Heterogeneous alteration of the ERBB3–MYC axis associated with MEK inhibitor resistance in a <i>KRAS</i>-mutated low-grade serous ovarian cancer patient
Why this work is in the frame
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Bibliographic record
Abstract
Low-grade serous ovarian cancer (LGSOC) is relatively chemoresistant, and no precision therapy is approved for this indication. Despite promising results in phase II trials, MEK inhibitors have failed to show improved progression-free survival in a phase III trial when compared to physician's choice chemotherapy. We report for the first time temporal changes in the tumor genome assessed in sequential tumor samples of a 48-yr-old patient with a KRAS -mutated LGSOC treated with the MEK inhibitor binimetinib. After an initial long-lasting partial response, rapidly progressive brain metastasis occurred, ultimately leading to patient death. Our study demonstrates that novel genomic alterations accumulated during the course of treatment as a result of therapeutic pressures led to MEK inhibitor resistance and, ultimately, disease evolution with an aggressive behavior observed in this patient. In particular, we describe the presence of ERBB3 amplification and aberrant ERBB3–MYC signaling as a potential mechanism of acquired MEK inhibitor resistance in a patient with LGSOC, which is similar to previous observations in KRAS -mutated colon and lung cancers. Our study highlights the need for an individualized approach to better understand tumor genome evolution and suggests that LGSOC patients may derive improved therapeutic benefit by using a combinatorial strategy used in other cancers in order to overcome emergent resistance to targeted therapies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it