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Circulating tumor DNA (ctDNA) detection of molecular residual disease (MRD) as a potential biomarker in localized soft tissue sarcoma (STS).

2022· article· en· W4281672405 on OpenAlexaff
Abdulazeez Salawu, Elizabeth G. Demicco, Peter Chung, Jordan Feeney, Jasmine Lee, Eoghan Ruadh Malone, Charles Catton, Limore Arones, Madeline Phillips, Philip Wong, Jay S. Wunder, Peter C. Ferguson, Stephen B. Willingham, David Shultz, Albiruni Ryan Abdul Razak

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsMount Sinai HospitalPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineInternal medicineOncologyCirculating tumor DNARadiation therapyBiomarkerMinimal residual diseasePopulationCancerGastroenterologyGeneticsBiology

Abstract

fetched live from OpenAlex

11547 Background: Surgery and (neo)adjuvant radiotherapy are the mainstay curative treatments for localized STS. Despite treatment, approximately 50% of STS patients (pts) experience metastatic relapse and routine use of adjuvant systemic therapy (AST) remains controversial. The presence of ctDNA following curative treatment of STS is a potential biomarker for MRD and may identify patients who benefit from AST. Given the genomic heterogeneity of STS, a histology-agnostic approach to ctDNA detection in this population is desirable. Methods: Pts with localized, high risk (size ≥ 5cm, grade ≥ 2) disease were enrolled prior to (neo) adjuvant radiotherapy and surgery. Blood for ctDNA was collected at diagnosis; post-radiotherapy, post-surgery and every 3 months for up to 2 years. Whole exome sequencing (WES) of archival tumor- and matched buffy coat-DNA were carried out to identify somatic variants. Personalized and tumor-informed, multiplex PCR next generation sequencing-based ctDNA assay (Signatera™ assay) was performed on plasma obtained at the serial timepoints. A sample level positive call required ≥ 2 variants above a confidence calling threshold. Absolute ctDNA levels were expressed as mean tumor molecules per milliliter (MTM/ml) of plasma, based on variant allele frequencies and quantity of cell free DNA. Standard radiologic surveillance (every 3 months) was performed following surgery. The primary endpoint was a ctDNA detection rate of 70% at diagnosis. Secondary endpoints included MRD detection and correlation of ctDNA levels with disease relapse. Results: Seventy-six plasma samples from 10 pts [8 males and 2 females; median age 64 years (range 46–84)] were obtained prospectively. STS subtypes were undifferentiated pleomorphic sarcoma (n = 4), myxofibrosarcoma (n = 2), dedifferentiated liposarcoma (n = 2), myxoid liposarcoma (n = 1), and pleomorphic liposarcoma (n = 1). All tumors successfully underwent WES with adequate data quality for Signatera™ assay design. The personalized ctDNA assay was performed on a median of 7 plasma samples per patient (range: 5 – 10). ctDNA was detected in 7 pts (70%) at diagnosis, with median ctDNA level of 1.6 MTM/ml (range: 0.2 – 137.8), achieving the study primary endpoint. Immediate post-surgery samples were negative in all pts. However, ctDNA was detected in 2 out of 2 pts who developed metastatic disease during follow-up. Conclusions: Personalized tumor-informed ctDNA assays in localized high-risk STS at diagnosis are feasible. In this series, all patients had undetectable levels of ctDNA post-surgery and patients who experienced disease relapse demonstrated a detectable rise in ctDNA levels. Further interrogation of this approach for detection of post-treatment MRD as a possible biomarker of benefit from AST is ongoing.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.060
GPT teacher head0.419
Teacher spread0.360 · 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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Citations2
Published2022
Admission routes1
Has abstractyes

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