Abstract 6700: Characterizing circulating tumor DNA release kinetics and fragment length in a chemotherapy resistant model of esophageal adenocarcinoma
Bibliographic record
Abstract
Abstract Background: While the incidence of esophageal adenocarcinoma (EAC) is rapidly increasing, the 5-year survival rate of this disease remains poor at less than 25%, with a substantial number of patients exhibiting inherent or acquired resistance to chemotherapy. Circulating tumor DNA (ctDNA) isolated from liquid biopsies of EAC patients holds clinical biomarker potential in this disease, but our understanding of how chemotherapy affects ctDNA release remains limited. Methods: In order to study the impact of cancer treatment on ctDNA emission in vitro, we first sought to establish a chemo-resistant model of EAC. As such, the EAC cell line OE19 was exposed to discontinuous cisplatin treatments for three months in order to establish drug resistance. Following this, established chemo-resistant OE19 cells and parental (chemo-sensitive) OE19 cells were exposed to five daily cisplatin treatments, and cell culture supernatant was collected every day throughout treatment. ctDNA was subsequently isolated from cell culture supernatant and quantified by Qubit fluorometer dsDNA HS assay. ctDNA quantities were additionally assessed by droplet digital PCR (ddPCR) assay using primers and probes targeting the TP53 N310K mutation in the OE19 cell line. Finally, ctDNA fragment lengths were analyzed using the Agilent Bioanalyzer 2100. Results: A chemo-resistant model of the OE19 cell line was established, as exemplified by marked morphological changes in the cells and significantly increased cell viability determined by CCK8 assay after 48-hour treatments of various doses of cisplatin (p<0.05). ctDNA levels, as measured by both Qubit fluorometer and mutant copies detected by ddPCR assay, increased after chemotherapy treatment for both chemo-sensitive and resistant cells. Notably, ctDNA emission was higher with larger amounts cell death, with chemo-sensitive cells emitting significantly higher levels of ctDNA compared to chemo-resistant cells throughout treatment (p<0.05). Fragmentomics analysis revealed ctDNA peaks corresponding to apoptosis (~167 bp) and necrosis (~10,000 bp). Moreover, chemotherapy treatment caused a shift in average fragment size, with larger fragments being observed during chemotherapy treatment in both chemo-sensitive and resistant cells. Conclusion: This study reveals that in vitro cancer models can be used to successfully study ctDNA emission during pre-clinical drug analyses. ctDNA release was found to be correlated to cytotoxicity and cell death, providing us with valuable insight into how clinical liquid biopsy data should be interpreted. Finally, fragment size analysis of ctDNA reveals a potential novel biomarker for treatment response in cancer patients. Citation Format: Alexandra Bartolomucci, Sarah Tadhg Ferrier, Thupten Tsering, Xin Su, Jonathan Cools-Lartigue, Julia V. Burnier. Characterizing circulating tumor DNA release kinetics and fragment length in a chemotherapy resistant model of esophageal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6700.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".