Studies on the Evolution of and Mutational Processes Driving Childhood Cancer in the Context of Genetic Predisposition
Bibliographic record
Abstract
Cancer predisposition syndromes (CPSs) are caused by heritable mutations, often affecting DNA-repair pathways, which dramatically increase cancer risk. Once diagnosed, screening of additional family members combined with cancer surveillance protocols have shown significant survival benefits. However, CPSs are largely underdiagnosed due to clinical heterogeneity and variants of uncertain significance. In this thesis I explore the hypothesis that CPS-associated cancers exhibit characteristic DNA-repair-associated mutational signatures—patterns of somatic mutation related to mutation aetiology¬— and/or evolutionary dynamics, which may be exploited to aid in CPS diagnosis and management. To address this hypothesis, I studied two model CPSs—constitutional mismatch repair deficiency (CMMRD) and Li-Fraumeni syndrome (LFS). CMMRD results from biallelic germline mutations in one of four mismatch repair genes, and is associated with childhood brain, colorectal and lymphocytic neoplasms. Recent work has shown cancers developing in CMMRD patients to possess recurrent somatic mutations in POLE/POLD1 and massively elevated numbers of somatic point mutations (hypermutation). To assess the frequency, timing, and aetiology of hypermutation in adult and childhood cancer, I performed a comprehensive analysis of hypermutation across >80,000 human cancers. Our work identified CMMRD in 15 patients, resulting in their enrollment on a surveillance protocol and immune checkpoint inhibitor trial, which has shown sustained responses for CMMRD patients. Li-Fraumeni syndrome (LFS) is a cancer predisposition syndrome caused by germline mutations in the TP53 tumor suppressor gene, and is associated with a wide range of cancers, including sarcomas, breast cancers, adrenocortical carcinomas and brain tumours. To investigate somatic mutational events driving tumourigenesis in LFS, I performed whole-genome sequencing (WGS) analysis of bulk and multi-region dissections of tumours derived from childhood and young adult patients with germline TP53 mutations. Our analyses revealed that the life history of LFS cancers is marked by early loss of heterozygosity of TP53, mutational signatures related to homologous recombination repair deficiency and in some cases previous chemotherapeutic treatment. In summary, my thesis research demonstrated CPS-related malignancies are often mutationally and evolutionarily distinct entities. The unique molecular features of these tumours reveal aspects of their aetiology and in some cases can be used to aid in diagnosing and treating these patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".