Early, On-Treatment Levels and Dynamic Changes of Genomic Instability in Circulating Tumor DNA Predict Response to Treatment and Outcome in Metastatic Breast Cancer Patients
Bibliographic record
Abstract
BACKGROUND: Circulating tumor DNA (ctDNA) offers high sensitivity and specificity in metastatic cancer. However, many ctDNA assays rely on specific mutations in recurrent genes or require the sequencing of tumor tissue, difficult to do in a metastatic disease. The purpose of this study was to define the predictive and prognostic values of the whole-genome sequencing (WGS) of ctDNA in metastatic breast cancer (MBC). METHODS: Plasma from 25 patients with MBC were taken at the baseline, prior to treatment (T0), one week (T1) and two weeks (T2) after treatment initiation and subjected to low-pass WGS. DNA copy number changes were used to calculate a Genomic Instability Number (GIN). A minimum predefined GIN value of 170 indicated detectable ctDNA. GIN values were correlated with the treatment response at three and six months by Response Evaluation Criteria in Solid Tumours assessed by imaging (RECIST) criteria and with overall survival (OS). RESULTS: GIN values were detectable (>170) in 64% of patients at the baseline and were significantly prognostic (41 vs. 18 months OS for nondetectable vs. detectable GIN). Detectable GIN values at T1 and T2 were significantly associated with poor OS. Declines in GIN at T1 and T2 of > 50% compared to the baseline were associated with three-month response and, in the case of T1, with OS. On the other hand, a rise in GIN at T2 was associated with a poor response at three months. CONCLUSIONS: Very early measurements using WGS of cell-free DNA (cfDNA) from the plasma of MBC patients provided a tumor biopsy-free approach to ctDNA measurement that was both predictive of the early tumor response at three months and prognostic.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".