Abstract B053: Targeting SMURF1 with low-dose proteasome inhibitors in pancreatic cancer organoids
Bibliographic record
Abstract
Abstract Global initiatives focused on whole genome and transcriptome analysis (WGTA) of metastatic pancreatic ductal adenocarcinoma (PDAC) tumor cohorts are driving progress in understanding the clinical impact of PDAC heterogeneity. This knowledge is key to continue the discovery of subsets of patients who could benefit from biomarker-informed targeted therapy and expand the currently limited PDAC treatment options. To identify clinically actionable subtypes in metastatic PDAC (mPDAC), our team analyzes prospectively collected WGTA data from patients enrolled in the PanGen trial (NCT02869802), in parallel with analysis of a set of patient-derived organoids (PDOs). In a cohort of 69 sequenced tumors, four cases (5.8%) had an amplification of chromosome 7q22, which included copy gains of transcriptional co-factor TRRAP, drug metabolizing cytochrome P450 genes CYP3A4 and CYP3A5, and SMURF1. SMURF1 is a ubiquitin-protein ligase that regulates TGFβ receptor signaling, in part via its interaction with SMAD7, and has been implicated in the epithelial-to-mesenchymal program and tumor invasiveness in PDAC. The proteasome inhibitor (PI) bortezomib has been shown to attenuate SMURF1 levels. We investigated whether SMURF1 signaling axis is associated with sensitivity to PIs in mPDAC, by analyzing the cytotoxic effects of the PIs bortezomib, carfilzomib and ixazomib in PDOs. Eight PDOs were treated with PIs at concentrations between 0.1pM-1mM. The presence of live and dead cells was quantified using the IN Cell Analyzer, and cell toxicity was analyzed using GRtoxic metrics (grcalculator.org). The PIs bind catalytic subunits of the 26S proteasome, primarily proteasome subunit beta-5, encoded by PSMB5. The cytotoxic activity of all three PIs negatively correlated with tumor biopsy PSMB5 expression (p<0.05), suggesting that PDOs established from tumors with low expression of PSMB5 are more sensitive to PI-induced cell death. There was a positive correlation between ixazomib cytotoxicity and SMURF1 expression (p<0.05). PanGen patients with high (>75th percentile) tumor SMURF1 expression showed shorter overall survival (OS) compared to the rest of the cohort (median OS 9 vs. 14 months, respectively; p=0.004). Immunohistochemistry analysis of a tumor tissue microarray comprising 175 resected PDAC cases detected an association of concomitant low SMURF1 and high SMAD7 protein levels with adjuvant therapy response (median OS: 40 vs. 12 months with no adjuvant treatment, p=0.002), suggesting that attenuation of SMURF1 in the presence of SMAD7 may improve chemotherapy response in early disease stages. In summary, we present data indicating increased sensitivity to PIs in a subset of tumors with low PSMB5 and high SMURF1 expression, and highlight the translational utility of the investigation of genomic and clinically annotated pre-clinical models in PDAC predictive biomarker discovery. Citation Format: Andrew Metcalfe, Joanna M. Karasinska, James T. Topham, Steve E. Kalloger, Hassan Ali, Dawn Ashforth, Marco A. Marra, Janessa Laskin, Patricia A. Tang, Rachel Goodwin, Oliver F. Bathe, Daniel J. Renouf, David F. Schaeffer. Targeting SMURF1 with low-dose proteasome inhibitors in pancreatic cancer organoids [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr B053.
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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 | 0.001 | 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".