Cancer in a drop: Liquid biopsy highlights from the World Conference on Lung Cancer (WCLC) 2025
Bibliographic record
Abstract
The role of liquid biopsy in oncological care continues to expand, with multiple studies presented at the International Association for the Study of Lung Cancer (IASLC) 2025 World Conference on Lung Cancer (WCLC 2025). This review summarizes recent advances in liquid biopsy for thoracic oncology, encompassing both non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). In early detection and screening, proteomic profiling has identified potential biomarkers predictive of future lung cancer risk. The integration of proteomics with clinical and imaging data can improve pulmonary nodule malignancy prediction. In resectable NSCLC, tumour-informed whole-genome sequencing (WGS) assay demonstrated high sensitivity for minimal residual disease (MRD) detection, with MRD clearance following neoadjuvant osimertinib or chemo-immunotherapy associated with favorable outcomes. In advanced NSCLC, longitudinal liquid biopsy analyses reveal dynamic subclonal evolution driving early treatment resistance. Circulating tumor DNA (ctDNA) clearance following targeted therapy in MET exon 14 skipping and BRAF-mutated tumors was associated with improved clinical outcomes. Emerging biomarkers such as ctDNA tumour fraction and circulating microRNA signatures are promising for radiotherapy stratification and prediction of immunotherapy-related toxicities. In SCLC, MRD monitoring enables earlier detection of disease progression and supports ctDNA-guided selection of patients for consolidation immunotherapy following chemotherapy. Overall, these advances demonstrate the expanding role of liquid biopsy in improving early detection, guiding treatment, and improving disease monitoring in lung cancer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".