RESUMEN
Conventional cancer clinical trials can be time-consuming and expensive, often yielding results with limited applicability to real-world scenarios and presenting challenges for patient participation. Real-world data (RWD) studies offer a promising solution to address evidence gaps and provide essential information about the effects of cancer treatments in real-world settings. The distinction between RWD and data derived from randomized clinical trials lies in the method of data collection, as RWD by definition are obtained at the point of care. Experimental designs resembling those used in traditional clinical trials can be utilized to generate RWD, thus offering multiple benefits including increased efficiency and a more equitable balance between internal and external validity. Real-world data can be utilized in the field of pharmacovigilance to facilitate the understanding of disease progression and to formulate external control groups. By utilizing prospectively collected RWD, it is feasible to conduct pragmatic clinical trials (PCTs) that can provide evidence to support randomized study designs and extend clinical research to the patient's point of care. To ensure the quality of real-world studies, it is crucial to implement auditable data abstraction methods and develop new incentives to capture clinically relevant data electronically at the point of care. The treatment landscape is constantly evolving, with the integration of front-line immune checkpoint inhibitors (ICIs), either alone or in combination with chemotherapy, affecting subsequent treatment lines. Real-world effectiveness and safety in underrepresented populations, such as the elderly and patients with poor performance status (PS), hepatitis, or human immunodeficiency virus, are still largely unexplored. Similarly, the cost-effectiveness and sustainability of these innovative agents are important considerations in the real world.
RESUMEN
Lung neuroendocrine tumors (LNETs) and gastroenteropancreatic neuroendocrine tumors (GEP-NETs) are two distinct types of neuroendocrine tumors (NETs) that have traditionally been treated as a single entity despite originating from different sources. Although they share certain phenotypic characteristics and the expression of neuroendocrine markers, they exhibit differences in their microenvironment, molecular mutations, and responses to various therapeutic regimens. Recent research has explored the genetic alterations in these tumors, revealing dissimilarities in the frequently mutated genes, the role of EGFR in carcinogenesis, the presence of transcription factors, and the immunogenicity of the tumor and its microenvironment. Spread Through Air Spaces (STAS), a phenomenon unique to lung carcinomas, appears to play a crucial role in LNET prognosis. These distinctions are also evident in the cascade response of lung and GI tract neuroendocrine tumors to somatostatin analogs, Peptide Receptor Radionuclide Therapy (PRRT), chemotherapy, and immunotherapy. Identifying similarities and differences between the two groups may improve our understanding of the underlying mechanisms and facilitate the development of more effective treatment strategies.
RESUMEN
Background: SARS-CoV-2 mortality rates are significantly higher in patients with lung cancer compared with the general population. However, little is known on their immunization status after vaccination. Methods: To evaluate the humoral response (seroconversion) of patients with lung cancer following vaccination against SARS-COV-2 (Group A), we obtained antibodies against SARS-CoV-2 spike (S) protein both at baseline and at different time points after the first dose of SARS-CoV-2 vaccine (two to three weeks [T1], six weeks ± one week [T2], 12 weeks ± three weeks [T3], and 24 weeks ± three weeks [T4]). Antibodies were also acquired from a control cohort of non-lung cancer patients (Group B) as well as a third cohort containing healthy controls (Group C) at all time points and at T4, respectively, to make comparisons with Group A. Analysis of antibody response at different time points, association with clinicopathologic parameters, and comparisons with control groups were performed. Results: A total of 125 patients with lung cancer were included in the analysis (96 males [74.3%], median age of 68 years [46−91]. All study participants received two vaccine doses (BNT162b2, mRNA-1273, AZD1222). Analysis of anti-SARS-CoV-2 S antibody titers showed minimal response at T1 (0.4 [0.4−48.6] IU/mL). Antibody response peaked at T2 (527.0 [0.4−2500] IU/mL) and declined over T3 (323.0 [0.4−2500] IU/mL) and T4 (141.0 [0.4−2500] IU/mL). Active smokers had lower antibody titers at T2 (p = 0.04), T3 (p = 0.04), and T4 (p < 0.0001) compared with former or never smokers. Peak antibody titers were not associated with any other clinicopathologic characteristic. No significant differences were observed compared with Group B. However, lung cancer patients exhibited significantly decreased antibody titers compared with Group C at T4 (p < 0.0001). Conclusions: Lung cancer patients demonstrate sufficient antibody response six weeks after the first dose of vaccine against SARS-CoV-2 when vaccinated with two-dose regimens. Rapidly declining antibody titers six weeks after the first dose underline the need for a third dose three months later, in patients with lung cancer, and especially active smokers.
