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Record W4411356627 · doi:10.1002/hon.70094_422

422 | MOLECULAR FEATURES ENCODED IN THE DYNAMIC ctDNA MONITORING REVEAL PROGNOSTIC VALUE, DIFFERENT CLINICAL COURSES AND CLONE EVOLUTION FOR DIFFERENT GENETIC SUBTYPES OF DLBCL

2025· article· en· W4411356627 on OpenAlexfundno aff
Jie Liang, Yao Wu, Wei Xu

Bibliographic record

VenueHematological Oncology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchJournal of Gastroenterology and Hepatology FoundationIncyteQuébec Consortium for Drug DiscoveryGilead Sciences
Keywordsclone (Java method)Value (mathematics)Somatic evolution in cancerOncologyComputational biologyBiologyInternal medicineMedicineGeneticsComputer scienceGeneCancerMachine learning

Abstract

fetched live from OpenAlex

Introduction: The ctDNA-based minimal residual disease (MRD) status at the end of treatment (MRDend) has been demonstrated as even stronger prognostic marker in both clinical trial and the real-world population for DLBCL patients (pts). However, no data has been shown (i) The clinical course embedded in the different genetic subtypes of DLBCL, (ii) Clonal evolution possessed in serial tissue and plasma samples. Methods: We enrolled 164 DLBCL pts from our center undergoing first line (1L) therapy for ctDNA profiling at 2 pre-defined milestones (matched baseline and end of treatment (EOT) plasma samples) using a 475 gene lymphoma-specific sequencing panel which had been detailed decribed in our previous work (Jinhua Liang, Leukemia). By the last visit in December 2024, the median follow-up duration was 25.4 (range, 14.9−43.7) months. All pts received R-CHOP regimens. Results: Among the 164 pts, the median age was 57 years, 57.3% had stage III−IV disease and 42.1% had IPI scores 3−5. Among the 164 pts, 46 pts (28.1%) were defined as MRDend positivity (MRDend+) (Figure 1A). The LymphGen subtype classification according to plasma and tissue were shown in Figure 1B. The MRDend− ratio in different genetype was shown in Figure 1C. EOT-MRD status is associated with worse PFS and OS (p < 0.001). Among the EOT-CR patients (N = 131), MRDend+ pts had trend towards worse PFS (p = 0.069) (Figure 1D-E). After analyzing the disease course of all pts according to the genetic subtypes, we found that the clinical problem for TP53 disruption pts is primary refractory, rather than relapse. However, MCD subtype that has poor prognosis because of a persistent high risk of relapse despite reaching CR while the clinical problem for BN2 pts is primary refractory and high rate of relapse (Figure 1F). MRDend negativity was unstable and prone to relapse for BN2 and MCD subtype patients (Figure 1G). The EOT top gene alterations (GAs) for MRDend+ pts was TP53mut (34.8%) which were significantly different from baseline plasma mutation profiling (Figure 1H). Firstly, we found that most EOT GAs for the MRDend+ pts were the predominant gene at baseline according the serial plasma samples (Figure 1I). However, 37 pts had additional GAs (Figure 1J) which were enriched in cell differentiation, cell cycle and TP53 disruption pathways (Figure 1L) in PD plasma samples in comparison with pretreatment plasma samples and the detailed number were shown in Figure 1K. Among the 38 pts of TP53mut, 18 pts were MRDend+ (47.4%); among these 18 MRDend+ pts, TP53mut was not cleared in 11 pts (61.1%) (Figure 1M). Conclusions: These data demonstrate that: (i) Different clinical courses were shown in different genetic subtypes for further personalized subtype-specific clinical trial design; (ii) TP53mut was the first GA which can not be cleared by the 1L induction therapy; (iii) New additional GAs in sequenced PD plasma samples were enriched in cell differentiation, cell cycle and TP53 disruption pathways. Research funding declaration: No funding disclosure Encore Abstract: Regional or national meetings with up to 1000 attendees Keywords: liquid biopsy; minimal residual disease; aggressive B-cell non-Hodgkin lymphoma No potential sources of conflict of interest.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.530

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.341
Teacher spread0.326 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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