Abstract LB-320: Systemic tumor-stroma interactions are prognostic indicators of breast tumor invasiveness
Bibliographic record
Abstract
Abstract Tumor invasiveness dysregulates homeostatic processes within tissues, driving both tumor and stroma cells to locally proliferate while also disrupting tissue boundaries and recruiting cells from distal sites to promote metastatic dissemination. Here, we use an RNA sequencing approach in mouse models of invasive and noninvasive breast cancer to directly test whether the transcriptional changes that occur as invasive tumors disrupt their microenvironment can be used to predict tumor subtype and patient outcomes. We find that tumor and stroma gene expression levels are largely synchronized within the tumor microenvironment, but that the expression levels of a subset of genes with high prognostic potential are strongly negatively correlated and may reflect opposing responses of tumor and normal stroma cells to tissue disruption. We clinically validate these observations in multiple human breast tumor gene expression datasets and demonstrate that stromal transcript levels enable superior classification of breast cancer subtypes relative to those expression changes observed between invasive and noninvasive tumor tissue. We show that local stromal remodelling is partially driven by differential recruitment of macrophages to invasive tumor sites by CCL5, and that invasion-specific gene expression changes that are prognostic for metastasis-free survival in human patients are detectable in stromal tissues far from the primary lesion. Our data indicate that dysregulation of tissue homeostasis by invasive tumors generates transcriptional changes both within the tumor microenvironment and in peripheral tissues that have significant diagnostic and prognostic potential and could facilitate novel patient therapeutic strategies. Citation Format: Russell Bainer, Casey Frankenberger, Daniel Rabe, Sadiq Saleh, Morag Park, Kiran Chada, Yoav Gilad, Marsha Rosner. Systemic tumor-stroma interactions are prognostic indicators of breast tumor invasiveness. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr LB-320. doi:10.1158/1538-7445.AM2014-LB-320
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.005 | 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".