Lung Cancer Multi-omics Digital Human Avatars for Integrating Precision Medicine Into Clinical Practice: the LANTERN Study
Conditions: Non Small Cell Lung Cancer
Sponsor: Fondazione Policlinico Universitario Agostino Gemelli IRCCS

 
Fondazione Policlinico Universitario Agostino Gemelli IRCCS 
Università Cattolica del Sacro Cuore 
 
 
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Version 1.0 del 16.12.2022 
 
TITLE: CANCER MULTI-OMICS AVATARS FOR INTEGRATED PRECISION MEDICINE  
 
SHORT TITLE: LANTERN 
 
PROMOTER: Fondazione Policlinico Universitario A. Gemelli, U.O.C. di Thoracic Surgery– I.R.C.C.S.  
  
PRINCIPAL INVESTIGATOR: Filippo Lococo, U.O.C. di Thoracic Surgery – I.R.C.C.S. Fondazione Policlinico 
Universitario A. Gemelli  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
Fondazione Policlinico Universitario Agostino Gemelli IRCCS 
Università Cattolica del Sacro Cuore 
 
 
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ABSTRACT 
 
Lung cancer is one of the most commonly diagnosed forms of cancer worldwide, accounting for 
approximately 12.2% of total cancer diagnoses. Despite the recent advances in treatment, prognosis still 
remains poor with a high mortality rate.  
The current therapeutic approaches have reached a significantly high level of complexity, due to the recent 
identification of mutations and other cancer heterogeneity drivers (i.e., actionable mutations or PD-L1 
expression for immunotherapy indication), thereby complicating the process of decision-making by clinicians.  
The integration of "omics" datasets in different domains (i.e., genomics, proteomics) may further support 
clinicians in identifying innovative variables to support a new kind of comprehensive cancer care, towards 
personalized medicine.  
In the last decade, significant improvements have been observed in the development of omics technologies, 
resulting in an overwhelming number of variables to be considered while making decisions that can be hardly 
handled by a human operator. Interestingly, with the advent of advanced Artificial intelligence (AI) 
techniques, more information can be obtained via "Internet of Things" technologies and various omics 
datasets which could be used efficaciously in creating predictive models.  
These models can be used can be used either individually or merged in a multi-omics patient avatars to assist 
in the clinical decision-making process and introduce new paradigms of care and research, evaluating 
unexpected links among variables that were previously unexplored.  
Therefore, the primary aim of the LANTERN project is to develop accurate predictive models for lung cancer 
patients, using various omics-based variables and integrating well-established clinical factors with "big data" 
and advanced imaging features. With this, individual patient avatars can be created which will serve as 
powerful tools in guiding clinicians and researchers in making more accurate decisions.  
 
AUTHORS 
 
Principal Investigators 
• 
Prof. Filippo Lococo - U.O.C. di Thoracic Surgery – Catholic University of Sacred Heart; I.R.C.C.S.  
Fondazione Policlinico Universitario A. Gemelli – filippo.lococo@policlinicogemelli.it  
 
Sub-Investigators 

 
Fondazione Policlinico Universitario Agostino Gemelli IRCCS 
Università Cattolica del Sacro Cuore 
 
 
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• 
Prof.  Troost Esther Chair of Department of Radiotherapy and Radiation Oncology of University 
Hospital and Faculty of Medicine Carl Gustav Carus of the Technische Universität Dresden, Dresden, 
Germany- esther.troost@uniklinikum-dresden.de; 
• 
Prof. Ádány Róza Head of the MTA-DE Public Health Research Group, University of Debrecen (DEB) - 
adany.roza@med.unideb.hu; 
• 
Dr. Farré Núria Radiation Oncologist, Consultant of Hospital de la Santa Creu i Sant Pau (HSCSP), 
Spain- nfarre@santpau.cat; 
• 
Dr. Van Doorne Dominique President of Accademia del Paziente Esperto EUPATI – AdPEE, Italy - 
segreteria@accademiadeipazienti.it 
• 
Dr. Öztürk Ece Asst. Prof. Of Koc University, Turkey- ozturkece@ku.edu.tr 
 
 
 
FINANCING 
Source 
The proposal is funded with reference to the Joint Transnational Call for Proposals (JTC) 2022 on 
"PREVENTION IN PERSONALISED MEDICINE" within the framework of ERA PerMed. 
 
 
RATIONALE OF THE STUDY 
 
To date, the current strategy for lung cancer treatment is still based on traditional clinical variables (e.g. age, 
sex, TNM stage) that appear clearly inadequate to deal with such a complex disease. In the last years, the 
advent of genomics and precision medicine approaches have transformed the horizon, firstly for scientists 
and physicians, and later for lung cancer patients. Besides the classical wealth of histological, clinical and 
demographic information, today’s technologies allow the easy capture of a wider range of data sources from 
genomics, proteomics, immunohistochemistry and imaging. All these additional sources need to be 
integrated by lung cancer physicians, in order to improve patient outcomes by stratifying or individualizing 
treatment decisions (personalised medicine approach). However, while this process is already available in 
the setting of controlled clinical trials, there is still a great limit to its application in daily clinical practice. 
Interestingly, with the introduction of Artificial Intelligence (AI) into clinical practice, physicians now 
potentially have the adequate technology to improve precision medicine approaches. Although, for AI 
applications to be successfully applied to the medical domain, an integrative machine learning (ML) system 
might be necessary, calling for the incorporation and fusion of heterogeneous datasets and diverse omics 
profiles.  
 

 
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Preliminary results from Consortium members  
 
Our consortium aims at fulfilling the objective of this year’s call through the intersectional, multinational and 
interdisciplinary collaboration between University and clinical research teams and private organisations 
towards the implementation of personalised medicine, by combining clinical research with bio-informatics 
components 
as 
well 
as 
ELSA 
research 
or 
implementation 
research. 
FPG 
in 
Rome 
(https://gstep.policlinicogemelli.it), the coordinating site, has a sound background in translational research 
of advanced quantitative imaging analysis and AI based applications for personalized medicine and has been 
involved in several international trials on lung cancer. In a retrospective study, FPG researchers explored 
technical issues in personalized medicine through the prediction of NSCLC survival by quantitative image 
analysis, demonstrating the usefulness of density correction of volumetric CT data (Farchione et al., 2020). 
Recently, this research team also participated in a study involving a distributed learning performed on over 
20,000 lung cancer patients by providing a privacy-by-design infrastructure (Personal Health Train), 
connecting FAIR (Findable, Accessible, Interoperable, Reusable) data sources from 5 different countries 
which enhanced distributed data analysis and ML (Deist et al., 2020). OncoRay, a national Centre for 
Radiation Research in Oncology, located in Dresden (TUD) (https://www.oncoray.de/center), is focused on 
applying ML/AI approaches to enable personalized radiation oncology based on big data, i.e. clinical imaging 
data (radiomics). In previous studies, their research team carried out a comparative study of machine learning 
methods for time-to-event survival data for radiomics risk modelling. From this study, they were able to 
identify a subset of algorithms which should be considered in future radiomics studies to develop stable and 
clinically relevant predictive models for time-to-event endpoints (Leger et al., 2017). HSCSP in Barcelona has 
a strong lung oncology research group, participating in numerous clinical trials in chemo-radiotherapy in lung 
cancer (Provencio et al., 2021), treating around 400 patients/year. In a recent retrospective study, HSCSP 
research team explored the effects of non-adherence to external beam radiation therapy on 1-year survival 
in cancer patients receiving treatment with a curative in Catalonia, Spain (Borras et al., 2020). DEB, is a 
renowned institution of higher education in Hungary, contributing immensely to the progress of clinical 
research with expertise in public health and play a major role in Ethico-legal aspects of personalised medicine 
research. In a Previous research project, FPG and DEB partnered with the PRECeDI consortium 
(http://www.precedi.eu/site), releasing a set of five recommendations for policy-makers, scientists and 
industry, including ethico-legal and policy recommendations, to foster the integration of PM approaches in 
the field of chronic disease prevention (Boccia et al., 2019). KU is a well recognized institute with over 150 
research centres, forums, and laboratories that conduct high quality scientific research projects with 
expertise in Thoracic Surgery, Pathology, Bioinformatics and Network Modelling and Engineered Cancer & 
Organ Models. In Previous studies, KU researchers were involved in the development of a three-dimensional 
hydrogel model that can mimic the native glycosaminoglycans in tissues (Öztürk et al., 2016; Öztürk et al., 
2017; Öztürket al., 2020). AdPEE is a patient organisation that ensures the active involvement of patients and 
caregivers in clinical research for constant and productive dialogue with decision-making bodies. 
(https://accademiadeipazienti.org/accademia-paziente-esperto-adpee/).  
 
 
 

 
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SUMMARY OF PROJECT RATIONALE AND WORKPLAN 
 
The LANTERN project was developed, based on 4 Work Packages (WP):  
 
i. WP1: Patient enrollment and omics data collection [Months 1-30].  
Objective: To gather information from all the clinical and omics based data sources acknowledged as clinically 
significant or decision support variables for lung cancer comprehensive diagnosis and therapy workflow 
definition. A structured terminological system will be developed for prospective data collection through 
specific Case Report Forms (CRFs).  
Methodology: The five main omics data tiers to be collected in this project, reflecting all the involved omics 
domains in the lung-cancer decision making pathway are: A. Demographic and physiological data; B. Medical 
history, clinic-functional, laboratory data and treatment information; C. Histo-pathological, immunological 
and genetic data; D. Radiomics and quantitative imaging data; E. Internet of Things derived data.  
 
ii. WP2: Omics data archiving and inter-actionability [Months 2-30].  
Objective: To ensure easy and effective omics data inter-actionability and to allow their complete integration 
into archiving systems and ad hoc digital platforms for data recording.  
Methodology: A standard unit of measurement will be used in order to record the single variables. Possible 
discrepancies originated from the use of different scales, will be solved through shared equivalence decision. 
All the laboratory data used in WP1 will undergo cross quality assurance (QA) procedures among the centres 
and all the images used in WP1 will have to adhere to the standard “dicom” format (.dcm).  
 
iii. WP3: Omics data modelling and avatar creation [Months 12-32].  
Objective: To set up dedicated multi-omics data models and create lung cancer Digital Human Avatars (DHA).  
Methodology: An exploratory analysis across all collected datasets to enable the start of the biomarker 
identification process; followed by the set-up of multiple distinct modular multivariate models, trained 
though advanced ML and AI techniques; and validated through internal and external validation to test their 
robustness, transferability and generalizability.  
 
iv. WP4: DHA towards implementation in healthcare [Months 1-36].  
Objective: To enable feedback data loops for preventive healthcare strategies and Quality of life (Qol) and to 
ensure that all the potential stakeholders are involved in the process of development of DHA for a fast uptake 
into daily practice.  
Methodology: All necessary factors (clinical, epidemiological, technical, economic, organizational, social, 
legal and ethical implications) will be considered for the proper implementation of the DHA in healthcare. To 

 
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this aim, a robust and rigorous scientific methodology will be applied by combining quantitative approaches 
(collection of scientific evidence and systematic literature reviews/meta-analysis; consultation of 
institutional flows and databases/hospital discharge forms/national price lists and data analyses; 
mathematical models; cost-utility/budget impact analyses) and qualitative research techniques (Consensus 
conference, Focus group, Delphi, Expert opinion).  
 
 
The LANTERN project is configured as highly innovative, since for the first time in the field of lung cancer 
research, we intend to develop a predictive platform powered by the integrated reading of data from the 
main omics sciences. The project has the ambition to combine data from radiomics with clinical and biological 
data, derived from biological omics (through the analysis of the patient's biological samples) on a digital 
platform. This project though seems ambiguous, has a well-thought out plan and is highly feasible through 
the cooperative work efforts and experience of each consortium partner in their respective fields. With the 
commitment of each partnering centre in undertaking their respective delegated tasks as described in the 
project work plans and tasks, We believe the LANTERN project will be completed within a timeframe of 3 
years which aligns with the expected time taken for the project completion as stated in the call document.  
 
Personalised medicine represents an evolution from traditional “one size fits all” strategies, extending the 
concept of stratifying cancer treatments on well-known clinical and pathological characteristics to a more 
individual, patients specific, truly personalized level. However, the current approach typically relies on 
molecular and cellular data-sets (i.e. mutations) that are very limited and relative only to subsets of the 
complex web of cellular data that determine treatment outcomes. Due to various histological, genetic, 
immunological and imaging characteristics that define its highly variable oncological outcomes, lung cancer 
represents an ideal case to run AI based multidimensional personalized medicine studies, thereby fitting into 
the purpose of this project.  
 
 
 
 
 
 
AIMS 
 
Main objectives 
The aim of LANTERN project is to develop accurate predictive models for lung cancer patients, using different 
omics-based variables, integrating well established clinical factors with big data and advanced imaging 

 
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features. This technological solution will be able to capture patients’ conditions at multiple levels, supporting 
an effective disease prevention and further reducing the healthcare costs through personalized treatments. 
More specifically, we will develop Digital Human Avatars (DHA) defined as computerized representations of 
individual patients and their characteristics, with a specific focus on advanced biological functions, as well as 
relevant pathological variables, which will be modelled, sourced and parametrized in a cutting-edge 
experimental “big data” setting.  
 
 
Secondary objectives 
Our main goals are:  
i. To gather information from all the clinical and omics-based data sources acknowledged as clinically 
significant  
ii. To ensure an easy and effective omics data inter-actionability and to allow their complete integration into 
archiving systems  
iii. To set up dedicated multi-omics data models and create a comprehensive lung cancer DHA  
iv. To enable feedback data loops for preventive healthcare strategies and quality of life (QoL) management  
v. To ensure that all the potential stakeholders are involved in the DHA development process.  
 
 
METHODOLOGY 
 
This is a prospective observational study 
 
WP1: Omics data collection 
The five main omics data tiers that will be identified as the ontology of this project, reflecting all the involved 
omics domains in the lung-cancer decision making pathway are: A. Demographic and physiological data; B. 
Medical history, clinic-functional, laboratory data and treatment information; C. Histo-pathological, 
immunological and genetic data; D. Radiomics and quantitative imaging data; E. Internet of Things derived 
data.  
A structured terminological system for prospective collection of anonymized data will be developed, creating 
a dedicated database for all omics domains considered. The data will be stored in a dedicated server and 
categorized according to the data reported in the WP1. Researchers will provide a structured terminological 
system for prospective collection of anonymized data in which all information will be recorded through 
specific Case Report Forms (CRFs), ensuring uniformity and consistency for all variables collected. Tumour 

 
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samples from all enrolling centres will be stored at the coordinating centre in a dedicated laboratory (FPG). 
In-house samples will be collected immediately after biopsy or surgery, while samples from other RUs will be 
delivered using an appropriate courier service following standard international rules and laws. A 
comprehensive molecular characterization and genomic profiling of tumour samples will be performed using 
the Illumina TruSightTM, which will allow the conversion of DNA and RNA extracted from formalin-fixed 
paraffin-embedded tissue samples into libraries enriched for cancer-related molecular targets. In our 
methodological approach, we will assess both DNA and RNA levels using a single high-throughput assay that 
evaluates a wide range of state-of-the-art clinical biomarkers. Specifically, we will analyze with high fidelity 
multiple somatic genomic alterations such as low-frequency somatic variants (single nucleotide variants, 
insertions and deletions, and splice variants) and copy number alterations (CNAs) in identified genes linked 
to cancer susceptibility and treatment.  
 
WP2: omics data archiving and inter-actionability 
The aim of this WP is to allow complete data integration into both existing and new archiving systems (Task 
2.1: Data archiving QA), and to ensure an easy and effective omics data inter-actionability (Task 2.2: Inter-
actionability QA). All the considered variables will be recorded according to a shared common ontology based 
on the variable-specific domains, in order to enhance their direct actionability. A standard unit of 
measurement will be used in order to record the single variables. Possible discrepancies originated from the 
use of different scales, will be solved through shared equivalence decision. All the laboratory data used in 
WP1 (Data A, B, C) will undergo cross quality assurance (QA) procedures among the involved centres prior to 
data upload in the shared platform. Patient data from KU, DEB and HSCSP will be transferred to FPG and TUD 
for the radiomic analysis. The shared general ontology will represent a structured terminological system for 
data archiving and analysis, granting uniformity and coherence for all the collected variables.  
Proposed radiomic analysis will be as follows: first, gross tumour volume (GTV) delineation will be performed 
retrospectively by experienced lung malignancy Radiotherapy Oncologists on each patient's images (CT 
images in DICOM (.dcm) format). DICOM files containing not only the CT images but also the RT structure file 
will be exported and processed using MODDICOM, an R package developed to optimize automatic loading of 
DICOM images and radiomic analysis. Radiomic features will then be extracted automatically. The extracted 
radiomic features belong to different families: Intensity-based, morphological, textural and fractal. All 
analyzed image features will be based on the standardization criteria of the IBSI initiative.  
 
WP3: omics data modelling, avatar creation and knowledge transfer 
Developing accurate predictive models and creating Digital Human Avatars (DHA) - Omics features 
identification and selection: In the first step, an exploratory analysis across all collected datasets (An estimate 
of ≈ 240 NSCLC patients) will enable the start of the biomarker identification process and restrict the cast 
amount of information towards a more selected pool of potential biomarkers. This first phase will employ 
robust data analysis techniques in order to identify relevant variables (T3.1.) in a univariate setting, taking 
individual statistical distributions, feature-relevant correlations and general descriptive statistics into 
account.  
 

 
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Patient selection 
INCLUSION CRITERIA: 
 
• 
Patients with (suspected) NSCLC 
• 
Age >18 yrs 
• 
ECOG 0-3 
• 
Written Informed Consent 
 
EXCLUSION CRITERIA:  
• 
ECOG 4 
• 
Psychosocial, or emotional conditions controindicating participation to the study 
 
 
STATISTICAL ANALYSIS METHODOLOGY 
 
An estimate of 600 Patients (both male and female) with ≥18 years of age with pathologically proven 
diagnosis of non-small cell lung cancer (NSCLC) will be enrolled. Sample size was calculated on the basis of G-
powerR program for “a priori” analysis, considering α=5% and power of 90%. 
The Wilcoxon Mann-Whitney (WMW) test will be used to investigate the ability of these features to predict 
clinical outcomes on univariate analysis, and the feature showing the lowest p-value will be considered as 
the most predictive parameter. Receiver Operating Feature curve analysis (ROC) will also be performed on 
the most significant feature obtained at WMW analysis, calculating the area under the curve (AUC) with the 
corresponding 95% confidence interval (CI) according to the Clopper-Pearson method. Model processing will 
be performed through advanced ML and AI techniques first on a dedicated training dataset to fit the model 
parameters. The obtained hyperparameters will be tuned on a dedicated validation dataset and finally a test 
dataset will be employed as a dataset independent of the training dataset but following the same probability 
distribution.  
 
 
Predictive model development 
 
 The objective of the second phase is creating multiple distinct but modular multivariate models, trained 
through advanced ML and AI techniques and segmented into specific modular areas of interest (T.3.2). 

 
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Different supervised models will be developed including logistic regression, decision tree, support vector 
machine, random forest, XGBoost classifier, and artificial neural networks. The k-fold cross-validation will be 
used for hyperparameters tuning. The final step in the proposed methodology is the statistical significance 
comparison of the performance of the ML models. This will be done evaluating predictive performances in 
terms of different metrics: the first evaluation will be based on accuracy (number of subjects correctly 
classified on the total number of patients) while the other will be on precision (true positive on total test 
positive), recall (sensitivity), F1 score (2*precision*recall/(precision+recall)) and AUC-ROC.  
We estimate about 300 NSCLC cases with complete data to start developing the models. Specific care will be 
taken to always ensure both user friendliness and model explainability as primary pillars of the model 
development strategies. Easily interpretable values such as SHAP (SHapley Additive exPlanations) values will 
be attached to each model in order to avoid any black-box approaches that might render model outputs, 
difficult to explain to the patients during their interactions with the clinicians.  
 
Predictive model validation 
 
Both the developed model and the comprehensive DHA will be validated in order to test their robustness, 
transferability and generalizability. Two consecutive validation strategies will be employed respectively: the 
internal and external validation techniques. We estimate a total number of ≈ 420 NSCLC cases to start the 
validation process. This process will include both internal and external validation. The internal validation step 
will be focused mainly on techniques such as K-fold cross validation. This will allow to achieve a Transparent 
Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) level 2b. At this 
TRIPOD level, data are non-randomly split (e.g., by location or time) into 2 groups: one to develop the 
prediction model and the remaining dataset to evaluate its predictive performance. In the case of suboptimal 
performances, all the failing models will be reverted back to the previous stage and the models will be 
retrained with new specifications in order to increase their robustness. At this point, the models can be 
validated using external datasets, reaching TRIPOD 3 and 4 levels (External validation). Finally, when the 
models reach a TRIPOD 4 level, data transferability and full actionability will be granted, allowing the 
successful and safe application of the model in the clinical Routine (T3.3).  
 
 
ETHICAL CONSIDERATIONS 
 
Considering the technicality and complexity of the LATERN Project, consortium partners are well aware of 
the various ethical and legal questions relating to the project. The main ethical issues related to this project 
include:  
the respect for persons and for human dignity; fair distribution of benefits and burden; the rights and 
interests of the participants; the need to ensure participants' informed consent; processing of personal data; 
concerns involving the development, deployment and/or use of artificial intelligence (AI)-based systems or 
techniques.  

 
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Therefore, LATERN partners will identify and implement a comprehensive set of ethical and legal 
requirements in accordance with Declaration of Helsinki principles to ensure compliance for all activities of 
the project. Appropriate ethical monitoring systems will also be set up.  
To this effect, LATERN will implement the following activities:  
 
Participation in this study must be entirely voluntary and fully informed consents of the participants must be 
obtained via project-specific Informed consent forms which must be documented before their participation 
in this project.  
 
• 
Obtaining ethical approvals by the relevant competent national/local ethics authorities in relation 
with project protocol (i.e., For using or storing human cells or tissues for genetic testing), according to each 
country’s specific ethics requirements and legislation  
• 
Personal data obtained in this study must be processed in an pseudomised format and Information 
about the data processing operations and the contact details of the data protection officer must be provided 
to the participants in accordance to applicable EU regulations (art 13/art 14 GDPR).  
• 
The development and deployment of AI-models will be based on specific ethical- approaches which 
must guarantee privacy and data protection throughout the system’s lifecycle. The adopted approach will be 
built upon key prerequisites that align with “Assessment List for Trustworthy Artificial Intelligence”(ALTAI) in 
order to establish procedures to detect, assess the level and address potential risks for the production of 
ethically sound AI models.  
• 
Monitoring compliance with set of ethical and legal requirements defined at EU and national level.  
 
 
 
 
 
 
  
  
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