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Lung cancer multi-omics digital human avatars for integrating precision medicine into clinical practice: the LANTERN study

BACKGROUND: The current management of lung cancer patients has reached a high level of complexity. Indeed, besides the traditional clinical variables (e.g., age, sex, TNM stage), new omics data have recently been introduced in clinical practice, thereby making more complex the decision-making proces...

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Autores principales: Lococo, Filippo, Boldrini, Luca, Diepriye, Charles-Davies, Evangelista, Jessica, Nero, Camilla, Flamini, Sara, Minucci, Angelo, De Paolis, Elisa, Vita, Emanuele, Cesario, Alfredo, Annunziata, Salvatore, Calcagni, Maria Lucia, Chiappetta, Marco, Cancellieri, Alessandra, Larici, Anna Rita, Cicchetti, Giuseppe, Troost, Esther G.C., Ádány, Róza, Farré, Núria, Öztürk, Ece, Van Doorne, Dominique, Leoncini, Fausto, Urbani, Andrea, Trisolini, Rocco, Bria, Emilio, Giordano, Alessandro, Rindi, Guido, Sala, Evis, Tortora, Giampaolo, Valentini, Vincenzo, Boccia, Stefania, Margaritora, Stefano, Scambia, Giovanni
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10262371/
https://www.ncbi.nlm.nih.gov/pubmed/37312079
http://dx.doi.org/10.1186/s12885-023-10997-x
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author Lococo, Filippo
Boldrini, Luca
Diepriye, Charles-Davies
Evangelista, Jessica
Nero, Camilla
Flamini, Sara
Minucci, Angelo
De Paolis, Elisa
Vita, Emanuele
Cesario, Alfredo
Annunziata, Salvatore
Calcagni, Maria Lucia
Chiappetta, Marco
Cancellieri, Alessandra
Larici, Anna Rita
Cicchetti, Giuseppe
Troost, Esther G.C.
Ádány, Róza
Farré, Núria
Öztürk, Ece
Van Doorne, Dominique
Leoncini, Fausto
Urbani, Andrea
Trisolini, Rocco
Bria, Emilio
Giordano, Alessandro
Rindi, Guido
Sala, Evis
Tortora, Giampaolo
Valentini, Vincenzo
Boccia, Stefania
Margaritora, Stefano
Scambia, Giovanni
author_facet Lococo, Filippo
Boldrini, Luca
Diepriye, Charles-Davies
Evangelista, Jessica
Nero, Camilla
Flamini, Sara
Minucci, Angelo
De Paolis, Elisa
Vita, Emanuele
Cesario, Alfredo
Annunziata, Salvatore
Calcagni, Maria Lucia
Chiappetta, Marco
Cancellieri, Alessandra
Larici, Anna Rita
Cicchetti, Giuseppe
Troost, Esther G.C.
Ádány, Róza
Farré, Núria
Öztürk, Ece
Van Doorne, Dominique
Leoncini, Fausto
Urbani, Andrea
Trisolini, Rocco
Bria, Emilio
Giordano, Alessandro
Rindi, Guido
Sala, Evis
Tortora, Giampaolo
Valentini, Vincenzo
Boccia, Stefania
Margaritora, Stefano
Scambia, Giovanni
author_sort Lococo, Filippo
collection PubMed
description BACKGROUND: The current management of lung cancer patients has reached a high level of complexity. Indeed, besides the traditional clinical variables (e.g., age, sex, TNM stage), new omics data have recently been introduced in clinical practice, thereby making more complex the decision-making process. With the advent of Artificial intelligence (AI) techniques, various omics datasets may be used to create more accurate predictive models paving the way for a better care in lung cancer patients. METHODS: The LANTERN study is a multi-center observational clinical trial involving a multidisciplinary consortium of five institutions from different European countries. The aim of this trial is to develop accurate several predictive models for lung cancer patients, through the creation of Digital Human Avatars (DHA), defined as digital representations of patients using various omics-based variables and integrating well-established clinical factors with genomic data, quantitative imaging data etc. A total of 600 lung cancer patients will be prospectively enrolled by the recruiting centers and multi-omics data will be collected. Data will then be modelled and parameterized in an experimental context of cutting-edge big data analysis. All data variables will be recorded according to a shared common ontology based on variable-specific domains in order to enhance their direct actionability. An exploratory analysis will then initiate the biomarker identification process. The second phase of the project will focus on creating multiple multivariate models trained though advanced machine learning (ML) and AI techniques for the specific areas of interest. Finally, the developed models will be validated in order to test their robustness, transferability and generalizability, leading to the development of the DHA. All the potential clinical and scientific stakeholders will be involved in the DHA development process. The main goals aim of LANTERN project are: i) To develop predictive models for lung cancer diagnosis and histological characterization; (ii) to set up personalized predictive models for individual-specific treatments; iii) to enable feedback data loops for preventive healthcare strategies and quality of life management. DISCUSSION: The LANTERN project will develop a predictive platform based on integration of multi-omics data. This will enhance the generation of important and valuable information assets, in order to identify new biomarkers that can be used for early detection, improved tumor diagnosis and personalization of treatment protocols. ETHICS COMMITTEE APPROVAL NUMBER: 5420 − 0002485/23 from Fondazione Policlinico Universitario Agostino Gemelli IRCCS – Università Cattolica del Sacro Cuore Ethics Committee. TRIAL REGISTRATION: clinicaltrial.gov - NCT05802771.
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spelling pubmed-102623712023-06-15 Lung cancer multi-omics digital human avatars for integrating precision medicine into clinical practice: the LANTERN study Lococo, Filippo Boldrini, Luca Diepriye, Charles-Davies Evangelista, Jessica Nero, Camilla Flamini, Sara Minucci, Angelo De Paolis, Elisa Vita, Emanuele Cesario, Alfredo Annunziata, Salvatore Calcagni, Maria Lucia Chiappetta, Marco Cancellieri, Alessandra Larici, Anna Rita Cicchetti, Giuseppe Troost, Esther G.C. Ádány, Róza Farré, Núria Öztürk, Ece Van Doorne, Dominique Leoncini, Fausto Urbani, Andrea Trisolini, Rocco Bria, Emilio Giordano, Alessandro Rindi, Guido Sala, Evis Tortora, Giampaolo Valentini, Vincenzo Boccia, Stefania Margaritora, Stefano Scambia, Giovanni BMC Cancer Study Protocol BACKGROUND: The current management of lung cancer patients has reached a high level of complexity. Indeed, besides the traditional clinical variables (e.g., age, sex, TNM stage), new omics data have recently been introduced in clinical practice, thereby making more complex the decision-making process. With the advent of Artificial intelligence (AI) techniques, various omics datasets may be used to create more accurate predictive models paving the way for a better care in lung cancer patients. METHODS: The LANTERN study is a multi-center observational clinical trial involving a multidisciplinary consortium of five institutions from different European countries. The aim of this trial is to develop accurate several predictive models for lung cancer patients, through the creation of Digital Human Avatars (DHA), defined as digital representations of patients using various omics-based variables and integrating well-established clinical factors with genomic data, quantitative imaging data etc. A total of 600 lung cancer patients will be prospectively enrolled by the recruiting centers and multi-omics data will be collected. Data will then be modelled and parameterized in an experimental context of cutting-edge big data analysis. All data variables will be recorded according to a shared common ontology based on variable-specific domains in order to enhance their direct actionability. An exploratory analysis will then initiate the biomarker identification process. The second phase of the project will focus on creating multiple multivariate models trained though advanced machine learning (ML) and AI techniques for the specific areas of interest. Finally, the developed models will be validated in order to test their robustness, transferability and generalizability, leading to the development of the DHA. All the potential clinical and scientific stakeholders will be involved in the DHA development process. The main goals aim of LANTERN project are: i) To develop predictive models for lung cancer diagnosis and histological characterization; (ii) to set up personalized predictive models for individual-specific treatments; iii) to enable feedback data loops for preventive healthcare strategies and quality of life management. DISCUSSION: The LANTERN project will develop a predictive platform based on integration of multi-omics data. This will enhance the generation of important and valuable information assets, in order to identify new biomarkers that can be used for early detection, improved tumor diagnosis and personalization of treatment protocols. ETHICS COMMITTEE APPROVAL NUMBER: 5420 − 0002485/23 from Fondazione Policlinico Universitario Agostino Gemelli IRCCS – Università Cattolica del Sacro Cuore Ethics Committee. TRIAL REGISTRATION: clinicaltrial.gov - NCT05802771. BioMed Central 2023-06-13 /pmc/articles/PMC10262371/ /pubmed/37312079 http://dx.doi.org/10.1186/s12885-023-10997-x Text en © The Author(s) 2023, corrected publication 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Study Protocol
Lococo, Filippo
Boldrini, Luca
Diepriye, Charles-Davies
Evangelista, Jessica
Nero, Camilla
Flamini, Sara
Minucci, Angelo
De Paolis, Elisa
Vita, Emanuele
Cesario, Alfredo
Annunziata, Salvatore
Calcagni, Maria Lucia
Chiappetta, Marco
Cancellieri, Alessandra
Larici, Anna Rita
Cicchetti, Giuseppe
Troost, Esther G.C.
Ádány, Róza
Farré, Núria
Öztürk, Ece
Van Doorne, Dominique
Leoncini, Fausto
Urbani, Andrea
Trisolini, Rocco
Bria, Emilio
Giordano, Alessandro
Rindi, Guido
Sala, Evis
Tortora, Giampaolo
Valentini, Vincenzo
Boccia, Stefania
Margaritora, Stefano
Scambia, Giovanni
Lung cancer multi-omics digital human avatars for integrating precision medicine into clinical practice: the LANTERN study
title Lung cancer multi-omics digital human avatars for integrating precision medicine into clinical practice: the LANTERN study
title_full Lung cancer multi-omics digital human avatars for integrating precision medicine into clinical practice: the LANTERN study
title_fullStr Lung cancer multi-omics digital human avatars for integrating precision medicine into clinical practice: the LANTERN study
title_full_unstemmed Lung cancer multi-omics digital human avatars for integrating precision medicine into clinical practice: the LANTERN study
title_short Lung cancer multi-omics digital human avatars for integrating precision medicine into clinical practice: the LANTERN study
title_sort lung cancer multi-omics digital human avatars for integrating precision medicine into clinical practice: the lantern study
topic Study Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10262371/
https://www.ncbi.nlm.nih.gov/pubmed/37312079
http://dx.doi.org/10.1186/s12885-023-10997-x
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