Human retinal organoids release extracellular vesicles that regulate gene expression in target human retinal progenitor cells.

The mechanisms underlying retinal development have not been completely elucidated. Extracellular vesicles (EVs) are novel essential mediators of cell-to-cell communication with emerging roles in developmental processes. Nevertheless, the identification of EVs in human retinal tissue, characterization of their cargo, and analysis of their potential role in retina development has not been accomplished. Three-dimensional retinal tissue derived from human induced pluripotent stem cells (hiPSC) provide an ideal developmental system to achieve this goal. Here we report that hiPSC-derived retinal organoids release exosomes and microvesicles with small noncoding RNA cargo. EV miRNA cargo-predicted targetome correlates with Gene Ontology (GO) pathways involved in mechanisms of retinogenesis relevant to specific developmental stages corresponding to hallmarks of native human retina development. Furthermore, uptake of EVs by human retinal progenitor cells leads to changes in gene expression correlated with EV miRNA cargo predicted gene targets, and mechanisms involved in retinal development, ganglion cell and photoreceptor differentiation and function.

Correction to: Sequencing and curation strategies for identifying candidate glioblastoma treatments.

Following publication of the original article [1], it was reported that the given name of the fourteenth author was incorrectly published. The incorrect and the correct names are given below.

Sequencing and curation strategies for identifying candidate glioblastoma treatments.

BACKGROUND: Prompted by the revolution in high-throughput sequencing and its potential impact for treating cancer patients, we initiated a clinical research study to compare the ability of different sequencing assays and analysis methods to analyze glioblastoma tumors and generate real-time potential treatment options for physicians. METHODS: A consortium of seven institutions in New York City enrolled 30 patients with glioblastoma and performed tumor whole genome sequencing (WGS) and RNA sequencing (RNA-seq; collectively WGS/RNA-seq); 20 of these patients were also analyzed with independent targeted panel sequencing. We also compared results of expert manual annotations with those from an automated annotation system, Watson Genomic Analysis (WGA), to assess the reliability and time required to identify potentially relevant pharmacologic interventions. RESULTS: WGS/RNAseq identified more potentially actionable clinical results than targeted panels in 90% of cases, with an average of 16-fold more unique potentially actionable variants identified per individual; 84 clinically actionable calls were made using WGS/RNA-seq that were not identified by panels. Expert annotation and WGA had good agreement on identifying variants [mean sensitivity = 0.71, SD = 0.18 and positive predictive value (PPV) = 0.80, SD = 0.20] and drug targets when the same variants were called (mean sensitivity = 0.74, SD = 0.34 and PPV = 0.79, SD = 0.23) across patients. Clinicians used the information to modify their treatment plan 10% of the time. CONCLUSION: These results present the first comprehensive comparison of technical and machine augmented analysis of targeted panel and WGS/RNA-seq to identify potential cancer treatments.

Functional equivalence of genome sequencing analysis pipelines enables harmonized variant calling across human genetics projects.

Hundreds of thousands of human whole genome sequencing (WGS) datasets will be generated over the next few years. These data are more valuable in aggregate: joint analysis of genomes from many sources increases sample size and statistical power. A central challenge for joint analysis is that different WGS data processing pipelines cause substantial differences in variant calling in combined datasets, necessitating computationally expensive reprocessing. This approach is no longer tenable given the scale of current studies and data volumes. Here, we define WGS data processing standards that allow different groups to produce functionally equivalent (FE) results, yet still innovate on data processing pipelines. We present initial FE pipelines developed at five genome centers and show that they yield similar variant calling results and produce significantly less variability than sequencing replicates. This work alleviates a key technical bottleneck for genome aggregation and helps lay the foundation for community-wide human genetics studies.

Different mutational rates and mechanisms in human cells at pregastrulation and neurogenesis.

Somatic mosaicism in the human brain may alter function of individual neurons. We analyzed genomes of single cells from the forebrains of three human fetuses (15 to 21 weeks postconception) using clonal cell populations. We detected 200 to 400 single-nucleotide variations (SNVs) per cell. SNV patterns resembled those found in cancer cell genomes, indicating a role of background mutagenesis in cancer. SNVs with a frequency of >2% in brain were also present in the spleen, revealing a pregastrulation origin. We reconstructed cell lineages for the first five postzygotic cleavages and calculated a mutation rate of ~1.3 mutations per division per cell. Later in development, during neurogenesis, the mutation spectrum shifted toward oxidative damage, and the mutation rate increased. Both neurogenesis and early embryogenesis exhibit substantially more mutagenesis than adulthood.

Comparing sequencing assays and human-machine analyses in actionable genomics for glioblastoma.

OBJECTIVE: To analyze a glioblastoma tumor specimen with 3 different platforms and compare potentially actionable calls from each. METHODS: Tumor DNA was analyzed by a commercial targeted panel. In addition, tumor-normal DNA was analyzed by whole-genome sequencing (WGS) and tumor RNA was analyzed by RNA sequencing (RNA-seq). The WGS and RNA-seq data were analyzed by a team of bioinformaticians and cancer oncologists, and separately by IBM Watson Genomic Analytics (WGA), an automated system for prioritizing somatic variants and identifying drugs. RESULTS: More variants were identified by WGS/RNA analysis than by targeted panels. WGA completed a comparable analysis in a fraction of the time required by the human analysts. CONCLUSIONS: The development of an effective human-machine interface in the analysis of deep cancer genomic datasets may provide potentially clinically actionable calls for individual patients in a more timely and efficient manner than currently possible. CLINICALTRIALSGOV IDENTIFIER: NCT02725684.

Autophagy orchestrates adaptive responses to targeted therapy in endometrial cancer.

Targeted therapies in endometrial cancer (EC) using kinase inhibitors rarely result in complete tumor remission and are frequently challenged by the appearance of refractory cell clones, eventually resulting in disease relapse. Dissecting adaptive mechanisms is of vital importance to circumvent clinical drug resistance and improve the efficacy of targeted agents in EC. Sorafenib is an FDA-approved multitarget tyrosine and serine/threonine kinase inhibitor currently used to treat hepatocellular carcinoma, advanced renal carcinoma and radioactive iodine-resistant thyroid carcinoma. Unfortunately, sorafenib showed very modest effects in a multi-institutional phase II trial in advanced uterine carcinoma patients. Here, by leveraging RNA-sequencing data from the Cancer Cell Line Encyclopedia and cell survival studies from compound-based high-throughput screenings we have identified the lysosomal pathway as a potential compartment involved in the resistance to sorafenib. By performing additional functional biology studies we have demonstrated that this resistance could be related to macroautophagy/autophagy. Specifically, our results indicate that sorafenib triggers a mechanistic MAPK/JNK-dependent early protective autophagic response in EC cells, providing an adaptive response to therapeutic stress. By generating in vivo subcutaneous EC cell line tumors, lung metastatic assays and primary EC orthoxenografts experiments, we demonstrate that targeting autophagy enhances sorafenib cytotoxicity and suppresses tumor growth and pulmonary metastasis progression. In conclusion, sorafenib induces the activation of a protective autophagic response in EC cells. These results provide insights into the unopposed resistance of advanced EC to sorafenib and highlight a new strategy for therapeutic intervention in recurrent EC.

Endometrial Carcinoma: Specific Targeted Pathways.

Endometrial cancer (EC) is the most common gynecologic malignancy in the western world with more than 280,000 cases per year worldwide. Prognosis for EC at early stages, when primary surgical resection is the most common initial treatment, is excellent. Five-year survival rate is around 70 %.Several molecular alterations have been described in the different types of EC. They occur in genes involved in important signaling pathways. In this chapter, we will review the most relevant altered pathways in EC, including PI3K/AKT/mTOR, RAS-RAF-MEK-ERK, Tyrosine kinase, WNT/ß-Catenin, cell cycle, and TGF-ß signaling pathways. At the end of the chapter, the most significant clinical trials will be briefly discussed.This information is important to identify specific targets for therapy.

Conpair: concordance and contamination estimator for matched tumor-normal pairs.

MOTIVATION: Sequencing of matched tumor and normal samples is the standard study design for reliable detection of somatic alterations. However, even very low levels of cross-sample contamination significantly impact calling of somatic mutations, because contaminant germline variants can be incorrectly interpreted as somatic. There are currently no sequence-only based methods that reliably estimate contamination levels in tumor samples, which frequently display copy number changes. As a solution, we developed Conpair, a tool for detection of sample swaps and cross-individual contamination in whole-genome and whole-exome tumor-normal sequencing experiments. RESULTS: On a ladder of in silico contaminated samples, we demonstrated that Conpair reliably measures contamination levels as low as 0.1%, even in presence of copy number changes. We also estimated contamination levels in glioblastoma WGS and WXS tumor-normal datasets from TCGA and showed that they strongly correlate with tumor-normal concordance, as well as with the number of germline variants called as somatic by several widely-used somatic callers. AVAILABILITY AND IMPLEMENTATION: The method is available at: https://github.com/nygenome/conpair CONTACT: egrabowska@gmail.com or mczody@nygenome.orgSupplementary information: Supplementary data are available at Bioinformatics online.

Context Sensitive Modeling of Cancer Drug Sensitivity.

Recent screening of drug sensitivity in large panels of cancer cell lines provides a valuable resource towards developing algorithms that predict drug response. Since more samples provide increased statistical power, most approaches to prediction of drug sensitivity pool multiple cancer types together without distinction. However, pan-cancer results can be misleading due to the confounding effects of tissues or cancer subtypes. On the other hand, independent analysis for each cancer-type is hampered by small sample size. To balance this trade-off, we present CHER (Contextual Heterogeneity Enabled Regression), an algorithm that builds predictive models for drug sensitivity by selecting predictive genomic features and deciding which ones should-and should not-be shared across different cancers, tissues and drugs. CHER provides significantly more accurate models of drug sensitivity than comparable elastic-net-based models. Moreover, CHER provides better insight into the underlying biological processes by finding a sparse set of shared and type-specific genomic features.

Interferon α/ß Enhances the Cytotoxic Response of MEK Inhibition in Melanoma.

Drugs that inhibit the MAPK pathway have therapeutic benefit in melanoma, but responses vary between patients, for reasons that are still largely unknown. Here we aim at explaining this variability using pre- and post-MEK inhibition transcriptional profiles in a panel of melanoma cell lines. We found that most targets are context specific, under the influence of the pathway in only a subset of cell lines. We developed a computational method to identify context-specific targets, and found differences in the activity levels of the interferon pathway, driven by a deletion of the interferon locus. We also discovered that IFNα/ß treatment strongly enhances the cytotoxic effect of MEK inhibition, but only in cell lines with low activity of interferon pathway. Taken together, our results suggest that the interferon pathway plays an important role in, and predicts, the response to MAPK inhibition in melanoma. Our analysis demonstrates the value of system-wide perturbation data in predicting drug response.

Integration of genomic data enables selective discovery of breast cancer drivers.

Identifying driver genes in cancer remains a crucial bottleneck in therapeutic development and basic understanding of the disease. We developed Helios, an algorithm that integrates genomic data from primary tumors with data from functional RNAi screens to pinpoint driver genes within large recurrently amplified regions of DNA. Applying Helios to breast cancer data identified a set of candidate drivers highly enriched with known drivers (p < 10(-14)). Nine of ten top-scoring Helios genes are known drivers of breast cancer, and in vitro validation of 12 candidates predicted by Helios found ten conferred enhanced anchorage-independent growth, demonstrating Helios's exquisite sensitivity and specificity. We extensively characterized RSF-1, a driver identified by Helios whose amplification correlates with poor prognosis, and found increased tumorigenesis and metastasis in mouse models. We have demonstrated a powerful approach for identifying driver genes and how it can yield important insights into cancer.

Harnessing gene expression to identify the genetic basis of drug resistance.

The advent of cost-effective genotyping and sequencing methods have recently made it possible to ask questions that address the genetic basis of phenotypic diversity and how natural variants interact with the environment. We developed Camelot (CAusal Modelling with Expression Linkage for cOmplex Traits), a statistical method that integrates genotype, gene expression and phenotype data to automatically build models that both predict complex quantitative phenotypes and identify genes that actively influence these traits. Camelot integrates genotype and gene expression data, both generated under a reference condition, to predict the response to entirely different conditions. We systematically applied our algorithm to data generated from a collection of yeast segregants, using genotype and gene expression data generated under drug-free conditions to predict the response to 94 drugs and experimentally confirmed 14 novel gene-drug interactions. Our approach is robust, applicable to other phenotypes and species, and has potential for applications in personalized medicine, for example, in predicting how an individual will respond to a previously unseen drug.

Modularity and interactions in the genetics of gene expression.

Understanding the effect of genetic sequence variation on phenotype is a major challenge that lies at the heart of genetics. We developed GOLPH (GenOmic Linkage to PHenotype), a statistical method to identify genetic interactions, and used it to characterize the landscape of genetic interactions between gene expression quantitative trait loci. Our results reveal that allele-specific interactions, in which a gene only exerts an influence on the phenotype in the presence of a particular allele at the primary locus, are widespread and that genetic interactions are predominantly nonadditive. The data portray a complex picture in which interacting loci influence the expression of modules of coexpressed genes involved in coherent biological processes and pathways. We show that genetic variation at a single gene can have a major impact on the global transcriptional response, altering interactions between genes through shutdown or activation of pathways. Thus, different cellular states occur not only in response to the external environment but also result from intrinsic genetic variation.

Bioinformatics approaches for disulfide connectivity prediction.

Protein structure prediction with computational methods has gained much attention in the research fields of protein engineering and protein folding studies. Due to the vastness of conformational space, one of the major tasks is to restrain the flexibility of protein structure and reduce the search space. Many studies have revealed that, with the information of disulfide connectivity available, the search in conformational space can be dramatically reduced and lead to significant improvements in the prediction accuracy. As a result, predicting disulfide connectivity using bioinformatics approaches is of great interest nowadays. In this mini-review, the prediction of disulfide connectivity in proteins will be discussed in four aspects: (1) how the problem formulated and the computational techniques used in the literatures; (2) the effects of the features adopted to encode the information and the biological meanings implied; (3) the problems encountered and limitations of disulfide connectivity prediction; and (4) the practical usages of predicted disulfide bond information in molecular simulation and the prospects in the future.

Disulfide connectivity prediction with 70% accuracy using two-level models.

Disulfide bridges stabilize protein structures covalently and play an important role in protein folding. Predicting disulfide connectivity precisely helps towards the solution of protein structure prediction. Previous methods for disulfide connectivity prediction either infer the bonding potential of cysteine pairs or rank alternative disulfide bonding patterns. As a result, these methods encode data according to cysteine pairs (pair-wise) or disulfide bonding patterns (pattern-wise). However, using either encoding scheme alone cannot fully utilize the local and global information of proteins, so the accuracies of previous methods are limited. In this work, we propose a novel two-level framework to predict disulfide connectivity. With this framework, both the pair-wise and pattern-wise encoding schemes are considered. Our models were validated on the datasets derived from SWISS-PROT 39 and 43, and the results demonstrate that our models can combine both local and global information. Compared to previous methods, significant improvements were obtained by our models. Our work may also provide insights to further improvements of disulfide connectivity prediction and increase its applicability in protein structure analysis and prediction.

Improving disulfide connectivity prediction with sequential distance between oxidized cysteines.

SUMMARY: Predicting disulfide connectivity precisely helps towards the solution of protein structure prediction. In this study, a descriptor derived from the sequential distance between oxidized cysteines (denoted as DOC) is proposed. An approach using support vector machine (SVM) method based on weighted graph matching was further developed to predict the disulfide connectivity pattern in proteins. When DOC was applied, prediction accuracy of 63% for our SVM models could be achieved, which is significantly higher than those obtained from previous approaches. The results show that using the non-local descriptor DOC coupled with local sequence profiles significantly improves the prediction accuracy. These improvements demonstrate that DOC, with a proper scaling scheme, is an effective feature for the prediction of disulfide connectivity. The method developed in this work is available at the web server PreCys (prediction of cys-cys linkages of proteins).