Abstract B15: Metastatic breast tumors regulate gene expression at distal mammary sites that predicts patient outcome
Bibliographic record
Abstract
Abstract The molecular interactions between cancer and stromal cells within the tumor microenvironment enable tumor invasion, intravasation, and metastasis at distant sites. However, the degree to which metastatic breast tumors reprogram stromal cells both locally and at distant mammary tissues is not well understood. To address this question, we used species-specific RNA sequencing in a mouse xenograft model to determine how the metastasis suppressor RKIP influences transcription in tumor and stroma tissues. Here we show that metastatic tumors prime mammary tissue at a distant site in a manner that reflects local stromal responses. In addition, gene expression in metastatic breast tumors is pervasively correlated with gene expression in local stroma of both mouse xenografts and human patients. Changes in local and distant stromal gene expression elicited by metastatic tumors are better predictors of subtype and patient survival than tumor gene expression, supporting the use of stromal-based strategies for the diagnosis and prognosis of breast cancer. One mechanism by which changes at contralateral distal mammary breast occur is through exosomes secreted by tumor cells. These results indicate that tumors prime contralateral mammary tissue in a manner that reflects local stromal changes and predicts metastatic disease. This study has future application to our understanding of contralateral breast cancer. Citation Format: Jiyoung Lee, Russell Bainer, Casey Frankenberger, Daniel Rabe, sadiq Saleh, Morag Park, Gary An, Yoav Gilad, Marsha Rich Rosner. Metastatic breast tumors regulate gene expression at distal mammary sites that predicts patient outcome. [abstract]. In: Proceedings of the AACR Special Conference: Metabolism and Cancer; Jun 7-10, 2015; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Res 2016;14(1_Suppl):Abstract nr B15.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".