Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients

IntroductionArtificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods. MethodsWe prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions. ResultsOf 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models' prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR. ConclusionsIn this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients

APOLLO 11 Project, Consortium in Advanced Lung Cancer Patients Treated With Innovative Therapies: Integration of Real-World Data and Translational Research

Introduction: Despite several therapeutic efforts, lung cancer remains a highly lethal disease. Novel therapeutic approaches encompass immune-checkpoint inhibitors, targeted therapeutics and antibody-drug conjugates, with different results. Several studies have been aimed at identifying biomarkers able to predict benefit from these therapies and create a prediction model of response, despite this there is a lack of information to help clinicians in the choice of therapy for lung cancer patients with advanced disease. This is primarily due to the complexity of lung cancer biology, where a single or few biomarkers are not sufficient to provide enough predictive capability to explain biologic differences; other reasons include the paucity of data collected by single studies performed in heterogeneous unmatched cohorts and the methodology of analysis. In fact, classical statistical methods are unable to analyze and integrate the magnitude of information from multiple biological and clinical sources (eg, genomics, transcriptomics, and radiomics). Methods and objectives: APOLLO11 is an Italian multicentre, observational study involving patients with a diagnosis of advanced lung cancer (NSCLC and SCLC) treated with innovative therapies. Retrospective and prospective collection of multiomic data, such as tissue- (eg, for genomic, transcriptomic analysis) and blood-based biologic material (eg, ctDNA, PBMC), in addition to clinical and radiological data (eg, for radiomic analysis) will be collected. The overall aim of the project is to build a consortium integrating different datasets and a virtual biobank from participating Italian lung cancer centers. To face with the large amount of data provided, AI and ML techniques will be applied will be applied to manage this large dataset in an effort to build an R-Model, integrating retrospective and prospective population-based data. The ultimate goal is to create a tool able to help physicians and patients to make treatment decisions. Conclusion: APOLLO11 aims to propose a breakthrough approach in lung cancer research, replacing the old, monocentric viewpoint towards a multicomprehensive, multiomic, multicenter model. Multicenter cancer datasets incorporating common virtual biobank and new methodologic approaches including artificial intelligence, machine learning up to deep learning is the road to the future in oncology launched by this project

Safety of extended interval dosing immune checkpoint inhibitors:a multicenter cohort study

BACKGROUND: Real-life spectrum and survival implications of immune-related adverse events (irAEs) in patients treated with extended interval dosing (ED) immune checkpoint inhibitors (ICIs) are unknown. METHODS: Characteristics of 812 consecutive solid cancer patients who received at least 1 cycle of ED monotherapy (pembrolizumab 400 mg Q6W or nivolumab 480 mg Q4W) after switching from canonical interval dosing (CD; pembrolizumab 200 mg Q3W or nivolumab 240 mg Q2W) or treated upfront with ED were retrieved. The primary objective was to compare irAEs patterns within the same population (before and after switch to ED). irAEs spectrum in patients treated upfront with ED and association between irAEs and overall survival were also described. RESULTS: A total of 550 (68%) patients started ICIs with CD and switched to ED. During CD, 225 (41%) patients developed any grade and 17 (3%) G3 or G4 irAEs; after switching to ED, any grade and G3 or G4 irAEs were experienced by 155 (36%) and 20 (5%) patients. Switching to ED was associated with a lower probability of any grade irAEs (adjusted odds ratio [aOR] = 0.83, 95% confidence interval [CI] = 0.64 to 0.99; P = .047), whereas no difference for G3 or G4 events was noted (aOR = 1.55, 95% CI = 0.81 to 2.94; P = .18). Among patients who started upfront with ED (n = 232, 32%), 107 (41%) developed any grade and 14 (5%) G3 or G4 irAEs during ED. Patients with irAEs during ED had improved overall survival (adjusted hazard ratio [aHR] = 0.53, 95% CI = 0.34 to 0.82; P = .004 after switching; aHR = 0.57, 95% CI = 0.35 to 0.93; P = .025 upfront). CONCLUSIONS: Switching ICI treatment from CD and ED did not increase the incidence of irAEs and represents a safe option also outside clinical trials.</p

Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients

IntroductionArtificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods.MethodsWe prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions.ResultsOf 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models’ prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR.ConclusionsIn this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients

Synthesis of biolubricants with high viscosity and high oxidation stability

The synthetic procedure as well as the main properties of obtained products of a group of complex esters are reported here. Complex esters were prepared using low molecular weight saturated fatty acids, trimethylolpropane and a dicarboxylic acid as a feedstock. By means of this procedure it is possible to obtain products having high viscosity and very good lubricating, thermal and cold properties. Thanks to the absence of unsaturations into the ester also the oxidation property is good, opening several application perspective for these products which are partly prepared from renewable source

Synthesis of biolubricants with high viscosity and high oxidation stability

The synthetic procedure as well as the main properties of obtained products of a group of complex esters are reported here. Complex esters were prepared using low molecular weight saturated fatty acids, trimethylolpropane and a dicarboxylic acid as a feedstock. By means of this procedure it is possible to obtain products having high viscosity and very good lubricating, thermal and cold properties. Thanks to the absence of unsaturations into the ester also the oxidation property is good, opening several application perspective for these products which are partly prepared from renewable source

Synthesis of biolubricants with high viscosity and high oxidation stability

The synthetic procedure as well as the main properties of obtained products of a group of complex esters are reported here. Complex esters were prepared using low molecular weight saturated fatty acids, trimethylolpropane and a dicarboxylic acid as a feedstock. By means of this procedure it is possible to obtain products having high viscosity and very good lubricating, thermal and cold properties. Thanks to the absence of unsaturations into the ester also the oxidation property is good, opening several application perspective for these products which are partly prepared from renewable source

PEOPLE (NTC03447678), a phase II trial to test pembrolizumab as first-line treatment in patients with advanced NSCLC with PD-L1

Background Chemoimmunotherapy represents the standard of care for patients with advanced non-small cell lung cancer (NSCLC) and programmed death-ligand 1 (PD-L1) &lt;50%. Although single-agent pembrolizumab has also demonstrated some activity in this setting, no reliable biomarkers yet exist for selecting patients likely to respond to single-agent immunotherapy. The main purpose of the study was to identify potential new biomarkers associated with progression-free-survival (PFS) within a multiomics analysis.Methods PEOPLE (NTC03447678) was a prospective phase II trial evaluating first-line pembrolizumab in patients with advanced EGFR and ALK wild type treatment-naïve NSCLC with PD-L1 &lt;50%. Circulating immune profiling was performed by determination of absolute cell counts with multiparametric flow cytometry on freshly isolated whole blood samples at baseline and at first radiological evaluation. Gene expression profiling was performed using nCounter PanCancer IO 360 Panel (NanoString) on baseline tissue. Gut bacterial taxonomic abundance was obtained by shotgun metagenomic sequencing of stool samples at baseline. Omics data were analyzed with sequential univariate Cox proportional hazards regression predicting PFS, with Benjamini-Hochberg multiple comparisons correction. Biological features significant with univariate analysis were analyzed with multivariate least absolute shrinkage and selection operator (LASSO).Results From May 2018 to October 2020, 65 patients were enrolled. Median follow-up and PFS were 26.4 and 2.9 months, respectively. LASSO integration analysis, with an optimal lambda of 0.28, showed that peripheral blood natural killer cells/CD56dimCD16+ (HR 0.56, 0.41–0.76, p=0.006) abundance at baseline and non-classical CD14dimCD16+monocytes (HR 0.52, 0.36–0.75, p=0.004), eosinophils (CD15+CD16−) (HR 0.62, 0.44–0.89, p=0.03) and lymphocytes (HR 0.32, 0.19–0.56, p=0.001) after first radiologic evaluation correlated with favorable PFS as well as high baseline expression levels of CD244 (HR 0.74, 0.62–0.87, p=0.05) protein tyrosine phosphatase receptor type C (HR 0.55, 0.38–0.81, p=0.098) and killer cell lectin like receptor B1 (HR 0.76, 0.66–0.89, p=0.05). Interferon-responsive factor 9 and cartilage oligomeric matrix protein genes correlated with unfavorable PFS (HR 3.03, 1.52–6.02, p 0.08 and HR 1.22, 1.08–1.37, p=0.06, corrected). No microbiome features were selected.Conclusions This multiomics approach was able to identify immune cell subsets and expression levels of genes associated to PFS in patients with PD-L1 &lt;50% NSCLC treated with first-line pembrolizumab. These preliminary data will be confirmed in the larger multicentric international I3LUNG trial (NCT05537922).Trial registration number 2017-002841-31

Circulating Fatty Acid Profile as a Biomarker for Immunotherapy in Advanced Non-Small Cell Lung Cancer

Lipid metabolism impacts immune cell differentiation, activation, and functions, modulating inflammatory mediators, energy homeostasis, and cell membrane composition. Despite preclinical evidence, data in humans lack concerning tumors and immunotherapy (IO). We aimed at investigating the correlations between circulating lipids and the outcome of non-small cell lung cancer (NSCLC) patients treated with IO