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Multidimensional analyses identify genes of high priority for pancreatic cancer research
Zeribe C. Nwosu, Heather M. Giza, Maya Nassif, Verodia Charlestin, Rosa E. Menjivar, Daeho Kim, Samantha B. Kemp, Peter Sajjakulnukit, Anthony Andren, Li Zhang, William K.M. Lai, Ian Loveless, Nina Steele, Jiantao Hu, Biao Hu, Shaomeng Wang, Marina Pasca di Magliano, Costas A. Lyssiotis
Zeribe C. Nwosu, Heather M. Giza, Maya Nassif, Verodia Charlestin, Rosa E. Menjivar, Daeho Kim, Samantha B. Kemp, Peter Sajjakulnukit, Anthony Andren, Li Zhang, William K.M. Lai, Ian Loveless, Nina Steele, Jiantao Hu, Biao Hu, Shaomeng Wang, Marina Pasca di Magliano, Costas A. Lyssiotis
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Research Article Gastroenterology Oncology

Multidimensional analyses identify genes of high priority for pancreatic cancer research

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Abstract

Pancreatic ductal adenocarcinoma (PDAC) is a drug-resistant and lethal cancer. Identification of the genes that consistently show altered expression across patient cohorts can expose effective therapeutic targets and strategies. To identify such genes, we separately analyzed 5 human PDAC microarray datasets. We defined genes as “consistent” if upregulated or downregulated in 4 or more datasets (adjusted P < 0.05). The genes were subsequently queried in additional datasets, including single-cell RNA-sequencing data, and we analyzed their pathway enrichment, tissue specificity, essentiality for cell viability, and association with cancer features, e.g., tumor subtype, proliferation, metastasis, and poor survival outcome. We identified 2,010 consistently upregulated and 1,928 downregulated genes, of which more than 50% to our knowledge were uncharacterized in PDAC. These genes spanned multiple processes, including cell cycle, immunity, transport, metabolism, signaling, and transcriptional/epigenetic regulation — cell cycle and glycolysis being the most altered. Several upregulated genes correlated with cancer features, and their suppression impaired PDAC cell viability in prior CRISPR/Cas9 and RNA interference screens. Furthermore, the upregulated genes predicted sensitivity to bromodomain and extraterminal (epigenetic) protein inhibition, which, in combination with gemcitabine, disrupted amino acid metabolism and in vivo tumor growth. Our results highlight genes for further studies in the quest for PDAC mechanisms, therapeutic targets, and biomarkers.

Authors

Zeribe C. Nwosu, Heather M. Giza, Maya Nassif, Verodia Charlestin, Rosa E. Menjivar, Daeho Kim, Samantha B. Kemp, Peter Sajjakulnukit, Anthony Andren, Li Zhang, William K.M. Lai, Ian Loveless, Nina Steele, Jiantao Hu, Biao Hu, Shaomeng Wang, Marina Pasca di Magliano, Costas A. Lyssiotis

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Figure 7

Expression of high-priority genes predicts therapy response.

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Expression of high-priority genes predicts therapy response.
(A) Heatmap...
(A) Heatmap showing the sensitivity of PDAC cell lines expressing high-priority genes to compounds tested in the Genomics of Drug Sensitivity in Cancer (GDSC2) data. In blue font are epigenetics drugs. EXP, expressing. (B) Viability assay of PDAC cell lines treated with BETi AZD5153 alone or in combination with gemcitabine (gem) for 72 hours, in quadruplicate. BETi, bromodomain and extraterminal motif (BET) protein inhibitor(s); DMSO, dimethyl sulfoxide. (C) Heatmaps showing the metabolomics profile of lysate (intracellular) and culture media (extracellular) extracts from human PDAC cell TU8902 treated with gemcitabine, AZD5153, or the combination, for 24 hours, in triplicate. AMP, adenosine monophosphate; NAD, nicotinamide adenine dinucleotide. (D) Viability assay of TU8902 treated with gemcitabine, BD-9136 (a bromodomain-containing protein 4 degrader), or both, for 48 hours. Represents more than 2 independent experiments. Below: Treatment with BD-9136 or AZD5153 alone or in combination with gemcitabine for 48 hours, in quadruplicate. (E) Workflow of the mouse tumor experiments. Mice were treated with gemcitabine at 100 mg/kg body weight 2 times per week and with BD-9136 at 20 mg/kg body weight 5 times per week. (F) Tumor weight of subcutaneously implanted TU8902 (n = 4 mice per group, injected on both flanks; n = 8 tumors per group except combination arm, n = 6). Right: Image depicting the size of tumors harvested at the end of the experiment. (G) Image depicting the size of KPC 7940b pancreatic orthotopic tumors following the indicated treatments (n = 6 mice per group). Below: Bar graph showing tumor weights. Statistical comparison of the indicated groups (F and G) was by 2-tailed t test with Welch’s correction. Comparison by 1-way ANOVA with Tukey’s post hoc correction was not statistically significant. Comparisons for B and D (below) were by unpaired t test and D was by 1-way ANOVA with Tukey’s post hoc test. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001 (B, D, F, and G).

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