Oberg, Ann L.

Abstract An accurate assessment of p53’s functional statuses is critical for cancer genomic medicine. However, there is a significant challenge in identifying tumors with non-mutational p53 inactivation which is not detectable through DNA sequencing. These undetected cases are often misclassified as p53-normal, leading to inaccurate prognosis and downstream association analyses. To address this issue, we built the support vector machine (SVM) models to systematically reassess p53’s functional statuses in TP53 wild-type (TP53WT) tumors from multiple The Cancer Genome Atlas (TCGA) cohorts. Cross-validation demonstrated the good performance of the SVM models with a mean area under the receiver operating characteristic curve (AUROC) of 0.9822, precision of 0.9747, and recall of 0.9784. Our study revealed that a significant proportion (87%–99%) of TP53WT tumors actually had compromised p53 function. Additional analyses uncovered that these genetically intact but functionally impaired (termed as predictively reduced function of p53 or TP53WT-pRF) tumors exhibited genomic and pathophysiologic features akin to TP53-mutant tumors: heightened genomic instability and elevated levels of hypoxia. Clinically, patients with TP53WT-pRF tumors experienced significantly shortened overall survival or progression-free survival compared to those with predictively normal function of p53 (TP53WT-pN) tumors, and these patients also displayed increased sensitivity to platinum-based chemotherapy and radiation therapy.

Abstract Aberrant activation of GLI transcription factors has been implicated in the pathogenesis of different tumor types including pancreatic ductal adenocarcinoma. However, the mechanistic link with established drivers of this disease remains in part elusive. In this study, using a new genetically engineered mouse model overexpressing constitutively active mouse form of GLI2 and a combination of genome-wide assays, we provide evidence of a novel mechanism underlying the interplay between KRAS, a major driver of pancreatic ductal adenocarcinoma development, and GLI2 to control oncogenic gene expression. These mice, also expressing KrasG12D, show significantly reduced median survival rate and accelerated tumorigenesis compared with the KrasG12D only expressing mice. Analysis of the mechanism using RNA sequencing demonstrate higher levels of GLI2 targets, particularly tumor growth–promoting genes, including Ccnd1, N-Myc, and Bcl2, in KrasG12D mutant cells. Furthermore, chromatin immunoprecipitation sequencing studies showed that in these cells KrasG12D increases the levels of trimethylation of lysine 4 of the histone 3 (H3K4me3) at the promoter of GLI2 targets without affecting significantly the levels of other major active chromatin marks. Importantly, Gli2 knockdown reduces H3K4me3 enrichment and gene expression induced by mutant Kras. In summary, we demonstrate that Gli2 plays a significant role in pancreatic carcinogenesis by acting as a downstream effector of KrasG12D to control gene expression.

Abstract Background: Pancreatic cancer is a leading cause of cancer-related death globally. Risk factors for pancreatic cancer include common genetic variants and potentially heavy alcohol consumption. We assessed if genetic variants modify the association between heavy alcohol consumption and pancreatic cancer risk. Methods: We conducted a genome-wide interaction analysis of single-nucleotide polymorphisms (SNP) by heavy alcohol consumption (more than three drinks per day) for pancreatic cancer in European ancestry populations from genome-wide association studies. Our analysis included 3,707 cases and 4,167 controls from case–control studies and 1,098 cases and 1,162 controls from cohort studies. Fixed-effect meta-analyses were conducted. Results: A potential novel region of association on 10p11.22, lead SNP rs7898449 (interaction P value (Pinteraction) = 5.1 × 10−8 in the meta-analysis; Pinteraction = 2.1 × 10−9 in the case–control studies; Pinteraction = 0.91 in the cohort studies), was identified. An SNP correlated with this lead SNP is an expression quantitative trait locus for the neuropilin 1 gene. Of the 17 genomic regions with genome-wide significant evidence of association with pancreatic cancer in prior studies, we observed suggestive evidence that heavy alcohol consumption modified the association for one SNP near LINC00673, rs11655237 on 17q25.1 (Pinteraction = 0.004). Conclusions: We identified a novel genomic region that may be associated with pancreatic cancer risk in conjunction with heavy alcohol consumption located near an expression quantitative trait locus for neuropilin 1, a protein that plays an important role in the development and progression of pancreatic cancer. Impact: This work can provide insights into the etiology of pancreatic cancer, particularly in heavy drinkers.

INTRODUCTION: Accurate risk prediction can facilitate screening and early detection of pancreatic cancer (PC). We conducted a systematic review to critically evaluate effectiveness of machine learning (ML) and artificial intelligence (AI) techniques applied to electronic health records (EHR) for PC risk prediction. METHODS: Ovid MEDLINE(R), Ovid EMBASE, Ovid Cochrane Central Register of Controlled Trials, Ovid Cochrane Database of Systematic Reviews, Scopus, and Web of Science were searched for articles that utilized ML/AI techniques to predict PC, published between January 1, 2012, and February 1, 2024. Study selection and data extraction were conducted by 2 independent reviewers. Critical appraisal and data extraction were performed using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist. Risk of bias and applicability were examined using prediction model risk of bias assessment tool. RESULTS: Thirty studies including 169,149 PC cases were identified. Logistic regression was the most frequent modeling method. Twenty studies utilized a curated set of known PC risk predictors or those identified by clinical experts. ML model discrimination performance (C-index) ranged from 0.57 to 1.0. Missing data were underreported, and most studies did not implement explainable-AI techniques or report exclusion time intervals. DISCUSSION: AI/ML models for PC risk prediction using known risk factors perform reasonably well and may have near-term applications in identifying cohorts for targeted PC screening if validated in real-world data sets. The combined use of structured and unstructured EHR data using emerging AI models while incorporating explainable-AI techniques has the potential to identify novel PC risk factors, and this approach merits further study.

Abstract Background: Ovarian cancer ranks fifth in cancer deaths among women. The gold standard of care for advanced disease includes cytoreductive surgery along with adjuvant or neoadjuvant platinum/taxane-based chemotherapy. Although response to this treatment is high, chemotherapy-induced nausea and vomiting due to platinum agents and allergic reactions due to both agents are common. For nearly half a century, the glucocorticoid (GC) dexamethasone (DEX) has been administered as part of supportive care. However, there have periodically been questions about the possible deleterious effects of DEX on chemotherapy response, especially in solid tumors. Methods: Platinum-sensitive, glucocorticoid receptor (GRa) expressing ovarian cancer cells were exposed to DEX prior to chemotherapy and clonogenic survival was assessed. The effects of DEX on cell cycle and apoptosis were assessed by flow cytometry. Changes in the tumor cell transcriptome after acute DEX treatment were determined by RNA sequencing. Mice bearing orthotopic ovarian cancer patient-derived xenografts (PDXs) were treated with chemotherapy ± DEX to assess changes in chemotherapy response in vivo. Findings: DEX pre-treatment decreased platinum cytotoxicity in ovarian cancer cell lines in vitro and diminished platinum response in some PDXs analyzed for chemotherapy response ex vivo. RNA-seq analysis revealed changes in transcripts encoding proteins involved in platinum detoxification and TNFa pro-survival signaling. In ovarian cancer PDXs, DEX decreased platinum response in one model and had limited impact on both platinum and platinum/taxane chemotherapy in three distinct studies. Interpretation: DEX appears to modulate chemotherapy response in some settings in vitro and ex-vivo. The impact of DEX on chemotherapy response in vivo appears to be limited. However, further investigation of this question in vivo in non-immunosuppressed murine models appears warranted. Citation Format: Annapoorna Venkatachalam, Cristina Correia, Cordelia Mcgehee, Sarah Finstuen-Magro, Paula A. Schneider, Rachel Hurley, Kevin L. Peterson, Ethan Heinzen, Melissa C. Larson, Xiaonon Hou, Ann L. Oberg, Saravut Weroha, Scott H. Kaufmann. Impact of dexamethasone on chemotherapy response in ovarian cancer [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr B098.

Abstract Background: The kinases ataxia-telangiectasia-mutated-and-Rad3-related kinase (ATR), checkpoint kinase 1 (CHK1) and WEE1 participate in the response to replication stress. Inhibitors of ATR, WEE1, and CHK1 are being tested for antineoplastic activity as monotherapies and in combination with chemotherapy. Although these agents impair cell cycle checkpoints, fork stabilization, origin firing, and HR, little is known about the apoptotic pathways that are engaged and the critical mediators activating cell death mechanisms. The present study utilizes high-grade serous ovarian cancer (HGSOC) cell lines and patient-derived xenografts (PDXs) to understand the predominant apoptotic players and assess changes involved in the resistance setting after treatment with these inhibitors. Methods: HGSOC cell lines were examined for responses to ceralasertib (ATRi), prexasertib (CHK1i), or adavosertib (WEE1i) using colony forming assays, immunoblotting, and assays for apoptosis. To characterize the cell death networks after prexasertib treatment, RNA-seq analysis was performed in two representative cell lines. Mechanisms involved in acquired resistance and downstream changes in cell fate were evaluated by generating prexasertib- resistant HGSOC cell lines. Results: Irrespective of the homologous recombination (HR) status, ovarian cell lines, and PDXs underwent cell death during prolonged exposure at clinically achievable concentrations. CDC25A-CDK2 signaling was predictive of responsiveness in vitro and in vivo. The subsequent apoptotic response was c-Jun N-terminal kinases (JNK) dependent and involved engagement of mitochondrial pathways rather than the TNFa-induced death receptor-mediated apoptosis we recently described in acute leukemia (H. Ding, N. Vincelette, et al., Cancer Res. 81:2666-2678, 2021). Additionally, ovarian lines selected for CHK1i resistance failed to die in response to either ATRi or WEE1i but were sensitive to front-line therapy options, including carboplatin and PARP inhibitor. Conclusions: Unlike AML, death receptor-mediated apoptosis was not observed; instead, the primary mechanism of cell killing in HGSOC lines involves activation of the mitochondrial apoptotic pathway. Our findings identify the key cell death-initiating events that serve as measures of response to replication checkpoint inhibitors in ovarian cancer. Acquired resistance to these therapies is multifactorial and mainly involves failure to exacerbate the replication stress response. Citation Format: Annapoorna Venkatachalam, Cristina Correia, Kevin L. Peterson, Xiaonan Hou, Annabella R. Strathman, Paula A Schneider, Karen S. Flatten, Laura N. Duffield, Cordelia D. McGehee, Melissa C. Larson, Xue Meng, Nicole D Vincelette, Ann L. Oberg, Hu Li, John Weroha, Scott H Kaufmann. Transcriptomic insights reveal role of BCL2 proteins in replication checkpoint inhibitor response [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr B012.

Exposure to polycyclic aromatic hydrocarbons (PAHs), byproducts of incomplete combustion, and their effects on the development of cancer are still being evaluated. Recent studies have analyzed the relationship between PAHs and tobacco or dietary intake in the form of processed foods and smoked/well-done meats. This study aims to assess the association of a blood biomarker and metabolite of PAHs, r-1,t-2,3,c-4-tetrahydroxy-1,2,3,4-tetrahydrophenanthrene (PheT), dietary intake, selected metabolism SNPs, and pancreatic cancer. Demographics, food-frequency data, SNPs, treatment history, and levels of PheT in plasma were determined from 400 participants (202 cases and 198 controls) and evaluated based on pancreatic adenocarcinoma diagnosis. Demographic and dietary variables were selected based on previously published literature indicating association with pancreatic cancer. A multiple regression model combined the significant demographic and food items with SNPs. Final multivariate logistic regression significant factors (p-value < 0.05) associated with pancreatic cancer included: Type 2 Diabetes [OR = 6.26 (95% CI = 2.83, 14.46)], PheT [1.03 (1.02, 1.05)], very well-done red meat [0.90 (0.83, 0.96)], fruit/vegetable servings [1.35 (1.06, 1.73)], recessive (rs12203582) [4.11 (1.77, 9.91)], recessive (rs56679) [0.2 (0.06, 0.85)], overdominant (rs3784605) [3.14 (1.69, 6.01)], and overdominant (rs721430) [0.39 (0.19, 0.76)]. Of note, by design, the level of smoking did not differ between our cases and controls. This study does not provide strong evidence that PheT is a biomarker of pancreatic cancer susceptibility independent of dietary intake and select metabolism SNPs among a nonsmoking population.

Abstract Background: There are conflicting data on whether nonalcoholic fatty liver disease (NAFLD) is associated with susceptibility to pancreatic cancer. Using Mendelian randomization (MR), we investigated the relationship between genetic predisposition to NAFLD and risk for pancreatic cancer. Methods: Data from genome-wide association studies (GWAS) within the Pancreatic Cancer Cohort Consortium (PanScan; cases n = 5,090, controls n = 8,733) and the Pancreatic Cancer Case Control Consortium (PanC4; cases n = 4,163, controls n = 3,792) were analyzed. We used data on 68 genetic variants with four different MR methods [inverse variance weighting (IVW), MR-Egger, simple median, and penalized weighted median] separately to predict genetic heritability of NAFLD. We then assessed the relationship between each of the four MR methods and pancreatic cancer risk, using logistic regression to calculate ORs and 95% confidence intervals (CI), adjusting for PC risk factors, including obesity and diabetes. Results: No association was found between genetically predicted NAFLD and pancreatic cancer risk in the PanScan or PanC4 samples [e.g., PanScan, IVW OR, 1.04; 95% confidence interval (CI), 0.88–1.22; MR-Egger OR, 0.89; 95% CI, 0.65–1.21; PanC4, IVW OR, 1.07; 95% CI, 0.90–1.27; MR-Egger OR, 0.93; 95% CI, 0.67–1.28]. None of the four MR methods indicated an association between genetically predicted NAFLD and pancreatic cancer risk in either sample. Conclusions: Genetic predisposition to NAFLD is not associated with pancreatic cancer risk. Impact: Given the close relationship between NAFLD and metabolic conditions, it is plausible that any association between NAFLD and pancreatic cancer might reflect host metabolic perturbations (e.g., obesity, diabetes, or metabolic syndrome) and does not necessarily reflect a causal relationship between NAFLD and pancreatic cancer.