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Erratum to: The Special Issue on "Computational Molecular Medicine", edited by Rachel Karchin and Melissa S. Cline

2015 Human Genetics  
doi:10.1007/s00439-015-1543-8 pmid:25800701 pmcid:PMC4643602 fatcat:z6h4o6hl5vewrmm5ncamaeoyhi

HLA Gene Expression Mediates Tumor Immunogenicity and Escape [article]

Xutong Gong, Rachel Karchin
2021 bioRxiv   pre-print
Human Leukocyte Antigen (HLA) expression contributes to the activation of anti-tumor immunity through interactions with T cell receptors. However, pan-cancer HLA expression in tumors has not been systematically studied. In a retrospective analysis using the Cancer Genome Atlas, we quantified HLA class I and class II expression across 33 tumor types, which strongly correlated with infiltration of various immune cell types, expression of pro-inflammatory genes, and immune checkpoint markers.
more » ... nts with high HLA allelic diversity and gene expression had better survival. Immune microenvironments could be predicted using a neural network model trained on HLA expression data with varied survival outcomes. Furthermore, we identified a subset of tumors which upregulated HLA class I but not class II genes and exploited HLA-mediated escape strategies. Our results suggest the potential of using HLA expression data to predict immunogenicity. Taken together, we emphasize the crucial role of HLA upregulation in shaping prolonged anti-tumor immunity.
doi:10.1101/2021.05.17.444511 fatcat:5r5ouhmzcfbvxek7uibth2cc4q

Evaluating the Evaluation of Cancer Driver Genes [article]

Collin Tokheim, Nickolas Papadopoulis, Kenneth W Kinzler, Bert Vogelstein, Rachel Karchin
2016 bioRxiv   pre-print
Sequencing has identified millions of somatic mutations in human cancers, but distinguishing cancer driver genes remains a major challenge. Numerous methods have been developed to identify driver genes, but evaluation of the performance of these methods is hindered by the lack of a gold standard, i.e., bona fide driver gene mutations. Here, we establish an evaluation framework that can be applied when a gold standard is not available. We used this framework to compare the performance of eight
more » ... iver gene prediction methods. One of these methods, newly described here, incorporated a machine learning-based ratiometric approach. We show that the driver genes predicted by each of these eight methods vary widely. Moreover, the p-values reported by several of the methods were inconsistent with the uniform values expected, thus calling into question the assumptions that were used to generate them. Finally, we evaluated the potential effects of unexplained variability in mutation rates on false positive driver gene predictions. Our analysis points to the strengths and weaknesses of each of the currently available methods and offers guidance for improving them in the future.
doi:10.1101/060426 fatcat:34iatvdfezelxk6ufau5npjeua

CHASMplus reveals the scope of somatic missense mutations driving human cancers [article]

Collin Tokheim, Rachel Karchin
2018 bioRxiv   pre-print
STAR MethodsCONTACT FOR REAGENT AND RESOURCE SHARINGFor additional information regarding the data, please contact Rachel Karchin: karchin@jhu.edu.EXPERIMENTAL MODEL AND SUBJECT DETAILSThe Cancer Genome  ...  ., and Karchin, R. (2016b). Evaluating the evaluation of cancer driver genes. Proc Natl Acad Sci U S A 113, 14330-14335.  ... 
doi:10.1101/313296 fatcat:gblqtkmf3zfohkid5axokfmlty

Human genetics special issue on computational molecular medicine

Rachel Karchin, Melissa S. Cline
2015 Human Genetics  
the era before high-throughput DNA sequencing. They describe how family information and older statistical and computational methods used to identify linkage and identity-by-descent can now be leveraged to improve WES/ WGS data quality and identification of inheritance models. Some of these models have previously been hard to identify, e.g., rare de novo germline mutations and sporadic somatic variants. The study of complex diseases has also been transformed by large-scale DNA and RNA sequencing projects.
doi:10.1007/s00439-015-1545-6 pmid:25805167 pmcid:PMC4405483 fatcat:fjyypj7varaahnxs2a4i6ulmgi

Integrating diverse genomic data using gene sets

Svitlana Tyekucheva, Luigi Marchionni, Rachel Karchin, Giovanni Parmigiani
2011 Genome Biology  
We introduce and evaluate data analysis methods to interpret simultaneous measurement of multiple genomic features made on the same biological samples. Our tools use gene sets to provide an interpretable common scale for diverse genomic information. We show we can detect genetic effects, although they may act through different mechanisms in different samples, and show we can discover and validate important disease-related gene sets that would not be discovered by analyzing each data type individually.
doi:10.1186/gb-2011-12-10-r105 pmid:22018358 pmcid:PMC3333775 fatcat:eygv3t4pqjelfbx6mmt5z375xq

Next generation tools for the annotation of human SNPs

Rachel Karchin
2009 Briefings in Bioinformatics  
Like HTML, it has markup symbols, but unlike HTML, these symbols are unlimited and self-defining. 38 Karchin (Table 1) . 40 Karchin (ii) metaservers that pull information from many servers,  ... 
doi:10.1093/bib/bbn047 pmid:19181721 pmcid:PMC2638621 fatcat:wrxufl5ayvd2nc7v5x3a4d3dr4

PROTEIN INTERACTIONS AND DISEASE PHENOTYPES IN THE ABC TRANSPORTER SUPERFAMILY

LIBUSHA KELLY, RACHEL KARCHIN, ANDREJ SALI
2006 Biocomputing 2007  
ABC transporter proteins couple the energy of ATP binding and hydrolysis to substrate transport across a membrane. In humans, clinical studies have implicated mutations in 19 of the 48 known ABC transporters in diseases such as cystic fibrosis and adrenoleukodystrophy. Although divergent in sequence space, the overall topology of these proteins, consisting of two transmembrane domains and two ATP-binding cassettes, is likely to be conserved across diverse organisms. We examine known
more » ... orter domain interfaces using crystallographic structures of isolated and complexed domains in ABC transporter proteins and find that the nucleotide binding domain interfaces are better conserved than interfaces at the transmembrane domains. We then apply this analysis to identify known disease-associated point and deletion mutants for which disruption of domain-domain interfaces might indicate the mechanism of disease. Finally, we suggest a possible interaction site based on conservation of sequence and disease-association of point mutants. * Corresponding author libusha@salilab.org Pacific Symposium on Biocomputing 12:51-63 (2007) proper function. One such example is the vitamin B12 transporter BtuCD in E. coli, in which the two BtuC proteins and two BtuD proteins associate for transport [3] . Because there are no complete, high-resolution structures of eukaryotic ABC transporters, it is not known how similar their structures and mechanisms are to those of their bacterial and archaeal homologs. However, the striking sequence conservation of domains (e.g., the motif conservation and sequence identity between NBDs of diverse organisms) suggests that, despite differences in gene organization, human ABC transporters are likely to have a quarternary structure similar to those observed in bacteria and archaea [5] . Four crystal structures of NBD dimers (PDB IDs 1L2T, 1XEF, 1L7V and 1Q12) all have a structurally similar NBD/NBD interface, with the Walker A phosphate binding loop of one NBD appearing directly across the interface from the highly conserved 'signature' motif of the opposite NBD [6, 7, 3, 8] . The Cα RMSD (computed with MODELLER's salign feature [17] ) between the structures is between 1.7 and 2.7 Å, further demonstrating that the NBD/NBD interface is well conserved among different ABC transporters. An unanswered question about ABC transporter associations is whether the "two NBD / two TMD" model can also include higher-order oligomeric states [9, 10] . ABC transporters are also known to interact with a number of other membrane and soluble proteins. The sulfonylurea transporters (ABCC8 and 9) interact with inwardly rectifying (Kir) potassium channels to form ATPsensitive potassium channels that modulate the electrical activity in cells [11] . The CFTR protein is known to interact with PDZ domains and likely has other binding partners, including adrenergic receptors [12] . Because of the lack of high-resolution structural data, the nature of these interactions at the amino acid residue level is not known. Point mutations at interfaces can affect the function of ABC transporters in several ways. First, the mutant might destabilize domain folding or association during folding and prevent proper maturation of the protein. A medically relevant example is the deletion mutant ΔF508 in CFTR that is the most common cause of cystic fibrosis. This mutation leads to an immature, lower molecular weight form of the protein that is retained in the endoplasmic reticulum and degraded, which leads to a lack of functional transporters localized to the membrane [2]. Second, the mutant might interfere with the function of an intact transporter by affecting ATP binding and hydrolysis. Third, the mutant might affect allosteric interactions between the domains that are required for substrate binding and transport. Given the importance of intra-and inter-protein interactions in the ABC transporters, coupled with the large body of data on disease-associated
doi:10.1142/9789812772435_0006 fatcat:my5ag4vlxnfvzeti6y4526bwsi

CRAVAT 4: Cancer-Related Analysis of Variants Toolkit [article]

David Masica, Christopher Douville, Collin Tokheim, Rohit Bhattacharya, RyangGuk Kim, Kyle Moad, Michael C. Ryan, Rachel Karchin
2017 bioRxiv   pre-print
Cancer sequencing studies are increasingly comprehensive and well-powered, returning long lists of somatic mutations that can be difficult to sort and interpret. Diligent analysis and quality control can require multiple computational tools of distinct utility and producing disparate output, creating additional challenges for the investigator. The Cancer-Related Analysis of Variants Toolkit (CRAVAT) is an evolving suite of informatics tools for mutation interpretation that includes mutation
more » ... ecting and quality control, impact prediction and extensive annotation, gene- and mutation-level interpretation including joint prioritization of all nonsilent consequence types, and structural and mechanistic visualization. Results from CRAVAT submissions are explored in an interactive, user-friendly web-environment with dynamic filtering and sorting designed to highlight the most informative mutation, even in the context of very large studies. CRAVAT can be run on a public web-portal, in the cloud, or downloaded for local use, and is easily integrated with other methods for cancer omics analysis.
doi:10.1101/162859 fatcat:5byjwmonhzg4llx4zbd6m4tvym

Epistasis in genomic and survival data of cancer patients

Dariusz Matlak, Ewa Szczurek, Rachel Karchin
2017 PLoS Computational Biology  
Cancer aggressiveness and its effect on patient survival depends on mutations in the tumor genome. Epistatic interactions between the mutated genes may guide the choice of anticancer therapy and set predictive factors of its success. Inhibitors targeting synthetic lethal partners of genes mutated in tumors are already utilized for efficient and specific treatment in the clinic. The space of possible epistatic interactions, however, is overwhelming, and computational methods are needed to limit
more » ... he experimental effort of validating the interactions for therapy and characterizing their biomarkers. Here, we introduce SurvLRT, a statistical likelihood ratio test for identifying epistatic gene pairs and triplets from cancer patient genomic and survival data. Compared to established approaches, SurvLRT performed favorable in predicting known, experimentally verified synthetic lethal partners of PARP1 from TCGA data. Our approach is the first to test for epistasis between triplets of genes to identify biomarkers of synthetic lethality-based therapy. SurvLRT proved successful in identifying the known gene TP53BP1 as the biomarker of success of the therapy targeting PARP in BRCA1 deficient tumors. Search for other biomarkers for the same interaction revealed a region whose deletion was a more significant biomarker than deletion of TP53BP1. With the ability to detect not only pairwise but twelve different types of triple epistasis, applicability of SurvLRT goes beyond cancer therapy, to the level of characterization of shapes of fitness landscapes. Author Summary Genomic alterations in tumors affect the fitness of tumor cells, controlling how well they replicate and survive compared to other cells. The landscape of tumor fitness is shaped by epistasis. Epistasis occurs when the contribution of gene alterations to the total fitness is non-linear. The type of epistatic genetic interactions with great potential for cancer therapy is synthetic lethality. Inhibitors targeting synthetic lethal partners of genes mutated in tumors can selectively kill tumor and not normal cells. Therapy based on synthetic lethality is, however, context dependent, and it is crucial to identify its biomarkers. Unfortunately, the space of possible interactions and their biomarkers is overwhelming for experimental validation. Computational pre-selection methods are required to limit the experimental effort. Here, we introduce a statistical approach called SurvLRT, for the identification of epistatic gene pairs and triplets based on patient genomic and survival data. First, we show that using SurvLRT, we can deliver synthetic lethal interactions of pairs of genes that are specific to cancer. Second, we demonstrate the applicability of SurvLRT to identify biomarkers for synthetic lethality, such as mutational status of other genes that can alleviate the synthetic effect.
doi:10.1371/journal.pcbi.1005626 pmid:28678836 pmcid:PMC5517071 fatcat:5sdycjdqmjfcdij3vovy52weka

CHASMplus Reveals the Scope of Somatic Missense Mutations Driving Human Cancers

Collin Tokheim, Rachel Karchin
2019 Cell Systems  
Graphical Abstract Author Manuscript * corresponding author: Rachel Karchin, Ph.D., 217A Hackerman Hall, 3400 N.  ...  Lead contact: Rachel Karchin, Ph.D Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication.  ... 
doi:10.1016/j.cels.2019.05.005 pmid:31202631 pmcid:PMC6857794 fatcat:5kznuxeiunfghjb7bwvaoe3kzm

IDENTIFICATION OF ABERRANT PATHWAY AND NETWORK ACTIVITY FROM HIGH-THROUGHPUT DATA

RACHEL KARCHIN, MICHAEL F. OCHS, JOSHUA M. STUART, JOEL S. BADER
2011 Biocomputing 2012  
The workshop focused on approaches to deduce changes in biological activity in cellular pathways and networks that drive phenotype from high-throughput data. Work in cancer has demonstrated conclusively that cancer etiology is driven not by single gene mutation or expression change, but by coordinated changes in multiple signaling pathways. These pathway changes involve different genes in different individuals, leading to the failure of gene-focused analysis to identify the full range of
more » ... ns or expression changes driving cancer development. There is also evidence that metabolic pathways rather than individual genes play the critical role in a number of metabolic diseases. Tools to look at pathways and networks are needed to improve our understanding of disease and to improve our ability to target therapeutics at appropriate points in these pathways.
doi:10.1142/9789814366496_0001 fatcat:4yzlyuv52zekhjrmtfsxcp47oa

Estimation of cancer cell fractions and clone trees from multi-region sequencing of tumors [article]

Lily Zheng, Laura Wood, Rachel Karchin, Robert B Scharpf
2021 bioRxiv   pre-print
Multi-region sequencing of one or multiple biopsies of solid tumors from a patient can be used to improve our understanding of the diversity of subclones in the patient's tumor and shed light on the evolutionary history of the disease. Due to the large number of possible evolutionary relationships between clones and the fundamental uncertainty of the mutational composition of subclones, elucidating the most probable evolutionary relationships poses statistical and computational challenges. We
more » ... veloped a Bayesian hierarchical model called PICTograph to model uncertainty in the assignment of mutations to subclones and an approach to reduce the space of possible graphical models that postulate their evolutionary origin. Compared to available methods, our approach provided more consistent and accurate estimates of cancer cell fractions and better tree topology reconstruction over a range of simulated clonal diversity. Application of PICTograph to whole exome sequencing data of individuals with pancreatic cancer precursor lesions confirmed known early occurring mutations and indicated substantial molecular diversity, including multiple distinct subclones (range 6 - 12) and intra-sample mixing of subclones. As the complete evolutionary history for some patients was not identifiable, we used ensemble-based visualizations to distinguish between highly probable evolutionary relationships recovered in multiple models from uncertain relationships occurring in a small subset of models. These analyses indicate that PICTograph provides a useful approximation to evolutionary inference, particularly when the evolutionary course of a patient's cancer is complex.
doi:10.1101/2021.06.12.448194 fatcat:rqjbvs7mkbcknlvuvno66r7yea

Evaluation of local structure alphabets based on residue burial

Rachel Karchin, Melissa Cline, Kevin Karplus
2004 Proteins: Structure, Function, and Bioinformatics  
Residue burial, which describes a protein residue's exposure to solvent and neighboring atoms, is key to protein structure prediction, modeling, and analysis. We assessed 21 alphabets representing residue burial, according to their predictability from amino acid sequence, conservation in structural alignments, and utility in one foldrecognition scenario. This follows upon our previous work in assessing nine representations of backbone geometry. 1 The alphabet found to be most effective overall
more » ... as seven states and is based on a count of C ␤ atoms within a 14 Å-radius sphere centered at the C ␤ of a residue of interest. When incorporated into a hidden Markov model (HMM), this alphabet gave us a 38% performance boost in fold recognition and 23% in alignment quality. Proteins 2004;55:508 -518.
doi:10.1002/prot.20008 pmid:15103615 fatcat:tncf6ezf3vco3clmums5vrdatm

Understanding Genotype-Phenotype Effects in Cancer via Network Approaches

Yoo-Ah Kim, Dong-Yeon Cho, Teresa M. Przytycka, Rachel Karchin
2016 PLoS Computational Biology  
Cancer is now increasingly studied from the perspective of dysregulated pathways, rather than as a disease resulting from mutations of individual genes. A pathway-centric view acknowledges the heterogeneity between genomic profiles from different cancer patients while assuming that the mutated genes are likely to belong to the same pathway and cause similar disease phenotypes. Indeed, network-centric approaches have proven to be helpful for finding genotypic causes of diseases, classifying
more » ... se subtypes, and identifying drug targets. In this review, we discuss how networks can be used to help understand patientto-patient variations and how one can leverage this variability to elucidate interactions between cancer drivers. Teresa M. Przytycka is an Associate Editor of PLOS Computational Biology. PLOS Computational Biology |
doi:10.1371/journal.pcbi.1004747 pmid:26963104 pmcid:PMC4786343 fatcat:p2zqfax3gfag7jutp3tq52tqve
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