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• Breast cancer women lack normal lifelong immune response after full-term pregnancies

  Immunological tolerance during pregnancy is necessary for women’s bodies to accept the semi-allogenic fetus. Interestingly, “immune evasion”, which consists of some similar immunological changes, was recently proposed as a hallmark of cancer. Looking at these results, it seems paradoxical that increasing number of full-term pregnancies (FTP) reduces the risk of breast cancer in women. Our aim was to use a systems epidemiology approach to investigate this paradox by modelling breast cancer incidence as a function of parity in the Norwegian Women and Cancer (NOWAC) cohort, then testing the mathematical model using gene expression from blood in a subcohort of these women: the NOWAC postgenome cohort. Lastly, we undertook a gene set enrichment analysis for immunological gene sets. A linear trend nicely fitted the dataset showing an 8% decrease in risk for each FTP, independent of stratification on other risk factors and lasting for decades after a woman’s last FTP. However, when we looked at gene expression we found that hundreds of genes showed linear trends in cancer-free controls, but this was not the case in /any/ of the samples from breast cancer cases. In the gene set enrichment analysis, samples consisted of human blood or cord blood and mouse bone marrow or spleen. We found more adaptive gene sets than expected in humans and more innate gene sets than expected in mice. None of the gene sets provided information about the long-term effects of immunological tolerance during pregnancy, but the lack of relevant reductionist experiments hampers our interpretation. We suggest that, after resolution at birth, the immunological tolerance induced by pregnancy augments immune response to cancer development.

  Study EGAS00001002616

• Analysis of error profiles in deep next-generation sequencing data

  BackgroundSequencing errors are key confounding factors for detecting low-frequency genetic variants that are important for cancer molecular diagnosis, treatment, and surveillance using deep next-generation sequencing (NGS). However, there is a lack of comprehensive understanding of errors introduced at various steps of a conventional NGS workflow, such as sample handling, library preparation, PCR enrichment, and sequencing. In this study, we use current NGS technology to systematically investigate these questions.ResultsBy evaluating read-specific error distributions, we discover that the substitution error rate can be computationally suppressed to 10-5 to 10-4, which is 10- to 100-fold lower than generally considered achievable (10-3) in the current literature. We then quantify substitution errors attributable to sample handling, library preparation, enrichment PCR, and sequencing by using multiple deep sequencing datasets. We find that error rates differ by nucleotide substitution types, ranging from 10-5 for A>C/T>G, C>A/G>T, and C>G/G>C changes to 10-4 for A>G/T>C changes. Furthermore, C>T/G>A errors exhibit strong sequence context dependency, sample-specific effects dominate elevated C>A/G>T errors, and target enrichment PCR led to aprox 6-fold increase of overall error rate. We also find that more than 70% of hotspot variants can be detected at 0.1% - 0.01% frequency with the current NGS technology by applying in-silico error suppression.ConclusionsWe present the first comprehensive analysis of sequencing error sources in conventional NGS workflows. The error profiles revealed by our study highlight new directions for further improving NGS analysis accuracy both experimentally and computationally, ultimately enhancing the precision of deep sequencing.

  Study EGAS00001003444

• Proteom characterization in primary colorectal cancer and corresponding liver metastasis

  Colorectal adenocarcinomas (CRC) are one of the most commonly diagnosed tumors worldwide. Colorectal adenocarcinomas primarily metastasize into the liver and (less often) into the peritoneum. Patients suffering from CRC-liver metastasis (CRC-LM) typically present with a dismal overall survival compared to non-metastasized CRC patients. The metastasis process and metastasis promoting factors in patients with CRC are under intensive debate. However, CRC studies investigating the proteome biology are lacking. Formalin-fixed paraffin-embedded (FFPE) tissue specimens provide a valuable resource for comprehensive proteomic studies of a broad variety of clinical malignancies. The presented pilot study compares the proteome of primary CRC and patient-matched CRC-LM. The applied protocol allows a reproducible and straightforward identification and quantification of over 2,600 proteins within the dissected tumorous tissue. Subsequent unsupervised clustering reveals distinct proteome biologies of the primary CRC and the corresponding CRC-LM. Statistical analysis yields multiple differentially abundant proteins in either primary CRC or their corresponding liver metastases. A more detailed analysis of dysregulated biological processes suggests an active immune response in the liver metastases, including several proteins of the complement system. Proteins with structural roles, e.g. cytoskeleton organization or cell junction assembly appear to be less prominent in liver metastases as compared to primary CRC. Immunohistochemistry corroborates proteomic high expression levels of metabolic proteins in CRC-LM. We further assessed how the in vitro inhibition of two in CRC-LM enriched metabolic proteins affected cell proliferation and chemosensitivity. The presented proteomic investigation in a small clinical cohort promotes a more comprehensive understanding of the distinct proteome biology of primary CRC and their corresponding liver metastases.

  Study EGAS00001005641

• European BestAgeing Study on microRNA candidates for cardiovascular disease

  Background: Circulating miRNAs have emerged as promising biomarker candidates due to their stability and their role in regulating key pathological pathways in cardiovascular disease (CVD). Yet, large-scale, multicenter studies examining their diagnostic and prognostic potential are scarce. This study evaluates the potential of miRNA expression profiles to inform disease classification and risk stratification across major CVD phenotypes, including acute coronary syndrome (ACS), chronic coronary artery disease (CAD), dilated cardiomyopathy (DCM), and ischemic cardiomyopathy (ICM), in a large, multicenter European cohort. Methods: We assessed genome-wide miRNA expression profiles in a total of 1,209 cardiovascular patients and 848 controls in a uniform, standardized fashion, which renders this study one of the largest prospective miRNA studies. To focus on only the most biologically plausible miRNAs for clinical translation, we mined all original studies of miRNA candidates in CVD and performed differential miRNA expression and enrichment analysis. We then trained disease-specific binary classification models to evaluate the diagnostic potential of miRNA signatures. Finally, we evaluated prognosis and disease severity based on distinct miRNA levels. Results: 634 original abstracts were identified, detailing 166 ACS, 181 CAD, 56 DCM, and 182 ICM miRNAs. Without further optimization, the signatures of a priori miRNAs already yielded very good diagnostic performance with ROC AUC of 0.83 – 0.95. There was an improvement when considering additional miRNAs in a discovery setting. Interestingly, in ACS, CAD and DCM we observed a significantly worse prognosis in probands with higher scores in the miRNA signatures, indicating additional prognostic information. Conclusions: The European BestAgeing miRNA study reveals emerging associations of several miRNA signatures with cardiovascular disease discrimination and prognostication, providing a foundation for future external validation and potential clinical translation of this class of markers.

  Study EGAS00001008346

• Exome sequencing from cfDNA blood samples. 159 samples at 2x101bp Illumina reads in Fastq format.

  Differential presence of exons (DPE) by next generation sequencing (NGS) is a method of interpretation of whole exome sequencing. This method has been proposed to design a predictive and diagnostic algorithm with clinical value in plasma from patients bearing colorectal cancer (CRC). The aim of the present study was to determine a common exonic signature to discriminate between different clinical pictures, such as non-metastatic, metastatic and non-disease (healthy), using a sustainable and novel technology in liquid biopsy. Through DPE analysis, we determined the differences in DNA exon levels circulating in plasma between patients bearing CRC vs. healthy, patients bearing CRC metastasis vs. non-metastatic and patients bearing CRC metastasis vs. healthy comparisons. We identified a set of 510 exons (469 up and 41 down) whose differential presence in plasma allowed us to group and classify between the three cohorts. Random forest classification (machine learning) was performed and an estimated out-of-bag (OOB) error rate of 35.9% was obtained and the predictive model had an accuracy of 75% with a confidence interval (CI) of 56.6-88.5. In conclusion, the DPE analysis allowed us to discriminate between different patho-physiological status such as metastatic, non-metastatic and healthy donors. In addition, this analysis allowed us to obtain very significant values with respect to previous published results, since we increased the number of samples in our study. These results suggest that circulating DNA in patient’s plasma may be actively released by cells and may be involved in intercellular communication and, therefore, may play a pivotal role in malignant transformation (genometastasis).

  Study EGAS00001006656

• Transcriptomics-driven classification of ALAL

  Acute leukemia of ambiguous lineage (ALAL) is a rare, poor-prognosis subtype of acute leukemia that cannot be assigned to a single hematopoietic lineage. Although ALAL patients are typically treated with acute myeloid leukemia (AML) or acute lymphoblastic leukemia (ALL) regimens, optimal treatment choice is hindered by their lineage ambiguity. Therefore, we investigated the added value of transcriptomics for improving lineage assignment, currently based mainly on surface markers. First, we used an in-house pipeline to comprehensively detect genetic lesions in RNA-sequencing data (n=23) with a sensitivity >90%, as confirmed by targeted DNA sequencing. Second, we compared ALAL gene expression profiles (GEPs) with representative AML (n=145), B-ALL (n=223) and T-ALL (n=85) cases. In a principal component analysis, ALALs did not form a separate group, but clustered with AML, B-ALL or T-ALL. Accordingly, a machine learning classifier trained with GEPs of acute leukemias segregated 20/23 ALALs into myeloid-, B- or T-lymphoid leukemia. These 20 cases harbored genetic abnormalities consistent with the classifier-assigned leukemia. Furthermore, ALAL GEPs were deconvoluted with single-cell transcriptional profiles of normal hematopoietic cells using CIBERSORTx, revealing enrichment for signatures of lineages corresponding to the leukemic type predicted by our algorithm. The classifier was validated on an external ALAL cohort (n=24), assigning 75% of the patients to a lineage matching their immunophenotypic and methylation profiles. In conclusion, integrative analysis of RNA-sequencing data can accurately classify most ALAL cases. The pipeline and classifier developed in our study are valuable tools to improve ALAL diagnosis and guide therapeutic decisions.

  Study EGAS00001007967

• Coagulation and Fibrinolysis in a Pediatric Insulin Titration Trial

  Summary of the Design and Aims: The Coagulation and Fibrinolysis in a Pediatric Insulin Titration Trial (CAF-PINT) is an ancillary study to the HALF PINT (NCT01565941) randomized, controlled trial that was originally designed to study changes in inflammatory and thrombosis pathways in the setting of an interventional trial. The parent HALF PINT study was designed to study the impact of tight glycemic control (TGC) using an explicit insulin titration algorithm to safely achieve a normal glucose in target range of 80-110 mg/dL versus 150-180mg/dL on clinical outcomes among critically ill hyperglycemic children with Heart and Lung Failure. The CAF PINT study collected blood samples pre randomization and at 2 and 4 days after randomization. Blood samples collected in EDTA tubes were centrifuged at local sites to separate the plasma from cell pellets and frozen prior to shipment. In addition, 0.5 mL of whole blood collected in PAXgene tubes modified for use in pediatrics frozen 24 hours after collection prior to shipment.Population Information: Setting: Twenty-one academic children's hospitals (HALF-PINT) and 1 academic children's hospital (IIT-SBPP). Participants: Critically ill hyperglycemic children requiring mechanical ventilation/vasoactive support (n=293).Interventions and Exposures: Patients in HALF PINT were randomized to achieve a low target glucose (80-110 mg/dL) vs. a higher target (150-180 mg/dL). The primary trial was stopped early due to low likelihood of achieving a statistically significant difference in the primary outcomes. Molecular Technologies Employed: Plasma biomarkers were assayed on thawed plasma using a Luminex panel. RNA was extracted from PAXgene Blood RNA Kits modified for pediatric use. Extracted RNA was sequenced with the Novaseq S4 system (Illumina) to generate 2 x 150 base paired end reads to a target depth of 50 million read-pairs per sample.Principal Findings of the Study: We tested for heterogeneity of treatment effects according to baseline blood inflammatory proteins using logistic regression with individual biomarkers as interaction terms and Cox regression analysis using biomarker-derived latent classes. Children with higher inflammatory proteins including TNFR-1, IL-6, IL-8, and IL-10 had lower mortality when treated with insulin to a target glucose of 80-110 mg/dL as compared to 150-180 mg/dL (interaction p<0.05). Causal forest estimates ranged from a 40% mortality reduction to a 15% mortality increase with TGC, depending on each patient’s individual biomarker profile. Latent class categorization strongly interacted with TGC with respect to mortality (Cox regression interaction p=0.005). Specifically, patients with higher inflammation benefited from TGC (6% mortality with TGC vs. 48% mortality, p=0.004) whereas hypoinflamed patients showed a trend towards harm (14% mortality with TGC vs. 7% mortality, p=0.055).Data available through dbGaP: Transcriptomic (Gene Counts) and phenotype data

  Study phs003016

• Cloning_of_the_breakpoint_of_a_novel_translocation_associated_with_T_acute_lymphoblastic_leukaemia

  Single case of T-ALL carrying t(4;6), a novel translocation.

  Study EGAS00001000520

• Myocardial Infarction Genetics Exome Sequencing Consortium: Malmo Diet and Cancer Study

  The Malmo Diet and Cancer Study (MDCS) is a community-based prospective epidemiologic cohort of 28,449 subjects who were recruited for baseline examination between 1991 and 1996. From this cohort, 6103 subjects were randomly selected to participate in a cardiovascular cohort (MDCSCC), which seeks to investigate risk factors for cardiovascular disease. This study is a subset of those samples. All exome sequencing was performed at the Broad Institute of Harvard and MIT; samples sequence capture was performed using Illumina's ICE Capture reagent and sequencing was performed on an Illumina HiSeq 2000 or 2500.

  Study phs001101

• Genomic Features of Lung Adenocarcinoma from Individuals with <= 10 Pack-Year Smoking History

  Actionable oncogenic alterations are identified in a subset of lung adenocarcinomas. Many of these driver alterations are enriched in lung adenocarcinomas from individuals with minimal smoking history and are generally mutually exclusive of one another. We generated whole-exome and RNA-seq data from lung adenocarcinomas obtained from individuals with &#8804;10 pack-year smoking history. Most of these tumors harbored an established oncogenic alteration. We identified a novel OCLN-RASGRF1 fusion from one tumor that lacked a known oncogenic driver. Functional studies demonstrated this fusion activates RAS signaling and promotes tumorigenesis in cell models.

  Study phs002556