Before the introduction of tyrosine kinase inhibitors (TKIs) for a particular subgroup of patients, despite platinum-based combination chemotherapy, the majority of patients affected by non-small-cell lung cancer (NSCLC) did not live longer than one year. With deeper understanding of tumor molecular biology, treatment of NSCLC has progressively entered the era of treatment customization according to tumor molecular characteristics, as well as histology. All this information allowed the development of personalized molecular targeted therapies. A series of studies have shown that, in some cases, cancer cells can grow and survive as result of the presence of a single driver genomic abnormality. This phenomenon, called oncogene-addiction, more often occurs in adenocarcinoma histology, in non-smokers (except BRAF mutations, also frequent in smoking patients), young, and female patients. Several different driver mutations have been identified and many studies have clearly shown that upfront TKI monotherapy may improve the overall outcome of these patients. The greater efficacy of these drugs is also associated with a better tolerability and safety than chemotherapy, with fewer side effects and an extremely good compliance to treatment. The most frequent oncogene-addicted disease is represented by those tumors carrying a mutation of the epidermal growth factor receptor (EGFR). The development of first, second and third generation TKIs against EGFR mutations have dramatically changed the prognosis of these patients. Currently, osimertinib (which demonstrated to improve efficacy with a better tolerability in comparison with first-generation TKIs) is considered the best treatment option for patients affected by NSCLC harboring a common EGFR mutation. EML4-ALK-driven disease (which gene re-arrangement occurs in 3–7% of NSCLC), has demonstrated to be significantly targeted by specific TKIs, which have improved outcome in comparison with chemotherapy. To date, alectinib is considered the best treatment option for these patients, with other newer agents upcoming. Other additional driver abnormalities, such as ROS1, BRAF, MET, RET and NTRK, have been identified as a target mirroring peculiar vulnerability to specific agents. Oncogene-addicted disease typically has a low early resistance rate, but late acquired resistance always develops and therefore therapy needs to be changed when progression occurs. In this narrative review, the state of art of scientific literature about targeted therapy options in oncogene-addicted disease is summarized and critically discussed. We also aim to analyze future perspectives to maximize benefits for this subgroup of patients.

Artificial Intelligence (AI) has revolutionized the management of non-small-cell lung cancer (NSCLC) by enhancing different aspects, including staging, prognosis assessment, treatment prediction, response evaluation, recurrence/prognosis prediction, and personalized prognostic assessment. AI algorithms may accurately classify NSCLC stages using machine learning techniques and deep imaging data analysis. This could potentially improve precision and efficiency in staging, facilitating personalized treatment decisions. Furthermore, there are data suggesting the potential application of AI-based models in predicting prognosis in terms of survival rates and disease progression by integrating clinical, imaging and molecular data. In the present narrative review, we will analyze the preliminary studies reporting on how AI algorithms could predict responses to various treatment modalities, such as surgery, radiotherapy, chemotherapy, targeted therapy, and immunotherapy. There is robust evidence suggesting that AI also plays a crucial role in predicting the likelihood of tumor recurrence after surgery and the pattern of failure, which has significant implications for tailoring adjuvant treatments. The successful implementation of AI in personalized prognostic assessment requires the integration of different data sources, including clinical, molecular, and imaging data. Machine learning (ML) and deep learning (DL) techniques enable AI models to analyze these data and generate personalized prognostic predictions, allowing for a precise and individualized approach to patient care. However, challenges relating to data quality, interpretability, and the ability of AI models to generalize need to be addressed. Collaboration among clinicians, data scientists, and regulators is critical for the responsible implementation of AI and for maximizing its benefits in providing a more personalized prognostic assessment. Continued research, validation, and collaboration are essential to fully exploit the potential of AI in NSCLC management and improve patient outcomes. Herein, we have summarized the state of the art of applications of AI in lung cancer for predicting staging, prognosis, and pattern of recurrence after treatment in order to provide to the readers a large comprehensive overview of this challenging issue.

Background: Pembrolizumab is approved in monotherapy for the first-line (1L) of advanced or metastatic NSCLC patients with high PD-L1 (≥50%). Despite a proportion of patients achieve long-term survival, about one-third of patients experience detrimental survival outcomes, including early death, hyperprogression, and fast progression. The impact of clinical factors on early progression (EP) development has not been widely explored. Methods: We designed a retrospective, multicenter study involving five Italian centers, in patients with metastatic NSCLC with PD-L1 ≥ 50%, treated with Pembrolizumab in a 1L setting. EP was defined as a progressive disease within three months from pembrolizumab initiation. Baseline clinical factors of patients with and without EP were collected and analyzed. Logistic regression was performed to identify clinical factors associated with EP and an EP prognostic score was developed based on the logistic model. Results: Overall, 321 out of 336 NSCLC patients treated with 1L pembrolizumab provided all the data for the analysis. EP occurred in 137 (42.7%) patients; the median PFS was 3.8 months (95% CI: 2.9–4.7), and median OS was not reached in the entire study population. Sex, Eastern Cooperative Oncology Group (ECOG) performance status (PS), steroids, metastatic sites ≥2, and the presence of liver/pleural metastasis were confirmed as independent factors for EP by multivariate analysis. By combining these factors, we developed an EP prognostic score ranging from 0–13, with three-risk group stratification: 0–2 (good prognosis), 3–6 (intermediate prognosis), and 7–13 (poor prognosis). The area under the curve (AUC) of the model was 0.76 (95% CI: 0.70–0.81). Conclusions: We identified six clinical factors independently associated with EP. We developed a prognostic score model for EP-risk to potentially improve clinical practice and patient selection for 1L pembrolizumab in NSCLC with high PD-L1, in the real-world clinical setting.

Background: Only few ES-SCLC patients experience long-term survival benefit by maintenance IT. Adipokines-induced metabolic meta-inflammation has been related to enhanced responsiveness to IT in obese patients; however, their prognostic role in SCLC is currently controversial. Methods: Pre-treatment CT scan was used for determining distribution of abdominal adiposity, and blood samples were collected at fasting for measuring glycemia, insulin, ghrelin, leptin and adipokines (TNF-α, IFN-γ, IL-6 and MCP-1). Patients with known history of DM type II or metabolic syndrome with HOMA index > 2.5 were considered insulin resistant (IR). Results: In ES-SCLC pts receiving maintenance IT, increased leptin concentration and higher leptin/visceral adipose tissue (VAT) ratio were significantly associated with prolonged PFS. By applying a hierarchical clustering algorithm, we identified a cluster of patients characterized by higher leptin values and lower pro-inflammatory cytokines (TNF-α, IFN-γ and IL-6) who experienced longer PFS (13.2 vs 8.05 months; HR: 0.42 [0.18–0.93] p = 0.02) and OS (18.04 vs 12.09 mo; HR: 0.53 [0.25–1.29] p = 0.07). Conclusions: Adipokines can play a crucial role to determining effectiveness of anti-cancer immunotherapy. The role of metabolic immune dysfunctions needs further pre-clinical validation and is currently investigated in the larger prospective cohort.

The use of epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) as first-line treatment in patients with lung adenocarcinoma (LUAD) harboring EGFR-activating mutations has resulted in a dramatic improvement in the management of the disease. However, the long-term clinical benefit is inevitably compromised by multiple resistance mechanisms. Accumulating evidence suggests that metabolic landscape remodeling is one of the mechanisms that EGFR-mutant LUAD cells activate, thus acquiring higher plasticity, tolerating EGFR TKI-mediated cytotoxic stress, and sustaining their oncogenic phenotype. Several metabolic pathways are upregulated in EGFR TKI-resistant models modulating the levels of numerous metabolites such as lipids, carbohydrates, and metabolic enzymes which have been suggested as potential mediators of resistance to EGFR TKIs. Moreover, metabolites have been shown to carry signals and stimulate oncogenic pathways and tumor microenvironment (TME) components such as fibroblasts, facilitating resistance to EGFR TKIs in various ways. Interestingly, metabolic signatures could function as predictive biomarkers of EGFR TKI efficacy, accurately classifying patients with EGFR-mutant LUAD. In this review, we present the identified metabolic rewiring mechanisms and how these act either independently or in concert with epigenetic or TME elements to orchestrate EGFR TKI resistance. Moreover, we discuss potential nutrient dependencies that emerge, highlighting them as candidate druggable metabolic vulnerabilities with already approved drugs which, in combination with EGFR TKIs, might counteract the solid challenge of resistance, hopefully prolonging the clinical benefit.

Background: Outcomes for locally advanced NSCLC with pathological complete response (pCR), i.e., pT0N0 after induction chemoradiotherapy (IT), have been seldom investigated. Herein, long-term results, in this highly selected group of patients, have been evaluated with the aim to identify prognostic predictive factors. Methods: Patients affected by locally advanced NSCLC (cT1-T4/N0-2/M0) who underwent IT, possibly following surgery, from January 1992 to December 2019, were considered for this retrospective analysis. Survival rates and prognostic factors have been studied with Kaplan-Meier analysis, log-rank and Cox regression analysis. Results: Three-hundred and forty-three consecutive patients underwent IT in the considered period. Out of them, 279 were addressed to surgery; among them, pCR has been observed in 62 patients (18% of the total and 22% of the operated patients). In the pCR-group, clinical staging was IIb in 3 (5%) patients, IIIa in 28 (45%) patients and IIIb in 31 (50%). Surgery consisted of (bi)lobectomy in the majority of cases (80.7%), followed by pneumonectomy (19.3%). Adjuvant therapy was administered in 33 (53.2%) patients. Five-year overall survival and disease-free survival have been respectively 56.18% and 48.84%. The relative risk of death, observed with the Cox regression analysis, was 4.4 times higher (95% confidence interval (CI): 1.632–11.695, p = 0.03) for patients with N2 multi-station disease, 2.6 times higher (95% CI: 1.066–6.407, p = 0.036) for patients treated with pneumonectomy and 3 times higher (95% CI: 1.302–6.809, p = 0.01) for patients who did not receive adjuvant therapy. Conclusions: Rewarding long-term results could be expected in locally advanced NSCLC patients with pCR after IT followed by surgery. Baseline N2 single-station disease and adjuvant therapy after surgery seem to be associated with better prognosis, while pneumonectomy is associated with poorer outcomes.