Gene expression profiling of the breast tumour microenvironment : characterization of gene expression heterogeneity in the breast tumour microenvironment and its influence on clinical outcome
Bibliographic record
Abstract
Breast cancer is a very heterogeneous disease. This heterogeneity can be observed at many levels, including gene expression, chromosomal aberrations, and disease pathology. A clear understanding of these differences is important since they impact upon treatment efficacy and clinical outcome. Recent studies have demonstrated that the tumour microenvironment also plays a critical role in cancer initiation and progression. Genomic technologies have been used to gain a better understanding of the impact of gene expression heterogeneity on breast cancer, and have identified gene expression signatures associated with clinical outcome, histopathological breast cancer subtypes, and a variety of cancer-related pathways and processes. However, little work has been done in this context to examine the role of the tumour microenvironment in determining breast cancer outcome, or in defining breast cancer heterogeneity. Additionally, little is known about gene expression in histologically normal tissue adjacent to breast tumour, if this is influenced by the tumour, and how this compares with non-tumour-bearing breast tissue. By applying laser--capture microdissection and gene expression profiling to clinical breast cancer specimens the research presented in this thesis addresses these questions.
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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 | 0.001 |
| 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 | 0.001 |
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".