Investigation of selective glucocorticoid receptor modulation in high-grade serous ovarian cancer PDX models.

OBJECTIVE: In ovarian cancer (OvCa), tumor cell high glucocorticoid receptor (GR) has been associated with poor patient prognosis. In vitro, GR activation inhibits chemotherapy-induced OvCa cell death in association with transcriptional upregulation of genes encoding anti-apoptotic proteins. A recent randomized phase II study demonstrated improvement in progression-free survival (PFS) for heavily pre-treated OvCa patients randomized to receive therapy with a selective GR modulator (SGRM) plus chemotherapy compared to chemotherapy alone. We hypothesized that SGRM therapy would improve carboplatin response in OvCa patient-derived xenograft (PDX). METHODS: Six high-grade serous (HGS) OvCa PDX models expressing GR mRNA (NR3C1) and protein were treated with chemotherapy +/- SGRM. Tumor size was measured longitudinally by peritoneal transcutaneous ultrasonography. RESULTS: One of the 6 GR-positive PDX models showed a significant improvement in PFS with the addition of a SGRM. Interestingly, the single model with an improved PFS was least carboplatin sensitive. Possible explanations for the modest SGRM activity include the high carboplatin sensitivity of 5 of the PDX tumors and the potential that SGRMs activate the tumor invasive immune cells in patients (absent from immunocompromised mice). The level of tumor GR protein expression alone appears insufficient for predicting SGRM response. CONCLUSION: The significant improvement in PFS shown in 1 of the 6 models after treatment with a SGRM plus chemotherapy underscores the need to determine predictive biomarkers for SGRM therapy in HGS OvCa and to better identify patient subgroups that are most likely to benefit from adding GR modulation to chemotherapy.

Genome-wide analysis to assess if heavy alcohol consumption modifies the association between SNPs and pancreatic cancer risk.

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 3 drinks per day) for pancreatic cancer in European ancestry populations from genome-wide association studies (GWAS). 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 (Pinteraction = 5.1 x 10-8 in the meta-analysis, Pinteraction = 2.1x10-9 in the case-control studies, Pinteraction = 0.91 cohort studies) was identified. A SNP correlated with this lead SNP is an eQTL for the NRP1 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 eQTL for the NRP1, a protein that plays an important role in the development and progression of pancreatic cancer Impact: This work can provide insight into the etiology of pancreatic cancer particularly in heavy drinkers.

KRAS promotes GLI2-dependent transcription during pancreatic carcinogenesis.

Aberrant activation of GLI transcription factors has been implicated in the pathogenesis of different tumor types including pancreatic ductal adenocarcinoma (PDAC). However, the mechanistic link with established drivers of this disease remains in part elusive. Here, 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 PDAC development, and GLI2 to control oncogenic gene expression. These mice, also expressing KrasG12D, show significantly reduced median survival rate and accelerated tumorigenesis compared to the KrasG12D only expressing mice. Analysis of the mechanism using RNA-seq demonstrate higher levels of GLI2 targets, particularly tumor growth promoting genes including Ccnd1, N-Myc and Bcl2, in KrasG12D mutant cells. Further, ChIP-seq studies showed that in these cells KrasG12D increases the levels of 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.

Machine Learning Models for Pancreatic Cancer Risk Prediction Using Electronic Health Record Data-A Systematic Review and Assessment.

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.

Polycyclic Aromatic Hydrocarbons and Pancreatic Cancer: An Analysis of the Blood Biomarker, r-1,t-2,3,c-4-Tetrahydroxy-1,2,3,4-tetrahydrophenanthrene and Selected Metabolism Gene SNPs.

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.

Genetic Susceptibility to Nonalcoholic Fatty Liver Disease and Risk for Pancreatic Cancer: Mendelian Randomization.

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.

Artificial intelligence assisted whole organ pancreatic fat estimation on magnetic resonance imaging and correlation with pancreas attenuation on computed tomography.

BACKGROUND: Fatty pancreas is associated with inflammatory and neoplastic pancreatic diseases. Magnetic resonance imaging (MRI) is the diagnostic modality of choice for measuring pancreatic fat. Measurements typically use regions of interest limited by sampling and variability. We have previously described an artificial intelligence (AI)-aided approach for whole pancreas fat estimation on computed tomography (CT). In this study, we aimed to assess the correlation between whole pancreas MRI proton-density fat fraction (MR-PDFF) and CT attenuation. METHODS: We identified patients without pancreatic disease who underwent both MRI and CT between January 1, 2015 and June 1, 2020. 158 paired MRI and CT scans were available for pancreas segmentation using an iteratively trained convolutional neural network (CNN) with manual correction. Boxplots were generated to visualize slice-by-slice variability in 2D-axial slice MR-PDFF. Correlation between whole pancreas MR-PDFF and age, BMI, hepatic fat and pancreas CT-Hounsfield Unit (CT-HU) was assessed. RESULTS: Mean pancreatic MR-PDFF showed a strong inverse correlation (Spearman -0.755) with mean CT-HU. MR-PDFF was higher in males (25.22 vs 20.87; p = 0.0015) and in subjects with diabetes mellitus (25.95 vs 22.17; p = 0.0324), and was positively correlated with age and BMI. The pancreatic 2D-axial slice-to-slice MR-PDFF variability increased with increasing mean whole pancreas MR-PDFF (Spearman 0.51; p < 0.0001). CONCLUSION: Our study demonstrates a strong inverse correlation between whole pancreas MR-PDFF and CT-HU, indicating that both imaging modalities can be used to assess pancreatic fat. 2D-axial pancreas MR-PDFF is variable across slices, underscoring the need for AI-aided whole-organ measurements for objective and reproducible estimation of pancreatic fat.

Diagnostic and prognostic potential of the microbiome in ovarian cancer treatment response.

Ovarian cancer (OC) is the second most common gynecological malignancy and the fifth leading cause of death due to cancer in women in the United States mainly due to the late-stage diagnosis of this cancer. It is, therefore, critical to identify potential indicators to aid in early detection and diagnosis of this disease. We investigated the microbiome associated with OC and its potential role in detection, progression as well as prognosis of the disease. We identified a distinct OC microbiome with general enrichment of several microbial taxa, including Dialister, Corynebacterium, Prevotella, and Peptoniphilus in the OC cohort in all body sites excluding stool and omentum which were not sampled from the benign cohort. These taxa were, however, depleted in the advanced-stage and high-grade OC patients compared to early-stage and low-grade OC patients suggestive of decrease accumulation in advanced disease and could serve as potential indicators for early detection of OC. Similarly, we also observed the accumulation of these mainly pathogenic taxa in OC patients with adverse treatment outcomes compared to those without events and could also serve as potential indicators for predicting patients' responses to treatment. These findings provide important insights into the potential use of the microbiome as indicators in (1) early detection of and screening for OC and (2) predicting patients' response to treatment. Given the limited number of patients enrolled in the study, these results would need to be further investigated and confirmed in a larger study.

A Clinician's Guide to Bioinformatics for Next-Generation Sequencing.

Next-generation sequencing (NGS) technologies are high-throughput methods for DNA sequencing and have become a widely adopted tool in cancer research. The sheer amount and variety of data generated by NGS assays require sophisticated computational methods and bioinformatics expertise. In this review, we provide background details of NGS technology and basic bioinformatics concepts for the clinician investigator interested in cancer research applications, with a focus on DNA-based approaches. We introduce the general principles of presequencing library preparation, postsequencing alignment, and variant calling. We also highlight the common variant annotations and NGS applications for other molecular data types. Finally, we briefly discuss the revealed utility of NGS methods in NSCLC research and study design considerations for research studies that aim to leverage NGS technologies for clinical care.

Culture media composition influences patient-derived organoid ability to predict therapeutic responses in gastrointestinal cancers.

BACKGROUNDA patient-derived organoid (PDO) platform may serve as a promising tool for translational cancer research. In this study, we evaluated PDO's ability to predict clinical response to gastrointestinal (GI) cancers.METHODSWe generated PDOs from primary and metastatic lesions of patients with GI cancers, including pancreatic ductal adenocarcinoma, colorectal adenocarcinoma, and cholangiocarcinoma. We compared PDO response with the observed clinical response for donor patients to the same treatments.RESULTSWe report an approximately 80% concordance rate between PDO and donor tumor response. Importantly, we found a profound influence of culture media on PDO phenotype, where we showed a significant difference in response to standard-of-care chemotherapies, distinct morphologies, and transcriptomes between media within the same PDO cultures.CONCLUSIONWhile we demonstrate a high concordance rate between donor tumor and PDO, these studies also showed the important role of culture media when using PDOs to inform treatment selection and predict response across a spectrum of GI cancers.TRIAL REGISTRATIONNot applicable.FUNDINGThe Joan F. & Richard A. Abdoo Family Fund in Colorectal Cancer Research, GI Cancer program of the Mayo Clinic Cancer Center, Mayo Clinic SPORE in Pancreatic Cancer, Center of Individualized Medicine (Mayo Clinic), Department of Laboratory Medicine and Pathology (Mayo Clinic), Incyte Pharmaceuticals and Mayo Clinic Hepatobiliary SPORE, University of Minnesota-Mayo Clinic Partnership, and the Early Therapeutic program (Department of Oncology, Mayo Clinic).

Machine-learning aided in situ drug sensitivity screening predicts treatment outcomes in ovarian PDX tumors.

Long-term treatment outcomes for patients with high grade ovarian cancers have not changed despite innovations in therapies. There is no recommended assay for predicting patient response to second-line therapy, thus clinicians must make treatment decisions based on each individual patient. Patient-derived xenograft (PDX) tumors have been shown to predict drug sensitivity in ovarian cancer patients, but the time frame for intraperitoneal (IP) tumor generation, expansion, and drug screening is beyond that for tumor recurrence and platinum resistance to occur, thus results do not have clinical utility. We describe a drug sensitivity screening assay using a drug delivery microdevice implanted for 24 h in subcutaneous (SQ) ovarian PDX tumors to predict treatment outcomes in matched IP PDX tumors in a clinically relevant time frame. The SQ tumor response to local microdose drug exposure was found to be predictive of the growth of matched IP tumors after multi-week systemic therapy using significantly fewer animals (10 SQ vs 206 IP). Multiplexed immunofluorescence image analysis of phenotypic tumor response combined with a machine learning classifier could predict IP treatment outcomes against three second-line cytotoxic therapies with an average AUC of 0.91.

Influence of Cancer Susceptibility Gene Mutations and ABO Blood Group of Pancreatic Cancer Probands on Concomitant Risk to First-Degree Relatives.

BACKGROUND: ABO blood group is associated with pancreatic cancer risk. Whether ABO blood group alone or when combined with inherited mutation status of index pancreatic cancer cases (probands) can enhance pancreatic cancer risk estimation in first-degree relatives (FDR) is unclear. We examined FDRs' risk for pancreatic cancer based on probands' ABO blood group and probands' cancer susceptibility gene mutation status. METHODS: Data on 23,739 FDRs, identified through 3,268 pancreatic cancer probands, were analyzed. Probands' ABO blood groups were determined serologically or genetically, and 20 cancer susceptibility genes were used to classify probands as "mutation-positive" or "mutation-negative." SIRs and 95% confidence intervals (CI) were calculated, comparing observed pancreatic cancer cases in the FDRs with the number expected in SEER-21 (reference population). RESULTS: Overall, FDRs had 2-fold risk of pancreatic cancer (SIR = 2.00; 95% CI = 1.79-2.22). Pancreatic cancer risk was higher in FDRs of mutation-positive (SIR = 3.80; 95% CI = 2.81-5.02) than mutation-negative (SIR = 1.79; 95% CI = 1.57-2.04) probands (P < 0.001). The magnitude of risk did not differ by ABO blood group alone (SIRblood-group-O = 1.57; 95% CI = 1.20-2.03, SIRnon-O = 1.83; 95% CI = 1.53-2.17; P = 0.33). Among FDRs of probands with non-O blood group, pancreatic cancer risk was higher in FDRs of mutation-positive (SIR = 3.98; 95% CI = 2.62-5.80) than mutation-negative (SIR = 1.66; 95% CI = 1.35-2.03) probands (P < 0.001), but risk magnitudes were statistically similar when probands had blood group O (SIRmutation-positive = 2.65; 95% CI = 1.09-5.47, SIRmutation-negative = 1.48; 95% CI = 1.06-5.47; P = 0.16). CONCLUSIONS: There is a range of pancreatic cancer risk to FDRs according to probands' germline mutation status and ABO blood group, ranging from 1.48 for FDRs of probands with blood group O and mutation-negative to 3.98 for FDRs of probands with non-O blood group and mutation-positive. IMPACT: Combined ABO blood group and germline mutation status of probands can inform pancreatic cancer risk estimation in FDRs.

Repurposing Ceritinib Induces DNA Damage and Enhances PARP Inhibitor Responses in High-Grade Serous Ovarian Carcinoma.

PARP inhibitors (PARPi) have activity in homologous recombination (HR) repair-deficient, high-grade serous ovarian cancers (HGSOC). However, even responsive tumors develop PARPi resistance, highlighting the need to delay or prevent the appearance of PARPi resistance. Here, we showed that the ALK kinase inhibitor ceritinib synergizes with PARPis by inhibiting complex I of the mitochondrial electron transport chain, which increases production of reactive oxygen species (ROS) and subsequent induction of oxidative DNA damage that is repaired in a PARP-dependent manner. In addition, combined treatment with ceritinib and PARPi synergized in HGSOC cell lines irrespective of HR status, and a combination of ceritinib with the PARPi olaparib induced tumor regression more effectively than olaparib alone in HGSOC patient-derived xenograft (PDX) models. Notably, the ceritinib and olaparib combination was most effective in PDX models with preexisting PARPi sensitivity and was well tolerated. These findings unveil suppression of mitochondrial respiration, accumulation of ROS, and subsequent induction of DNA damage as novel effects of ceritinib. They also suggest that the ceritinib and PARPi combination warrants further investigation as a means to enhance PARPi activity in HGSOC, particularly in tumors with preexisting HR defects. SIGNIFICANCE: The kinase inhibitor ceritinib synergizes with PARPi to induce tumor regression in ovarian cancer models, suggesting that ceritinib combined with PARPi may be an effective strategy for treating ovarian cancer.

CHFR and Paclitaxel Sensitivity of Ovarian Cancer.

The poly(ADP-ribose) binding protein CHFR regulates cellular responses to mitotic stress. The deubiquitinase UBC13, which regulates CHFR levels, has been associated with better overall survival in paclitaxel-treated ovarian cancer. Despite the extensive use of taxanes in the treatment of ovarian cancer, little is known about expression of CHFR itself in this disease. In the present study, tissue microarrays containing ovarian carcinoma samples from 417 women who underwent initial surgical debulking were stained with anti-CHFR antibody and scored in a blinded fashion. CHFR levels, expressed as a modified H-score, were examined for association with histology, grade, time to progression (TTP) and overall survival (OS). In addition, patient-derived xenografts from 69 ovarian carcinoma patients were examined for CHFR expression and sensitivity to paclitaxel monotherapy. In clinical ovarian cancer specimens, CHFR expression was positively associated with serous histology (p = 0.0048), higher grade (p = 0.000014) and higher stage (p = 0.016). After correction for stage and debulking, there was no significant association between CHFR staining and overall survival (p = 0.62) or time to progression (p = 0.91) in patients with high grade serous cancers treated with platinum/taxane chemotherapy (N = 249). Likewise, no association between CHFR expression and paclitaxel sensitivity was observed in ovarian cancer PDXs treated with paclitaxel monotherapy. Accordingly, differences in CHFR expression are unlikely to play a major role in paclitaxel sensitivity of high grade serous ovarian cancer.

Group III phospholipase A2 downregulation attenuated survival and metastasis in ovarian cancer and promotes chemo-sensitization.

BACKGROUND: Aberrant lipogenicity and deregulated autophagy are common in most advanced human cancer and therapeutic strategies to exploit these pathways are currently under consideration. Group III Phospholipase A2 (sPLA2-III/PLA2G3), an atypical secretory PLA2, is recognized as a regulator of lipid metabolism associated with oncogenesis. Though recent studies reveal that high PLA2G3 expression significantly correlates with poor prognosis in several cancers, however, role of PLA2G3 in ovarian cancer (OC) pathogenesis is still undetermined. METHODS: CRISPR-Cas9 and shRNA mediated knockout and knockdown of PLA2G3 in OC cells were used to evaluate lipid droplet (LD) biogenesis by confocal and Transmission electron microscopy analysis, and the cell viability and sensitization of the cells to platinum-mediated cytotoxicity by MTT assay. Regulation of primary ciliation by PLA2G3 downregulation both genetically and by metabolic inhibitor PFK-158 induced autophagy was assessed by immunofluorescence-based confocal analysis and immunoblot. Transient transfection with GFP-RFP-LC3B and confocal analysis was used to assess the autophagic flux in OC cells. PLA2G3 knockout OVCAR5 xenograft in combination with carboplatin on tumor growth and metastasis was assessed in vivo. Efficacy of PFK158 alone and with platinum drugs was determined in patient-derived primary ascites cultures expressing PLA2G3 by MTT assay and immunoblot analysis. RESULTS: Downregulation of PLA2G3 in OVCAR8 and 5 cells inhibited LD biogenesis, decreased growth and sensitized cells to platinum drug mediated cytotoxicity in vitro and in in vivo OVCAR5 xenograft. PLA2G3 knockdown in HeyA8MDR-resistant cells showed sensitivity to carboplatin treatment. We found that both PFK158 inhibitor-mediated and genetic downregulation of PLA2G3 resulted in increased number of percent ciliated cells and inhibited cancer progression. Mechanistically, we found that PFK158-induced autophagy targeted PLA2G3 to restore primary cilia in OC cells. Of clinical relevance, PFK158 also induces percent ciliated cells in human-derived primary ascites cells and reduces cell viability with sensitization to chemotherapy. CONCLUSIONS: Taken together, our study for the first time emphasizes the role of PLA2G3 in regulating the OC metastasis. This study further suggests the therapeutic potential of targeting phospholipases and/or restoration of PC for future OC treatment and the critical role of PLA2G3 in regulating ciliary function by coordinating interface between lipogenesis and metastasis.

Experimental design of preclinical experiments: number of PDX lines vs subsampling within PDX lines.

BACKGROUND: Appropriately designed preclinical patient-derived xenograft (PDX) experiments are important to accurately inform human clinical trials. There is little experimental design guidance regarding choosing the number of PDX lines to study, and the number of mice within each PDX line. METHODS: Retrospective data from IDH-wildtype glioblastoma preclinical experiments evaluating a uniform regimen of fractionated radiation (RT), temozolomide (TMZ) chemotherapy, and concurrent RT/TMZ across 27 PDX lines were used to evaluate experimental designs and empirically estimate statistical power for ANOVA and Cox regression. RESULTS: Increasing the number of PDX lines resulted in more precise and reproducible estimates of effect size. To achieve 80% statistical power using ANOVA, experiments using a single PDX line required subsampling of 6 mice per PDX for each treatment group to detect a difference in survival of 135 days, and 9 mice per PDX to detect a difference of 100 days. Alternatively, a design that used 10 PDX lines had greater than 80% power to detect a difference of 135 days with a single mouse per PDX per treatment group, a difference of 100 days with 2 mice per PDX per treatment, and 35 days with more than 10 mice per PDX per treatment. Power for Cox regression was slightly smaller than ANOVA for very small experiments regardless of effect size and slightly higher than ANOVA for detecting a smaller effect size of 35 days difference in survival for moderate-to-large experiments. CONCLUSIONS: Experimental designs using few mice across many PDX lines can provide robust results and account for inter-tumor variability.

Statistical analysis of comparative tumor growth repeated measures experiments in the ovarian cancer patient derived xenograft (PDX) setting.

Repeated measures studies are frequently performed in patient-derived xenograft (PDX) models to evaluate drug activity or compare effectiveness of cancer treatment regimens. Linear mixed effects regression models were used to perform statistical modeling of tumor growth data. Biologically plausible structures for the covariation between repeated tumor burden measurements are explained. Graphical, tabular, and information criteria tools useful for choosing the mean model functional form and covariation structure are demonstrated in a Case Study of five PDX models comparing cancer treatments. Power calculations were performed via simulation. Linear mixed effects regression models applied to the natural log scale were shown to describe the observed data well. A straight growth function fit well for two PDX models. Three PDX models required quadratic or cubic polynomial (time squared or cubed) terms to describe delayed tumor regression or initial tumor growth followed by regression. Spatial(power), spatial(power) + RE, and RE covariance structures were found to be reasonable. Statistical power is shown as a function of sample size for different levels of variation. Linear mixed effects regression models provide a unified and flexible framework for analysis of PDX repeated measures data, use all available data, and allow estimation of tumor doubling time.

Biomarker Discovery and Validation: Statistical Considerations.

Biomarkers have various applications including disease detection, diagnosis, prognosis, prediction of response to intervention, and disease monitoring. In this era of precision medicine, having validated biomarkers to inform clinical decision making is more important than ever. In this article, we discuss best the practices and potential issues in biomarker discovery and validation. We encourage team science partnerships to bring cutting-edge discovery from bench to bedside, leading to improved patient care and outcomes.

Smoking Modifies Pancreatic Cancer Risk Loci on 2q21.3.

Germline variation and smoking are independently associated with pancreatic ductal adenocarcinoma (PDAC). We conducted genome-wide smoking interaction analysis of PDAC using genotype data from four previous genome-wide association studies in individuals of European ancestry (7,937 cases and 11,774 controls). Examination of expression quantitative trait loci data from the Genotype-Tissue Expression Project followed by colocalization analysis was conducted to determine whether there was support for common SNP(s) underlying the observed associations. Statistical tests were two sided and P < 5 × 10-8 was considered statistically significant. Genome-wide significant evidence of qualitative interaction was identified on chr2q21.3 in intron 5 of the transmembrane protein 163 (TMEM163) and upstream of the cyclin T2 (CCNT2). The most significant SNP using the Empirical Bayes method, in this region that included 45 significantly associated SNPs, was rs1818613 [per allele OR in never smokers 0.87, 95% confidence interval (CI), 0.82-0.93; former smokers 1.00, 95% CI, 0.91-1.07; current smokers 1.25, 95% CI 1.12-1.40, P interaction = 3.08 × 10-9). Examination of the Genotype-Tissue Expression Project data demonstrated an expression quantitative trait locus in this region for TMEM163 and CCNT2 in several tissue types. Colocalization analysis supported a shared SNP, rs842357, in high linkage disequilibrium with rs1818613 (r 2 = 0. 94) driving both the observed interaction and the expression quantitative trait loci signals. Future studies are needed to confirm and understand the differential biologic mechanisms by smoking status that contribute to our PDAC findings. SIGNIFICANCE: This large genome-wide interaction study identifies a susceptibility locus on 2q21.3 that significantly modified PDAC risk by smoking status, providing insight into smoking-associated PDAC, with implications for prevention.

Co-expression patterns of chimeric antigen receptor (CAR)-T cell target antigens in primary and recurrent ovarian cancer.

OBJECTIVE: Chimeric antigen receptor (CAR)-T cell strategies ideally target a surface antigen that is exclusively and uniformly expressed by tumors; however, no such antigen is known for high-grade serous ovarian carcinoma (HGSC). A potential solution involves combinatorial antigen targeting with AND or OR logic-gating. Therefore, we investigated co-expression of CA125, Mesothelin (MSLN) and Folate Receptor alpha (FOLRA) on individual tumor cells in HGSC. METHODS: RNA expression of CA125, MSLN, and FOLR1 was assessed using TCGA (HGSC) and GTEx (healthy tissues) databases. Antigen expression profiles and CD3+, CD8+ and CD20+ tumor-infiltrating lymphocyte (TIL) patterns were assessed in primary and recurrent HGSC by multiplex immunofluorescence and immunohistochemistry. RESULTS: At the transcriptional level, each antigen was overexpressed in >90% of cases; however, MSLN and FOLR1 showed substantial expression in healthy tissues. At the protein level, CA125 was expressed by the highest proportion of cases and tumor cells per case, followed by MSLN and FOLRA. The most promising pairwise combination was CA125 and/or MSLN (OR gate), with 51.9% of cases containing ≥90% of tumor cells expressing one or both antigens. In contrast, only 5.8% of cases contained ≥90% of tumor cells co-expressing CA125 and MSLN (AND gate). Antigen expression patterns showed modest correlations with TIL. Recurrent tumors retained expression of all three antigens and showed increased TIL densities. CONCLUSIONS: An OR-gated CAR-T cell strategy against CA125 and MSLN would target the majority of tumor cells in most cases. Antigen expression and T-cell infiltration patterns are favorable for this strategy in primary and recurrent disease.