Basal biomarkers nestin and INPP4B predict gemcitabine benefit in metastatic breast cancer: Samples from the phase III SBG0102 clinical trial
Bibliographic record
Abstract
In a formal prospective-retrospective analysis of the phase III SBG0102 clinical trial randomizing metastatic breast cancer patients to gemcitabine-docetaxel or to single agent docetaxel, patients with basal-like tumors by PAM50 gene expression had significantly better overall survival in the gemcitabine arm. By immunohistochemistry (IHC), triple negative status was not predictive, but more specific biomarkers have since become available defining basal-like by nestin positivity or loss of inositol-polyphosphate-4-phosphate (INPP4B). Here, we evaluate their capacity to identify which patients benefit from gemcitabine in the metastatic setting. Nestin and INPP4B staining and interpretation followed published methods. A prespecified statistical plan evaluated the primary hypothesis that patients with basal-like breast cancer, defined as "nestin+ or INPP4B-", would have superior overall survival on gemcitabine-docetaxel when compared to docetaxel. Interaction tests, Kaplan-Meier curves and forest plots were used to assess prognostic and predictive capacities of biomarkers relative to treatment. Among 239 cases evaluable for our study, 36 (15%) had been classified as basal-like by PAM50. "Nestin+ or INPP4B-" was observed in 41 (17%) of the total cases and was significantly associated with PAM50 basal-like subtype. Within an estimated median follow-up of 13 years, patients assigned as IHC basal "nestin+ or INPP4B-" had significantly better overall survival on gemcitabine-docetaxel versus docetaxel monotherapy (HR = 0.31, 95%CI: 0.16-0.60), whereas no differences were observed for other patients (HR = 0.99), p-interaction < 0.01. In the metastatic setting, women with IHC basal breast cancers defined as "nestin+ or INPP4B-" have superior overall survival when randomized to gemcitabine-containing chemotherapy compared to docetaxel alone. These findings need to be validated using larger prospective-retrospective phase III clinical trials series.
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How this classification was reachedexpand
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 | 0.001 | 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) | 0.001 | 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 teacher head, 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".