Detecting drug resistance in pancreatic cancer organoids guides optimized chemotherapy treatment.

Drug combination therapies for cancer treatment show high efficacy but often induce severe side effects, resulting in dose or cycle number reduction. We investigated the impact of neoadjuvant chemotherapy (neoCTx) adaptions on treatment outcome in 59 patients with pancreatic ductal adenocarcinoma (PDAC). Resections with tumor-free margins were significantly more frequent when full-dose neoCTx was applied. We determined if patient-derived organoids (PDOs) can be used to personalize poly-chemotherapy regimens by pharmacotyping of treatment-naïve and post-neoCTx PDAC PDOs. Five out of ten CTx-naïve PDO lines exhibited a differential response to either the FOLFIRINOX or the Gem/Pac regimen. NeoCTx PDOs showed a poor response to the neoadjuvant regimen that had been administered to the respective patient in 30% of cases. No significant difference in PDO response was noted when comparing modified treatments in which the least effective single drug was removed from the complete regimen. Drug testing of CTx-naïve PDAC PDOs and neoCTx PDOs may be useful to guide neoadjuvant and adjuvant regimen selection, respectively. Personalizing poly-chemotherapy regimens by omitting substances with low efficacy could potentially result in less severe side effects, thereby increasing the fraction of patients receiving a full course of neoadjuvant treatment. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Deep Learning Improves Pancreatic Cancer Diagnosis Using RNA-Based Variants.

For optimal pancreatic cancer treatment, early and accurate diagnosis is vital. Blood-derived biomarkers and genetic predispositions can contribute to early diagnosis, but they often have limited accuracy or applicability. Here, we seek to exploit the synergy between them by combining the biomarker CA19-9 with RNA-based variants. We use deep sequencing and deep learning to improve differentiating pancreatic cancer and chronic pancreatitis. We obtained samples of nucleated cells found in peripheral blood from 268 patients suffering from resectable, non-resectable pancreatic cancer, and chronic pancreatitis. We sequenced RNA with high coverage and obtained millions of variants. The high-quality variants served as input together with CA19-9 values to deep learning models. Our model achieved an area under the curve (AUC) of 96% in differentiating resectable cancer from pancreatitis using a test cohort. Moreover, we identified variants to estimate survival in resectable cancer. We show that the blood transcriptome harbours variants, which can substantially improve noninvasive clinical diagnosis.

CFTR Expression Analysis for Subtyping of Human Pancreatic Cancer Organoids.

BACKGROUND: Organoid cultures of human pancreatic ductal adenocarcinoma (PDAC) have become a promising tool for tumor subtyping and individualized chemosensitivity testing. PDACs have recently been grouped into different molecular subtypes with clinical impact based on cytokeratin-81 (KRT81) and hepatocyte nuclear factor 1A (HNF1A). However, a suitable antibody for HNF1A is currently unavailable. The present study is aimed at establishing subtyping in PDAC organoids using an alternative marker. METHODS: A PDAC organoid biobank was generated from human primary tumor samples containing 22 lines. Immunofluorescence staining was established and done for 10 organoid lines for cystic fibrosis transmembrane conductance regulator (CFTR) and KRT81. Quantitative real-time PCR (qPCR) was performed for CFTR and HNF1A. A chemotherapeutic drug response analysis was done using gemcitabine, 5-FU, oxaliplatin, and irinotecan. RESULTS: A biobank of patient-derived PDAC organoids was established. The efficiency was 71% (22/31) with 68% for surgical resections and 83% for fine needle aspirations. Organoids could be categorized into the established quasimesenchymal, exocrine-like, and classical subtypes based on KRT81 and CFTR immunoreactivity. CFTR protein expression was confirmed on the transcript level. CFTR and HNF1A transcript expression levels positively correlated (n = 10; r = 0.927; p = 0.001). PDAC subtypes of the primary tumors and the corresponding organoid lines were identical for most of the cases analyzed (6/7). Treatment with chemotherapeutic drugs revealed tendencies but no significant differences regarding drug responses. CONCLUSIONS: Human PDAC organoids can be classified into known subtypes based on KRT81 and CFTR immunoreactivity. CFTR and HNF1A mRNA levels correlated well. Furthermore, subtype-specific immunoreactivity matched well between PDAC organoids and the respective primary tumor tissue. Subtyping of human PDACs using CFTR might constitute an alternative to HNF1A and should be further investigated.

Erratum to "CFTR Expression Analysis for Subtyping of Human Pancreatic Cancer Organoids".

[This corrects the article DOI: 10.1155/2019/1024614.].

The G Protein-Coupled Receptor RAI3 Is an Independent Prognostic Factor for Pancreatic Cancer Survival and Regulates Proliferation via STAT3 Phosphorylation.

Pancreatic Ductal Adenocarcinoma (PDAC) is one of the deadliest tumors worldwide. Understanding the function of gene expression alterations is a prerequisite for developing new strategies in diagnostic and therapy. GPRC5A (RAI3), coding for a seven transmembrane G protein-coupled receptor is known to be overexpressed in pancreatic cancer and might be an interesting candidate for therapeutic intervention. Expression levels of RAI3 were compared using a tissue microarray of 435 resected patients with pancreatic cancer as well as 209 samples from chronic pancreatitis (CP), intra-ductal papillary mucinous neoplasm (IPMN) and normal pancreatic tissue. To elucidate the function of RAI3 overexpression, siRNA based knock-down was used and transfected cells were analyzed using proliferation and migration assays. Pancreatic cancer patients showed a statistically significant overexpression of RAI3 in comparison to normal and chronic pancreatitis tissue. Especially the loss of apical RAI3 expression represents an independent prognostic parameter for overall survival of patients with pancreatic cancer. Suppression of GPRC5a results in decreased cell growth, proliferation and migration in pancreatic cancer cell lines via a STAT3 modulated pathway, independent from ERK activation.

Simultaneous gene silencing of KRAS and anti-apoptotic genes as a multitarget therapy.

Pancreatic cancer is one of the most lethal tumor types worldwide and an effective therapy is still elusive. Targeted therapy focused against a specific alteration is by definition unable to attack broad pathway signaling modification. Tumor heterogeneity will render targeted therapies ineffective based on the regrowth of cancer cell sub-clones. Therefore multimodal therapy strategies, targeting signaling pathways simultaneously should improve treatment.SiRNAs against KRAS and the apoptosis associated genes BCLXL, FLIP, MCL1L, SURVIVIN and XIAP were transfected into human and murine pancreatic cancer cell lines. Induction of apoptosis was measured by Caspase 3/7 activation, subG1 FACS analysis and PARP cleavage. The therapeutic approach was tested in a subcutaneous allograft model with a murine cancer cell line.By using siRNAs as a systematic approach to remodel signal transduction in pancreatic cancer the results showed increasing inhibition of proliferation and apoptosis induction in vitro and in vivo. Thus, siRNAs are suitable to model multimodal therapy against signaling pathways in pancreatic cancer. Improvements in in vivo delivery of siRNAs against a multitude of targets might therefore be a potential therapeutic approach.

Google goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes.

Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice.

Hepatocyte nuclear factor (HNF) 4α expression distinguishes ampullary cancer subtypes and prognosis after resection.

OBJECTIVE: To investigate biological differences and prognostic indicators of different ampullary cancer (AC) subtypes. BACKGROUND: AC is associated with a favorable prognosis compared with other periampullary carcinomas. Aside from other prognostic factors, the histological origin of AC may determine survival. Specifically, the pancreatobiliary subtype of AC displays worse prognosis compared with the intestinal subtype. However, knowledge of inherent molecular characteristics of different periampullary tumors and their effects on prognosis has been limited. METHODS: Gene expression profiling was used to screen for differential gene expression between 6 PDAC cases and 12 AC cases. Among others, hepatocyte nuclear factor 4α (HNF4α) mRNA overexpression was observed in AC cases. Nuclear HNF4α protein expression was assessed using tissue microarrays consisting of 99 individual AC samples. The correlation of HNF4α expression with clinicopathological data (n = 99) and survival (n = 84) was assessed. RESULTS: HNF4α mRNA is 7.61-fold up-regulated in AC compared with that in PDAC. Bioinformatics analyses indicated its key role in dysregulated signaling pathways. Nuclear HNF4α expression correlates with histological subtype, grading, CDX2 positivity, MUC1 negativity and presence of adenomatous components in the carcinoma. The presence of HNF4α is a univariate predictor of survival in AC mean survival (50 months versus 119 months, P = 0.002). Multivariate analysis revealed that HNF4α negativity (HR = 17.95, 95% CI: 2.35-136.93, P = 0.005) and lymph node positivity (HR = 3.33, 95% CI: 1.36-8.18, P = 0.009) are independent negative predictors of survival. CONCLUSIONS: Immunohistochemical determination of HNF4α expression is an effective tool for distinguishing different AC subtypes. Similarly, HNF4α protein expression is an independent predictor of favorable prognosis in carcinoma of the papilla of Vater and may serve for risk stratification after curative resection.

Activation of Wnt signalling in stroma from pancreatic cancer identified by gene expression profiling.

Pancreatic ductal adenocarcinoma (PDAC) is characterized by an abundant desmoplastic stroma. Interactions between cancer and stromal cells play a critical role in tumour invasion, metastasis and chemoresistance. Therefore, we hypothesized that gene expression profile of the stromal components of pancreatic carcinoma is different from chronic pancreatitis and reflects the interaction with the tumour. We investigated the gene expression of eleven stromal tissues from PDAC, nine from chronic pancreatitis and cell lines of stromal origin using the Affymetrix U133 GeneChip set. The tissue samples were microdissected, the RNA was extracted, amplified and labelled using a repetitive in vitro transcription protocol. Differentially expressed genes were identified and validated using quantitative RT-PCR and immuno-histochemistry. We found 255 genes to be overexpressed and 61 genes to be underexpressed within the stroma of pancreatic carcinoma compared to the stroma of chronic pancreatitis. Analysis of the involved signal transduction pathways revealed a number of genes associated with the Wnt pathway of which the differential expression of SFRP1 and WNT5a was confirmed using immunohistochemistry. Moreover, we could demonstrate that WNT5a expression was induced in fibroblasts during cocultivation with a pancreatic carcinoma cell line. The identified differences in the expression profile of stroma cells derived from tumour compared to cells of inflammatory origin suggest a specific response of the tissue surrounding malignant cells. The overexpression of WNT5a, a gene involved in the non canonical Wnt signalling and chondrocyte development might contribute to the strong desmoplastic reaction seen in pancreatic cancer.

Gene expression profiling of microdissected pancreatic ductal carcinomas using high-density DNA microarrays.

Pancreatic ductal adenocarcinoma (PDAC) remains an important cause of malignancy-related death and is the eighth most common cancer with the lowest overall 5-year relative survival rate. To identify new molecular markers and candidates for new therapeutic regimens, we investigated the gene expression profile of microdissected cells from 11 normal pancreatic ducts, 14 samples of PDAC, and 4 well-characterized pancreatic cancer cell lines using the Affymetrix U133 GeneChip set. RNA was extracted from microdissected samples and cell lines, amplified, and labeled using a repetitive in vitro transcription protocol. Differentially expressed genes were identified using the significance analysis of microarrays program. We found 616 differentially expressed genes. Within these, 140 were also identified in PDAC by others, such as Galectin-1, Galectin-3, and MT-SP2. We validated the differential expression of several genes (e.g., CENPF, MCM2, MCM7, RAMP, IRAK1, and PTTG1) in PDAC by immunohistochemistry and reverse transcription polymerase chain reaction. We present a whole genome expression study of microdissected tissues from PDAC, from microdissected normal ductal pancreatic cells and pancreatic cancer cell lines using high-density microarrays. Within the panel of genes, we identified novel differentially expressed genes, which have not been associated with the pathogenesis of PDAC before.