Heat shock protein 27 as a prognostic and predictive biomarker in pancreatic ductal adenocarcinoma

A role of heat shock protein 27 (HSP27) as a potential biomarker has been reported in various tumour entities, but comprehensive studies in pancreatic cancer are lacking. Applying tissue microarray (TMA) analysis, we correlated HSP27 protein expression status with clinicopathologic parameters in pancreatic ductal adenocarcinoma specimens from 86 patients. Complementary, we established HSP27 overexpression and RNA-interference models to assess the impact of HSP27 on chemo- and radiosensitivity directly in pancreatic cancer cells. In the TMA study, HSP27 expression was found in 49% of tumour samples. Applying univariate analyses, a significant correlation was found between HSP27 expression and survival. In the multivariate Cox-regression model, HSP27 expression emerged as an independent prognostic factor. HSP27 expression also correlated inversely with nuclear p53 accumulation, indicating either protein interactions between HSP27 and p53 or TP53 mutation-dependent HSP27-regulation in pancreatic cancer. In the sensitivity studies, HSP27 overexpression rendered HSP27 low-expressing PL5 pancreatic cancer cells more susceptible towards treatment with gemcitabine. Vice versa, HSP27 protein depletion in HSP27 high-expressing AsPC-1 cells caused increased gemcitabine resistance. Importantly, HSP27 expression was inducible in pancreatic cancer cell lines as well as primary cells. Taken together, our study suggests a role for HSP27 as a prognostic and predictive marker in pancreatic cancer. Assessment of HSP27 expression could thus facilitate the identification of specific patient subpopulations that might benefit from individualized treatment options. Additional studies need to clarify whether modulation of HSP27 expression could represent an attractive concept to support the incorporation of hyperthermia in clinical treatment protocols for pancreatic cancer.     

Reprogramming of human cancer cells to pluripotency for models of cancer progression

The ability to study live cells as they progress through the stages of cancer provides the opportunity to discover dynamic networks underlying pathology, markers of early stages, and ways to assess therapeutics. Genetically engineered animal models of cancer, where it is possible to study the consequences of temporal‐specific induction of oncogenes or deletion of tumor suppressors, have yielded major insights into cancer progression. Yet differences exist between animal and human cancers, such as in markers of progression and response to therapeutics. Thus, there is a need for human cell models of cancer progression. Most human cell models of cancer are based on tumor cell lines and xenografts of primary tumor cells that resemble the advanced tumor state, from which the cells were derived, and thus do not recapitulate disease progression. Yet a subset of cancer types have been reprogrammed to pluripotency or near‐pluripotency by blastocyst injection, by somatic cell nuclear transfer and by induced pluripotent stem cell (iPS) technology. The reprogrammed cancer cells show that pluripotency can transiently dominate over the cancer phenotype. Diverse studies show that reprogrammed cancer cells can, in some cases, exhibit early‐stage phenotypes reflective of only partial expression of the cancer genome. In one case, reprogrammed human pancreatic cancer cells have been shown to recapitulate stages of cancer progression, from early to late stages, thus providing a model for studying pancreatic cancer development in human cells where previously such could only be discerned from mouse models. We discuss these findings, the challenges in developing such models and their current limitations, and ways that iPS reprogramming may be enhanced to develop human cell models of cancer progression.     

Integrated proteomic profiling of cell line conditioned media and pancreatic juice for the identification of pancreatic cancer biomarkers

Pancreatic cancer is one of the leading causes of cancer-related deaths, for which serological biomarkers are urgently needed. Most discovery-phase studies focus on the use of one biological source for analysis. The present study details the combined mining of pancreatic cancer-related cell line conditioned media and pancreatic juice for identification of putative diagnostic leads. Using strong cation exchange chromatography, followed by LC-MS/MS on an LTQ-Orbitrap mass spectrometer, we extensively characterized the proteomes of conditioned media from six pancreatic cancer cell lines (BxPc3, MIA-PaCa2, PANC1, CAPAN1, CFPAC1, and SU.86.86), the normal human pancreatic ductal epithelial cell line HPDE, and two pools of six pancreatic juice samples from ductal adenocarcinoma patients. All samples were analyzed in triplicate. Between 1261 and 2171 proteins were identified with two or more peptides in each of the cell lines, and an average of 521 proteins were identified in the pancreatic juice pools. In total, 3479 nonredundant proteins were identified with high confidence, of which ～ 40% were extracellular or cell membrane-bound based on Genome Ontology classifications. Three strategies were employed for identification of candidate biomarkers: (1) examination of differential protein expression between the cancer and normal cell lines using label-free protein quantification, (2) integrative analysis, focusing on the overlap of proteins among the multiple biological fluids, and (3) tissue specificity analysis through mining of publically available databases. Preliminary verification of anterior gradient homolog 2, syncollin, olfactomedin-4, polymeric immunoglobulin receptor, and collagen alpha-1(VI) chain in plasma samples from pancreatic cancer patients and healthy controls using ELISA, showed a significant increase (p < 0.01) of these proteins in plasma from pancreatic cancer patients. The combination of these five proteins showed an improved area under the receiver operating characteristic curve to CA19.9 alone. Further validation of these proteins is warranted, as is the investigation of the remaining group of candidates.     

Integration of clinical and gene expression data has a synergetic effect on predicting breast cancer outcome

Breast cancer outcome can be predicted using models derived from gene expression data or clinical data. Only a few studies have created a single prediction model using both gene expression and clinical data. These studies often remain inconclusive regarding an obtained improvement in prediction performance. We rigorously compare three different integration strategies (early, intermediate, and late integration) as well as classifiers employing no integration (only one data type) using five classifiers of varying complexity. We perform our analysis on a set of 295 breast cancer samples, for which gene expression data and an extensive set of clinical parameters are available as well as four breast cancer datasets containing 521 samples that we used as independent validation.mOn the 295 samples, a nearest mean classifier employing a logical OR operation (late integration) on clinical and expression classifiers significantly outperforms all other classifiers. Moreover, regardless of the integration strategy, the nearest mean classifier achieves the best performance. All five classifiers achieve their best performance when integrating clinical and expression data. Repeating the experiments using the 521 samples from the four independent validation datasets also indicated a significant performance improvement when integrating clinical and gene expression data. Whether integration also improves performances on other datasets (e.g. other tumor types) has not been investigated, but seems worthwhile pursuing. Our work suggests that future models for predicting breast cancer outcome should exploit both data types by employing a late OR or intermediate integration strategy based on nearest mean classifiers.     

Selective radiosensitization of p53 mutant pancreatic cancer cells by combined inhibition of Chk1 and PARP1

We have recently shown that inhibition of HRR (homologous recombination repair) by Chk1 (checkpoint kinase 1) inhibition radiosensitizes pancreatic cancer cells and others have demonstrated that Chk1 inhibition selectively sensitizes p53 mutant tumor cells. Furthermore, PARP1 [poly (ADP-ribose) polymerase-1] inhibitors dramatically radiosensitize cells with DNA double strand break repair defects. Thus, we hypothesized that inhibition of HRR (mediated by Chk1 via AZD7762) and PARP1 [via olaparib (AZD2281)] would selectively sensitize p53 mutant pancreatic cancer cells to radiation. We also used 2 isogenic p53 cell models to assess the role of p53 status in cancer cells and intestinal epithelial cells to assess overall cancer specificity. DNA damage response and repair were assessed by flow cytometry, γH2AX, and an HRR reporter assay. We found that the combination of AZD7762 and olaparib produced significant radiosensitization in p53 mutant pancreatic cancer cells and in all of the isogenic cancer cell lines. The magnitude of radiosensitization by AZD7762 and olaparib was greater in p53 mutant cells compared with p53 wild type cells. Importantly, normal intestinal epithelial cells were not radiosensitized. The combination of AZD7762 and olaparib caused G₂ checkpoint abrogation, inhibition of HRR, and persistent DNA damage responses. These findings demonstrate that the combination of Chk1 and PARP1 inhibition selectively radiosensitizes p53 mutant pancreatic cancer cells. Furthermore, these studies suggest that inhibition of HRR by Chk1 inhibitors may be a useful strategy for selectively inducing a BRCA1/2 ‘deficient-like’ phenotype in p53 mutant tumor cells, while sparing normal tissue.     

Mechanisms of measles virus oncolytic immunotherapy

The study of measles virus (MeV) as a cancer immunotherapeutic was prompted by clinical observations of leukemia and lymphoma regressions in patients following measles virus infection in the 1970s and 1980s. Since then, numerous preclinical studies have confirmed the oncolytic activity of MeV vaccine strains as well as their potential to promote long-lasting tumor-specific immune responses. Early clinical data indicate that some of these effects may translate to the treatment of cancer patients. In this review, we provide a structured summary of current evidence for the anti-tumor immune activity of oncolytic MeV. We start with an overview of MeV oncolysis and MeV-induced immunogenic cell death. Next, we relate findings on MeV-mediated activation of antigen-presenting cells, T cell priming and effector mechanisms to the cancer immunity cycle. We discuss additional factors in the tumor microenvironment which are modulated by MeV treatment as well as the role of anti-viral immunity. Based on these findings, we highlight avenues for rational enhancement of oncolytic MeV immunotherapy by vector engineering. We further point to advantages and drawbacks of experimental models and propose areas warranting promising research. Lastly, we review the available immunomonitoring data from several Phase I clinical trials. While this review presents data for MeV, the concepts and principles introduced herein apply to other oncolytic viruses, providing a framework to assess novel cancer immunotherapies.     

Prediction of head and neck squamous cell carcinoma survival based on the expression of 15 lncRNAs

Recent evidence suggests that long noncoding RNAs (lncRNAs) are essential regulators of many cancer-related processes, including cancer cell proliferation, invasion, and migration. There is thus a reason to believe that the detection of lncRNAs may be useful as a diagnostic and prognostic strategy for cancer detection, however, at present no effective genome-wide tests are available for clinical use, constraining the use of such a strategy. In this study, we performed a comprehensive assessment of lncRNAs expressed in samples in the head and neck squamous cell carcinoma (HNSCC) cohort available in The Cancer Genome Atlas database. A risk score (RS) model was constructed based on the expression data of these 15 lncRNAs in the validation data set of HNSCC patients and was subsequently validated in validation data set and the entire data set. We were able to stratify patients into high- and low-risk categories, using our lncRNA expression panel to determine an RS, with significant differences in overall survival (OS) between these two groups in our test set (median survival, 1.863 vs. 5.484 years; log-rank test, p < 0.001). We were able to confirm the predictive value of our 15-lncRNA signature using both a validation data set and a full data set, finding our signature to be reproducible and effective as a means of predicting HNSCC patient OS. Through the multivariate Cox regression and stratified analyses, we were further able to confirm that the predictive value of this RS was independent of other predictive factors such as clinicopathological parameters. The Gene set enrichment analysis revealed potential functional roles for these 15 lncRNAs in tumor progression. Our findings indicate that an RS established based on a panel of lncRNA expression signatures can effectively predict OS and facilitate patient stratification in HNSCC.     

Selective radiosensitization of p53 mutant pancreatic cancer cells by combined inhibition of Chk1 and PARP1

We have recently shown that inhibition of HRR (homologous recombination repair) by Chk1 (checkpoint kinase 1) inhibition radiosensitizes pancreatic cancer cells, and others have demonstrated that Chk1 inhibition selectively sensitizes p53 mutant tumor cells. Furthermore, PARP1 [poly (ADP-ribose) polymerase-1] inhibitors dramatically radiosensitize cells with DNA double-strand break repair defects. Thus, we hypothesized that inhibition of HRR (mediated by Chk1 via AZD7762) and PARP1 [via olaparib (AZD2281)] would selectively sensitize p53 mutant pancreatic cancer cells to radiation. We also used two isogenic p53 cell models to assess the role of p53 status in cancer cells and intestinal epithelial cells to assess overall cancer specificity. DNA damage response and repair were assessed by flow cytometry, γH2AX and an HRR reporter assay. We found that the combination of AZD7762 and olaparib produced significant radiosensitization in p53 mutant pancreatic cancer cells and in all of the isogenic cancer cell lines. The magnitude of radiosensitization by AZD7762 and olaparib was greater in p53 mutant cells compared with p53 wild-type cells. Importantly, normal intestinal epithelial cells were not radiosensitized. The combination of AZD7762 and olaparib caused G₂ checkpoint abrogation, inhibition of HRR and persistent DNA damage responses. These findings demonstrate that the combination of Chk1 and PARP1 inhibition selectively radiosensitizes p53 mutant pancreatic cancer cells. Furthermore, these studies suggest that inhibition of HRR by Chk1 inhibitors may be a useful strategy for selectively inducing a BRCA1/2 “deficient-like” phenotype in p53 mutant tumor cells, while sparing normal tissue.Key words: pancreatic cancer, Chk1, PARP1, radiosensitization, p53     

Definitive Characterization of CA 19-9 in Resectable Pancreatic Cancer Using a Reference Set of Serum and Plasma Specimens

The validation of candidate biomarkers often is hampered by the lack of a reliable means of assessing and comparing performance. We present here a reference set of serum and plasma samples to facilitate the validation of biomarkers for resectable pancreatic cancer. The reference set includes a large cohort of stage I-II pancreatic cancer patients, recruited from 5 different institutions, and relevant control groups. We characterized the performance of the current best serological biomarker for pancreatic cancer, CA 19–9, using plasma samples from the reference set to provide a benchmark for future biomarker studies and to further our knowledge of CA 19–9 in early-stage pancreatic cancer and the control groups. CA 19–9 distinguished pancreatic cancers from the healthy and chronic pancreatitis groups with an average sensitivity and specificity of 70–74%, similar to previous studies using all stages of pancreatic cancer. Chronic pancreatitis patients did not show CA 19–9 elevations, but patients with benign biliary obstruction had elevations nearly as high as the cancer patients. We gained additional information about the biomarker by comparing two distinct assays. The two CA 9–9 assays agreed well in overall performance but diverged in measurements of individual samples, potentially due to subtle differences in antibody specificity as revealed by glycan array analysis. Thus, the reference set promises be a valuable resource for biomarker validation and comparison, and the CA 19–9 data presented here will be useful for benchmarking and for exploring relationships to CA 19–9.     

A semiparametric empirical likelihood method for biased sampling schemes with auxiliary covariates

We consider a semiparametric inference procedure for data from epidemiologic studies conducted with a two-component sampling scheme where both a simple random sample and multiple outcome- or outcome-/auxiliary-dependent samples are observed. This sampling scheme allows the investigators to oversample certain subpopulations believed to have more information about the regression model while still gaining insights about the underlying population through the simple random sample. We focus on settings where there is no additional information about the parent cohort and the sampling probability is nonidentifiable. We motivate our problem with an ongoing study to assess the association between the mutation level of epidermal growth factor receptor (EGFR) and the antitumor response to EGFR-targeted therapy among nonsmall cell lung cancer patients. The proposed method applies to both binary and multicategorical outcome data and allows an arbitrary link function in the framework of generalized linear models. Simulation studies show that the proposed estimator has nice small sample properties. The proposed method is illustrated with a data example.     

Metformin Treatment Does Not Inhibit Growth of Pancreatic Cancer Patient-Derived Xenografts

There is currently tremendous interest in developing anti-cancer therapeutics targeting cell signaling pathways important for both cancer cell metabolism and growth. Several epidemiological studies have shown that diabetic patients taking metformin have a decreased incidence of pancreatic cancer. This has prompted efforts to evaluate metformin, a drug with negligible toxicity, as a therapeutic modality in pancreatic cancer. Preclinical studies in cell line xenografts and one study in patient-derived xenograft (PDX) models were promising, while recently published clinical trials showed no benefit to adding metformin to combination therapy regimens for locally advanced and metastatic pancreatic cancer. PDX models in which patient tumors are directly engrafted into immunocompromised mice have been shown to be excellent preclinical models for biomarker discovery and therapeutic development. We evaluated the response of four PDX tumor lines to metformin treatment and found that all four of our PDX lines were resistant to metformin. We found that the mechanisms of resistance may occur through lack of sustained activation of adenosine monophosphate-activated protein kinase (AMPK) or downstream reactivation of the mammalian target of rapamycin (mTOR). Moreover, combined treatment with metformin and mTOR inhibitors failed to improve responses in cell lines, which further indicates that metformin alone or in combination with mTOR inhibitors will be ineffective in patients, and that resistance to metformin may occur through multiple pathways. Further studies are required to better understand these mechanisms of resistance and inform potential combination therapies with metformin and existing or novel therapeutics.     

Pancreatic cancer stem cells: Fact or fiction?

The terms cancer-initiating or cancer stem cells have been the subject of great interest in recent years. In this review we will use pancreatic cancer as an overall theme to draw parallels with historical findings to compare to recent reports of stem-like characteristics in pancreatic cancer. We will cover such topics as label-retaining cells (side-population), ABC transporter pumps, telomerase, quiescence, cell surface stem cell markers, and epithelial–mesenchymal transitions. Finally we will integrate the available findings into a pancreatic stem cell model that also includes metastatic disease.     

Biomarker discovery from pancreatic cancer secretome using a differential proteomic approach

Quantitative proteomics can be used as a screening tool for identification of differentially expressed proteins as potential biomarkers for cancers. Candidate biomarkers from such studies can subsequently be tested using other techniques for use in early detection of cancers. Here we demonstrate the use of stable isotope labeling with amino acids in cell culture (SILAC) method to compare the secreted proteins (secretome) from pancreatic cancer-derived cells with that from non-neoplastic pancreatic ductal cells. We identified 145 differentially secreted proteins (>1.5-fold change), several of which were previously reported as either up-regulated (e.g. cathepsin D, macrophage colony stimulation factor, and fibronectin receptor) or down-regulated (e.g. profilin 1 and IGFBP-7) proteins in pancreatic cancer, confirming the validity of our approach. In addition, we identified several proteins that have not been correlated previously with pancreatic cancer including perlecan (HSPG2), CD9 antigen, fibronectin receptor (integrin beta1), and a novel cytokine designated as predicted osteoblast protein (FAM3C). The differential expression of a subset of these novel proteins was validated by Western blot analysis. In addition, overexpression of several proteins not described previously to be elevated in human pancreatic cancer (CD9, perlecan, SDF4, apoE, and fibronectin receptor) was confirmed by immunohistochemical labeling using pancreatic cancer tissue microarrays suggesting that these could be further pursued as potential biomarkers. Lastly the protein expression data from SILAC were compared with mRNA expression data obtained using gene expression microarrays for the two cell lines (Panc1 and human pancreatic duct epithelial), and a correlation coefficient (r) of 0.28 was obtained, confirming previously reported poor associations between RNA and protein expression studies.     

Analyzing electrical activities of pancreatic β cells using mathematical models

Bursts of repetitive action potentials are closely related to the regulation of glucose-induced insulin secretion in pancreatic β cells. Mathematical studies with simple β-cell models have established the central principle that the burst-interburst events are generated by the interaction between fast membrane excitation and slow cytosolic components. Recently, a number of detailed models have been developed to simulate more realistic β cell activity based on expanded findings on biophysical characteristics of cellular components. However, their complex structures hinder our intuitive understanding of the underlying mechanisms, and it is becoming more difficult to dissect the role of a specific component out of the complex network. We have recently developed a new detailed model by incorporating most of ion channels and transporters recorded experimentally (the Cha-Noma model), yet the model satisfies the charge conservation law and reversible responses to physiological stimuli. Here, we review the mechanisms underlying bursting activity by applying mathematical analysis tools to representative simple and detailed models. These analyses include time-based simulation, bifurcation analysis and lead potential analysis. In addition, we introduce a new steady-state I-V (ssI-V) curve analysis. We also discuss differences in electrical signals recorded from isolated single cells or from cells maintaining electrical connections within multi-cell preparations. Towards this end, we perform simulations with our detailed pancreatic β-cell model.     

Meta-analysis of epidemiological studies demonstrates significant association of PTGS2 polymorphism rs689470 and no significant association of rs20417 with prostate cancer

Evidence is accumulating that chronic inflammation has an important role in prostate cancer. Two common polymorphisms in the prostaglandin-endoperoxide synthase 2 (PTGS2) gene, rs20417 and rs689470, have been found to alter the risk for prostate cancer, but the various studies are not in agreement. To derive a more precise estimation of this association, all available studies were considered in a meta-analysis, with 10,700 patients and 13,021 controls for rs20417 and 4087 patients and 3761 controls for rs689470. We used odds ratios (ORs) to assess the strength of the association, and 95% confidence intervals (CIs) to determine the precision of the estimate. When all groups were pooled, we did not detect a significant association of rs20417 polymorphism with prostate cancer risk. Similarly, no associations were found in the subgroup analysis. However, we found that rs689470 was significantly associated with a trend towards increased prostate cancer risk when using both additive (OR = 2.15, 95%CI = 1.04-4.44, P = 0.04) and recessive models (OR = 2.07, 95%CI = 1.07-4.03, P = 0.03) to analyze the data. In subgroup analyses stratified by ethnicity, there was no evidence that rs689470 has a significant association with prostate cancer in Caucasians. Based on our meta-analysis, rs689470 polymorphism is significantly associated with prostate cancer risk in the overall population. Nevertheless, we suggest that further studies should be made to confirm these findings.     

Bioinformatics for cancer immunology and immunotherapy

Recent mechanistic insights obtained from preclinical studies and the approval of the first immunotherapies has motivated increasing number of academic investigators and pharmaceutical/biotech companies to further elucidate the role of immunity in tumor pathogenesis and to reconsider the role of immunotherapy. Additionally, technological advances (e.g., next-generation sequencing) are providing unprecedented opportunities to draw a comprehensive picture of the tumor genomics landscape and ultimately enable individualized treatment. However, the increasing complexity of the generated data and the plethora of bioinformatics methods and tools pose considerable challenges to both tumor immunologists and clinical oncologists. In this review, we describe current concepts and future challenges for the management and analysis of data for cancer immunology and immunotherapy. We first highlight publicly available databases with specific focus on cancer immunology including databases for somatic mutations and epitope databases. We then give an overview of the bioinformatics methods for the analysis of next-generation sequencing data (whole-genome and exome sequencing), epitope prediction tools as well as methods for integrative data analysis and network modeling. Mathematical models are powerful tools that can predict and explain important patterns in the genetic and clinical progression of cancer. Therefore, a survey of mathematical models for tumor evolution and tumor–immune cell interaction is included. Finally, we discuss future challenges for individualized immunotherapy and suggest how a combined computational/experimental approaches can lead to new insights into the molecular mechanisms of cancer, improved diagnosis, and prognosis of the disease and pinpoint novel therapeutic targets.