Although circulating cell-free DNA(cfDNA)methylation has emerged as the mainstream approach in multi-cancer detection blood tests(MCDBTs),the potential of integrating proteins and mutations,to enhance its performance ...Although circulating cell-free DNA(cfDNA)methylation has emerged as the mainstream approach in multi-cancer detection blood tests(MCDBTs),the potential of integrating proteins and mutations,to enhance its performance remains unclear.The PROMISE study(NCT04972201)was conducted to investigate the feasibility of a multi-omics integration strategy in MCDBTs across nine types of cancers in head and neck(excluding nasopharynx),esophagus,lung,stomach,liver,biliary tract,pancreas,colorectum,and ovary.Blood samples were pro-spectively collected from 1,706 participants(840 non-cancer;866 can-cer)and then randomly divided into training and validation sets.The complementarity between various omics were investigated,and specific omics features were carefully selected for further multimodal model construction.The methylation-based classifier outperformed both the mutation-based and protein-based classifiers.As 95.0%of cancer cases detected by the mutation-based classifier were simultaneously identified by the methylation-based classifier,while 14.0%of the protein-positive samples were missed,protein markers may provide complementary value to the methylation-based classifier.Compared with the methyl-ation-based classifier,the multimodal classifier combining methylation and protein features exhibited an improved sensitivity of 75.1%(95%confidence interval[CI],69.3%-80.3%)at the same specificity of 98.8%with the accuracy of top predicted origin(TPO1)of 73.1%(95%CI,66.2%-79.2%).Notably,the TPO1 accuracy reached 100%in liver and ovarian cancers with negative results of the methylation-based classi-fier.Collectively,these data suggest that the integration of protein markers in the multimodal classifier can offer additional benefits to the methylation-based classifier,particularly in identifying liver and ovarian cancers.展开更多
基金supported by the Science and Education Cultivation Fund of the National Cancer and Regional Medical Center of Shanxi Provincial Cancer Hospital(TD2023002)CAMS Innovation Fund for Medical Sciences(2024-I2M-ZD-004,to Z.W.)+5 种基金Noncommunicable Chronic Diseases-National Science and Technology Major Project(2024ZD0519700,to J.W.)Beijing Natural Science Foundation(7242114 to Jiachen Xu)National Natural Science Foundation of China of China(82102886 to Jiachen Xu)Beijing Nova Program(20220484119 to Jiachen Xu)Medical Oncology Key Foundation of Cancer Hospital Chinese Academy of Medical Sciences(CICAMS-MOCP2022003,CICAMS-MOY&M202405 to Jiachen Xu)Key Research Program of Department of Science and Technology of Heilongjiang Province(2022ZXJ03C0).
文摘Although circulating cell-free DNA(cfDNA)methylation has emerged as the mainstream approach in multi-cancer detection blood tests(MCDBTs),the potential of integrating proteins and mutations,to enhance its performance remains unclear.The PROMISE study(NCT04972201)was conducted to investigate the feasibility of a multi-omics integration strategy in MCDBTs across nine types of cancers in head and neck(excluding nasopharynx),esophagus,lung,stomach,liver,biliary tract,pancreas,colorectum,and ovary.Blood samples were pro-spectively collected from 1,706 participants(840 non-cancer;866 can-cer)and then randomly divided into training and validation sets.The complementarity between various omics were investigated,and specific omics features were carefully selected for further multimodal model construction.The methylation-based classifier outperformed both the mutation-based and protein-based classifiers.As 95.0%of cancer cases detected by the mutation-based classifier were simultaneously identified by the methylation-based classifier,while 14.0%of the protein-positive samples were missed,protein markers may provide complementary value to the methylation-based classifier.Compared with the methyl-ation-based classifier,the multimodal classifier combining methylation and protein features exhibited an improved sensitivity of 75.1%(95%confidence interval[CI],69.3%-80.3%)at the same specificity of 98.8%with the accuracy of top predicted origin(TPO1)of 73.1%(95%CI,66.2%-79.2%).Notably,the TPO1 accuracy reached 100%in liver and ovarian cancers with negative results of the methylation-based classi-fier.Collectively,these data suggest that the integration of protein markers in the multimodal classifier can offer additional benefits to the methylation-based classifier,particularly in identifying liver and ovarian cancers.
