Relapse in children with acute lymphoblastic leukemia involving selection of a preexisting drug-resistant subclone
Bibliographic record
Abstract
Relapse following remission induction chemotherapy remains a barrier to survival in approximately 20% of children suffering from acute lymphoblastic leukemia (ALL). To investigate the mechanism of relapse, 27 matched diagnosis and relapse ALL samples were analyzed for clonal populations using polymerase chain reaction (PCR)-based detection of multiple antigen receptor gene rearrangements. These clonal markers revealed the emergence of apparently new populations at relapse in 13 patients. More sensitive clone-specific PCR revealed that, in 8 cases, these "relapse clones" were present at diagnosis and a significant relationship existed between presence of the relapse clone at diagnosis and time to first relapse (P < .007). Furthermore, in cases where the relapse clone could be quantified, time to first relapse was dependent on the amount of the relapse clone at diagnosis (r = -0.84; P = .018). This observation, together with demonstrated differential chemosensitivity between subclones at diagnosis, argues against therapy-induced acquired resistance as the mechanism of relapse in the informative patients. Instead these data indicate that relapse in ALL patients may commonly involve selection of a minor intrinsically resistant subclone that is undetectable by routine PCR-based methods. Relapse prediction may be improved with strategies to detect minor potentially resistant subclones early during treatment, hence allowing intensification of therapy.
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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 | 0.002 |
| 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".