Applications of massively parallel sequencing technology in the evaluation of haematological malignancies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Massively parallel sequencing (MPS) technology has revolutionised the genomic exploration of human disease.This is especially true in the case of cancer, which is primarily driven by the development of acquired genomic aberrations.The body of work described within this thesis represents a broad yet in-depth array of novel applications of MPS technology in the evaluation of haematological malignancies.This field is currently surging in relevance and clinical utility as the ongoing movement of MPS technology from the research to the routine diagnostic setting continues to facilitate the development of increasingly personalised medicine.High impact contributions have been made in a number of areas encompassing myeloid and lymphoid malignancies as well as haematological malignancies as a collective.Key achievements include: quantifying the risk of incidentally detecting germline variants of potential clinical significance during unpaired MPS testing of cancer samples; definitively proving that ASXL1 NM_015338.5:c.1934dup;p.Gly646Trpfs*12 is a true somatic alteration and developing an accurate and sensitive assay for its detection; exploring the pathogenesis of and mechanisms of resistance to histone deacetylase inhibitors in cutaneous T-cell lymphomas as well as defining the clinical features, outcomes and genomic landscape of transformed marginal zone lymphoma.This thesis represents a diverse portfolio of novel research with a strong translational focus.Despite the wide scope of the individual lines of inquiry described herein there is a common thread that binds the narrative together: the pursuit of innovative yet practical ways of utilising the powerful technology now available to improve the genomic characterisation of haematological malignancies and ultimately the lives of the patients and families they affect.vi
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it