Li, Hu

Abstract Pancreatic ductal adenocarcinoma (PDAC) is a deadly malignancy with a dismal 5-year survival rate, highlighting the urgent need to improve drug response and overcome primary or acquired (secondary) resistance to conventional chemotherapy. Glycogen synthase kinase 3β (GSK-3β) is a target in PDAC and the GSK-3 inhibitor, elraglusib, has been shown to improve chemosensitivity of PDAC and survival in preclinical cancer models. In addition, data from phase II clinical trials in patients with previously untreated metastatic pancreatic cancer is demonstrating preliminary evidence of clinical benefit in patients receiving a combination of gemcitabine/nab-paclitaxel. While this early evidence is highly encouraging, the biological determinants of elraglusib response are unknown. Using genome wide CRISPR/Cas9 library screening, we identify aurora kinase A (AURKA), an oncogenic kinase with major roles in mitosis, as a critical regulator for elraglusib sensitivity. Elraglusib treatment activates AURKA by inducing a G2/M cell cycle arrest. We show that knocking out AURKA reduces the phosphorylation of polo like kinase 1 (PLK1), arresting cells in mitosis, and leading to mitotic catastrophe and significant apoptosis after elraglusib treatment. Strikingly, the AURKA inhibitor, alisertib, works synergistically with elraglusib to inhibit PDAC cell line growth and induce apoptosis. In summary, our results demonstrate that targeting AURKA is an effective approach to sensitize PDAC tumors to elraglusib. Citation Format: Li Ding, Easton Maeder, Cheng Zhang, Taylor Weiskittel, Daniel Schmitt, Andrew Mazar, Hu Li, Daniel Billadeau. Genome wide CRISPR/Cas9 library screening identifies aurora kinase A as a regulator of elraglusib sensitivity in pancreatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4658.

Abstract Metastatic pancreatic cancer patients who previously progressed on nab-paclitaxel/gemcitabine (average of 3 previous lines if treatment) were treated with elraglusib in combination with a rechallenge of this combination. The majority of patients demonstrated a prolonged progression free survival. While this early clinical evidence is highly encouraging, the biological determinants of elraglusib response are unknown. In vitro screens of elraglusib on pancreatic PDX cell lines showed that some tumor cells possess modest intrinsic resistance to elraglusib as measured by LD50 which ranged from 316.9 nM to 1080 nM in a screen of 10 cell lines. To explore this phenomena further, we induced resistance in three cell line models by growing cells in the presence of increasing concentrations of elraglusib. We then sequenced the transcriptome of the parental and resistant lines to identify potential mechanisms of elraglusib resistance. Comparison of RNA sequencing profiles identified upregulation of the Aldo-keto reductase (AKR) family of genes. This upregulation was confirmed by qRT-PCR and by immunoblot demonstrating that AKRs were upregulated at both the transcriptomic and protein level. We further quantified the AKR levels in the remainder of the 10 cell lines and found that many of the more resistant lines had high expression of AKR1C2. However, not all of the resistant lines had high expression which could be because they have a separate mechanism of resistance to elraglusib. A large body of literature has found that AKR genes provide resistance to many other small molecule inhibitors either by direct metabolism or by indirectly relieving oxidative stress. Given these results, we identify aldo-keto reductases as potentially associated with elraglusib resistance, but further investigation is warranted to establish a causal link between AKR and the resistant phenotype. Citation Format: Taylor Weiskittel, Ding Li, Lingling Han, Joseph McDermott, Benedito Caneiro, Hu Li, Daniel Billadeau, Andrew Mazar. Exploring mechanisms of resistance to elraglusib in pancreatic cancer PDX models. [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 3857.

Abstract Elraglusib is a selective GSK-3 inhibitor that has activity on a broad array of neoplasms as well as their immune microenvironment. However the determinants of tumor response have not been elucidated. Here, we use genomic panels of cancer driver genes to predict patient response to elraglusib therapy as measured by RECIST criteria. Patient response in the Phase I 1801 trial (NCT03678883) was matched to genomic data from patient tumors, with 80 cases used to train Machine Learning models and 26 cases reserved as a test set for model evaluation. When predicting response from mutation status alone our models reached a modest accuracy of 65% in the test set, which we attributed to the sparsity of genomic data. To remedy this, we engineered pathway-based features to provide combinatorial information while ensuring feature stability. To select the optimal combination of features, we designed an iterative design process that first randomized feature combinations and then created refined models from the highest performing pathway features. This resulted in several final models with greater than 88% accuracy. We retained several models of similar accuracy to more comprehensively identify potential biomarkers and combinatorial relationships. These models were then interpreted using SHapley Additive exPlanations (SHAP) where we were able to identify highly predictive features for both the entire pan-cancer cohort and specific histologies. We found the feature constructed from the Reactome pathway “Chromatin Modifying Enzymes” frequently occurred in high performing models with a high-ranking SHAP value. This feature included genes such as SMARCA4, HDAC2, and KDM5A, and was negatively associated with patient response. Another frequently observed high-ranking pathway feature with a negative response association was based on the “Innate Immune System” Reactome pathway. Many of the features highlighted in our models were negatively associated with elraglusib response and thus could be general markers of poor prognosis. To address this, we altered our pipeline to test only positively associated features which substantially reduced our feature pool to only 285 features but still produced models with accuracies of up to 81%. Analysis of key features used by these models identified several positive markers of elraglusib including mutations in the POLE gene, which previously has been linked to DNA-damage response deficiency and anti-tumor immune response. In sum, we forecast elraglusib response for a variety of tumor histologies and simultaneously reveal potential mechanisms of elraglusib sensitivity and biological action. In future work we will investigate the utility of using POLE and other identified biomarker candidates to predict the likelihood of patient response to elraglusib, as well as whether our machine learning models will be an effective tool to guide patient stratification. Citation Format: Joseph McDermott, Taylor Weiskittel, Daniel Billadeau, Benedito Carneiro, Hu Li, Andrew Mazar. Elraglusib response prediction and mechanistic discovery using iterative machine learning. [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 5355.