Role of Circulating Tumor DNA in Adapting Immunotherapy Approaches in Breast Cancer
Bibliographic record
Abstract
Immunotherapy has a defined role in the treatment of both early- and late-stage triple-negative breast cancer (TNBC) and is under active exploration in human epidermal receptor 2-positive as well as high-risk hormone-receptor-positive subtypes. It is critical to balance the efficacy and toxicity of immunotherapy while keeping the cost and duration of treatment in check. In addition to the immunohistochemistry testing of PD-L1 expression, which only predicts the efficacy of immunotherapy in metastatic TNBC, there is a lack of biomarkers that are better standardized to predict efficacy and treatment response, detect early relapse, and guide prognosis in breast cancer patients treated with immunotherapy. Circulating tumor DNA (ctDNA) is a minimally invasive, dynamic, real-time, blood-based biomarker that has shown promising value in the management of solid tumors, including breast cancer. This review discusses the emerging evidence for the potential application of ctDNA to further refine patient-centered care and personalize treatment based on a molecularly defined risk assessment for breast cancer patients treated with immunotherapy-based approaches. We further discuss the challenges and barriers to widespread adoption of this promising tool in the management of breast cancer patients requiring immunotherapy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".