Abstract ES8-2: Breast Cancer Stroma: A Predictor of Clinical Outcome and Tumour Heterogeneity
Bibliographic record
Abstract
Abstract Breast cancer heterogeneity is one of the principal obstacles both to predicting outcome and to determining an effective course of treatment for this disease. Individual cases demonstrate heterogeneity at multiple levels, including those parameters assessed by studies of gene expression, chromosomal aberrations, and classical and immuno-pathology. Although genomic technologies have been used to gain a better understanding of the impact of gene expression heterogeneity on breast cancer outcome by identifying gene expression signatures associated with clinical outcome, histopathological breast cancer subtypes, and a variety of cancer–related pathways and processes, relatively little is known about the effects of heterogeneity in the tumor microenvironment. We have addressed changes in stroma by analyzing changes in gene expression in stromal tissue associated with breast tumors when compared to normal breast tissue. We have integrated gene expression data from laser capture microdissected breast tumor stroma with matched normal stroma. Using this approach we have identified that the microenvironment of a breast tumor can be classified into one of six distinct molecular phenotypes exhibiting distinct biological functions and carrying prognostic information independent of existing therapeutic biomarkers and tumor subtypes. A trained predictor of 23 genes was developed and contains new information to stratify breast cancer subtypes. This is independent of clinical parameters and published predictors of outcome and identifies patients with poor outcome in multiple breast cancer expression data generated using using whole tissue. The stromal predictor selects poor outcome patients from multiple clinical subtypes of breast cancer and contains genes representing distinct biological features, including differential immune response, angiogenic response, as well as a hypoxic response. Elements of this signature are present in murine models of breast cancer. These results highlight the complex relationship between the tumor and its microenvironment, and underline the role that the stroma plays in tumor progression. These results demonstrate an important role for the tumor microenvironment in defining breast cancer heterogeneity, with a consequent impact upon clinical outcome. Novel therapies could be targeted at the processes that define the stroma classes, suggesting new avenues for the development of individualized treatment regimens. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr ES8-2.
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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 | 0.000 | 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) | 0.003 | 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".