Functional Copy-Number Alterations as Diagnostic and Prognostic Biomarkers in Neuroendocrine Tumors.

Functional copy-number alterations (fCNAs) are DNA copy-number changes with concordant differential gene expression. These are less likely to be bystander genetic lesions and could serve as robust and reproducible tumor biomarkers. To identify candidate fCNAs in neuroendocrine tumors (NETs), we integrated chromosomal microarray (CMA) and RNA-seq differential gene-expression data from 31 pancreatic (pNETs) and 33 small-bowel neuroendocrine tumors (sbNETs). Tumors were resected from 47 early-disease-progression (<24 months) and 17 late-disease-progression (>24 months) patients. Candidate fCNAs that accurately differentiated these groups in this discovery cohort were then replicated using fluorescence in situ hybridization (FISH) on formalin-fixed, paraffin-embedded (FFPE) tissues in a larger validation cohort of 60 pNETs and 82 sbNETs (52 early- and 65 late-disease-progression samples). Logistic regression analysis revealed the predictive ability of these biomarkers, as well as the assay-performance metrics of sensitivity, specificity, and area under the curve. Our results indicate that copy-number changes at chromosomal loci 4p16.3, 7q31.2, 9p21.3, 17q12, 18q21.2, and 19q12 may be used as diagnostic and prognostic NET biomarkers. This involves a rapid, cost-effective approach to determine the primary tumor site for patients with metastatic liver NETs and to guide risk-stratified therapeutic decisions.

Prioritization of Fluorescence In Situ Hybridization (FISH) Probes for Differentiating Primary Sites of Neuroendocrine Tumors with Machine Learning.

Determining neuroendocrine tumor (NET) primary sites is pivotal for patient care as pancreatic NETs (pNETs) and small bowel NETs (sbNETs) have distinct treatment approaches. The diagnostic power and prioritization of fluorescence in situ hybridization (FISH) assay biomarkers for establishing primary sites has not been thoroughly investigated using machine learning (ML) techniques. We trained ML models on FISH assay metrics from 85 sbNET and 59 pNET samples for primary site prediction. Exploring multiple methods for imputing missing data, the impute-by-median dataset coupled with a support vector machine model achieved the highest classification accuracy of 93.1% on a held-out test set, with the top importance variables originating from the ERBB2 FISH probe. Due to the greater interpretability of decision tree (DT) models, we fit DT models to ten dataset splits, achieving optimal performance with k-nearest neighbor (KNN) imputed data and a transformation to single categorical biomarker probe variables, with a mean accuracy of 81.4%, on held-out test sets. ERBB2 and MET variables ranked as top-performing features in 9 of 10 DT models and the full dataset model. These findings offer probabilistic guidance for FISH testing, emphasizing the prioritization of the ERBB2, SMAD4, and CDKN2A FISH probes in diagnosing NET primary sites.

Creation and validation of models to predict response to primary treatment in serous ovarian cancer.

Nearly a third of patients with high-grade serous ovarian cancer (HGSC) do not respond to initial therapy and have an overall poor prognosis. However, there are no validated tools that accurately predict which patients will not respond. Our objective is to create and validate accurate models of prediction for treatment response in HGSC. This is a retrospective case-control study that integrates comprehensive clinical and genomic data from 88 patients with HGSC from a single institution. Responders were those patients with a progression-free survival of at least 6 months after treatment. Only patients with complete clinical information and frozen specimen at surgery were included. Gene, miRNA, exon, and long non-coding RNA (lncRNA) expression, gene copy number, genomic variation, and fusion-gene determination were extracted from RNA-sequencing data. DNA methylation analysis was performed. Initial selection of informative variables was performed with univariate ANOVA with cross-validation. Significant variables (p < 0.05) were included in multivariate lasso regression prediction models. Initial models included only one variable. Variables were then combined to create complex models. Model performance was measured with area under the curve (AUC). Validation of all models was performed using TCGA HGSC database. By integrating clinical and genomic variables, we achieved prediction performances of over 95% in AUC. Most performances in the validation set did not differ from the training set. Models with DNA methylation or lncRNA underperformed in the validation set. Integrating comprehensive clinical and genomic data from patients with HGSC results in accurate and robust prediction models of treatment response.

Presacral neuroendocrine tumors associated with the Currarino syndrome.

Currarino syndrome (CS) is an autosomal dominant syndrome caused by mutations in MNX1 and characterized by anorectal abnormalities, partial sacral agenesis, and presacral masses. The presacral masses are typically benign; however, malignant degeneration can occur, and presacral neuroendocrine tumors (NETs) have been reported in six cases. We report three individuals from two families affected by CS in which multiple individuals developed presacral NETs. The first family, 491, had six members with features of CS, including two siblings who presented with presacral, Grade 2 NETs, one of which had metastasized to bone and lymph nodes. A germline c.874C>T (p.Arg292Trp) mutation was found in a highly conserved region of MNX1 in three affected members who underwent sequencing. A second somatic variant/deletion in MNX1 was not detected in either patient's tumor. In the second family, 342, the proband presented with an incidentally discovered presacral NET. The proband's father had previously undergone resection of a presacral NET, and so genetic testing was performed, which did not reveal an MNX1 mutation or copy number variants. The lack of a second, somatic mutation in the tumors from family 491 argues against MNX1 acting as a tumor suppressor, and the absence of a germline MNX1 mutation in family 342 suggests that other genetic and anatomic factors contribute to the development of presacral NETs. These cases highlight the variable presentation of CS, and the potential for malignancy in these patients.

Gene Expression Signatures Identify Novel Therapeutics for Metastatic Pancreatic Neuroendocrine Tumors.

PURPOSE: Pancreatic neuroendocrine tumors (pNETs) are uncommon malignancies noted for their propensity to metastasize and comparatively favorable prognosis. Although both the treatment options and clinical outcomes have improved in the past decades, most patients will die of metastatic disease. New systemic therapies are needed. EXPERIMENTAL DESIGN: Tissues were obtained from 43 patients with well-differentiated pNETs undergoing surgery. Gene expression was compared between primary tumors versus liver and lymph node metastases using RNA-Seq. Genes that were selectively elevated at only one metastatic site were filtered out to reduce tissue-specific effects. Ingenuity pathway analysis (IPA) and the Connectivity Map (CMap) identified drugs likely to antagonize metastasis-specific targets. The biological activity of top identified agents was tested in vitro using two pNET cell lines (BON-1 and QGP-1). RESULTS: A total of 902 genes were differentially expressed in pNET metastases compared with primary tumors, 626 of which remained in the common metastatic profile after filtering. Analysis with IPA and CMap revealed altered activity of factors involved in survival and proliferation, and identified drugs targeting those pathways, including inhibitors of mTOR, PI3K, MEK, TOP2A, protein kinase C, NF-kB, cyclin-dependent kinase, and histone deacetylase. Inhibitors of MEK and TOP2A were consistently the most active compounds. CONCLUSIONS: We employed a complementary bioinformatics approach to identify novel therapeutics for pNETs by analyzing gene expression in metastatic tumors. The potential utility of these drugs was confirmed by in vitro cytotoxicity assays, suggesting drugs targeting MEK and TOP2A may be highly efficacious against metastatic pNETs. This is a promising strategy for discovering more effective treatments for patients with pNETs.

A TFAP2C Gene Signature Is Predictive of Outcome in HER2-Positive Breast Cancer.

The AP-2γ transcription factor, encoded by the TFAP2C gene, regulates the expression of estrogen receptor-alpha (ERα) and other genes associated with hormone response in luminal breast cancer. Little is known about the role of AP-2γ in other breast cancer subtypes. A subset of HER2+ breast cancers with amplification of the TFAP2C gene locus becomes addicted to AP-2γ. Herein, we sought to define AP-2γ gene targets in HER2+ breast cancer and identify genes accounting for physiologic effects of growth and invasiveness regulated by AP-2γ. Comparing HER2+ cell lines that demonstrated differential response to growth and invasiveness with knockdown of TFAP2C, we identified a set of 68 differentially expressed target genes. CDH5 and CDKN1A were among the genes differentially regulated by AP-2γ and that contributed to growth and invasiveness. Pathway analysis implicated the MAPK13/p38δ and retinoic acid regulatory nodes, which were confirmed to display divergent responses in different HER2+ cancer lines. To confirm the clinical relevance of the genes identified, the AP-2γ gene signature was found to be highly predictive of outcome in patients with HER2+ breast cancer. We conclude that AP-2γ regulates a set of genes in HER2+ breast cancer that drive cancer growth and invasiveness. The AP-2γ gene signature predicts outcome of patients with HER2+ breast cancer and pathway analysis predicts that subsets of patients will respond to drugs that target the MAPK or retinoic acid pathways. IMPLICATIONS: A set of genes regulated by AP-2γ in HER2+ breast cancer that drive proliferation and invasion were identified and provided a gene signature that is predictive of outcome in HER2+ breast cancer.

Machine learning with the TCGA-HNSC dataset: improving usability by addressing inconsistency, sparsity, and high-dimensionality.

BACKGROUND: In the era of precision oncology and publicly available datasets, the amount of information available for each patient case has dramatically increased. From clinical variables and PET-CT radiomics measures to DNA-variant and RNA expression profiles, such a wide variety of data presents a multitude of challenges. Large clinical datasets are subject to sparsely and/or inconsistently populated fields. Corresponding sequencing profiles can suffer from the problem of high-dimensionality, where making useful inferences can be difficult without correspondingly large numbers of instances. In this paper we report a novel deployment of machine learning techniques to handle data sparsity and high dimensionality, while evaluating potential biomarkers in the form of unsupervised transformations of RNA data. We apply preprocessing, MICE imputation, and sparse principal component analysis (SPCA) to improve the usability of more than 500 patient cases from the TCGA-HNSC dataset for enhancing future oncological decision support for Head and Neck Squamous Cell Carcinoma (HNSCC). RESULTS: Imputation was shown to improve prognostic ability of sparse clinical treatment variables. SPCA transformation of RNA expression variables reduced runtime for RNA-based models, though changes to classifier performance were not significant. Gene ontology enrichment analysis of gene sets associated with individual sparse principal components (SPCs) are also reported, showing that both high- and low-importance SPCs were associated with cell death pathways, though the high-importance gene sets were found to be associated with a wider variety of cancer-related biological processes. CONCLUSIONS: MICE imputation allowed us to impute missing values for clinically informative features, improving their overall importance for predicting two-year recurrence-free survival by incorporating variance from other clinical variables. Dimensionality reduction of RNA expression profiles via SPCA reduced both computation cost and model training/evaluation time without affecting classifier performance, allowing researchers to obtain experimental results much more quickly. SPCA simultaneously provided a convenient avenue for consideration of biological context via gene ontology enrichment analysis.

RABL6A inhibits tumor-suppressive PP2A/AKT signaling to drive pancreatic neuroendocrine tumor growth.

Hyperactivated AKT/mTOR signaling is a hallmark of pancreatic neuroendocrine tumors (PNETs). Drugs targeting this pathway are used clinically, but tumor resistance invariably develops. A better understanding of factors regulating AKT/mTOR signaling and PNET pathogenesis is needed to improve current therapies. We discovered that RABL6A, a new oncogenic driver of PNET proliferation, is required for AKT activity. Silencing RABL6A caused PNET cell-cycle arrest that coincided with selective loss of AKT-S473 (not T308) phosphorylation and AKT/mTOR inactivation. Restoration of AKT phosphorylation rescued the G1 phase block triggered by RABL6A silencing. Mechanistically, loss of AKT-S473 phosphorylation in RABL6A-depleted cells was the result of increased protein phosphatase 2A (PP2A) activity. Inhibition of PP2A restored phosphorylation of AKT-S473 in RABL6A-depleted cells, whereas PP2A reactivation using a specific small-molecule activator of PP2A (SMAP) abolished that phosphorylation. Moreover, SMAP treatment effectively killed PNET cells in a RABL6A-dependent manner and suppressed PNET growth in vivo. The present work identifies RABL6A as a new inhibitor of the PP2A tumor suppressor and an essential activator of AKT in PNET cells. Our findings offer what we believe is a novel strategy of PP2A reactivation for treatment of PNETs as well as other human cancers driven by RABL6A overexpression and PP2A inactivation.

Correction to: Genomics of NSCLC patients both affirm PD-L1 expression and predict their clinical responses to anti-PD-1 immunotherapy.

It has been highlighted that in the original manuscript [1] Table S3 'An example of the predictive computational modeling process. Specific details on an annexure section of the PD-L1 pathway show the step-by-step reactions, mechanisms, and reaction equations that occur. Such reactions also occurred in all of the other pathways' was omitted and did not appear in the Additional files and that the Additional files were miss-numbered thereafter. This Correction shows the correct and incorrect Additional files. The original article has been updated.

Genomics of NSCLC patients both affirm PD-L1 expression and predict their clinical responses to anti-PD-1 immunotherapy.

BACKGROUND: Programmed Death Ligand 1 (PD-L1) is a co-stimulatory and immune checkpoint protein. PD-L1 expression in non-small cell lung cancers (NSCLC) is a hallmark of adaptive resistance and its expression is often used to predict the outcome of Programmed Death 1 (PD-1) and PD-L1 immunotherapy treatments. However, clinical benefits do not occur in all patients and new approaches are needed to assist in selecting patients for PD-1 or PD-L1 immunotherapies. Here, we hypothesized that patient tumor cell genomics influenced cell signaling and expression of PD-L1, chemokines, and immunosuppressive molecules and these profiles could be used to predict patient clinical responses. METHODS: We used a recent dataset from NSCLC patients treated with pembrolizumab. Deleterious gene mutational profiles in patient exomes were identified and annotated into a cancer network to create NSCLC patient-specific predictive computational simulation models. Validation checks were performed on the cancer network, simulation model predictions, and PD-1 match rates between patient-specific predicted and clinical responses. RESULTS: Expression profiles of these 24 chemokines and immunosuppressive molecules were used to identify patients who would or would not respond to PD-1 immunotherapy. PD-L1 expression alone was not sufficient to predict which patients would or would not respond to PD-1 immunotherapy. Adding chemokine and immunosuppressive molecule expression profiles allowed patient models to achieve a greater than 85.0% predictive correlation among predicted and reported patient clinical responses. CONCLUSIONS: Our results suggested that chemokine and immunosuppressive molecule expression profiles can be used to accurately predict clinical responses thus differentiating among patients who would and would not benefit from PD-1 or PD-L1 immunotherapies.

Changes in gene expression in small bowel neuroendocrine tumors associated with progression to metastases.

BACKGROUND: Small bowel neuroendocrine tumors (SBNETs) present frequently with metastases, yet little is known about the molecular basis of this progression. This study sought to identify the serial differential expression of genes between normal small bowel, primary small bowel neuroendocrine tumors, and liver metastases. METHODS: RNA isolated from matched normal small bowel tissue, primary small bowel neuroendocrine tumors, and liver metastases in 12 patients was analyzed with whole transcriptome expression microarrays and RNA-Seq. Changes in gene expression between primary small bowel neuroendocrine tumors and normal small bowels, and liver metastases versus primary small bowel neuroendocrine tumors were calculated. Common genes that were differentially expressed serially (increasing or decreasing from normal small bowel to primary small bowel neuroendocrine tumors to liver metastases) were identified, and 10 were validated using qPCR. RESULTS: Use of 2 transcriptome platforms allowed for a robust discrimination of genes important in small bowel neuroendocrine tumors progression. Serial differential expression was validated in 7/10 genes, all of which had been described previously in abdominal cancers, and with several interacting with members of the AKT, MYC, or MAPK3 pathways. Liver metastases had consistent underexpression of PMP22, while high expression of SERPINA10 and SYT13 was characteristic of both pSBTs and liver metastases. CONCLUSION: Identification of the serial differential expression of genes from normal tissues to primary tumors to metastases lends insight into important pathways for SBNETs progression. Differential expression of various genes, including PMP22, SYT13 and SERPINA10, are associated with the progression of SBNETs and warrant further investigation.

Clinically Focused Molecular Investigation of 1000 Consecutive Families with Inherited Retinal Disease.

PURPOSE: To devise a comprehensive multiplatform genetic testing strategy for inherited retinal disease and to describe its performance in 1000 consecutive families seen by a single clinician. DESIGN: Retrospective series. PARTICIPANTS: One thousand consecutive families seen by a single clinician. METHODS: The clinical records of all patients seen by a single retina specialist between January 2010 and June 2016 were reviewed, and all patients who met the clinical criteria for a diagnosis of inherited retinal disease were included in the study. Each patient was assigned to 1 of 62 diagnostic categories, and this clinical diagnosis was used to define the scope and order of the molecular investigations that were performed. The number of nucleotides evaluated in a given subject ranged from 2 to nearly 900 000. MAIN OUTCOME MEASURES: Sensitivity and false genotype rate. RESULTS: Disease-causing genotypes were identified in 760 families (76%). These genotypes were distributed across 104 different genes. More than 75% of these 104 genes have coding sequences small enough to be packaged efficiently into an adeno-associated virus. Mutations in ABCA4 were the most common cause of disease in this cohort (173 families), whereas mutations in 80 genes caused disease in 5 or fewer families (i.e., 0.5% or less). Disease-causing genotypes were identified in 576 of the families without next-generation sequencing (NGS). This included 23 families with mutations in the repetitive region of RPGR exon 15 that would have been missed by NGS. Whole-exome sequencing of the remaining 424 families revealed mutations in an additional 182 families, and whole-genome sequencing of 4 of the remaining 242 families revealed 2 additional genotypes that were invisible by the other methods. Performing the testing in a clinically focused tiered fashion would be 6.1% more sensitive and 17.7% less expensive and would have a significantly lower average false genotype rate than using whole-exome sequencing to assess more than 300 genes in all patients (7.1% vs. 128%; P < 0.001). CONCLUSIONS: Genetic testing for inherited retinal disease is now more than 75% sensitive. A clinically directed tiered testing strategy can increase sensitivity and improve statistical significance without increasing cost.

Esophageal cancer in a family with hamartomatous tumors and germline PTEN frameshift and SMAD7 missense mutations.

Germline mutations in the PTEN tumor-suppressor gene cause autosomal-dominant conditions such as Cowden and Bannayan-Riley-Ruvalcaba syndromes with variable presentations, including hamartomatous gastrointestinal tumors, dermatologic abnormalities, neurologic symptoms, and elevated cancer risk. We describe a father and son with extensive hamartomatous gastrointestinal polyposis who both developed early-onset esophageal cancer. Exome sequencing identified a novel germline PTEN frameshift mutation (c.568_569insC, p.V191Sfs*11). In addition, a missense mutation of SMAD7 (c.115G>A, p.G39R) with an allele frequency of 0.3% in the Exome Variant Server was detected in both affected individuals. Fluorescence in situ hybridization for PTEN in the resected esophageal cancer specimen demonstrated no PTEN copy loss in malignant cells; however, results of an immunohistochemical analysis demonstrated a loss of PTEN protein expression. While the risks of many cancers are elevated in the PTEN hamartoma tumor syndromes, association between esophageal adenocarcinoma and these syndromes has not been previously reported. Esophageal adenocarcinoma and extensive polyposis/ganglioneuromatosis could represent less common features of these syndromes, potentially correlating with this novel PTEN frameshift and early protein termination genotype. Alternatively, because simultaneous disruption of both the PTEN and TGF-ß/SMAD4 pathways is associated with development of esophageal cancer in a mouse model and because SMAD4 mutations cause gastrointestinal hamartomas in juvenile polyposis syndrome, the SMAD7 mutation may represent an additional modifier of these individuals' PTEN-mutant phenotype.

Patient-specific iPSC-derived photoreceptor precursor cells as a means to investigate retinitis pigmentosa.

Next-generation and Sanger sequencing were combined to identify disease-causing USH2A mutations in an adult patient with autosomal recessive RP. Induced pluripotent stem cells (iPSCs), generated from the patient's keratinocytes, were differentiated into multi-layer eyecup-like structures with features of human retinal precursor cells. The inner layer of the eyecups contained photoreceptor precursor cells that expressed photoreceptor markers and exhibited axonemes and basal bodies characteristic of outer segments. Analysis of the USH2A transcripts of these cells revealed that one of the patient's mutations causes exonification of intron 40, a translation frameshift and a premature stop codon. Western blotting revealed upregulation of GRP78 and GRP94, suggesting that the patient's other USH2A variant (Arg4192His) causes disease through protein misfolding and ER stress. Transplantation into 4-day-old immunodeficient Crb1 (-/-) mice resulted in the formation of morphologically and immunohistochemically recognizable photoreceptor cells, suggesting that the mutations in this patient act via post-developmental photoreceptor degeneration. DOI:http://dx.doi.org/10.7554/eLife.00824.001.

Differentiation of small bowel and pancreatic neuroendocrine tumors by gene-expression profiling.

BACKGROUND: Between 10% and 20% of patients with neuroendocrine tumors (NETs) present with metastases of unknown primary site. Because knowledge of the primary site has important implications for treatment, we set out to define gene-expression profiles to differentiate between small-bowel NETs (SBNETs) and pancreatic NETs (PNETs). METHODS: RNA was extracted from tumor and normal tissues in 11 patients with SBNETs and 15 patients with PNETs, and qPCR was performed for 367 GPCR genes. Differentially expressed genes were identified using the RT2 Profiler. Whole genome expression analysis was performed on 11 SBNETs, 5 PNETS, and corresponding normal tissues. Statistical significance was evaluated by the Student t test and ANOVA. RESULTS: Whole-genome analysis revealed 173 significantly differentially expressed genes in SBNETs and normal tissues and in 52 in PNETs. GPCR arrays identified 28 genes in SBNETs and 18 in PNETs, with significant expression differences from normal tissues. In all SBNETs, 2 genes were significantly upregulated by more than fivefold: OXTR and GPR113. No PNETs shared this profile, whereas 73% had a greater than fivefold downregulation of ADORA1 and SCTR. These genes also allowed for determination of the primary site in 8 of 10 liver metastases. CONCLUSION: Differential expression patterns using as few as 2 to 4 GPCR genes successfully discriminated primary sites in small bowel and pancreatic NETs.

Calpain-5 mutations cause autoimmune uveitis, retinal neovascularization, and photoreceptor degeneration.

Autosomal dominant neovascular inflammatory vitreoretinopathy (ADNIV) is an autoimmune condition of the eye that sequentially mimics uveitis, retinitis pigmentosa, and proliferative diabetic retinopathy as it progresses to complete blindness. We identified two different missense mutations in the CAPN5 gene in three ADNIV kindreds. CAPN5 encodes calpain-5, a calcium-activated cysteine protease that is expressed in retinal photoreceptor cells. Both mutations cause mislocalization from the cell membrane to the cytosol, and structural modeling reveals that both mutations lie within a calcium-sensitive domain near the active site. CAPN5 is only the second member of the large calpain gene family to cause a human Mendelian disorder, and this is the first report of a specific molecular cause for autoimmune eye disease. Further investigation of these mutations is likely to provide insight into the pathophysiologic mechanisms of common diseases ranging from autoimmune disorders to diabetic retinopathy.

Prediction of cochlear implant performance by genetic mutation: the spiral ganglion hypothesis.

BACKGROUND: Up to 7% of patients with severe-to-profound deafness do not benefit from cochlear implantation. Given the high surgical implantation and clinical management cost of cochlear implantation (>$1 million lifetime cost), prospective identification of the worst performers would reduce unnecessary procedures and healthcare costs. Because cochlear implants bypass the membranous labyrinth but rely on the spiral ganglion for functionality, we hypothesize that cochlear implant (CI) performance is dictated in part by the anatomic location of the cochlear pathology that underlies the hearing loss. As a corollary, we hypothesize that because genetic testing can identify sites of cochlear pathology, it may be useful in predicting CI performance. METHODS: 29 adult CI recipients with idiopathic adult-onset severe-to-profound hearing loss were studied. DNA samples were subjected to solution-based sequence capture and massively parallel sequencing using the OtoSCOPE(®) platform. The cohort was divided into three CI performance groups (good, intermediate, poor) and genetic causes of deafness were correlated with audiometric data to determine whether there was a gene-specific impact on CI performance. RESULTS: The genetic cause of deafness was determined in 3/29 (10%) individuals. The two poor performers segregated mutations in TMPRSS3, a gene expressed in the spiral ganglion, while the good performer segregated mutations in LOXHD1, a gene expressed in the membranous labyrinth. Comprehensive literature review identified other good performers with mutations in membranous labyrinth-expressed genes; poor performance was associated with spiral ganglion-expressed genes. CONCLUSIONS: Our data support the underlying hypothesis that mutations in genes preferentially expressed in the spiral ganglion portend poor CI performance while mutations in genes expressed in the membranous labyrinth portend good CI performance. Although the low mutation rate in known deafness genes in this cohort likely relates to the ascertainment characteristics (postlingual hearing loss in adult CI recipients), these data suggest that genetic testing should be implemented as part of the CI evaluation to test this association prospectively.

Informatics methods to enable sharing of quantitative imaging research data.

INTRODUCTION: The National Cancer Institute Quantitative Research Network (QIN) is a collaborative research network whose goal is to share data, algorithms and research tools to accelerate quantitative imaging research. A challenge is the variability in tools and analysis platforms used in quantitative imaging. Our goal was to understand the extent of this variation and to develop an approach to enable sharing data and to promote reuse of quantitative imaging data in the community. METHODS: We performed a survey of the current tools in use by the QIN member sites for representation and storage of their QIN research data including images, image meta-data and clinical data. We identified existing systems and standards for data sharing and their gaps for the QIN use case. We then proposed a system architecture to enable data sharing and collaborative experimentation within the QIN. RESULTS: There are a variety of tools currently used by each QIN institution. We developed a general information system architecture to support the QIN goals. We also describe the remaining architecture gaps we are developing to enable members to share research images and image meta-data across the network. CONCLUSIONS: As a research network, the QIN will stimulate quantitative imaging research by pooling data, algorithms and research tools. However, there are gaps in current functional requirements that will need to be met by future informatics development. Special attention must be given to the technical requirements needed to translate these methods into the clinical research workflow to enable validation and qualification of these novel imaging biomarkers.

Genetic characterization of commonly used glioma cell lines in the rat animal model system.

OBJECT: Animal models have been used extensively to discern the molecular biology of diseases and to gain insight into treatments. Nevertheless, discrepancies in the effects of treatments and procedures have been encountered during the transition from these animal models to application of the information to clinical trials in humans. To assess the genetic similarities between human gliomas and four cell lines used routinely in animal models, the authors used microarray technology to characterize the similarities and differences in gene expression. METHODS: To define the changes in gene expression, normal rat astrocytes were compared with four rat glioma cell lines (C6, 9L, F98, and RG2). The data were analyzed using two different methods: fold-change analysis and statistical analysis with t statistics. The gene products that were highlighted after intersecting the lists generated by the two methods of analysis were scrutinized against changes in gene expression reported in the literature. Tumorigenesis involves three major steps: the accumulation of genetic alterations, uncontrolled growth, and selected survival of transformed cells. The discussion of the results focuses attention on genes whose primary function is in pathways involved in glioma proliferation, infiltration, and neovascularization. A comparative microarray analysis of differentially expressed genes for four of the commonly used rat tumor cell lines is presented here. CONCLUSIONS: Due to the variances between the cell lines and results from analyses in humans, caution must be observed in interpreting as well as in the translation of information learned from animal models to its application in human trials.