Abstract 2516: Analysis of CLL Celllular Environment and Response (ACCER) is a novel method to understand the microenvironment in CLL
Bibliographic record
Abstract
Abstract Microenvironments such as lymph nodes and bone marrow allow chronic lymphocytic leukemia (CLL) cells to survive after drug treatments. There are limited methods to study the to study the contribution of the microenvironment. We have adapted a solid tumour microenvironment cell culture system that provides elements of the CLL microenvironment called Analysis of CLL Cellular Environment and Response (ACCER). We optimized the cell number for patient’s primary CLL cells and HS-5 human bone marrow stromal cell line that will give sufficient cell number and viability with the ACCER. We then determined the amount of collagen type 1 to give the best extracellular matrix to seed CLL cells to the membrane. Finally, we determined that ACCER provide CLL cell protection against cell death following treatment with fludarabine and ibrutinib compared to co-culture conditions. This describes novel microenvironment model to investigate factors that promote drug resistance and cell survival in CLL. In the future, this will further validate new treatment strategies to overcome microenvironmental CLL cell survival signals. Citation Format: Spencer Gibson, Tricia Choquette, Elizabeth S. Henson, Xioyan Yang, James B. Johnston. Analysis of CLL Celllular Environment and Response (ACCER) is a novel method to understand the microenvironment in CLL [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 2516.
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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 | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".