Abstract 3666: The genomic landscape and clonal evolution of tumours arising in <i>TP53</i> mutation carriers
Bibliographic record
Abstract
Abstract Li-Fraumeni syndrome (LFS) is a familial cancer predisposition syndrome (CPS) caused by germline mutations in TP53, associated with a high frequency of sarcomas, breast cancers, adrenocortical carcinomas and CNS tumours. Recently, our group and others have identified distinct mutational signatures, defined by the spectrum and context of somatic mutations, in tumours arising in individuals with another CPS, constitutional mismatch repair deficiency (CMMRD). CMMRD tumours frequently harbor somatic mutations disrupting the proofreading function of the DNA polymerases POLE and POLD1, which consequently leads to an ultra-hypermutant cancer genome, with early MMR mutational signatures and late POLE/POLD1 mutational signatures. This ultrahypermutant tumor phenotype is essentially diagnostic for the syndrome and provides a rational for immune checkpoint inhibitors which have shown success in this context. Based on our results in CMMRD tumors we hypothesize that LFS tumours might likewise harbor distinct mutational events and/or evolutionary dynamics from sporadic tumours of the same histiotype. Although several cancer genomics landscape studies have included a handful of tumours from LFS patients, to our knowledge no study to date has attempted to comprehensively characterize the cancer genomes of LFS patients. To investigate the somatic mutational events driving tumourigenesis in LFS we performed whole-genome sequencing (WGS) analysis of 22 tumours derived from patients with pathogenic germline TP53 mutations. Tumours from germline TP53 wildtype patients were analyzed by the same methods to serve as a control data set. For each tumour sample, where possible, we performed WGS on multiple spatially distinct micro-dissected tumour regions in order to reconstruct the evolutionary history of each cancer. Somatic variant calling was performed at high sensitivity using in silico reconstructed high-depth bulk WGS (80-120X coverage), with somatic mutations, structural variants, copy number alterations, mutational signatures and subclones identified using MuTect2, delly, battenberg, SigProfiler and phyloWGS respectively. High confidence variants were identified using in-house designed filtering pipelines. The resulting analyses reveals the life history of LFS cancers is marked by a high frequency of early catastrophic genomic rearrangement events, a diverse range of somatic driver events and in at least some cases marked intratumoural spatial heterogeneity of CNVs and SNVs. Citation Format: Nicholas Light, Matthew Zatzman, Nathaniel Anderson, Vallijah Subasri, Mehdi Layeghifard, Ana Novokmet, James Tran, Richard de Borja, Fabio Fuligni, Joshua Schiffman, David Malkin, Adam Shlien. The genomic landscape and clonal evolution of tumours arising in TP53 mutation carriers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3666.
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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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".