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

2024· article· en· W4399629256 on OpenAlexafffund
Aisha Alshibany, Maggie Zhou, Emilie A. K. Warren, Kristen N. Ganjoo, Elizabeth G. Demicco, Jordan Feeney, Jasmine Lee, David Shultz, Erik Spickard, Nam Q. Bui, Kenneth Cardona, Abdulazeez Salawu, Albiruni Ryan Abdul Razak

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsUniversity of TorontoMount Sinai HospitalPrincess Margaret Cancer CentreUniversity Health Network
FundersUniversity Health Network
KeywordsMedicineCirculating tumor DNASoft tissue sarcomaMinimal residual diseaseSoft tissueSarcomaBespokeCancer researchDNAPathologyOncologyInternal medicineCancerBone marrowBiologyGenetics

Abstract

fetched live from OpenAlex

11537 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: This multicentre prospective study enrolled patients with localized, high-risk (grade ≥ 2, size ≥ 5 cm) STS. Blood samples were collected at diagnosis, post- radiotherapy, post-surgery, and at serial longitudinal time points for up to 2 years. Standard radiologic follow-up was performed concurrently. Whole exome sequencing of tumor tissue was carried out to identify patient-specific, single nucleotide variants. Personalized, and tumor-informed multiplex PCR next-generation sequencing-based ctDNA (Signatera) assays were used to track ctDNA in serial plasma samples. The primary endpoint was ctDNA detection rate of >70% at diagnosis. Secondary endpoints were MRD detection after local therapy and correlation of ctDNA detection with disease relapse. Results: A total of 76 subjects (female n = 43; median age [range]: 58 [21- 84] years) were included in this study from Princess Margaret Cancer Center, Stanford University, and Emory University. The most common STS types observed were leiomyosarcoma (LMS, n = 28), liposarcoma (n = 14), (predominantly dedifferentiated [n = 6]), and myxofibrosarcoma (n = 11). Among 38 pts who had blood samples collected at baseline (time of surgery), ctDNA was positive in 30/38 (79%). A baseline sample prior to radiotherapy was collected in 24 pts. Following neoadjuvant radiotherapy, 8/24 pts (33%) who were ctDNA positive at baseline became ctDNA negative, while 29/30 became ctDNA-negative after surgical resection. Median follow-up was 19 months, and 19/76 pts (25%) experienced disease recurrence Among these 19 pts, ctDNA was detected in all pts with baseline studies. ctDNA was detected at or before radiologic recurrence in 9/19 pts (47%), predominantly LMS 4/9, with a median lead time of 64.8 days (range: 0-197 days). ctDNA was detected in 14/38 patients at baseline with no radiological recurrence during follow-up period. Conclusions: Personalized, tumor-informed ctDNA assays can detect MRD and has prognostic value after definitive therapy, surgery and (neo)adjuvant radiotherapy, in localized STS patients. Additional studies are ongoing to evaluate ctDNA as a predictive biomarker for benefit from AST in STS. 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)0.002
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 score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.102
GPT teacher head0.466
Teacher spread0.364 · 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
Published2024
Admission routes2
Has abstractyes

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