Evolution of Human BCR-ABL1 lymphoblastic Leukaemia-Initiating Cells.
Bibliographic record
Abstract
Abstract Abstract 1023 Many tumour types are composed of genetically diverse cells, however little is known of how diversity evolves or the impact that diversity has on functional properties. Here, using xenografting and DNA copy number alteration (CNA) profiling of human BCR-ABL1 acute lymphoblastic leukaemia, we demonstrate that genetic diversity occurs in functionally defined leukaemia-initiating cells and that many diagnostic patient samples contain multiple genetically distinct subclones. Reconstruction of the subclonal genetic ancestry of several samples by CNA profiling demonstrated a branching multiclonal evolution model of leukaemogenesis, rather than linear succession. For some patient samples, the predominant diagnostic clone repopulated xenografts, while in others it was outcompeted by minor subclones. Reconstitution with the predominant diagnosis clone was associated with more aggressive growth properties in xenografts, deletion of CDKN2A/CDKN2B, and poor patient outcome. Our findings link clonal diversity with function and underscore the importance of developing therapies that eradicate all subclones. Disclosures: No relevant conflicts of interest to declare.
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 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 | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".