Raheleh Salari

Mutations in early follicular lymphoma progenitors are associated with suppressed antigen presentation

Significance Follicular lymphoma (FL) is a disease characterized by multiple relapses that are linked by a common progenitor bearing only a subset of the mutations found within the tumor that presents clinically. Inability to cure this disease may therefore be linked to the failure of current therapies to clear these early tumor-propagating clones. Here we further define the genetic hallmarks of this disease and model the steps in evolution through phylogenetic analysis of serial tumor biopsies. This identified CREBBP mutations as early events in genome evolution that are enriched within tumor cell progenitors and provided evidence that these mutations act by allowing immune evasion. This highlights CREBBP mutations as an attractive therapeutic target in FL and provides insight into their pathogenic mechanism. Follicular lymphoma (FL) is incurable with conventional therapies and has a clinical course typified by multiple relapses after therapy. These tumors are genetically characterized by B-cell leukemia/lymphoma 2 (BCL2) translocation and mutation of genes involved in chromatin modification. By analyzing purified tumor cells, we identified additional novel recurrently mutated genes and confirmed mutations of one or more chromatin modifier genes within 96% of FL tumors and two or more in 76% of tumors. We defined the hierarchy of somatic mutations arising during tumor evolution by analyzing the phylogenetic relationship of somatic mutations across the coding genomes of 59 sequentially acquired biopsies from 22 patients. Among all somatically mutated genes, CREBBP mutations were most significantly enriched within the earliest inferable progenitor. These mutations were associated with a signature of decreased antigen presentation characterized by reduced transcript and protein abundance of MHC class II on tumor B cells, in line with the role of CREBBP in promoting class II transactivator (CIITA)-dependent transcriptional activation of these genes. CREBBP mutant B cells stimulated less proliferation of T cells in vitro compared with wild-type B cells from the same tumor. Transcriptional signatures of tumor-infiltrating T cells were indicative of reduced proliferation, and this corresponded to decreased frequencies of tumor-infiltrating CD4 helper T cells and CD8 memory cytotoxic T cells. These observations therefore implicate CREBBP mutation as an early event in FL evolution that contributes to immune evasion via decreased antigen presentation.

A partition function algorithm for interacting nucleic acid strands

Recent interests, such as RNA interference and antisense RNA regulation, strongly motivate the problem of predicting whether two nucleic acid strands interact. Motivation: Regulatory non-coding RNAs (ncRNAs) such as microRNAs play an important role in gene regulation. Studies on both prokaryotic and eukaryotic cells show that such ncRNAs usually bind to their target mRNA to regulate the translation of corresponding genes. The specificity of these interactions depends on the stability of intermolecular and intramolecular base pairing. While methods like deep sequencing allow to discover an ever increasing set of ncRNAs, there are no high-throughput methods available to detect their associated targets. Hence, there is an increasing need for precise computational target prediction. In order to predict base-pairing probability of any two bases in interacting nucleic acids, it is necessary to compute the interaction partition function over the whole ensemble. The partition function is a scalar value from which various thermodynamic quantities can be derived. For example, the equilibrium concentration of each complex nucleic acid species and also the melting temperature of interacting nucleic acids can be calculated based on the partition function of the complex. Results: We present a model for analyzing the thermodynamics of two interacting nucleic acid strands considering the most general type of interactions studied in the literature. We also present a corresponding dynamic programming algorithm that computes the partition function over (almost) all physically possible joint secondary structures formed by two interacting nucleic acids in O(n6) time. We verify the predictive power of our algorithm by computing (i) the melting temperature for interacting RNA pairs studied in the literature and (ii) the equilibrium concentration for several variants of the OxyS–fhlA complex. In both experiments, our algorithm shows high accuracy and outperforms competitors. Availability: Software and web server is available at http://compbio.cs.sfu.ca/taverna/pirna/ Contact: cenk@cs.sfu.ca; backofen@informatik.uni-freiburg.de Supplementary information: Supplementary data are avaliable at Bioinformatics online.

Genome evolution during progression to breast cancer.

Cancer evolution involves cycles of genomic damage, epigenetic deregulation, and increased cellular proliferation that eventually culminate in the carcinoma phenotype. Early neoplasias, which are often found concurrently with carcinomas and are histologically distinguishable from normal breast tissue, are less advanced in phenotype than carcinomas and are thought to represent precursor stages. To elucidate their role in cancer evolution we performed comparative whole-genome sequencing of early neoplasias, matched normal tissue, and carcinomas from six patients, for a total of 31 samples. By using somatic mutations as lineage markers we built trees that relate the tissue samples within each patient. On the basis of these lineage trees we inferred the order, timing, and rates of genomic events. In four out of six cases, an early neoplasia and the carcinoma share a mutated common ancestor with recurring aneuploidies, and in all six cases evolution accelerated in the carcinoma lineage. Transition spectra of somatic mutations are stable and consistent across cases, suggesting that accumulation of somatic mutations is a result of increased ancestral cell division rather than specific mutational mechanisms. In contrast to highly advanced tumors that are the focus of much of the current cancer genome sequencing, neither the early neoplasia genomes nor the carcinomas are enriched with potentially functional somatic point mutations. Aneuploidies that occur in common ancestors of neoplastic and tumor cells are the earliest events that affect a large number of genes and may predispose breast tissue to eventual development of invasive carcinoma.

Fast prediction of RNA-RNA interaction

BackgroundRegulatory antisense RNAs are a class of ncRNAs that regulate gene expression by prohibiting the translation of an mRNA by establishing stable interactions with a target sequence. There is great demand for efficient computational methods to predict the specific interaction between an ncRNA and its target mRNA(s). There are a number of algorithms in the literature which can predict a variety of such interactions - unfortunately at a very high computational cost. Although some existing target prediction approaches are much faster, they are specialized for interactions with a single binding site.MethodsIn this paper we present a novel algorithm to accurately predict the minimum free energy structure of RNA-RNA interaction under the most general type of interactions studied in the literature. Moreover, we introduce a fast heuristic method to predict the specific (multiple) binding sites of two interacting RNAs.ResultsWe verify the performance of our algorithms for joint structure and binding site prediction on a set of known interacting RNA pairs. Experimental results show our algorithms are highly accurate and outperform all competitive approaches.

Fast and scalable inference of multi-sample cancer lineages

Somatic variants can be used as lineage markers for the phylogenetic reconstruction of cancer evolution. Since somatic phylogenetics is complicated by sample heterogeneity, novel specialized tree-building methods are required for cancer phylogeny reconstruction. We present LICHeE (Lineage Inference for Cancer Heterogeneity and Evolution), a novel method that automates the phylogenetic inference of cancer progression from multiple somatic samples. LICHeE uses variant allele frequencies of somatic single nucleotide variants obtained by deep sequencing to reconstruct multi-sample cell lineage trees and infer the subclonal composition of the samples. LICHeE is open source and available at http://viq854.github.io/lichee.

Inferring cancer subnetwork markers using density-constrained biclustering

Motivation: Recent genomic studies have confirmed that cancer is of utmost phenotypical complexity, varying greatly in terms of subtypes and evolutionary stages. When classifying cancer tissue samples, subnetwork marker approaches have proven to be superior over single gene marker approaches, most importantly in cross-platform evaluation schemes. However, prior subnetwork-based approaches do not explicitly address the great phenotypical complexity of cancer. Results: We explicitly address this and employ density-constrained biclustering to compute subnetwork markers, which reflect pathways being dysregulated in many, but not necessarily all samples under consideration. In breast cancer we achieve substantial improvements over all cross-platform applicable approaches when predicting TP53 mutation status in a well-established non-cross-platform setting. In colon cancer, we raise prediction accuracy in the most difficult instances from 87% to 93% for cancer versus non−cancer and from 83% to (astonishing) 92%, for with versus without liver metastasis, in well-established cross-platform evaluation schemes. Availability: Software is available on request. Contact: alexsch@math.berkeley.edu; ester@cs.sfu.ca Supplementary information: Supplementary data are available at Bioinformatics online.

Time and space efficient RNA-RNA interaction prediction via sparse folding

In the past years, a large set of new regulatory ncRNAs have been identified, but the number of experimentally verified targets is considerably low Thus, computational target prediction methods are on high demand Whereas all previous approaches for predicting a general joint structure have a complexity of O(n6) running time and O(n4) space, a more time and space efficient interaction prediction that is able to handle complex joint structures is necessary for genome-wide target prediction problems In this paper we show how to reduce both the time and space complexity of the RNA-RNA interaction prediction problem as described by Alkan et al [1] via dynamic programming sparsification - which allows to discard large portions of DP tables without loosing optimality Applying sparsification techniques reduces the complexity of the original algorithm from O(n6) time and O(n4) space to O(n4ψ(n)) time and O(n2ψ(n)+n3) space for some function ψ(n), which turns out to have small values for the range of n that we encounter in practice Under the assumption that the polymer-zeta property holds for RNA-structures, we demonstrate that ψ(n)=O(n) on average, resulting in a linear time and space complexity improvement over the original algorithm We evaluate our sparsified algorithm for RNA-RNA interaction prediction by total free energy minimization, based on the energy model of Chitsaz et al.[2], on a set of known interactions Our results confirm the significant reduction of time and space requirements in practice.

The Effect of Insertions and Deletions on Wirings in Protein-Protein Interaction Networks: A Large-Scale Study

Although insertions and deletions (indels) are a common type of sequence variation, their origin and their functional consequences have not yet been fully understood. It has been known that indels preferably occur in the loop regions of the affected proteins. Moreover, it has recently been demonstrated that indels are significantly more strongly correlated with functional changes than substitutions. In sum, there is substantial evidence that indels, not substitutions, are the predominant evolutionary factor when it comes to structural changes in proteins. As a consequence it comes natural to hypothesize that sizable indels can modify protein interaction interfaces, causing a gain or loss of protein-protein interactions, thereby significantly rewiring the interaction networks. In this paper, we have analyzed this relationship in a large-scale study. We have computed all paralogous protein pairs in Saccharomyces cerevisiae (Yeast) and Drosophila melanogaster (Fruit Fly), and sorted the respective alignments according to whether they contained indels of significant lengths as per a pair Hidden Markov Model (HMM)-based framework of a recent study. We subsequently computed well known centrality measures for proteins that participated in indel alignments (indel proteins) and those that did not. We found that indel proteins indeed showed greater variation in terms of these measures. This demonstrates that indels have a significant influence when it comes to rewiring of the interaction networks due to evolution, which confirms our hypothesis. In general, this study may yield relevant insights into the functional interplay of proteins and the evolutionary dynamics behind it.

A Gene-Specific Method for Predicting Hemophilia-Causing Point Mutations

A fundamental goal of medical genetics is the accurate prediction of genotype-phenotype correlations. As an approach to develop more accurate in silico tools for prediction of disease-causing mutations of structural proteins, we present a gene- and disease-specific prediction tool based on a large systematic analysis of missense mutations from hemophilia A (HA) patients. Our HA-specific prediction tool, HApredictor, showed disease prediction accuracy comparable to other publicly available prediction software. In contrast to those methods, its performance is not limited to non-synonymous mutations. Given the role of synonymous mutations in disease and drug codon optimization, we propose that utilizing a gene- and disease-specific method can be highly useful to make functional predictions possible even for synonymous mutations. Incorporating computational metrics at both nucleotide and amino acid levels along with multiple protein sequence/structure alignment significantly improved the predictive performance of our tool. HApredictor is freely available for download at http://www.ncbi.nlm.nih.gov/CBBresearch/Przytycka/HA_Predict/index.htm.

taveRNA: a web suite for RNA algorithms and applications

We present taveRNA, a web server package that hosts three RNA web services: alteRNA, inteRNA and pRuNA. alteRNA is a new alternative for RNA secondary structure prediction. It is based on a dynamic programming solution that minimizes the sum of energy density and free energy of an RNA structure. inteRNA is the first RNA-RNA interaction structure prediction web service. It also employs a dynamic programming algorithm to minimize the free energy of the resulting joint structure of the two interacting RNAs. Lastly, pRuNA is an efficient database pruning service; which given a query RNA, eliminates a significant portion of an ncRNA database and returns only a few ncRNAs as potential regulators. taveRNA is available at http://compbio.cs.sfu.ca/taverna.We present taveRNA, a web server package that hosts three RNA web services: alteRNA, inteRNA and pRuNA. alteRNA is a new alternative for RNA secondary structure prediction. It is based on a dynamic programming solution that minimizes the sum of energy density and free energy of an RNA structure. inteRNA is the first RNA–RNA interaction structure prediction web service. It also employs a dynamic programming algorithm to minimize the free energy of the resulting joint structure of the two interacting RNAs. Lastly, pRuNA is an efficient database pruning service; which given a query RNA, eliminates a significant portion of an ncRNA database and returns only a few ncRNAs as potential regulators. taveRNA is available at http://compbio.cs.sfu.ca/taverna.