Circulating tumor DNA (ctDNA) is emerging as a minimally invasive biomarker for risk stratification and treatment-response assessment in lung cancer immunotherapy, but its routine use for treatment selection has not been established. This review critically evaluates evidence across neoadjuvant, adjuvant, consolidation after definitive chemoradiotherapy, and advanced/metastatic settings, with emphasis on serial sampling and on the distinction among prognostic, response-associated, and predictive roles. Across disease stages, baseline or post-definitive-treatment ctDNA detectability consistently identifies patients at increased risk of recurrence or death and is therefore principally prognostic. Early on-treatment decline or clearance frequently precedes radiographic change and is associated with pathological response, progression-free survival, and overall survival, supporting ctDNA as a response-associated biomarker. By contrast, evidence that ctDNA identifies differential benefit from a specific immunotherapy remains limited, because most analyses are single-arm and/or retrospective, formal treatment-by-biomarker interaction tests are uncommon, and prospective ctDNA-guided trials have not yet demonstrated clinical utility. Nevertheless, ctDNA dynamics provide a biologically and clinically coherent framework for future risk-adapted strategies, including enrichment of molecular residual disease-positive patients, early identification of resistance, and prospective testing of treatment escalation, de-escalation, or duration. Translation into routine care will require harmonized assays and sampling time points, improved sensitivity at low disease burden, control of clonal hematopoiesis, and randomized interventional validation. Thus, ctDNA currently functions mainly as a prognostic and response-associated biomarker in lung cancer immunotherapy, while its predictive, decision-defining role remains an important but investigational objective.
Yan Li, Guanyu Lu, Wei Song et al.· Frontiers in Immunology· 0 citations
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.
Yan Li, Guanyu Lu, Xiangliang Liu et al.· Critical reviews in oncology...· 0 citations
This review examines how tumor-derived signals activate IRE1α/XBP1 to upregulate both the autophagy-lysosome pathway (ALP) and ubiquitin-proteasome system (UPS); its crosstalk with inflammatory and metabolic networks; and the therapeutic potential of IRE1α inhibitors, XBP1-directed strategies, and nutritional approaches including arginine.
Guanran Ding, Wang Yang, Yixin Zhao et al.· Biochimica et biophysica act...· 0 citations
