Abstract A28: Mutational landscape of TP53 in localized prostate cancer
Bibliographic record
Abstract
Abstract Background: We performed a comprehensive interrogation of the mutational landscape of TP53 in the context of localized prostate cancer using a large clinical/molecular-paired dataset from the Canadian Prostate Cancer Gene Network (CPC-GENE). We further test the associations of TP53 mutations with outcomes post-image-guided radiotherapy (IGRT) or radical prostatectomy (RadP). Methods: Copy number status (N = 284), single nucleotide variants (SNV) (N = 123), methylation status (N = 117), and mRNA abundance profiling (N = 115) were assessed using the Affymetrix Oncoscan FFPE express v3.0 assay, whole genome sequencing (up to 100-200x), Illumina 450K methylation array, and Affymetrix HuGene 2.0 array, respectively. Patient cohort comprised of NCCN-defined intermediate-risk prostate cancer who underwent either IGRT (N = 146) or RadP (N = 137). Biochemical-relapse free rate (bRFR) was assessed as the primary clinical end-point. Results: We identified 65 cases (22.9%) with mono-/bi-allelic copy number alteration (CNA) of TP53, and 7 cases (5.7%; 6 non-synonymous and 1 splice variant) with TP53 SNV in our cohort, which was comparable with the TCGA (30% CNA, 7% SNV) and MSKCC (17% CNA, 2.9% SNV) cohorts of low to high-risk localized prostate cancers. Epigenomic profiling revealed specific sites of DNA hypermethylation (β-value >0.7) within the body and 5' UTR gene-regions, while the TSS gene-region was unaffected. Genomic mutations (CNA and/or SNV) of TP53 were associated with global genomic instability (percent genome aberration of 9.5 vs 6.4, p = 0.001) and reduced mRNA levels (mRNA abundance Z-Score: -0.58 vs 0.22, p-value = 0.0011), but methylation status had no consequence on these indices. Neither TP53 genomic mutations (HR = 1.35, 95% CI 0.91-2.00, Wald's p = 0.14) nor mRNA abundance (HR = 1.39, 95% CI 0.71-2.75, Wald's p = 0.34) was associated with bRFR on multivariable analyses. However, stratification by combinatorial genomic and mRNA abundance indices identified an unfavorable subgroup that was associated with poorer bRFR on multivariable analysis (HR = 2.95, 95% CI 1.42-6.12, Wald's p = 0.004). Conclusions: This is the first comprehensive interrogation of the mutational landscape of TP53 in localized prostate cancer. Our findings suggest that functional TP53 loss at both the copy number and transcription level accounts for a subset of non-indolent localized prostate cancer. Citation Format: Osman Mahamud, Melvin L.K Chua, Stephane Supiot, Emilie Lalonde, Alan Dal Pra, Alejandro Berlin, Michèle Orain, Valerie Picard, Helene Hovington, Alain Bergeron, Yves Fradet, Bernard Têtu, Gaetano Zafarana, Alice Meng, Julie Livingstone, Melania Pintilie, Michael Fraser, Theodorus van der Kwast, Paul C. Boutros, Bristow G. Robert. Mutational landscape of TP53 in localized prostate cancer [abstract]. In: Proceedings of the AACR Special Conference on DNA Repair: Tumor Development and Therapeutic Response; 2016 Nov 2-5; Montreal, QC, Canada. Philadelphia (PA): AACR; Mol Cancer Res 2017;15(4_Suppl):Abstract nr A28.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".