Molecular Residual Disease in Breast Cancer: Detection and Therapeutic Interception
Bibliographic record
Abstract
Breast cancer remains a leading cause of cancer-related death in women despite screening and therapeutic advances. Early detection allows for resection of local disease; however, patients can develop metastatic recurrences years after curative treatment. There is no reliable blood-based monitoring after curative therapy, and radiographic evaluation for metastatic disease is performed only in response to symptoms. Advances in circulating tumor DNA (ctDNA) assays have allowed for a potential option for blood-based monitoring. The detection of ctDNA in the absence of overt metastasis or recurrent disease indicates molecular evidence of cancer, defined as molecular residual disease (MRD). Multiple studies have shown that MRD detection is strongly associated with disease recurrence, with a lead time prior to clinical evidence of recurrence of many months. Importantly, it is still unclear whether treatment changes in response to ctDNA detection will improve outcomes. There are currently ongoing trials evaluating the efficacy of therapy escalation in the setting of MRD, and these studies are being conducted in all major breast cancer subtypes. Additional therapies under study include CDK4/6 inhibitors, PARP inhibitors, HER2-targeted therapies, and immunotherapy. This review will summarize the underlying scientific principles of various MRD assays, their known prognostic roles in early breast cancer, and the ongoing clinical trials assessing the efficacy of therapy escalation in the setting of MRD.
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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 | 0.002 |
| 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) | 0.003 | 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".