Abstract 3496: Self-renewal as a therapeutic target in human colorectal cancer
Bibliographic record
Abstract
Abstract Tumor recurrence following treatment remains a major clinical challenge. Evidence from xenograft models and human trials indicates selective enrichment of cancer-initiating cells (C-ICs) in tumors that survive therapy. Together with recent reports showing that C-IC gene signatures influence patient survival, these studies predict that targeting self-renewal, the key stemness property unique to C-ICs, may represent a new paradigm in cancer therapy. Here we demonstrate that tumor formation, and more specifically human colorectal C-IC function are dependent on the canonical self-renewal regulator BMI-1. Down-regulation of BMI-1 inhibits the ability of colorectal C-ICs to self-renew resulting in the abrogation of their tumorigenic potential. Treatment of primary colorectal cancer xenografts with small molecule BMI-1 inhibitors resulted in colorectal C-IC loss with long-term and irreversible impairment of tumor growth. Targeting the Bmi-1 related self-renewal machinery provides the basis for a new therapeutic approach in the treatment of colorectal cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3496. doi:1538-7445.AM2012-3496
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".