Evaluation of stored liquid biopsies for molecular profiling in patients with non-small cell lung cancer (NSCLC).
Bibliographic record
Abstract
9024 Background: Molecular profiling is often limited by access to sufficient tumour tissue for comprehensive analysis and due to tumour heterogeneity, the complete range of tumor DNA abnormalities may not be represented nor accurately reflect the clinical evolution of disease. Circulating tumour DNA (ctDNA) can be used as a liquid biopsy for molecular abnormalities detection, quantification and monitoring for personalised treatment strategies. Methods: Plasma was collected at baseline (BL) and during study therapy from advanced NSCLC patients (pts) enrolled in a placebo controlled phase III trial of a novel irrerversible EGFR inhibitor; all patients had received standard therapy with chemotherapy and gefitinib or erlotinib. Archival tissue was collected when available but biopsy was not required prior to enrolment. BL Plasma ( < 3ml), stored for ~8 years was used to extract DNA and analysed using InVision (enhanced tagged-amplicon sequencing). Results: BL plasma from 387 pts was tested; 289 pts had available tissue results (from archival tissue collected at diagnosis) for EGFR (174WT/115Mut) and 243 for KRAS (205WT/38Mut). Despite age of plasma samples, ctDNA analysis detected cancer mutations in 310 pts (82%): TP53 (45%), KRAS (15%), and EGFR (43%; 56% were EGFR del19 and 29% L858R); T790M was detected in 80 patients. EGFR mutations were identified in 29 patients and KRAS in 10 patients with unknown tissue status. Also of note, STK11 (32 pts, 12 with KRAS), BRAF (5pts, 3 with V600E), MET(7 pts, 4 with METamp), ERBB2 (16pts, 10 with ERBB2amp) were identified in ctDNA analysis. Median time and median number of lines of systemic therapy between tissue biopsy and blood was 714 days and 3 lines respectively. Further analyses of ctDNA analyses in context of patient and trial outcomes are in progress. Conclusions: Liquid biopsies provide opportunity to evaluate molecular mutation profile in NSCLC patients. We demonstrate a highly sensitive method for ctDNA analysis which is complementary to tissue molecular analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".