WHOLE GENOME SEQUENCING OF MATCHED PRIMARY AND RELAPSED DLBCL REVEALS DISTINCT EVOLUTIONARY DYNAMICS ASSOCIATED WITH RELAPSE TIMING
Bibliographic record
Abstract
DNTM3A (4/10), TP53 (2/10), ASXL1 (1/10) and CBL (1/10).CHIP was associated with age in newly diagnosed DLBCL (p <0.01), but not IPI Score, tumor volume and tumor DNA (ctDNA) level in plasma.Strikingly, previously untreated pts with CHIP had a significant inferior overall (OS) and event-free survival (EFS) (p = 0.048 and p = 0.024 respectively) (Figure 1A) following immunochemotherapy with curative intent.In contrast, no such impact of CHIP was observed in rrDLBCL treated with Axicabtagene Ciloleucel (p = 0.99 and p = 0.35, respectively).CHIP was associated with lower DLBCL response rates: among evaluable pts without CHIP, 98% achieved a complete or partial remission (CR/PR) compared to only 82% in pts with CHIP (p = 0.05).Of note, therapy-related myelosuppression appeared similar in newly diagnosed DLBCL pts with and without CHIP, as we observed no significant difference in transfusion needs or infection rates (Figure 1C,D,E). Conclusions:In this study CHIP mutations were detected in 12.4% of DLBCL pts.While CHIP is associated with lower response rates and inferior survival outcomes after frontline cytotoxic therapy of DLBCL, this affect appears context-specific, since it was not observed in the rrDLBCL pts treated with CART19.Of interest, the mutational CHIP landscape differed between our 2 cohorts, suggesting that cytotoxic therapy shapes CHIP evolution.
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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 | 0.001 |
| 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) | 0.001 | 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".