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

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

Immunotherapy and chemo-immunotherapy for non–small cell lung cancer with novel actionable oncogenic driver alterations.

Background: Immune checkpoint inhibitors (IO) single agent or in combination with platinum chemotherapy (CT-IO) are standard of care for Stage IV non-small cell lung cancer (NSCLC) according to PD-L1 expression. While the efficacy of IO among patients (pts) with common EGFR and ALK alterations appears to be limited, its activity in pts with novel oncogenic drivers alterations is not well characterized. Compared to non-oncogene-addicted NSCLC, the overall response rate (ORR) seems to be similar in BRAF and c-MET altered NSCLC, lower in RET altered NSCLC, while data are less consistent in HER2 and EGFR exon 20 (EGFRex20) altered NSCLCs. Methods: From January 2016 to January 2022, we retrospectively enrolled pts with Stage IV NSCLC that received IO or combination CT– IO in any line, ECOG PS 0 - 2 and detection of MET exon 14 skipping mutations (METex14), BRAF mutations (V600E or non-V600E), RET rearrangement, HER2 point mutations (HER2mut)/exon 20 insertions (HER2ex20) or uncommon EGFR mutations (uEGFRmut)/EGFRex20. A review of clinicopathologic and molecular features and an analysis of response to combination or single-agent IO were conducted. Results: Among sixty-four pts enrolled, 20 (31%) had METex14, 19 (30%) had EGFR alterations [12 (19%) EGFRex20, 7 (11%) uEGFRmut], 8 (12%) had BRAF mutation (3 V600E and 5 non-V600E), 13(20%) had HER2 alterations [7 (11%) HER2ex20, 6 (10%) HER2mut] and 4 (6%) were RET rearranged. 43 received IO single agent and 21 received CT-IO. With a median follow up of 22 months (m), median progression free survival (mPFS) was 5.40 m (0.95 CI 4.73-6.9) overall, 6.77m in CT-IO arm (0.95 CI 5.37-NA) and 5.10m in IO arm (0.95 CI 2.60-6.7), with a trend to better mPFS for CT-IO (p 0.054). Regarding specific mutations irrespectively from treatment arm, NSCLC harboring METex14 showed a mPFS of 5.33 m (0.95 CI, 2.30-13.9), BRAF 9.9 m (0.95 CI, 6.70-NA), EGFR 4.93 m (0.95 CI, 1.80-6.9), HER2 11.4 (0.95 CI, 4.2-NA), RET 5.28 (0.95 CI, 1.42-NA). Disease control rate (DCR) was better in the CT-IO arm vs IO one in the overall population (84.2% vs 50%, p 0.013). Conclusions: Novel driver alterations seem to show a benefit from IO treatments. CT-IO seems to have a better outcome in terms of DCR. Therefore, IO-based treatment should be evaluated also in tumors harboring novel driver alterations

Prognostic role of neutrophil-to-lymphocyte ratio and EPSILoN score in advanced non-small-cell lung cancer patients treated with first-line chemo-immunotherapy

Plain language summary Patients affected by inoperable lung cancer, due to great extension or to the presence of metastases, are currently treated with intravenous drugs that act on immune system activation alone or in combination with chemotherapy as first-line treatment. The characteristics of these patients (both their medical history and their blood exams) need to be studied to find out if some of them can help clinicians to predict if they will benefit from the combination of immunotherapy with chemotherapy. The authors collected the data of patients with advanced lung cancer treated in their hospital and found out that a value calculated from their blood exams, collected before the start of treatment, and a combination of values named EPSILoN score (which considers patients' clinical condition, their history of tobacco smoking, the presence of metastases in the liver and two blood exam parameters, namely the neutrophil-to-lymphocyte ratio and LDH level) can predict how their disease will evolve during first-line treatment with chemotherapy in combination with immunotherapy.Background: Clinical and laboratory biomarkers in patients with advanced non-small-cell lung cancer (aNSCLC) receiving chemo-immunotherapy (CIT) are still poorly explored. Materials & methods: All consecutive aNSCLC patients who received at least one cycle of first-line CIT were enrolled. The impact of several clinical and laboratory biomarkers on outcomes was evaluated through Cox proportional hazard models. Results: Higher neutrophil-to-lymphocyte ratio was shown to be an independent prognostic biomarker of both worse progression-free survival and worse overall survival. The EPSILoN score was able to divide patients into three different prognostic groups, with a median overall survival of 73.2, 45.6 and 8.6 months for the favorable, intermediate and poor groups, respectively. Conclusion: The neutrophil-to-lymphocyte ratio and EPSILoN score were shown to have a prognostic value in aNSCLC patients treated with CIT

Evaluation of Drug-Drug Interactions in EGFR-Mutated Non-Small-Cell Lung Cancer Patients during Treatment with Tyrosine-Kinase Inhibitors

(1) Background. The onset of a drug-drug interaction (DDI) may affect treatment efficacy and toxicity of advanced non-small-cell lung cancer (aNSCLC) patients during epidermal growth factor receptor (EGFR) tyrosine-kinase inhibitor (TKI) use. Here we present the use of Drug-PIN(R) (Personalized Interactions Network) software to detect DDIs in aNSCLC patients undergoing EGFR-TKIs. (2) Methods. We enrolled patients with Stage IV aNSCLC already treated with or candidates to receive EGFR-TKIs, in any line; ECOG PS 0-2; taking at least one concomitant drug. Cancer treatments, concomitant drugs, and clinical and laboratory data were collected and inserted in Drug-PIN(R). (3) Results. Ninety-two patients, median age of 68.5 years (range 43-89), were included. In total, 20 clinically relevant DDIs needing medical intervention in a total of 14 patients were identified; the 14 major DDIs were related to a high-grade interaction between TKIs and SSRIs, antipsychotics, antiepileptics, H2-receptor antagonist and calcium antagonists. A negative association between statin intake and PFS was identified (p = 0.02; HR 0.281, 95% CI 0.096-0.825). (4) Conclusions. This is the first retrospective study assessing the prevalence of DDIs, the clinical need for medical intervention and the impact of concomitant drugs on EGFR-TKIs survival in aNSCLC

Machine Learning Using Real-World and Translational Data to Improve Treatment Selection for NSCLC Patients Treated with Immunotherapy

Simple Summary In this paper, the authors show that artificial intelligence (AI) and machine learning (ML) are useful approaches to integrate multifactorial data and helpful for personalized prediction. In detail, compared to PD-L1 for advanced non-small cell lung cancer (NSCLC), ML tools predicted better responder (R) and non-responder (NR) patients to immunotherapy (IO). It was also able to indirectly foresee OS and PFS of R and NR patients. Given the high incidence of NSCLC, and the absence of reliable biomarkers to predict the response to IO other than PD-L1, the authors believe this research may be of great interest to anyone involved in thoracic oncology. Furthermore, given the growing interest from the scientific community in AI and ML, the authors believe that this manuscript could represent a fascinating topic to anyone who needs to exploit the enormous potential of these tools in the treatment of cancer. (1) Background: In advanced non-small cell lung cancer (aNSCLC), programmed death ligand 1 (PD-L1) remains the only biomarker for candidate patients to immunotherapy (IO). This study aimed at using artificial intelligence (AI) and machine learning (ML) tools to improve response and efficacy predictions in aNSCLC patients treated with IO. (2) Methods: Real world data and the blood microRNA signature classifier (MSC) were used. Patients were divided into responders (R) and non-responders (NR) to determine if the overall survival of the patients was likely to be shorter or longer than 24 months from baseline IO. (3) Results: One-hundred sixty-four out of 200 patients (i.e., only those ones with PD-L1 data available) were considered in the model, 73 (44.5%) were R and 91 (55.5%) NR. Overall, the best model was the linear regression (RL) and included 5 features. The model predicting R/NR of patients achieved accuracy ACC = 0.756, F1 score F1 = 0.722, and area under the ROC curve AUC = 0.82. LR was also the best-performing model in predicting patients with long survival (24 months OS), achieving ACC = 0.839, F1 = 0.908, and AUC = 0.87. (4) Conclusions: The results suggest that the integration of multifactorial data provided by ML techniques is a useful tool to select NSCLC patients as candidates for IO

Immune-Checkpoint Inhibitors in Advanced Non-Small Cell Lung Cancer With Uncommon Histology

ICIs role aNSCLC with UH is still unclear. In this retrospective study conducted in 375 pts - with 79 pts having a UH - no significant difference was found between the UH and CH group treated with ICIs. Given the retrospective nature of this study, further prospective trials are needed to clarify ICIs role in UH patients.Background: Immune-checkpoint inhibitors (ICIs) have significantly improved outcome of advanced non-small cell lung cancer (aNSCLC) patients. However, their efficacy remains uncertain in uncommon histologies (UH). Materials and Methods: Data from ICI treated aNSCLC patients (April,2013-January,2021) in one Institution were retrospectively collected. Univariate and multivariate survival analyses were estimated by Kaplan-Meier and Cox proportional hazards regression model, respectively. Objective response rate (ORR) and disease control rate (DCR) were assessed. Results: Of 375 patients, 79 (21.1%) had UH: 19 (24.1%) sarcomatoid carcinoma, 15 (19.0%) mucinous adenocarcinoma, 10 (12.6%) enteric adenocarcinoma, 8 (10.1%) adenocarcinoma not otherwise specified, 7 (8.9%) large-cell neuroendocrine carcinoma, 6 (7.6%) mixed histology non-adenosquamous, 5 (6.3%) adenosquamous carcinoma, 9 (11.4%) other UH. In UH group, programmed death-ligand 1 (PD-L1) < 1%, 1-49%, >= 50% and unknown expression were reported in 27.8%, 22.8%, 31.7% and 17.7% patients respectively and ICI was the second/further-line in the majority of patients. After a median follow-up of 35.64 months (m), median progression-free survival (mPFS) was 2.5 m in UH [95% CI 2.2-2.9 m] versus (vs.) 2.7 m in CH [95% CI 2.3-3.2 m, P-value = .584]; median overall survival (mOS) was 8.8 m [95% CI 4.9-12.6 m] vs. 9.7 m [95% CI 8.0-11.3 m, P-value = .653]. At multivariate analyses only ECOG PS was a confirmed prognostic factor in UH. ORR and DCR were 25.3% and 40.5% in UH vs. 21.6% and 49.5% in CH [ P-value = .493 and .155 respectively]. Conclusions: No significant differences were detected between UH and CH groups. Prospective trials are needed to understand ICIs role in UH population. (C) 2021 Published by Elsevier Inc

High bone tumor burden to identify advanced non-small cell lung cancer patients with survival benefit upon bone targeted agents and immune checkpoint inhibitors

Background: Bone-targeted agents (BTA), such as denosumab (DN) and zoledronic acid (ZA), have historically reduced the risk of skeletal related events in cancer patients with bone metastases (BM), with no improvement in survival outcomes. In the immunotherapy era, BM have been associated with poor prognosis upon immune-checkpoint inhibitors (ICI). Currently, the impact of bone tumor burden on survival upon BTAs in advanced non-small cell lung cancer (aNSCLC) patients treated with ICI remains unknown. Methods: Data from ICI-treated aNSCLC patients with BM (4/2013-5/2022) in one institution were retrospectively collected. BTA-ICI concurrent treatment was defined as BTA administration at any time before or within 90 days from ICI start. High bone tumor burden (HBTB) was defined as ≥ 3 sites of BM. Median OS (mOS) was estimated with Kaplan-Meier. Aikaike's information criterion (AIC) was used to select the best model for data analysis adjusted for clinical variables. Results: Of 134 patients included, 51 (38 %) received BTA. At a mFU of 39.6 months (m), BTA-ICIs concurrent treatment did not significantly impact on mOS [8.3 m (95% CI 3.9-12.8) versus (vs) 6.8 m (95% CI 4.0-9.6) p = 0.36]; these results were confirmed after adjustment for clinical variables selected by AIC. A multivariate model showed a significant interaction between BTA use and HBTB or radiation therapy to BM. In subgroup analyses, only HBTB confirmed to be associated with significantly longer mOS [8.3 m (95% CI 2.4-14.2) vs 3.5 m (95% CI 2.9-4.1), p = 0.003] and mPFS [3.0 m (95% CI 1.6-4.4) vs 1.8 m (95% CI 1.6-2.0) p = 0.001] upon BTA-ICI concurrent treatment, with the most pronounced OS benefit observed for DN-ICI concurrent regimen [15.2 m (95% CI 0.1-30.7) vs 3.5 m (95% CI 2.9-4.1) p = 0.002]. Conclusions: In the immunotherapy era, HBTB can identify patients experiencing survival benefit with BTA, especially with DN-ICI combination. HBTB should be included as a stratification factor in the upcoming trials assessing BTA and ICI combinations in patients with aNSCLC and BM