Lung Cancer, Lung Cancer Diagnosis, Non-small Cell Lung Cancer Stage I, Non-small Cell Lung Cancer Stage II
Conditions
Keywords
Lung cancer, early diagnosis, Biomarkers, Proteomic, Volatomic
Brief summary
The goal of this prospective, case-control study is to discover the specific omics biomarkers of early stage of lung cancer using the non-invasive samples (breath, urine and serum) in a total of 200 subjects (100 healthy controls and 100 lung cancer patient). The main questions it aims to answer are: * Which are the omics biomarkers that characterize the early stage of lung cancer? * How to Translate Laboratory Data into Clinical Data? For each participant we will collected the breath, urine and blood samples. In lung cancer patients group the samples will be sample before lung cancer resection. The samples of Breath, urine and serum will be analysed using different type of analysis: eNose and the Gas Chromatography combined with Ion Mass Spectrometry (GC/IMS). Moreover, Serum will be analyzed by mass-spectrometry-based proteomics. The purpose of these analyses will be to find biomarkers capable of distinguishing the early-stage of lung cancer from the healthy group. Followup will be performed to evaluate the possible change of the volatolomic and proteomic profile.
Detailed description
All partecipants will sign the Informed Consent before the sampling procedures. In addition, they will complete the clinical questionnaire containing medical history, smoking history and psychological evaluation.We will conduct the trial according to the ICH Good Clinical Practice (GCP) guidelines. Keeping accurate and consistent records is essential to a cooperative study.The IEO Data Management Office will responsible of the study database and data management.
Interventions
OTHERBreath, urine and blood analysis
breath sampling: all subjects exhale into two sterile Tedlar bags, connected to a mouthpiece, in a condition similar to traditional spirometry. Urine will be collected usual urine container. The blood sample (\ 5 ml) will be taken with a serum separator tube.
Sponsors
University of Rome Tor Vergata
European Institute of Oncology
Study design
Observational model
CASE_CONTROL
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
50 Years to 80 Years
Healthy volunteers
Yes
Inclusion criteria
1. Lung cancer group * Diagnosis of early stage - lung cancer * Signed Informed Consent * Completed questionnaire 2. Healthy subjects * high risk individuals (heavy smokers, subjects with pulmonary disease non-cancer related or with a familiar history of lung cancer disease) * Recent (within 6 months) negative Chest X-ray or CT scan
Exclusion criteria
Both groups * No previous chemo or radiotherapy for lung cancer * No previous malignancies within last 5 years * No abuse of alcohol (no more than 1 litre of wine for day). * No patients with psychiatric, addictive, or any disorder, which compromises ability to give informed consent for participation in this study
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Comparison of the proteomic and volatolomic signature in samples of respiratory exhalation, serum, and urine from patients with lung cancer (stage I/II) and healthy individuals at high risk. | 12 months | Enrolling two cohorts at baseline: healthy individuals at high risk with negative LDCT (no suspicious oncological findings) vs. patients with early-stage I/II lung cancer candidates for surgical resection; sampling in lung cancer patients will be performed pre-intervention. We will assess and compare the serum and urinary proteomic and volatile organic compound profiles, serum and respiratory, of the two cohorts under study at baseline and changes in proteomic and volatile organic compound signature at 12 months from baseline. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Omics-Data intagration. | 6 months | The serum proteomic and serum volatomomic, urinary, and respiratory data will be compared using algorithms based on artificial intelligence and deep learning. Data from each test, including patient follow-up, will be analyzed using multivariate statistical analysis of samples with multivariable logistic and Cox proportional hazards regression models to identify the most significant variables. |
Countries
Italy
Contacts
Primary ContactRoberto Gasparri, MD, PhD
Outcome results
None listed