Abstract 1135: DRD2 is critical for pancreatic cancer and promises pharmacological therapy by already established antagonists
Bibliographic record
Abstract
Abstract Introduction and aims: Although the overall five-year survival of all patients with cancer stands at 63%, for pancreatic cancer patients, it is a disheartening 8% - a number that remains largely unchanged for three decades. Of the patients diagnosed with pancreatic cancer, about 85% exhibit pancreatic ductal adenocarcinoma (PDAC). Most of these patients die within 4 to 6 months after diagnosis. The poor prognosis is caused by the detection at only late stages, and lack of effective options for chemotherapy. The widely used chemotherapeutic agent gemcitabine, confers a median survival advantage of only 6 months, and resistance to therapy develops in the vast majority of patients. Given this poor prognosis of patients with PDAC, there is an urgent need to find more effective therapies. Experimental procedures: Microarrays were used to perform global gene expression profiling in 195 PDAC and 41 normal pancreatic tissue samples. Using these profiling data, we undertook an extensive analysis of PDAC transcriptome by superimposing the pathway context and interaction networks of aberrantly expressed genes to identify factors with central roles in PDAC pathways. Next, tissue microarray analysis (TMA) were used to verify the expression of the candidate target in independent set of 152 samples comprising 40 normal pancreatic tissues, 49 chronic pancreatitis sections (CP) and 63 PDAC samples. We further validated the functional relevance of the candidate molecule through RNA interference (RNAi) and pharmacological inhibition in vitro and in vivo. Results: We identified dopamine receptor D2 (DRD2) as a key modulator of cancer pathways in PDAC. DRD2 up-regulation at the protein level was validated in a large independent sample cohort. Most importantly, we found that blockade of DRD2, through RNAi or pharmacological inhibition using FDA-approved antagonists hampers the proliferative and invasive capacities of pancreatic cancer cells while modulating cAMP and endoplasmic reticulum stress pathways. Also, we observed a potent effect of DRD2 antagonists on inhibition of cancer cell proliferation using different model of primary and metastatic tumor cells derived from spontaneous pancreatic cancer mouse models and patient-derived pancreatic adenocarcinoma mouse xenograft (PDX) models. Conclusions: Our findings demonstrate that inhibiting DRD2 represents a novel therapeutic approach for PDAC. Since DRD2 inhibitors are already in the clinic for the management of schizophrenia, our results from this study could support a drug repurposing strategy to expedite clinical evaluation of these agents as novel therapy against pancreatic cancer. Citation Format: Pouria Jandaghi, Hamed S. Najafabadi, Andrea Bauer, Andreas I. Papadakis, Matteo Fassan, Anita Hall, Anie Monast, Maryam Safisamghabadi, Magnus von Knebel Doeberitz, John P. Neoptolemos, Eithne Costello, William Greenhalf, Aldo Scarpa, Bence Sipos, Daniel Auld, Mark Lathrop, Morag Park, Markus W. Büchler, Oliver Strobel, Thilo Hackert, Nathalia Giese, George Zogopoulos, Veena Sangwan, Sidong Huang, Jörg D. Hoheisel, Yaser Riazalhosseini. DRD2 is critical for pancreatic cancer and promises pharmacological therapy by already established antagonists [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1135. doi:10.1158/1538-7445.AM2017-1135
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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) | 0.005 | 0.002 |
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".