Abstract 5112: Investigating the use of circulating tumor DNA for sarcoma management
Bibliographic record
Abstract
Abstract Circulating tumor DNA (ctDNA) has the potential to detect sarcoma recurrence and metastasis but requires highly sensitive methods to detect and quantify genetic variants present in very low quantities. Plasma was isolated from 20mL peripheral blood samples collected from over 400 pre-operative sarcoma patients, and matched tumor samples from surgical resection were frozen and stored. Cell-free DNA (cfDNA) extracted from plasma was quantified using qPCR, and the quality was assessed using capillary electrophoresis. A subset of these cases were selected for whole exome sequencing (WES). WES of bulk tumor and whole blood samples identified tumor-specific genetic alterations, which then serve as personalized biomarkers of tumor DNA in patient plasma. We previously showed that droplet digital PCR (ddPCR) can detect and quantify ctDNA, by targeting patient-specific variants. However, ddPCR is limited in that it can only investigate one tumor variant sequence at a time. The purpose of the present study is to investigate methods of targeting multiple tumor variants simultaneously, increasing the chances of detecting ctDNA in patient blood. To this end, four cases were selected for multiplex PCR (mPCR) followed by targeted amplicon sequencing. For each case, six to eight of the tumor variants identified by WES were selected as targets, and primers were designed to amplify these sequences concurrently by mPCR. The amplicons will then be sequenced to detect the tumor variants. Additionally, two of the four cases have plasma collected at two different time points. To assess the viability of this method as a way to monitor disease surveillance, these cfDNA samples will be compared to determine how the abundance and nature of ctDNA changes over time. To date, cfDNA has been extracted from over 100 cases, the majority of which were positive for cfDNA. For each of the cases whole exome sequenced, a variety of tumor-specific variations were identified. The variants chosen as targets were selected based on having the highest variant allele frequency (VAF), with priority being given to mutations that alter the protein coding sequence. Thus far, mPCR primers have been designed and optimized for four separate cases. Across all cases analyzed by amplicon sequencing, the variant sequences could be detected in the amplicons generated by mPCR of tumor DNA. Furthermore, amplicon sequencing was able to recapitulate the variant allele frequency observed in WES. This indicates that the mPCR successfully amplified the sequences of interest in the tumor DNA, and that the sequencing results are accurate. Furthermore, no tumor variants were detected in the amplicons generated from blood DNA, which is to be expected. The cfDNA amplicons for these cases will be sequenced in this manner to investigate the presence of ctDNA. If successful, the ability to detect ctDNA in plasma will be an important first step in developing a testing protocol for clinical use. Citation Format: Paige Darville-O'Quinn, Nalan Gokgoz, Kim M. Tsoi, Jay S. Wunder, Irene L. Andrulis. Investigating the use of circulating tumor DNA for sarcoma management [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5112.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".