Abstract PR003: ctDNA release kinetics and fragmentation to monitor treatment response and resistance in esophageal adenocarcinoma
Bibliographic record
Abstract
Abstract Background: Esophageal adenocarcinoma (EAC) has a low 5-year survival rate and limited options for targeted therapies. Circulating tumor (ct)DNA isolated from liquid biopsies could help monitor patients to personalize treatment decisions. However, ctDNA studies in EAC remain sparse, and significant knowledge gaps exist in our understanding of ctDNA emission in response to therapy. The goal of this study was 1) to conduct a multimodal, longitudinal analysis of plasma cell free (cf)DNA in EAC patients and 2) to better understand the biology of ctDNA release during drug treatment. Methods: 1) A total of 189 longitudinal blood samples were collected from 41 EAC patients throughout treatment. cfDNA was isolated from baseline and post-neoadjuvant chemo samples for targeted deep sequencing alongside white blood cell controls. Shallow whole-genome sequencing was conducted on cfDNA for ichorCNA and fragmentomics analysis. 2) To dynamically monitor ctDNA fluctuations during EAC treatment, cfDNA was isolated from all other timepoints for digital (d)PCR quantification of the mutations identified from sequencing. ctDNA release kinetics were studied in vitro using three cell lines (OE19, FLO-1, A549), as well as our newly established cisplatin resistant model of OE19. ctDNA was analyzed by Qubit, dPCR, and Bioanalyzer. Annexin-V/PI flow cytometry was used to assess percentages of apoptotic and necrotic cells to correlate ctDNA to release mechanism. Results: 1) Sequencing has been completed for 21/41 patients and is ongoing for 20/41 patients. In the first 21 patients, somatic mutations were found in potential EAC driver genes, such as KRAS, TP53, ACVR2A, NOTCH1, and APC. Interestingly, in one patient’s cfDNA sample sequenced post neoadjuvant chemo, a novel mutation emerged in FBXW7, representing a potential resistance driver. ichorCNA tumor fraction (TF) values overall were low, and when using the recommended 0.03 TF estimate cutoff, there were significantly larger baseline TF values in metastatic (stage IVB) patients than those with localized disease (stage III) (p<0.05). 2) In vitro viable cancer cell numbers correlated to ctDNA levels, as measured by qubit and dPCR analysis. Additionally, cisplatin and 5-FU chemo treatments led to larger ctDNA release in all cells. ctDNA emission kinetics correlated to cytotoxicity (apoptosis and necrosis as shown through flow cytometry), with higher levels of ctDNA released by cisplatin-sensitive vs. resistant cells (p<0.05). Additionally, cisplatin treatment caused a shift in average ctDNA fragment size, with larger fragments observed during treatment, corresponding to an increased proportion of necrotic cells. Conclusions: This study reveals that multimodal cfDNA analysis can successfully be used in EAC patients to monitor treatment response and potentially identify resistance mechanisms. Moreover, in vitro models demonstrated a correlation of ctDNA emission and fragment length with chemo-cytotoxicity, shedding light on the release kinetics of DNA from cancer cells. Citation Format: Alexandra Bartolomucci, Laura Kienzle, Sarah Tadhg Ferrier, Lisa-Monique Edward, Jeffrey Bruce, Kwang-Bo Joung, Stephenie Prokopec, Wotan Zeng, Abirami Sharma, Kyle Dickinson, Nicholas Bertos, Jonathan Cools-Lartigue, Lorenzo Ferri, Trevor J. Pugh, Julia V. Burnier. ctDNA release kinetics and fragmentation to monitor treatment response and resistance in esophageal adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr PR003.
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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 | 0.001 |
| 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 | 0.001 | 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".