A Case-Matched Gender Comparison Transcriptomic Screen Identifies eIF4E and eIF5 as Potential Prognostic Markers in Male Breast Cancer
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Bibliographic record
Abstract
Abstract Purpose: Breast cancer affects both genders, but is understudied in men. Although still rare, male breast cancer (MBC) is being diagnosed more frequently. Treatments are wholly informed by clinical studies conducted in women, based on assumptions that underlying biology is similar. Experimental Design: A transcriptomic investigation of male and female breast cancer was performed, confirming transcriptomic data in silico. Biomarkers were immunohistochemically assessed in 697 MBCs (n = 477, training; n = 220, validation set) and quantified in pre- and posttreatment samples from an MBC patient receiving everolimus and PI3K/mTOR inhibitor. Results: Gender-specific gene expression patterns were identified. eIF transcripts were upregulated in MBC. eIF4E and eIF5 were negatively prognostic for overall survival alone (log-rank P = 0.013; HR = 1.77, 1.12–2.8 and P = 0.035; HR = 1.68, 1.03–2.74, respectively), or when coexpressed (P = 0.01; HR = 2.66, 1.26–5.63), confirmed in the validation set. This remained upon multivariate Cox regression analysis [eIF4E P = 0.016; HR = 2.38 (1.18–4.8), eIF5 P = 0.022; HR = 2.55 (1.14–5.7); coexpression P = 0.001; HR = 7.04 (2.22–22.26)]. Marked reduction in eIF4E and eIF5 expression was seen post BEZ235/everolimus, with extended survival. Conclusions: Translational initiation pathway inhibition could be of clinical utility in MBC patients overexpressing eIF4E and eIF5. With mTOR inhibitors that target this pathway now in the clinic, these biomarkers may represent new targets for therapeutic intervention, although further independent validation is required. Clin Cancer Res; 23(10); 2575–83. ©2016 AACR.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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