The Investigation of Genomic Microsatellite Indel Signatures in Replication Repair Deficient Germline and Cancerous Tissues
Bibliographic record
Abstract
Replication repair deficiency (RRD) is the inactivation of either/both of the mechanisms that repair mistakes during DNA synthesis, the mismatch repair (MMR) system and polymerase proofreading. RRD is associated with different malignancies at all stages throughout life, and among the different forms, one that is particularly aggressive is Constitutional Mismatch Repair Deficiency (CMMRD). It is characterized by the germline biallelic loss of one of the four MMR genes, MSH2, MSH6, MLH1, and PMS2. Individuals with CMMRD have no MMR activity from conception, and develop cancers early in life, primarily in the brain, GI, and haematopoietic systems. These cancers are commonly hypermutated (>10 mut/Mb), and the analysis of their mutations led to insights on their etiology, mutational kinetics, and signatures. Conversely, there is another category of mutations that is associated with RRD but has not been investigated in CMMRD tissues, called microsatellite insertion/deletions (MS-indels). These MS-indels are canonically known to be repaired by the MMR system, and are highly prevalent in adult MMR-deficient (MMRD) cancers. This thesis presents a comprehensive, genomic analysis of MS-indels in RRD tissues, and the associated biological discoveries and clinical implications. Chapter 1 highlights important findings in the pertinent fields of replication repair, microsatellites, modern therapies/prevention, and current challenges with diagnosis of RRD. Chapter 2 describes the methods used in this thesis, including bioinformatic pipelines, statistical analyses, as well as any laboratory experiments that were included in the subsequent chapters. Chapter 3 describes the landscape of MS-indels in RRD cancers, defining novel MS-indel signatures (MS-sigs) that uncover important biological questions. Chapter 4 details the clinical use of MS-sigs in the diagnosis of RRD in cancer, and its efficacy in predicting the response of RRD tumours to immune checkpoint inhibitors (ICI). Chapter 5 outlines the ability of MS-sigs to detect RRD in the germline, and clarifies the heterogeneity of MSI in CMMRD individuals. Chapter 6 is focused on the future directions of this work, and discusses the potential limitations to the above studies. This thesis clarifies the association between MS-indels and RRD, and describes how microsatellite mutations can impact the treatment and clinical management of individuals with RRD.
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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 | 0.001 |
| 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 | 0.001 |
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".