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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.We identified all patients with advanced NSCLC treated with IO at our Institution with available baseline plasma samples. Fatty acids (FAs) were analyzed through gas chromatography. Survival curves were estimated by the Kaplan-Meier method. Cox multivariate models were constructed through a stepwise procedure, with entry and exit P value set at .2.We identified 112 patients, mostly with performance status 1 (65.2%) and PD-L1≥1% (75.3%). Median progression-free survival (PFS) and overall survival (OS) were 2.8 and 11.0 months, respectively. Multivariable model for survival identified a positive association of circulating free (FFA) C16:0 (P .005) and esterified (EFA) C16:1 (P .030) with PFS, and a positive association of EFA C16:1 (P .001) and EFA C18:0 (P .020) with OS. EFA C16:0 was negatively associated with PFS (P .008).FFA C16:0 and FAs derived from its unsaturation (EFA C16:1) and elongation (EFA C18:0) are associated with a better outcome in NSCLC patients treated with IO. It is conceivable that the ratio among those FAs may modify membrane fluidity and receptor activity, influencing IO efficacy. These data pave the way for the investigation of lipid-modulating strategies in association with IO in NSCLC.

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.

Background: Immune checkpoint-inhibitors (ICIs) are changing outcomes in different cancer settings, notably for patients with non-small-cell lung cancer (NSCLC). There are, however, still important gaps of evidence for clinical practice when using these novel treatments. In this study, we assessed physicians' opinion and experience on challenges for clinical practice with ICIs monotherapy in NSCLC. Methods: A survey was conducted on experienced physicians treating patients with NSCLC with ICIs. Two rounds of pilot tests were carried out for validation among a group of experts. Topics under analysis were in relation to treatment of elderly populations, performance status, brain metastases, use of steroids or antibiotics, the effects of gut microbiome, autoimmune diseases, human immunodeficiency virus infection, solid organ transplants, use of anti-programmed cell death protein 1 versus anti-programmed death-ligand 1 drugs, atypical tumour responses, predictors of response, duration of treatment and a final open question on additional relevant challenges. Results: Two hundred and twenty-one answers were collected, including 106 (48%) valid answers from experts for final analysis (physicians who have treated at least 20 patients with NSCLC with ICIs). The vast majority agreed that the selected topics in this study are important challenges ahead and more evidence is needed. Moreover, predictors of response, treating brain metastasis, shorter duration of treatment, the effects of gut microbiome and concomitant use of steroids were voted the most important topics to be further addressed in prospective clinical research. Conclusions: This survey contributed to understanding which are the main challenges for clinical practice with ICIs monotherapy in NSCLC. It can also contribute to guide further clinical research, considering the opinions and experience of those who regularly treat NSCLC patients with ICIs. info:eu-repo/semantics/publishedVersion

BACKGROUND: Malignant pleural mesothelioma (MPM) is an aggressive tumor but approximately 12% of patients survive more than 3 years. The biological differences underlying better outcomes are not known. Several targeted agents and immunotherapy have been ineffective. Hedgehog (Hh) is one emerging pathway. We compared the biological profiles of patients with different survival, investigating the most frequently altered genes, including the Hh pathway. METHODS: We analyzed 56 MPM. A 36-month overall survival (OS) cut-off divided patients into 32 normo (NS) and 24 long (LS) survivors. We used next generation sequencing to test 21 genes, immunohistochemistry to evaluate SMO expression. Mutation differences between NS and LS and their associations with clinical features were analysed by Fisher’s test, OS with the Kaplan-Meier method and its association with mutations by univariate and multivariate Cox proportional hazard models. RESULTS: Clinical features were similar in both groups. Eighteen out of 56 patients (32%) were wild-type for the genes analysed. At least five had mutations in BAP1, NF2, TP53, SMO and PTCH1 with no significant differences between the groups except for SMO. SMO, a member of the Hh pathway, was mutated only in NS (15.6%) and only SMO mutations were significantly associated with poor prognosis at univariate (HR =4.36, 95% CI: 2.32–8.18, P

The use of immune checkpoint inhibitors (ICIs) offers new possibilities in modern treatment of many types of cancers. Few data regarding safety and efficacy of ICIs are available, and are mainly from retrospective studies and case reports rather than from clinical trials, in the context of preexisting autoimmune disease, mainly due to the risk of severe toxicity. We present an unexpected life-threatening reactivation of systemic lupus erythematosus after one dose of chemo-immunotherapy with pembrolizumab for oligometastatic non-small-cell lung cancer. We analyze data coming from the published literature in this setting and discuss the risk–benefit balance of immunotherapy in patients with preexisting severe autoimmune disease.

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.

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).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.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].No significant differences were detected between UH and CH groups. Prospective trials are needed to understand ICIs role in UH population.

Most trials with Immune Checkpoint Inhibitors (ICIs) for Non-Small Cell Lung Cancer (NSCLC) included only small subgroups of patients aged ≥65. As NSCLC is often diagnosed in patients aged ≥70, real-world data about efficacy and safety of immunotherapy (IO) in elderly patients are essential.We retrospectively collected data about all patients with advanced NSCLC treated with IO at our Institution between April 2013 and March 2019. The patients were stratified for age as follows:70 year-old, 70-79 year-old, ≥80 year-old. Chi-square test was used to compare qualitative variables. Survival was estimated with Kaplan-Meier method. Log-rank test was used to compare curves. Multivariate analyses were performed with Cox model.We reviewed 290 cases, with a median age of 67 (range: 29-89). Patients aged70, 70-79 and ≥80 year-old were 180, 94 and 16, respectively. Clinical/pathological variables were uniformly distributed across age classes, except for a higher rate of males (p 0.0228) and squamous histology (p 0.0071) in the intermediate class. Response Rate (RR) was similar across age groups (p 0.9470). Median Progression Free Survival (PFS) and Overall Survival (OS) did not differ according to age (p 0.2020 and 0.9144, respectively). Toxicity was comparable across subgroups (p 0.6493). The only variables influencing outcome were performance status (PS) (p 0.0001 for PFS, p 0.0192 for OS), number of metastatic sites (p 0.0842 for PFS, p 0.0235 for OS) and IO line (p 0.0001 for both PFS and OS).Advanced age was not associated to a reduced efficacy of IO in our case series. Furthermore, no toxicity concern emerged even among the eldest pts. To our opinion, ICIs should be considered irrespective of age, provided an optimal PS at baseline. Of note, IO is often the only therapeutic option applicable to these cases considering the toxicity of chemotherapy.

Introduction: In advanced Non-Small Cell Lung Cancer (NSCLC), Programmed Death Ligand 1 (PD-L1) remains the only used biomarker to candidate patients to immunotherapy (IO) with many limits. Given the complex dynamics of the immune system it is improbable that a single biomarker could be able to profile prediction with high accuracy. A promising solution cope with this complexity is provided by Artificial Intelligence (AI) and Machine Learning (ML), which are techniques able to analyse and interpret big multifactorial data. The present study aims at using AI tools to improve response and efficacy prediction in NSCLC patients treated with IO.MethodsReal world data (clinical data, PD-L1, histology, molecular, lab tests) and the blood microRNA signature classifier (MSC), which include 24 different microRNAs, were used. Patients were divided into responders (R), who obtained a complete or partial response or stable disease as best response, and non-responders (NR), who experienced progressive or hyperprogressive disease and those who died before the first radiologic evaluation. Moreover, we used the same data to determine if the overall survival of the patients was likely to be shorter or longer than 24 months from baseline IO. For A literature review and forward feature selection technique was used to extract a specific subset of the patients’ data. To develop the final predictive model, different ML methods have been tested, i.e., Feedforward Neural Network (FFNN), Logistic Regression (LR), K-nearest neighbours (K-NN), Support Vector Machines (SVM), and Random Forest (RF).Results 200 patients were included. 164 out of 200 (i.e., only those patients 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 LR and included 5 features: 2 clinical features including the ECOG performance status and IO-line of therapy; 1 tissue feature such as PD-L1 tumour expression; and 2 blood features including the MSC test and the neutrophil-to-lymphocyte ratio (NLR). The model predicting R/NR of the patient achieves accuracy ACC= 0.756, F1 score F1=0.722, and Area Under the ROC Curve AUC=0.82. The use of the PD-L1 alone has an ACC=0.655. The accuracy of the ML models excluding some of the features from the model were as follow: without PD-L1 value (ACC=0.726), MSC (ACC=0.750), and both PD-L1 and MSC (ACC=0.707), i.e., considering only clinical features. At data cut-off (Nov 2020), median Overall Survival (mOS) for R was 38.5 months (m) (95%IC 23.9 - 53.1) vs 3.8 m (95%IC 2.8 - 4.7) for NR, with pConclusionsThe results suggest that the integration of multifactorial data provided by ML techniques is a useful tool to improve personalized selection of NSCLC patients candidates to IO. In particular, compared to PD-L1 alone the expected improvement was around 10%. In particular, the model shows that the higher the ECOG, NLR value, IO-line, and MSC test level the lower the response, and the higher PD-L1 the higher the response. Considering the difference in survival among R and NR groups, these results suggest that the model can also be used to indirectly predict survival. Moreover, a second model was able to predict long survival patients with good accuracy.

Peripheral blood inflammatory indices, like the neutrophil-to-lymphocyte ratio (NLR), may reflect the host's pro-inflammatory status and systemic immune response to cancer-related inflammation. We reviewed 22 combined prognostic scores based on peripheral blood-derived inflammatory indices for aNSCLC patients treated with single-agent or combination immune-checkpoint inhibitors (ICI) as first-line or subsequent therapy lines and attempted evidence strength assessment and scoring. The Lung Immune Prognostic Index (LIPI), consisting of derived NLR and LDH, was the most studied score with validated prognostic value in over five thousand aNSCLC ICI-naïve or pretreated patients. The combination of NLR and tumour programmed-cell-death-ligand1 (PD-L1) expression showed a predictive value. The Lung-Immune-Prognostic score (LIPS) might help identify patients with poor performance status but a favourable outcome following first-line ICI. These non-expensive scores can help clinicians discuss the prognosis with aNSCLC patients approaching ICI, identify those less likely to benefit from single-agent ICI and orient future clinical research.

Background: The introduction of immunotherapy has improved the prognosis of patients with Non-Small Cell Lung Cancer (NSCLC). However, data in poor ECOG Performance Status (PS) patients remain scant due to their exclusion from randomized trials. Material and methods: We analyzed data of patients with advanced NSCLC treated with immunotherapy in two Italian Centers, to evaluate the impact of PS (0-1 vs 2) on disease control rate (DCR), progression free survival (PFS) and overall survival (OS). Chi-square test was used to compare clinical-pathological variables, their impact on survival was evaluated through Cox proportional hazard models. Results: Among 404 patients included, PS was 0 in 137 (33.9 %), 1 in 208 (51.5 %) and 2 in 59 (14.6 %) patients; 143 were female and 90 had squamous NSCLC. Clinical-pathological variables were uniformly distributed except for higher prevalence of liver metastases in patients with poor PS. We found that PS2 patients showed worse outcomes in terms of DCR (21.8 % vs 50.3 %, p = 0.001), PFS [2.0 (95 % CI 1.6–3.0) vs 3.0 (95 % CI 2.7–4.0) months, p < 0.0001] and OS [4.0 (95 % CI 2.8–5.7) vs 13.2 (95 % CI 11.0−15.8) months, p < 0.0001]. PS2 status, negative PDL1 expression and early corticosteroids exposure as well as higher Neutrophil to Lymphocyte Ratio and LDH at baseline were associated with worse outcomes at univariate and multivariable analysis. Subgroup analysis confirmed poor outcomes in PS2 patients with high LDH and concomitant corticosteroid therapies. The incidence of Grade 3/4 adverse events was 11.3 % in PS 0−1 and 10.2 % in PS 2 patients (p = 0.81). Conclusion: Our data confirm reduced efficacy of immunotherapy in patients with poor PS even though a good safety. Despite PS remains the most powerful independent prognostic factor for NSCLC, LDH levels and steroids exposure could support the decision making in PS2 patients.

Immune checkpoint blockade has been a pivotal development in the management of advanced non-small-cell lung cancer (NSCLC). Although durable antitumour activity and improved survival have been observed in a subset of patients, there is a need for additional predictive biomarkers to improve patient selection and avoid toxicity in potential non-responders. This review will address the use and limitations of tumour programmed death-ligand 1 expression as a predictive biomarker and review emerging biomarker strategies specifically related to NSCLC including genetic alterations (tumour mutation burden, loss and gain activated mutations), tumour-related factors (tumour microenvironment) and factors related to the host immune system. Novel approaches in biomarker detection such as peripheral blood monitoring will also be reviewed.

Simple Summary The aim of this innovative review is to highlight the treatment strategies beyond immune-checkpoint inhibitor (ICI)-based first-line therapy failure according to different patterns of progression (i.e., oligo or systemic progression) and to discuss the ongoing and potential future therapeutic approaches to overcome resistance to immunotherapy. Many therapeutic strategies can be adapted in advanced non-small cell lung cancer patients with oligo and systemic progression to personalize the treatment approach based on to re-characterization of the tumors, previous ICI response and type of progression. Abstract First-line immune-checkpoint inhibitor (ICI)-based therapy has deeply changed the treatment landscape and prognosis in advanced non-small cell lung cancer (aNSCLC) patients with no targetable alterations. Nonetheless, a percentage of patients progressed on ICI as monotherapy or combinations. Open questions remain on patients’ selection, the identification of biomarkers of primary resistance to immunotherapy and the treatment strategies to overcome secondary resistance to first-line immunotherapy. Local ablative approaches are the main therapeutic strategies in oligoprogressive disease, and their role is emerging in patients treated with immunotherapy. Many therapeutic strategies can be adapted in aNSCLC patients with systemic progression to personalize the treatment approach according to re-characterization of the tumors, previous ICI response, and type of progression. This review’s aim is to highlight and discuss the current and potential therapeutic approaches beyond first-line ICI-based therapy in aNSCLC patients based on the pattern of disease progression (oligoprogression versus systemic progression).

BACKGROUND: Immune checkpoint inhibitors (ICIs) represent a great breakthrough in the treatment of advanced non-small cell lung cancer (aNSCLC). However, whether immunotherapy beyond progression (IBP) is effective for aNSCLC has yet to be established. Therefore, a retrospective clinical study was conducted to investigate the efficacy of IBP in patients with aNSCLC under real-world conditions. METHODS: A total of 125 Chinese patients with aNSCLC who experienced progressive disease (PD) after receiving monotherapy or combination therapy (combined with chemotherapy or/and antiangiogenic therapy) with programmed cell death-1 (PD-1)/programmed cell death ligand-1 (PD-L1) inhibitors between January 2015 and March 2019 were enrolled. Patients who were treated with ICIs for more than 6 weeks after PD were defined as IBP (n=39), while those who received ICI treatment for less than 6 weeks or discontinued it due to PD were defined as non-IBP (n=86). Patient clinical characteristics were evaluated. An optimization-based method was applied to balance the clinical baseline characteristics between the two groups. RESULTS: In total population, the IBP group had longer overall survival (median OS, 26.6 vs. 9.5 months; HR, 0.40; 95% CI: 0.23–0.69; P

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.

Giving the potential contribute in cancer initiation and progression, lung microbiota represents a promising topic in cancer research, although still unexplored. We performed a systematic literature search to identify clinical studies evaluating lung microbiota composition, its correlation with lung cancer patients' clinico-pathological features and prognosis. Of the identified 370 studies, 21 were eligible and included. Although studies were heterogeneous, lung cancer resulted to be enriched in peculiar microbial communities, with differences in composition and diversity according to clinico-pathological parameters. Few studies explored how lung microbiota influences cancer outcome. In light of these findings and borrowing the suggestions coming from gut microbiota, we speculate that respiratory microbiome may influence pathogenesis, progression and outcome of lung cancer. Taking advantage of the experience of chronical lung diseases, prospective studies should be designed to evaluate lung microbiota changes throughout any phase of lung cancer course, particularly with the advent of immunotherapy as pivotal treatment.

BACKGROUND: Several biomarkers have been separately described to select patients for immunotherapy (IO), but few studies integrate these markers. Di Maio, EPSILoN and the plasma microRNA signature classifier (MSC), are three different clinico, biochemical and molecular markers able to independently predict prognosis in non-small cell lung cancer (NSCLC). METHODS: Complete data such as sex, histology, ECOG-PS, stage, smoking status, presence of liver metastasis, LDH and neutrophils-to-lymphocyte ratio were collected to generate Di Maio and EPSILoN. The MSC risk level was prospectively assessed in plasma samples collected at baseline IO. The 3 markers were integrated into the DEMo score system prospectively tested in a cohort of 200 advanced NSCLC patients treated with IO. Endpoints were overall survival (OS), progression-free survival (PFS) and overall response rate (ORR). RESULTS: DEMo separated patients in 7-risk groups whose median OS had a trend ranging from 29.7 to 1.5 months (P

Other than the programmed cell death ligand 1 (PD-L1) value, oncologists have only the clinical characteristics of patients with advanced non-small-cell lung cancer (aNSCLC) to determine candidates for immunotherapy. A clinical prognostic score composed of the Eastern Cooperative Oncology Group performance status, sex, histologic type, stage, platinum-based first-line therapy, and response to first-line therapy has categorized 3 prognostic groups for patients undergoing second-line chemotherapy. We sought to validate the same score for patients with aNSCLC treated with second- or further-line immunotherapy.We collected data from 2 Italian centers. A score was generated to divide patients into 3 prognostic groups: best, score 5; intermediate, score 5 to 9; and worst, score9. Overall survival (OS) and progression-free survival (PFS) were the endpoints.A total of 347 patients were included for analysis. Their median age was 66 years (range, 30-88 years), most were aged 70 years (67.5%), 70.7% were men, 79.5% were smokers, and 74.6% had had adenocarcinoma. The Eastern Cooperative Oncology Group performance status was 0 for 23%, 1 for 54.5%, and 2 for 22.5%. Of the 347 patients, 28% were in the best prognosis, 51% in the intermediate, and 21% in the worst prognosis group, respectively. The median OS was 18.0 months for the best, 8.5 months for the intermediate (hazard ratio [HR] vs. best, 1.83; 95% confidence interval [CI], 1.35-2.47; P .001) and 2.6 months for worst (HR vs. best, 5.77; 95% CI, 3.99-8.33; P .001) group. The median PFS was 3.4 months for the best, 3.7 months for the intermediate (HR vs. best, 1.35; 95% CI, 1.03-1.77; P = .032), and 1.9 months for the worst (HR vs. best, 2.51; 95% CI, 1.80-3.50; P .001) group.The prognostic score was able to predict the outcomes of patients with aNSCLC who had received immunotherapy. The worst category showed a dismal life expectancy and probably would not benefit from active systemic therapy. Thus, for these patients, best supportive care could be the best choice.

PD-1 pathway blockade has been shown to promote proatherogenic T-cell responses and destabilization of atherosclerotic plaques. Moreover, preclinical evidence suggests a potential synergy of antiplatelet drugs with immune checkpoint inhibitors (ICIs). We conducted an analysis within a prospective observational protocol (APOLLO study) to investigate the rates, predictors, and prognostic significance of thromboembolic events (TE) and thromboprophylaxis in patients with advanced NSCLC treated with ICIs. Among 217 patients treated between April 2014 and September 2018, 13.8% developed TE events. Current smoking status (HR 3.61 (95% CI 1.52&ndash, 8.60), p = 0.004) and high (&gt, 50%) PD-L1 (HR 2.55 (95% CI 1.05&ndash, 6.19), p = 0.038) resulted in being independent TE predictors. An increased risk of death following a diagnosis of TE (HR 2.93, 95% CI 1.59&ndash, 5.42, p = 0.0006) was observed. Patients receiving antiplatelet treatment experienced longer progression-free survival (PFS) (6.4 vs. 3.4 months, HR 0.67 (95% CI 0.48&ndash, 0.92), p = 0.015) and a trend toward better OS (11.2 vs. 9.6 months, HR 0.78 (95% CI 0.55&ndash, 1.09), p = 0.14), which were not confirmed in a multivariate model. No impact of anticoagulant treatment on patients&rsquo, outcomes was observed. NSCLC patients treated with ICIs bear a consistent risk for thrombotic complications, with a detrimental effect on survival. The impact of antiplatelet drugs on ICIs efficacy deserves further investigation in prospective trials.

Small-cell lung cancer (SCLC) is a very aggressive disease characterized by a high response rate to first-line chemotherapy, but most patients relapse within 1 year with disappointing results to second-line treatments. Chemotherapy has reached a plateau of effectiveness and new therapeutic strategies are needed to change the natural history of SCLC. Areas covered: This review will focus on the current results and the future development of the therapeutic approaches for the treatment of SCLC. Expert commentary: Immunotherapy is becoming a new frontier for the management of SCLC with preliminary interesting results. To date, no targeted drugs have been approved for clinical practice but several novel agents are in an advanced stage of clinical development in SCLC.

Aim: A minority of patients gains advantage from immunotherapy (IO). Predictive variables of long-term benefit (LTB) are incompletely understood. Materials & methods: We retrospectively collected data about metastatic non-small-cell lung cancer patients treated with IO from April 2013 to July 2017. We defined LTB to IO as complete response (CR), partial response (PR) or disease stability as best response and maintaining it for ≥12 months. Results: Thirty-five of the 147 patients had LTB. More LTB patients than controls showed CR/PR as first and best response to IO. Only CR/PR as best response to IO retained association to LTB at multivariate analyses. Conclusion: Objective response appears as a central factor for LTB from IO.

Background: Beyond programmed death ligand 1 (PD-L1), no other biomarkers for immunotherapy are used in daily practice. We previously created EPSILoN (Eastern Cooperative Oncology Group performance status (ECOG PS), smoking, liver metastases, lactate dehydrogenase (LDH), neutrophil-to-lymphocyte ratio (NLR)) score, a clinical/biochemical prognostic score, in 154 patients treated with second/further-line immunotherapy. This study&rsquo, s aim was to validate EPSILoN score in a different population group. Methods: 193 patients were included at National Cancer Institute of Milan (second-line immunotherapy, 61%, further-line immunotherapy, 39%). Clinical/laboratory parameters such as neutrophil-to-lymphocyte ratio and lactate dehydrogenase levels were collected. Kaplan&ndash, Meier and Cox hazard methods were used for survival analysis. Results: Overall median progression-free survival and median overall survival were 2.3 and 7.6 months, respectively. Multivariate analyses for Progression-Free Survival (PFS) identified heavy smokers (hazard ratio (HR) 0.71, p = 0.036) and baseline LDH &lt, 400 mg/dL (HR 0.66, p = 0.026) as independent positive factors and liver metastases (HR 1.48, p = 0.04) and NLR &ge, 4 (HR 1.49, p = 0.029) as negative prognostic factors. These five factors were included in the EPSILoN score which was able to stratify patients in three different prognostic groups, high, intermediate and low, with PFS of 6.0, 3.8 and 1.9 months, respectively (HR 1.94, p &lt, 0.001), high, intermediate and low prognostic groups had overall survival (OS) of 24.5, 8.9 and 3.4 months, respectively (HR 2.40, p &lt, 0.001). Conclusions: EPSILoN, combining five baseline clinical/blood parameters (ECOG PS, smoking, liver metastases, LDH, NLR), may help to identify advanced non-small-cell lung cancer (aNSCLC) patients who most likely benefit from immune checkpoint inhibitors (ICIs).

Introduction In advanced Non-Small Cell Lung Cancer (aNSCLC), Programmed Death Ligand 1 (PD-L1) remains the only used biomarker to candidate patients (pts) to immunotherapy (IO) even if its predictive accuracy is not satisfactory. Indeed, given the complex dynamics underlying the cross-talk between the tumor and its microenvironment, it is unlikely that a single biomarker could be able to profile prediction with high precision. Artificial Intelligence (AI) and machine learning (ML) are techniques able to analyze and interpret big data, which cope with this complexity. The present study aims at using AI tools to improve response and efficacy prediction in aNSCLC pts treated with IO. Methods A classification task to determine if a pt is likely to benefit from IO was formulated using complete clinical data, PD-L1, histology, molecular data, and the blood microRNA signature classifier (MSC), which include 24 different microRNAs. Pts were divided into responders (R), who obtained a partial response or stable disease as best response, and non-R, who experienced progressive disease. A forward feature selection technique based on the Akaike Information Criterion was used to extract a specific subset of the pts data, being the most informative ones for the task. To develop the final predictive model, different ML methods have been tested: K-nearest neighbors, Logistic Regression, Kernel Support Vector Machines, Feedforward Neural Network, and Random Forest. Results Of 164 enrolled pts, 73 (44.5%) were R and 91 (55.5%) non-R. At data cut-off (Nov 2020), median Overall Survival (mOS) was 10.1 (95%IC 7.0 - 13.2) months (m). mOS for R pts was 38.5 m (95%IC 23.9 - 53.1) vs 3.8 m (95%IC 2.8 - 4.7) of non-R, p Citation Format: Arsela Prelaj, Mattia Boeri, Alessandro Robuschi, Claudia Proto, Giuseppe Lo Russo, Roberto Ferrara, Giulia Galli, Alessandro De Toma, Marta Brambilla, Mario Occhipinti, Sara Manglaviti, Alice Labianca, Teresa Beninato, Marta Bini, Mavis Mensah, Monica Ganzinelli, Nicoletta Zilembo, Filippo de Braud, Gabriella Sozzi, Marcello Restelli, Alessandra Pedrocchi, Marina Chiara Garassino, Francesco Trovo. Artificial intelligence to improve selection for NSCLC patients treated with immunotherapy [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-065.

Background Second-line immunotherapy (IO) has shown an overall survival benefit. However, only 18% to 20% of patients with advanced non–small-cell lung cancer (aNSCLC) will respond, with a median progression-free survival (PFS) of 2 to 4 months. Thus, biomarkers to select those patients most likely to benefit from IO are greatly needed. Patients and Methods We conducted a retrospective analysis of 154 patients with aNSCLC who had received anti–programmed cell death 1 therapy as second line or further treatment. We assessed the absolute neutrophil, lymphocyte, monocyte, and eosinophil counts at baseline (T0) and the second (T1) and third (T2) cycles. The neutrophil/lymphocyte ratio (NLR), derived-NLR (dNLR), lymphocyte/monocyte ratio (LMR), and their percentage of change at T1 and T2 compared with T0 were evaluated. The clinical characteristics and lactate dehydrogenase (LDH) level were also considered. Univariate and multivariate analyses were performed. Significant biomarkers for PFS on multivariate analysis were combined in a prognostic score. Results For overall survival, the negative prognostic biomarkers were Eastern Cooperative Oncology Group (ECOG) performance status (PS) 2, NLR at T0, and dNLR at T1; the LMR at T0, T1, and T2 was identified as a positive prognostic biomarker. For PFS, the negative prognostic biomarkers were ECOG PS 2, liver metastases, NLR at T0, dNLR at T1 and T2, and ≥ 30% increase of NLR from T0 to T1; the positive prognostic biomarkers were heavy smoking, LDH, and LMR at T2. The ≥ 30% increase of LMR from T0 to T1 and T0 to T2 correlated with the overall response rate. A prognostic score (EPSILoN score; smoking, ECOG PS, liver metastases, LDH, NLR) identified 3 prognostic groups (median PFS, 10.2, 4.9, and 1.7 months, respectively; P Conclusions The EPSILoN score combines 5 baseline clinical and blood biomarkers and can help to identify patients with aNSCLC who will most likely benefit from second-line IO. Further studies are warranted.

e21601 Background: The introduction of Immunotherapy (IO) has dramatically improved the prognosis of patients (pts) with advanced Non-Small Cell Lung Cancer (NSCLC). However, data regarding the role of IO in ECOG Performance Status (PS) 2 pts are generally limited in randomized trials, and real-world evidences could support clinical decision-making. Methods: We retrospectively analyzed data about pts with stage IV NSCLC treated with IO between April 2013 and December 2019 in two Italian Centers. The aim of our study was to assess the impact of PS status (0-1 vs 2) on disease control rate (DCR), progression-free survival (PFS) and overall survival (OS). Response was classified according to RECIST v1.1 criteria. PFS and OS were estimated by the Kaplan-Meier method. Chi-square test was used to compare clinical-pathological variables: gender, age ( < 70, 70-79, ≥80 years-old), smoking status, histology (squamous, non-squamous), PDL1 expression ( < 1%, ≥1%), IO line (1°, ≥2°), number (N) of metastatic sites (1, ≥2), presence of liver and/or brain metastasis. Their impact on survival was evaluated through Cox proportional hazard models. Results: Four-hundred-one pts (35.7% female) with median age of 65.4 years (range 27-88) were studied. Baseline PS was 0 in 134 pts (33.4%), 1 in 209 pts (52.1%) and 2 in 58 pts (14.5%). 312 pts had non-squamous NSCLC, 89 squamous NSCLC. Clinical-pathological variables were uniformly distributed across PS groups except for a higher rate of liver metastasis in PS2 pts ( p= 0.046). Response evaluation was available for 386 pts. DCR was 49.7% in PS0-1 pts and 25.9% in PS2 pts ( p= 0.006). At a median follow-up of 29 months (mos), median PFS was 3.0 mos (95% CI 2.63-4.00) and 2.04 mos (95% CI 1.84-3.00) in pts with PS0-1 and 2 ( p< 0.0001). Median OS was 13.2 mos (95% CI 11.18-15.78) and 4.0 mos (95% CI 2.66-5.62) in pts with PS 0-1 and 2 respectively ( p< 0.0001). Univariate analysis showed significant correlation of PS2 status, negative PDL1, IO line ≥2, N of metastatic sites ≥2 and liver metastasis, for both PFS and OS. Multivariate analysis confirmed an independent association of PS ( p= 0.0013 for PFS, p< 0.0001 for OS), PDL1 ( p= 0.0002 for PFS, p= 0.02 for OS) and liver metastasis ( p= 0.017 for PFS, p= 0.02 for OS). The incidence of Grade 3/4 adverse events was 10.5% in PS 0-1 pts and 13.7% in PS 2 pts ( p= 0.41). Conclusions: Our data confirm reduced efficacy of IO in pts with poor PS, regardless of the N of prior therapy lines or PDL1 expression. Despite IO appears to be safe and tolerable its role remains uncertain in PS2 pts based on worse survival outcomes.

Background The DiMaio (D), EPSILoN (E) and plasma microRNA signature classifier (MSC), are 3 diverse clinico-biochemical and molecular scores able to independently predict prognosis in advanced non-small cell lung cancer (aNSCLC) patients (pts) treated with immunotherapy (IO). By assessing the ability of each test a combined score (SC) called DEMo was developed. The study aims to prospectively evaluate the prognostic value of DEMo in aNSCLC pts treated with IO. Methods We included in the analyses 166 aNSCLC pts treated in 1L (n = 47) and further-lines (n = 119) with IO at Istituto Nazionale Tumori of Milan. For all pts we obtained complete necessary data for both clinical SC: D (sex, histology, ECOG-PS stage, uses of platinum-based therapy at 1L and response to 1L) and E (ECOG-PS, Smoke, Liver, LDH, NL-ratio). MSC was prospectively evaluated in plasma samples collected at baseline IO and the risk level was assessed. Endpoints were median overall survival (mOS), progression-free survival (mPFS) and overall response rate (ORR). Kaplan-Meier and Cox model were used to generate survival curves and multivariate analyses, respectively. Results In multivariate analysis adjusted for age, sex, smoke and ECOG-PS each score remain independently significant for both PFS (D: HR = 1.99, CI95% 1.21–3.03, p = 0.007; E: HR = 1.87 CI95% 1.12 – 3.10, p = 0.016; MSC: HR = 1.56, CI95% 1.03–2.37, p = 0.0370) and OS (D: HR = 3.12, CI95% 1.80–5.41, p = 0.0001; E: HR = 2.21, CI95% 1.28–3.79, p = 0.0041; MSC: HR = 2.03, CI95% 1.30–3.17, p = 0.0019). DEMo separated patients in 4-risk groups (gr) based on the presence of 3–2–1–0 poor prognostic SC. Strata had 0%–7%–20%–46% 18 months (mo) PFS (p Conclusions We created a composite clinic-molecular combined biomarker classifier able to better predict prognosis compared to each single SC and to select patients who less likely benefit from IO. DEMo could be a useful tool to guide choices in aNSCLC. Clinical trial identification The authors. Legal entity responsible for the study Fondazione IRCCS Istituto Nazionale Tumori of Milan, Italy. Funding Has not received any funding. Disclosure All authors have declared no conflicts of interest.

e14077 Background: Beyond PD-L1, nowadays oncologists can only use clinical characteristics to candidate patients for immunotherapy (IO). Previously, a clinical prognostic score composed by ECOG performance status (PS), sex, histology, stage, uses of platin-based therapy at first-line (1L) and response to 1L categorized 3 different prognostic groups for patients treated with second-line (2L) chemotherapy (CHT) (Di Maio, EJC. 2010 Mar;46(4):735-43.). The aim of this study is to assess if the same score is able to discriminate the outcome of aNSCLC pts treated in 2L or further-line IO, potentially helping decision making. Methods: We recorded data of patients collected from two institutional databases: Istituto Nazionale Tumori of Milan and IRCCS Oncologico Giovanni Paolo II of Bari, Italy. Overall survival (OS) was the primary endpoint and also progression-free survival (PFS) was assessed. Prognostic score was generated, and pts were divided into 3 prognostic groups: best (B: < 5), intermediate (I:5-9), worst (W: > 9). Results: Overall, 347 pts were included in the analysis (193 from Milan and 154 from Bari). Median age was 66 years (y) (30 – 88y), most were < 70 y (67.5%), male (70.7%), smokers (79.5%) and adenocarcinoma (74.6%). ECOG PS was: 0 (23%), 1 (54.5%) and 2 (22.5%). Pts distribution was: 28%, 51% and 21% in the B, I and W groups, respectively. Median OS was 18.0 months for B group, 8.5 months for I group (HR vs B 1.83, 95%CI 1.35 – 2.47, p < 0.001) and 2.6 months for W group (HR vs B 5.77, 95%CI 3.99 – 8.33, p < 0.001). Median PFS was 3.4 months for B group, 3.7 months for I group (HR vs B 1.35, 95% CI 1.03 – 1.77, p = 0.032) and 1.9 months for W group (HR vs B 2.51. 95% CI 1.80- 3.50, p < 0.001). Similar results were obtained stratifying the model by Institution. Conclusions: This clinical prognostic score, that was generated in patients treated with second-line chemotherapy, is able to highly predict outcomes of patients treated with IO. These results demonstrated that in pre-treated aNSCLC pts, the worst category has a dismal absolute life expectancy, and probably would not benefit from any active systemic therapy (independently if CHT or IO). Perhaps for these pts best supportive care could be the best choice.

e20691 Background: Immunotherapy (IO) is effective against metastatic Non Small Cell Lung Cancer (mNSCLC). Nonetheless, a minority of patients (pts) gains a benefit from IO and predictive variables of response are scarcely understood. Previous retrospective studies have showed a superior outcome for obese pts and preclinical data suggest a role of lipid profile. We aimed at investigating these topics in a cohort of pts with mNSCLC. Methods: We retrospectively collected data about all consecutive pts with mNSCLC treated with IO at our Institution between 12/2017 and 12/2018. Body Mass Index (BMI) was calculated as weight/height2; pts were classified as overweight if having a BMI≥25 kg/m2. Basal Cholesterolemia (BC) was dosed between one month (mo) before and one mo after the beginning of IO, with a cut-off for normality of 200 mg/dL. Survival was estimated with Kaplan-Meier method. Cox model was used for multivariate analyses. Results: We identified 55 pts. Thirty-three had a normal baseline BMI, the remaining were overweighted. Median BC was 171.5 mg/dL (range 15-313); 14 pts had hypercholesterolemia. No correlation was evident between BMI and BC ( p .1772). Neither BMI nor BC were associated to first and best response to IO. Median Progression Free Survival (PFS) was 3.75 mos; median Overall Survival (OS) was 6.78 mos. Hypercholesterolemia was related to longer PFS (9.44 vs 3.71 mos, p .0484), confirmed at multivariate analysis ( p.0392). Both hypercholesterolemia and BMI≥25 kg/m2 were associated to longer OS (15.56 vs 5.13 mos, p.0411, and 16.67 vs 4.97 mos, p.0222, respectively), but only BMI retained significance at multivariate analysis ( p .0279). Conclusions: Hypercholesterolemia shows a positive impact on outcome of mNSCLC treated with IO. The independence of this correlation from BMI suggests the existence of a true different metabolic pathways. Moreover, overweight patients have a longer OS than those with normal BMI. This result confirms previous data on obese patients, extending the range of BMI values that are linked to a better prognosis. In conclusion, the role of metabolism and body composition in defining the benefit from IO in mNSCLC is likely multifaceted, warranting further investigation.