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Molecular residual disease (MRD) detection using bespoke circulating tumor DNA (ctDNA) assays in localized soft tissue sarcoma (STS).

2023· article· en· W4379282163 on OpenAlexaff
Abdulazeez Salawu, Elizabeth G. Demicco, Peter Chung, Jordan Feeney, Erik Spickard, Richa Rathore, Jasmine Lee, Philip Wong, Eoghan Ruadh Malone, Charles Catton, Limore Arones, Madeline Phillips, Peter C. Ferguson, Jay S. Wunder, Himanshu Sethi, Minetta C. Liu, David Shultz, Albiruni Ryan Abdul Razak

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsUniversity of TorontoMount Sinai HospitalPrincess Margaret Cancer CentreUniversity Health Network
FundersNateraConquer Cancer Foundation
KeywordsMedicineCirculating tumor DNAInternal medicineOncologyAdjuvantMinimal residual diseasePopulationCancerClinical endpointSarcomaGastroenterologyPathologyClinical trialLeukemia

Abstract

fetched live from OpenAlex

11509 Background: Surgery and (neo)adjuvant radiotherapy (RT) are the mainstay curative treatments for localized STS. Despite treatment, up to 50% of STS patients experience metastatic relapse, and routine use of adjuvant systemic therapy (AST) remains controversial. The presence of ctDNA following curative-intent treatment of STS is a potential biomarker for MRD and may identify patients who are likely to benefit from AST. Given the genomic heterogeneity of STS, a histology-agnostic approach to ctDNA detection in this population is desirable. Methods: Patients (pts) with localized, high risk (size ≥ 5cm, grade ≥ 2) STS were enrolled prior to (neo)adjuvant RT and surgery. Pts who received (neo)adjuvant systemic treatment were excluded. Blood samples for ctDNA analysis were collected at diagnosis, post-RT, post-surgery and every 3 months for up to 2 years. Whole exome sequencing (WES) of archival tumor and matched normal were carried out to identify patient-specific, somatic, single nucleotide variants. Personalized and tumor-informed, multiplex PCR next generation sequencing-based ctDNA (Signatera) assays were then developed to track ctDNA in serially collected plasma samples. ctDNA levels were expressed as mean tumor molecules per milliliter (MTM/ml) of plasma. Radiologic surveillance was performed every 3 months following surgery. The primary endpoint was a ctDNA detection rate of >70% at diagnosis. Secondary endpoints included MRD detection after local therapy and correlation of ctDNA levels with disease relapse. Results: A total of 140 plasma samples from 22 pts [18 male and 4 female; median age: 65 years, range: 30 – 84] were obtained. RT was preoperative in 19/22 pts. Of the 22 tumor samples, 20 had adequate tissue quality for WES to enable ctDNA assay design. Tumor histologic subtypes included undifferentiated pleomorphic sarcoma (n = 6), myxofibrosarcoma (n = 5), and liposarcoma (n = 9). A median of 7 plasma samples per patient (range: 2 – 10) were analyzed. ctDNA was detected in 80% of pts (16/20) at diagnosis, with median ctDNA level of 3.4 MTM/mL (range: 0.2 – 1393.9). Of these 16 pts, 15 (94%) became ctDNA negative at the immediate post-surgical timepoint. In addition, ctDNA was detected in 4 pts (80%) prior to or around radiologic relapse with a median lead time of 92 days. Conclusions: Personalized, tumor-informed ctDNA assays can detect MRD after definitive local therapy and/or prior to radiologic recurrence in patients with localized high-risk STS. As such, serial ctDNA monitoring provides prognostic value and may further identify patients that will benefit from AST treatment. Additional studies evaluating ctDNA as a predictive biomarker for AST benefit are 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)0.001
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0010.000
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 head0.067
GPT teacher head0.417
Teacher spread0.350 · 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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Citations5
Published2023
Admission routes1
Has abstractyes

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