Prediction of response to anti-PD-1 therapy in patients with non-small-cell lung cancer by electronic nose analysis of exhaled breath

Idiopathic pulmonary fibrosis (IPF): Diagnostic routes using novel biomarkers

Idiopathic pulmonary fibrosis (IPF) diagnosis is still the diagnosis of exclusion. Differentiating from other forms of interstitial lung diseases (ILDs) is essential, given the various therapeutic approaches. The IPF course is now unpredictable for individual patients, although some genetic factors and several biomarkers have already been associated with various IPF prognoses. Since its early stages, IPF may be asymptomatic, leading to a delayed diagnosis. The present review critically examines the recent literature on molecular biomarkers potentially useful in IPF diagnostics. The examined biomarkers are grouped into breath and sputum biomarkers, serologically assessed extracellular matrix neoepitope markers, and oxidative stress biomarkers in lung tissue. Fibroblasts and complete blood count have also gained recent interest in that respect. Although several biomarker candidates have been profiled, there has yet to be a single biomarker that proved specific to the IPF disease. Nevertheless, various IPF biomarkers have been used in preclinical and clinical trials to verify their predictive and monitoring potential.

Application of electronic nose and machine learning used to detect soybean gases under water stress and variability throughout the daytime

The development of non-invasive methods and accessible tools for application to plant phenotyping is considered a breakthrough. This work presents the preliminary results using an electronic nose (E-Nose) and machine learning (ML) as affordable tools. An E-Nose is an electronic system used for smell global analysis, which emulates the human nose structure. The soybean (Glycine Max) was used to conduct this experiment under water stress. Commercial E-Nose was used, and a chamber was designed and built to conduct the measurement of the gas sample from the soybean. This experiment was conducted for 22 days, observing the stages of plant growth during this period. This chamber is embedded with relative humidity [RH (%)], temperature (°C), and CO2 concentration (ppm) sensors, as well as the natural light intensity, which was monitored. These systems allowed intermittent monitoring of each parameter to create a database. The soil used was the red-yellow dystrophic type and was covered to avoid evapotranspiration effects. The measurement with the electronic nose was done daily, during the morning and afternoon, and in two phenological situations of the plant (with the healthful soy irrigated with deionized water and underwater stress) until the growth V5 stage to obtain the plant gases emissions. Data mining techniques were used, through the software "Weka™" and the decision tree strategy. From the evaluation of the sensors database, a dynamic variation of plant respiration pattern was observed, with the two distinct behaviors observed in the morning (~9:30 am) and afternoon (3:30 pm). With the initial results obtained with the E-Nose signals and ML, it was possible to distinguish the two situations, i.e., the irrigated plant standard and underwater stress, the influence of the two periods of daylight, and influence of temporal variability of the weather. As a result of this investigation, a classifier was developed that, through a non-invasive analysis of gas samples, can accurately determine the absence of water in soybean plants with a rate of 94.4% accuracy. Future investigations should be carried out under controlled conditions that enable early detection of the stress level.

Established and emerging biomarkers of immunotherapy in renal cell carcinoma

Immunotherapies, such as immune checkpoint inhibitors, have heralded impressive progress for patient care in renal cell carcinoma (RCC). Despite this success, some patients' disease fails to respond, and other patients experience significant side effects. Thus, development of biomarkers is needed to ensure that patients can be selected to maximize benefit from immunotherapies. Improving clinicians' ability to predict which patients will respond to immunotherapy and which are most at risk of adverse events - namely through clinical biomarkers - is indispensable for patient safety and therapeutic efficacy. Accordingly, an evolving suite of therapeutic biomarkers continues to be investigated. This review discusses biomarkers for immunotherapy in RCC, highlighting current practices and emerging innovations, aiming to contribute to improved outcomes for patients with RCC.

Use of Sensor Array Analysis to Detect Ovarian Cancer through Breath, Urine, and Blood: A Case-Control Study

Ovarian cancer (OC) is the eighth most common cancer in women. Since screening programs do not exist, it is often diagnosed in advanced stages. Today, the detection of OC is based on clinical examination, transvaginal ultrasound (US), and serum biomarker (Carbohydrate Antigen 125 (CA 125) and Human Epididymis Protein 4 (HE4)) dosage, with a sensitivity of 88% and 95%, respectively, and a specificity of 84% for US and 76% for biomarkers. These methods are clearly not enough, and OC in its early stages is often missed. Many scientists have recently focused their attention on volatile organic compounds (VOCs). These are gaseous molecules, found in the breath, that could provide interesting information on several diseases, including solid tumors. To detect VOCs, an electronic nose was invented by a group of researchers. A similar device, the e-tongue, was later created to detect specific molecules in liquids. For the first time in the literature, we investigated the potential use of the electronic nose and the electronic tongue to detect ovarian cancer not just from breath but also from urine, blood, and plasma samples.

Prediction of response to neoadjuvant chemo-immunotherapy in patients with esophageal squamous cell carcinoma by a rapid breath test

BACKGROUND

Neoadjuvant chemo-immunotherapy combination has shown remarkable advances in the management of esophageal squamous cell carcinoma (ESCC). However, the identification of a reliable biomarker for predicting the response to this chemo-immunotherapy regimen remains elusive. While computed tomography (CT) is widely utilized for response evaluation, its inherent limitations in terms of accuracy are well recognized. Therefore, in this study, we present a novel technique to predict the response of ESCC patients before receiving chemo-immunotherapy by testing volatile organic compounds (VOCs) in exhaled breath.

METHODS

This study employed a prospective-specimen-collection, retrospective-blinded-evaluation design. Patients' baseline breath samples were collected and analyzed using high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS). Subsequently, patients were categorized as responders or non-responders based on the evaluation of therapeutic response using pathology (for patients who underwent surgery) or CT images (for patients who did not receive surgery).

RESULTS

A total of 133 patients were included in this study, with 91 responders who achieved either a complete response (CR) or a partial response (PR), and 42 non-responders who had stable disease (SD) or progressive disease (PD). Among 83 participants who underwent both evaluations with CT and pathology, the paired t-test revealed significant differences between the two methods (p < 0.05). For the breath test prediction model using breath test data from all participants, the validation set demonstrated mean area under the curve (AUC) of 0.86 ± 0.06. For 83 patients with pathological reports, the breath test achieved mean AUC of 0.845 ± 0.123.

CONCLUSIONS

Since CT has inherent weakness in hollow organ assessment and no other ideal biomarker has been found, our study provided a noninvasive, feasible, and inexpensive tool that could precisely predict ESCC patients' response to neoadjuvant chemo-immunotherapy combination using breath test based on HPPI-TOFMS.

Exhaled Breath Analysis Detects the Clearance of Staphylococcus aureus from the Airways of Children with Cystic Fibrosis

BACKGROUND

Electronic nose (eNose) technology can be used to characterize volatile organic compound (VOC) mixes in breath. While previous reports have shown that eNose can detect lung infections with pathogens such as Staphylococcus aureus (SA) in people with cystic fibrosis (CF), the clinical utility of eNose for longitudinally monitoring SA infection status is unknown.

METHODS

In this longitudinal study, a cloud-connected eNose, the SpiroNose, was used for the breath profile analysis of children with CF at two stable visits and compared based on changes in SA infection status between visits. Data analysis involved advanced sensor signal processing, ambient correction, and statistics based on the comparison of breath profiles between baseline and follow-up visits.

RESULTS

Seventy-two children with CF, with a mean (IQR) age of 13.8 (9.8-16.4) years, were studied. In those with SA-positive airway cultures at baseline but SA-negative cultures at follow-up (n = 19), significant signal differences were detected between Baseline and Follow-up at three distinct eNose sensors, i.e., S4 (p = 0.047), S6 (p = 0.014), and S7 (p = 0.014). Sensor signal changes with the clearance of SA from airways were unrelated to antibiotic treatment. No changes in sensor signals were seen in patients with unchanged infection status between visits.

CONCLUSIONS

Our results demonstrate the potential applicability of the eNose as a non-invasive clinical tool to longitudinally monitor pulmonary SA infection status in children with CF.

Comparing modeling strategies combining changes in multiple serum tumor biomarkers for early prediction of immunotherapy non-response in non-small cell lung cancer

BACKGROUND

Patients treated with immune checkpoint inhibitors (ICI) are at risk of adverse events (AEs) even though not all patients will benefit. Serum tumor markers (STMs) are known to reflect tumor activity and might therefore be useful to predict response, guide treatment decisions and thereby prevent AEs.

OBJECTIVE

This study aims to compare a range of prediction methods to predict non-response using multiple sequentially measured STMs.

METHODS

Nine prediction models were compared to predict treatment non-response at 6-months (n = 412) using bi-weekly CYFRA, CEA, CA-125, NSE, and SCC measurements determined in the first 6-weeks of therapy. All methods were applied to six different biomarker combinations including two to five STMs. Model performance was assessed based on sensitivity, while model training aimed at 95% specificity to ensure a low false-positive rate.

RESULTS

In the validation cohort, boosting provided the highest sensitivity at a fixed specificity across most STM combinations (12.9% -59.4%). Boosting applied to CYFRA and CEA achieved the highest sensitivity on the validation data while maintaining a specificity >95%.

CONCLUSIONS

Non-response in NSCLC patients treated with ICIs can be predicted with a specificity >95% by combining multiple sequentially measured STMs in a prediction model. Clinical use is subject to further external validation.

Advancing Colorectal Cancer Diagnosis with AI-Powered Breathomics: Navigating Challenges and Future Directions

Early detection of colorectal cancer is crucial for improving outcomes and reducing mortality. While there is strong evidence of effectiveness, currently adopted screening methods present several shortcomings which negatively impact the detection of early stage carcinogenesis, including low uptake due to patient discomfort. As a result, developing novel, non-invasive alternatives is an important research priority. Recent advancements in the field of breathomics, the study of breath composition and analysis, have paved the way for new avenues for non-invasive cancer detection and effective monitoring. Harnessing the utility of Volatile Organic Compounds in exhaled breath, breathomics has the potential to disrupt colorectal cancer screening practices. Our goal is to outline key research efforts in this area focusing on machine learning methods used for the analysis of breathomics data, highlight challenges involved in artificial intelligence application in this context, and suggest possible future directions which are currently considered within the framework of the European project ONCOSCREEN.

Impact of Different Treatment Regimens and Timeframes in the Plasmatic Metabolic Profiling of Patients with Lung Adenocarcinoma

In recent years, the treatment of advanced non-small cell lung cancer (NSCLC) has suffered a variety of alterations. Chemotherapy (CTX), immunotherapy (IT) and tyrosine kinase inhibitors (TKI) have shown remarkable results. However, not all patients with NSCLC respond to these drug treatments or receive durable benefits. In this framework, metabolomics has been applied to improve the diagnosis, treatment, and prognosis of lung cancer and particularly lung adenocarcinoma (AdC). In our study, metabolomics was used to analyze plasma samples from 18 patients with AdC treated with CTX or IT via 1H-NMR spectroscopy. Relevant clinical information was gathered, and several biochemical parameters were also evaluated throughout the treatments. During the follow-up of patients undergoing CTX or IT, imaging control is recommended in order to assess the effectiveness of the therapy. This evaluation is usually performed every three treatments. Based on this procedure, all the samples were collected before the beginning of the treatment and after three and six treatments. The identified and quantified metabolites in the analyzed plasma samples were the following: isoleucine, valine, alanine, acetate, lactate, glucose, tyrosine, and formate. Multivariate/univariate statistical analyses were performed. Our data are in accordance with previous published results, suggesting that the plasma glucose levels of patients under CTX become higher throughout the course of treatment, which we hypothesize could be related to the tumor response to the therapy. It was also found that alanine levels become lower during treatment with CTX regimens, a fact that could be associated with frailty. NMR spectra of long responders' profiles also showed similar results. Based on the results of the study, metabolomics can represent a potential option for future studies, in order to facilitate patient selection and the monitoring of therapy efficacy in treated patients with AdC. Further studies are needed to improve the prospective identification of predictive markers, particularly glucose and alanine levels, as well as confer guidance to NSCLC treatment and patient stratification, thus avoiding ineffective therapeutic strategies.

Prospective Detection of Early Lung Cancer in Patients With COPD in Regular Care by Electronic Nose Analysis of Exhaled Breath

BACKGROUND

Patients with COPD are at high risk of lung cancer developing, but no validated predictive biomarkers have been reported to identify these patients. Molecular profiling of exhaled breath by electronic nose (eNose) technology may qualify for early detection of lung cancer in patients with COPD.

RESEARCH QUESTION

Can eNose technology be used for prospective detection of early lung cancer in patients with COPD?

STUDY DESIGN AND METHODS

BreathCloud is a real-world multicenter prospective follow-up study using diagnostic and monitoring visits in day-to-day clinical care of patients with a standardized diagnosis of asthma, COPD, or lung cancer. Breath profiles were collected at inclusion in duplicate by a metal-oxide semiconductor eNose positioned at the rear end of a pneumotachograph (SpiroNose; Breathomix). All patients with COPD were managed according to standard clinical care, and the incidence of clinically diagnosed lung cancer was prospectively monitored for 2 years. Data analysis involved advanced signal processing, ambient air correction, and statistics based on principal component (PC) analysis, linear discriminant analysis, and receiver operating characteristic analysis.

RESULTS

Exhaled breath data from 682 patients with COPD and 211 patients with lung cancer were available. Thirty-seven patients with COPD (5.4%) demonstrated clinically manifest lung cancer within 2 years after inclusion. Principal components 1, 2, and 3 were significantly different between patients with COPD and those with lung cancer in both training and validation sets with areas under the receiver operating characteristic curve of 0.89 (95% CI, 0.83-0.95) and 0.86 (95% CI, 0.81-0.89). The same three PCs showed significant differences (P < .01) at baseline between patients with COPD who did and did not subsequently demonstrate lung cancer within 2 years, with a cross-validation value of 87% and an area under the receiver operating characteristic curve of 0.90 (95% CI, 0.84-0.95).

INTERPRETATION

Exhaled breath analysis by eNose identified patients with COPD in whom lung cancer became clinically manifest within 2 years after inclusion. These results show that eNose assessment may detect early stages of lung cancer in patients with COPD.

Diagnostic accuracy of eNose 'breathprints' for therapeutic drug monitoring of Tacrolimus trough levels in lung transplantation

In order to prevent long-term immunity-related complications after lung transplantation, close monitoring of immunosuppressant levels using therapeutic drug monitoring (TDM) is paramount. Novel electronic nose (eNose) technology may be a non-invasive alternative to the current invasive procedures for TDM. We investigated the diagnostic and categorization capacity of eNose breathprints for Tacrolimus trough blood plasma levels (TACtrough) in lung transplant recipients (LTRs). We performed eNose measurements in stable LTR attending the outpatient clinic. We evaluated (1) the correlation between eNose measurements and TACtrough, (2) the diagnostic capacity of eNose technology for TACtrough, and (3) the accuracy of eNose technology for categorization of TACtroughinto three clinically relevant categories (low: <7µg ml-1, medium: 7-10µg ml-1, and high: >10µg ml-1). A total of 186 measurements from 86 LTR were included. There was a weak but statistically significant correlation (r= 0.21,p= 0.004) between the eNose measurements and TACtrough. The root mean squared error of prediction for the diagnostic capacity was 3.186 in the training and 3.131 in the validation set. The accuracy of categorization ranged between 45%-63% for the training set and 52%-69% in the validation set. There is a weak correlation between eNose breathprints and TACtroughin LTR. However, the diagnostic as well as categorization capacity for TACtroughusing eNose breathprints is too inaccurate to be applicable in TDM.

Does the last 20 years paradigm of clinical research using volatile organic compounds to non-invasively diagnose cancer need to change? Challenges and future direction

PURPOSE

Volatile organic compounds (VOCs) have shown great potential as novel biomarkers for cancer detection; however, comprehensive quantitative analysis is lacking. In this study, we performed a bibliometric analysis of non-invasive cancer diagnosis using VOCs to better characterise international trends and to predict future hotspots in this field, and then we focussed on human studies to analyse clinical characteristics for presenting the current controversies and future perspectives of further clinical work.

METHODS

Publications, from 2002 to 2022, were retrieved from the Web of Science Core Collection database. CiteSpace and VOSviewer were used to generate network maps and identify the annual publications, top countries, authors, institutions, journals, references, and keywords. Then, we further screened clinical trials, and the key information was extracted into Microsoft Excel for further systematical analysis.

RESULTS

Six hundred and forty-one articles were identified to evaluate research trends, of which 301 clinical trials were selected for further systematical analysis. Overall, the annual publications in this area increased, with an overall upward trend, while the quality of clinical research remains remarkably uneven.

CONCLUSION

The study of non-invasive cancer diagnosis using VOCs would continue to be an active field. However, without stringent clinical design criteria, most suitable acquisition and analysis devices and statistical approaches, a list of exclusive, specific, reliable and reproducible VOCs to identify a disease and these VOCs appearing in a breath at detectable levels at early stage disease, the clinical utility of VOC tests will be difficult to have any breakthroughs.

Exhaled volatile organic compounds for diagnosis and monitoring of asthma

The asthmatic inflammatory process results in the generation of volatile organic compounds (VOCs), which are subsequently secreted by the airways. The study of these elements through gas chromatography-mass spectrometry (GC-MS), which can identify individual molecules with a discriminatory capacity of over 85%, and electronic-Nose (e-NOSE), which is able to perform a quick onboard pattern-recognition analysis of VOCs, has allowed new prospects for non-invasive analysis of the disease in an "omics" approach. In this review, we aim to collect and compare the progress made in VOCs analysis using the two methods and their instrumental characteristics. Studies have described the potential of GC-MS and e-NOSE in a multitude of relevant aspects of the disease in both children and adults, as well as differential diagnosis between asthma and other conditions such as wheezing, cystic fibrosis, COPD, allergic rhinitis and last but not least, the accuracy of these methods compared to other diagnostic tools such as lung function, FeNO and eosinophil count. Due to significant limitations of both methods, it is still necessary to improve and standardize techniques. Currently, e-NOSE appears to be the most promising aid in clinical practice, whereas GC-MS, as the gold standard for the structural analysis of molecules, remains an essential tool in terms of research for further studies on the pathophysiologic pathways of the asthmatic inflammatory process. In conclusion, the study of VOCs through GC-MS and e-NOSE appears to hold promise for the non-invasive diagnosis, assessment, and monitoring of asthma, as well as for further research studies on the disease.

Potential non-invasive biomarkers in tumor immune checkpoint inhibitor therapy: response and prognosis prediction

Immune checkpoint inhibitors (ICIs) have dramatically enhanced the treatment outcomes for diverse malignancies. Yet, only 15-60% of patients respond significantly. Therefore, accurate responder identification and timely ICI administration are critical issues in tumor ICI therapy. Recent rapid developments at the intersection of oncology, immunology, biology, and computer science have provided an abundance of predictive biomarkers for ICI efficacy. These biomarkers can be invasive or non-invasive, depending on the specific sample collection method. Compared with invasive markers, a host of non-invasive markers have been confirmed to have superior availability and accuracy in ICI efficacy prediction. Considering the outstanding advantages of dynamic monitoring of the immunotherapy response and the potential for widespread clinical application, we review the recent research in this field with the aim of contributing to the identification of patients who may derive the greatest benefit from ICI therapy.

Potential for Early Noninvasive COVID-19 Detection Using Electronic-Nose Technologies and Disease-Specific VOC Metabolic Biomarkers

The established efficacy of electronic volatile organic compound (VOC) detection technologies as diagnostic tools for noninvasive early detection of COVID-19 and related coronaviruses has been demonstrated from multiple studies using a variety of experimental and commercial electronic devices capable of detecting precise mixtures of VOC emissions in human breath. The activities of numerous global research teams, developing novel electronic-nose (e-nose) devices and diagnostic methods, have generated empirical laboratory and clinical trial test results based on the detection of different types of host VOC-biomarker metabolites from specific chemical classes. COVID-19-specific volatile biomarkers are derived from disease-induced changes in host metabolic pathways by SARS-CoV-2 viral pathogenesis. The unique mechanisms proposed from recent researchers to explain how COVID-19 causes damage to multiple organ systems throughout the body are associated with unique symptom combinations, cytokine storms and physiological cascades that disrupt normal biochemical processes through gene dysregulation to generate disease-specific VOC metabolites targeted for e-nose detection. This paper reviewed recent methods and applications of e-nose and related VOC-detection devices for early, noninvasive diagnosis of SARS-CoV-2 infections. In addition, metabolomic (quantitative) COVID-19 disease-specific chemical biomarkers, consisting of host-derived VOCs identified from exhaled breath of patients, were summarized as possible sources of volatile metabolic biomarkers useful for confirming and supporting e-nose diagnoses.

Prognostic and predictive biomarkers for immunotherapy in advanced renal cell carcinoma

The therapeutic algorithm of renal cell carcinoma has been revolutionized by the approval of immunotherapy agents by regulatory agencies. However, objective and durable responses are still not observed in a large number of patients, and prognostic and predictive biomarkers for immunotherapy response are urgently needed. Prognostic models used in clinical practice are based on clinical and laboratory factors (such as hypercalcaemia, neutrophil count or Karnofsky Performance Status), but, with progress in molecular biology and genome sequencing techniques, new renal cell carcinoma molecular features that might improve disease course and outcomes prediction have been highlighted. An implementation of current models is needed to improve the accuracy of prognosis in the immuno-oncology era. Moreover, several potential biomarkers are currently under evaluation, but effective markers to select patients who might benefit from immunotherapy and to guide therapeutic strategies are still far from validation.

Diagnosing Non-Small Cell Lung Cancer by Exhaled Breath Profiling Using an Electronic Nose: A Multicenter Validation Study

BACKGROUND

Despite the potential of exhaled breath analysis of volatile organic compounds to diagnose lung cancer, clinical implementation has not been realized, partly due to the lack of validation studies.

RESEARCH QUESTION

This study addressed two questions. First, can we simultaneously train and validate a prediction model to distinguish patients with non-small cell lung cancer from non-lung cancer subjects based on exhaled breath patterns? Second, does addition of clinical variables to exhaled breath data improve the diagnosis of lung cancer?

STUDY DESIGN AND METHODS

In this multicenter study, subjects with non-small cell lung cancer and control subjects performed 5 min of tidal breathing through the aeoNose, a handheld electronic nose device. A training cohort was used for developing a prediction model based on breath data, and a blinded cohort was used for validation. Multivariable logistic regression analysis was performed, including breath data and clinical variables, in which the formula and cutoff value for the probability of lung cancer were applied to the validation data.

RESULTS

A total of 376 subjects formed the training set, and 199 subjects formed the validation set. The full training model (including exhaled breath data and clinical parameters from the training set) were combined in a multivariable logistic regression analysis, maintaining a cut off of 16% probability of lung cancer, resulting in a sensitivity of 95%, a specificity of 51%, and a negative predictive value of 94%; the area under the receiver-operating characteristic curve was 0.87. Performance of the prediction model on the validation cohort showed corresponding results with a sensitivity of 95%, a specificity of 49%, a negative predictive value of 94%, and an area under the receiver-operating characteristic curve of 0.86.

INTERPRETATION

Combining exhaled breath data and clinical variables in a multicenter, multi-device validation study can adequately distinguish patients with lung cancer from subjects without lung cancer in a noninvasive manner. This study paves the way to implement exhaled breath analysis in the daily practice of diagnosing lung cancer.

CLINICAL TRIAL REGISTRATION

The Netherlands Trial Register; No.: NL7025; URL: https://trialregister.nl/trial/7025.

An improved AdaBoost algorithm for identification of lung cancer based on electronic nose

The research developed an improved intelligent enhancement learning algorithm based on AdaBoost, that can be applied for lung cancer breath detection by the electronic nose (eNose). First, collected the breath signals from volunteers by eNose, including healthy individuals and people who had lung cancer. Additionally, the signals' features were extracted and optimized. Then, multi sub-classifiers were obtained, and their coefficients were derived from the training error. To improve generalization performance, K-fold cross-validation was used when constructing each sub-classifier. The prediction results of a sub-classifier on the test set were then achieved by the voting method. Thus, an improved AdaBoost classifier would be built through heterogeneous integration. The results shows that the average precision of the improved algorithm classifier for distinguishing between people with lung cancer and healthy individuals could reach 98.47%, with 98.33% sensitivity and 97% specificity. And in 100 independent and randomized tests, the coefficient of variation of the classifier's performance hardly exceeded 4%. Compared with other integrated algorithms, the generalization and stability of the improved algorithm classifier are more superior. It is clear that the improved AdaBoost algorithm may help screen out lung cancer more comprehensively. Additionally, it will significantly advance the use of eNose in the early identification of lung cancer.

LC-MS/MS Based Volatile Organic Compound Biomarkers Analysis for Early Detection of Lung Cancer

(1) Background: lung cancer is the world's deadliest cancer, but early diagnosis helps to improve the cure rate and thus reduce the mortality rate. Annual low-dose computed tomography (LD-CT) screening is an efficient lung cancer-screening program for a high-risk population. However, LD-CT has often been characterized by a higher degree of false-positive results. To meet these challenges, a volatolomic approach, in particular, the breath volatile organic compounds (VOCs) fingerprint analysis, has recently received increased attention for its application in early lung cancer screening thanks to its convenience, non-invasiveness, and being well tolerated by patients. (2) Methods: a LC-MS/MS-based volatolomics analysis was carried out according to P/N 5046800 standard based breath analysis of VOC as novel cancer biomarkers for distinguishing early-stage lung cancer from the healthy control group. The discriminatory accuracy of identified VOCs was assessed using subject work characterization and a random forest risk prediction model. (3) Results: the proposed technique has good performance compared with existing approaches, the differences between the exhaled VOCs of the early lung cancer patients before operation, three to seven days after the operation, as well as four to six weeks after operation under fasting and 1 h after the meal were compared with the healthy controls. The results showed that only 1 h after a meal, the concentration of seven VOCs, including 3-hydroxy-2-butanone (TG-4), glycolaldehyde (TG-7), 2-pentanone (TG-8), acrolein (TG-11), nonaldehyde (TG-19), decanal (TG-20), and crotonaldehyde (TG-22), differ significantly between lung cancer patients and control, with the invasive adenocarcinoma of the lung (IAC) having the most significant difference. (4) Conclusions: this novel, non-invasive approach can improve the detection rate of early lung cancer, and LC-MS/MS-based breath analysis could be a promising method for clinical application.

Safety and tolerability of stereotactic radiotherapy combined with durvalumab with or without tremelimumab in advanced non-small cell lung cancer, the phase I SICI trial

INTRODUCTION

This phase I study primarily addresses the safety and tolerability of Stereotactic radiotherapy on the primary tumor combined with double Immune Checkpoint Inhibition (SICI) in patients with non-small cell lung cancer (NSCLC). Increasing the release of neoantigens by radiotherapy might enhance response to immunotherapy. Especially, by targeting trunk mutations in the primary tumor.

MATERIALS AND METHODS

In three sequential cohorts, immunotherapy regimes combined with stereotactic body radiotherapy (SBRT) on the primary tumor (1x20 Gy on 9 cc) were studied in stage IIIB/IV NSCLC patients progressing on chemotherapy. The first cohort (n = 3) received durvalumab. The second (n = 6) received a combination of tremelimumab and durvalumab followed by durvalumab monotherapy. The third cohort (n = 6) was similar except that the combination was reversed. Descriptive statistics were used to assess safety parameters and the exploratory outcomes of efficacy. Adverse events were reported using NCI CTCAE version 4.03. Exhaled breath was analyzed at baseline.

RESULTS

Fifteen patients were included. Median irradiated volume was 9.13 cc, on a median primary tumor volume of 79 cc. There were seven patients with grade 1-2, and two patients with grade 3 treatment related adverse events. There was 1 dose limiting toxicity (colitis) with double immunotherapy.

CONCLUSION

The combination of SBRT to the primary tumor and double immunotherapy in advanced NSCLC patients is safe and feasible.

Exhaled Biomarkers for Point-of-Care Diagnosis: Recent Advances and New Challenges in Breathomics

Cancers, chronic diseases and respiratory infections are major causes of mortality and present diagnostic and therapeutic challenges for health care. There is an unmet medical need for non-invasive, easy-to-use biomarkers for the early diagnosis, phenotyping, predicting and monitoring of the therapeutic responses of these disorders. Exhaled breath sampling is an attractive choice that has gained attention in recent years. Exhaled nitric oxide measurement used as a predictive biomarker of the response to anti-eosinophil therapy in severe asthma has paved the way for other exhaled breath biomarkers. Advances in laser and nanosensor technologies and spectrometry together with widespread use of algorithms and artificial intelligence have facilitated research on volatile organic compounds and artificial olfaction systems to develop new exhaled biomarkers. We aim to provide an overview of the recent advances in and challenges of exhaled biomarker measurements with an emphasis on the applicability of their measurement as a non-invasive, point-of-care diagnostic and monitoring tool.

Modeling strategies to analyse longitudinal biomarker data: An illustration on predicting immunotherapy non-response in non-small cell lung cancer

Serum tumor markers acquired through a blood draw are known to reflect tumor activity. Their non-invasive nature allows for more frequent testing compared to traditional imaging methods used for response evaluations. Our study aims to compare nine prediction methods to accurately, and with a low false positive rate, predict progressive disease despite treatment (i.e. non-response) using longitudinal tumor biomarker data. Bi-weekly measurements of CYFRA, CA-125, CEA, NSE, and SCC were available from a cohort of 412 advanced stage non-small cell lung cancer (NSCLC) patients treated up to two years with immune checkpoint inhibitors. Serum tumor marker measurements from the first six weeks after treatment initiation were used to predict treatment response at 6 months. Nine models with varying complexity were evaluated in this study, showing how longitudinal biomarker data can be used to predict non-response to immunotherapy in NSCLC patients.

The Use of Breath Analysis in the Management of Lung Cancer: Is It Ready for Primetime?

Breath analysis is a promising non-invasive method for the detection and management of lung cancer. Exhaled breath contains a complex mixture of volatile and non-volatile organic compounds that are produced as end-products of metabolism. Several studies have explored the patterns of these compounds and have postulated that a unique breath signature is emitted in the setting of lung cancer. Most studies have evaluated the use of gas chromatography and mass spectrometry to identify these unique breath signatures. With recent advances in the field of analytical chemistry and machine learning gaseous chemical sensing and identification devices have also been created to detect patterns of odorant molecules such as volatile organic compounds. These devices offer hope for a point-of-care test in the future. Several prospective studies have also explored the presence of specific genomic aberrations in the exhaled breath of patients with lung cancer as an alternative method for molecular analysis. Despite its potential, the use of breath analysis has largely been limited to translational research due to methodological issues, the lack of standardization or validation and the paucity of large multi-center studies. It is clear however that it offers a potentially non-invasive alternative to investigations such as tumor biopsy and blood sampling.

Deciphering radiological stable disease to immune checkpoint inhibitors

BACKGROUND

'Stable disease (SD)' as per RECIST is a common but ambiguous outcome in patients receiving immune checkpoint inhibitors (ICIs). This study aimed to characterize SD and identify the subset of patients with SD who are benefiting from treatment. Understanding SD would facilitate drug development and improve precision in correlative research.

PATIENTS AND METHODS

A systematic review was carried out to characterize SD in ICI trials. SD and objective response were compared to proliferation index using The Cancer Genome Atlas gene expression data. To identify a subgroup of SD with outcomes mirroring responders, we examined a discovery cohort of non-small-cell lung cancer (NSCLC). Serial cutpoints of two variables, % best overall response and progression-free survival (PFS), were tested to define a subgroup of patients with SD with similar survival as responders. Results were then tested in external validation cohorts.

RESULTS

Among trials of ICIs (59 studies, 14 280 patients), SD ranged from 16% to 42% in different tumor types and was associated with disease-specific proliferation index (ρ = -0.75, P = 0.03), a proxy of tumor kinetics, rather than relative response to ICIs. In a discovery cohort of NSCLC [1220 patients, 313 (26%) with SD to ICIs], PFS ranged widely in SD (0.2-49 months, median 4.9 months). The subset with PFS >6 months and no tumor growth mirrored partial response (PR) minor (overall survival hazard ratio 1.0) and was proposed as the definition of SD responder. This definition was confirmed in two validation cohorts from trials of NSCLC treated with durvalumab and found to apply in tumor types treated with immunotherapy in which depth and duration of benefit were correlated.

CONCLUSIONS

RECIST-defined SD to immunotherapy is common, heterogeneous, and may largely reflect tumor growth rate rather than ICI response. In patients with NSCLC and SD to ICIs, PFS >6 months and no tumor growth may be considered 'SD responders'. This definition may improve the efficiency of and insight derivable from clinical and translational research.

High Expression of TACC3 Is Associated with the Poor Prognosis and Immune Infiltration in Lung Adenocarcinoma Patients

Background

Lung adenocarcinoma (LUAD) has been recognized as one of the commonest aggressive malignant tumors occurring in humans. The transforming acidic coiled-coil-containing protein 3 (TACC3) seems to be a probable prognostic marker and treatment target for non-small-cell lung cancer (NSCLC). Nevertheless, there exist no reports on the association between TACC3 and immunotherapy or other therapeutic interventions in LUAD.

Methods

Premised on the data accessed from The Cancer Genome Atlas- (TCGA-) LUAD, we carried out bioinformatics analysis. The TACC3 expression in LUAD was analyzed utilizing the GEPIA. A survival module was constructed to evaluate the effect of TACC3 on the survival of patients with LUAD. Logistic regression was undertaken to examine the relationship between TACC3 expression and clinical factors. Protein-protein interaction analysis was performed in the GeneMANIA database, and enrichment analysis and identification of predicted signaling pathways were performed using Gene Ontology and Kyoto Encyclopedia of Genes. Additionally, the Cox regression was used to assess the clinicopathologic features linked to the overall survival in TCGA patients. Lastly, we investigated the link between TACC3 and tumor-infiltrating immune cells (TIICs) through CIBERSORT and the “Correlation” module of GEPIA. The association between TACC3 gene expression and drug response was analyzed using the CellMiner database to predict drug sensitivity.

Results

The outcomes illustrated that TACC3 was upregulated and considerably correlated with dismal prognosis in LUAD patients. Moreover, the multivariate Cox regression analysis depicted TACC3 as an independent prognostic marker in LUAD patients. It was also revealed that the expression of TACC3 was related to clinical stage (P = 0.014), age (P = 0.002), and T classification (P ≤ 0.018). Moreover, we discovered that the expression of TACC3 was considerably linked to a wide range of TIICs, especially the T cells and NK cells. Single-cell results found that TACC3 was mainly expressed in the immune cells (especially tprolif cells) and malignant cells. TACC3 gene expression was positively correlated with TMB and MSI, and TACC3 may provide a prediction of the efficacy of immunotherapy. Moreover, the correlation analysis between TACC3 gene expression and immune checkpoint gene expression revealed that TACC3 may coordinate the activities of these ICP genes in different signal transduction pathways. TACC3 is related to biological progress (BP), cellular component (CC), and molecular function (MF). The pathways involved in the interaction network involving TACC3 include nonhomologous end-joining, RNA transport, pantothenate and CoA biosynthesis, homologous recombination, and nucleotide excision repair. Furthermore, we investigated the association between the expression of TACC3 and the use of antitumor drugs, and TACC3 was positively correlated with response to most drugs.

Conclusion

The findings from this research offer robust proof that the expression of TACC3 could be a prognostic marker correlated with TIICs in LUAD. TACC3 can also provide new ideas for immunotherapy as a potential therapeutic target.

Analyses of lung cancer-derived volatiles in exhaled breath and in vitro models

Lung cancer is one of the leading causes of cancer incidence and cancer-related deaths in the world. Early diagnosis of pulmonary tumors results in improved survival compared to diagnosis with more advanced disease, yet early disease is not reliably indicated by symptoms. Despite of the improved testing and monitoring techniques for lung cancer in the past decades, most diagnostic tests, such as sputum cytology or tissue biopsies, are invasive and risky, rendering them unfeasible for large population screening. The non-invasive analysis of exhaled breath has gained attentions as an innovative screening method to measure chemical alterations within the human volatilome profile as a result of oncogenesis. More importantly, volatile organic compounds (VOCs) have been correlated to the pathophysiology of disease since the source of volatile compounds relies mostly on endogenous metabolic processes that are altered as a result of disease onset. Therefore, studying VOCs emitted from human breath may assist lung cancer diagnosis, treatment monitoring, and other surveillance of this devastating disease. In this mini review, we evaluated recent human studies that have attempted to identify lung cancer-derived volatiles in exhaled breath of patients. We also examined reported volatiles in cell cultures of lung cancer to better understand the origins of cancer-associated VOCs. We highlight the metabolic processes of lung cancer that could be responsible for the endogenous synthesis of these VOCs and pinpoint the protein-encoding genes involved in these pathways. Finally, we highlight the potential value of a breath test in lung cancer and propose prominent areas for future research required for the incorporation of VOCs-based testing into clinical settings.

STOP: an open label crossover trial to study ICS withdrawal in patients with a combination of obesity and low-inflammatory asthma and evaluate its effect on asthma control and quality of life

Background

Asthma patients with obesity often have a high disease burden, despite the use of high-dose inhaled corticosteroids (ICS). In contrast to asthmatics with normal weight, the efficacy of ICS in patients with obesity and asthma is often relatively low. Meanwhile, patients do suffer from side effects, such as weight gain, development of diabetes, cataract, or high blood pressure. The relatively poor response to ICS might be explained by the low prevalence of type 2 inflammatory patterns (T2-low) in patients with asthma and obesity. T2-low inflammation is characterized by low eosinophilic count, low Fractional exhaled NO (FeNO), no clinically allergy-driven asthma, and no need for maintenance oral corticosteroids (OCS). We aim to study whether ICS can be safely withdrawn in patients with T2-low asthma and obesity while maintaining an equal level of asthma control. Secondary outcomes focus on the prevalence of ‘false-negative’ T2-low phenotypes (i.e. T2-hidden) and the effect of ICS withdrawal on parameters of the metabolic syndrome. This study will lead to a better understanding of this poorly understood subgroup and might find new treatable traits.

Methods

The STOP trial is an investigator-initiated, multicenter, non-inferiority, open-label, crossover study aiming to assess whether ICS can be safely withdrawn in adults aged 17–75 years with T2-low asthma and obesity (body mass index (BMI) ≥ 30 kg/m2). Patients will be randomly divided into two arms (both n = 60). One arm will start with fixed-dose ICS (control group) and one arm will taper and subsequently stop ICS (intervention group). Patients in the intervention group will remain ICS naïve for ten weeks. After a washout of 4 weeks, patients will crossover to the other study arm. The crossover study takes 36 weeks to complete. Patients will be asked to participate in the extension study, to investigate the long-term metabolic benefits of ICS withdrawal.

Discussion

This study yields valuable data on ICS tapering in patients with T2-low asthma and obesity. It informs future guidelines and committees on corticosteroid-sparing algorithms in these patients.

Trial registration Netherlands Trial Register, NL8759, registered 2020–07-06, https://www.trialregister.nl/trial/8759.

Protocol version and date: version 2.1, 20 November 2020.

How paediatric drug development and use could benefit from OMICs: a c4c expert group white paper

The safety and efficacy of pharmacotherapy in children, particularly preterms, neonates, and infants, is limited by a paucity of good quality data from prospective clinical drug trials. A specific challenge is the establishment of valid biomarkers. OMICs technologies may support these efforts, by complementary information about targeted and non-targeted molecules through systematic characterization and quantitation of biological samples. OMICs technologies comprise at least genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics in addition to the patient's phenotype. OMICs technologies are in part hypothesis-generating allowing an in depth understanding of disease pathophysiology and pharmacological mechanisms. Application of OMICs technologies in paediatrics faces major challenges before routine adoption. First, developmental processes need to be considered, including a sub-division into specific age groups as developmental changes clearly impact OMICs data. Second, compared to the adult population, the number of patients is limited as well as type and amount of necessary biomaterial, especially in neonates and preterms. Thus, advanced trial designs and biostatistical methods, non-invasive biomarkers, innovative biobanking concepts including data and samples from healthy children, as well as analytical approaches (e.g. liquid biopsies) should be addressed to overcome these obstacles. The ultimate goal is to link OMICs technologies with innovative analysis tools, like artificial intelligence at an early stage. The use of OMICs data based on a feasible approach will contribute to identify complex phenotypes and subpopulations of patients to improve development of medicines for children with potential economic advantages.

Odors and cancer: Current status and future directions

Cancer is the second leading cause of death in the world. Because tumors detected at early stages are easier to treat, the search for biomarkers-especially non-invasive ones-that allow early detection of malignancies remains a central goal to reduce cancer mortality. Cancer, like other pathologies, often alters body odors, and much has been done by scientists over the last few decades to assess the value of volatile organic compounds (VOCs) as signatures of cancers. We present here a quantitative review of 208 studies carried out between 1984 and 2020 that explore VOCs as potential biomarkers of cancers. We analyzed the main findings of these studies, listing and classifying VOCs related to different cancer types while considering both sampling methods and analysis techniques. Considering this synthesis, we discuss several of the challenges and the most promising prospects of this research direction in the war against cancer.

Localized delivery of immunotherapeutics: A rising trend in the field

Immunotherapy is becoming a new standard of care for multiple cancers, while several limitations are impending its further clinical success. Immunotherapeutic agents often have inappropriate pharmacokinetics on their own and/or exhibit limited specificity to tumor cells, leading to severe immuno-related adverse effects and limited efficacy. Suitable formulating strategies that confer prolonged contact with or efficient proliferation in tumors while reducing exposure to normal tissues are highly worthy to explore. With the assistance of biomaterial carriers, targeted therapy can be achieved artificially by implanting or injecting drug depots into desired sites, about which the wisdoms in literature have been rich. The relevant results have suggested a "local but systemic" effect, that is, local replenishment of immune modulators achieves a high treatment efficacy that also governs distant metastases, thereby building another rationale for localized delivery. Particularly, implantable scaffolds have been further engineered to recruit disseminated tumor cells with an efficiency high enough to reduce tumor burdens at typical metastatic organs, and simultaneously provide diagnostic signals. This review introduces recent advances in this emerging area along with a perspective on the opportunities and challenges in the way to clinical application.

Identification of multi-concentration aromatic fragrances with electronic nose technology using a support vector machine

Due to the concentration effect, there is a major challenge for the electronic nose system to identify different odor samples with multiple concentrations. The development of artificial intelligence provides new ways to solve such problems. This article attempts to use support vector machine (SVM) technology to distinguish four fragrance samples with three concentrations, including roman chamomile, jasmine, lavender, and orange. The responses of these samples were collected by an 11-sensor electronic nose. After baseline correction, data smoothing, and removal of non-responsive sensors, the signals of 8 sensors were used for subsequent model analysis. Due to the concentration effect, when the primary signal intensities were used as features, the electronic nose cannot distinguish between different aroma types (accuracy less than 50%). When the normalized maximum signal intensity X_mr was used, the accuracy of the model was greatly improved. Graphic analysis and PCA showed that the normalized feature effectively eliminates the concentration effect, and appropriately reducing some sensors can enhance the ability to distinguish odors. The SVM correctly classified all 14 aromas when feeding 8 sets of data to train the radial kernel C-classification SVM. This showed that the cross-interference of the sensors was reduced, and the resolving power of the electronic nose was enhanced after the feature reduction.

The smell of lung disease: a review of the current status of electronic nose technology

There is a need for timely, accurate diagnosis, and personalised management in lung diseases. Exhaled breath reflects inflammatory and metabolic processes in the human body, especially in the lungs. The analysis of exhaled breath using electronic nose (eNose) technology has gained increasing attention in the past years. This technique has great potential to be used in clinical practice as a real-time non-invasive diagnostic tool, and for monitoring disease course and therapeutic effects. To date, multiple eNoses have been developed and evaluated in clinical studies across a wide spectrum of lung diseases, mainly for diagnostic purposes. Heterogeneity in study design, analysis techniques, and differences between eNose devices currently hamper generalization and comparison of study results. Moreover, many pilot studies have been performed, while validation and implementation studies are scarce. These studies are needed before implementation in clinical practice can be realised. This review summarises the technical aspects of available eNose devices and the available evidence for clinical application of eNose technology in different lung diseases. Furthermore, recommendations for future research to pave the way for clinical implementation of eNose technology are provided.

An Update on the Use of Exhaled Breath Analysis for the Early Detection of Lung Cancer

Lung cancer has historically been the main responsible for cancer associated deaths. Owing to this is our current inability to screen for and diagnose early pathological findings, preventing us from a timely intervention when cure is still achievable. Over the last decade, together with the extraordinary progress in therapeutical alternatives in the field, there has been an ongoing search for a biomarker that would allow for this. Numerous technologies have been developed but their clinical application is yet to come. In this review, we provide an update on volatile organic compounds, a non-invasive method that can hold the key for detecting early metabolic pathway changes in carcinogenesis. For its compilation, web-based search engines of scientific literature such as PubMed were explored and reviewed, using articles, research, and papers deemed meaningful by authors discretion. After a brief description, we depict how this technique can complement current methods and present the value of electronic noses in the identification of the “breathprint”. Lastly, we bring some of the latest updates in the field together with the current limitations and final remarks.

eNose analysis for early immunotherapy response monitoring in non-small cell lung cancer

OBJECTIVES

Exhaled breath analysis by electronic nose (eNose) has shown to be a potential predictive biomarker before start of anti-PD-1 therapy in patients with non-small cell lung carcinoma (NSCLC). We hypothesized that the eNose could also be used as an early monitoring tool to identify responders more accurately at early stage of treatment when compared to baseline. In this proof-of-concept study we aimed to definitely discriminate responders from non-responders after six weeks of treatment.

MATERIALS AND METHODS

This was a prospective observational study in patients with advanced NSCLC eligible for anti-PD-1 treatment. The efficacy of treatment was assessed by the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 at 3-month follow-up. We analyzed SpiroNose exhaled breath data of 94 patients (training cohort n = 62, validation cohort n = 32). Data analysis involved signal processing and statistics based on Independent Samples T-tests and Linear Discriminant Analysis (LDA) followed by Receiver Operating Characteristic (ROC) analysis.

RESULTS

In the training cohort, a specificity of 73% was obtained at a 100% sensitivity level to identify objective responders. The Area Under the Curve (AUC) was 0.95 (CI: 0.89-1.00). In the validation cohort, these results were confirmed with an AUC of 0.97 (CI: 0.91-1.00).

CONCLUSION

Exhaled breath analysis by eNose early during treatment allows for a highly accurate, non-invasive and low-cost identification of advanced NSCLC patients who benefit from anti-PD-1 therapy.

Study of confounding factors influence on lung cancer diagnostics effectiveness using gas chromatography-mass spectrometry analysis of exhaled breath

Aim: The purpose of this study was to estimate volatile organic compounds (VOCs) ability to distinguish exhaled breath samples of lung cancer patients and healthy volunteers and to assess the effect of smoking status and gender on parameters. Patients & methods: Exhaled breath samples of 40 lung cancer patients and 40 healthy individuals were analyzed using gas chromatography-mass spectrometry. Influence of other factors on the exhaled breath VOCs profile was investigated. Results: Some parameters correlating with the disease status were affected by other factors. Excluding these parameters allows creating a logistic regression diagnostic model with 83% sensitivity and 81% specificity. Conclusion: Influence of other factors on the exhaled breath VOCs profile has to be taken into account to avoid misleading results.

Immune checkpoint inhibitors in oncogene-addicted non-small cell lung cancer: a systematic review and meta-analysis

Background

Treatment of oncogene-addicted non-small cell lung cancer (NSCLC) has been changed by the advent of tyrosine kinase inhibitors (TKIs). Albeit great benefits are achieved with target therapies, resistance invariably occurs and recourse to alternative treatments is unavoidable. Immune checkpoint inhibitors (ICIs) role and the best setting of immunotherapy administration in oncogene-driven NSCLC are matter of debate.

Methods

We performed a systematic literature review through PubMed, in order to gather all the available information regarding ICI activity and efficacy in oncogene-addicted NSCLC, from both prospective trials and retrospective series. A meta-analysis of objective response rate in different molecular subgroups was provided. Combinatorial strategies including ICIs and related toxicities were also recorded.

Results

Eighty-seven studies were included in the qualitative analysis. EGFR mutation may be a biomarker of poor response to single-agent ICIs (7% of EGFR-mutant NSCLC patients achieved disease response in prospective trials), while encouraging results have been shown with combination strategies. KRAS-mutated disease (response rate, RR, 22%) has different clinical and pathological characteristics, and the co-existence of additional mutations (e.g., STK11 or TP53) influence tumor microenvironment and response to immunotherapy. Other molecular alterations have been marginally considered prospectively, and data from clinical practice are variegated, given poor effectiveness of ICIs in ALK-rearranged disease (RR 9.5%, pooling the data of retrospective studies) or some encouraging results in BRAF-(RR 25%, retrospective data) or MET-driven one (with estimations conditioned by the presence of both exon 14 skipping mutations and gene amplification in reported series).

Conclusions

In oncogene-addicted NSCLC (with the exception of KRAS-mutated), ICIs are usually administered at the failure of other treatment options, but administering single-agent immunotherapy in later disease phases may limit its efficacy. With the progressive administration of TKIs and ICIs in early-stage disease, molecular characterization will become fundamental in this setting.

Nose in malignant mesothelioma-Prediction of response to immune checkpoint inhibitor treatment

INTRODUCTION

Recent clinical trials with immune checkpoint inhibitors (ICIs) have shown that a subgroup of patients with malignant pleural mesothelioma (MPM) could benefit from these agents. However, there are no accurate biomarkers to predict who will respond. The aim of this study was to assess the accuracy of exhaled breath analysis using electronic technology (eNose) for discriminating between responders to ICI and non-responders.

METHODS

This proof-of-concept prospective observational study was part of an intervention study (INITIATE) in patients with recurrent MPM who were treated with nivolumab (anti-PD-1) plus ipilimumab (anti-CTLA-4). At baseline and after six weeks of treatment, breath profiles were collected by an eNose. Modified Response Evaluation Criteria in Solid Tumors were used to assess efficacy at 6-month follow-up. For data processing and statistics, we used independent t-test analyses followed by linear discriminant and receiver-operating characteristic (ROC) analysis.

RESULTS

Exhaled breath data of 31 MPM patients who received nivolumab plus ipilimumab were available at baseline. There were 16 with and 15 without a response after 6 months of treatment. At baseline, breath profiles significantly differed between responders and non-responders, with a cross validation value of 71%. The ROC-AUC after internal cross-validation was 0.90 (confidence interval: 0.80-1.00).

CONCLUSION

An eNose is able to discriminate at baseline between responders and non-responders to nivolumab plus ipilimumab in MPM, thereby potentially identifying a subgroup of patients that will benefit from ICI treatment.

Prognostic and Predictive Biomarkers in Non-Small Cell Lung Cancer Patients on Immunotherapy-The Role of Liquid Biopsy in Unraveling the Puzzle

In the last decade, immunotherapy has been one of the most important advances in the non-small cell lung cancer (NSCLC) treatment landscape. Nevertheless, only a subset of NSCLC patients benefits from it. Currently, the only Food and Drug Administration (FDA) approved diagnostic test for first-line immunotherapy in metastatic NSCLC patients uses tissue biopsies to determine the programmed death ligand 1 (PD-L1) status. However, obtaining tumor tissue is not always feasible and puts the patient at risk. Liquid biopsy, which refers to the tumor-derived material present in body fluids, offers an alternative approach. This less invasive technique gives real-time information on the tumor characteristics. This review addresses different promising liquid biopsy based biomarkers in NSCLC patients that enable the selection of patients who benefit from immunotherapy and the monitoring of patients during this therapy. The challenges and the opportunities of blood-based biomarkers such as cell-free DNA (cfDNA), circulating tumor cells (CTCs), exosomes, epigenetic signatures, microRNAs (miRNAs) and the T cell repertoire will be addressed. This review also focuses on the less-studied feces-based and breath-based biomarkers.

Breathomics: Review of Sample Collection and Analysis, Data Modeling and Clinical Applications

Metabolomics research is rapidly gaining momentum in disease diagnosis, on top of other Omics technologies. Breathomics, as a branch of metabolomics is developing in various frontiers, for early and noninvasive monitoring of disease. This review starts with a brief introduction to metabolomics and breathomics. A number of important technical issues in exhaled breath collection and factors affecting the sampling procedures are presented. We review the recent progress in metabolomics approaches and a summary of their applications on the respiratory and non-respiratory diseases investigated by breath analysis. Recent reports on breathomics studies retrieved from Scopus and Pubmed were reviewed in this work. We conclude that analyzing breath metabolites (both volatile and nonvolatile) is valuable in disease diagnoses, and therefore believe that breathomics will turn into a promising noninvasive discipline in biomarker discovery and early disease detection in personalized medicine. The problem of wide variations in the reported metabolite concentrations from breathomics studies should be tackled by developing more accurate analytical methods and sophisticated numerical analytical alogorithms.

Laminin γ2-mediating T cell exclusion attenuates response to anti-PD-1 therapy

PD-1/PD-L1 blockade therapies provide notable clinical benefits for patients with advanced cancers, but the factors influencing the effectiveness of the treatment remain incompletely cataloged. Here, the up-regulation of laminin γ2 (Ln-γ2) predicted the attenuated efficacy of anti-PD-1 drugs and was associated with unfavorable outcomes in patients with lung cancer or esophageal cancer. Furthermore, Ln-γ2 was transcriptionally activated by transforming growth factor-β1 (TGF-β1) secreted from cancer-associated fibroblasts via JNK/AP1 signaling, which blocked T cell infiltration into the tumor nests by altering the expression of T cell receptors. Coadministration of the TGF-β receptor inhibitor galunisertib and chemotherapy drugs provoked vigorous antitumor activity of anti-PD-1 therapy in mouse tumor models. Therefore, Ln-γ2 may represent a useful biomarker to optimize clinical decisions and predict the response of cancer patients to treatment with anti-PD-1 drugs.

Exhaled breath analysis by use of eNose technology: a novel diagnostic tool for interstitial lung disease

INTRODUCTION

Early and accurate diagnosis of interstitial lung diseases (ILDs) remains a major challenge. Better noninvasive diagnostic tools are much needed. We aimed to assess the accuracy of exhaled breath analysis using eNose technology to discriminate between ILD patients and healthy controls, and to distinguish ILD subgroups.

METHODS

In this cross-sectional study, exhaled breath of consecutive ILD patients and healthy controls was analysed using eNose technology (SpiroNose). Statistical analyses were done using partial least square discriminant analysis and receiver operating characteristic analysis. Independent training and validation sets (2:1) were used in larger subgroups.

RESULTS

A total of 322 ILD patients and 48 healthy controls were included: sarcoidosis (n=141), idiopathic pulmonary fibrosis (IPF) (n=85), connective tissue disease-associated ILD (n=33), chronic hypersensitivity pneumonitis (n=25), idiopathic nonspecific interstitial pneumonia (n=10), interstitial pneumonia with autoimmune features (n=11) and other ILDs (n=17). eNose sensors discriminated between ILD and healthy controls, with an area under the curve (AUC) of 1.00 in the training and validation sets. Comparison of patients with IPF and patients with other ILDs yielded an AUC of 0.91 (95% CI 0.85-0.96) in the training set and an AUC of 0.87 (95% CI 0.77-0.96) in the validation set. The eNose reliably distinguished between individual diseases, with AUC values ranging from 0.85 to 0.99.

CONCLUSIONS

eNose technology can completely distinguish ILD patients from healthy controls and can accurately discriminate between different ILD subgroups. Hence, exhaled breath analysis using eNose technology could be a novel biomarker in ILD, enabling timely diagnosis in the future.

Potential of the Electronic Nose for the Detection of Respiratory Diseases with and without Infection

Respiratory tract infections are common, and when affecting the lower airways and lungs, can result in significant morbidity and mortality. There is an unfilled need for simple, non-invasive tools that can be used to screen for such infections at the clinical point of care. The electronic nose (eNose) is a novel technology that detects volatile organic compounds (VOCs). Early studies have shown that certain diseases and infections can result in characteristic changes in VOC profiles in the exhaled breath. This review summarizes current knowledge on breath analysis by the electronic nose and its potential for the detection of respiratory diseases with and without infection.

Metabolomics to Assess Response to Immune Checkpoint Inhibitors in Patients with Non-Small-Cell Lung Cancer

Simple Summary

Recently, immunotherapy has presented new opportunities for clinical development in the treatment of non-small cell lung cancer (NSCLC). Although effective in sustaining overall survival in several clinical trials, not all the NSCLC patients respond to these treatments. Thus, a better patient selection, as well as the identification of predictive biomarkers of treatment efficacy, are of paramount importance. In this work, metabolomics was used with the aim of identifying responder with respect to non-responder subjects. We show that the metabolomic fingerprint of serum samples, collected before therapy, acts as a predictive biomarker to treatment response. Prospective identification of subjects that will benefit from immunotherapy could improve patient stratification, thus optimizing the treatment and avoiding unsuccessful strategies.

Abstract

In the treatment of advanced non-small cell lung cancer (NSCLC), immune checkpoint inhibitors have shown remarkable results. However, not all patients with NSCLC respond to this drug treatment or receive durable benefits. Thus, patient stratification and selection, as well as the identification of predictive biomarkers, represent pivotal aspects to address. In this framework, metabolomics can be used to support the discrimination between responders and non-responders. Here, metabolomics was used to analyze the sera samples from 50 patients with NSCL treated with immune checkpoint inhibitors. All the samples were collected before the beginning of the treatment and were analyzed by NMR spectroscopy and multivariate statistical analyses. Significantly, we show that the metabolomic fingerprint of serum acts as a predictive “collective” biomarker to immune checkpoint inhibitors response, being able to predict individual therapy outcome with > 80% accuracy. Metabolomics represents a potential strategy for the real-time selection and monitoring of patients treated with immunotherapy. The prospective identification of responders and non-responders could improve NSCLC treatment and patient stratification, thus avoiding ineffective therapeutic strategies.

First-Line Immune-Checkpoint Inhibitors in Non-Small Cell Lung Cancer: Current Landscape and Future Progress

Lung cancer is one of the most common cancers and the leading cause of cancer-related deaths worldwide. Most of these patients with non-small cell lung cancer (NSCLC) present with the advanced stage of the disease at the time of diagnosis, and thus decrease the 5-year survival rate to about 5%. Immune checkpoint inhibitors (ICIs) can act on the inhibitory pathway of cancer immune response, thereby restoring and maintaining anti-tumor immunity. There are already ICIs targeting different pathways, including the programmed cell death 1 (PD-1), programmed cell death ligand 1 (PD-L1), and cytotoxic T lymphocyte antigen 4 (CTLA-4) pathway. Since March 2015, the US Food and Drug Administration (FDA) approved nivolumab (anti-PD-1 antibody) as the second-line option for treatment of patients with advanced squamous NSCLC. Additionally, a series of inhibitors related to PD-1/PD-L1 immune-checkpoints have helped in the immunotherapy of NSCLC patients, and modified the original treatment model. However, controversies remain regarding the use of ICIs in a subgroup with targeted oncogene mutations is a problem that we need to solve. On the other hand, there are continuous efforts to find biomarkers that effectively predict the response of ICIs to screen suitable populations. In this review, we have reviewed the history of the continuous developments in cancer immunotherapy, summarized the mechanism of action of the immune-checkpoint pathways. Finally, based on the results of the first-line recent trials, we propose a potential first-line immunotherapeutic strategy for the treatment of the patients with NSCLC.