Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics.

The Cancer Genome Atlas (TCGA) has catalyzed systematic characterization of diverse genomic alterations underlying human cancers. At this historic junction marking the completion of genomic characterization of over 11,000 tumors from 33 cancer types, we present our current understanding of the molecular processes governing oncogenesis. We illustrate our insights into cancer through synthesis of the findings of the TCGA PanCancer Atlas project on three facets of oncogenesis: (1) somatic driver mutations, germline pathogenic variants, and their interactions in the tumor; (2) the influence of the tumor genome and epigenome on transcriptome and proteome; and (3) the relationship between tumor and the microenvironment, including implications for drugs targeting driver events and immunotherapies. These results will anchor future characterization of rare and common tumor types, primary and relapsed tumors, and cancers across ancestry groups and will guide the deployment of clinical genomic sequencing.

A workflow to study mechanistic indicators for driver gene prediction with Moonlight.

Prediction of driver genes (tumor suppressors and oncogenes) is an essential step in understanding cancer development and discovering potential novel treatments. We recently proposed Moonlight as a bioinformatics framework to predict driver genes and analyze them in a system-biology-oriented manner based on -omics integration. Moonlight uses gene expression as a primary data source and combines it with patterns related to cancer hallmarks and regulatory networks to identify oncogenic mediators. Once the oncogenic mediators are identified, it is important to include extra levels of evidence, called mechanistic indicators, to identify driver genes and to link the observed gene expression changes to the underlying alteration that promotes them. Such a mechanistic indicator could be for example a mutation in the regulatory regions for the candidate gene. Here, we developed new functionalities and released Moonlight2 to provide the user with a mutation-based mechanistic indicator as a second layer of evidence. These functionalities analyze mutations in a cancer cohort to classify them into driver and passenger mutations. Those oncogenic mediators with at least one driver mutation are retained as the final set of driver genes. We applied Moonlight2 to the basal-like breast cancer subtype, lung adenocarcinoma and thyroid carcinoma using data from The Cancer Genome Atlas. For example, in basal-like breast cancer, we found four oncogenes (COPZ2, SF3B4, KRTCAP2 and POLR2J) and nine tumor suppressor genes (KIR2DL4, KIF26B, ARL15, ARHGAP25, EMCN, GMFG, TPK1, NR5A2 and TEK) containing a driver mutation in their promoter region, possibly explaining their deregulation. Moonlight2R is available at https://github.com/ELELAB/Moonlight2R.

High-resolution and quantitative spatial analysis reveal intra-ductal phenotypic and functional diversification in pancreatic cancer.

A 'classical' and a 'basal-like' subtype of pancreatic cancer have been reported, with differential expression of GATA6 and different dosages of mutant KRAS. We established in situ detection of KRAS point mutations and mRNA panels for the consensus subtypes aiming to project these findings to paraffin-embedded clinical tumour samples for spatial quantitative analysis. We unveiled that, next to inter-patient and intra-patient inter-ductal heterogeneity, intraductal spatial phenotypes exist with anti-correlating expression levels of GATA6 and KRASG12D . The basal-like mRNA panel better captured the basal-like cell states than widely used protein markers. The panels corroborated the co-existence of the classical and basal-like cell states in a single tumour duct with functional diversification, i.e. proliferation and epithelial-to-mesenchymal transition respectively. Mutant KRASG12D detection ascertained an epithelial origin of vimentin-positive cells in the tumour. Uneven spatial distribution of cancer-associated fibroblasts could recreate similar intra-organoid diversification. This extensive heterogeneity with functional cooperation of plastic tumour cells poses extra challenges to therapeutic approaches. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

EDIR: exome database of interspersed repeats.

MOTIVATION: Intragenic exonic deletions are known to contribute to genetic diseases and are often flanked by regions of homology. RESULTS: In order to get a more clear view of these interspersed repeats encompassing a coding sequence, we have developed EDIR (Exome Database of Interspersed Repeats) which contains the positions of these structures within the human exome. EDIR has been calculated by an inductive strategy, rather than by a brute force approach and can be queried through an R/Bioconductor package or a web interface allowing the per-gene rapid extraction of homology-flanked sequences throughout the exome. AVAILABILITY AND IMPLEMENTATION: The code used to compile EDIR can be found at https://github.com/lauravongoc/EDIR. The full dataset of EDIR can be queried via an Rshiny application at http://193.70.34.71:3857/edir/. The R package for querying EDIR is called 'EDIRquery' and is available on Bioconductor. The full EDIR dataset can be downloaded from https://osf.io/m3gvx/ or http://193.70.34.71/EDIR.tar.gz. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identification of RAD17 as a candidate cancer predisposition gene in families with histories of pancreatic and breast cancers.

BACKGROUND: Among the 10% of pancreatic cancers that occur in a familial context, around a third carry a pathogenic variant in a cancer predisposition gene. Genetic studies of pancreatic cancer predisposition are limited by high mortality rates amongst index patients and other affected family members. The genetic risk for pancreatic cancer is often shared with breast cancer susceptibility genes, most notably BRCA2, PALB2, ATM and BRCA1. Therefore, we hypothesized that additional shared genetic etiologies might be uncovered by studying families presenting with both breast and pancreatic cancer. METHODS: Focusing on a multigene panel of 276 DNA Damage Repair (DDR) genes, we performed next-generation sequencing in a cohort of 41 families with at least three breast cancer cases and one pancreatic cancer. When the index patient with pancreatic cancer was deceased, close relatives (first or second-degree) affected with breast cancer were tested (39 families). RESULTS: We identified 27 variants of uncertain significance in DDR genes. A splice site variant (c.1605 + 2T > A) in the RAD17 gene stood out, as a likely loss of function variant. RAD17 is a checkpoint protein that recruits the MRN (MRE11-RAD50-NBS1) complex to initiate DNA signaling, leading to DNA double-strand break repair. CONCLUSION: Within families with breast and pancreatic cancer, we identified RAD17 as a novel candidate predisposition gene. Further genetic studies are warranted to better understand the potential pathogenic effect of RAD17 variants and in other DDR genes.

A milder form of NSRP1-associated neurodevelopmental disorder, caused by a missense variant in the nuclear localization signal.

Nuclear Speckle Splicing Regulator Protein 1 (NSRP1) is a splice factor found in nuclear speckles, which are small membrane-free organelles implicated in epigenetic regulation, chromatin organization, DNA repair, and RNA modification. Bi-allelic loss-of-function variants in NSRP1 have recently been identified in patients suffering from a severe neurodevelopmental disorder, presenting with neurodevelopmental delay, epilepsy, microcephaly, hypotonia, and spastic cerebral palsy. Described patients acquired neither independent walking nor speech and often showed anomalies on cerebral MRI. Here we describe the case of a 14-year-old girl with motor and language delay as well as intellectual disability, who presents an ataxic gait but walks without assistance and speaks in short sentences. Whole-genome sequencing revealed the compound heterozygous NSRP1 variants c.114 + 2T > G and c.1595T > A (p.Val532Glu). Functional validation using HEK293T cells transfected with either wild-type or mutated GFP-tagged Nsrp1 suggests that the Val532Glu variant interferes with the function of the nuclear localization signal, and leads to mislocalization of NSRP1 in the cytosol, thus confirming the pathogenicity of the observed variant. This case helps to expand the phenotypic and genetic spectrum associated with pathogenic NSRP1 variants and indicates that this diagnosis should also be suspected in patients with milder phenotypes.

The endometrial transcriptome of infertile women with and without implantation failure.

INTRODUCTION: Implantation failure after transferring morphologically "good-quality" embryos in in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) may be explained by impaired endometrial receptivity. Analyzing the endometrial transcriptome analysis may reveal the underlying processes and could help in guiding prognosis and using targeted interventions for infertility. This exploratory study investigated whether the endometrial transcriptome profile was associated with short-term or long-term implantation outcomes (ie success or failure). MATERIAL AND METHODS: Mid-luteal phase endometrial biopsies of 107 infertile women with one full failed IVF/ICSI cycle, obtained within an endometrial scratching trial, were subjected to RNA-sequencing and differentially expressed genes analysis with covariate adjustment (age, body mass index, luteinizing hormone [LH]-day). Endometrial transcriptomes were compared between implantation failure and success groups in the short term (after the second fresh IVF/ICSI cycle) and long term (including all fresh and frozen cycles within 12 months). The short-term analysis included 85/107 women (33 ongoing pregnancy vs 52 no pregnancy), excluding 22/107 women. The long-term analysis included 46/107 women (23 'fertile' group, ie infertile women with a live birth after ≤3 embryos transferred vs 23 recurrent implantation failure group, ie no live birth after ≥3 good quality embryos transferred), excluding 61/107 women not fitting these categories. As both analyses drew from the same pool of 107 samples, there was some sample overlap. Additionally, cell type enrichment scores and endometrial receptivity were analyzed, and an endometrial development pseudo-timeline was constructed to estimate transcriptomic deviations from the optimum receptivity day (LH + 7), denoted as ΔWOI (window of implantation). RESULTS: There were no significantly differentially expressed genes between implantation failure and success groups in either the short-term or long-term analyses. Principal component analysis initially showed two clusters in the long-term analysis, unrelated to clinical phenotype and no longer distinct following covariate adjustment. Cell type enrichment scores did not differ significantly between groups in both analyses. However, endometrial receptivity analysis demonstrated a potentially significant displacement of the WOI in the non-pregnant group compared with the ongoing pregnant group in the short-term analysis. CONCLUSIONS: No distinct endometrial transcriptome profile was associated with either implantation failure or success in infertile women. However, there may be differences in the extent to which the WOI is displaced.

Comprehensive evaluation of the implementation of episignatures for diagnosis of neurodevelopmental disorders (NDDs).

Episignatures are popular tools for the diagnosis of rare neurodevelopmental disorders. They are commonly based on a set of differentially methylated CpGs used in combination with a support vector machine model. DNA methylation (DNAm) data often include missing values due to changes in data generation technology and batch effects. While many normalization methods exist for DNAm data, their impact on episignature performance have never been assessed. In addition, technologies to quantify DNAm evolve quickly and this may lead to poor transposition of existing episignatures generated on deprecated array versions to new ones. Indeed, probe removal between array versions, technologies or during preprocessing leads to missing values. Thus, the effect of missing data on episignature performance must also be carefully evaluated and addressed through imputation or an innovative approach to episignatures design. In this paper, we used data from patients suffering from Kabuki and Sotos syndrome to evaluate the influence of normalization methods, classification models and missing data on the prediction performances of two existing episignatures. We compare how six popular normalization methods for methylarray data affect episignature classification performances in Kabuki and Sotos syndromes and provide best practice suggestions when building new episignatures. In this setting, we show that Illumina, Noob or Funnorm normalization methods achieved higher classification performances on the testing sets compared to Quantile, Raw and Swan normalization methods. We further show that penalized logistic regression and support vector machines perform best in the classification of Kabuki and Sotos syndrome patients. Then, we describe a new paradigm to build episignatures based on the detection of differentially methylated regions (DMRs) and evaluate their performance compared to classical differentially methylated cytosines (DMCs)-based episignatures in the presence of missing data. We show that the performance of classical DMC-based episignatures suffers from the presence of missing data more than the DMR-based approach. We present a comprehensive evaluation of how the normalization of DNA methylation data affects episignature performance, using three popular classification models. We further evaluate how missing data affect those models' predictions. Finally, we propose a novel methodology to develop episignatures based on differentially methylated regions identification and show how this method slightly outperforms classical episignatures in the presence of missing data.

Patient-reported outcome measures on mental health and psychosocial factors in patients with Brugada syndrome.

AIMS: Brugada syndrome (BrS) is a hereditary arrhythmic disease, associated with sudden cardiac death. To date, little is known about the psychosocial correlates and impacts associated with this disease. The aim of this study was to assess a set of patient-reported psychosocial outcomes, to better profile these patients, and to propose a tailored psychosocial care. METHODS AND RESULTS: Patients were recruited at the European reference Centre for BrS at Universitair Ziekenhuis Brussel, Belgium. Recruitment was undertaken in two phases: phase 1 (retrospective), patients with confirmed BrS, and phase 2 (prospective), patients referred for ajmaline testing who had an either positive or negative diagnosis. BrS patients were compared to controls from the general population. Two hundred and nine questionnaires were analysed (144 retrospective and 65 prospective). Collected patient-reported outcomes were on mental health (12 item General Health Questionnaire; GHQ-12), social support (Oslo Social Support Scale), health-related quality of life, presence of Type-D personality (Type-D Scale; DS14), coping styles (Brief-COPE), and personality dimensions (Ten Item Personality Inventory). Results showed higher mental distress (GHQ-12) in BrS patients (2.53 ± 3.03) than in the general population (P < 0.001) and higher prevalence (32.7%) of Type D personality (P < 0.001) in patients with confirmed Brugada syndrome (BrS +). A strong correlation was found in the BrS + group (0.611, P < 0.001) between DS14 negative affectivity subscale and mental distress (GHQ-12). CONCLUSION: Mental distress and type D personality are significantly more common in BrS patients compared to the general population. This clearly illustrates the necessity to include mental health screening and care as standard for BrS.

Analysis of parental contribution for aneuploidy detection (APCAD): a novel method to detect aneuploidy and mosaicism in preimplantation embryos.

RESEARCH QUESTION: Can (mosaic) aneuploidy be reliably detected in preimplantation embryos after multiple displacement amplification and single nucleotide polymorphism detection, independent of haplotyping and copy number detection, with a new method 'analysis of parental contribution for aneuploidy detection' or 'APCAD'? DESIGN: This method is based on the maternal contribution, a parameter that reflects the proportion of DNA that is of maternal origin for a given chromosome or chromosome segment. A maternal contribution deviating from 50% for autosomes is strongly indicative of a (mosaic) chromosomal anomaly. The method was optimized using cell mixtures with varying ratios of euploid and aneuploid (47,XY,+21) lymphocytes. Next, the maternal contribution was retrospectively measured for all chromosomes from 349 Karyomapping samples. RESULTS: Retrospective analysis showed a skewed maternal contribution (<36.4 or >63.6%) in 57 out of 59 autosome meiotic trisomies and all autosome monosomies (n = 57), with values close to theoretical expectation. Thirty-two out of 7436 chromosomes, for which no anomalies had been observed with Karyomapping, showed a similarly skewed maternal contribution. CONCLUSIONS: APCAD was used to measure the maternal contribution, which is an intuitive parameter independent of copy number detection. This method is useful for detecting copy number neutral anomalies and can confirm diagnosis of (mosaic) aneuploidy detected based on copy number. Mosaic and complete aneuploidy can be distinguished and the parent of origin for (mosaic) chromosome anomalies can be determined. Because of these benefits, the APCAD method has the potential to improve aneuploidy detection carried out by comprehensive preimplantation genetic testing methods.

New functionalities in the TCGAbiolinks package for the study and integration of cancer data from GDC and GTEx.

The advent of Next-Generation Sequencing (NGS) technologies has opened new perspectives in deciphering the genetic mechanisms underlying complex diseases. Nowadays, the amount of genomic data is massive and substantial efforts and new tools are required to unveil the information hidden in the data. The Genomic Data Commons (GDC) Data Portal is a platform that contains different genomic studies including the ones from The Cancer Genome Atlas (TCGA) and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) initiatives, accounting for more than 40 tumor types originating from nearly 30000 patients. Such platforms, although very attractive, must make sure the stored data are easily accessible and adequately harmonized. Moreover, they have the primary focus on the data storage in a unique place, and they do not provide a comprehensive toolkit for analyses and interpretation of the data. To fulfill this urgent need, comprehensive but easily accessible computational methods for integrative analyses of genomic data that do not renounce a robust statistical and theoretical framework are required. In this context, the R/Bioconductor package TCGAbiolinks was developed, offering a variety of bioinformatics functionalities. Here we introduce new features and enhancements of TCGAbiolinks in terms of i) more accurate and flexible pipelines for differential expression analyses, ii) different methods for tumor purity estimation and filtering, iii) integration of normal samples from other platforms iv) support for other genomics datasets, exemplified here by the TARGET data. Evidence has shown that accounting for tumor purity is essential in the study of tumorigenesis, as these factors promote confounding behavior regarding differential expression analysis. With this in mind, we implemented these filtering procedures in TCGAbiolinks. Moreover, a limitation of some of the TCGA datasets is the unavailability or paucity of corresponding normal samples. We thus integrated into TCGAbiolinks the possibility to use normal samples from the Genotype-Tissue Expression (GTEx) project, which is another large-scale repository cataloging gene expression from healthy individuals. The new functionalities are available in the TCGAbiolinks version 2.8 and higher released in Bioconductor version 3.7.

Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis.

BACKGROUND: Modern high-throughput genomic technologies represent a comprehensive hallmark of molecular changes in pan-cancer studies. Although different cancer gene signatures have been revealed, the mechanism of tumourigenesis has yet to be completely understood. Pathways and networks are important tools to explain the role of genes in functional genomic studies. However, few methods consider the functional non-equal roles of genes in pathways and the complex gene-gene interactions in a network. RESULTS: We present a novel method in pan-cancer analysis that identifies de-regulated genes with a functional role by integrating pathway and network data. A pan-cancer analysis of 7158 tumour/normal samples from 16 cancer types identified 895 genes with a central role in pathways and de-regulated in cancer. Comparing our approach with 15 current tools that identify cancer driver genes, we found that 35.6% of the 895 genes identified by our method have been found as cancer driver genes with at least 2/15 tools. Finally, we applied a machine learning algorithm on 16 independent GEO cancer datasets to validate the diagnostic role of cancer driver genes for each cancer. We obtained a list of the top-ten cancer driver genes for each cancer considered in this study. CONCLUSIONS: Our analysis 1) confirmed that there are several known cancer driver genes in common among different types of cancer, 2) highlighted that cancer driver genes are able to regulate crucial pathways.

TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data.

The Cancer Genome Atlas (TCGA) research network has made public a large collection of clinical and molecular phenotypes of more than 10 000 tumor patients across 33 different tumor types. Using this cohort, TCGA has published over 20 marker papers detailing the genomic and epigenomic alterations associated with these tumor types. Although many important discoveries have been made by TCGA's research network, opportunities still exist to implement novel methods, thereby elucidating new biological pathways and diagnostic markers. However, mining the TCGA data presents several bioinformatics challenges, such as data retrieval and integration with clinical data and other molecular data types (e.g. RNA and DNA methylation). We developed an R/Bioconductor package called TCGAbiolinks to address these challenges and offer bioinformatics solutions by using a guided workflow to allow users to query, download and perform integrative analyses of TCGA data. We combined methods from computer science and statistics into the pipeline and incorporated methodologies developed in previous TCGA marker studies and in our own group. Using four different TCGA tumor types (Kidney, Brain, Breast and Colon) as examples, we provide case studies to illustrate examples of reproducibility, integrative analysis and utilization of different Bioconductor packages to advance and accelerate novel discoveries.

PharmacoGx: an R package for analysis of large pharmacogenomic datasets.

UNLABELLED: Pharmacogenomics holds great promise for the development of biomarkers of drug response and the design of new therapeutic options, which are key challenges in precision medicine. However, such data are scattered and lack standards for efficient access and analysis, consequently preventing the realization of the full potential of pharmacogenomics. To address these issues, we implemented PharmacoGx, an easy-to-use, open source package for integrative analysis of multiple pharmacogenomic datasets. We demonstrate the utility of our package in comparing large drug sensitivity datasets, such as the Genomics of Drug Sensitivity in Cancer and the Cancer Cell Line Encyclopedia. Moreover, we show how to use our package to easily perform Connectivity Map analysis. With increasing availability of drug-related data, our package will open new avenues of research for meta-analysis of pharmacogenomic data. AVAILABILITY AND IMPLEMENTATION: PharmacoGx is implemented in R and can be easily installed on any system. The package is available from CRAN and its source code is available from GitHub. CONTACT: bhaibeka@uhnresearch.ca or benjamin.haibe.kains@utoronto.ca SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SpidermiR: An R/Bioconductor Package for Integrative Analysis with miRNA Data.

Gene Regulatory Networks (GRNs) control many biological systems, but how such network coordination is shaped is still unknown. GRNs can be subdivided into basic connections that describe how the network members interact e.g., co-expression, physical interaction, co-localization, genetic influence, pathways, and shared protein domains. The important regulatory mechanisms of these networks involve miRNAs. We developed an R/Bioconductor package, namely SpidermiR, which offers an easy access to both GRNs and miRNAs to the end user, and integrates this information with differentially expressed genes obtained from The Cancer Genome Atlas. Specifically, SpidermiR allows the users to: (i) query and download GRNs and miRNAs from validated and predicted repositories; (ii) integrate miRNAs with GRNs in order to obtain miRNA-gene-gene and miRNA-protein-protein interactions, and to analyze miRNA GRNs in order to identify miRNA-gene communities; and (iii) graphically visualize the results of the analyses. These analyses can be performed through a single interface and without the need for any downloads. The full data sets are then rapidly integrated and processed locally.

NetBenchmark: a bioconductor package for reproducible benchmarks of gene regulatory network inference.

BACKGROUND: In the last decade, a great number of methods for reconstructing gene regulatory networks from expression data have been proposed. However, very few tools and datasets allow to evaluate accurately and reproducibly those methods. Hence, we propose here a new tool, able to perform a systematic, yet fully reproducible, evaluation of transcriptional network inference methods. RESULTS: Our open-source and freely available Bioconductor package aggregates a large set of tools to assess the robustness of network inference algorithms against different simulators, topologies, sample sizes and noise intensities. CONCLUSIONS: The benchmarking framework that uses various datasets highlights the specialization of some methods toward network types and data. As a result, it is possible to identify the techniques that have broad overall performances.

Inference and validation of predictive gene networks from biomedical literature and gene expression data.

Although many methods have been developed for inference of biological networks, the validation of the resulting models has largely remained an unsolved problem. Here we present a framework for quantitative assessment of inferred gene interaction networks using knock-down data from cell line experiments. Using this framework we are able to show that network inference based on integration of prior knowledge derived from the biomedical literature with genomic data significantly improves the quality of inferred networks relative to other approaches. Our results also suggest that cell line experiments can be used to quantitatively assess the quality of networks inferred from tumor samples.

mRMRe: an R package for parallelized mRMR ensemble feature selection.

MOTIVATION: Feature selection is one of the main challenges in analyzing high-throughput genomic data. Minimum redundancy maximum relevance (mRMR) is a particularly fast feature selection method for finding a set of both relevant and complementary features. Here we describe the mRMRe R package, in which the mRMR technique is extended by using an ensemble approach to better explore the feature space and build more robust predictors. To deal with the computational complexity of the ensemble approach, the main functions of the package are implemented and parallelized in C using the openMP Application Programming Interface. RESULTS: Our ensemble mRMR implementations outperform the classical mRMR approach in terms of prediction accuracy. They identify genes more relevant to the biological context and may lead to richer biological interpretations. The parallelized functions included in the package show significant gains in terms of run-time speed when compared with previously released packages. AVAILABILITY: The R package mRMRe is available on Comprehensive R Archive Network and is provided open source under the Artistic-2.0 License. The code used to generate all the results reported in this application note is available from Supplementary File 1. CONTACT: bhaibeka@ircm.qc.ca SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Predictive networks: a flexible, open source, web application for integration and analysis of human gene networks.

Genomics provided us with an unprecedented quantity of data on the genes that are activated or repressed in a wide range of phenotypes. We have increasingly come to recognize that defining the networks and pathways underlying these phenotypes requires both the integration of multiple data types and the development of advanced computational methods to infer relationships between the genes and to estimate the predictive power of the networks through which they interact. To address these issues we have developed Predictive Networks (PN), a flexible, open-source, web-based application and data services framework that enables the integration, navigation, visualization and analysis of gene interaction networks. The primary goal of PN is to allow biomedical researchers to evaluate experimentally derived gene lists in the context of large-scale gene interaction networks. The PN analytical pipeline involves two key steps. The first is the collection of a comprehensive set of known gene interactions derived from a variety of publicly available sources. The second is to use these 'known' interactions together with gene expression data to infer robust gene networks. The PN web application is accessible from http://predictivenetworks.org. The PN code base is freely available at https://sourceforge.net/projects/predictivenets/.

Heterotopia of salivary gland tissue in the pancreas.

Heterotopia of the salivary gland occurs mainly in the head and neck region of the human body, rarely in regions such as the rectum, but has never been demonstrated in the pancreas. Within a screening effort of pancreatic samples for detecting ΔNp63 expression, we discovered two pancreatic samples from a 35-year-old male showing salivary gland heterotopia. Immunohistochemical stainings were done for markers of healthy and neoplastic salivary glands and showed expression of calponin, CD142 and KRT14 but not of S100p, GFAP or CD117. A PAS-staining and Alcian Blue staining showed the presence of acid mucins. These staining patterns were consistent with non-neoplastic submandibular gland tissue comprised of abundant seromucous glands, basal cells and myoepithelial cells, all features typically absent in the pancreas. Also, no pancreatic islets of Langerhans were detected. We show for the first time that salivary gland heterotopia can occur at the location of the pancreas.