The impact of decision tools during oncological consultation with lung cancer patients: A systematic review within the I3LUNG project.

INTRODUCTION: To date, lung cancer is one of the most lethal diagnoses worldwide. A variety of lung cancer treatments and modalities are available, which are generally presented during the patient and doctor consultation. The implementation of decision tools to facilitate patient's decision-making and the management of their healthcare process during medical consultation is fundamental. Studies have demonstrated that decision tools are helpful to promote health management and decision-making of lung cancer patients during consultations. The main aim of the present work within the I3LUNG project is to systematically review the implementation of decision tools to facilitate medical consultation about oncological treatments for lung cancer patients. METHODS: In the present study, we conducted a systematic review following the PRISMA guidelines. We used an electronic computer-based search involving three databases, as follows: Embase, PubMed, and Scopus. 10 articles met the inclusion criteria and were included. They explicitly refer to decision tools in the oncological context, with lung cancer patients. RESULTS: The discussion highlights the most encouraging results about the positive role of decision aids during medical consultations about oncological treatments, especially regarding anxiety, decision-making, and patient knowledge. However, no one main decision aid tool emerged as essential. Opting for a more recent timeframe to select eligible articles might shed light on the current array of decision aid tools available. CONCLUSION: Future review efforts could utilize alternative search strategies to explore other lung cancer-specific outcomes during medical consultations for treatment decisions and the implementation of decision aid tools. Engaging with experts in the fields of oncology, patient decision-making, or health communication could provide valuable insights and recommendations for relevant literature or research directions that may not be readily accessible through traditional search methods. The development of guidelines for future research were provided with the aim to promote decision aids focused on patients' needs.

Gender Difference in sidE eFfects of ImmuNotherapy: a possible clue to optimize cancEr tReatment (G-DEFINER): study protocol of an observational prospective multicenter study.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have significantly improved outcomes in various cancers. ICI treatment is associated with the incidence of immune-related adverse events (irAEs) which can affect any organ. Data on irAEs occurrence in relation to sex- differentiation and their association with gender-specific factors are limited. AIMS: The primary objective of the G-DEFINER study is to compare the irAEs incidence in female and male patients who undergo ICI treatment. Secondary objectives are: to compare the irAEs incidence in pre- and postmenopausal female patients; to compare the irAEs incidence in female and male patients according to different clinical and gender-related factors (lifestyle, psychosocial, and behavioral factors). Exploratory objectives of the study are to compare and contrast hormonal, gene-expression, SNPs, cytokines, and gut microbiota profiles in relation to irAEs incidence in female and male patients. METHODS AND RESULTS: The patients are recruited from Fondazione IRCCS Istituto Nazionale dei Tumori, Italy, St Vincent's University Hospital, Ireland, Oslo University Hospital, Norway, and Karolinska Insitutet/Karolinska University Hospital, Sweden. The inclusion of patients was delayed due to the Covid pandemic, leading to a total of 250 patients recruited versus a planned number of 400 patients. Clinical and translational data will be analyzed. INTERPRETATION: The expected outcomes are to improve the management of cancer patients treated with ICIs, leading to more personalized clinical approaches that consider potential toxicity profiles. The real world nature of the trial makes it highly applicable for timely irAEs diagnosis.

Demographic Analysis of Cancer Research Priorities and Treatment Correlations.

Understanding the diversity in cancer research priorities and the correlations among different treatment modalities is essential to address the evolving landscape of oncology. This study, conducted in collaboration with the European Cancer Patient Coalition (ECPC) and Childhood Cancer International-Europe (CCI-E) as part of the "UNCAN.eu" initiative, analyzed data from a comprehensive survey to explore the complex interplay of demographics, time since cancer diagnosis, and types of treatments received. Demographic analysis revealed intriguing trends, highlighting the importance of tailoring cancer research efforts to specific age groups and genders. Individuals aged 45-69 exhibited highly aligned research priorities, emphasizing the need to address the unique concerns of middle-aged and older populations. In contrast, patients over 70 years demonstrated a divergence in research priorities, underscoring the importance of recognising the distinct needs of older individuals in cancer research. The analysis of correlations among different types of cancer treatments underscored the multidisciplinary approach to cancer care, with surgery, radiotherapy, chemotherapy, precision therapy, and biological therapies playing integral roles. These findings support the need for personalized and combined treatment strategies to achieve optimal outcomes. In conclusion, this study provides valuable insights into the complexity of cancer research priorities and treatment correlations in a European context. It emphasizes the importance of a multifaceted, patient-centred approach to cancer research and treatment, highlighting the need for ongoing support, adaptation, and collaboration to address the ever-changing landscape of oncology.

Response to lorlatinib rechallenge in a case of ALK-rearranged metastatic NSCLC with a resistance mutation to second generation TKIs.

INTRODUCTION: Several anaplastic lymphoma kinase (ALK) tyrosine kinase inhibitors (TKIs) have been developed for the treatment of echinoderm microtubule-associated protein-like 4 (EML4)-ALK-rearranged non-small cell lung cancer (NSCLC), with the newer generation agents brigatinib, alectinib and lorlatinib showing prolonged responses. With the increasing number of target therapies available, the optimal sequence is yet to be defined, as resistance profiles may evolve over time and in response to sequential ALK inhibitors. Therefore, ALK-targeted strategies may be personalized based upon the presence of specific ALK resistance mutations. METHODS: Here, we report on the case of a patient who has been treated with a sequence of three ALK TKIs after receiving diagnosis of ALK-rearranged metastatic NSCLC in 2015 and gained further benefit upon lorlatinib rechallenge after the acquisition of the G1202R resistance mutation to second generation TKIs. RESULTS AND CONCLUSION: In this case, the first ALK resistance mutation detected after progression on first line TKI, the I1171N, is a common resistance mutation after alectinib and confers sensitivity to brigatinib, that the patient received afterwards with a long-term disease stability. The second ALK resistance mutation detected after a chemotherapy interval, the G1202R, is the most common resistance mutation after second generation ALK TKIs and has been associated with sensitivity to third generation TKIs, such as lorlatinib. This case of a patient with EML4-ALK-rearranged NSCLC shows that sequential treatment with next-generation ALK TKIs, including rechallenge, can induce profound remissions, even in heavily pretreated patients, and that ALK-targeted strategies may be personalized by considering the presence of distinct ALK resistance mutations.

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.

Tackling the implementation gap for the uptake of NGS and advanced molecular diagnostics into healthcare systems.

Next-generation sequencing (NGS) and liquid biopsy (LB) showed positive results in the fight against different cancer types. This paper aims to assess the uptake of advanced molecular diagnostics/NGS for quick and efficient genetic profiles of tumour cells. For that purpose, the European Alliance for Personalised Medicine conducted a series of expert interviews to ascertain the current status across member states. One stakeholder meeting was additionally conducted to prioritize relevant factors by stakeholders. Seven common pillars were identified, and twenty-five measures were defined based on these pillars. Results showed that a multi-faceted approach is necessary for successful NGS implementation and that regional differences may be influenced by healthcare policies, resources, and infrastructure. It is important to consider different correlations when interpreting the results and to use them as a starting point for further discussion.

Aligning Cancer Research Priorities in Europe with Recommendations for Conquering Cancer: A Comprehensive Analysis.

Improvements in cancer care require a new degree of collaboration beyond the purely medical sphere, extending deeply into the world of other stakeholders-preeminently patients but also the other stakeholders in the hardware and software of care. Cancer remains a global health challenge, necessitating collaborative efforts to understand, prevent, and treat this complex disease. To achieve this goal, a comprehensive analysis was conducted, aligning the prioritization of cancer research measures in 13 European countries with 13 key recommendations for conquering cancer in the region. The study utilized a survey involving both patients and citizens, alongside data from IQVIA, a global healthcare data provider, to assess the availability and access to single-biomarker tests in multiple European countries. The results revealed a focused approach toward understanding, preventing, and treating cancer, with each country emphasizing specific research measures tailored to its strengths and healthcare objectives. This analysis highlights the intricate relationship between research priorities, access to biomarker tests, and financial support. Timely access to tests and increased availability positively influence research areas such as cancer prevention, early detection, ageing, and data utilization. The alignment of these country-specific measures with 13 recommendations for conquering cancer in Europe underscores the importance of tailored strategies for understanding, preventing, and treating cancer.

Association Between Lung Immune Prognostic Index and Durvalumab Consolidation Outcomes in Patients With Locally Advanced Non-Small-Cell Lung Cancer.

INTRODUCTION: The LIPI, based on pretreatment derived neutrophils/[leukocytes-neutrophils] ratio (dNLR) and LDH, is associated with immune checkpoint inhibitors (ICI) outcomes in advanced non-small-cell lung cancer (NSCLC). We aimed to assess baseline LIPI correlation with durvalumab consolidation outcomes in the locally advanced setting. MATERIAL AND METHODS: Multicentre retrospective study (330 patients) with stage III unresectable NSCLC treated with durvalumab after chemo-radiotherapy between April 2015 and December 2020; 65 patients treated with chemo-radiotherapy only. Baseline LIPI characterized 3 groups: good (dNLR≤3+LDH≤ULN), intermediate (dNLR>3/LDH>ULN) and poor (dNLR>3+LDH>ULN). Primary endpoint was overall survival (OS). RESULTS: In the durvalumab cohort, median age was 67 years, 95% smokers, 98% with a performance status of 0-1; 60% had nonsquamous histology and 16% a PD-L1 expression <1%. Radiotherapy was delivered concurrently in 81%. LIPI was evaluable in 216 patients: 66% good, 31% intermediate, 3% poor. LIPI significantly correlated with median OS (median follow-up: 19 months): 18.1 months vs. 47.0 months vs. not reached in poor, intermediate and good LIPI groups, respectively (P = .03). A trend between objective response rate and LIPI groups was observed: 0% vs. 41% vs. 45%, respectively (P = .05). The pooled intermediate/poor LIPI group was associated with shorter OS (HR 1.97; P = .03) and higher risk of progressive disease (OR 2.68; P = .047). Survivals and response were not influenced in the control cohort. CONCLUSION: Baseline LIPI correlated with outcomes in patients with locally advanced NSCLC treated with durvalumab consolidation, but not in those who only received chemo-radiotherapy, providing further evidence of its prognostic and potential predictive role of ICI benefit in NSCLC.

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.

Exploring the Role of Immunotherapy-Based Treatments for Advanced Non-Small-Cell Lung Cancer With Novel Driver Alterations.

BACKGROUND: Immunotherapy (IO) single agent or combined with chemotherapy (CT-IO) is the standard treatment for advanced non-small-cell lung cancer (aNSCLC) without driver alterations. IO efficacy in patients with novel driver alterations is not well reported. MATERIALS AND METHODS: Data of aNSCLC patients treated with IO or CT-IO in any line from January 2016 to September 2022 were retrospectively collected. Patients harboring novel driver alterations (m-cohort), including MET exon 14 skipping, BRAF (V600E or atypical), RET rearrangements, HER2 point mutations/exon 20 insertions or uncommon EGFR mutations/EGFR exon 20 insertions, and wild type patients (wt-cohort) were eligible. Clinico-pathological data were extracted from Institutional databases and compared through chi square or Fisher's exact test. Survivals were estimated through Kaplan-Meier method and compared by log-rank test. RESULTS: m-cohort and wt-cohort included 84 and 444 patients, respectively. Progression free survival (PFS) was 5.53 vs. 4.57 months (P= .846) and overall survival (OS) was 25.1 vs. 9.37 months, (P < .0001) for m-cohort compared to wt-cohort. Within the m-cohort, BRAF atypical mutations had the better outcomes (Overall Response Rate [ORR], PFS), targeted agents timing did not affect response to IO and CT-IO had better ORR and disease control rate (DCR) compared to IO single agent (P = .0160 and P = .0152). In the PD-L1≥50% group, first line IO single agent resulted in inferior ORR (P = .027) and PFS (P = .022) in m-cohort compared to wt-cohort. CONCLUSION: IO based treatments seem not detrimental for patients harboring novel driver alteration. Adding CT could improve modest responses to IO alone. Confirmation on larger datasets is required.

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.

Artificial Intelligence in Clinical Oncology: From Data to Digital Pathology and Treatment.

Recently, a wide spectrum of artificial intelligence (AI)-based applications in the broader categories of digital pathology, biomarker development, and treatment have been explored. In the domain of digital pathology, these have included novel analytical strategies for realizing new information derived from standard histology to guide treatment selection and biomarker development to predict treatment selection and response. In therapeutics, these have included AI-driven drug target discovery, drug design and repurposing, combination regimen optimization, modulated dosing, and beyond. Given the continued advances that are emerging, it is important to develop workflows that seamlessly combine the various segments of AI innovation to comprehensively augment the diagnostic and interventional arsenal of the clinical oncology community. To overcome challenges that remain with regard to the ideation, validation, and deployment of AI in clinical oncology, recommendations toward bringing this workflow to fruition are also provided from clinical, engineering, implementation, and health care economics considerations. Ultimately, this work proposes frameworks that can potentially integrate these domains toward the sustainable adoption of practice-changing AI by the clinical oncology community to drive improved patient outcomes.

STYLE (NCT03449173): A Phase 2 Trial of Sunitinib in Patients With Type B3 Thymoma or Thymic Carcinoma in Second and Further Lines.

INTRODUCTION: Thymic malignancies are rare tumors with few therapeutic options. The STYLE trial was aimed to evaluate activity and safety of sunitinib in advanced or recurrent type B3 thymoma (T) and thymic carcinoma (TC). METHODS: In this multicenter, Simon 2 stages, phase 2 trial, patients with pretreated T or TC were enrolled in two cohorts and assessed separately. Sunitinib was administered 50 mg daily for 4 weeks, followed by a 2-week rest period (schedule 4/2), until disease progression or unacceptable toxicity. The primary endpoint was objective response rate (ORR). Progression-free survival, overall survival, disease control rate and safety were secondary endpoints. RESULTS: From March 2017 to January 2022, 12 patients with T and 32 patients with TC were enrolled. At stage 1, ORR was 0% (90% confidence interval [CI]: 0.0-22.1) in T and 16.7% (90% CI: 3.1-43.8) in TC, so the T cohort was closed. At stage 2, the primary endpoint was met for TC with ORR of 21.7% (90% CI: 9.0%-40.4%). In the intention-to-treat analysis, disease control rate was 91.7% (95% CI: 61.5%-99.8%) in Ts and 89.3% (95% CI: 71.8%-97.7%) in TCs. Median progression-free survival was 7.7 months (95% CI: 2.4-45.5) in Ts and 8.8 months (95% CI: 5.3-11.1) in TCs; median overall survival was 47.9 months (95% CI: 4.5-not reached) in Ts and 27.8 months (95% CI: 13.2-53.2) in TCs. Adverse events occurred in 91.7% Ts and 93.5% TCs. Grade 3 or greater treatment-related adverse events were reported in 25.0% Ts and 51.6% TCs. CONCLUSIONS: This trial confirms the activity of sunitinib in patients with TC, supporting its use as a second-line treatment, albeit with potential toxicity that requires dose adjustment.

Poziotinib in Treatment-Naive NSCLC Harboring HER2 Exon 20 Mutations: ZENITH20-4, A Multicenter, Multicohort, Open-Label, Phase 2 Trial (Cohort 4).

INTRODUCTION: ERBB2 or HER2 alterations are found in approximately 2% to 5% of NSCLCs; most are exon 20 insertion mutations. The efficacy and safety of poziotinib, an oral tyrosine kinase inhibitor, were assessed in patients with treatment-naive NSCLC whose tumors harbor HER2 exon 20 insertions. METHODS: ZENITH20 is an open-label, multicohort, multicenter, global, phase 2 trial. ZENITH20-C4 enrolled treatment-naive patients with NSCLC with tumors harboring HER2 exon 20 insertions. Poziotinib was administered 16 mg once daily (QD) or 8 mg twice daily (BID). The primary end point was objective response rate (ORR) by independent central review. Secondary and exploratory end points included disease control rate, duration of response, progression-free survival, and safety. RESULTS: A total of 80 patients (16 mg QD, n = 47; 8 mg BID, n = 33) were treated in ZENITH20-C4. ORR was 39% (95% confidence interval [CI]: 28%-50%; 31 of 80), with a disease control rate of 73% (95% CI: 61%-82%; 58 of 80); 80% of the patients experienced tumor reduction. Median duration of response was 5.7 (95% CI: 4.6-11.9) months, and median progression-free survival was 5.6 (95% CI: 5.4-7.3) months. The most common grade 3 treatment-related adverse events were rash (QD, 45%; BID, 39%), stomatitis (QD, 21%; BID, 15%), and diarrhea (QD, 15%; BID, 21%). Among all subtypes of HER2 exon 20 insertions, seven patients (9%) harboring tumors with G778_P780dupGSP had the best clinical outcomes (ORR, 71%). CONCLUSIONS: Poziotinib was found to have clinically meaningful efficacy with a manageable toxicity profile for patients with treatment-naive NSCLC harboring HER2 exon 20 mutations.

The EU-funded I3LUNG Project: Integrative Science, Intelligent Data Platform for Individualized LUNG Cancer Care With Immunotherapy.

Although immunotherapy (IO) has changed the paradigm for the treatment of patients with advanced non-small cell lung cancers (aNSCLC), only around 30% to 50% of treated patients experience a long-term benefit from IO. Furthermore, the identification of the 30 to 50% of patients who respond remains a major challenge, as programmed Death-Ligand 1 (PD-L1) is currently the only biomarker used to predict the outcome of IO in NSCLC patients despite its limited efficacy. Considering the dynamic complexity of the immune system-tumor microenvironment (TME) and its interaction with the host's and patient's behavior, it is unlikely that a single biomarker will accurately predict a patient's outcomes. In this scenario, Artificial Intelligence (AI) and Machine Learning (ML) are becoming essential to the development of powerful decision-making tools that are able to deal with this high-complexity and provide individualized predictions to better match treatments to individual patients and thus improve patient outcomes and reduce the economic burden of aNSCLC on healthcare systems. I3LUNG is an international, multicenter, retrospective and prospective, observational study of patients with aNSCLC treated with IO, entirely funded by European Union (EU) under the Horizon 2020 (H2020) program. Using AI-based tools, the aim of this study is to promote individualized treatment in aNSCLC, with the goals of improving survival and quality of life, minimizing or preventing undue toxicity and promoting efficient resource allocation. The final objective of the project is the construction of a novel, integrated, AI-assisted data storage and elaboration platform to guide IO administration in aNSCLC, ensuring easy access and cost-effective use by healthcare providers and patients.

Molecular Epidemiology and Treatment Patterns of Patients With EGFR Exon 20-Mutant NSCLC in the Precision Oncology Era: The European EXOTIC Registry.

Introduction: Real-world evidence regarding molecular epidemiology and management patterns of patients with EGFR exon-20 mutated, advanced NSCLC outside the context of clinical trials is lacking. Methods: We created a European registry for patients with advanced EGFR exon 20-mutant NSCLC diagnosed from January 2019 to December 2021. Patients enrolled in clinical trials were excluded. Clinicopathologic and molecular epidemiology data were collected, and treatment patterns were recorded. Clinical end points according to treatment assignment were assessed using Kaplan-Meier curves and Cox regression models. Results: Data on 175 patients from 33 centers across nine countries were included in the final analysis. Median age was 64.0 (range: 29.7-87.8) years. Main features included female sex (56.3%), never or past smokers (76.0%), adenocarcinoma (95.4%), and tropism for bone (47.4%) and brain (32.0%) metastases. Mean programmed death-ligand 1 tumor proportional score was 15.8% (range: 0%-95%) and mean tumor mutational burden was 7.06 (range: 0-18.8) mutations per megabase. Exon 20 was detected in the tissue (90.7%), plasma (8.7%), or both (0.6%), using mostly targeted next-generation sequencing (64.0%) or polymerase chain reaction (26.0%). Mutations were mainly insertions (59.3%), followed by duplications (28.1%), deletions-insertions (7.7%), and the T790M (4.5%). Insertions and duplications were located mainly in the near loop (codons 767-771, 83.1%) and the far loop (codons 771-775, 13%) and only in 3.9% within the C helix (codons 761-766). Main co-alterations included mutations in TP53 (61.8%) and MET amplifications (9.4%). Treatment on mutation identification included chemotherapy (CT) (33.8%), CT-immunotherapy (IO) (18.2%), osimertinib (22.1%), poziotinib (9.1%), mobocertinib (6.5%), mono-IO (3.9%), and amivantamab (1.3%). Disease control rates were 66.2% with CT plus or minus IO, 55.8% with osimertinib, 64.8% with poziotinib, and 76.9% with mobocertinib. Corresponding median overall survival was 19.7, 15.9, 9.2, and 22.4 months, respectively. In multivariate analysis, type of treatment (new targeted agents versus CT ± IO) affected progression-free survival (p = 0.051) and overall survival (p = 0.03). Conclusions: EXOTIC represents the largest academic real-world evidence data set on EGFR exon 20-mutant NSCLC in Europe. Indirectly compared, treatment with new exon 20-targeting agents is likely to confer survival benefit than CT plus or minus IO.

Comparative assessment of early mortality risk upon immune checkpoint inhibitors alone or in combination with other agents across solid malignancies: a systematic review and meta-analysis.

BACKGROUND: The early crossing of survival curves in randomised clinical trials (RCTs) with immune checkpoint blockers suggests an excess of mortality in the first months of treatment. However, the exact estimation of the early death (ED) rate, the comparison between ED upon immune checkpoint inhibitors (ICI) alone or in combination with other agents and the impact of tumour type, and PD-L1 expression on ED are unknown. METHODS: RCTs comparing ICI alone (ICI-only group) or in combination with other non-ICI therapies (ICI-OT group) (experimental arms) versus non-ICI treatments (control arm) were included. ED was defined as death within the first 3 months of treatment. The primary outcome was the comparison of ED between experimental and control arms, and the secondary outcome was the comparison of ED risk between ICI-only and ICI-OT. ED rates estimated by risk ratio (RR) were pooled by random effect model. RESULTS: A total of 56 RCTs (40,215 participants, 14 cancer types) were included. ED occurred in 14.2% and 6.7% of patients in ICI-only and ICI-OT groups, respectively. ED risk significantly increased with ICI-only (RR: 1.29, 95% CI 1.05-1.57) versus non-ICI therapies, while it was lower with ICI-OT versus non-ICI treatments (RR: 0.81, 95% CI 0.73-0.90). ED risk was significantly higher upon ICI-only compared to ICI-OT (RR: 1.57, 95% CI 1.26-1.95). Gastric and urothelial carcinoma were at higher risk of ED. PD-L1 expression and ICI drug classes were not associated with ED. CONCLUSIONS: ED upon first-line ICI is a clinically relevant phenomenon across solid malignancies, not predictable by PD-L1 expression but preventable through the addition of other treatments to ICI.

Circulating CD81-expressing extracellular vesicles as biomarkers of response for immune-checkpoint inhibitors in advanced NSCLC.

PD-L1 in tumor cells is the only used biomarker for anti PD1/PD-L1 immune-checkpoints inhibitors (ICI) in Non Small Cell Lung Cancer (NSCLC) patients. However, this parameter is inaccurate to predict response, especially in patients with low tumor PD-L1. Here, we evaluated circulating EVs as possible biomarkers for ICI in advanced NSCLC patients with low tumoral PD-L1. EVs were isolated from plasma of 64 PD-L1 low, ICI-treated NSCLC patients, classified either as responders (R; complete or partial response by RECIST 1.1) or non-responders (NR). EVs were characterized following MISEV guidelines and by flow cytometry. T cells from healthy donors were triggered in vitro using patients' EVs. Unsupervised statistical approach was applied to correlate EVs' and patients' features to clinical response. R-EVs showed higher levels of tetraspanins (CD9, CD81, CD63) than NR-EVs, significantly associated to better overall response rate (ORR). In multivariable analysis CD81-EVs correlated with ORR. Unsupervised analysis revealed a cluster of variables on EVs, including tetraspanins, significantly associated with ORR and improved survival. R-EVs expressed more costimulatory molecules than NR-EVs although both increased T cell proliferation and partially, activation. Tetraspanins levels on EVs could represent promising biomarkers for ICI response in NSCLC.

Biological Rationale for Peripheral Blood Cell-Derived Inflammatory Indices and Related Prognostic Scores in Patients with Advanced Non-Small-Cell Lung Cancer.

PURPOSE OF REVIEW: To describe the biological rationale of peripheral blood cells (PBC)-derived inflammatory indexes and assess the related prognostic scores for patients with advanced non-small cell lung cancer (aNSCLC) treated with immune-checkpoint inhibitors (ICI). RECENT FINDINGS: Inflammatory indexes based on PBC may indicate a pro-inflammatory condition affecting the immune response to cancer. The lung immune prognostic index (LIPI), consisting of derived neutrophils-to-lymphocyte ratio (NLR) and lactate dehydrogenase, is a validated prognostic tool, especially for pretreated aNSCLC patients, where the combination of NLR and PD-L1 tumour expression might also be predictive of immunotherapy benefit. In untreated high-PD-L1 aNSCLC patients, the Lung-Immune-Prognostic score (LIPS), including NLR, ECOG PS and concomitant steroids, is prognostic, and its modified version might indicate patients with favourable outcomes despite an ECOG PS of 2. NLR times platelets (i.e., SII), included in the NHS-Lung score, might improve the prognostication for combined chemoimmunotherapy. PBC-derived inflammatory indexes and related scores represent accurate, reproducible and non-expensive prognostic tools with clinical and research utility.

Prognostic scores including peripheral blood-derived inflammatory indices in patients with advanced non-small-cell lung cancer treated with immune checkpoint inhibitors.

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.