Abstract 2801: Identification of oncogenic/metastatic driver genes that cooperate with p53 or p53/Rb-loss to induce triple-negative breast cancer
Bibliographic record
Abstract
Abstract Triple-negative breast cancer (TNBCs) is an aggressive subtype with poor prognosis; drug-resistant metastases are common and highly lethal for which identification of therapeutic targets is of major medical interest. Previously we demonstrated that deletion of p53 and Rb in mouse mammary epithelium induced TNBC-like tumors but with limited capacity to metastasize. Therefore, we decided to employ sleeping beauty (SB) mutagenesis system that can identify oncogenic networks responsible to drive primary and metastatic tumors. In order to do this, we generated mice in which p53 (p53f/f) or p53 plus Rb (p53f/f:Rbf/f ) are deleted and the SB transposon is mobilized from chromosome 9 as well as controls in which both p53 and Rb are wild type. Forty primary tumors and forty metastases from each cohort will be harvested to identify common integration sites followed by deep sequencing. Then, the top 5-10 candidate oncogenes/tumor suppressors will be validated to determine whether their loss/activation would accelerate tumorigenesis/metastasis. H&E staining of mammary tumors collected so far from different genotypes revealed histologically diverse tumor types which includes but not limited to poorly differentiated tumors with areas of mesenchymal/spindle-like cells, acinar adenocarcinomas, and well differentiated tumors. All mice analyzed developed multiple mammary tumors in more than one mammary gland with average latency of 108, 158 and 170 days for p53/Rb deleted (N=7), p53 deleted (N=12), and wild type mice (N=2), respectively (p < 0.05). However, we did not always observe macro metastases in mice with primary mammary tumors and we decided to perform primary tumor survival surgery when tumors are oversized and let the mice live longer to develop metastases. With this technique, 3 out of 4 mice that underwent tumor removal surgery developed lung metastases when sacrificed. In total, lung metastases were observed in 5 out of 28 mice analyzed so far with/without surgery (18%) which histologically resemble their primary tumors. Since removing tumors from multiple glands is challenging, we decided to take a transplantation approach of the primary mammary epithelial cells of mid-pregnancy (13-16 dpc) p53f/f:Rbf/f:SB+ females and injecting them into the mammary glands of 3-4 week old NSG mice. So far, four NSG mice developed mammary tumors with average latency of 176 days. These tumors resemble spindle-shape histology. SB mutagenesis can promote metastasis in mouse models with limited dissemination potential such as the model proposed herein. The proposed experiments will identify novel oncogenic events that cooperate with p53-loss or combined p53/Rb-loss to induce primary and metastatic TNBCs which may serve as therapeutic targets for this aggressive disease. I have generated 3 sets of cohort of SB mutagenesis to identify these oncogenic events which are currently being analyzed in details. Citation Format: Ronak Ghanbari Azarnier, Agatha Zuchelkowski, Philip.E Chung, Zhe Jiang, Eldad Zacksenhaus. Identification of oncogenic/metastatic driver genes that cooperate with p53 or p53/Rb-loss to induce triple-negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2801. doi:10.1158/1538-7445.AM2017-2801
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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 | 0.000 | 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) | 0.003 | 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".