Neoplasms

Respiratory Tract (Lung and Bronchial) Diseases

All Conditions

Lung Neoplasms

Respiratory Tract Neoplasms

Thoracic Neoplasms

Lung Diseases

Respiratory Tract Diseases

Anti-Infective Agents

All Drugs and Chemicals

Central Nervous System Depressants

Anti-Bacterial Agents

Antibiotics, Antitubercular

Ethanol

192 patients diagnosed with lung cancer with the following inclusion criteria:

* To be between the ages of 18 and 65,
* Not having been previously diagnosed with and treated for lung cancer,
* Not having received any cancer treatment in the last 2 years,
* Not having been operated on within the last two years,
* 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 probiotic (lozenges, sublingual drops) for at least the last six months,
* Not being pregnant, not 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 , viral-origin lesions, dominant bacterial infections like tonsillitis, and/or thermal or chemical mucosal traumas in the mouth.

192 patients diagnosed with non-cancer diseases with the following inclusion criteria:

* To be between the ages of 18 and 65,
* To have visited our clinic with complaints related to the lungs and/or respiratory tract,
* Not having been diagnosed with lung cancer after clinical evaluation,
* Not having received any cancer treatment in the last 2 years,
* Not having been operated on within the last two years,
* 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 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, viral-origin lesions, dominant bacterial infections like tonsillitis, and/or thermal or chemical mucosal traumas in the mouth.

292 individuals without lung cancer or another lung disease diagnosis with the following 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, viral-origin lesions, dominant bacterial infections like tonsillitis, and/or thermal or chemical mucosal traumas in the mouth.

For diagnostic evaluation, the necessary procedures from the standard protocols consisting of anamnesis, physical examination, laboratory tests, radiological imaging methods, and tissue biopsy will be followed. Computerized Tomography (CT) and Positron Emission Tomography-Computed Tomography (PET-CT) will be used as imaging methods, while fiberoptic bronchoscopy and video-assisted mediastinoscopy will be applied for tissue diagnosis and staging.

NCCN (National Comprehensive Cancer Network) diagnosis

Aycan Gundogdu, PhD

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.

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.

Oral Microbiome Diagnostics of Lung Cancer

Developing an Artificial Intelligence-based Diagnostic Method Based on Oral Microbiome for Non-invasive Diagnosis of Lung Cancer

Diagnostic technology under investigation will be evaluated using sensitivity, specificity, area under receiver operating curve. Cross validation will be used for testing and NCCN diagnosis and patient follow-ups will be considered as the ground truth.

THE SCIENTIFIC AND TECHNOLOGICAL RESEARCH COUNCIL OF TÜRKİYE

TC Erciyes University

Associate Professor

Digestive System Diseases

Barrett Esophagus

Precancerous Conditions

Esophageal Diseases

Gastrointestinal Diseases

Patients who have undergone an endoscopy within the last three years and have never been diagnosed with Barrett's esophagus.

Patients who have undergone an endoscopy within the last three years and have histologically confirmed Barrett's esophagus. BE segment must be M\>1cm, and has not be treated with endoscopic eradication therapy (focal mucosal resection without subsequent eradication therapy is allowed).

Sample collection

Julian Abrams, MD

Katharine Boyce, RN

Joel H. Rubenstein, MD

Amy Liu

This is a longitudinal cohort study to assess the impact of repeated sampling of an oral microbiome signature for Barrett's esophagus (BE). Potential participants will be identified through chart review of patients who have had an endoscopy in the past three years.

Saliva collection is non-invasive and can be performed in clinical and non-clinical settings. As such, saliva testing is a highly attractive method for diagnosing and/or monitoring disease. The investigators previously showed that the bacterial make-up ("microbiome") of saliva is highly distinct in patients with and without Barrett's esophagus (BE), a precursor to esophageal cancer that normally can only be diagnosed by undergoing an upper endoscopy. While the microbiome of saliva is felt to be relatively stable over time, it is unclear whether a microbiome signature for Barrett's esophagus is reproducible within individuals. Also, repeated testing of saliva may improve the ability to diagnose Barrett's esophagus.

Longitudinal Oral Microbiome Sampling for BE

Repeated Sampling of the Oral Microbiome to Improve Detection of Barrett's Esophagus

Using a classifier developed from 16S rRNA gene sequencing data, the investigators will calculate the area under the receiver operating curve (AUROC) and associated 95% confidence interval for a classifier with three repeated measures per subject.

This will be measured to determine the degree of consistency of a microbiome-based classifier for each individual's salivary microbiome assessments (three total per subject).

University of Michigan

National Cancer Institute (NCI)

Columbia University

Associate Professor of Medicine and Epidemiology

Behaviors and Mental Disorders

Mouth and Tooth Diseases

Rare Diseases

Carcinoma, Squamous Cell

Gastroesophageal Reflux

Alcohol Drinking

Mouth Neoplasms

Neoplasms, Squamous Cell

Carcinoma

Head and Neck Neoplasms

Mouth Diseases

Stomatognathic Diseases

Neoplasms, Glandular and Epithelial

Neoplasms by Histologic Type

Oral Cancer

Lip and Oral Cavity Cancer

Complement System Proteins

From PLCO and ACS CPS II, identified 140 UADSCC cases. These cases are frequency matched 3:1 (420 controls) by incidence density sampling for study (ACS. PLCO),

From PLCO and ACS CPS II, we have identified 140 UADSCC cases. These cases are frequency matched 3:1 (420 controls) by incidence density sampling for study (ACS. PLCO),

The sequence-based approach is selected because it classifies samples by considering the entire difference (genetic distance) in 16S rRNA gene sequences among samples analyzed, unbiased by artificial details such as taxonomies, sequence length, or even quality scores.

Classification of Oral Microbiome Using 16S rRNA gene sequence-based approach

The taxonomic approach is a complement to the sequence-based approach in that it will further identify specific taxonomic groups or species that can explain the difference found by overall analyses.

Classification of Oral Microbiome Using taxonomic approach

Jiyoung Ahn, MD

The human oral cavity is a diverse habitat that contains approximately 700 prokaryotic species. The oral microbiome is comprised of 44% named species, 12% isolates representing unnamed species, and 44% phylotypes known only from 16S rRNA based cloning studies (http://www.homd.org/). Species from 11 phyla have been identified: Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, Spirochaetes, Fusobacteria, TM7, Synergistetes, Chlamydiae, Chloroflexi and SR1 (http://www.homd.org/). Because these observations have been mainly based on data generated from traditional Sanger sequencing, the diversity of oral microbiome is highly likely underestimated. Application of high throughput sequencing to the oral microbiome similar to the scale of the microbiome studies of other body sites (GI tract, skin, and vagina) under the Human Microbiome Project is necessary to obtain data essential for understanding the diversity and community structure of the oral microbiome in health and disease.

The Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial and The American Cancer Society Cancer Prevention Study II (CPS-II) investigators have identified 140 UADSCC cases that have been frequency matched 3:1 (420 controls) by incidence density sampling for study (ACS. PLCO), gender, ethnic background, and age, within 10 years.

140 UADSCC cases and 420 controls using Roche 454 16S rRNA gene sequencing.

The Oral Microbiome and Upper Aerodigestive Squamous Cell Cancer

Data alignment and clustering to determine microbiome species diversity

National Institutes of Health (NIH)

NYU Langone Health

Gland and Hormone Related Diseases

Pancreatic Neoplasms

Gastrointestinal Neoplasms

Digestive System Neoplasms

Endocrine Gland Neoplasms

Pancreatic Diseases

Endocrine System Diseases

Pancreatic Cancer

Gastrointestinal Agents

Pancrelipase

Pancreatin

Pancreatic cancer patients evaluated at the Pancreatic Surgery Unit of the Pancreas Institute of the University of Verona will be enrolled. Fifteen PC patients per stage will be selected (15 Stage I/II, Stage III, Stage IV). Patients may have been scheduled for surgery or for biopsy for future systemic treatments. An oral microbiota (dental plaque) sample will be collected and a periodontal and dental assessment will be performed, before any therapeutic procedure, together with orthopantomography

Age-matched (+- 5 years) healthy controls referring to the Odontostomatology Unit for oral procedure according to clinical practice, with a recent (not longer than 12 months) cross-sectional abdominal imaging excluding gastrointestinal cancers.

Pancreatic cancer patients evaluated at the Pancreatic Surgery Unit of the Pancreas Institute of the University of Verona will be enrolled. Fifteen PC patients per stage will be selected (15 Stage I/II, Stage III, Stage IV). Patients may have been scheduled for surgery or for biopsy for future systemic treatments.

Patients suffering from IPMNs evaluated at outpatients clinics or scheduled for surgery.

Dental plaque sampling

qPCR

Salvatore Paiella, MD, PhD

Erica Secchettin, PharmD

Salvatore Paiella

Erica Secchettin

Francesca Zotti

Pancreatic cancer (PC) is a solid malignancy with a dismal prognosis. It has a 5-year survival rate of approximately 8%. This is due to the usually late diagnosis, to chemoresistance, and to intrinsic biological aggressiveness. Risk factors for PC are smoking, alcohol, chronic pancreatitis, obesity, and diabetes. Recently, research has been dedicated to the identification of a causal connection between certain pathogenic microorganisms, especially of the oral flora, and PC. This would ultimately allow to identify new biomarkers to adopt for early diagnosis, or to create new strategies for prevention.

Oral microbiota, periodontal disease and neoplastic risk

When referring to "oral microbiota" (OM), about 700 hundreds bacterial species are mentioned, colonizing the oral cavity. A change in the normal flora of the oral cavity is commonly indicated with the term "dysbiosis".

The causal connection between oral microbiota, periodontal disease and neoplastic risk is possibly triple. First, it has been found that oral flora substantially differentiates between cancer patients and controls. In particular, the most predominant phyla in cancer patients are Firmicutes and Actinobacteria, whereas Proteobacteria, Fusobacteria, and Bacteroides are more common in healthy controls. This highlights the possibility of a direct causal connection between dysbiosis and neoplastic risk. Second, oral dysbiosis represents the main risk factor of PD that per sé is a risk factor of many cancers. Third, the conditions leading to oral dysbiosis (alcohol, smoking, obesity, diabetes, chronic drugs intake, dietary habits, etc.) are the most well known risk factors either for cancer and oral dysbiosis. The common denominator is always represented by chronic inflammation and migration of microorganisms to distant sites, ultimately promoting neoplastic progression.

This tangled net of causal connections sheds light on the potential important role of the oral cavity and PD as independent risk factors for many cancers, and as modifiable elements to reduce the neoplastic risk and to perform prevention(15).

Oral microbiota, periodontal disease and pancreatic cancer

In 2012, the pioneering study by Farrell et al. showed that bacteria of the OM can discriminate PC patients from healthy subjects. Since then, few other studies have shown that changes of the OM are independent risk factors for PC and that the OM of PC patients differs than controls. The involved bacterial species are many and their role seems to be contrasting on the basis of the study considered. Farrell et al. found that the combined adoption of Neisseria elongata and Streptococcus mitis distinguished PC patients from healthy controls (both showed low levels in PC patients, AUC of combined sensitivity 0.9), and that higher levels of Granulicatella adiacens and Streptococcus Mitis distinguished PC patients from chronic pancreatitis ones. Torres et al. found a higher ratio of Leptotrichia to Porphyromonas in PC patients. Fan et al. reported that Porphyromonas gingivalis, Prevotella intermedia, Alloprevotella and Aggregatibacter actinomycetemcomitans are associated with a higher risk of PC, whereas Fusobacteria and Leptotrichia were associated with a decreased risk. Another study evaluating the diversity of OM in three groups of individuals (PC patients, patients suffering from Intraductal papillary mucinous neoplasms \[a pancreatic preneoplastic condition\], and healthy controls), excluding current smokers and users of antibiotics, found no differences in the OM, although patients with PC had a higher proportion of Firmicutes compared with Intraductal Papillary Mucinous Neoplasms (IPMNs) and controls. Lastly, a recent study by Gaiser et al. showed that the cystic fluid of patients submitted to surgery for IPMNs contained bacterial species that are commonly found in the oral cavity, including, among the others, Granulicatella adiacens, Fusobacterium nucleatum. These two, in particular, were higher in the cohort of individuals with IPMNs with high-grade dysplasia, indicating a pivotal role in tumorigenesis(19).

As regards PD, the first studies demonstrating an association between PD and PC date back to the mid of 2000's, and they were confirmed afterward, even adjusting confounders such as diabetes, pancreatitis, hyperlipemia, smoking or alcohol-related conditions. PD is strictly connected to oral hygiene, that seems to be associated to an increased risk of PC. It is now clear that PD can concur to development of PC in several ways, promoting chronic inflammation, spreading continuously to distant organs (including pancreas) pro-tumorigenic bacteria, or promoting a chronic alteration of the immune function that make the individual more prone to develop a cancer.

Pancreatic Cancer and Oral Microbiome

Pancreatic canceR pErioDontAl sTatus and Oral micRobiome - PREDATOR Study

According to the periodontal status (appropriate scores will be used), patients will be divided into distinct groups. The proportional means of abundance of selected genera and species, and bacterial load (real-time data) between these groups will be analyzed using the Mann-Whitney U-test. Correlations between mean distributions of the genera and species and the parodontal status scores values will be analysed using the Spearman Correlation Coefficient.

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Oral Microbiome Diagnostics of Lung Cancer	NCT06270992	Lung Cancer	NCCN (National ...	18 Years - 65 Years	TC Erciyes University
Longitudinal Oral Microbiome Sampling for BE	NCT05133102	Barrett Esophag...	Sample collecti...	18 Years -	Columbia University
Oral Microbiome Diagnostics of Lung Cancer	NCT06270992	Lung Cancer	NCCN (National ...	18 Years - 65 Years	TC Erciyes University
The Oral Microbiome and Upper Aerodigestive Squamous Cell Cancer	NCT03304132	Oral Cancer	Classification ... Classification ...	40 Years - 92 Years	NYU Langone Health
Pancreatic Cancer and Oral Microbiome	NCT04993846	Oral Microbiome Pancreatic Canc... IPMN Parodontopathy	Dental plaque s... qPCR	18 Years - 75 Years	Azienda Ospedaliera Universitaria Integrata Verona

Spots Global Cancer Trial Database for oral microbiome