Concordance between liquid and tissue biopsy in participants with newly diagnosed recurrent breast cancer.
Bibliographic record
Abstract
1028 Background: Tissue biopsy is recommended to confirm breast cancer (BC) recurrence. Liquid biopsy [including circulating tumour cells (CTCs) and circulating tumour DNA (ctDNA)] is a non-invasive approach for detecting cancer that may provide information to identify treatment choices and replace invasive biopsies. This ongoing study aims to assess the concordance between tissue and liquid biopsy testing in subjects presenting with suspicion of distant recurrence from BC. Methods: Patients with suspected metastatic BC were enrolled; tumour characteristics and treatment were recorded. Blood samples were collected within 30 days before tissue biopsy, or within 7-28 days after tissue biopsy and before any systemic or radiation treatment. Samples were shipped to EPIC Sciences and processed within 96 hours; after plasma isolation, nucleated cells were plated; slides and plasma were banked. CTCs were identified using Epic Sciences digital imaging and machine learning algorithms. Single-cell isolation for genomic ctDNA analysis was performed. Cell free DNA was analyzed using a validated NGS panel to detect ctDNA alterations. The presence of metastases was classified as suspicious, highly suspicious, definitely metastatic BC or other by the treating oncologist based on the patient's clinical presentation and biopsy pathology results. Epic Sciences classified samples into similar categories based on CTC and ctDNA assay results. These classifications were performed independently. Sensitivity of the Epic Sciences methodology to detect metastatic BC (as determined by the treating oncologist), and its false positive rate were calculated. Results: 100 patients were enrolled from June 2020 to October 2022; shipping delays precluded EPIC assays in six patients; 94 patients were analyzed. Of 83 cases deemed suspicious, highly suspicious, or definitely metastatic BC by the treating oncologist, 61 were also deemed so by Epic Sciences (sensitivity of 73.5%, 95% confidence interval, CI 63.1% - 81.9%). Of 66 cases assigned as suspicious, highly suspicious, or definitely metastatic BC by the Epic Sciences, 4 had new primary cancers (3 lung cancer, 1 hepatocarcinoma), for a false positive rate of 6.1% (95% CI 1.9% - 15.0%). One additional case was classified as un-specified adenocarcinoma (possibly breast) by the treating oncologist; resolution awaits further follow-up. Conclusions: Preliminary results show that 73.5% of distant BC recurrences were correctly identified by liquid biopsy. A small number of false positive results occurred in patients with other new primary cancers. Additional analyses with CTC characterization are ongoing. [Table: see text]
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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 | 0.002 | 0.006 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".