Low-pass Whole-genome Sequencing of Circulating Cell-free DNA Demonstrates Dynamic Changes in Genomic Copy Number in a Squamous Lung Cancer Clinical Cohort
Bibliographic record
Abstract
Abstract Purpose: We developed a method to monitor copy number variations (CNV) in plasma cell-free DNA (cfDNA) from patients with metastatic squamous non–small cell lung cancer (NSCLC). We aimed to explore the association between tumor-derived cfDNA and clinical outcomes, and sought CNVs that may suggest potential resistance mechanisms. Experimental Design: Sensitivity and specificity of low-pass whole-genome sequencing (LP-WGS) were first determined using cell line DNA and cfDNA. LP-WGS was performed on baseline and longitudinal cfDNA of 152 patients with squamous NSCLC treated with chemotherapy, or in combination with pictilisib, a pan-PI3K inhibitor. cfDNA tumor fraction and detected CNVs were analyzed in association with clinical outcomes. Results: LP-WGS successfully detected CNVs in cfDNA with tumor fraction ≥10%, which represented approximately 30% of the first-line NSCLC patients in this study. The most frequent CNVs were gains in chromosome 3q, which harbors the PIK3CA and SOX2 oncogenes. The CNV landscape in cfDNA with a high tumor fraction generally matched that of corresponding tumor tissue. Tumor fraction in cfDNA was dynamic during treatment, and increases in tumor fraction and corresponding CNVs could be detected before radiographic progression in 7 of 12 patients. Recurrent CNVs, such as MYC amplification, were enriched in cfDNA from posttreatment samples compared with the baseline, suggesting a potential resistance mechanism to pictilisib. Conclusions: LP-WGS offers an unbiased and high-throughput way to investigate CNVs and tumor fraction in cfDNA of patients with cancer. It may also be valuable for monitoring treatment response, detecting disease progression early, and identifying emergent clones associated with therapeutic resistance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".