Aging · 2018
Re-exploring the core genes and modules in the human frontal cortex during chronological aging: insights from network-based analysis of transcriptomic studies.
СтатьяМетаанализПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Lee Kyeryoung, Paek Hunki, Huang Liang-Chin, Hilton C Beau, Datta Surabhi, Higashi Josh, Ofoegbu Nneka, Wang Jingqi, Rubinstein Samuel M, Cowan Andrew J, Kwok Mary, Warner Jeremy L, Xu Hua, Wang Xiaoyan
Аннотация
BACKGROUND: Initial insights into oncology clinical trial outcomes are often gleaned manually from conference abstracts. We aimed to develop an automated system to extract safety and efficacy information from study abstracts with high precision and fine granularity, transforming them into computable data for timely clinical decision-making.
METHODS: We collected clinical trial abstracts from key conferences and PubMed (2012-2023). The SEETrials system was developed with three modules: preprocessing, prompt engineering with knowledge ingestion, and postprocessing. We evaluated the system's performance qualitatively and quantitatively and assessed its generalizability across different cancer types- multiple myeloma (MM), breast, lung, lymphoma, and leukemia. Furthermore, the efficacy and safety of innovative therapies, including CAR-T, bispecific antibodies, and antibody-drug conjugates (ADC), in MM were analyzed across a large scale of clinical trial studies.
RESULTS: SEETrials achieved high precision (0.964), recall (sensitivity) (0.988), and F1 score (0.974) across 70 data elements present in the MM trial studies Generalizability tests on four additional cancers yielded precision, recall, and F1 scores within the 0.979-0.992 range. Variation in the distribution of safety and efficacy-related entities was observed across diverse therapies, with certain adverse events more common in specific treatments. Comparative performance analysis using overall response rate (ORR) and complete response (CR) highlighted differences among therapies: CAR-T (ORR: 88 %, 95 % CI: 84-92 %; CR: 95 %, 95 % CI: 53-66 %), bispecific antibodies (ORR: 64 %, 95 % CI: 55-73 %; CR: 27 %, 95 % CI: 16-37 %), and ADC (ORR: 51 %, 95 % CI: 37-65 %; CR: 26 %, 95 % CI: 1-51 %). Notable study heterogeneity was identified (>75 % I 2 heterogeneity index scores) across several outcome entities analyzed within therapy subgroups.
CONCLUSION: SEETrials demonstrated highly accurate data extraction and versatility across different therapeutics and various cancer domains. Its automated processing of large datasets facilitates nuanced data comparisons, promoting the swift and effective dissemination of clinical insights.
METHODS: We collected clinical trial abstracts from key conferences and PubMed (2012-2023). The SEETrials system was developed with three modules: preprocessing, prompt engineering with knowledge ingestion, and postprocessing. We evaluated the system's performance qualitatively and quantitatively and assessed its generalizability across different cancer types- multiple myeloma (MM), breast, lung, lymphoma, and leukemia. Furthermore, the efficacy and safety of innovative therapies, including CAR-T, bispecific antibodies, and antibody-drug conjugates (ADC), in MM were analyzed across a large scale of clinical trial studies.
RESULTS: SEETrials achieved high precision (0.964), recall (sensitivity) (0.988), and F1 score (0.974) across 70 data elements present in the MM trial studies Generalizability tests on four additional cancers yielded precision, recall, and F1 scores within the 0.979-0.992 range. Variation in the distribution of safety and efficacy-related entities was observed across diverse therapies, with certain adverse events more common in specific treatments. Comparative performance analysis using overall response rate (ORR) and complete response (CR) highlighted differences among therapies: CAR-T (ORR: 88 %, 95 % CI: 84-92 %; CR: 95 %, 95 % CI: 53-66 %), bispecific antibodies (ORR: 64 %, 95 % CI: 55-73 %; CR: 27 %, 95 % CI: 16-37 %), and ADC (ORR: 51 %, 95 % CI: 37-65 %; CR: 26 %, 95 % CI: 1-51 %). Notable study heterogeneity was identified (>75 % I 2 heterogeneity index scores) across several outcome entities analyzed within therapy subgroups.
CONCLUSION: SEETrials demonstrated highly accurate data extraction and versatility across different therapeutics and various cancer domains. Its automated processing of large datasets facilitates nuanced data comparisons, promoting the swift and effective dissemination of clinical insights.
Предметные рубрики MeSH
AdultAgedAged, 80 and overAnimalsHumansMiddle AgedYoung AdultAge FactorsAgingAlzheimer DiseaseBehavior, Animalcdc42 GTP-Binding ProteinFrontal LobeGene Expression ProfilingGene Regulatory NetworksHistone Deacetylase 1Maze LearningMice, Inbred C57BLModels, AnimalOligonucleotide Array Sequence AnalysisProto-Oncogene MasProto-Oncogene Proteins c-yesRNA, MessengerTranscriptome