Abstract SY31-02: Determinants of hypoxia in pancreatic cancer and identification of ULK1 as a potential therapeutic target
Bibliographic record
Abstract
Abstract The effectiveness of radiation treatment is impacted by spatially defined features of the tumor microenvironment including areas of tumor hypoxia. This is due in part to direct protection against radiation damage by hypoxia, as well as through the influence of hypoxia on tumor, stroma and immune cell phenotypes. Despite this understanding, our progress in advancing therapies to address tumor hypoxia in patients have been largely unsuccessful. This is driven in part to the incredible variability in tumor hypoxia across patients as well as to a limited understanding of the underlying causes and consequences of hypoxia on an individual patient basis. As a step towards more precision approaches for hypoxia directed therapies we are characterizing hypoxia and its causes in individual patients and in matched patient derived xenografts. Our data reveal a striking variation in hypoxia across patients, and a corresponding variation in both oxygen consumption and hypoxia tolerance in patient derived organoids. We hypothesize that levels of hypoxia within individual tumors are determined through separate processes that drive changes in oxygen demand coupled with those that influence adaptation and survival to hypoxic stress. We have also identified ULK1 as a key contributor to both of these processes. Loss or inhibition of ULK1, a gene activated downstream of the unfolded protein response during hypoxia, results in defects in both mitochondrial and ER turnover leading to increases in both oxygen consumption and ER stress. Consequently, knockdown of ULK1 in established pancreatic cancer xenografts results in development of more hypoxia, while at the same time promotes the death of cells that become hypoxic. This has a dramatic impact on tumor growth, leading to complete remission in some models and suggesting that ULK1 should be pursued as a tumor microenvironment therapeutic target. Citation Format: Ji Zhang, Dan Cojocari, Rob Cairns, Marianne Koritzinsky, Bradly G. Wouters. Determinants of hypoxia in pancreatic cancer and identification of ULK1 as a potential therapeutic target. [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 SY31-02.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".