Abstract A43: Systemic tumor-stroma interactions are prognostic indicators of breast tumor invasiveness
Bibliographic record
Abstract
Abstract Cancer progression is critically dependent on specific molecular interactions between tumor cells and their microenvironment, but little is known about the global dynamics of this crosstalk within and across individuals. Here, we used an RNA sequencing approach with species-of-origin information in a xenograft mouse model to computationally resolve and compare tumor versus stromal transcriptional changes. Our study employed a triple-negative breast cancer (TNBC) model in which primary tumor invasiveness is controlled by the metastasis suppressor Raf Kinase Inhibitory Protein (RKIP) allowing systemic observation of stromal response to invasive or noninvasive breast tumours at both proximal and distal sites. Initially, we observed that expression levels of gene homologs in human tumor and mouse stroma are highly synchronized. Surprisingly, the set of genes most prognostic for metastasis-free survival in multiple clinical data sets were those whose mRNA expression was inversely correlated between tumor and stroma. These results were confirmed in an independent set of microarray data from tumor and stroma micro-dissected from primary human breast cancer patient tissue. Additionally, we found broad-based and invasion-specific gene expression changes in stromal tissues far from the primary lesion, with these results being confirmed in an independent set of samples. Of note, genes differentially expressed in the tumor-associated stroma were better classifiers of tumor phenotype than genes differentially expressed in the tumor alone. As well, stromal genes differentially expressed both locally and distally, are significant predictors of patient prognosis. These findings demonstrate the potential for using patient tissue remote from the primary tumor as indicators of disease aggressiveness. Furthermore, these results suggest that stromal gene expression, acting to compensate for changes in the tumor, may have both prognostic and therapeutic implications. Citation Format: Casey A. Frankenberger, Russell O. Bainer, Daniel C. Rabe, Sadiq Saleh, Morag Park, Yoav Gilad, Marsha R. Rosner. Systemic tumor-stroma interactions are prognostic indicators of breast tumor invasiveness. [abstract]. In: Abstracts: AACR Special Conference on Cellular Heterogeneity in the Tumor Microenvironment; 2014 Feb 26-Mar 1; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(1 Suppl):Abstract nr A43. doi:10.1158/1538-7445.CHTME14-A43
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".