Kosmas V. Kepesidis, Philip Jacob, Wolfgang Schweinberger, Marinus Huber, Nico Feiler, Frank Fleischmann, Michael Trubetskov, Liudmila Voronina, Jacqueline Aschauer, Tarek Eissa, Lea Gigou, Patrik Karandušovsky, Ioachim Pupeza, Alexander Weigel, Abdallah Azzeer, Christian G. Stief, Michael Chaloupka, Niels Reinmuth, Jürgen Behr, Thomas Kolben, Nadia Harbeck, Maximilian Reiser, Ferenc Krausz and Mihaela Žigman*,
{"title":"电场分子指纹技术探测癌症","authors":"Kosmas V. Kepesidis, Philip Jacob, Wolfgang Schweinberger, Marinus Huber, Nico Feiler, Frank Fleischmann, Michael Trubetskov, Liudmila Voronina, Jacqueline Aschauer, Tarek Eissa, Lea Gigou, Patrik Karandušovsky, Ioachim Pupeza, Alexander Weigel, Abdallah Azzeer, Christian G. Stief, Michael Chaloupka, Niels Reinmuth, Jürgen Behr, Thomas Kolben, Nadia Harbeck, Maximilian Reiser, Ferenc Krausz and Mihaela Žigman*, ","doi":"10.1021/acscentsci.4c0216410.1021/acscentsci.4c02164","DOIUrl":null,"url":null,"abstract":"<p >Human biofluids serve as indicators of various physiological states, and recent advances in molecular profiling technologies hold great potential for enhancing clinical diagnostics. Leveraging recent developments in laser-based electric-field molecular fingerprinting, we assess its potential for <i>in vitro</i> diagnostics. In a proof-of-concept clinical study involving 2533 participants, we conducted randomized measurement campaigns to spectroscopically profile bulk venous blood plasma across lung, prostate, breast, and bladder cancer. Employing machine learning, we detected infrared signatures specific to therapy-naı̈ve cancer states, distinguishing them from matched control individuals with a cross-validation ROC AUC of 0.88 for lung cancer and values ranging from 0.68 to 0.69 for the other three cancer entities. In an independent held-out test data set, designed to reflect different experimental conditions from those used during model training, we achieved a lung cancer detection ROC AUC of 0.81. Our study demonstrates that electric-field molecular fingerprinting is a robust technological framework broadly applicable to disease phenotyping under real-world conditions.</p><p >Laser-based infrared molecular fingerprinting detects cancer, demonstrating its potential for clinical disease diagnostics.</p>","PeriodicalId":10,"journal":{"name":"ACS Central Science","volume":"11 4","pages":"560–573 560–573"},"PeriodicalIF":12.7000,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acscentsci.4c02164","citationCount":"0","resultStr":"{\"title\":\"Electric-Field Molecular Fingerprinting to Probe Cancer\",\"authors\":\"Kosmas V. 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In a proof-of-concept clinical study involving 2533 participants, we conducted randomized measurement campaigns to spectroscopically profile bulk venous blood plasma across lung, prostate, breast, and bladder cancer. Employing machine learning, we detected infrared signatures specific to therapy-naı̈ve cancer states, distinguishing them from matched control individuals with a cross-validation ROC AUC of 0.88 for lung cancer and values ranging from 0.68 to 0.69 for the other three cancer entities. In an independent held-out test data set, designed to reflect different experimental conditions from those used during model training, we achieved a lung cancer detection ROC AUC of 0.81. 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Electric-Field Molecular Fingerprinting to Probe Cancer
Human biofluids serve as indicators of various physiological states, and recent advances in molecular profiling technologies hold great potential for enhancing clinical diagnostics. Leveraging recent developments in laser-based electric-field molecular fingerprinting, we assess its potential for in vitro diagnostics. In a proof-of-concept clinical study involving 2533 participants, we conducted randomized measurement campaigns to spectroscopically profile bulk venous blood plasma across lung, prostate, breast, and bladder cancer. Employing machine learning, we detected infrared signatures specific to therapy-naı̈ve cancer states, distinguishing them from matched control individuals with a cross-validation ROC AUC of 0.88 for lung cancer and values ranging from 0.68 to 0.69 for the other three cancer entities. In an independent held-out test data set, designed to reflect different experimental conditions from those used during model training, we achieved a lung cancer detection ROC AUC of 0.81. Our study demonstrates that electric-field molecular fingerprinting is a robust technological framework broadly applicable to disease phenotyping under real-world conditions.
Laser-based infrared molecular fingerprinting detects cancer, demonstrating its potential for clinical disease diagnostics.
期刊介绍:
ACS Central Science publishes significant primary reports on research in chemistry and allied fields where chemical approaches are pivotal. As the first fully open-access journal by the American Chemical Society, it covers compelling and important contributions to the broad chemistry and scientific community. "Central science," a term popularized nearly 40 years ago, emphasizes chemistry's central role in connecting physical and life sciences, and fundamental sciences with applied disciplines like medicine and engineering. The journal focuses on exceptional quality articles, addressing advances in fundamental chemistry and interdisciplinary research.