Front Cell Dev Biol. 2026 Aug 24;14:1867246. doi: 10.3389/fcell.2026.1867246. eCollection 2026.

ABSTRACT

Acquired resistance to third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) presents a formidable challenge in the treatment of non-small cell lung cancer (NSCLC). Despite the remarkable efficacy of these agents, resistance inevitably develops, typically within approximately 10 months of treatment initiation. This review elucidates the multifaceted mechanisms driving this resistance, broadly categorized into on-target EGFR-dependent alterations and off-target EGFR-independent bypass pathway activations. On-target mechanisms include the emergence of tertiary EGFR mutations, most notably C797S, which disrupts TKI binding. Off-target mechanisms encompass the activation of alternative signaling pathways such as MET and HER2/HER3 amplification, as well as histological transformations and complex changes within the tumor microenvironment. Furthermore, recent discoveries highlight the role of epigenetic dysregulation and metabolic reprogramming in fostering resistance. To counter this pervasive adaptability, advanced diagnostic methodologies, including liquid biopsy and high-resolution omics technologies, are crucial for real-time molecular profiling. The field is actively exploring emerging combination therapeutic strategies to circumvent these diverse resistance pathways, aiming to prolong clinical benefits and improve patient outcomes. The persistent emergence of resistance underscores that current targeted therapies, while revolutionary, are primarily disease-modifying rather than curative, necessitating continuous innovation to overcome the inherent biological challenge of tumor adaptability and heterogeneity.

PMID:42707604 | PMC:PMC13547778 | DOI:10.3389/fcell.2026.1867246

Crit Rev Oncol Hematol. 2026 Sep;225:105483. doi: 10.1016/j.critrevonc.2026.105483. Epub 2026 Jul 10.

ABSTRACT

Low-dose CT (LDCT) lung cancer screening significantly reduces mortality but has dramatically increased the detection of pulmonary nodules. Most of these nodules are benign, leading to a high false-positive rate that triggers unnecessary invasive procedures and patient anxiety, underscoring the need for more precise noninvasive diagnostic tools. Critically, single-modality liquid biopsy biomarkers, including circulating tumor cells, cell-free DNA mutations, or individual microRNAs, have demonstrated insufficient sensitivity or specificity for independent clinical deployment when used in isolation. This necessitates a paradigm shift toward multimodal molecular integration, wherein complementary biomarker classes are combined to overcome the inherent limitations of any single analyte. Traditional clinical prediction models (Mayo, VA, Brock, Herder) assist in estimating malignancy risk, yet their accuracy remains modest. Emerging approaches harness radiomics and artificial intelligence (AI) to extract high-dimensional imaging features from chest CT scans, improving risk stratification beyond human assessment alone. In parallel, minimally invasive liquid biopsy biomarkers offer complementary avenues to detect occult malignancy signals. Additionally, bronchial airway gene expression classifiers leverage the "field-of-injury" effect in normal respiratory epithelium to help identify lung cancer even when the nodule itself cannot be directly sampled via biopsy. Integrating these radiologic and molecular data streams into a multi-omics framework has the potential to enhance diagnostic precision for indeterminate pulmonary nodules, enabling more confident discrimination between benign and malignant lesions. However, most of these emerging tools have not yet been validated in large prospective trials and face technological barriers as well as challenges in real-world implementation. This review focuses primarily on LDCT screening detected pulmonary nodules, while incorporating evidence from incidentally detected and other indeterminate nodule cohorts when relevant to broader CT based management. By synthesizing advances in radiomics, AI, liquid biopsy, airway classifiers, and multi-omics integration, we highlight the need for prospective validation and multidisciplinary collaboration to translate these approaches into clinically useful pathways that improve early lung cancer detection, reduce unnecessary interventions, and enhance patient outcomes.

PMID:42431477 | DOI:10.1016/j.critrevonc.2026.105483