A Dramatic Clinical Response to Trastuzumab-Deruxtecan in a Patient with HER-2 Low Breast Cancer with Untreated Leptomeningeal Metastasis and Hydrocephalus
Bibliographic record
Abstract
Leptomeningeal metastasis (LM) is a rare and challenging manifestation of advanced breast cancer (ABC) with severe morbidity and mortality. Patients with LM may be asymptomatic, or present with non-specific neurologic deficits, thereby possibly delaying diagnosis. Treatment typically requires a multimodal approach for effective management, symptom relief, and quality-of-life improvement. Trastuzumab-deruxtecan (T-DXd), a humanized monoclonal antibody drug conjugate, demonstrated efficacy across diverse breast cancer subtypes expressing variable levels of HER2 proteins. Currently, T-DXd is the standard of care for patients with advanced, pretreated, HER2 low breast cancer. There is limited evidence of the response of brain metastases (BM) and leptomeningeal metastases (LM) to T-DXd in HER2-low patients, with most data extrapolated from HER2-positive breast cancer studies. This case report presents the first documented instance of a patient with debilitating, symptomatic, untreated LM and hydrocephalus demonstrating a rapid and dramatic clinical response to T-DXd. This finding holds crucial clinical relevance, highlighting the potential benefit of initiating effective systemic therapy for LM early in treatment to address both central nervous system (CNS) and non-CNS disease burden, rather than delaying systemic therapy until after radiation therapy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".