Abstract 4205: Investigating micro-environmental changes in a syngeneic radio-recurrent prostate cancer model
Bibliographic record
Abstract
Abstract Approximately 1 in 8 men in Canada will be diagnosed with prostate cancer (PCa) in their lifetime, with ~20% presenting with high-risk disease. A standard treatment is radiotherapy (RT) and while many patients respond well to RT, up to 40% of men with high-risk disease can recur, often within the prostate. It can be challenging to salvage prostate relapses due to the morbidity of re-irradiation and the high rate of subsequent progression. The tumor micro-environment (TME) is altered after radiotherapy with increased immune-suppressor and immune-stimulatory effects occurring concurrently. Therapies capitalizing on these changes in the TME might provide an advantage to tackling radiation-recurrent cancer. Identifying TME changes in radio-recurrent PCa is the first step; however, existing PCa animal models do not accurately model the TME. Our collaborator’s lab has created the DVL3 mouse model with a clinically relevant TME. It forms tumors with distinct glandular morphology and displays micro-environmental responses to RT like what is seen in high-risk PCa patients, making this model an innovative tool for investigating radio-recurrence. The objective of this study is to create a radio-recurrent DVL3 PCa cell line to explore changes in the TME both in vitro and in vivo via orthotopic injection. To generate the radio-recurrent model, the DVL3 Parental cells (DVL3-Par) underwent a conventionally fractionated RT schedule (78Gy/39fx), herein referred to as DVL3-CF cells. The first part of the project involves in vitro characterization. Radiation resistance was evaluated via clonogenic assay which demonstrated a significant increase in clonogenic survival in the DVL3-CF cells relative to DVL3-Par. Characterization of DVL3-CF cells demonstrates that they are more proliferative at baseline, they recover quicker following G2/M blocks, they have decreased senescence following high dose radiation, and they are more invasive in vitro. The second part of the project evaluates the radio-recurrent model in vivo using an orthotopic mouse model as this best emulates the TME and clinical tumor progression. Parental and CF cells will be injected orthotopically into the prostate of C57/BL-6 mice and monitored for growth by MRI which will provide information on tumor volume as well as vasculature. At the time of sacrifice, the prostate and draining lymph nodes will be cryo-preserved and formalin fixed for downstream histological analysis. Transcriptional analyses, MRI data, and histological analysis of the tumors and draining lymph nodes will be compared across the radio-recurrent model and its parental cell line to evaluate environmental changes and potentially implicated mechanisms. This study will elucidate some of the complex interactions that are occurring within the TME of radio-recurrent PCa, increasing our understanding of mechanisms of radio-recurrence. Citation Format: Stephanie D. White, Xiaoyong Huang, Ian Mills, Stanley K. Liu. Investigating micro-environmental changes in a syngeneic radio-recurrent prostate cancer model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4205.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".