536. TUMOR-INFORMED “LIQUID BIOPSY” FOR ESOPHAGEAL ADENOCARCINOMA FROM MATCHED CANCER ORGANOID CULTURE
Bibliographic record
Abstract
Abstract Background The absence of recurrent mutations in esophageal adenocarcinoma (EAC) poses a challenge in detecting circulating tumor DNA (ctDNA) in plasma and may hinder the advancement of liquid biopsy methods. To address this, we cultured patient-derived EAC organoids (PDOs), speculating that they could serve as a guide for identifying ctDNA in the patient's blood samples. This approach aims to leverage organoids as a potential tool to overcome the complexity of identifying ctDNA in EAC, offering a promising avenue for refining liquid biopsy strategies in clinical practice. Methods PDOs were generated from EAC tumor tissue in Matrigel domes and expanded in suspension culture. To isolate mononucleosomes (147 bp), chromatin from PDOs was extracted and digested with micrococcal nuclease (MNase). Fragments larger than 147 bp were removed through size selection. MNase-sequencing was performed to generate a mutation map with preferential coverage of nucleosome-protected DNA for each sample. Matched whole genome sequencing of the tumor for each respective PDO sample was used as a control. Primers were designed for the identified mutations in nucleosome-protected DNA and used to amplify patient cfDNA for sequencing. Results DNA from five different PDOs were collected and MNase digested. MNase concentration and digestion time were optimized for each sample. MNase digestion produced mononucleosomes of approximately 147 bp for all samples. MNase-sequencing identified 24 mutations in peaks (mononucleosomes) in 24 genes, including known oncogenes. Among these were 16 missense, 2 frameshift, and 1 nonsense mutations, and 5 mutations in splice regions. To date, amplicons of expected size were detected by PCR for six genes using either total PDO DNA or normal cell-free DNA, confirming the detectability of these genes. PCR amplification using patient ctDNA and next-generation sequencing is ongoing. Conclusion These findings show that we are able to isolate and detect somatic mutations in nucleosomes from different PDOs, allowing us to generate a nucleosome SNV map for each sample. Preliminary data indicate these regions can be PCR amplified from normal cfDNA. Amplification and sequence verification of mutated regions from corresponding patient blood ctDNA is ongoing. The mapping of patient-specific variants will enable the development of targeted personalized PCR panels, aiding in recurrence prediction and enhancing drug screening accuracy. This advancement holds promise for early cancer detection and improving prognoses for individuals with EAC by addressing gaps in recurrence prediction.
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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) | 0.006 | 0.004 |
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".