Combination of Osimertinib with Concurrent Chemotherapy and Hormonal Therapy for Synchronous NSCLC, Hormone Receptor-Positive Breast Cancer, and Triple-Negative Breast Cancer: Case Report
Bibliographic record
Abstract
Patients presenting with multiple primary malignancies remain a growing challenge for physicians due to a lack of data for generalizable guidelines. Identification of driver mutations in carcinogenesis leads to the development of targeted treatment of many different cancer types, but its combination with other anti-cancer therapy is not well understood. We report a case of a 66-year-old woman who presented with triple-negative breast cancer, multifocal hormone receptor-positive breast cancer, primary epidermal growth factor receptor-mutated lung adenocarcinoma, possible primary lung adenocarcinoma of unspecified mutational status in the contralateral lung, and a solitary metastatic lesion in the brain from one of her primary cancers. She was treated with stereotactic radiosurgery and osimertinib in combination with carboplatin/nab-paclitaxel, doxorubicin/cyclophosphamide, and letrozole, with excellent clinical and radiographical response. We did not observe synergistic toxicity or unexpected adverse events from the treatment. To the best of our knowledge, this is the first report of concurrent osimertinib with these chemotherapy and hormonal therapy agents. As large-scale studies are difficult to conduct for these rare cases requiring exceptional treatment, it is important for physicians to build on the community's shared experience via case reports to better predict efficacy and safety of combining targeted agents with other conventional systemic treatments.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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".