Transcriptomes and shRNA Suppressors in a <i>TP53</i> Allele–Specific Model of Early-Onset Colon Cancer in African Americans
Bibliographic record
Abstract
UNLABELLED: African Americans are disproportionately affected by early-onset, high-grade malignancies. A fraction of this cancer health disparity can be explained by genetic differences between individuals of African or European descent. Here the wild-type Pro/Pro genotype at the TP53Pro72Arg (P72R) polymorphism (SNP: rs1042522) is more frequent in African Americans with cancer than in African Americans without cancer (51% vs. 37%), and is associated with a significant increase in the rates of cancer diagnosis in African Americans. To test the hypothesis that Tp53 allele-specific gene expression may contribute to African American cancer disparities, TP53 hemizygous knockout variants were generated and characterized in the RKO colon carcinoma cell line, which is wild type for TP53 and heterozygous at the TP53Pro72Arg locus. Transcriptome profiling, using RNAseq, in response to the DNA-damaging agent etoposide revealed a large number of Tp53-regulated transcripts, but also a subset of transcripts that were TP53Pro72Arg allele specific. In addition, a shRNA-library suppressor screen for Tp53 allele-specific escape from Tp53-induced arrest was performed. Several novel RNAi suppressors of Tp53 were identified, one of which, PRDM1β (BLIMP-1), was confirmed to be an Arg-specific transcript. Prdm1β silences target genes by recruiting H3K9 trimethyl (H3K9me3) repressive chromatin marks, and is necessary for stem cell differentiation. These results reveal a novel model for African American cancer disparity, in which the TP53 codon 72 allele influences lifetime cancer risk by driving damaged cells to differentiation through an epigenetic mechanism involving gene silencing. IMPLICATIONS: TP53 P72R polymorphism significantly contributes to increased African American cancer disparity.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".