The pro-apoptotic paradox: the BH3-only protein Bcl-2 interacting killer (Bik) is prognostic for unfavorable outcomes in breast cancer
Bibliographic record
Abstract
// Vrajesh Pandya 1 , Darryl Glubrecht 2 , Larissa Vos 2 , John Hanson 2 , Sambasivarao Damaraju 3 , John Mackey 2 , Judith Hugh 3 , Ing Swie Goping 1, 2 1 Department of Biochemistry, University of Alberta, Edmonton, Alberta T6G 2H7, Canada 2 Department of Oncology, University of Alberta, Edmonton, Alberta T6G 2H7, Canada 3 Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta T6G 2H7, Canada Correspondence to: Ing Swie Goping, e-mail: igoping@ualberta.ca Keywords: breast cancer, BH3-only proteins, Bcl-2 interacting killer, BiK, autophagy Received: March 02, 2016 Accepted: April 10, 2016 Published: April 22, 2016 ABSTRACT Breast cancer is the leading cause of cancer-associated deaths in women worldwide. Clinical biomarkers give information on disease progression and identify relevant biological pathways. A confounding factor that uncouples markers from disease outcome is the ability of tumor cells to mutate and evade clinical intervention. Therefore, we focussed on apoptotic genes that modulate tumor regression. Using gene and tissue microarray analyses, we identified an association of Bcl-2 interacting killer (Bik) with poor breast cancer prognosis. Bik prognostic ability was independent of Estrogen Receptor/Progesterone Receptor and Her2 status. Additionally, Bik was independent of anti-apoptotic Bcl-2, Bcl-xL, Mcl-1 and Bcl-w suggesting a complex mechanism of tumor promotion identified by Bik high tumors. Bik also stimulates autophagy, which can contribute to enhanced tumor fitness. We found a significant association between the autophagy marker ATG5 and Bik. Combined high expression level of ATG5 and Bik was a stronger predictor of outcome than either alone. Thus, our study identifies Bik as a novel, independent prognostic biomarker for poor outcomes in breast cancer and suggests that Bik-mediated autophagy contributes to disease recurrence.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".