Identification of genetic variants predictive of early onset pancreatic cancer through a population science analysis of functional genomic datasets

Genetic and non-genetic risk factors for early-onset pancreatic cancer

BACKGROUND

Early-onset pancreatic cancer (EOPC) represents 5-10% of all pancreatic ductal adenocarcinoma (PDAC) cases, and the etiology of this form is poorly understood. It is not clear if established PDAC risk factors have the same relevance for younger patients. This study aims to identify genetic and non-genetic risk factors specific to EOPC.

METHODS

A genome-wide association study was performed, analysing 912 EOPC cases and 10 222 controls, divided into discovery and replication phases. Furthermore, the associations between a polygenic risk score (PRS), smoking, alcohol consumption, type 2 diabetes and PDAC risk were also assessed.

RESULTS

Six novel SNPs were associated with EOPC risk in the discovery phase, but not in the replication phase. The PRS, smoking, and diabetes affected EOPC risk. The OR comparing current smokers to never-smokers was 2.92 (95% CI 1.69-5.04, P = 1.44 × 10-4). For diabetes, the corresponding OR was 14.95 (95% CI 3.41-65.50, P = 3.58 × 10-4).

CONCLUSION

In conclusion, we did not identify novel genetic variants associated specifically with EOPC, and we found that established PDAC risk variants do not have a strong age-dependent effect. Furthermore, we add to the evidence pointing to the role of smoking and diabetes in EOPC.

Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles

BACKGROUND AND OBJECTIVES

This paper presents an in-depth review of the state-of-the-art genetic variations analysis to discover complex genes associated with the brain's genetic disorders. We first introduce the genetic analysis of complex brain diseases, genetic variation, and DNA microarrays. Then, the review focuses on available machine learning methods used for complex brain disease classification. Therein, we discuss the various datasets, preprocessing, feature selection and extraction, and classification strategies. In particular, we concentrate on studying single nucleotide polymorphisms (SNP) that support the highest resolution for genomic fingerprinting for tracking disease genes. Subsequently, the study provides an overview of the applications for some specific diseases, including autism spectrum disorder, brain cancer, and Alzheimer's disease (AD). The study argues that despite the significant recent developments in the analysis and treatment of genetic disorders, there are considerable challenges to elucidate causative mutations, especially from the viewpoint of implementing genetic analysis in clinical practice. The review finally provides a critical discussion on the applicability of genetic variations analysis for complex brain disease identification highlighting the future challenges.

METHODS

We used a methodology for literature surveys to obtain data from academic databases. Criteria were defined for inclusion and exclusion. The selection of articles was followed by three stages. In addition, the principal methods for machine learning to classify the disease were presented in each stage in more detail.

RESULTS

It was revealed that machine learning based on SNP was widely utilized to solve problems of genetic variation for complex diseases related to genes.

CONCLUSIONS

Despite significant developments in genetic diseases in the past two decades of the diagnosis and treatment, there is still a large percentage in which the causative mutation cannot be determined, and a final genetic diagnosis remains elusive. So, we need to detect the variations of the genes related to brain disorders in the early disease stages.

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.

Genome-wide association study identifies an early onset pancreatic cancer risk locus

Early onset pancreatic cancer (EOPC) is a rare disease with a very high mortality rate. Almost nothing is known on the genetic susceptibility of EOPC, therefore, we performed a genome-wide association study (GWAS) to identify novel genetic variants specific for patients diagnosed with pancreatic ductal adenocarcinoma (PDAC) at younger ages. In the first phase, conducted on 821 cases with age of onset ≤60 years, of whom 198 with age of onset ≤50, and 3227 controls from PanScan I-II, we observed four SNPs (rs7155613, rs2328991, rs4891017 and rs12610094) showing an association with EOPC risk (P < 1 × 10-4 ). We replicated these SNPs in the PANcreatic Disease ReseArch (PANDoRA) consortium and used additional in silico data from PanScan III and PanC4. Among these four variants rs2328991 was significant in an independent set of 855 cases with age of onset ≤60 years, of whom 265 with age of onset ≤50, and 4142 controls from the PANDoRA consortium while in the in silico data, we observed no statistically significant association. However, the resulting meta-analysis supported the association (P = 1.15 × 10-4 ). In conclusion, we propose a novel variant rs2328991 to be involved in EOPC risk. Even though it was not possible to find a mechanistic link between the variant and the function, the association is supported by a solid statistical significance obtained in the largest study on EOPC genetics present so far in the literature.

SMAD2 rs4940086 heterozygosity increases the risk of cervical cancer development among the women in Bangladesh

SMAD2 is a critical signal transducer molecule in the TGFβ- SMAD pathway which is also known for its tumor suppressor role. Genetic variations in SMAD2 render cells insensitive to its anti-proliferative signals leading to tumor formation. In this study, we demonstrate the impact of single nucleotide polymorphisms (SNPs) of SMAD2 (rs4940086 and rs8085335) on cervical cancer risk development in Bangladeshi population. 132 cervical cancer patients and 98 control volunteers were enrolled in the study and genotyped utilizing polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method. The association between cervical cancer susceptibility and the chosen SNPs were evaluated through multiple logistic regression. SMAD2 rs4940086 heterozygous genotype (T/C) was associated with a 3.89 times higher risk of cervical cancer development (P = 0.001, AOR 3.89, 95% CI 1.777-8.513). The T/C and C/C genotypes in combination also significantly elevated cervical cancer risk (P = 0.035, AOR 1.876, 95% CI 1.047-3.364). Urban cancer patients had a significantly higher chance of carrying the rs4940086 polymorphism as compared to rural cancer patients (P = 0.045, OR 2.59 95% CI 1.02-6.59). SMAD2 rs8085335 heterozygous variant (A/G) demonstrated modest effects in increasing cervical cancer susceptibility (P = 0.594, AOR 1.247, 95% CI 0.554-2.809). Our results suggest that polymorphic variations in SMAD2, particularly rs4940086, can potentially elevate cervical cancer susceptibility in Bangladeshi women.

Analysis of association of potentially functional genetic variants within genes encoding miR-34b/c, miR-378 and miR-143/145 with prostate cancer in Serbian population

MiRNA-associated genetic variants occurring in regulatory regions can affect the efficiency of transcription and potentially modify pri-miRNA or pre-miRNA processing. Since miRNA-based mechanisms are shown to be involved in the pathogenesis of prostate cancer (PCa), the aim of the present study was to evaluate the effect of rs4938723, rs1076064 and rs4705343 occurring in regulatory regions of miR-34b/c, miR-143/145 and miR-378, respectively, on PCa risk and progression in Serbian population. We examined a total of 1060 subjects, of which 350 were patients with PCa, 354 were patients with benign prostatic hyperplasia (BPH), while 356 healthy volunteers were included in the control group. Genotyping of rs4938723, rs1076064 and rs4705343 was performed by using Taqman^® SNP Genotyping Assays. Allele C of rs4705342 was found to increase the risk of PCa (P=0.031 for codominant model, P=0.0088 for recessive model). Rs1076064 minor allele G was found to associate with serum PSA score, as well as with PCa T category and disease aggressiveness. For rs4938723 minor allele C was shown to be associated with the lower PCa T category (P_dom=0.0046; OR=0.36, 95 % CI 0.17-0.76) in T2 vs. T1 comparison. Rs4705342 was identified as PCa susceptibility variant in Serbian population, while for rs1076064 and rs4938723 association with PCa progression parameters was found.

Translational genomics in pancreatic ductal adenocarcinoma: A review with re-analysis of TCGA dataset

Malignancy of the pancreas is a leading cause of cancer-related mortality, with the highest case-fatality of all cancers. Nevertheless, the lack of sensitive biomarkers and presence of biological heterogeneity precludes its early detection and effective treatment. The recent introduction of next-generation sequencing allows characterization of multiple driver mutations at genome- and exome-wide levels. Sequencing of DNA and RNA from circulating tumour cells has also opened an exciting era of non-invasive procedures for tumour detection and prognostication. This massively-parallel sequencing technology has uncovered the previously obscure molecular mechanisms, providing clues for better stratification of patients and identification of druggable targets for the disease. Identification of active oncogenic pathways and gene-gene interactions may reveal oncogene addiction and synthetic lethality. Relevant findings can be extrapolated to develop targeted and personalized therapeutic interventions. In addition to known mutational events, the role of chromosomal rearrangements in pancreatic neoplasms is gradually uncovered. Coupled with bioinformatics pipelines and epidemiological analyses, a better framework for risk stratification and prognostication of pancreatic cancer will be possible in the near future. In this review, we discuss how translational genomic studies facilitate our understanding of pathobiology, and development of novel diagnostics and therapeutics for pancreatic ductal adenocarcinoma with emphases on whole genome sequencing, whole exome sequencing, and liquid biopsies. We have also re-analyzed The Cancer Genome Atlas (TCGA) dataset to look for genetic features associated with altered survival in patients with pancreatic ductal adenocarcinoma.

Poorly differentiated ductal adenocarcinoma of the pancreas with rapid progression in a young man

Pancreatic cancer in young adults is very rare. We report a case of young-onset poorly differentiated pancreatic ductal adenocarcinoma with rapid progression and poor prognosis in a 31-year-old Japanese man with no obvious family history of malignancy. Preoperative examinations revealed a mass lesion in the body of the pancreas, accompanied by a slightly dilated main pancreatic duct distal to the mass lesion. Pancreatic cancer with acute pancreatitis was suspected because of an elevation of serum pancreatic enzyme and tumor marker, along with imaging findings. Distal pancreatectomy with resection of the common hepatic artery and splenectomy along with lymph node dissection was performed. Microscopically, the tumor was mainly composed of poorly differentiated ductal adenocarcinoma. The postoperative course was uneventful, but the patient had multiple liver metastases 2 months postoperatively, in spite of adjuvant chemotherapy, and died 8 months postoperatively. This case may represent a rare instance of young-onset poorly differentiated ductal adenocarcinoma with rapid progression and may indicate potential risk factors of pancreatic cancer in young adults.

Comparative analysis of Notch1 and Notch2 binding sites in the genome of BxPC3 pancreatic cancer cells

Notch signaling plays a key role in the development of pancreatic cancer. Among the four identified Notch receptors, Notch1 and Notch2 share the highest homology. Notch1 has been reported to be an oncogene but some reports indicate that Notch2, not Notch1, plays a key role in pancreatic carcinogenesis. As both are transcription factors, examination of their genomic binding sites might reveal interesting functional differences between them. Notch proteins do not have DNA-binding domain. In the canonical Notch signaling pathway, ligand binding induces the release and nuclear translocation of Notch receptor intracellular domains (NICDs), which then interact with the transcription factor CSL, resulting in subsequent activation of the canonical Notch target genes. We investigated the binding site profiles of Notch1and Notch2 in the BxPC3 genome using CHIP-Seq and bioinfomatics. We found that Notch1, Notch2 and CSL generally bound to different target genes. We also found that only a small subset of Notch1 and Notch2 binding sites overlap with that of CSL, but about half of the CSL binding overlap with that of Notch1 or Notch2, indicating most Notch signaling activities are CSL-independent.