Volume 35, Issue 6 pp. 664-670
REVIEW

Exploring the potential of multiomics liquid biopsy testing in the clinical setting of lung cancer

Andrea Gottardo

Andrea Gottardo

Department of Precision Medicine in Medical, Surgical and Critical Care (Me.Pre.C.C.), University of Palermo, Palermo, Italy

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Tancredi Didier Bazan Russo

Tancredi Didier Bazan Russo

Department of Precision Medicine in Medical, Surgical and Critical Care (Me.Pre.C.C.), University of Palermo, Palermo, Italy

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Alessandro Perez

Alessandro Perez

Department of Precision Medicine in Medical, Surgical and Critical Care (Me.Pre.C.C.), University of Palermo, Palermo, Italy

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Marco Bono

Marco Bono

Department of Precision Medicine in Medical, Surgical and Critical Care (Me.Pre.C.C.), University of Palermo, Palermo, Italy

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Emilia Di Giovanni

Emilia Di Giovanni

Department of Precision Medicine in Medical, Surgical and Critical Care (Me.Pre.C.C.), University of Palermo, Palermo, Italy

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Enrico Di Marco

Enrico Di Marco

Department of Precision Medicine in Medical, Surgical and Critical Care (Me.Pre.C.C.), University of Palermo, Palermo, Italy

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Rita Siino

Rita Siino

Department of Precision Medicine in Medical, Surgical and Critical Care (Me.Pre.C.C.), University of Palermo, Palermo, Italy

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Carla Ferrante Bannera

Carla Ferrante Bannera

Department of Precision Medicine in Medical, Surgical and Critical Care (Me.Pre.C.C.), University of Palermo, Palermo, Italy

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Clarissa Mujacic

Clarissa Mujacic

Department of Precision Medicine in Medical, Surgical and Critical Care (Me.Pre.C.C.), University of Palermo, Palermo, Italy

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Maria Concetta Vitale

Maria Concetta Vitale

Department of Precision Medicine in Medical, Surgical and Critical Care (Me.Pre.C.C.), University of Palermo, Palermo, Italy

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Silvia Contino

Silvia Contino

Department of Precision Medicine in Medical, Surgical and Critical Care (Me.Pre.C.C.), University of Palermo, Palermo, Italy

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Giuliana Iannì

Giuliana Iannì

Department of Precision Medicine in Medical, Surgical and Critical Care (Me.Pre.C.C.), University of Palermo, Palermo, Italy

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Giulia Busuito

Giulia Busuito

Department of Precision Medicine in Medical, Surgical and Critical Care (Me.Pre.C.C.), University of Palermo, Palermo, Italy

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Federica Iacono

Federica Iacono

A.R.N.A.S. Hospital Di Cristina, Palermo, Italy

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Lorena Incorvaia

Lorena Incorvaia

Department of Precision Medicine in Medical, Surgical and Critical Care (Me.Pre.C.C.), University of Palermo, Palermo, Italy

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Giuseppe Badalamenti

Giuseppe Badalamenti

Department of Precision Medicine in Medical, Surgical and Critical Care (Me.Pre.C.C.), University of Palermo, Palermo, Italy

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Antonio Galvano

Antonio Galvano

Department of Precision Medicine in Medical, Surgical and Critical Care (Me.Pre.C.C.), University of Palermo, Palermo, Italy

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Antonio Russo

Corresponding Author

Antonio Russo

Department of Precision Medicine in Medical, Surgical and Critical Care (Me.Pre.C.C.), University of Palermo, Palermo, Italy

Correspondence

Antonio Russo, Department of Precision Medicine in Medical, Surgical and Critical Care (Me.Pre.C.C.), “P. Giaccone” University Hospital (A.O.U.P.) of Palermo, Via del Vespro 129, 90127 Palermo, Italy.

Email: [email protected]

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Viviana Bazan

Viviana Bazan

Department of Biomedicine, Neuroscience and Advanced Diagnostic (Bi.N.D.), University of Palermo, Palermo, Italy

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Valerio Gristina

Valerio Gristina

Department of Precision Medicine in Medical, Surgical and Critical Care (Me.Pre.C.C.), University of Palermo, Palermo, Italy

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First published: 01 June 2024
Citations: 5

Andrea Gottardo and Tancredi Didier Bazan Russo contributed equally to this work.

Viviana Bazan and Valerio Gristina Co-last authors.

Abstract

The transformative role of artificial intelligence (AI) and multiomics could enhance the diagnostic and prognostic capabilities of liquid biopsy (LB) for lung cancer (LC). Despite advances, the transition from tissue biopsies to more sophisticated, non-invasive methods like LB has been impeded by challenges such as the heterogeneity of biomarkers and the low concentration of tumour-related analytes. The advent of multiomics – enabled by deep learning algorithms – offers a solution by allowing the simultaneous analysis of various analytes across multiple biological fluids, presenting a paradigm shift in cancer diagnostics. Through multi-marker, multi-analyte and multi-source approaches, this review showcases how AI and multiomics are identifying clinically valuable biomarker combinations that correlate with patients' health statuses. However, the path towards clinical implementation is fraught with challenges, including study reproducibility and lack of methodological standardization, thus necessitating urgent solutions to solve these common issues.

Graphical Abstract

A flow diagram to visualize how multiomics approaches can be split into multi-marker, multi-analyte and multi-source approach; then, their link to AI, to decrypt and use in the clinical setting the messages hidden within them. The combined use of Artificial Intelligence (AI) and multiomics could improve the diagnosis and prognosis of Lung Cancer (LC) via Liquid Biopsy (LB); through multi-marker, multi-analyte, and multi-source analysis, the way is paved for the achievement of these goals, once tested through appropriate large-scale multi-center studies.

CONFLICT OF INTEREST STATEMENT

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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