Genomic data in prognostic models—what is lost in translation? The case of deletion 17p and mutant<i>TP53</i>in chronic lymphocytic leukaemia
Bibliographic record
Abstract
Genomic technologies are revolutionizing the practice of haematology-oncology, leading to improved disease detection, more accurate prognostication and targeted treatment decisions. These advances, however, have also introduced new clinical challenges, which include problems of prognostic underdetermination and its attendant risks of over- and undertreatment. Genomic data is generated from different technologies, from cytogenetics to next-generation sequencing, which are often interpreted interchangeably and in a binary fashion-as the presence or absence of a given chromosomal deletion or mutation-an oversimplification which may lead to mistaken prognosis. We discuss the clinical use of one such prognostic marker, represented by sequence and copy number alterations in TP53, located on chromosome 17p. Mutations in TP53 are strongly linked to poor prognosis in a variety of haematological malignancies, including chronic lymphocytic leukaemia (CLL). We review studies in CLL which utilize the 17p deletion or TP53 mutations for prognostic stratification with specific focus on the technologies used for detection, the thresholds established for clinical significance, and the clinical contexts in which these alterations are identified. The case of CLL illustrates issues arising from simplistic, binary interpretation of genetic testing and highlights the need to apply a critical lens when incorporating genomics into prognostic models.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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.
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".