Limitations of Personalized Medicine and Gene Assays for Breast Cancer
Bibliographic record
Abstract
Adjuvant systemic treatments reduce the risk of breast cancer recurrence following the local treatment of primary stage I-III breast cancers. For patients with hormone-positive breast cancers receiving hormonal therapy, the risk of distant recurrence is under 20% and therefore, many patients may potentially be spared of chemotherapy. Consequently, several molecular signatures based on gene expression were developed to better determine which breast cancer patients would benefit from chemotherapy. We present the case of a 62-year-old woman diagnosed with an early stage hormone receptor-positive breast cancer that was treated with a partial mastectomy. Oncotype DX (Genomic Health, Redwood City, CA) molecular testing was performed on the surgical specimen, which reported a recurrence score of 0. The patient commenced adjuvant radiotherapy during which she developed symptoms suggestive of bone metastasis and was subsequently diagnosed with a spinal cord compression that required neurosurgery and radiotherapy. Pathology review of the specimen from the spine surgery revealed a metastatic breast carcinoma with neuroendocrine differentiation. Molecular assays such as Oncotype DX are increasingly used to prognosticate patient outcomes and help determine who may avoid chemotherapy. This case report seeks to illustrate that such assays should not be used in the presence of rare histological subtypes like neuroendocrine breast cancers, which are often under-reported. The current status of personalized medicine and gene assays in breast cancer is reviewed and potential strategies are suggested to identify these rare cases to better orient diagnostic and treatment decisions.
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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 | 0.081 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".