EPSILoN score: Validation cohort of a prognostic score in advanced non-small cell lung cancer (aNSCLC) patients treated with immunotherapy

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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

Classification criteria for autoinflammatory recurrent fevers.

BACKGROUND: Different diagnostic and classification criteria are available for hereditary recurrent fevers (HRF)-familial Mediterranean fever (FMF), tumour necrosis factor receptor-associated periodic fever syndrome (TRAPS), mevalonate kinase deficiency (MKD) and cryopyrin-associated periodic syndromes (CAPS)-and for the non-hereditary, periodic fever, aphthosis, pharyngitis and adenitis (PFAPA). We aimed to develop and validate new evidence-based classification criteria for HRF/PFAPA. METHODS: Step 1: selection of clinical, laboratory and genetic candidate variables; step 2: classification of 360 random patients from the Eurofever Registry by a panel of 25 clinicians and 8 geneticists blinded to patients\u27 diagnosis (consensus ≥80%); step 3: statistical analysis for the selection of the best candidate classification criteria; step 4: nominal group technique consensus conference with 33 panellists for the discussion and selection of the final classification criteria; step 5: cross-sectional validation of the novel criteria. RESULTS: The panellists achieved consensus to classify 281 of 360 (78%) patients (32 CAPS, 36 FMF, 56 MKD, 37 PFAPA, 39 TRAPS, 81 undefined recurrent fever). Consensus was reached for two sets of criteria for each HRF, one including genetic and clinical variables, the other with clinical variables only, plus new criteria for PFAPA. The four HRF criteria demonstrated sensitivity of 0.94-1 and specificity of 0.95-1; for PFAPA, criteria sensitivity and specificity were 0.97 and 0.93, respectively. Validation of these criteria in an independent data set of 1018 patients shows a high accuracy (from 0.81 to 0.98). CONCLUSION: Eurofever proposes a novel set of validated classification criteria for HRF and PFAPA with high sensitivity and specificity

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

Afatinib in the treatment of squamous non-small cell lung cancer: A new frontier or an old mistake?

Lung squamous cell carcinoma represents approximately 20% of all non-small cell lung cancer (NSCLC) and is associated with a very poor prognosis. In the randomized phase III LUX-Lung 8 trial afatinib showed a statistical significant efficacy advantage compared to erlotinib as second-line treatment of advanced/metastatic squamous NSCLC. Despite its well-built design and the statistical significant results, in our opinion the study is still far from being clinically relevant for this subset of patients. Moreover, during the last years other drugs have shown encouraging activity with low toxicity in pretreated lung squamous cell carcinomas. In particular, nivolumab in the treatment of platinum-pretreated squamous NSCLC has recently radically changed the treatment paradigms in this histology. Sure, LUX-Lung 8 trial achieved its primary endpoint progression-free survival showing some afatinib activity in one of the most difficult-to treat and genetically complex neoplasm but we haven’t found the most active drug in this subset of patients yet. The purpose of this editorial is to discuss some of the most controversial aspects of the LUX-Lung 8 trial focusing especially on its rational and desig

Novel patterns of progression upon immunotherapy in other thoracic malignancies and uncommon populations

In the immunotherapy era, considering the prolonged survival benefit and responses observed with immunecheckpoint inhibitors (ICI) in many cancer types, the identification of patients with rapid progression (PD) and deaths upon ICI has found some skepticism and resistance among the scientific community. Nevertheless, an acceleration of tumour during ICI, defined as hyperprogressive disease (HPD), has been recognized across different cancer types and evidence regarding rapid PDs and deaths are emerging in patients with malignant pleural mesothelioma (MPM), small cell lung cancer (SCLC) and thymic malignancies and in uncommon non-small cell lung cancer (NSCLC) populations. Of note, PD and early deaths (ED) rates upon single agent ICI were up to 60% and 30% in MPM and 70% and 38% in SCLC patients, respectively. Similarly, rapid PDs and deaths were observed in clinical trials and retrospective studies including patients with poor performance status (PS), HIV infection and rare NSCLC histologies. Atypical patterns of response, such as pseudoprogression (PsPD) may also occur in other thoracic malignancies (MPM) and in some uncommon populations (i.e., HIV patients), however probably at lower rate compared to HPD. The characterizations of HPD and PsPD mechanisms and the identification of common definition criteria are the next future challenges in this area of cancer research

Treatment in EGFR-mutated non-small cell lung cancer: how to block the receptor and overcome resistance mechanisms

In non-small cell lung cancer (NSCLC), the identification of epidermal growth factor receptor (EGFR) mutations and the parallel development of EGFR tyrosine kinase inhibitors (TKIs) have radically changed the therapeutic management strategies. Currently, erlotinib, gefitinib, and afatinib are all approved as standard first-line treatment in EGFR-mutated NSCLC. However, despite the proven efficacy, some EGFR-mutated NSCLCs do not respond to EGFR TKIs, while some patients, after a favorable and prolonged response to EGFR TKIs, inevitably progress within about 10-14 months. Epidermal growth factor receptor-dependent mechanisms, activation of alternative pathways, or phenotypic transformation can cause the resistance to EGFR TKIs. The exon 20 p.Thr790Met point mutation (T790M) is responsible for about 60% of cases of resistance when progression occurs. A third-generation TKI, osimertinib, improved outcome in patients harboring T790M after first- and second-generation TKI treatment. However, resistance develops even after treatment with third-generation drugs. To date, the Cys797Ser (C797S) mutation in exon 20 of EGFR is the most well-known resistance mutation after osimertinib. Fourth-generation TKIs are already under development. Nevertheless, additional information is needed to better understand and effectively overcome resistance. The aim of this review is to report recent advances and future perspectives in the treatment of EGFR-mutated NSCLC, highlighting the resistance mechanisms that underlie disease progression. © 2017 Wichtig Publishing

Concomitant EML4-ALK rearrangement and EGFR mutation in non-small cell lung cancer patients: a literature review of 100 cases

The discovery of EGFR mutations and EML4-ALK gene rearrangements has radically changed the therapeutic scenario for patients with advanced non-small cell lung cancer. ALK and EGFR tyrosine-kinase inhibitors showed better activity and efficacy than standard chemotherapy in the first and second line treatment settings, leading to a clear advantage in overall survival of advanced non-small cell lung cancer patients harboring these genetic alterations. Historically the coexistence of EGFR mutations and EML4-ALK rearrangements in the same tumor has been described as virtually impossible. Nevertheless many recent observations seem to show that it is not true in all cases. In this review we will discuss the available literature data regarding this rare group of patients in order to give some suggestions useful for their clinical management. Furthermore we report here two cases of concomitant presence of both alterations that will help us in the development of discussion

Epidermal growth factor receptor tyrosine kinase inhibitors for the treatment of central nervous system metastases from non-small cell lung cancer: the present and the future

Lung cancer is one of the major causes of cancer related mortality worldwide. Brain metastases (BM) complicate clinical evolution of non-small cell lung cancer (NSCLC) in approximately 25-40% of cases, adversely influencing quality of life (QoL) and overall survival (OS). Systemic therapy remains the standard strategy for metastatic disease. Nevertheless, the blood-brain barrier (BBB) makes central nervous system (CNS) a sanctuary site. To date, the combination of chemotherapy with whole brain radiation therapy (WBRT), surgery and/or stereotactic radiosurgery (SRS) represents the most used treatment for patients (pts) with intracranial involvement. However, due to their clinical conditions, many pts are not able to undergo local treatments. Targeted therapies directed against epidermal growth factor receptor (EGFR), such as gefitinib, erlotinib and afatinib, achieved important improvements in EGFR mutated NSCLC with favorable toxicity profile. Although their role is not well defined, the reported objective response rate (ORR) and the good tolerance make EGFR-tyrosine kinase inhibitors (TKIs) an interesting valid alternative for NSCLC pts with BM, especially for those harboring EGFR mutations. Furthermore, new-generation TKIs, such as osimertinib and rociletinib, have already shown important activity on intracranial disease and several trials are still ongoing to evaluate their efficacy. In this review we want to highlight literature data about the use and the effectiveness of EGFR-TKIs in pts with BM from NSCLC. © Translational lung cancer research. All rights reserved

Treatment of lung large cell neuroendocrine carcinoma

Lung large cell neuroendocrine carcinoma (L-LCNEC) is a rare, aggressive, and difficult-to-treat tumor. It is classified as a neuroendocrine subtype of large cell lung carcinoma (LCLC) belonging to the non-small cell lung cancer (NSCLC) group, but it is also included in the neuroendocrine tumor (NET) group. Most of the available data related to its treatment derive from retrospective analyses or small case series. For patients with L-LCNEC, prognosis is generally very poor. In early stages (I–II–III), surgery is recommended but does not seem to be sufficient. Platinum-based adjuvant chemotherapy may be useful while the role of neoadjuvant chemotherapy is still not well defined. In patients with advanced L-LCNEC, the chemotherapy regimens used in SCLC still remain the standard of treatment, but results are not satisfactory. Due to their peculiar clinical and biological features and the lack of literature data, there is an emerging need for a consensus on the best treatment strategy for L-LCNEC and for the identification of new therapeutic options. In this review, we will discuss the key aspects of L-LCNEC management with the aim to clarify the most controversial issues. © 2016, International Society of Oncology and BioMarkers (ISOBM)