Clinical trial NCT06270992: Oral Microbiome Diagnostics of Lung Cancer
Official title: Developing an Artificial Intelligence-based Diagnostic Method Based on Oral Microbiome for Non-invasive Diagnosis of Lung Cancer
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Lung Cancer
Keywords: oral microbiome
non-invasive diagnosis
artificial intelligence
Lead sponsor: TC Erciyes University
Sponsor class: OTHER
Collaborators: name: The Scientific and Technological Research Council of Turkey
First posted: 2024-02-21
Enrollment: 676
Sex: ALL
Minimum age (years): 18
Maximum age (years): 65
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: CASE_CONTROL
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: The population consists of a lung cancer group of 192 patients, a non-lung cancer benign lung disease group of 192 patients, and 292 healthy controls. Each subgroups are gender and age matched.

Brief summary: The study aims to develop a deep learning-based diagnostic method for lung cancer using the oral microbiome. This innovative approach involves establishing an observational cohort of 576 individuals, including lung cancer patients, non-cancerous benign lung disease patients, and healthy controls, to collect tongue swab samples for 16S rRNA sequencing. Additionally, an international cohort of approximately 1700 individuals will be formed using in silico data. The project will utilize deep learning methods to analyze all data integratively and develop an AI diagnostic algorithm capable of distinguishing lung cancer patients from others. The diagnostic method's performance will be tested in a pilot clinical trial with 96 individuals using a PRoBE design. Led by experts in chest surgery, molecular microbiology, and bioinformatics, the project spans over 30 months and aims to create a non-invasive, easily accessible lung cancer screening method that could lead to significant diagnostic advancements and potential spin-off companies in the field of liquid biopsy/molecular diagnosis.

Detailed description: Cancer is a global health issue that is on an increasing trend in terms of incidence and mortality rates, hindering the increase in life expectancy. According to the World Health Organization, lung, colorectal, and liver cancers are among the most common causes of cancer-related deaths. In Turkey, the incidence and mortality rates of lung cancer are higher than the world average. are among the risk factors that may increase the risk of lung cancer. In addition to risk factors like family history, smoking, different studies have shown that dysbiotic oral microbiome may contribute to the risk of lung cancer.

The oral microbiome is the second most diverse microenvironment in our body and has been associated with many diseases, including lung cancer. Studies to date on lung cancer-oral microbiome have generally involved designs aimed at resolving cause-and-effect relationships through statistical differences and/or mechanisms involving microbiome units.

However, there is no literature on any study aimed at developing a deep learning-based diagnostic method that focuses on the oral microbiome.Therefore, the proposed study aims to develop a microbiome based deep learning diagnostic method for lung cancer diagnosis. To this end, an observation cohort will be established consisting of 192 lung cancer patients, 192 non-cancerous benign lung disease patients, and 192 healthy controls. Tongue swab samples belonging to the cohort will be collected, and 16S rRNA sequencing will be performed. At the same time, an international observation cohort of approximately 1700 individuals will be created using in silico data. All data will be analyzed integratively, and an artificial intelligence diagnostic algorithm that can differentiate lung cancer patients from other lung diseases and healthy individuals will be developed using deep learning methods. In the final stage, the performance of the diagnostic method developed for a pilot clinical trial cohort of 96 individuals will be tested using a PRoBE (prospective specimen collection before outcome ascertainment and retrospective blinded evaluation) design.

The original aspects of the project are the proposal of a novel design in the literature, the creation of an experimental design/clinical trial and the presentation of a potential solution proposal that may have a high impact on an important diagnostic problem.

If the project is successfully completed, an artificial intelligence-based method that can potentially diagnose lung cancer through non-invasive oral microbiome samples will be developed. In addition to its patentability, if the method is further developed (in the medium to long term) into a product, it will enable lung cancer screening to be easily performed even in primary healthcare institutions with a simple oral swab sample.

Eligibility criteria: Inclusion Criteria:

* To be between the ages of 18 and 65,
* Not to have a diagnosed lung disease or suspicion thereof,
* Not to have complaints related to the lungs and/or respiratory tract,
* Not to have alcohol or severe substance dependency,
* Not having a hospitalization history in the last year,
* Not having used antibiotics in the last six months,
* Not having used products manufactured to support the oral microbiome, such as probiotics (lozenges, sublingual drops) for at least the last six months,
* Not being pregnant or breastfeeding,
* Not having undergone dental procedures such as root canal treatment, implants, prostheses, tooth extraction, fillings in the last 6 months
* Not having dominant immune-origin lesions (such as aphthous ulcers, erythema multiforme, pemphigus), viral-origin lesions (such as herpes, Koplik spots, herpangina), dominant bacterial infections like tonsillitis, and/or thermal or chemical mucosal traumas in the mouth.

Exclusion Criteria:

* Not to satisfy inclusion or declining to participate even though all the inclusion criteria are satisfied.

Primary outcomes:
measure: Diagnostics accuracy assessment
timeFrame: 30 months
Secondary outcomes:
Other outcomes:
Locations:
city: Kayseri
country: Turkey
facility: Erciyes University Hospital
Study officials:
References: