathologists, whose ICCs ranged from 0.78 to 0.91 in urothelial carcinoma cases, both with and without divergent differentiation/ subtypes. After applying AI-driven NCP, 190 CNV (24.2%) were reclassified with 66 (8.4%) and 78 (9.9%) moved to amplification and loss, respectively, from neutral/minor CNV. The neutral/minor CNV proportion decreased by 6%. Conclusions These results suggest that AI models could assist human pathologists in repetitive and cumbersome NCP calculations.