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Detecting ctDNA using personalized structural variants to forecast recurrence in localized soft tissue sarcoma (STS).

2025· article· en· W4410800338 on OpenAlexaff
Changsu Lawrence Park, Elizabeth G. Demicco, Karen Howarth, Mitchell J. Elliott, Peter Chung, Jasmine Lee, Núria Seguí, Limore Arones, Madeline Phillips, Völundur Hafstað, Peter C. Ferguson, Samuel Woodhouse, Jay S. Wunder, David Shultz, Abdulazeez Salawu, Albiruni Ryan Abdul Razak

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsMount Sinai HospitalPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineSoft tissue sarcomaSoft tissueSarcomaInternal medicineOncologyPathology

Abstract

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11511 Background: The current standard for definitive management of localized STS involves surgery and (neo)adjuvant radiation (RT). Unfortunately, up to 50% of these patients (pts) recur but the role of adjuvant systemic therapy remains controversial. Circulating tumor DNA (ctDNA) is a promising biomarker for molecular residual disease (MRD) in STS but its clinical validity and utility remains unclear. Given that structural variants (SVs) are prevalent in the tumor genome of STS pts, this longitudinal study aims to utilize an ultra-sensitive, tumor-informed MRD assay that tracks somatic SVs for the detection of ctDNA. Methods: Pts with newly diagnosed, localized, high-risk (≥ 5cm, grade ≥2) STS planned for curative-intent (neo)adjuvant RT and surgery were recruited from Feb 2019 to Aug 2023. Blood samples for ctDNA analyses were collected at diagnosis, post RT, post-surgery and every 3 months for up to two years in tandem with radiologic surveillance. The MRD window was defined as the first 8 weeks after surgery. Whole genome sequencing (WGS) was performed on archival tumor samples to detect all genomic SVs. A personalized multiplex digital PCR assay was then designed based on WGS data to track up to 16 somatic SVs in cell-free DNA from serial plasma samples for ctDNA detection and quantification. ctDNA data was then correlated to clinical outcomes (last updated on Jan 2025). Results: A total of 228 plasma samples from 32 pts were analyzed with a median follow-up of 20.1 months. STS subtypes included myxofibrosarcoma (12), undifferentiated pleomorphic sarcoma (10), dedifferentiated liposarcoma (6), pleomorphic liposarcoma (2), myxoid liposarcoma (1) and leiomyosarcoma (1). The ctDNA detection rate at diagnosis was 97% (31/32 pts). Of the cohort, 22 pts received preoperative RT and had blood collected within the MRD window. ctDNA was detectable at baseline and in the MRD window in 4/22 pts (18%). All 4 (100%) developed metastatic disease with a median lead time of 136 days (range: 28-210 days) in ctDNA detection prior to radiologic relapse. Of the 18 pts who were ctDNA-negative in the MRD window, 3 (17%) developed metastatic recurrence, all of which was preceded by detectable ctDNA with a median lead time of 87 days (range: 80-147 days). The median time from surgery to recurrence was 153 days (range: 57-224 days) vs 521 days (range: 406-631 days) for pts with detectable vs undetectable ctDNA within the MRD window, respectively. Conclusions: Detection of ctDNA using personalized tumor-informed assays for somatic SV tracking was feasible and highly sensitive in localized high-risk STS pts prior to surgery. Positive ctDNA within the MRD window was predictive of subsequent and earlier radiologic relapse. Based on this data, an interception trial of adjuvant systemic therapy for MRD-positive STS pts is planned. Future analysis, including the measurement of circulating extrachromosomal DNA (ecDNA) is planned. Clinical trial information: NCT03818412 .

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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.148
GPT teacher head0.499
Teacher spread0.351 · 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
Published2025
Admission routes1
Has abstractyes

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