Abstract 2558: MYC-dependent transformation model of triple-negative breast cancer in vivo
Bibliographic record
Abstract
Abstract Recent comprehensive breast cancer studies examining mutations and genomic alterations have determined that deregulation of MYC and the PI3K pathway occur frequently during breast cancer progression and may be useful targets for therapy. As a result, there have been large efforts to develop PI3K, AKT and mTOR inhibitors (PAM inhibitors) for clinical use, however clinical trial data demonstrates that many patients treated with PAM inhibitors develop resistant disease. An alternative strategy would be to target Myc, though a lack of effective and specific inhibitors makes this difficult. To identify the core vulnerabilities in these cancers we developed an in vivo xenograft model of triple-negative breast cancer driven by deregulated PI3K signaling and MYC. We hypothesize that determining how these pathways co-operate to transform normal human breast cells into breast carcinomas will reveal a tumor progression signature and highlight new therapeutic opportunities. We developed our model using the spontaneously immortalized, basal, triple-negative MCF10A cell line. By expressing the hotspot PIK3caH1047R protein alone in MCF10A cells (MCF10.H) in addition to MYC (MCF10.HM), we can model normal/early breast cancer and invasive ductal carcinoma respectively. This is the first in vivo human model of breast cancer dependent on MYC for transformation. When injected into female NOD-SCID mice, MCF10A.H cells form organized acinar ducts embedded in extracellular matrix. MCF10A.H ducts form with hollow lumen and a single layer of myoepithelial cells, recapitulating normal human breast histology. Alternatively, MCF10A.HM cells grow as high-grade carcinomas indicative of invasive disease. MCF10A.H benign growths and MCF10A.HM tumors remain basal-like and triple-negative by immunohistochemistry. Importantly, MCF10A.HM tumors are sensitive to MYC repression and therefore may be a suitable model to evaluate direct and indirect anti-MYC therapies. Having relevant human xenograft samples representing both normal and IDC tissue, we performed RNA-seq to identify a MYC-signature driving breast cancer transformation. Our current work will involve targeting the resulting MYC-driven pathways identified by RNA-seq to therapeutically target MYC in breast cancer. Citation Format: Corey Lourenco, Manpreet Kalkat, Dharmesh Dingar, Jason De Melo, Rosemary Yu, Linda Penn. MYC-dependent transformation model of triple-negative breast cancer in vivo [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 2558. doi:10.1158/1538-7445.AM2017-2558
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".