Abstract B48: Cancer Stem Cells: characteristics, roles in metastatic disease and targeting therapy for the medically underserved
Bibliographic record
Abstract
Abstract Cancer stem cells have been observed to lead to the proliferation and relapse of cancers. While they share many properties with normal stem cells, CSCs are distinguished by their incredibly high survivability, drug resistance, and the ability to facilitate tumor progression. In this review, we discuss the origin, major properties used to identify and characterize CSCs, and also how they behave and are regulated with tumor progression and metastasis. In addition, we discuss multiple therapeutic strategies that were developed to target CSCs. Amongst these we review several natural compounds that in recent studies have shown great potential for preventing tumorigenesis and in the treatment of cancer cells by interacting with CSCs. Natural compounds are being widely researched for efficacy as cancer and CSC targeting agents. Natural drugs consume much less funding to produce. Also, as dietary compounds, they are less stigmatized by patients than artificially synthesized drugs, and they are inexpensive and available to even the medically underserved. Finally, we discuss several strategies that are used to maximize the effect of therapeutic strategies on the CSC fraction and further directions for this field of tumor biology. Note: This abstract was not presented at the conference. Citation Format: Zhenya Morgatskaya, Reza Bayat Mokhtari, Albina Tyker, Parandis Kazemi, Tina Homayouni, Narges Baluch, Sara Dhalla, Shreya Rekhi, Sushil Kumar, Herman Yeger. Cancer Stem Cells: characteristics, roles in metastatic disease and targeting therapy for the medically underserved. [abstract]. In: Proceedings of the Ninth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2016 Sep 25-28; Fort Lauderdale, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(2 Suppl):Abstract nr B48.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".