Abstract 5526: State-of-the-art biobanking of gastroesophageal adenocarcinoma samples: Integrating non-viable biospecimens with 3D and 2D cell models and their characterization, validation, and utilization of cell models for precision oncology
Bibliographic record
Abstract
Abstract Traditionally, biobanking platforms have collected and stored non-viable biological specimens such as serum, plasma, and fresh-frozen or formalin-fixed paraffin-embedded (FFPE) tissues. These constitute key resources for clinical and contemporary genomics, transcriptomics, and proteomics studies. However, such specimens cannot be used for studies involving drug testing, high throughput target validation, and implementation for personalized medicine. Next-generation biobanking strategies rectify this issue by combining the collection of non-viable samples from patients with the propagation of viable tissue fractions as in vitro 2D and 3D cell models or in vivo xenograft models. Such cutting-edge biobanking strategies enable downstream cell-based high-throughput functional assays promoting the discovery of therapeutic targets or assessing treatment responses and resistance to treatment. Here, we provide comprehensive details of our establishment of a state-of-the-art biobanking platform for gastroesophageal adenocarcinoma (GEA) samples (n=389). Our approach opens new directions for understanding disease biology and conducting translational cancer studies. Patient-derived 2D cells (n=376), organoids (PDOs) (n=185), and xenografts (PDXs) (n= 99) included in our pipeline retain crucial features of the original human tumors, serving as valuable tools for clinical and experimental analyses in the context of precision oncology. We also discuss the entire approach of next-generation biobanking and emphasize the importance of integrating the propagation of PDOs and PDXs simultaneously. In addition, we explain each protocol optimized to propagate patient tissue-derived cell models. We validate that these models recapitulate tissue heterogeneity and are relevant preclinical models. Taken together, we described each approach used to develop one of the largest next-level biobanks for GEA. Citation Format: Mingyang Iris Kong, Sanjima Pal, Julie Berube, France Bourdeau, Betty Giannias, Nicholas Bertos, Veena Sangwan, Lorenzo Ferri. State-of-the-art biobanking of gastroesophageal adenocarcinoma samples: Integrating non-viable biospecimens with 3D and 2D cell models and their characterization, validation, and utilization of cell models for precision oncology. [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 5526.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".