Circulating tumor DNA (ctDNA) detection of molecular residual disease (MRD) as a potential biomarker in localized soft tissue sarcoma (STS).
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".