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.

Desmoplastic small round cell tumor: from state of the art to future clinical prospects.

INTRODUCTION: Desmoplastic small round cell tumor (DSRCT) is an extremely rare and highly aggressive soft tissue sarcoma, presenting mainly in male adolescents and young adults with multiple nodules disseminated within the abdominopelvic cavity. Despite a multimodal approach including aggressive cytoreductive surgery, intensive multi-agent chemotherapy, and postoperative whole abdominopelvic radiotherapy, the prognosis for DSRCT remains dismal. Median progression-free survival ranges between 4 and 21 months, and overall survival between 17 and 60 months, with the 5-year overall survival rate in the range of 10-20%. AREA COVERED: This review discusses the treatment strategies used for DSRCT over the years, the state of the art of current treatments, and future clinical prospects. EXPERT OPINION: The unsatisfactory outcomes for patients with DSRCT warrant investigations into innovative treatment combinations. An international multidisciplinary and multi-stakeholder collaboration, involving both pediatric and adult sarcoma communities, is needed to propel preclinical model generation and drug development, and innovative clinical trial designs to enable the timely testing of treatments involving novel agents guided by biology to boost the chances of survival for patients with this devastating disease.

Long-term survivors with desmoplastic small round cell tumor (DSRCT): Results from a retrospective single-institution case series analysis.

OBJECTIVE: To report on a retrospective study of primary DSRCT aiming at characterizing long-term survivors (LTS). METHODS: All consecutive patients treated at our institution for a primary DSRCT between 2000 and 2021 were retrospectively identified. Patients received multiagent chemotherapy ± surgery ± hyperthermic intraperitoneal chemotherapy (HIPEC) ± whole abdomino-pelvic radiotherapy (WAP-RT) ± high-dose chemotherapy ± maintenance chemotherapy (MC). Event-free survival (EFS) and overall survival (OS) were estimated by Kaplan-Meier method. Patients alive, without evidence of disease at ≥36 months from diagnosis, were defined as LTS. RESULTS: Thirty-eight patients were identified. All received multiagent chemotherapy; 27/38 (71%) surgery (7/27 [26%] plus HIPEC), 9/38 (24%) WAP-RT, 12/38 (32%) MC. At a median-follow-up of 37 months (IQR 18-63), overall median-EFS and median-OS were 15 and 37 months, respectively. All events occurred within 35 months. In patients who underwent surgery, median-EFS and median-OS were 19 and 37 months (23 and 43 months after R0/R1, and 10 and 19 months after R2 resection), respectively. LTS were 5/38 (13%), alive at 37, 39, 53, 64, 209 months. None had liver or extra-abdominal metastasis at diagnosis, they all received R0/R1 resection, 3/5 had WAP-RT, 2/5 MC, 1/5 received high-dose chemotherapy, none HIPEC. CONCLUSIONS: In our series cure was likely achieved in 13% of DSRCT. LTS had no liver/extra-abdominal disease, were treated with complete surgery, and possibly WAP-RT/MC.

PEOPLE (NTC03447678), a phase II trial to test pembrolizumab as first-line treatment in patients with advanced NSCLC with PD-L1 <50%: a multiomics analysis.

BACKGROUND: Chemoimmunotherapy represents the standard of care for patients with advanced non-small cell lung cancer (NSCLC) and programmed death-ligand 1 (PD-L1) <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 <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 <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.

Management of Vascular Sarcoma.

Vascular sarcomas encompass 3 well-defined sarcoma types: hemangioendothelioma, Kaposi sarcoma, and angiosarcoma. These distinct types are exceedingly rare and very different in terms of clinical behavior, biological features, and treatment approach. Because of this rarity and heterogeneity, it is crucial that vascular sarcomas are treated in sarcoma reference centers or networks, in order to ensure optimal management. The diversity of vascular sarcomas also needs to be taken into account in the design of clinical trials, in order to produce meaningful results that can be consistently translated into everyday clinical practice.

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

Introduction: Artificial 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. Methods: We 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. Results: Of 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. Conclusions: In 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.

Subnotificação de acidentes de trabalho entre profissionais de enfermagem / Under-reporting of occupational accidents among nursing professionals

O acidente de trabalho pode provocar agravos à saúde do trabalhador. A subnotificação é um fator limitante do ponto de vista prevencionista e jurídico. O objetivo do estudo foi levantar na literatura artigos que abordam a subnotificação de acidentes de trabalho entre profissionais de enfermagem. Foi realizado um estudo de revisão bibliográfica abordando publicações de 1992 a 2005. A carga horária de trabalho foi uma das principais causas de acidentes de trabalho.