Identification of germline genomic copy number variation in familial pancreatic cancer

Inactivation of HIPK2 attenuates KRASG12D activity and prevents pancreatic tumorigenesis

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) features KRAS mutations in approximately 90% of human cases and excessive stromal response, termed desmoplastic reaction. Oncogenic KRAS drives pancreatic carcinogenesis by acting on both epithelial cells and tumor microenvironment (TME). We have previously shown that Homeodomain-Interacting Protein Kinase 2 (HIPK2) cooperates with KRAS in sustaining ERK1/2 phosphorylation in human colorectal cancers. Here, we investigated whether HIPK2 contributes to oncogenic KRAS-driven tumorigenesis in vivo, in the onset of pancreatic cancer.

METHODS

We employed an extensively characterized model of KRASG12D-dependent preinvasive PDAC, the Pdx1-Cre;LSL-KRasG12D/+ (KC) mice. In these mice, HIPK2 was inhibited by genetic knockout in the pancreatic epithelial cells (KCH-/-) or by pharmacologic inactivation with the small molecule 5-IodoTubercidin (5-ITu). The development of preneoplastic acinar-to-ductal metaplasia (ADM), intraepithelial neoplasia (PanIN), and their associated desmoplastic reaction were analyzed.

RESULTS

In Hipk2-KO mice (KCH-/-), ERK phosphorylation was lowered, the appearance of ADM was slowed down, and both the number and pathologic grade of PanIN were reduced compared to Hipk2-WT KC mice. The pancreatic lesion phenotype in KCH-/- mice was characterized by abundant collagen fibers and reduced number of αSMA+ and pSTAT3+ desmoplastic cells. These features were reminiscent of the recently described human "deserted" sub-TME, poor in cells, rich in matrix, and associated with tumor differentiation. In contrast, the desmoplastic reaction of KC mice resembled the "reactive" sub-TME, rich in stromal cells and associated with tumor progression. These observations were confirmed by the pharmacologic inhibition of HIPK2 in KC mice.

CONCLUSION

This study demonstrates that HIPK2 inhibition weakens oncogenic KRAS activity and pancreatic tumorigenesis providing a rationale for testing HIPK2 inhibitors to mitigate the incidence of PDAC development in high-risk individuals.

Copy Number Variations in Pancreatic Cancer: From Biological Significance to Clinical Utility

Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer, characterized by high tumor heterogeneity and a poor prognosis. Inter- and intra-tumoral heterogeneity in PDAC is a major obstacle to effective PDAC treatment; therefore, it is highly desirable to explore the tumor heterogeneity and underlying mechanisms for the improvement of PDAC prognosis. Gene copy number variations (CNVs) are increasingly recognized as a common and heritable source of inter-individual variation in genomic sequence. In this review, we outline the origin, main characteristics, and pathological aspects of CNVs. We then describe the occurrence of CNVs in PDAC, including those that have been clearly shown to have a pathogenic role, and further highlight some key examples of their involvement in tumor development and progression. The ability to efficiently identify and analyze CNVs in tumor samples is important to support translational research and foster precision oncology, as copy number variants can be utilized to guide clinical decisions. We provide insights into understanding the CNV landscapes and the role of both somatic and germline CNVs in PDAC, which could lead to significant advances in diagnosis, prognosis, and treatment. Although there has been significant progress in this field, understanding the full contribution of CNVs to the genetic basis of PDAC will require further research, with more accurate CNV assays such as single-cell techniques and larger cohorts than have been performed to date.

Copy Number Variants Are Ovarian Cancer Risk Alleles at Known and Novel Risk Loci

BACKGROUND

Known risk alleles for epithelial ovarian cancer (EOC) account for approximately 40% of the heritability for EOC. Copy number variants (CNVs) have not been investigated as EOC risk alleles in a large population cohort.

METHODS

Single nucleotide polymorphism array data from 13 071 EOC cases and 17 306 controls of White European ancestry were used to identify CNVs associated with EOC risk using a rare admixture maximum likelihood test for gene burden and a by-probe ratio test. We performed enrichment analysis of CNVs at known EOC risk loci and functional biofeatures in ovarian cancer-related cell types.

RESULTS

We identified statistically significant risk associations with CNVs at known EOC risk genes; BRCA1 (PEOC = 1.60E-21; OREOC = 8.24), RAD51C (Phigh-grade serous ovarian cancer [HGSOC] = 5.5E-4; odds ratio [OR]HGSOC = 5.74 del), and BRCA2 (PHGSOC = 7.0E-4; ORHGSOC = 3.31 deletion). Four suggestive associations (P < .001) were identified for rare CNVs. Risk-associated CNVs were enriched (P < .05) at known EOC risk loci identified by genome-wide association study. Noncoding CNVs were enriched in active promoters and insulators in EOC-related cell types.

CONCLUSIONS

CNVs in BRCA1 have been previously reported in smaller studies, but their observed frequency in this large population-based cohort, along with the CNVs observed at BRCA2 and RAD51C gene loci in EOC cases, suggests that these CNVs are potentially pathogenic and may contribute to the spectrum of disease-causing mutations in these genes. CNVs are likely to occur in a wider set of susceptibility regions, with potential implications for clinical genetic testing and disease prevention.

Integrated Workflow for the Label-Free Isolation and Genomic Analysis of Single Circulating Tumor Cells in Pancreatic Cancer

As pancreatic cancer is the third deadliest cancer in the U.S., the ability to study genetic alterations is necessary to provide further insight into potentially targetable regions for cancer treatment. Circulating tumor cells (CTCs) represent an especially aggressive subset of cancer cells, capable of causing metastasis and progressing the disease. Here, we present the Labyrinth-DEPArray pipeline for the isolation and analysis of single CTCs. Established cell lines, patient-derived CTC cell lines and freshly isolated CTCs were recovered and sequenced to reveal single-cell copy number variations (CNVs). The resulting CNV profiles of established cell lines showed concordance with previously reported data and highlight several gains and losses of cancer-related genes such as FGFR3 and GNAS. The novel sequencing of patient-derived CTC cell lines showed gains in chromosome 8q, 10q and 17q across both CTC cell lines. The pipeline was used to process and isolate single cells from a metastatic pancreatic cancer patient revealing a gain of chromosome 1q and a loss of chromosome 5q. Overall, the Labyrinth-DEPArray pipeline offers a validated workflow combining the benefits of antigen-free CTC isolation with single cell genomic analysis.

Determining the accuracy of next generation sequencing based copy number variation analysis in Hereditary Breast and Ovarian Cancer

BACKGROUND

Copy number variations (CNVs) are commonly associated with malignancies, including hereditary breast and ovarian cancers. Next generation sequencing (NGS) provides solutions for CNV detection in a single run. This study aimed to compare the accuracy of CNV detection by NGS analyzing tool against Multiplex Ligation Dependent Probe Amplification (MLPA).

RESEARCH DESIGN AND METHODS

In total, 1276 cases were studied by targeted NGS panels and 691 cases (61 calls in 58 NGS-CNV positive and 633 NGS-CNV negative cases) were validated by MLPA.

RESULTS

Twenty-eight (46%) NGS-CNV positive calls were consistent, whereas 33 (54%) calls showed discordance with MLPA. Two cases were detected as SNV by the NGS and CNV by the MLPA analysis. In total, 2% of the cases showed an MLPA confirmed CNV region in BRCA1/2. The results of this study showed that despite the high false positive call rate of the NGS-CNV algorithm, there were no false negative calls. The cases that were determined to be negative by the NGS and positive by the MLPA were actually carrying SNVs that were located on the MLPA probe binding sites.

CONCLUSION

The diagnostic performance of NGS-CNV analysis is promising; however, the need for confirmation by different methods remains.

Shall genomic correlation structure be considered in copy number variants detection?

Copy number variation has been identified as a major source of genomic variation associated with disease susceptibility. With the advent of whole-exome sequencing (WES) technology, massive WES data have been generated, allowing for the identification of copy number variants (CNVs) in the protein-coding regions with direct functional interpretation. We have previously shown evidence of the genomic correlation structure in array data and developed a novel chromosomal breakpoint detection algorithm, LDcnv, which showed significantly improved detection power through integrating the correlation structure in a systematic modeling manner. However, it remains unexplored whether the genomic correlation exists in WES data and how such correlation structure integration can improve the CNV detection accuracy. In this study, we first explored the correlation structure of the WES data using the 1000 Genomes Project data. Both real raw read depth and median-normalized data showed strong evidence of the correlation structure. Motivated by this fact, we proposed a correlation-based method, CORRseq, as a novel release of the LDcnv algorithm in profiling WES data. The performance of CORRseq was evaluated in extensive simulation studies and real data analysis from the 1000 Genomes Project. CORRseq outperformed the existing methods in detecting medium and large CNVs. In conclusion, it would be more advantageous to model genomic correlation structure in detecting relatively long CNVs. This study provides great insights for methodology development of CNV detection with NGS data.

Integrating genomic correlation structure improves copy number variations detection

MOTIVATION

Copy number variation plays important roles in human complex diseases. The detection of copy number variants (CNVs) is identifying mean shift in genetic intensities to locate chromosomal breakpoints, the step of which is referred to as chromosomal segmentation. Many segmentation algorithms have been developed with a strong assumption of independent observations in the genetic loci, and they assume each locus has an equal chance to be a breakpoint (i.e. boundary of CNVs). However, this assumption is violated in the genetics perspective due to the existence of correlation among genomic positions, such as linkage disequilibrium (LD). Our study showed that the LD structure is related to the location distribution of CNVs, which indeed presents a non-random pattern on the genome. To generate more accurate CNVs, we proposed a novel algorithm, LDcnv, that models the CNV data with its biological characteristics relating to genetic dependence structure (i.e. LD).

RESULTS

We theoretically demonstrated the correlation structure of CNV data in SNP array, which further supports the necessity of integrating biological structure in statistical methods for CNV detection. Therefore, we developed the LDcnv that integrated the genomic correlation structure with a local search strategy into statistical modeling of the CNV intensities. To evaluate the performance of LDcnv, we conducted extensive simulations and analyzed large-scale HapMap datasets. We showed that LDcnv presented high accuracy, stability and robustness in CNV detection and higher precision in detecting short CNVs compared to existing methods. This new segmentation algorithm has a wide scope of potential application with data from various high-throughput technology platforms.

AVAILABILITY AND IMPLEMENTATION

https://github.com/FeifeiXiaoUSC/LDcnv.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

An accurate and powerful method for copy number variation detection

MOTIVATION

Integration of multiple genetic sources for copy number variation detection (CNV) is a powerful approach to improve the identification of variants associated with complex traits. Although it has been shown that the widely used change point based methods can increase statistical power to identify variants, it remains challenging to effectively detect CNVs with weak signals due to the noisy nature of genotyping intensity data. We previously developed modSaRa, a normal mean-based model on a screening and ranking algorithm for copy number variation identification which presented desirable sensitivity with high computational efficiency. To boost statistical power for the identification of variants, here we present a novel improvement that integrates the relative allelic intensity with external information from empirical statistics with modeling, which we called modSaRa2.

RESULTS

Simulation studies illustrated that modSaRa2 markedly improved both sensitivity and specificity over existing methods for analyzing array-based data. The improvement in weak CNV signal detection is the most substantial, while it also simultaneously improves stability when CNV size varies. The application of the new method to a whole genome melanoma dataset identified novel candidate melanoma risk associated deletions on chromosome bands 1p22.2 and duplications on 6p22, 6q25 and 19p13 regions, which may facilitate the understanding of the possible roles of germline copy number variants in the etiology of melanoma.

AVAILABILITY AND IMPLEMENTATION

http://c2s2.yale.edu/software/modSaRa2 or https://github.com/FeifeiXiaoUSC/modSaRa2.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Updated risk factors to inform early pancreatic cancer screening and identify high risk patients

Pancreatic adenocarcinoma (PDAC) is associated with poor clinical outcomes and incomplete responses to conventional therapy. Therefore, there is an unmet clinical need to better understand the predisposing factors for pancreatic cancer in hopes of providing early screening to high-risk patients. While select risk factors such as age, race, and family history, or predisposing syndromes are unavoidable, there are several new and established risk factors that allow for intervention, namely by counseling patients to make the appropriate lifestyle modifications. Here, we discuss the best-studied risk factors for PDAC such as tobacco use and chronic pancreatitis, as well as newly emerging risk factors including select nutritional deficits, bacterial infections, and psychosocial factors. As several of these risk factors appear to be additive or synergistic, by understanding their relationships and offering coordinated, multidisciplinary care to high-risk patients, it may be possible to reduce pancreatic cancer incidence and improve clinical outcomes through early detection.

Chronic pancreatitis and lipomatosis are associated with defective function of ciliary genes in pancreatic ductal cells

Genetic diseases associated with defects in primary cilia are classified as ciliopathies. Pancreatic lesions and ductal cysts are found in patients with ciliopathic polycystic kidney diseases suggesting a close connection between pancreatic defects and primary cilia. Here we investigate the role of two genes whose deletion is known to cause primary cilium defects, namely Hnf6 and Lkb1, in pancreatic ductal homeostasis. We find that mice with postnatal duct-specific deletion of Hnf6 or Lkb1 show duct dilations. Cells lining dilated ducts present shorter cilia with swollen tips, suggesting defective intraciliary transport. This is associated with signs of chronic pancreatitis, namely acinar-to-ductal metaplasia, acinar proliferation and apoptosis, presence of inflammatory infiltrates, fibrosis and lipomatosis. Our data reveal a tight association between ductal ciliary defects and pancreatitis with perturbed acinar homeostasis and differentiation. Such injuries can account for the increased risk to develop pancreatic cancer in Peutz-Jeghers patients who carry LKB1 loss-of-function mutations.

Germline copy number variations are associated with breast cancer risk and prognosis

Breast cancer is one of the most common cancers among women, and susceptibility is explained by genetic, lifestyle and environmental components. Copy Number Variants (CNVs) are structural DNA variations that contribute to diverse phenotypes via gene-dosage effects or cis-regulation. In this study, we aimed to identify germline CNVs associated with breast cancer susceptibility and their relevance to prognosis. We performed whole genome CNV genotyping in 422 cases and 348 controls using Human Affymetrix SNP 6 array. Principal component analysis for population stratification revealed 84 outliers leaving 366 cases and 320 controls of Caucasian ancestry for association analysis; CNVs with frequency > 10% and overlapping with protein coding genes were considered for breast cancer risk and prognostic relevance. Coding genes within the CNVs identified were interrogated for gene- dosage effects by correlating copy number status with gene expression profiles in breast tumor tissue. We identified 200 CNVs associated with breast cancer (q-value < 0.05). Of these, 21 CNV regions (overlapping with 22 genes) also showed association with prognosis. We validated representative CNVs overlapping with APOBEC3B and GSTM1 genes using the TaqMan assay. Germline CNVs conferred dosage effects on gene expression in breast tissue. The candidate CNVs identified in this study warrant independent replication.

Rare Germline Copy Number Variations and Disease Susceptibility in Familial Melanoma

Mounting evidence suggests that copy number variations (CNVs) can contribute to cancer susceptibility. The main goal of this study was to evaluate the role of germline CNVs in melanoma predisposition in high-risk melanoma families. We used genome-wide tiling comparative genomic hybridization and single nucleotide polymorphism arrays to characterize CNVs in 335 individuals (240 melanoma cases) from American melanoma-prone families (22 with germline CDKN2A or CDK4 mutations). We found that the global burden of overall CNVs (or deletions or duplications separately) was not significantly associated with case-control or CDKN2A/CDK4 mutation status after accounting for the familial dependence. However, we identified several rare CNVs that either involved known melanoma genes (e.g., PARP1, CDKN2A) or cosegregated with melanoma (duplication on 10q23.23, 3p12.2 and deletions on 8q424.3, 2q22.1) in families without mutations in known melanoma high-risk genes. Some of these CNVs were correlated with expression changes in disrupted genes based on RNASeq data from a subset of melanoma cases included in the CNV study. These results suggest that rare cosegregating CNVs may influence melanoma susceptibility in some melanoma-prone families and genes found in our study warrant further evaluation in future genetic analyses of melanoma.

Genome-wide association studies and epigenome-wide association studies go together in cancer control

Completion of the human genome a decade ago laid the foundation for: using genetic information in assessing risk to identify individuals and populations that are likely to develop cancer, and designing treatments based on a person's genetic profiling (precision medicine). Genome-wide association studies (GWAS) completed during the past few years have identified risk-associated single nucleotide polymorphisms that can be used as screening tools in epidemiologic studies of a variety of tumor types. This led to the conduct of epigenome-wide association studies (EWAS). This article discusses the current status, challenges and research opportunities in GWAS and EWAS. Information gained from GWAS and EWAS has potential applications in cancer control and treatment.

The association of copy number variation and percent mammographic density

Background

Percent mammographic density (PD) estimates the proportion of stromal, fat, and epithelial breast tissues on the mammogram image. Adjusted for age and body mass index (BMI), PD is one of the strongest risk factors for breast cancer [1]. Inherited factors are hypothesized to explain between 30 and 60% of the variance in this trait [2–5]. However, previously identified common genetic variants account for less than 6% of the variance in PD, leaving much of the genetic contribution to this trait unexplained. We performed the first study to examine whether germline copy number variation (CNV) are associated with PD. Two genome-wide association studies (GWAS) of percent density conducted on the Illumina 660W-Quad were used to identify and replicate the association between candidate CNVs and PD: the Minnesota Breast Cancer Family Study (MBCFS) and controls from the Mayo Venous Thromboembolism (Mayo VTE) Case–Control Study, with 585 and 328 women, respectively. Linear models were utilized to examine the association of each probe with PD, adjusted for age, menopausal status and BMI. Segmentation was subsequently performed on the probe-level test statistics to identify candidate CNV regions that were associated with PD.

Results

Sixty-one probes from five chromosomal regions [3q26.1 (2 regions), 8q24.22, 11p15.3, and 17q22] were significantly associated with PD in MBCFS (p-values <0.0001). A CNV at 3q26.1 showed the greatest evidence for association with PD; a region without any known SNPs. Conversely, the CNV at 17q22 was largely due to the association between SNPs and PD in the region. SNPs in the 8q24.22 region have been shown to be associated with risk of many cancers; however, SNPs in this region were not responsible for the observed CNV association. While we were unable to replicate the associations with PD, two of the five CNVs (3q26.1 and 11p15.3) were also observed in the Mayo VTE controls.

Conclusions

CNVs may help to explain some of the variability in PD that is currently unexplained by SNPs. While we were able to replicate the existence of two CNVs across the two GWAS studies, we were unable to replicate the associations with PD. Even so, the proximity of the identified CNV regions to loci known to be associated with breast cancer risk suggests further investigation and potentially shared genetic mechanisms underlying the PD and breast cancer association.

Electronic supplementary material

The online version of this article (doi:10.1186/s13104-015-1212-y) contains supplementary material, which is available to authorized users.

Rare germline copy number deletions of likely functional importance are implicated in endometrial cancer predisposition

Endometrial cancer is the most common invasive gynaecological cancer in women, and relatively little is known about inherited risk factors for this disease. This is the first genome-wide study to explore the role of common and rare germline copy number variants (CNVs) in predisposition to endometrial cancer. CNVs were called from germline DNA of 1,209 endometrioid endometrial cancer cases and 528 cancer-unaffected female controls. Overall CNV load of deletions or DNA gains did not differ significantly between cases and controls (P > 0.05), but cases presented with an excess of rare germline deletions overlapping likely functional genomic regions including genes (P = 8 × 10(-10)), CpG islands (P = 1 × 10(-7)) and sno/miRNAs regions (P = 3 × 10(-9)). On average, at least one additional gene and two additional CpG islands were disrupted by rare deletions in cases compared to controls. The most pronounced difference was that over 30 sno/miRNAs were disrupted by rare deletions in cases for every single disruption event in controls. A total of 13 DNA repair genes were disrupted by rare deletions in 19/1,209 cases (1.6%) compared to one gene in 1/528 controls (0.2%; P = 0.007), and this increased DNA repair gene loss in cases persisted after excluding five individuals carrying CNVs disrupting mismatch repair genes MLH1, MSH2 and MSH6 (P = 0.03). There were 34 miRNA regions deleted in at least one case but not in controls, the most frequent of which encompassed hsa-mir-661 and hsa-mir-203. Our study implicates rare germline deletions of functional and regulatory regions as possible mechanisms conferring endometrial cancer risk, and has identified specific regulatory elements as candidates for further investigation.

Germline DNA variations in breast cancer predisposition and prognosis: a systematic review of the literature

Breast cancer is the most common cancer and the second leading cause of death in women worldwide. The disease is caused by a combination of genetic, environmental, lifestyle, and reproductive risk factors. Linkage and family-based studies have identified many pathological germline mutations, which account for around 20% of the genetic risk of familial breast cancer. In recent years, single nucleotide polymorphism-based genetic association studies, especially genome-wide association studies (GWASs), have been very successful in uncovering low-penetrance common variants associated with breast cancer risk. These common variants alone may explain up to an additional 30% of the familial risk of breast cancer. With the advent of available genetic resources and growing collaborations among researchers across the globe, the much needed large sample size to capture variants with small effect sizes and low population frequencies is being addressed, and hence many more common variants are expected to be discovered in the coming days. Here, major GWASs conducted for breast cancer predisposition and prognosis until 2013 are summarized. Few studies investigating other forms of genetic variations contributing to breast cancer predisposition and disease outcomes are also discussed. Finally, the potential utility of the GWAS-identified variants in disease risk models and some future perspectives are presented.

Application of Machine Learning to Development of Copy Number Variation-based Prediction of Cancer Risk

In the present study, recurrent copy number variations (CNVs) from non-tumor blood cell DNAs of Caucasian non-cancer subjects and glioma, myeloma, and colorectal cancer-patients, and Korean non-cancer subjects and hepatocellular carcinoma, gastric cancer, and colorectal cancer patients, were found to reveal for each of the two ethnic cohorts highly significant differences between cancer patients and controls with respect to the number of CN-losses and size-distribution of CN-gains, suggesting the existence of recurrent constitutional CNV-features useful for prediction of predisposition to cancer. Upon identification by machine learning, such CNV-features could extensively discriminate between cancer-patient and control DNAs. When the CNV-features selected from a learning-group of Caucasian or Korean mixed DNAs consisting of both cancer-patient and control DNAs were employed to make predictions on the cancer predisposition of an unseen test group of mixed DNAs, the average prediction accuracy was 93.6% for the Caucasian cohort and 86.5% for the Korean cohort.

Genome-wide analysis of the role of copy-number variation in pancreatic cancer risk

Although family history is a risk factor for pancreatic adenocarcinoma, much of the genetic etiology of this disease remains unknown. While genome-wide association studies have identified some common single nucleotide polymorphisms (SNPs) associated with pancreatic cancer risk, these SNPs do not explain all the heritability of this disease. We hypothesized that copy number variation (CNVs) in the genome may play a role in genetic predisposition to pancreatic adenocarcinoma. Here, we report a genome-wide analysis of CNVs in a small hospital-based, European ancestry cohort of pancreatic cancer cases and controls. Germline CNV discovery was performed using the Illumina Human CNV370 platform in 223 pancreatic cancer cases (both sporadic and familial) and 169 controls. Following stringent quality control, we asked if global CNV burden was a risk factor for pancreatic cancer. Finally, we performed in silico CNV genotyping and association testing to discover novel CNV risk loci. When we examined the global CNV burden, we found no strong evidence that CNV burden plays a role in pancreatic cancer risk either overall or specifically in individuals with a family history of the disease. Similarly, we saw no significant evidence that any particular CNV is associated with pancreatic cancer risk. Taken together, these data suggest that CNVs do not contribute substantially to the genetic etiology of pancreatic cancer, though the results are tempered by small sample size and large experimental variability inherent in array-based CNV studies.

Familial pancreatic cancer: genetic advances

Beset by poor prognosis, pancreatic ductal adenocarcinoma is classified as familial or sporadic. This review elaborates on the known genetic syndromes that underlie familial pancreatic cancer, where there are opportunities for genetic counseling and testing as well as clinical monitoring of at-risk patients. Such subsets of familial pancreatic cancer involve germline cationic trypsinogen or PRSS1 mutations (hereditary pancreatitis), BRCA2 mutations (usually in association with hereditary breast-ovarian cancer syndrome), CDKN2 mutations (familial atypical mole and multiple melanoma), or DNA repair gene mutations (e.g., ATM and PALB2, apart from those in BRCA2). However, the vast majority of familial pancreatic cancer cases have yet to have their genetic underpinnings elucidated, waiting in part for the results of deep sequencing efforts.

Genetic and molecular alterations in pancreatic cancer: implications for personalized medicine

Recent advances in human genomics and biotechnologies have profound impacts on medical research and clinical practice. Individual genomic information, including DNA sequences and gene expression profiles, can be used for prediction, prevention, diagnosis, and treatment for many complex diseases. Personalized medicine attempts to tailor medical care to individual patients by incorporating their genomic information. In a case of pancreatic cancer, the fourth leading cause of cancer death in the United States, alteration in many genes as well as molecular profiles in blood, pancreas tissue, and pancreas juice has recently been discovered to be closely associated with tumorigenesis or prognosis of the cancer. This review aims to summarize recent advances of important genes, proteins, and microRNAs that play a critical role in the pathogenesis of pancreatic cancer, and to provide implications for personalized medicine in pancreatic cancer.

Prolonged survival and delayed progression of pancreatic intraepithelial neoplasia in LSL-KrasG12D/+;Pdx-1-Cre mice by vitamin E δ-tocotrienol

The highly lethal nature of pancreatic cancer and the increasing recognition of high-risk individuals have made research into chemoprevention a high priority. Here, we tested the chemopreventive activity of δ-tocotrienol, a bioactive vitamin E derivative extracted from palm fruit, in the LSL-Kras(G12D/+);Pdx-1-Cre pancreatic cancer mouse model. At 10 weeks of age, mice (n = 92) were randomly allocated to three groups: (i) no treatment; (ii) vehicle and (iii) δ-tocotrienol (200mg/kg × 2/day, PO). Treatment was continued for 12 months. Mice treated with δ-tocotrienol showed increased median survival from the onset of treatment (11.1 months) compared with vehicle-treated mice (9.7 months) and non-treated mice (8.5 months; P < 0.025). Importantly, none of the mice treated with δ-tocotrienol harbored invasive cancer compared with 10% and 8% in vehicle-treated and non-treated mice, respectively. Furthermore, δ-tocotrienol treatment also resulted in significant suppression of mouse pancreatic intraepithelial neoplasm (mPanIN) progression compared with vehicle-treated and non-treated mice: mPanIN-1: 47-50% (P < 0.09), mPanIN-2: 6-11% (P < 0.001), mPanIN-3: 3-15% (P < 0.001) and invasive cancer: 0-10% (P < 0.001). δ-Tocotrienol treatment inhibited mutant Kras-driven pathways such as MEK/ERK, PI3K/AKT and NF-kB/p65, as well as Bcl-xL and induced p27. δ-Tocotrienol also induced biomarkers of apoptosis such as Bax and activated caspase 3 along with an increase in plasma levels of CK18. In summary, δ-tocotrienol's ability to interfere with oncogenic Kras pathways coupled with the observed increase in median survival and significant delay in PanIN progression highlights the chemopreventative potential of δ-tocotrienol and warrants further investigation of this micronutrient in individuals at high risk for pancreatic cancer.