Data from Gene Expression Profiling in Cervical Cancer: An Exploration of Intratumor Heterogeneity
Bibliographic record
Abstract
Abstract Purpose: To explore intratumor heterogeneity in gene expression profiles from patients with cervical cancer. Experimental Design: A total of 33 biopsies were obtained from 11 patients, sampling between two and five different areas for each tumor. The extracted RNA was hybridized onto the Affymetrix U133 Plus 2.0 oligonucleotide chip. The variance of expression within a patient (W), between patients (B) and the total variance (T = W + B) were calculated for each ProbeSet, and the ratio W/T was used as a measure of intratumor heterogeneity. Gene Ontology functional analysis was done to assess the function of genes that had high W/T (top 10%) and low W/T (bottom 10%) values. Results: In total, 448 ProbeSets (2.2% of the total) had W/T < 0.10, indicating low intratumor heterogeneity, and 537 ProbeSets (2.7% of the total) had W/T > 0.90, indicating high intratumor heterogeneity. In total 14,473 ProbeSets (72.4%) had higher intertumor than intratumor heterogeneity (W/T < 0.5). Genes with low intratumor heterogeneity were characterized by a statistically significant enrichment of immune-related functions (P < 0.0001). Genes with high intratumor heterogeneity were characterized by a significant tendency towards nuclear localization and nucleic acid binding (both P < 0.0001). For genes with W/T > 0.5, more than six biopsies would be required to minimize the intratumoral heterogeneity to <0.15; if W/T is 0.3 to 0.4, four biopsies are required; and for low W/T of 0.16 to 0.3, only two to three biopsies would be needed. Conclusion: Although the intratumor heterogeneity was low for the majority of the tested ProbeSets, for many genes, multiple biopsies are required to obtain a reliable estimate of gene expression.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".