Risk Assessment With Ultra-Low-Pass Whole-Genome Sequencing of Cell-Free DNA for Large B-Cell Lymphoma
Bibliographic record
Abstract
PURPOSE Although deep targeted DNA sequencing of liquid biopsies has shown prognostic utility in large B-cell lymphoma (LBCL), the routine clinical adoption of these assays remains limited because of their high costs. MATERIALS AND METHODS Here, leveraging a well-annotated cohort encompassing both frontline and relapsed/refractory (R/R) LBCL, we profiled patient plasma samples with two complementary modalities—ultra-low-pass whole-genome sequencing (ULP-WGS) and deep targeted DNA sequencing, the former being a cost-effective method to profile large scale chromosomal abnormalities and estimate tumor burden. RESULTS Our findings revealed a strong association of high cell-free tumor burden by both genomic profiling modalities with established measures of tumor burden and patient survival. Notably, the associations with survival remained statistically significant after accounting for international prognostic index scoring. Furthermore, we showed that del(17p) in circulating tumor DNA as detected by ULP-WGS was strongly associated with TP53 mutation status and predicted for significantly inferior outcome in frontline LBCL patients but not in patients with R/R LBCL. CONCLUSION Our study demonstrates that ULP-WGS can provide robust prognostic biomarkers for both frontline and R/R LBCL, highlighting its broad applicability for risk stratification.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".