Validation of cellular barcoding enabled clonal tracking in Barrett's esophagus stem cell populations
Bibliographic record
Abstract
Esophageal adenocarcinoma (EAC) is one of the deadliest cancers, with a five-year net survival rate of only 16% according to the Canadian Cancer Society. A key factor contributing to its high mortality is late-stage diagnosis. Barrett’s Esophagus (BE), an intestinal-type metaplasia arising in the distal esophagus in response to chronic inflammation, is a precursor lesion for EAC. Although the overall risk of progression from BE to EAC is low, it increases significantly following the development of high-grade dysplasia (HGD), a genetically unstable, precancerous dysplastic state. Currently, the detection of BE relies on endoscopic surveillance and histological assessment, and predicting which patients will progress to HGD before the phenotypic switch takes place is not possible. Beyond the classical oncogenic mutations (i.e. TP53, CDKN2A) which often precede catastrophic genomic events that drive progression, the genetic mutations that may initiate BE progression but are lost through to dysplasia, remain poorly characterized. The field of carcinogenesis is lacking a method by which to track clonal competition of BE stem cells in real time to identify potentially malignant clones before they drive phenotypic shifts. This project aims to develop and validate a method to uncover the clonal competition that drives clonal expansion of malignant clones in BE. To achieve this, we adapted a previously established DNA barcoding technology for use in our adult tissue resident stem cell (ASC) populations to track clonal competition in real time. To validate this tool’s ability to detect clones with a selective growth advantage, we introduced a faster-growing cell line carrying a known barcode sequence into a barcoded population of BE-ASCs. The faster-growing cell line could be informatically recovered, and sequencing data revealed clonal competition within BE-ASC populations, establishing an empirical basis to further explore the capability of this methodology to identify potentially malignant BE clones. This approach has the potential to identify previously unknown causative genes involved in BE progression, as well as improve early detection, and risk assessments of EAC; ultimately enabling the development of less invasive, cost-effective screening strategies for BE and improving the ability to detect high-risk individuals before the progression to cancer.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".