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Dynamic monitoring of cerebrospinal fluid circulating tumor DNA to identify unique genetic profiles of brain metastatic tumors and to better predict intracranial tumor response in patients with non–small cell lung cancer with brain metastases: A prospective cohort study.

2022· article· en· W4286298705 on OpenAlexaff
Meichen Li, Xue Hou, Jing Chen, Baishen Zhang, Juan Yu, Na Wang, Delan Li, Yang Shao, Dongqin Zhu, Chuqiao Liang, Qiuxiang Ou, Yutong Ma, Likun Chen

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineLiquid biopsyCerebrospinal fluidConcordanceLung cancerInternal medicineCirculating tumor cellBrain tumorCell-free fetal DNAOncologyCancerPrimary tumorCirculating tumor DNAPathologyMetastasisBiology

Abstract

fetched live from OpenAlex

2023 Background: Cerebrospinal fluid (CSF) can be used as a type of liquid biopsy to detect brain tumors. We aimed to explore the genetic profiles of CSF-derived circulating tumor DNA (ctDNA) to predict intracranial tumor response and monitor mutational evolution during systemic treatment in non-small cell lung cancer (NSCLC) patients with brain metastases. Methods: We conducted aprospective study of 92 newly diagnosed NSCLC patients with brain metastases. Paired CSF and plasma samples were collected at baseline, 8 weeks after treatment initiation, and at disease progression. Primary extracranial tumor samples were available for 58 patients and all samples underwent targeted next generation sequencing of 425 cancer-related genes. Results: At baseline, the positive detection rates of ctDNA in CSF, plasma, and extracranial tumors were 63.7% (58/91), 91.1% (82/90), and 100% (58/58), respectively. A high level of heterogeneity was observed between paired CSF and plasma samples, while concordance in driver mutations was also observed. A higher number of unique copy number variations (CNVs) were detected in CSF ctDNA than in plasma. CtDNA-positivity in baseline CSF samples was associated with poor outcomes (HR = 2.565, P = 0.003). Moreover, patients with increased concentrations of ctDNA in CSF after 8 weeks of treatment had significantly shorter intracranial progression-free survivals (PFS) than patients with decreased concentrations of CSF ctDNA (6.13 months vs 13.27 months, HR = 3.92, P = 0.007). Increased concentrations of plasma ctDNA were associated with shorter extracranial PFS (6.13 months vs 11.57 months, HR = 2.626, P = 0.032). From clonal evolution analyses, the accumulation of subclonal mutations in CSF ctDNA was observed after 8 weeks of systemic treatment. The clonal mutations remained more than 80% in CSF after 8-weeks of treatment also predicted a shorter intracranial PFS (HR = 3.785, P = 0.039). Conclusions: CSF ctDNA revealed unique genetic profiles of brain metastases, and dynamic changes in CSF ctDNA could better predict intracranial tumor response and track clonal evolution during treatment in NSCLC patients with brain metastases.

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)0.000
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.347
Teacher spread0.335 · 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
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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Citations1
Published2022
Admission routes1
Has abstractyes

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