Raman Analysis of Saliva as Biomarker of COPD

Study Overview
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Brief Summary
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Chronic Obstructive Pulmonary Disease (COPD) is a debilitating and chronic lung syndrome that causes accelerated lung function decline and death in the 20% of cases. Mostly, the non-adherence to therapy contributes to symptoms increase, mortality, inability and therapies failure, highly influencing the management costs associated to COPD. The existing procedure of diagnosing COPD is effective and fast. The acute treatment and the subsequent disease management, instead, strictly depend on the currently long and complex process of identification of three factors: COPD phenotype, adherence to chosen therapy and probability of exacerbation events. The knowledge of these factors is needed by clinicians to stratify patients and personalise the therapies and rehabilitation procedures, to initiate an effective disease management. The application of Raman spectroscopy on saliva, representing an easy collectable and highly informative biofluid, has been already proposed for different infective, neurological and cancer diseases, with promising results in the diagnostic and monitoring fields. In this project, we propose the use of Deep Learning analysis of Raman spectra collected from COPD patient's saliva to be combined with other clinical data for the development of a system able to provide fast and sensitive information regarding COPD phenotypes, adherence and exacerbation risks. This will support clinicians to personalise COPD therapies and treatments, and to monitor their effectiveness.

Detailed Description
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The main goal of the project is to create and validate a new method based on the Raman spectroscopy (RS) analysis of saliva for the optimised and personalised management of patients with Chronic Obstructive Pulmonary disease (COPD). The combination of the clinical instrumental data with the RS-approach will increase the quality of the clinical practice through appropriate stratification of patients, i.e., early identification of COPD phenotypes, consequent attribution of precise therapies, assessment of potential exacerbation risk and adherence to therapy. By the integration of instrumental and RS measures with Artificial Intelligence (AI), patients' COPD phenotype will be predicted allowing to efficiently direct the resources of the health-care system. The feasibility of the work is corroborated by the use of a sensitive, fast and miniaturized RS, used by non-specialized personnel and for the creation of a point-of care (POC) on an accessible biofluid. The multidisciplinary approach in pre-clinical, clinical and big data management fields is achieved through collaboration of academy, clinical research and industry. Starting from the unmet clinical need, CORSAI will build a close link between biomedical research, clinical research, data science towards the integration of PM into clinical practice and on ethical, legal, and social implications across the participating countries and beyond. The main objective is the collection of RS signals from the saliva of COPD patients, characterized for severity stages and phenotypes using GERA instruments, and corresponding CTRL and asthma patients (AsP). The creation and correlation of the dataset will lead to the accomplishment of specific objectives: I) Identification of the specific COPD, CTRL and AsP RF; II) Monitoring of therapy adherence through the drug signal in saliva; III) Definition of COPD phenotypes on the base of the RF correlated with instrumental GERA data; IV) Monitoring of the rehabilitation procedures and effects; V) Association of a high exacerbation risk to specific COPD patients; VI) Creation of a classification model from the RS database; VII) Application of high-performance computing for data analysis; VIII) Integration of the portable RS as POC. The novelty of CORSAI relies in the advanced methodology, brought to the bed side thanks to portable instruments. The minimal invasive procedure used for the saliva collection and the velocity for the Raman acquisition represent relevant advantages allowing the continuous monitoring of patients' adherence to therapy, and the contemporary discrimination of COPD phenotypes with high rate of exacerbation. The feasibility of the project is directly related to the biological sample and proposed technology, already tested in the clinical setting19: i)easy collection and storage of saliva fits the clinical scenario; ii) minimal sample preparation and portable device enable POC use by non-specialized personnel, with AI remote decision guidance. SAMPLE COLLECTION: Saliva collection from all the selected subjects will be performed following the Salivette (SARSTEDT) manufacturer's instructions. To limit variability in salivary content not related to COPD, saliva will be obtained from all subjects at a fixed time, after an appropriate lag time from feeding and teeth brushing. Pre-analytical parameters (i.e. storage temperature and time between collection and processing), dietary and smoking habit will be properly recorded. Briefly, the swab will be removed, placed in the mouth and chewed for 60 seconds to stimulate salivation. Then the swab will be centrifuged for 2 minutes at 1,000 g to remove cells fragments and food debris. Collected samples will be stored at -80° C. SAMPLE PROCESSING: For the Raman analysis, a drop of each sample will be casted on an aluminium foil in order to achieve the Surface Enhanced Raman Scattering (SERS). DATA COLLECTION: SERS spectra will be acquired using an Aramis Raman microscope (Horiba Jobin-Yvon, France) equipped with a laser light source operating at 785 nm with laser power ranging from 25-100% (Max power 512 mW). Acquisition time between 10-30 seconds will be used. The instrument will be calibrated before each analysis using the reference band of silicon at 520.7 cm-1. Raman spectra will be collected from 35 points following a line-map from the edge to the centre of the drop. Spectra will be acquired in the region between 400 and 1600 cm-1 using a 50x objective (Olympus, Japan). Spectra resolution is about 1.2 cm-1. The software package LabSpec 6 (Horiba Jobin-Yvon, France) will be used for map design and the acquisition of spectra. DATA PROCESSING: All the acquired spectra will be fit with a fourth-degree polynomial baseline and normalized by unit vector using the dedicated software LabSpec 6. The contribution of the substrate will be removed from each spectra. The statistical analysis to validate the method, will be performed using a multivariate analysis approach. Principal Component analysis (PCA) will be performed in order to reduce data dimensions and to evidence major trends. The first 20 resultant Principal Components (PCs) will be used in a classification model, Linear Discriminant Analysis (LDA), to discriminate the data maximizing the variance between the selected groups. The smallest number of PCs will be selected to prevent data overfitting. Leave-one-out cross-validation and confusion matrix test will be used to evaluate the method sensitivity, precision and accuracy of the LDA model. Mann-Whitney will be performed on PCs scores to verify the differences statistically relevant between the analysed groups. Correlation and partial correlation analysis will be performed using the Spearman's test, assuming as valid correlation only the coefficients with a p-value lower than 0.05. The statistical analysis will be performed using Origin2018 (OriginLab, USA). DEEP LEARNING: The datasets will be analysed and processed using Deep Learning models with the aim to discover significant patterns that can be used to confirm and analyse trends and to develop predictions and decision support about the COPD stratification. Techniques of data augmentation and automatic hyperparameter optimization will be developed in order to enhance classification performances and improve generalization ability. In order to reach a tradeoff between predictive accuracy and interpretability, a class activation mapping (CAM)-based approach will be applied to visualize the active variables in the spectra in order to identify discriminative pattern to extract the most informative spectral features. UNIMIB and GERA will implement an explanation mechanism to identify the active variables in whole spectrum and interpret the internal feature representations and data transformation pipeline of the CNN model. UNIMIB and GERA will integrate the various computational modules in a modular computational pipeline for patient-wise classification.

Official Title
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Raman Analysis of Saliva From COPD Patients as New Biomarker: AI-based Point-of-care for the Disease Monitoring and Management

Conditions
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Chronic Obstructive Pulmonary Disease

Intervention / Treatment
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* Procedure: Collection and Raman analysis of saliva for the database


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: COPD patients will be defined as a postbronchodilator ratio of FEV1/FEV <0.7. The severity of airflow limitation and phenotypes will be defined as described by the GOLD grading system, including Grade 2, 3 or 4. Overlapped Asthma - COPD will be established by the presence of a combination of the following factors: history of asthma and/or atopy, reversibility in the bronchodilator test, notable eosinophilia in respiratory and/or peripheral secretions, high IgE, positive prick test to pneumoallergens and high concentrations of exhaled NO Sex and age matched HC and AsP (bronchial asthma according to The Global Strategy for Asthma Management and Prevention 2018 from at least 6 months) will be recruited as controls. Exclusion Criteria: Exclusion criteria will be the combination with obstructive sleep apnea, cancer, MMSE<24, at least 4 weeks from the last acute exacerbation, cardiovascular, neurologic and kidney diseases, age<18 Bacterial or fungal oral infections in progress

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Asthma-COPD Overlapped (aCOPD)<br>50 subjects affected by Asthma-COPD Overlapped comparable by age and sex with the other recruited subjects. The diagnosis of the mixed phenotypes will be established by the presence of a combination of the following factors: history of asthma and/or atopy, reversibility in the bronchodilator test, notable eosinophilia in respiratory and/or peripheral secretions, high IgE, positive prick test to pneumoallergens and high concentrations of exhaled NO | Procedure: Collection and Raman analysis of saliva for the database<br>* Saliva will be collected and processed for the Raman analysis. The collected data will be computed for the creation of the classification model<br>|
| Non-Exacerbator COPD (neCOPD)<br>50 subjects affected by Non-Exacerbator COPD comparable by age and sex with the other recruited subjects | Procedure: Collection and Raman analysis of saliva for the database<br>* Saliva will be collected and processed for the Raman analysis. The collected data will be computed for the creation of the classification model<br>|
| frequent Excacerbator with Emphysema COPD (eeCOPD)<br>50 subjects affected by frequent exacerbation with emphysema COPD comparable by age and sex with the other recruited subjects | Procedure: Collection and Raman analysis of saliva for the database<br>* Saliva will be collected and processed for the Raman analysis. The collected data will be computed for the creation of the classification model<br>|
| frequent Excacerbator with chronic Bronchitis COPD (ebCOPD)<br>50 subjects affected by frequent excacerbation with chronic bronchitis COPD comparable by age and sex with the other recruited subjects | Procedure: Collection and Raman analysis of saliva for the database<br>* Saliva will be collected and processed for the Raman analysis. The collected data will be computed for the creation of the classification model<br>|
| Asthma patients (AST)<br>200 subjects affected by asthma comparable by age and sex with the other recruited subjects | Procedure: Collection and Raman analysis of saliva for the database<br>* Saliva will be collected and processed for the Raman analysis. The collected data will be computed for the creation of the classification model<br>|
| Healthy subjects (CTRL)<br>200 healthy subjects in a good health state comparable by age and sex with the other recruited subjects | Procedure: Collection and Raman analysis of saliva for the database<br>* Saliva will be collected and processed for the Raman analysis. The collected data will be computed for the creation of the classification model<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Identification of the salivary COPD Raman signature | Raman spectroscopy will be used to analyse saliva of COPD patients, leading to the characterization of a specific COPD signature highlighting the differences between the one of asthma patients and healthy subjects. Using multivariate analysis, the possibility to create a classification model will be tested. | Two years | 
| Characterization of the spectral differences of COPD patients | Raman data will be interpreted comparing the signatures of the different experimental groups (COPD vs asthma vs healthy subjects), identifing the molecular classes responsible for the principal differences | Two years | 
| Stratification of the 4 COPD phenotypes through the Raman signature | An intra COPD class analysis will be performed, identifying the specific Raman signature of each phenotype considered in the study. The multivariate analysis will be performed evaluating the possibility to create a classification model able to perform a fast diagnosis based on the analysis of saliva | Two years | 
| Monitoring of therapy adherence and effects | Raman data will be correlated with the clinical parameters, identifying hidden trends and relationships between the two investigated factors. In particular, the effects of a full and missing therapy adherence will be evaluated in terms of changing in salivary Raman signatures | Two years | 
| Determination of the exacerbation index | The Raman signal associated with frequently exacerbator patients will be computed through linear discriminant analysis, obtaing coefficients related to the exacerbation event. In this way, a measurable parameter will be created in order to monitor and potentially forecast the exacerbation events | Two years | 
| Application of a portable Raman spectrometer as Point of Care | All the data, databases and classification models created in the previous outcomes will be integrated in a portable Raman instrument that will be applied directly on new patinets, in order to test the reliability of the methodology. At the same time, the new data will be used to train the model, increasing the discriminatory power in terms of accuracy, precision, sensitivity and specificity | Three years | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Raman Spectroscopy, COPD, Machine Learning, Biomarker