Integration of <i>ERG</i> gene mapping and gene‐expression profiling identifies distinct categories of human prostate cancer
Bibliographic record
Abstract
OBJECTIVE: To integrate the mapping of ERG alterations with the collection of expression microarray (EMA) data, as previous EMA analyses have failed to consider the genetic heterogeneity and complex patterns of ERG alteration frequently found in cancerous prostates. MATERIALS AND METHODS: We determined genome-wide expression levels with GeneChip Human Exon 1.0 ST arrays (Affymetrix, Santa Clara, CA, USA) using RNA prepared from 35 specimens of prostate cancer from 28 prostates. RESULTS: The expression profiles showed clustering, in unsupervised hierarchical analyses, into two distinct prostate cancer categories, with one group strongly associated with indicators of poor clinical outcome. The two categories are not tightly linked to ERG status. By analysis of the data we identified a subgroup of cancers lacking ERG rearrangements that showed an outlier pattern of SPINK1 mRNA expression. There was a major distinction between ERG rearranged and non-rearranged cancers that involves the levels of expression of genes linked to exposure to beta-oestradiol, and to retinoic acid. CONCLUSIONS: Expression profiling of prostate cancer samples containing single patterns of ERG alterations can provide novel insights into the mechanism of prostate cancer development, and support the view that factors other than ERG status are the major determinants of poor clinical outcome.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".