Abstract 1984: Investigating circulating tumor DNA as a biomarker of cancer progression and recurrence in sarcoma
Bibliographic record
Abstract
Abstract Objective: Circulating tumour DNA (ctDNA) is an active area of research in many types of cancer due to its value as a non-invasive diagnostic tool. Despite making up a small fraction of total cell-free DNA (cfDNA), detection of ctDNA has been made possible given advances in next generation sequencing and Digital Droplet PCR (ddPCR). CtDNA testing has some key advantages compared to tissue biopsy; ease of repetition provides a means of real-time monitoring of treatment response and signs of recurrence in cancer. This has specific relevance in sarcoma, where early detection of recurrence may provide an opportunity for faster intervention and can potentially lead to better prognostic outcomes. The purpose of this study is to determine if ctDNA is detectable in plasma samples collected from sarcoma patients and to validate our pre-analytical procedures and to identify opportunities to optimize our protocols for future use. Methods: 20 ml of blood samples were collected from 270 soft tissue sarcoma and osteosarcoma patients with or without pre-operative adjuvant treatment and processed to separate the plasma. Cf DNA was isolated from 2ml of plasma and the quantity and quality assessment was performed by qRT-PCR and capillary electrophoresis, respectively. Whole exome sequencing (WES) was performed on 6 matched tumor-blood DNA samples to identify tumour specific single nucleotide alterations. Sequence variants identified from WES analysis were used to design primers and probes to detect the tumor specific alterations in the corresponding ctDNA in those 6 cases using ddPCR. Results: Of the 60 patient samples analysed to date, the majority were positive for cfDNA. Quality assessment of 41cfDNA by capillary electrophoresis showed peaks approximately 170 bp in size, characteristic of cfDNA. WES of 6 patients' matched tumor-blood samples identified many tumor specific variants. Six of those sequence variants on SMAD4, COL19A1, DDX3X, ADGRG4, HECW1 and FOXR2 genes (Table 1) were used to design primers and probes to detect the tumour specific alterations in the corresponding ctDNA using dd PCR. We observed the presence tumour specific mutations in all corresponding ctDNAs tested in this study. Table 1:Identification of tumour specific variants by WESCaseGeneSNVSarcoma SubtypeVariant Allele Frequency1SMAD4T1584CMyxofibrosarcoma86%2COL19A1G1252AUndifferentiated pleomorphic sarcoma50%3DDX3XA1038COsteosarcoma54%4ADGRG4T6361CLiposarcoma65%5HECW1C1874AMyxofibrosarcoma25%6FOXR2C130GMyxofibrosarcoma24% Conclusions: Our ongoing study to evaluate the use of ctDNA as a biomarker in sarcoma monitoring shows promise related to the efficiency of our protocols and assays. Further testing is ongoing to assess the clinical value in sarcoma. Citation Format: Nalan Gokgoz, Paige Darville-O'Quinn, Ainaz Malekoltojari, Patrick Prochazka, Peter C. Ferguson, Jay S. Wunder, Irene L. Andrulis. Investigating circulating tumor DNA as a biomarker of cancer progression and recurrence in sarcoma [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1984.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".