Publications by authors named "Roland L Dunbrack"

97 Publications

Delineating The RAS Conformational Landscape.

Cancer Res 2022 May 10. Epub 2022 May 10.

Fox Chase Cancer Center, Philadelphia, PA, United States.

Mutations in RAS isoforms (KRAS, NRAS, and HRAS) are among the most frequent oncogenic alterations in many cancers, making these proteins high priority therapeutic targets. Effectively targeting RAS isoforms requires an exact understanding of their active, inactive, and druggable conformations. However, there is no structural catalog of RAS conformations to guide therapeutic targeting or examining the structural impact of RAS mutations. Here we present an expanded classification of RAS conformations based on analyses of the catalytic switch 1 (SW1) and switch 2 (SW2) loops. From 721 human KRAS, NRAS, and HRAS structures available in the Protein Data Bank (206 RAS-protein co-complexes, 190 inhibitor-bound, and 325 unbound, including 204 WT and 517 mutated structures), we created a broad conformational classification based on the spatial positions of Y32 in SW1 and Y71 in SW2. Clustering all well-modeled SW1 and SW2 loops using a density-based machine learning algorithm defined additional conformational subsets, some previously undescribed. Three SW1 conformations and nine SW2 conformations were identified, each associated with different nucleotide states (GTP-bound, nucleotide-free, and GDP-bound) and specific bound proteins or inhibitor sites. The GTP-bound SW1 conformation could be further subdivided based on the hydrogen bond type made between Y32 and the GTP γ-phosphate. Further analysis clarified the catalytic impact of G12D and G12V mutations and the inhibitor chemistries that bind to each druggable RAS conformation. Overall, this study has expanded our understanding of RAS structural biology, which could facilitate future RAS drug discovery.
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http://dx.doi.org/10.1158/0008-5472.CAN-22-0804DOI Listing
May 2022

PyRosetta Jupyter Notebooks Teach Biomolecular Structure Prediction and Design.

Biophysicist (Rockv) 2021 Apr 14;2(1):108-122. Epub 2021 Apr 14.

Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States.

Biomolecular structure drives function, and computational capabilities have progressed such that the prediction and computational design of biomolecular structures is increasingly feasible. Because computational biophysics attracts students from many different backgrounds and with different levels of resources, teaching the subject can be challenging. One strategy to teach diverse learners is with interactive multimedia material that promotes self-paced, active learning. We have created a hands-on education strategy with a set of sixteen modules that teach topics in biomolecular structure and design, from fundamentals of conformational sampling and energy evaluation to applications like protein docking, antibody design, and RNA structure prediction. Our modules are based on a Python library that encapsulates all computational modules and methods in the Rosetta software package. The workshop-style modules are implemented as Jupyter Notebooks that can be executed in the Google Colaboratory, allowing learners access with just a web browser. The digital format of Jupyter Notebooks allows us to embed images, molecular visualization movies, and interactive coding exercises. This multimodal approach may better reach students from different disciplines and experience levels as well as attract more researchers from smaller labs and cognate backgrounds to leverage PyRosetta in their science and engineering research. All materials are freely available at https://github.com/RosettaCommons/PyRosetta.notebooks.
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http://dx.doi.org/10.35459/tbp.2019.000147DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8813091PMC
April 2021

PP2A/B55α substrate recruitment as defined by the retinoblastoma-related protein p107.

Elife 2021 10 18;10. Epub 2021 Oct 18.

Fels Cancer Institute for Personalized Medicine, Temple University Lewis Katz School of Medicine, Philadelphia, United States.

Protein phosphorylation is a reversible post-translation modification essential in cell signaling. This study addresses a long-standing question as to how the most abundant serine/threonine protein phosphatase 2 (PP2A) holoenzyme, PP2A/B55α, specifically recognizes substrates and presents them to the enzyme active site. Here, we show how the PP2A regulatory subunit B55α recruits p107, a pRB-related tumor suppressor and B55α substrate. Using molecular and cellular approaches, we identified a conserved region 1 (R1, residues 615-626) encompassing the strongest p107 binding site. This enabled us to identify an 'HxRVxxV' short linear motif (SLiM) in p107 as necessary for B55α binding and dephosphorylation of the proximal pSer-615 in vitro and in cells. Numerous B55α/PP2A substrates, including TAU, contain a related SLiM C-terminal from a proximal phosphosite, '[]-x(4,10)-[]--x-x-[]-.' Mutation of conserved SLiM residues in TAU dramatically inhibits dephosphorylation by PP2A/B55α, validating its generality. A data-guided computational model details the interaction of residues from the conserved p107 SLiM, the B55α groove, and phosphosite presentation. Altogether, these data provide key insights into PP2A/B55α's mechanisms of substrate recruitment and active site engagement, and also facilitate identification and validation of new substrates, a key step towards understanding PP2A/B55α's role in multiple cellular processes.
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http://dx.doi.org/10.7554/eLife.63181DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8575462PMC
October 2021

Kincore: a web resource for structural classification of protein kinases and their inhibitors.

Nucleic Acids Res 2022 01;50(D1):D654-D664

Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA 19148, USA.

The active form of kinases is shared across different family members, as are several commonly observed inactive forms. We previously performed a clustering of the conformation of the activation loop of all protein kinase structures in the Protein Data Bank (PDB) into eight classes based on the dihedral angles that place the Phe side chain of the DFG motif at the N-terminus of the activation loop. Our clusters are strongly associated with the placement of the activation loop, the C-helix, and other structural elements of kinases. We present Kincore, a web resource providing access to our conformational assignments for kinase structures in the PDB. While other available databases provide conformational states or drug type but not both, KinCore includes the conformational state and the inhibitor type (Type 1, 1.5, 2, 3, allosteric) for each kinase chain. The user can query and browse the database using these attributes or determine the conformational labels of a kinase structure using the web server or a standalone program. The database and labeled structure files can be downloaded from the server. Kincore will help in understanding the conformational dynamics of these proteins and guide development of inhibitors targeting specific states. Kincore is available at http://dunbrack.fccc.edu/kincore.
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http://dx.doi.org/10.1093/nar/gkab920DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8728253PMC
January 2022

PDBrenum: A webserver and program providing Protein Data Bank files renumbered according to their UniProt sequences.

PLoS One 2021 6;16(7):e0253411. Epub 2021 Jul 6.

Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, Pennsylvania, United States of America.

The Protein Data Bank (PDB) was established at Brookhaven National Laboratories in 1971 as an archive for biological macromolecular crystal structures. In mid 2021, the database has almost 180,000 structures solved by X-ray crystallography, nuclear magnetic resonance, cryo-electron microscopy, and other methods. Many proteins have been studied under different conditions, including binding partners such as ligands, nucleic acids, or other proteins; mutations, and post-translational modifications, thus enabling extensive comparative structure-function studies. However, these studies are made more difficult because authors are allowed by the PDB to number the amino acids in each protein sequence in any manner they wish. This results in the same protein being numbered differently in the available PDB entries. For instance, some authors may include N-terminal signal peptides or the N-terminal methionine in the sequence numbering and others may not. In addition to the coordinates, there are many fields that contain structural and functional information regarding specific residues numbered according to the author. Here we provide a webserver and Python3 application that fixes the PDB sequence numbering problem by replacing the author numbering with numbering derived from the corresponding UniProt sequences. We obtain this correspondence from the SIFTS database from PDBe. The server and program can take a list of PDB entries or a list of UniProt identifiers (e.g., "P04637" or "P53_HUMAN") and provide renumbered files in mmCIF format and the legacy PDB format for both asymmetric unit files and biological assembly files provided by PDBe.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0253411PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8259974PMC
November 2021

Structure of TFIIK for phosphorylation of CTD of RNA polymerase II.

Sci Adv 2021 04 7;7(15). Epub 2021 Apr 7.

Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.

During transcription initiation, the general transcription factor TFIIH marks RNA polymerase II by phosphorylating Ser5 of the carboxyl-terminal domain (CTD) of Rpb1, which is followed by extensive modifications coupled to transcription elongation, mRNA processing, and histone dynamics. We have determined a 3.5-Å resolution cryo-electron microscopy (cryo-EM) structure of the TFIIH kinase module (TFIIK in yeast), which is composed of Kin28, Ccl1, and Tfb3, yeast homologs of CDK7, cyclin H, and MAT1, respectively. The carboxyl-terminal region of Tfb3 was lying at the edge of catalytic cleft of Kin28, where a conserved Tfb3 helix served to stabilize the activation loop in its active conformation. By combining the structure of TFIIK with the previous cryo-EM structure of the preinitiation complex, we extend the previously proposed model of the CTD path to the active site of TFIIK.
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http://dx.doi.org/10.1126/sciadv.abd4420DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8026125PMC
April 2021

Hiding in plain sight: structure and sequence analysis reveals the importance of the antibody DE loop for antibody-antigen binding.

MAbs 2020 Jan-Dec;12(1):1840005

Institute for Cancer Research, Fox Chase Cancer Center , Philadelphia, PA, USA.

Antibody variable domains contain "complementarity-determining regions" (CDRs), the loops that form the antigen binding site. CDRs1-3 are recognized as the canonical CDRs. However, a fourth loop sits adjacent to CDR1 and CDR2 and joins the D and E strands on the antibody v-type fold. This "DE loop" is usually treated as a framework region, even though mutations in the loop affect the conformation of the CDRs and residues in the DE loop occasionally contact antigen. We analyzed the length, structure, and sequence features of all DE loops in the Protein Data Bank (PDB), as well as millions of sequences from HIV-1 infected and naïve patients. We refer to the DE loop as H4 and L4 in the heavy and light chains, respectively. Clustering the backbone conformations of the most common length of L4 (6 residues) reveals four conformations: two κ-only clusters, one λ-only cluster, and one mixed κ/λ cluster. Most H4 loops are length-8 and exist primarily in one conformation; a secondary conformation represents a small fraction of H4-8 structures. H4 sequence variability exceeds that of the antibody framework in naïve human high-throughput sequences, and both L4 and H4 sequence variability from λ and heavy germline sequences exceed that of germline framework regions. Finally, we identified dozens of structures in the PDB with insertions in the DE loop, all related to broadly neutralizing HIV-1 antibodies (bNabs), as well as antibody sequences from high-throughput sequencing studies of HIV-infected individuals, illuminating a possible role in humoral immunity to HIV-1.
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http://dx.doi.org/10.1080/19420862.2020.1840005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7671036PMC
September 2021

Multifaceted analysis of training and testing convolutional neural networks for protein secondary structure prediction.

PLoS One 2020 6;15(5):e0232528. Epub 2020 May 6.

Temple University, Philadelphia, PA, United States of America.

Protein secondary structure prediction remains a vital topic with broad applications. Due to lack of a widely accepted standard in secondary structure predictor evaluation, a fair comparison of predictors is challenging. A detailed examination of factors that contribute to higher accuracy is also lacking. In this paper, we present: (1) new test sets, Test2018, Test2019, and Test2018-2019, consisting of proteins from structures released in 2018 and 2019 with less than 25% identity to any protein published before 2018; (2) a 4-layer convolutional neural network, SecNet, with an input window of ±14 amino acids which was trained on proteins ≤25% identical to proteins in Test2018 and the commonly used CB513 test set; (3) an additional test set that shares no homologous domains with the training set proteins, according to the Evolutionary Classification of Proteins (ECOD) database; (4) a detailed ablation study where we reverse one algorithmic choice at a time in SecNet and evaluate the effect on the prediction accuracy; (5) new 4- and 5-label prediction alphabets that may be more practical for tertiary structure prediction methods. The 3-label accuracy (helix, sheet, coil) of the leading predictors on both Test2018 and CB513 is 81-82%, while SecNet's accuracy is 84% for both sets. Accuracy on the non-homologous ECOD set is only 0.6 points (83.9%) lower than the results on the Test2018-2019 set (84.5%). The ablation study of features, neural network architecture, and training hyper-parameters suggests the best accuracy results are achieved with good choices for each of them while the neural network architecture is not as critical as long as it is not too simple. Protocols for generating and using unbiased test, validation, and training sets are provided. Our data sets, including input features and assigned labels, and SecNet software including third-party dependencies and databases, are downloadable from dunbrack.fccc.edu/ss and github.com/sh-maxim/ss.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0232528PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7202669PMC
July 2020

ProtCID: a data resource for structural information on protein interactions.

Nat Commun 2020 02 5;11(1):711. Epub 2020 Feb 5.

Fox Chase Cancer Center, 333 Cottman Avenue, Philadelphia, PA, 19111, USA.

Structural information on the interactions of proteins with other molecules is plentiful, and for some proteins and protein families, there may be 100s of available structures. It can be very difficult for a scientist who is not trained in structural bioinformatics to access this information comprehensively. Previously, we developed the Protein Common Interface Database (ProtCID), which provided clusters of the interfaces of full-length protein chains as a means of identifying biological assemblies. Because proteins consist of domains that act as modular functional units, we have extended the analysis in ProtCID to the individual domain level. This has greatly increased the number of large protein-protein clusters in ProtCID, enabling the generation of hypotheses on the structures of biological assemblies of many systems. The analysis of domain families allows us to extend ProtCID to the interactions of domains with peptides, nucleic acids, and ligands. ProtCID provides complete annotations and coordinate sets for every cluster.
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http://dx.doi.org/10.1038/s41467-020-14301-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7002494PMC
February 2020

A Structurally-Validated Multiple Sequence Alignment of 497 Human Protein Kinase Domains.

Sci Rep 2019 12 24;9(1):19790. Epub 2019 Dec 24.

Fox Chase Cancer Center, 333 Cottman Avenue, Philadelphia, PA, 19111, USA.

Studies on the structures and functions of individual kinases have been used to understand the biological properties of other kinases that do not yet have experimental structures. The key factor in accurate inference by homology is an accurate sequence alignment. We present a parsimonious, structure-based multiple sequence alignment (MSA) of 497 human protein kinase domains excluding atypical kinases. The alignment is arranged in 17 blocks of conserved regions and unaligned blocks in between that contain insertions of varying lengths present in only a subset of kinases. The aligned blocks contain well-conserved elements of secondary structure and well-known functional motifs, such as the DFG and HRD motifs. From pairwise, all-against-all alignment of 272 human kinase structures, we estimate the accuracy of our MSA to be 97%. The remaining inaccuracy comes from a few structures with shifted elements of secondary structure, and from the boundaries of aligned and unaligned regions, where compromises need to be made to encompass the majority of kinases. A new phylogeny of the protein kinase domains in the human genome based on our alignment indicates that ten kinases previously labeled as "OTHER" can be confidently placed into the CAMK group. These kinases comprise the Aurora kinases, Polo kinases, and calcium/calmodulin-dependent kinase kinases.
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http://dx.doi.org/10.1038/s41598-019-56499-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6930252PMC
December 2019

Protein modeling to assess the pathogenicity of rare variants of SERPINA1 in patients suspected of having Alpha 1 Antitrypsin Deficiency.

BMC Med Genet 2019 07 15;20(1):125. Epub 2019 Jul 15.

Biocerna LLC, Fulton, Maryland, USA.

Background: Alpha 1 Antitrypsin (AAT) is a key serum proteinase inhibitor encoded by SERPINA1. Sequence variants of the gene can cause Alpha 1 Antitrypsin Deficiency (AATD), a condition associated with lung and liver disease. The majority of AATD cases are caused by the 'Z' and 'S' variants - single-nucleotide variations (SNVs) that result in amino acid substitutions of E342K and E264V. However, SERPINA1 is highly polymorphic, with numerous potentially clinically relevant variants reported. Novel variants continue to be discovered, and without reports of pathogenicity, it can be difficult for clinicians to determine the best course of treatment.

Methods: We assessed the utility of next-generation sequencing (NGS) and predictive computational analysis to guide the diagnosis of patients suspected of having AATD. Blood samples on serum separator cards were submitted to the DNA Advanced Screening Program (Biocerna LLC, Fulton, Maryland, USA) by physicians whose patients were suspected of having AATD. Laboratory analyses included quantification of serum AAT levels, qualitative analysis by isoelectric focusing, and targeted genotyping and NGS of the SERPINA1 gene. Molecular modeling software UCSF Chimera (University College of San Francisco, CA) was used to visualize the positions of amino acid changes as a result of rare/novel SNVs. Predictive software was used to assess the potential pathogenicity of these variants; methods included a support vector machine (SVM) program, PolyPhen-2 (Harvard University, Cambridge, MA), and FoldX (Centre for Genomic Regulation, Barcelona, Spain).

Results: Samples from 23 patients were analyzed; 21 rare/novel sequence variants were identified by NGS, including splice variants (n = 2), base pair deletions (n = 1), stop codon insertions (n = 2), and SNVs (n = 16). Computational modeling of protein structures caused by the novel SNVs showed that 8 were probably deleterious, and two were possibly deleterious. For the majority of probably/possibly deleterious SNVs (I50N, P289S, M385T, M221T, D341V, V210E, P369H, V333M and A142D), the mechanism is probably via disruption of the packed hydrophobic core of AAT. Several deleterious variants occurred in combination with more common deficiency alleles, resulting in very low AAT levels.

Conclusions: NGS and computational modeling are useful tools that can facilitate earlier, more precise diagnosis, and consideration for AAT therapy in AATD.
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http://dx.doi.org/10.1186/s12881-019-0852-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6631921PMC
July 2019

Principles and characteristics of biological assemblies in experimentally determined protein structures.

Curr Opin Struct Biol 2019 04 6;55:34-49. Epub 2019 Apr 6.

Institute for Cancer Research, Fox Chase Cancer Center, 333 Cottman Ave., Philadelphia, PA 19148, USA. Electronic address:

More than half of all structures in the PDB are assemblies of two or more proteins, including both homooligomers and heterooligomers. Structural information on these assemblies comes from X-ray crystallography, NMR, and cryo-EM spectroscopy. The correct assembly in an X-ray structure is often ambiguous, and computational methods have been developed to identify the most likely biologically relevant assembly based on physical properties of assemblies and sequence conservation in interfaces. Taking advantage of the large number of structures now available, some of the most recent methods have relied on similarity of interfaces and assemblies across structures of homologous proteins.
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http://dx.doi.org/10.1016/j.sbi.2019.03.006DOI Listing
April 2019

Defining a new nomenclature for the structures of active and inactive kinases.

Proc Natl Acad Sci U S A 2019 04 13;116(14):6818-6827. Epub 2019 Mar 13.

Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA 19111

Targeting protein kinases is an important strategy for intervention in cancer. Inhibitors are directed at the active conformation or a variety of inactive conformations. While attempts have been made to classify these conformations, a structurally rigorous catalog of states has not been achieved. The kinase activation loop is crucial for catalysis and begins with the conserved DFGmotif. This motif is observed in two major classes of conformations, DFGin-a set of active and inactive conformations where the Phe residue is in contact with the C-helix of the N-terminal lobe-and DFGout-an inactive form where Phe occupies the ATP site exposing the C-helix pocket. We have developed a clustering of kinase conformations based on the location of the Phe side chain (DFGin, DFGout, and DFGinter or intermediate) and the backbone dihedral angles of the sequence X-D-F, where X is the residue before the DFGmotif, and the DFG-Phe side-chain rotamer, utilizing a density-based clustering algorithm. We have identified eight distinct conformations and labeled them based on the Ramachandran regions (A, alpha; B, beta; L, left) of the XDF motif and the Phe rotamer (minus, plus, trans). Our clustering divides the DFGin group into six clusters including BLAminus, which contains active structures, and two common inactive forms, BLBplus and ABAminus. DFGout structures are predominantly in the BBAminus conformation, which is essentially required for binding type II inhibitors. The inactive conformations have specific features that make them unable to bind ATP, magnesium, and/or substrates. Our structurally intuitive nomenclature will aid in understanding the conformational dynamics of kinases and structure-based development of kinase drugs.
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http://dx.doi.org/10.1073/pnas.1814279116DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6452665PMC
April 2019

A new clustering and nomenclature for beta turns derived from high-resolution protein structures.

PLoS Comput Biol 2019 03 7;15(3):e1006844. Epub 2019 Mar 7.

Fox Chase Cancer Center, Philadelphia PA, United States of America.

Protein loops connect regular secondary structures and contain 4-residue beta turns which represent 63% of the residues in loops. The commonly used classification of beta turns (Type I, I', II, II', VIa1, VIa2, VIb, and VIII) was developed in the 1970s and 1980s from analysis of a small number of proteins of average resolution, and represents only two thirds of beta turns observed in proteins (with a generic class Type IV representing the rest). We present a new clustering of beta-turn conformations from a set of 13,030 turns from 1074 ultra-high resolution protein structures (≤1.2 Å). Our clustering is derived from applying the DBSCAN and k-medoids algorithms to this data set with a metric commonly used in directional statistics applied to the set of dihedral angles from the second and third residues of each turn. We define 18 turn types compared to the 8 classical turn types in common use. We propose a new 2-letter nomenclature for all 18 beta-turn types using Ramachandran region names for the two central residues (e.g., 'A' and 'D' for alpha regions on the left side of the Ramachandran map and 'a' and 'd' for equivalent regions on the right-hand side; classical Type I turns are 'AD' turns and Type I' turns are 'ad'). We identify 11 new types of beta turn, 5 of which are sub-types of classical beta-turn types. Up-to-date statistics, probability densities of conformations, and sequence profiles of beta turns in loops were collected and analyzed. A library of turn types, BetaTurnLib18, and cross-platform software, BetaTurnTool18, which identifies turns in an input protein structure, are freely available and redistributable from dunbrack.fccc.edu/betaturn and github.com/sh-maxim/BetaTurn18. Given the ubiquitous nature of beta turns, this comprehensive study updates understanding of beta turns and should also provide useful tools for protein structure determination, refinement, and prediction programs.
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http://dx.doi.org/10.1371/journal.pcbi.1006844DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6424458PMC
March 2019

Interaction of germline variants in a family with a history of early-onset clear cell renal cell carcinoma.

Mol Genet Genomic Med 2019 03 24;7(3):e556. Epub 2019 Jan 24.

Cancer Prevention and Control Program, Fox Chase Cancer Center, Philadelphia, Pennsylvania.

Background: Identification of genetic factors causing predisposition to renal cell carcinoma has helped improve screening, early detection, and patient survival.

Methods: We report the characterization of a proband with renal and thyroid cancers and a family history of renal and other cancers by whole-exome sequencing (WES), coupled with WES analysis of germline DNA from additional affected and unaffected family members.

Results: This work identified multiple predicted protein-damaging variants relevant to the pattern of inherited cancer risk. Among these, the proband and an affected brother each had a heterozygous Ala45Thr variant in SDHA, a component of the succinate dehydrogenase (SDH) complex. SDH defects are associated with mitochondrial disorders and risk for various cancers; immunochemical analysis indicated loss of SDHB protein expression in the patient's tumor, compatible with SDH deficiency. Integrated analysis of public databases and structural predictions indicated that the two affected individuals also had additional variants in genes including TGFB2, TRAP1, PARP1, and EGF, each potentially relevant to cancer risk alone or in conjunction with the SDHA variant. In addition, allelic imbalances of PARP1 and TGFB2 were detected in the tumor of the proband.

Conclusion: Together, these data suggest the possibility of risk associated with interaction of two or more variants.
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http://dx.doi.org/10.1002/mgg3.556DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6418363PMC
March 2019

Mouse modeling and structural analysis of the p.G307S mutation in human cystathionine β-synthase () reveal effects on CBS activity but not stability.

J Biol Chem 2018 09 20;293(36):13921-13931. Epub 2018 Jul 20.

From the Cancer Biology Program, Fox Chase Cancer Center, Philadelphia, Pennsylvania 19111 and

Mutations in the cystathionine β-synthase () gene are the cause of classical homocystinuria, the most common inborn error in sulfur metabolism. The p.G307S mutation is the most frequent cause of CBS deficiency in Ireland, which has the highest prevalence of CBS deficiency in Europe. Individuals homozygous for this mutation tend to be severely affected and are pyridoxine nonresponsive, but the molecular basis for the strong effects of this mutation is unclear. Here, we characterized a transgenic mouse model lacking endogenous and expressing human p.G307S CBS protein from a zinc-inducible metallothionein promoter (). Unlike mice expressing other mutant alleles, the transgene could not efficiently rescue neonatal lethality of in a C57BL/6J background. In a C3H/HeJ background, zinc-induced mice expressed high levels of p.G307S CBS in the liver, and this protein variant forms multimers, similarly to mice expressing WT human CBS. However, the p.G307S enzyme had no detectable residual activity. Moreover, treating mice with proteasome inhibitors failed to significantly increase CBS-specific activity. These findings indicated that the G307S substitution likely affects catalytic function as opposed to causing a folding defect. Using molecular dynamics simulation techniques, we found that the G307S substitution likely impairs catalytic function by limiting the ability of the tyrosine at position 308 to assume the proper conformational state(s) required for the formation of the pyridoxal-cystathionine intermediate. These results indicate that the p.G307S CBS is stable but enzymatically inert and therefore unlikely to respond to chaperone-based therapy.
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http://dx.doi.org/10.1074/jbc.RA118.002164DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6130948PMC
September 2018

RosettaAntibodyDesign (RAbD): A general framework for computational antibody design.

PLoS Comput Biol 2018 04 27;14(4):e1006112. Epub 2018 Apr 27.

Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA, United States of America.

A structural-bioinformatics-based computational methodology and framework have been developed for the design of antibodies to targets of interest. RosettaAntibodyDesign (RAbD) samples the diverse sequence, structure, and binding space of an antibody to an antigen in highly customizable protocols for the design of antibodies in a broad range of applications. The program samples antibody sequences and structures by grafting structures from a widely accepted set of the canonical clusters of CDRs (North et al., J. Mol. Biol., 406:228-256, 2011). It then performs sequence design according to amino acid sequence profiles of each cluster, and samples CDR backbones using a flexible-backbone design protocol incorporating cluster-based CDR constraints. Starting from an existing experimental or computationally modeled antigen-antibody structure, RAbD can be used to redesign a single CDR or multiple CDRs with loops of different length, conformation, and sequence. We rigorously benchmarked RAbD on a set of 60 diverse antibody-antigen complexes, using two design strategies-optimizing total Rosetta energy and optimizing interface energy alone. We utilized two novel metrics for measuring success in computational protein design. The design risk ratio (DRR) is equal to the frequency of recovery of native CDR lengths and clusters divided by the frequency of sampling of those features during the Monte Carlo design procedure. Ratios greater than 1.0 indicate that the design process is picking out the native more frequently than expected from their sampled rate. We achieved DRRs for the non-H3 CDRs of between 2.4 and 4.0. The antigen risk ratio (ARR) is the ratio of frequencies of the native amino acid types, CDR lengths, and clusters in the output decoys for simulations performed in the presence and absence of the antigen. For CDRs, we achieved cluster ARRs as high as 2.5 for L1 and 1.5 for H2. For sequence design simulations without CDR grafting, the overall recovery for the native amino acid types for residues that contact the antigen in the native structures was 72% in simulations performed in the presence of the antigen and 48% in simulations performed without the antigen, for an ARR of 1.5. For the non-contacting residues, the ARR was 1.08. This shows that the sequence profiles are able to maintain the amino acid types of these conserved, buried sites, while recovery of the exposed, contacting residues requires the presence of the antigen-antibody interface. We tested RAbD experimentally on both a lambda and kappa antibody-antigen complex, successfully improving their affinities 10 to 50 fold by replacing individual CDRs of the native antibody with new CDR lengths and clusters.
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http://dx.doi.org/10.1371/journal.pcbi.1006112DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5942852PMC
April 2018

Role of a selecting ligand in shaping the murine γδ-TCR repertoire.

Proc Natl Acad Sci U S A 2018 02 5;115(8):1889-1894. Epub 2018 Feb 5.

Blood Cell Development and Function Program, Fox Chase Cancer Center, Philadelphia, PA 19111;

Unlike αβ-T lineage cells, where the role of ligand in intrathymic selection is well established, the role of ligand in the development of γδ-T cells remains controversial. Here we provide evidence for the role of a bona fide selecting ligand in shaping the γδ-T cell-receptor (TCR) repertoire. Reactivity of the γδ-TCR with the major histocompatibility complex (MHC) Class Ib ligands, H2-T10/22, is critically dependent upon the EGYEL motif in the complementarity determining region 3 (CDR3) of TCRδ. In the absence of H2-T10/22 ligand, the commitment of H2-T10/22 reactive γδ-T cells to the γδ fate is diminished, and the specification of those γδ committed cells to the IFN-γ or interleukin-17 effector fate is altered. Furthermore, those cells that do adopt the γδ fate and mature exhibit a profound alteration in the γδTCR repertoire, including depletion of the EGYEL motif and reductions in both CDR3δ length and charge. Taken together, these data suggest that ligand plays an important role in shaping the TCR repertoire of γδ-T cells.
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http://dx.doi.org/10.1073/pnas.1718328115DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5828614PMC
February 2018

BRCA2, EGFR, and NTRK mutations in mismatch repair-deficient colorectal cancers with MSH2 or MLH1 mutations.

Oncotarget 2017 Jun;8(25):39945-39962

Laboratory of Translational Oncology and Experimental Cancer Therapeutics, Fox Chase Cancer Center, Philadelphia, PA, USA.

Deficient mismatch repair (MMR) and microsatellite instability (MSI) contribute to ~15% of colorectal cancer (CRCs). We hypothesized MSI leads to mutations in DNA repair proteins including BRCA2 and cancer drivers including EGFR. We analyzed mutations among a discovery cohort of 26 MSI-High (MSI-H) and 558 non-MSI-H CRCs profiled at Caris Life Sciences. Caris-profiled MSI-H CRCs had high mutation rates (50% vs 14% in non-MSI-H, P < 0.0001) in BRCA2. Of 1104 profiled CRCs from a second cohort (COSMIC), MSH2/MLH1-mutant CRCs showed higher mutation rates in BRCA2 compared to non-MSH2/MLH1-mutant tumors (38% vs 6%, P < 0.0000001). BRCA2 mutations in MSH2/MLH1-mutant CRCs included 75 unique mutations not known to occur in breast or pancreatic cancer per COSMIC v73. Only 5 deleterious BRCA2 mutations in CRC were previously reported in the BIC database as germ-line mutations in breast cancer. Some BRCA2 mutations were predicted to disrupt interactions with partner proteins DSS1 and RAD51. Some CRCs harbored multiple BRCA2 mutations. EGFR was mutated in 45.5% of MSH2/MLH1-mutant and 6.5% of non-MSH2/MLH1-mutant tumors (P < 0.0000001). Approximately 15% of EGFR mutations found may be actionable through TKI therapy, including N700D, G719D, T725M, T790M, and E884K. NTRK gene mutations were identified in MSH2/MLH1-mutant CRC including NTRK1 I699V, NTRK2 P716S, and NTRK3 R745L. Our findings have clinical relevance regarding therapeutic targeting of BRCA2 vulnerabilities, EGFR mutations or other identified oncogenic drivers such as NTRK in MSH2/MLH1-mutant CRCs or other tumors with mismatch repair deficiency.
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http://dx.doi.org/10.18632/oncotarget.18098DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5522275PMC
June 2017

Functional analysis of rare variants in mismatch repair proteins augments results from computation-based predictive methods.

Cancer Biol Ther 2017 Jul 11;18(7):519-533. Epub 2017 May 11.

a Molecular Therapeutics Program , Fox Chase Cancer Center , Philadelphia , PA , USA.

The cancer-predisposing Lynch Syndrome (LS) arises from germline mutations in DNA mismatch repair (MMR) genes, predominantly MLH1, MSH2, MSH6, and PMS2. A major challenge for clinical diagnosis of LS is the frequent identification of variants of uncertain significance (VUS) in these genes, as it is often difficult to determine variant pathogenicity, particularly for missense variants. Generic programs such as SIFT and PolyPhen-2, and MMR gene-specific programs such as PON-MMR and MAPP-MMR, are often used to predict deleterious or neutral effects of VUS in MMR genes. We evaluated the performance of multiple predictive programs in the context of functional biologic data for 15 VUS in MLH1, MSH2, and PMS2. Using cell line models, we characterized VUS predicted to range from neutral to pathogenic on mRNA and protein expression, basal cellular viability, viability following treatment with a panel of DNA-damaging agents, and functionality in DNA damage response (DDR) signaling, benchmarking to wild-type MMR proteins. Our results suggest that the MMR gene-specific classifiers do not always align with the experimental phenotypes related to DDR. Our study highlights the importance of complementary experimental and computational assessment to develop future predictors for the assessment of VUS.
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http://dx.doi.org/10.1080/15384047.2017.1326439DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5639829PMC
July 2017

The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design.

J Chem Theory Comput 2017 Jun 12;13(6):3031-3048. Epub 2017 May 12.

Department of Chemical and Biomolecular Engineering, Johns Hopkins University , 3400 North Charles Street, Baltimore, Maryland 21218, United States.

Over the past decade, the Rosetta biomolecular modeling suite has informed diverse biological questions and engineering challenges ranging from interpretation of low-resolution structural data to design of nanomaterials, protein therapeutics, and vaccines. Central to Rosetta's success is the energy function: a model parametrized from small-molecule and X-ray crystal structure data used to approximate the energy associated with each biomolecule conformation. This paper describes the mathematical models and physical concepts that underlie the latest Rosetta energy function, called the Rosetta Energy Function 2015 (REF15). Applying these concepts, we explain how to use Rosetta energies to identify and analyze the features of biomolecular models. Finally, we discuss the latest advances in the energy function that extend its capabilities from soluble proteins to also include membrane proteins, peptides containing noncanonical amino acids, small molecules, carbohydrates, nucleic acids, and other macromolecules.
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http://dx.doi.org/10.1021/acs.jctc.7b00125DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5717763PMC
June 2017

Benchmarking predictions of allostery in liver pyruvate kinase in CAGI4.

Hum Mutat 2017 09 2;38(9):1123-1131. Epub 2017 May 2.

Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, Pennsylvania.

The Critical Assessment of Genome Interpretation (CAGI) is a global community experiment to objectively assess computational methods for predicting phenotypic impacts of genomic variation. One of the 2015-2016 competitions focused on predicting the influence of mutations on the allosteric regulation of human liver pyruvate kinase. More than 30 different researchers accessed the challenge data. However, only four groups accepted the challenge. Features used for predictions ranged from evolutionary constraints, mutant site locations relative to active and effector binding sites, and computational docking outputs. Despite the range of expertise and strategies used by predictors, the best predictions were marginally greater than random for modified allostery resulting from mutations. In contrast, several groups successfully predicted which mutations severely reduced enzymatic activity. Nonetheless, poor predictions of allostery stands in stark contrast to the impression left by more than 700 PubMed entries identified using the identifiers "computational + allosteric." This contrast highlights a specialized need for new computational tools and utilization of benchmarks that focus on allosteric regulation.
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http://dx.doi.org/10.1002/humu.23222DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5561472PMC
September 2017

Modeling and docking of antibody structures with Rosetta.

Nat Protoc 2017 02 26;12(2):401-416. Epub 2017 Jan 26.

Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA.

We describe Rosetta-based computational protocols for predicting the 3D structure of an antibody from sequence (RosettaAntibody) and then docking the antibody to protein antigens (SnugDock). Antibody modeling leverages canonical loop conformations to graft large segments from experimentally determined structures, as well as offering (i) energetic calculations to minimize loops, (ii) docking methodology to refine the V-V relative orientation and (iii) de novo prediction of the elusive complementarity determining region (CDR) H3 loop. To alleviate model uncertainty, antibody-antigen docking resamples CDR loop conformations and can use multiple models to represent an ensemble of conformations for the antibody, the antigen or both. These protocols can be run fully automated via the ROSIE web server (http://rosie.rosettacommons.org/) or manually on a computer with user control of individual steps. For best results, the protocol requires roughly 1,000 CPU-hours for antibody modeling and 250 CPU-hours for antibody-antigen docking. Tasks can be completed in under a day by using public supercomputers.
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http://dx.doi.org/10.1038/nprot.2016.180DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5739521PMC
February 2017

Biological function derived from predicted structures in CASP11.

Proteins 2016 09 15;84 Suppl 1:370-91. Epub 2016 Jun 15.

Fox Chase Cancer Center, Philadelphia, Pennsylvania, 19111.

In CASP11, the organizers sought to bring the biological inferences from predicted structures to the fore. To accomplish this, we assessed the models for their ability to perform quantifiable tasks related to biological function. First, for 10 targets that were probable homodimers, we measured the accuracy of docking the models into homodimers as a function of GDT-TS of the monomers, which produced characteristic L-shaped plots. At low GDT-TS, none of the models could be docked correctly as homodimers. Above GDT-TS of ∼60%, some models formed correct homodimers in one of the largest docked clusters, while many other models at the same values of GDT-TS did not. Docking was more successful when many of the templates shared the same homodimer. Second, we docked a ligand from an experimental structure into each of the models of one of the targets. Docking to the models with two different programs produced poor ligand RMSDs with the experimental structure. Measures that evaluated similarity of contacts were reasonable for some of the models, although there was not a significant correlation with model accuracy. Finally, we assessed whether models would be useful in predicting the phenotypes of missense mutations in three human targets by comparing features calculated from the models with those calculated from the experimental structures. The models were successful in reproducing accessible surface areas but there was little correlation of model accuracy with calculation of FoldX evaluation of the change in free energy between the wild-type and the mutant. Proteins 2016; 84(Suppl 1):370-391. © 2016 Wiley Periodicals, Inc.
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http://dx.doi.org/10.1002/prot.24997DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4963311PMC
September 2016

Assessment of template-based modeling of protein structure in CASP11.

Proteins 2016 09 15;84 Suppl 1:200-20. Epub 2016 Jun 15.

Fox Chase Cancer Center, Institute for Cancer Research, Philadelphia, Pennsylvania, 19111.

We present the assessment of predictions submitted in the template-based modeling (TBM) category of CASP11 (Critical Assessment of Protein Structure Prediction). Model quality was judged on the basis of global and local measures of accuracy on all atoms including side chains. The top groups on 39 human-server targets based on model 1 predictions were LEER, Zhang, LEE, MULTICOM, and Zhang-Server. The top groups on 81 targets by server groups based on model 1 predictions were Zhang-Server, nns, BAKER-ROSETTASERVER, QUARK, and myprotein-me. In CASP11, the best models for most targets were equal to or better than the best template available in the Protein Data Bank, even for targets with poor templates. The overall performance in CASP11 is similar to the performance of predictors in CASP10 with slightly better performance on the hardest targets. For most targets, assessment measures exhibited bimodal probability density distributions. Multi-dimensional scaling of an RMSD matrix for each target typically revealed a single cluster with models similar to the target structure, with a mode in the GDT-TS density between 40 and 90, and a wide distribution of models highly divergent from each other and from the experimental structure, with density mode at a GDT-TS value of ∼20. The models in this peak in the density were either compact models with entirely the wrong fold, or highly non-compact models. The results argue for a density-driven approach in future CASP TBM assessments that accounts for the bimodal nature of these distributions instead of Z scores, which assume a unimodal, Gaussian distribution. Proteins 2016; 84(Suppl 1):200-220. © 2016 Wiley Periodicals, Inc.
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http://dx.doi.org/10.1002/prot.25049DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5030193PMC
September 2016

Assessment of refinement of template-based models in CASP11.

Proteins 2016 09 15;84 Suppl 1:260-81. Epub 2016 Jun 15.

Fox Chase Cancer Center, Philadelphia, Pennsylvania, 19111.

CASP11 (the 11th Meeting on the Critical Assessment of Protein Structure Prediction) ran a blind experiment in the refinement of protein structure predictions, the fourth such experiment since CASP8. As with the previous experiments, the predictors were provided with one starting structure from the server models of each of a selected set of template-based modeling targets and asked to refine the coordinates of the starting structure toward native. We assessed the refined structures with the Z-scores of the standard CASP measures, which compare the model-target similarities of the models from all the predictors. Furthermore, we assessed the refined structures with "relative measures," which compare the improvement in accuracy of each model with respect to the starting structure. The latter provides an assessment of the extent to which each predictor group is able to improve the starting structures toward native. We utilized heat maps to display improvements in the Calpha-Calpha distance matrix for each model. The heat maps labeled with each element of secondary structure helped us to identify regions of refinement toward native in each model. Most positively scoring models show modest improvements in multiple regions of the structure, while in some models we were able to identify significant repositioning of N/C-terminal segments and internal elements of secondary structure. The best groups were able to improve more than 70% of the targets from the starting models, and by an average of 3-5% in the standard CASP measures. Proteins 2016; 84(Suppl 1):260-281. © 2016 Wiley Periodicals, Inc.
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http://dx.doi.org/10.1002/prot.25048DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5030126PMC
September 2016

CASP 11 target classification.

Proteins 2016 09 27;84 Suppl 1:20-33. Epub 2016 Jan 27.

Howard Hughes Medical Institute, University of Texas Southwestern Medical Center at Dallas, Dallas, Texas 75390-9050.

Protein target structures for the Critical Assessment of Structure Prediction round 11 (CASP11) and CASP ROLL were split into domains and classified into categories suitable for assessment of template-based modeling (TBM) and free modeling (FM) based on their evolutionary relatedness to existing structures classified by the Evolutionary Classification of Protein Domains (ECOD) database. First, target structures were divided into domain-based evaluation units. Target splits were based on the domain organization of available templates as well as the performance of servers on whole targets compared to split target domains. Second, evaluation units were classified into TBM and FM categories using a combination of measures that evaluate prediction quality and template detectability. Generally, target domains with sequence-related templates and good server prediction performance were classified as TBM, whereas targets without sequence-identifiable templates and low server performance were classified as FM. As in previous CASP experiments, the boundaries for classification were blurred due to the presence of significant insertions and deteriorations in the targets with respect to homologous templates, as well as the presence of templates with partial coverage of new folds. The FM category included 45 target domains, which represents an unprecedented number of difficult CASP targets provided for modeling. Proteins 2016; 84(Suppl 1):20-33. © 2016 Wiley Periodicals, Inc.
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http://dx.doi.org/10.1002/prot.24982DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4940306PMC
September 2016

Identifying three-dimensional structures of autophosphorylation complexes in crystals of protein kinases.

Sci Signal 2015 Dec 1;8(405):rs13. Epub 2015 Dec 1.

Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA 19111, USA.

Protein kinase autophosphorylation is a common regulatory mechanism in cell signaling pathways. Crystal structures of several homomeric protein kinase complexes have a serine, threonine, or tyrosine autophosphorylation site of one kinase monomer located in the active site of another monomer, a structural complex that we call an "autophosphorylation complex." We developed and applied a structural bioinformatics method to identify all such autophosphorylation complexes in x-ray crystallographic structures in the Protein Data Bank (PDB). We identified 15 autophosphorylation complexes in the PDB, of which five complexes had not previously been described in the publications describing the crystal structures. These five complexes consist of tyrosine residues in the N-terminal juxtamembrane regions of colony-stimulating factor 1 receptor (CSF1R, Tyr(561)) and ephrin receptor A2 (EPHA2, Tyr(594)), tyrosine residues in the activation loops of the SRC kinase family member LCK (Tyr(394)) and insulin-like growth factor 1 receptor (IGF1R, Tyr(1166)), and a serine in a nuclear localization signal region of CDC-like kinase 2 (CLK2, Ser(142)). Mutations in the complex interface may alter autophosphorylation activity and contribute to disease; therefore, we mutated residues in the autophosphorylation complex interface of LCK and found that two mutations impaired autophosphorylation (T445V and N446A) and mutation of Pro(447) to Ala, Gly, or Leu increased autophosphorylation. The identified autophosphorylation sites are conserved in many kinases, suggesting that, by homology, these complexes may provide insight into autophosphorylation complex interfaces of kinases that are relevant drug targets.
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http://dx.doi.org/10.1126/scisignal.aaa6711DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4766099PMC
December 2015

Systematic evaluation of underlying defects in DNA repair as an approach to case-only assessment of familial prostate cancer.

Oncotarget 2015 Nov;6(37):39614-33

Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia, PA, USA.

Risk assessment for prostate cancer is challenging due to its genetic heterogeneity. In this study, our goal was to develop an operational framework to select and evaluate gene variants that may contribute to familial prostate cancer risk. Drawing on orthogonal sources, we developed a candidate list of genes relevant to prostate cancer, then analyzed germline exomes from 12 case-only prostate cancer patients from high-risk families to identify patterns of protein-damaging gene variants. We described an average of 5 potentially disruptive variants in each individual and annotated them in the context of public databases representing human variation. Novel damaging variants were found in several genes of relevance to prostate cancer. Almost all patients had variants associated with defects in DNA damage response. Many also had variants linked to androgen signaling. Treatment of primary T-lymphocytes from these prostate cancer patients versus controls with DNA damaging agents showed elevated levels of the DNA double strand break (DSB) marker γH2AX (p < 0.05), supporting the idea of an underlying defect in DNA repair. This work suggests the value of focusing on underlying defects in DNA damage in familial prostate cancer risk assessment and demonstrates an operational framework for exome sequencing in case-only prostate cancer genetic evaluation.
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http://dx.doi.org/10.18632/oncotarget.5554DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4741850PMC
November 2015
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