Mutational Landscape and Tumor Burden Assessed by Cell-free DNA in Diffuse Large B-Cell Lymphoma in a Population-Based Study
Bibliographic record
Abstract
Abstract Purpose: We analyzed the utility of cell-free DNA (cfDNA) in a prospective population-based cohort to determine the mutational profile, assess tumor burden, and estimate its impact in response rate and outcome in patients with diffuse large B-cell lymphoma (DLBCL). Experimental Design: A total of 100 patients were diagnosed with DLBCL during the study period. Mutational status of 112 genes was studied in cfDNA by targeted next-generation sequencing. Paired formalin-fixed, paraffin-embedded samples and volumetric PET/CT were assessed when available. Results: Appropriate cfDNA to perform the analyses was obtained in 79 of 100 cases. At least one mutation could be detected in 69 of 79 cases (87%). The sensitivity of cfDNA to detect the mutations was 68% (95% confidence interval, 56.2–78.7). The mutational landscape found in cfDNA samples was highly consistent with that shown in the tissue and allowed genetic classification in 43% of the cases. A higher amount of circulating tumor DNA (ctDNA) significantly correlated with clinical parameters related to tumor burden (elevated lactate dehydrogenase and β2-microglobulin serum levels, advanced stage, and high-risk International Prognostic Index) and total metabolic tumor volume assessed by PET/CT. In patients treated with curative intent, high ctDNA levels (>2.5 log hGE/mL) were associated with lower complete response (65% vs. 96%; P < 0.004), shorter progression-free survival (65% vs. 85%; P = 0.038), and overall survival (73% vs. 100%; P = 0.007) at 2 years, although it did not maintain prognostic value in multivariate analyses. Conclusions: In a population-based prospective DLBCL series, cfDNA resulted as an alternative source to estimate tumor burden and to determine the tumor mutational profile and genetic classification, which have prognostic implications and may contribute to a future tailored treatment.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".