Abstract B090: Targeting the unfolded protein response enhances sensitivity to chemotherapy
Bibliographic record
Abstract
Abstract Introduction: Pancreatic ductal adenocarcinoma (PDAC) is currently the 3rd leading cause of cancer-related deaths in North American with ~10% survival 5 years after diagnosis. A contributing factor to this low survival rate is chemotherapy resistance. Major risk factors for PDAC, including smoking and chronic pancreatitis, promote activation of the unfolded protein response (UPR). The UPR reduces protein load to alleviate stress, and cancer cells can utilize the UPR to promote cell survival. We previously showed loss of Activating Transcription Factor 3 (ATF3), a mediator of the UPR, restricted early events in PDAC progression. The goal of this study was to determine if ATF3 and the UPR promote resistance to chemotherapy in PDAC. We hypothesized that cancer cells activate the UPR and ATF3 to reduce cell stress and promote chemotherapeutic resistance. Methods: Murine-derived organoids (MDOs) were developed from pancreatic tissue expressing KRASG12D with (Ptf1acreERT/+KRASG12D/+) or without Atf3 (Atf3-/-Ptf1acreERT/+KRASG12D; referred to as APK) expression, and patient-derived organoids (PDOs) were examined for mediators of the UPR. Transcriptomic changes related to ATF3 loss was determined by RNA-sequencing of Ptf1acreERT/+KRASG12D/+ and APK organoids. UPR activation was examined by qRT-PRC in MDOs and PDOs with and without gemcitabine treatment, and related to organoid viability and phenotype. PDOs viability was examined following treatment with gemcitabine +/- UPR inhibitors. Results: RNA-seq analysis of Ptf1acreERT/+KRASG12D/+ and APK MDOs identified drug metabolism cytochrome P450, metabolism of xenobiotics by cytochrome P450, and platinum drug resistance as key pathways affected by the loss of ATF3 suggesting reduced UPR activation impacts drug metabolism in KRASG12D expressing cells. Ptf1acreERT/+KRASG12D/+ and APK MDOs showed a differential response in cell viability and UPR activation to gemcitabine treatment suggesting an importance of ATF3 in gemcitabine resistance. Consistent with this role, treatment of PDOs with gemcitabine resulted in UPR activation in a patient-specific fashion, particularly regarding increased expression of ATF3 and spliced XBP1. In lines showing enhanced UPR activation to gemcitabine, combinatorial treatment with UPR inhibitors showed increased cell death compared to gemcitabine alone. Conclusions: This study shows the UPR and ATF3 are rapidly activated in response to gemcitabine, but in a patient-specific fashion. These results suggest cancer cells activate the UPR to increase resistance to chemotherapies, and a subset of patients may benefit from combined gemcitabine treatment with UPR inhibitors. Since the tumor microenvironment is believed to be a major contributing factor to chemotherapy resistance, future experiments will include the tumor microenvironment interactions and UPR inhibition. Citation Format: Mickenzie B. Martin, Teresa Borello, Fatemeh Mousavi, Nelson Dusetti, Christopher L. Pin. Targeting the unfolded protein response enhances sensitivity to chemotherapy [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr B090.
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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) | 0.005 | 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".