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Record W7132981103

Clinical, Molecular, and Management Perspectives on Replication Repair Deficiency Syndromes

2025· dissertation· W7132981103 on OpenAlexafffund
Ayse B. Ercan

Bibliographic record

VenueTSpace · 2025
Typedissertation
Language
FieldMedicine
TopicGenetic factors in colorectal cancer
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchStand Up To CancerFondation Brain CanadaHospital for Sick ChildrenMinisterstvo Zdravotnictví Ceské RepublikyGarron Family Cancer CentreBristol-Myers SquibbV Foundation for Cancer Research
KeywordsDNA mismatch repairCancerGenetic testingGenomePathognomonicGenomics
DOInot available

Abstract

fetched live from OpenAlex

Faithful replication of the human genome is essential for maintaining genomic stability, a process upheld by DNA polymerase and the mismatch (MMR) system. Defects in these mechanisms lead to replication repair deficiency (RRD), a major driver of hypermutant cancers in humans. These malignancies exhibit distinct genomic characteristics, are often resistant to conventional chemotherapies, yet demonstrate susceptibility to immune-based therapies. Constitutional mismatch repair deficiency (CMMRD) is an aggressive pediatric cancer predisposition syndrome caused by biallelic pathogenic variants in the MMR genes (MLH1, MSH2, MSH6, PMS2). Patients with CMMRD are susceptible to multisystem cancers, primarily brain, gastrointestinal and hematopoietic malignancies, alongside diverse non-malignant manifestations that resemble other genetic disorders such as neurofibromatosis type 1. A majority of patients succumb to malignant brain tumors prior to reaching young adulthood. Despite its severity, CMMRD remains underrecognized and current knowledge on it is largely derived from small cohort studies and literature reviews. The absence of a well-defined clinical phenotype, comprehensive spectrum and genomic profile of CMMRD cancers, genotype-phenotype correlations, and evidence-based management strategies contributes to diagnostic delays and poor outcomes. This thesis aims to address these gaps by providing the most comprehensive analysis of the clinical and biological landscape of CMMRD to date, leveraging data from the International Replication Repair Deficiency Consortium (IRRDC) and its global collaborators. Chapter 1 highlights significant findings on replication repair deficiency, diagnostic and management strategies, current challenges as well as recent and emerging approaches. Chapter 2 presents the largest international cohort of CMMRD patients, defining key clinical and biological pathognomonic features, characterizing the mutational landscape of CMMRD-driven cancers, uncovering novel genotype-phenotype associations, and its implication for current management strategies. Chapter 3 systematically evaluates the effectiveness of surveillance guidelines on early detection of malignancies and low-grade tumors, and its impact on overall survival in CMMRD patients. This provides critical evidence to optimize future surveillance strategies for these patients. Chapter 4 focuses on the significance of this work, potential limitations, and future directions. Collectively, this doctoral thesis advances understanding of CMMRD pathophysiology, the mutagenic processes underlying these cancers, and lays the foundation for precision-guided approaches in diagnosis, surveillance and treatment of affected individuals.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.002

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.

Opus teacher head0.037
GPT teacher head0.414
Teacher spread0.377 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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