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Lung cancer is the leading cause of cancer-related fatalities globally, accounting for the highest mortality rate among both men and women. Mutations in the epidermal growth factor receptor (EGFR) gene are frequently found in non-small cell lung cancer (NSCLC). Since curcumin and CB[2]UN support various medicinal applications in drug delivery and design, we investigated the effect of curcumin and CB[2]UN-based drugs in controlling EGFR-mutant NSCLC through a dodecagonal computational approach.

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This study demonstrates the analytical and clinical validity of the approved (United States and Japan) plasma-based Guardant360 companion diagnostic (CDx) test for selecting patients with human epidermal growth factor receptor 2 (HER2 [ERBB2])-mutated (HER2m) non-small-cell lung cancer (NSCLC) for trastuzumab deruxtecan (T-DXd) treatment. Concordance between the Guardant360 CDx test and the plasma-based AVENIO ctDNA Expanded Kit Assay (AVENIO), as well as the tissue-based clinical trial assays (CTAs) was investigated. Clinical utility was assessed by comparing T-DXd clinical efficacy results of patients in DESTINY-Lung01/02 who tested positive for HER2 mutations using the Guardant360 CDx test to benchmark efficacy results from DESTINY-Lung01/02.

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Introduction: Lung cancer is the leading cause of cancer-related mortality globally, with non-small cell lung cancer (NSCLC) comprising the majority of cases. For advanced NSCLC, immunotherapy offers substantial survival benefits but is often accompanied by severe immune-related adverse events symptoms, significantly affecting health-related quality of life (HRQoL). Routinely collection of patient-reported outcomes (PROs) followed by automated alerts has been shown to improve overall survival and HRQoL for cancers.

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AI decision support systems can assist clinicians in planning adaptive treatment strategies that can dynamically react to individuals' cancer progression for effective personalized care. However, AI's imperfections can lead to suboptimal therapeutics if clinicians over or under rely on AI. To investigate such collaborative decision-making process, we conducted a Human-AI interaction study on response-adaptive radiotherapy for non-small cell lung cancer and hepatocellular carcinoma.

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Background: Different doses of radiotherapy (RT) exert diverse effects on tumor immunity, although the precise irradiation method remains unknown. This study sought to elucidate the influence of combining different doses of RT with immune checkpoint inhibitors (ICIs) on the infiltration of CD8T cells within tumors, thereby augmenting the anti-tumor response.

Methods: Constructing a mouse model featuring bilateral lung cancer tumors subjected to high and low dose irradiation, the analysis of RNA transcriptome sequencing data and immunohistochemical validation for tumors exposed to various dosages guided the selection of the optimal low-dose irradiation scheme.

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