Abstract 3034: A high-content screen to identify novel chromosome instability genes
Bibliographic record
Abstract
Abstract Chromosome instability (CIN) is defined as an increase in the rate at which whole chromosomes or large chromosomal fragments are gained or lost. It is a characteristic of virtually all cancer types that is frequently observed in highly aggressive, drug resistant tumors. Despite this, the majority of human CIN genes have yet to be elucidated, highlighting the need for studies aimed at identifying the defective genes that underlie CIN. In this study we developed image-based approaches capable of detecting CIN-associated phenotypes following RNAi-based silencing of candidate CIN genes. The first assay involves quantifying nuclear areas following silencing, where changes in mean nuclear area relative to controls act as a surrogate marker of CIN. The second approach monitors micronucleus (MN) formation where increases in the number of micronuclei are indicative of CIN. These assays were employed in a high-content screen of 164 human candidate CIN genes in two unrelated cell lines, HT1080 and hTERT. In HT1080, the nuclear area and MN enumeration assays identified 43 and 83 putative CIN genes, respectively. In hTERT, the nuclear area and MN assays identified 55 and 48 putative CIN genes, respectively. Preliminary data collected through Western blotting, mitotic spreads and flow cytometry, has provided evidence to support the validation of a subset of these putative CIN genes (e.g. SKP1), as bona fide human CIN genes. Identifying novel CIN genes will provide critical insights into CIN and tumorigenesis, as well as identify potential targets that could be exploited for the development of superior therapeutic strategies. Citation Format: Laura L. Thompson, Allison Baergen, Zelda Lichtensztejn, Kirk J. McManus. A high-content screen to identify novel chromosome instability genes. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3034. doi:10.1158/1538-7445.AM2015-3034
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".