Antibody–drug conjugates (ADCs) represent a transformative therapeutic class in non-small cell lung cancer (NSCLC), moving precision oncology beyond traditional targeted therapies. These agents, composed of a monoclonal antibody, linker, and potent cytotoxic payload, enable targeted drug delivery to tumor cells expressing specific antigens like TROP2, HER2, and c-MET. Recent clinical trials have demonstrated remarkable efficacy of ADCs, both as monotherapy in molecularly defined populations and in combination with immunotherapy, offering new hope for patients with advanced disease. This review summarizes the latest clinical progress of ADCs in NSCLC, highlighting key agents and their impact, while also addressing the associated challenges and future directions for optimizing this promising treatment modality.
Xi Chen, Sirui Wu, Feiyang Li et al.· Holistic Integrative Oncolog...· 0 citations
BACKGROUNDS
Breast cancer (BRCA) represents the most prevalent malignancy globally, with projections indicating 3.2 million new cases anticipated by 2050. Current treatment modalities, encompassing surgical intervention, chemotherapy, and immunotherapy, are markedly hindered by treatment resistance and the inherent complexity of BRCA, thereby impeding effective disease management. Consequently, this study is designed to elucidate cellular heterogeneity within the tumor microenvironment (TME) and to identify prospective therapeutic targets, thus enabling individualized treatment approaches for individuals with BRCA, which remains crucial.
METHODS
GEO and TCGA databases were the sources of all analytical data employed in this investigation. Single-cell RNA sequencing data were employed to identify macrophage-associated genes in BRCA, followed by the development of a machine-learning-based prognostic model (PM) integrating data from TCGA and GEO databases. This model stratifies individuals into cohorts of either low or high risk, enabling a disparity evaluation in survival outcomes and tumor immune microenvironment characteristics. Gene co-expression patterns were examined through an analysis of gene co-expression network weighted by high dimension for screening pivotal central genes implicated in tumor immunity. Clinical PMs were subsequently constructed utilizing machine learning algorithms, with validation performed on training and test sets. Furthermore, XGBoost and LightGBM machine learning algorithms were implemented to pinpoint potential biomarkers. Ultimately, validation of these biomarkers was conducted through ELISA, CCK-8 assay, flow cytometry, Western blotting, and immunoprecipitation assays.
RESULTS
Findings indicated a pronounced elevation of TAMs in BRCA patients, with their phenotypic attributes exhibiting a strong correlation with prognosis. The developed clinical PM demonstrated high accuracy and robust predictive capability concerning survival rates of 1, 3, or 5 years. The comprehensive evaluation determined that high-risk cohort's immune microenvironment exhibited a greater inclination toward immune suppression, whereas the low-risk cohort was markedly predisposed to anti-tumor immune responses. Notably, the trifolium factor 1 (TFF1) gene was identified as a key determinant, with its overexpression being markedly associated with tumor invasiveness and immune evasion.
CONCLUSIONS
The study underscores cellular heterogeneity inherent in BRCA TME, while PM, predicated on macrophage subpopulation-related genes, offers a novel approach for risk stratification and personalized therapeutic interventions for BRCA patients. Moreover, TFF1 has been established as a prospective therapeutic target, yielding important directions for developing specific treatment interventions.
Lin Chen, Zhiqiang Chen, Bailu Shi et al.· Cancer Immunology and Immuno...· 0 citations