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Record W3123323752 · doi:10.1002/ctm2.236

Association of circulating tumor DNA from the cerebrospinal fluid with high‐risk CNS involvement in patients with diffuse large B‐cell lymphoma

2021· letter· en· W3123323752 on OpenAlexaffabout
Xiaoxiao Wang, Yan Gao, Changguo Shan, Mingyao Lai, Haixia He, Bing Bai, Liqin Ping, Qixiang Rong, Ruyu Ai, Lei Wen, Zhaoming Zhou, Ruoying Yu, Qiuxiang Ou, Xue Wu, Xiaoxia Wang, Yang Shao, Linbo Cai, Huiqiang Huang

Bibliographic record

VenueClinical and Translational Medicine · 2021
Typeletter
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsOntario Power Generation
FundersNatural Science Foundation of Guangdong ProvinceNational Natural Science Foundation of China
KeywordsDiffuse large B-cell lymphomaMedicineCerebrospinal fluidLiquid biopsyLymphomaPathologyOncologyInternational Prognostic IndexInternal medicineOncogeneCancer researchCancerCell cycle

Abstract

fetched live from OpenAlex

Association of circulating tumor DNA from the cerebrospinal fluid with high-risk CNS involvement in patients with diffuse large B-cell lymphomaDear Editor, Central nervous system (CNS) involvement in diffuse large B-cell lymphoma (DLBCL) patients correlates with dismal outcomes, and the detection sensitivity of conventional diagnosis of lymphoma is restricted.[1][2][3][4] Circulating tumor DNA from cerebrospinal fluid (CSF-ctDNA) has played an important part in the application of liquid biopsy for patients with CNS cancers.5 In this study, we provided new insights into feasibility of CSF-derived biomarkers for CNS relapse diagnosis in DLBCL patients.In clinical setting, the diagnosis of CNS involvement is based on several clinical risk factors including individual international prognostic index (IPI), number of extranodal involvement (testicular/adrenal/kidney), and serum lactate dehydrogenase (LDH).6 CNS-IPI, which is a six-risk-factor model developed by a German group (five IPI factors with kidney/adrenal involvement) for CNS diagnosis, has been validated and proved to be useful in clinical settings.7 Other reported biological risk factors for CNS involvement included MYC gene rearrangements or MYC (MYC proto-oncogene) and BCL2 (B-cell lymphoma 2) dual translocations.8,9 To assess the correlation between CSF-ctDNA and CNS involvement in DLBCL, targeted mutational profiling was performed on CSF-and plasma-derived ctDNA together with matched systemic tumor tissues in 67 DLBCL patients clinically diagnosed as high risk for CNS involvement (Figure S1, Table S1).Genomic landscape of this DLBCL cohort in systemic tumor tissue is shown in Figure 1A.Considering both single nucleotide variant (SNV) and copy number variant (CNV), commonly mutated genes cohort included Pim-1 proto-oncogene (PIM1, 37.3%), lysine methyltransferase 2D (KMT2D,33.3%),BCL2 (27.5%), myeloid differentiation primary response 88 (MYD88, 27.5%), B-cell translocation gene 2 (BTG2, 23.5%), and tumor protein p53 (TP53, 23.5%).Majority of altered genes were involved in four important pathways including epigenetic regulation

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.019
GPT teacher head0.255
Teacher spread0.236 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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".

Quick stats

Citations18
Published2021
Admission routes2
Has abstractyes

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