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.

Caloric restriction and metformin selectively improved LKB1-mutated NSCLC tumor response to chemo- and chemo-immunotherapy.

BACKGROUND: About 10% of NSCLCs are mutated in KRAS and impaired in STK11/LKB1, a genetic background associated with poor prognosis, caused by an increase in metastatic burden and resistance to standard therapy. LKB1 is a protein involved in a number of biological processes and is particularly important for its role in the regulation of cell metabolism. LKB1 alterations lead to protein loss that causes mitochondria and metabolic dysfunction that makes cells unable to respond to metabolic stress. Different studies have shown how it is possible to interfere with cancer metabolism using metformin and caloric restriction (CR) and both modify the tumor microenvironment (TME), stimulating the switch from "cold" to "hot". Given the poor therapeutic response of KRASmut/LKB1mut patients, and the role of LKB1 in cell metabolism, we examined whether the addition of metformin and CR enhanced the response to chemo or chemo-immunotherapy in LKB1 impaired tumors. METHODS: Mouse cell lines were derived from lung nodules of transgenic mice carrying KRASG12D with either functional LKB1 (KRASG12D/LKB1wt) or mutated LKB1 (KRASG12D/LKB1mut). Once stabilized in vitro, these cell lines were inoculated subcutaneously and intramuscularly into immunocompetent mice. Additionally, a patient-derived xenograft (PDX) model was established by directly implanting tumor fragments from patient into immunocompromised mice. The mice bearing these tumor models were subjected to treatment with chemotherapy or chemo-immunotherapy, both as standalone regimens and in combination with metformin and CR. RESULTS: Our preclinical results indicate that in NSCLC KRASmut/LKB1mut tumors, metformin and CR do enhance the response to chemo and chemo-immunotherapy, inducing a metabolic stress condition that these tumors are not able to overcome. Analysis of immune infiltrating cells did not bring to light any strong correlation between the TME immune-modulation and the tumor response to metformin and CR. CONCLUSION: Our in vitro and in vivo preliminary studies confirm our hypothesis that the addition of metformin and CR is able to improve the antitumor activity of chemo and chemoimmunotherapy in LKB1 impaired tumors, exploiting their inability to overcome metabolic stress.

Prognostic Impact of Blood Lipid Profile in Patients With Advanced Solid Tumors Treated With Immune Checkpoint Inhibitors: A Multicenter Cohort Study.

BACKGROUND: Specific components of lipid profile seem to differently impact on immune activity against cancer and unraveling their prognostic role in patients with solid cancer treated with immune checkpoint inhibitors (ICIs) is needed. MATERIALS AND METHODS: We retrospectively collected baseline clinicopathological characteristics including circulating lipid profile (total cholesterol [TC], triglycerides [TG], low-density lipoproteins [LDL], high-density lipoproteins [HDL]) of patients with consecutive solid cancer treated with ICIs, and we investigated their role in predicting clinical outcomes. RESULTS: At a median follow-up of 32.9 months, among 430 enrolled patients, those with TC ≥ 200 mg/dl showed longer median progression-free survival (mPFS; 6.6 vs. 4.7 months, P = .4), although not reaching statistical significance, and significantly longer median overall survival (mOS; 19.4 vs. 10.8 months, P = .02) compared to those with TC < 200 mg/dl. Conversely, patients with TG ≥150 mg/dl displayed shorter PFS (3.4 vs. 5.1 months, P = .02) and OS (7.1 vs. 12.9 months, P = .009) compared to those with TG <150 mg/dl. TC and TG were then combined in a "LIPID score" identifying three subgroups: good-risk (GR) (TC ≥200 mg/dl and TG <150 mg/dl), intermediate-risk (IR) (TC <200 mg/dl and TG <150 mg/dl or TC ≥200 mg/dl and TG ≥150 mg/dl) and poor-risk (PR) (TC <200 mg/dl and TG ≥150 mg/dl). The mPFS of GR, IR, and PR groups was 7.8, 4.3, and 2.5 months, respectively (P = .005); mOS of GR, IR, and PR was 20.4, 12.4, and 5.3 months, respectively (P < .001). At multivariable analysis, the PR profile represented an independent poor prognostic factor for both PFS and OS. CONCLUSIONS: We developed a lipid score that defined subgroups of patients with cancer who differently benefit from ICIs. Further mechanistic insights are warranted to clarify the prognostic and predictive role of lipid profile components in patients treated with ICIs.

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.

Epithelioid Mesothelioma Patients with Very Long Survival Display Defects in DNA Repair.

AIM: DNA repair has an important role in malignant pleural mesothelioma (MPM) tumorigenesis and progression. Prognostic/predictive biomarkers for better management of MPM patients are needed. In the present manuscript, we analyzed the expression of more than 700 genes in a cohort of MPM patients to possibly find biomarkers correlated with survival. METHODS: A total of 54 MPM patients, all with epithelioid histology, whose survival follow-up and formalin-fixed paraffin-embedded tumors were available, were included in the study. Gene expression profiles were evaluated using a Nanostring platform analyzing 760 genes involved in different cellular pathways. The percentages of proliferating tumor cells positive for RAD51 and BRCA1 foci were evaluated using an immunofluorescence assay, as a readout of homologous recombination repair status. RESULTS: Patient median survival time was 16.9 months, and based on this value, they were classified as long and short survivors (LS/SS) with, respectively, an overall survival ≥ and <16.9 months as well as very long and very short survivors (VLS/VSS) with an overall survival ≥ than 33.8 and < than 8.45 months. A down-regulation in the DNA damage/repair expression score was observed in LS and VLS as compared to SS and VSS. These findings were validated by the lower number of both RAD51 and BRCA1-positive tumor cells in VLS as compared to VSS. CONCLUSIONS: The down-regulation of DNA repair signature in VLS was functionally validated by a lower % of RAD51 and BRCA1-positive tumor cells. If these data can be corroborated in a prospective trial, an easy, cost-effective test could be routinely used to better manage treatment in MPM patients.

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.

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.

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.

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.

Circulating Fatty Acid Profile as a Biomarker for Immunotherapy in Advanced Non-Small Cell Lung Cancer.

INTRODUCTION: 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. MATERIALS AND METHODS: 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. RESULTS: 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). CONCLUSION: 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.

Case Report: Exceptional Response to Poziotinib in Patient with Metastatic Non-Small Cell Lung Cancer With EGFR Exon 20 Insertion Mutation.

Among the several next-generation tyrosine kinase inhibitors (TKIs) tested against uncommon EFGR alterations, poziotinib has been demonstrated to be a powerful agent for metastatic non-small-cell lung cancer (mNSCLC) with aberrations in HER2 exon 20, and FDA approval is being sought in the previously-treated population. Poziotinib has also shown activity in mNSCLC with aberrations in EGFR exon 20. Herein, we report the first published case of a patient affected by mNSCLC harbouring an EGFR exon 20 insertion (EGFRex20ins) mutation who achieved a complete response (CR) under treatment with poziotinib as part of the ZENITH20 trial. In January 2021, a former smoker 62-year-old female patient was diagnosed with relapse, after two surgeries and post-operative chemotherapy of mNSCLC, at liver and retroperitoneal nodes. Given the identification by Next Generation Sequencing (NGS) of EGFRex20ins mutation, she was enrolled in ZENITH20-cohort 5 trial, a phase 2 multicentre study aimed to assess the efficacy and safety of poziotinib in patients with EGFR or HER2 exon 20 insertion mutations. Poziotinib as first-line systemic therapy for metastatic disease was initiated at the end of January 2021 and administrated at the initial dosage of 8 mg orally twice daily (BID). The most common side effects from the beginning of the treatment included alopecia, macular skin rash, diarrhoea, xerostomia, and conjunctivitis. Due to these adverse events, poziotinib was discontinued during the first 3 months and then reduced to 6 mg orally BID in April 2021. After the dose de-escalation, the adverse events ameliorated, and the patient better tolerated the treatment without further interruption. Since the first reevaluation (after 4 weeks of therapy), the treatment with poziotinib resulted to be remarkably effective, with a partial response (PR) subsequently confirmed in May and July 2021. Then, in October 2021, a CT scan confirmed a CR, maintained with good tolerance at the last reevaluation in February 2022. Treatment is still ongoing at the same dosage. In this case, poziotinib has represented a successful and well-tolerated first-line treatment alternative to chemotherapy in this patient with EGFR exon 20 insertion mutated mNSCLC.

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

Background: Clinical and laboratory biomarkers in patients with advanced non-small-cell lung cancer (aNSCLC) receiving chemo-immunotherapy (CIT) are still poorly explored. Materials & methods: All consecutive aNSCLC patients who received at least one cycle of first-line CIT were enrolled. The impact of several clinical and laboratory biomarkers on outcomes was evaluated through Cox proportional hazard models. Results: Higher neutrophil-to-lymphocyte ratio was shown to be an independent prognostic biomarker of both worse progression-free survival and worse overall survival. The EPSILoN score was able to divide patients into three different prognostic groups, with a median overall survival of 73.2, 45.6 and 8.6 months for the favorable, intermediate and poor groups, respectively. Conclusion: The neutrophil-to-lymphocyte ratio and EPSILoN score were shown to have a prognostic value in aNSCLC patients treated with CIT.

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.

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

(1) Background: In advanced non-small cell lung cancer (aNSCLC), programmed death ligand 1 (PD-L1) remains the only biomarker for candidate patients to immunotherapy (IO). This study aimed at using artificial intelligence (AI) and machine learning (ML) tools to improve response and efficacy predictions in aNSCLC patients treated with IO. (2) Methods: Real world data and the blood microRNA signature classifier (MSC) were used. Patients were divided into responders (R) and non-responders (NR) to determine if the overall survival of the patients was likely to be shorter or longer than 24 months from baseline IO. (3) Results: One-hundred sixty-four out of 200 patients (i.e., only those ones with PD-L1 data available) were considered in the model, 73 (44.5%) were R and 91 (55.5%) NR. Overall, the best model was the linear regression (RL) and included 5 features. The model predicting R/NR of patients achieved accuracy ACC = 0.756, F1 score F1 = 0.722, and area under the ROC curve AUC = 0.82. LR was also the best-performing model in predicting patients with long survival (24 months OS), achieving ACC = 0.839, F1 = 0.908, and AUC = 0.87. (4) Conclusions: The results suggest that the integration of multifactorial data provided by ML techniques is a useful tool to select NSCLC patients as candidates for IO.

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

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

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.

Predictive modeling of gene expression regulation.

BACKGROUND: In-depth analysis of regulation networks of genes aberrantly expressed in cancer is essential for better understanding tumors and identifying key genes that could be therapeutically targeted. RESULTS: We developed a quantitative analysis approach to investigate the main biological relationships among different regulatory elements and target genes; we applied it to Ovarian Serous Cystadenocarcinoma and 177 target genes belonging to three main pathways (DNA REPAIR, STEM CELLS and GLUCOSE METABOLISM) relevant for this tumor. Combining data from ENCODE and TCGA datasets, we built a predictive linear model for the regulation of each target gene, assessing the relationships between its expression, promoter methylation, expression of genes in the same or in the other pathways and of putative transcription factors. We proved the reliability and significance of our approach in a similar tumor type (basal-like Breast cancer) and using a different existing algorithm (ARACNe), and we obtained experimental confirmations on potentially interesting results. CONCLUSIONS: The analysis of the proposed models allowed disclosing the relations between a gene and its related biological processes, the interconnections between the different gene sets, and the evaluation of the relevant regulatory elements at single gene level. This led to the identification of already known regulators and/or gene correlations and to unveil a set of still unknown and potentially interesting biological relationships for their pharmacological and clinical use.

LKB1 Down-Modulation by miR-17 Identifies Patients With NSCLC Having Worse Prognosis Eligible for Energy-Stress-Based Treatments.

INTRODUCTION: Preclinical models recently unveiled the vulnerability of LKB1/KRAS comutated NSCLC to metabolic stress-based treatments. Because miR-17 is a potential epigenetic regulator of LKB1, we hypothesized that wild-type LKB1 (LKB1WT) NSCLC with high miR-17 expression may be sensitive to an energetic stress condition, and eligible for metabolic frailties-based therapeutic intervention. METHODS: We took advantage of NSCLC cell lines with different combinations of KRAS mutation and LKB1 deletion and of patient-derived xenografts (PDXs) with high (LKB1WT/miR-17 high) or low (LKB1WT/miR-17 low) miR-17 expression. We evaluated LKB1 pathway impairment and apoptotic response to metformin. We retrospectively evaluated LKB1 and miR-17 expression levels in tissue specimens of patients with NSCLC and PDXs. In addition, a lung cancer series from The Cancer Genome Atlas data set was analyzed for miR-17 expression and potential correlation with clinical features. RESULTS: We identified miR-17 as an epigenetic regulator of LKB1 in NSCLC and confirmed targeting of miR-17 to LKB1 3' untranslated region by luciferase reporter assay. We found that miR-17 overexpression functionally impairs the LKB1/AMPK pathway. Metformin treatment prompted apoptosis on miR-17 overexpression only in LKB1WT cell lines, and in LKB1WT/miR-17 high PDXs. A retrospective analysis in patients with NSCLC revealed an inverse correlation between miR-17 and LKB1 expression and highlighted a prognostic role of miR-17 expression in LKB1WT patients, which was further confirmed by The Cancer Genome Atlas data analysis. CONCLUSIONS: We identified miR-17 as a mediator of LKB1 expression in NSCLC tumors. This study proposes a miR-17 expression score potentially exploitable to discriminate LKB1WT patients with NSCLC with impaired LKB1 expression and poor outcome, eligible for energy-stress-based treatments.

Poziotinib for EGFR and HER2 exon 20 insertion mutation in advanced NSCLC: Results from the expanded access program.

BACKGROUND: The treatment of metastatic non-small-cell lung cancer (mNSCLC) patients with EGFR/HER2 exon 20 insertion mutation (i-mut) remains an unmet clinical need. Poziotinib, a new generation tyrosine kinase inhibitor, is currently under investigation as a potential targeted therapy. This compassionate study of its use aims to describe the activity/toxicity of poziotinib in mNSCLC with EGFR/HER2-exon-20-i-mut. PATIENTS AND METHODS: NSCLC patients who were treated either with EGFR or HER2 exon 20-i-mut within an expanded access program were included in this study. Poziotinib (16 mg or less) was administrated orally quaque die (QD). The primary end-point was the overall response rate (ORR) assessed by central review using RECIST v1.1, and secondary end-points were median progression free survival (PFS), disease control rate (DCR), median overall survival (OS) and toxicity. RESULTS: The median age of all the 30 patients was 58 years (25-80 years), most of them were females (73%); ECOG 0-1 (83%), EGFR i-mut (73%) and pre-treated (83%). 73% started with poziotinib at a dose of 16 mg. At data cut-off, 22 of 33 patients (73%) experienced a progress in the disease and 12 of 30 (40%) died. Median PFS was 5.6 months (95% CI: 3.6-6.7 months) and the mOS 9.5 months (95% CI: 5.3 - not-reached months). The ORR was 30% (EGFR/HER2: 23/50%) and DCR 80%. G3 AEs were reported in 66% of the patients and were found with skin rash (50%), diarrhoea (17.6%), mucositis (7%) and paronychia (3%). G5, possibly associated with pneumonitis might also have occurred. CONCLUSIONS: Poziotinib exhibited effects in mNSCLC patients with EGFR/HER2-exon 20-i-mut. The toxicity rate was high leading to frequent dose interruption and reduction, thereby reducing mPFS in patients with good ORR/DCR. ZENITH20 trial is now being used to evaluate the low dose and new scheduled dose (e.g. bis in die (BID)).