Abstract P1-06-02: Identifying hypermethylated tumor suppressor genes in breast cancer with an in vivo total genome knockdown screen
Bibliographic record
Abstract
Abstract Changes in gene expression are required for the progression of breast cancer; and DNA methylation, which silences tumor suppressor genes, is often responsible for these changes. Several de-methylating/DNA synthesis inhibiting drugs (such as decitabine) are potential breast cancer therapies; however, for their application to be successful, we need to identify which patients would most benefit from these treatments. To accomplish this, the critical genes, which must be silenced by hypermethylation in order for breast cancer to progress, need to be identified. For this purpose, we have performed an in vivo total genome knockdown screen. A genome-wide lentiviral-based shRNA screen was performed with breast cancer cell line MDA-MB-231 tumors implanted in the mammary fat pads of female NOD/SCID mice, with or without decitabine treatment (50mg/kg). The resulting tumors were harvested and shRNA sequences retrieved and hybridized to a Decode microarray, allowing for the identification of shRNA sequences that were enriched or depleted under decitabine treatment. The enriched shRNA sequences likely target genes of two categories: 1) they are methylated in the tumor and when expressed have tumor suppressive function, or 2) they are required for decitabine-mediated DNA synthesis inhibition and apoptosis. To determine if the shRNA-targeted genes identified in the screen fall within category 1 or 2, further analysis was done using GEO datasets for methylation in breast cancer cell lines, primary tumors, and normal breast tissues. In replicates of 6 mice, 111 shRNA sequences were enriched more than 2-fold in 5/6 mice. Of the 111 genes, 29 genes showed significant enrichment (p<0.05) in the decitabine-treated tumors. Using the GEO datasets, 19 of those significantly enriched genes fit a tumor suppressor methylation profile and merit further investigation of their methylation status in the MDA-MB-231 cell line. The remaining enriched genes may play a role in DNA synthesis inhibition and apoptosis- this will be confirmed with in vitro apoptosis assays. DAVID analyses did not reveal any functional clustering of the genes of interest, but did implicate several pathways that involved a single potential hypermethylated tumor suppressor gene. We have identified several genes that are hypermethylated in breast cancer and are potentially involved in tumor progression. Confirmation experiments will reveal a list of genes that when found hypermethylated in patient tumors would identify candidate breast cancer patients who would benefit from decitabine treatment. Citation Format: Margaret Thomas, Krysta Coyle, Mohammad Sultan, Luzhe Pan, Dae-Gyun Ahn, Patrick Lee, Carman Giacomantonio, Paola Marcato. Identifying hypermethylated tumor suppressor genes in breast cancer with an in vivo total genome knockdown screen [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P1-06-02.
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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) | 0.001 | 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 | 0.000 | 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".