Circulating Tumor DNA in Aggressive B-Cell Lymphomas: Tumor Cell Characterization and Disease Dynamics
Bibliographic record
Abstract
B-cell lymphomas represent a heterogeneous group of malignancies, with diffuse large B-cell lymphoma (DLBCL) being the most common and aggressive subtype. Aggressive B-cell lymphomas may arise de novo, through transformation from indolent lymphomas such as marginal zone lymphoma (MZL), or in specific clinical contexts such as post-transplant lymphoproliferative disorders (PTLD) and relapsed/refractory DLBCL (R/R DLBCL). This thesis investigates the biological mechanisms underlying lymphoma progression and transformation, and evaluates the utility of circulating tumor DNA (ctDNA) as a biomarker across various aggressive B-cell lymphoma subtypes. Clinical and molecular analyses identified risk factors for MZL transformation and revealed that transformed MZL frequently acquires features of germinal center B cells. Multi-omics approaches showed only subtle genomic and transcriptomic changes during transformation. Across subtypes, ctDNA emerged as a promising non-invasive biomarker for diagnosis, prognosis, and disease monitoring. In PTLD, ctDNA profiling revealed recurrent genetic alterations and closely reflected tumor characteristics and guide treatment decisions. In R/R DLBCL, a high ctDNA tumor fraction was associated with poor prognosis. Persistent ctDNA mutations or copy number alterations provided complementary diagnostic value when combined with PET-CT, supporting their integrated use in assessing disease progression and guiding treatment decisions. Together, these findings underscore ctDNA as a clinically informative biomarker across aggressive B-cell lymphoma subtypes, with potential to improve risk stratification and therapeutic guidance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".