Search

Treatment resistance remains a major issue in aggressive prostate cancer (PC), and novel genomic biomarkers may guide better treatment selection. Circulating tumor DNA (ctDNA) can provide minimally invasive information about tumor genomes, but the genomic landscape of aggressive PC based on whole‐genome sequencing (WGS) of ctDNA remains incompletely characterized. Thus, we here performed WGS of tumor tissue (n = 31) or plasma ctDNA (n = 10) from a total of 41 aggressive PC patients, including 11 hormone‐naïve, 15 hormone‐sensitive, and 15 castration‐resistant patients. Across all variant types, we found progressively more altered tumor genomic profiles in later stages of aggressive PC. The potential driver genes most frequently affected by single‐nucleotide variants or insertions/deletions included the known PC‐related genes TP53, CDK12, and PTEN and the novel genes COL13A1, KCNH3, and SENP3. Etiologically, aggressive PC was associated with age‐related and DNA repair‐related mutational signatures. Copy number variants most frequently affected 14q11.2 and 8p21.2, where no well‐recognized PC‐related genes are located, and also frequently affected regions near the known PC‐related genes MYC, AR, TP53, PTEN, and BRCA1. Structural variants most frequently involved not only the known PC‐related genes TMPRSS2 and ERG but also the less extensively studied gene in this context, PTPRD. Finally, clinically actionable variants were detected throughout all stages of aggressive PC and in both plasma and tissue samples, emphasizing the potential clinical applicability of WGS of minimally invasive plasma samples. Overall, our study highlights the feasibility of using liquid biopsies for comprehensive genomic characterization as an alternative to tissue biopsies in advanced/aggressive PC. [ABSTRACT FROM AUTHOR]

Circulating tumor DNA (ctDNA) is a promising biomarker, reflecting the presence of tumor cells. Sequencing-based detection of ctDNA at low tumor fractions is challenging due to the crude error rate of sequencing. To mitigate this challenge, we developed ultra-deep mutation-integrated sequencing (UMIseq), a fixed-panel deep targeted sequencing approach, which is universally applicable to all colorectal cancer (CRC) patients. UMIseq features UMI-mediated error correction, the exclusion of mutations related to clonal hematopoiesis, a panel of normal samples for error modeling, and signal integration from single-nucleotide variations, insertions, deletions, and phased mutations. UMIseq was trained and independently validated on pre-operative (pre-OP) plasma from CRC patients (n = 364) and healthy individuals (n = 61). UMIseq displayed an area under the curve surpassing 0.95 for allele frequencies (AFs) down to 0.05%. In the training cohort, the pre-OP detection rate reached 80% at 95% specificity, while it was 70% in the validation cohort. UMIseq enabled the detection of AFs down to 0.004%. To assess the potential for detection of residual disease, 26 post-operative plasma samples from stage III CRC patients were analyzed. From this we found that the detection of ctDNA was associated with recurrence. In conclusion, UMIseq demonstrated robust performance with high sensitivity and specificity, enabling the detection of ctDNA at low allele frequencies. [ABSTRACT FROM AUTHOR]

Simple Summary: Epithelial tissues are the most common sites for the development of cancer. Loss of epithelial cell characteristics and dedifferentiation are hallmarks of cancer. A specialized and complex function in epithelial cells is receptor-mediated endocytosis. LRP2 (megalin) is the largest known endocytic membrane receptor of absorptive epithelia and mediates uptake of numerous ligands. However, its role and regulation in cancer has not been delineated. Therefore, we examined LRP2 expression across 33 cancer types in The Cancer Genome Atlas. As expected, we found the highest LRP2 expression in cancers arising from LRP2-expressing epithelia. However, in a subset of these tumors, we observed epigenetic silencing of LRP2. Interestingly, low LRP2 expression was associated with tumor cell dedifferentiation and poorer patient outcome, suggesting LRP2 is a potential cancer biomarker. Based on this, we warrant further studies on the functional role of LRP2 in tumors of epithelial origin and the implications of LRP2 downregulation. More than 80% of human cancers originate in epithelial tissues. Loss of epithelial cell characteristics are hallmarks of tumor development. Receptor-mediated endocytosis is a key function of absorptive epithelial cells with importance for cellular and organismal homeostasis. LRP2 (megalin) is the largest known endocytic membrane receptor and is essential for endocytosis of various ligands in specialized epithelia, including the proximal tubules of the kidney, the thyroid gland, and breast glandular epithelium. However, the role and regulation of LRP2 in cancers that arise from these tissues has not been delineated. Here, we examined the expression of LRP2 across 33 cancer types in The Cancer Genome Atlas. As expected, the highest levels of LRP2 were found in cancer types that arise from LRP2-expressing absorptive epithelial cells. However, in a subset of tumors from these cancer types, we observed epigenetic silencing of LRP2. LRP2 expression showed a strong inverse correlation to methylation of a specific CpG site (cg02361027) in the first intron of the LRP2 gene. Interestingly, low expression of LRP2 was associated with poor patient outcome in clear cell renal cell carcinoma, papillary renal cell carcinoma, mesothelioma, papillary thyroid carcinoma, and invasive breast carcinoma. Furthermore, loss of LRP2 expression was associated with dedifferentiated histological and molecular subtypes of these cancers. These observations now motivate further studies on the functional role of LRP2 in tumors of epithelial origin and the potential use of LRP2 as a cancer biomarker. [ABSTRACT FROM AUTHOR]

DNA methylation and gene expression are interdependent and both implicated in cancer development and progression, with many individual biomarkers discovered. A joint analysis of the two data types can potentially lead to biological insights that are not discoverable with separate analyses. To optimally leverage the joint data for identifying perturbed genes and classifying clinical cancer samples, it is important to accurately model the interactions between the two data types. Here, we present EBADIMEX for jointly identifying differential expression and methylation and classifying samples. The moderated t-test widely used with empirical Bayes priors in current differential expression methods is generalised to a multivariate setting by developing: (1) a moderated Welch t-test for equality of means with unequal variances; (2) a moderated F-test for equality of variances; and (3) a multivariate test for equality of means with equal variances. This leads to parametric models with prior distributions for the parameters, which allow fast evaluation and robust analysis of small data sets. EBADIMEX is demonstrated on simulated data as well as a large breast cancer (BRCA) cohort from TCGA. We show that the use of empirical Bayes priors and moderated tests works particularly well on small data sets. [ABSTRACT FROM AUTHOR]

Motivation Understanding the mutational processes that act during cancer development is a key topic of cancer biology. Nevertheless, much remains to be learned, as a complex interplay of processes with dependencies on a range of genomic features creates highly heterogeneous cancer genomes. Accurate driver detection relies on unbiased models of the mutation rate that also capture rate variation from uncharacterized sources. Results Here, we analyse patterns of observed-to-expected mutation counts across 505 whole cancer genomes, and find that genomic features missing from our mutation-rate model likely operate on a megabase length scale. We extend our site-specific model of the mutation rate to include the additional variance from these sources, which leads to robust significance evaluation of candidate cancer drivers. We thus present ncdDetect v.2, with greatly improved cancer driver detection specificity. Finally, we show that ranking candidates by their posterior mean value of their effect sizes offers an equivalent and more computationally efficient alternative to ranking by their P -values. Availability and implementation ncdDetect v.2 is implemented as an R-package and is freely available at http://github.com/TobiasMadsen/ncdDetect2 Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

The first epigenomes from archaic hominins (AH) and ancient anatomically modern humans (AMH) have recently been characterized, based, however, on a limited number of samples. The extent to which ancient genome-wide epigenetic landscapes can be reconstructed thus remains contentious. Here, we present epiPALEOMIX, an open-source and userfriendly pipeline that exploits post-mortem DNA degradation patterns to reconstruct ancient methylomes and nucleosome maps from shotgun and/or capture-enrichment data. Applying epiPALEOMIX to the sequence data underlying 35 ancient genomes including AMH, AH, equids and aurochs, we investigate the temporal, geographical and preservation range of ancient epigenetic signatures. We first assess the quality of inferred ancient epigenetic signatures within wellcharacterized genomic regions. We find that tissue-specific methylation signatures can be obtained across a wider range of DNA preparation types than previously thought, including when no particular experimental procedures have been used to remove deaminated cytosines prior to sequencing. We identify a large subset of samples for which DNA associated with nucleosomes is protected from post-mortem degradation, and nucleosome positioning patterns can be reconstructed. Finally, we describe parameters and conditions such as DNA damage levels and sequencing depth that limit the preservation of epigenetic signatures in ancient samples. When such conditions are met, we propose that epigenetic profiles of CTCF binding regions can be used to help data authentication. Our work, including epiPALEOMIX, opens for further investigations of ancient epigenomes through time especially aimed at tracking possible epigenetic changes during major evolutionary, environmental, socioeconomic, and cultural shifts. [ABSTRACT FROM AUTHOR]

Motivation: Recently, new RNA secondary structure probing techniques have been developed, including Next Generation Sequencing based methods capable of probing transcriptome-wide. These techniques hold great promise for improving structure prediction accuracy. However, each new data type comes with its own signal properties and biases, which may even be experiment specific. There is therefore a growing need for RNA structure prediction methods that can be automatically trained on new data types and readily extended to integrate and fully exploit multiple types of data. Results: Here, we develop and explore a modular probabilistic approach for integrating probing data in RNA structure prediction. It can be automatically trained given a set of known structures with probing data. The approach is demonstrated on SHAPE datasets, where we evaluate and selectively model specific correlations. The approach often makes superior use of the probing data signal compared to other methods. We illustrate the use of ProbFold on multiple data types using both simulations and a small set of structures with both SHAPE, DMS and CMCT data. Technically, the approach combines stochastic context-free grammars (SCFGs) with probabilistic graphical models. This approach allows rapid adaptation and integration of new probing data types. [ABSTRACT FROM AUTHOR]

Background: As the use of next-generation sequencing technologies is becoming more widespread, the need for robust software to help with the analysis is growing as well. A key challenge when analyzing sequencing data is the prediction of genotypes from the reads, i.e. correct inference of the underlying DNA sequences that gave rise to the sequenced fragments. For diploid organisms, the genotyper should be able to predict both alleles in the individual. Variations between the individual and the population can then be analyzed by looking for SNPs (single nucleotide polymorphisms) in order to investigate diseases or phenotypic features. To perform robust and high confidence genotyping and SNP calling, methods are needed that take the technology specific limitations into account and can model different sources of error. As an example, ancient DNA poses special challenges as the data is often shallow and subject to errors induced by post mortem damage. Findings: We present a novel approach to the genotyping problem where a probabilistic framework describing the process from sampling to sequencing is implemented as a graphical model. This makes it possible to model technology specific errors and other sources of variation that can affect the result. The inferred genotype is given a posterior probability to signify the confidence in the result. SNPest has already been used to genotype large scale projects such as the first ancient human genome published in 2010. Conclusions: We compare the performance of SNPest to a number of other widely used genotypers on both real and simulated data, covering both haploid and diploid genomes. We investigate the effects of read depth, of removing adapters before mapping and genotyping, of using different mapping tools, and of using the correct model in the genotyping process. We show that the performance of SNPest is comparable to existing methods, and we also illustrate cases where SNPest has an advantage over other methods, e.g. when dealing with simulated ancient DNA. [ABSTRACT FROM AUTHOR]

Summary: Bladder cancer (or urothelial cell carcinoma [UCC]) is characterized by field disease (malignant alterations in surrounding mucosa) and frequent recurrences. Whole-genome, exome, and transcriptome sequencing of 38 tumors, including four metachronous tumor pairs and 20 superficial tumors, identified an APOBEC mutational signature in one-third. This was biased toward the sense strand, correlated with mean expression level, and clustered near breakpoints. A > G mutations were up to eight times more frequent on the sense strand (p < 0.002) in [ACG]AT contexts. The patient-specific APOBEC signature was negatively correlated to repair-gene expression and was not related to clinicopathological parameters. Mutations in gene families and single genes were related to tumor stage, and expression of chromatin modifiers correlated with survival. Evolutionary and subclonal analyses of early/late tumor pairs showed a unitary origin, and discrete tumor clones contained mutated cancer genes. The ancestral clones contained Pik3ca/Kdm6a mutations and may reflect the field-disease mutations shared among later tumors. [Copyright &y& Elsevier]

Formalin-fixed, paraffin-embedded (FFPE) tissues are an invaluable resource for clinical research. However, nucleic acids extracted from FFPE tissues are fragmented and chemically modified making them challenging to use in molecular studies. We analysed 23 fresh-frozen (FF), 35 FFPE and 38 paired FF/FFPE specimens, representing six different human tissue types (bladder, prostate and colon carcinoma; liver and colon normal tissue; reactive tonsil) in order to examine the potential use of FFPE samples in next-generation sequencing (NGS) based retrospective and prospective clinical studies. Two methods for DNA and three methods for RNA extraction from FFPE tissues were compared and were found to affect nucleic acid quantity and quality. DNA and RNA from selected FFPE and paired FF/FFPE specimens were used for exome and transcriptome analysis. Preparations of DNA Exome-Seq libraries was more challenging (29.5% success) than that of RNA-Seq libraries, presumably because of modifications to FFPE tissue-derived DNA. Libraries could still be prepared from RNA isolated from two-decade old FFPE tissues. Data were analysed using the CLC Bio Genomics Workbench and revealed systematic differences between FF and FFPE tissue-derived nucleic acid libraries. In spite of this, pairwise analysis of DNA Exome-Seq data showed concordance for 70–80% of variants in FF and FFPE samples stored for fewer than three years. RNA-Seq data showed high correlation of expression profiles in FF/FFPE pairs (Pearson Correlations of 0.90 +/- 0.05), irrespective of storage time (up to 244 months) and tissue type. A common set of 1,494 genes was identified with expression profiles that were significantly different between paired FF and FFPE samples irrespective of tissue type. Our results are promising and suggest that NGS can be used to study FFPE specimens in both prospective and retrospective archive-based studies in which FF specimens are not available. [ABSTRACT FROM AUTHOR]

The discoveries of microRNAs and riboswitches, among others, have shown functional RNAs to be biologically more important and genomically more prevalent than previously anticipated. We have developed a general comparative genomics method based on phylogenetic stochastic context-free grammars for identifying functional RNAs encoded in the human genome and used it to survey an eight-way genome-wide alignment of the human, chimpanzee, mouse, rat, dog, chicken, zebra-fish, and puffer-fish genomes for deeply conserved functional RNAs. At a loose threshold for acceptance, this search resulted in a set of 48,479 candidate RNA structures. This screen finds a large number of known functional RNAs, including 195 miRNAs, 62 histone 3'UTR stem loops, and various types of known genetic recoding elements. Among the highest-scoring new predictions are 169 new miRNA candidates, as well as new candidate selenocysteine insertion sites, RNA editing hairpins, RNAs involved in transcript auto regulation, and many folds that form singletons or small functional RNA families of completely unknown function. While the rate of false positives in the overall set is difficult to estimate and is likely to be substantial, the results nevertheless provide evidence for many new human functional RNAs and present specific predictions to facilitate their further characterization. [ABSTRACT FROM AUTHOR]