Inherited cancer predisposing mutations in patients with therapy-related myeloid neoplasms.

Some patients with therapy-related myeloid neoplasms (t-MN) may have unsuspected inherited cancer predisposition syndrome (CPS). We propose a set of clinical criteria to identify t-MN patients with high risk of CPS (HR-CPS). Among 225 t-MN patients with an antecedent non-myeloid malignancy, our clinical criteria identified 52 (23%) HR-CPS patients. Germline whole-exome sequencing identified pathogenic or likely pathogenic variants in 10 of 27 HR-CPS patients compared to 0 of 9 low-risk CPS patients (37% vs. 0%, p = 0.04). These simple clinical criteria identify t-MN patients most likely to benefit from genetic testing for inherited CPS.

Tumour characteristics provide evidence for germline mismatch repair missense variant pathogenicity.

BACKGROUND: Pathogenic variants in mismatch repair (MMR) genes (MLH1, MSH2, MSH6 and PMS2) increase risk for Lynch syndrome and related cancers. We quantified tumour characteristics to assess variant pathogenicity for germline MMR genes. METHODS: Among 4740 patients with cancer with microsatellite instability (MSI) and immunohistochemical (IHC) results, we tested MMR pathogenic variant association with MSI/IHC status, and estimated likelihood ratios which we used to compute a tumour characteristic likelihood ratio (TCLR) for each variant. Predictive performance of TCLR in combination with in silico predictors, and a multifactorial variant prediction (MVP) model that included allele frequency, co-occurrence, co-segregation, and clinical and family history information was assessed. RESULTS: Compared with non-carriers, carriers of germline pathogenic/likely pathogenic (P/LP) variants were more likely to have abnormal MSI/IHC status (p<0.0001). Among 150 classified missense variants, 73.3% were accurately predicted with TCLR alone. Models leveraging in silico scores as prior probabilities accurately classified >76.7% variants. Adding TCLR as quantitative evidence in an MVP model (MVP +TCLR Pred) increased the proportion of accurately classified variants from 88.0% (MVP alone) to 98.0% and generated optimal performance statistics among all models tested. Importantly, MVP +TCLR Pred resulted in the high yield of predicted classifications for missense variants of unknown significance (VUS); among 193 VUS, 62.7% were predicted as P/PL or benign/likely benign (B/LB) when assessed according to American College of Medical Genetics and Genomics/Association for Molecular Pathology guidelines. CONCLUSION: Our study demonstrates that when used separately or in conjunction with other evidence, tumour characteristics provide evidence for germline MMR missense variant assessment, which may have important implications for genetic testing and clinical management.

Validation of a prostate cancer polygenic risk score.

BACKGROUND: Genome-wide association studies have identified over 100 single-nucleotide polymorphisms (SNPs) associated with prostate cancer (PrCa), and polygenic risk scores (PRS) based on their combined genotypes have been developed for risk stratification. We aimed to assess the contribution of PRS to PrCa risk in a large multisite study. METHODS: The sample included 1972 PrCa cases and 1919 unaffected controls. Next-generation sequencing was used to assess pathogenic variants in 14 PrCa-susceptibility genes and 72 validated PrCa-associated SNPs. We constructed a population-standardized PRS and tested its association with PrCa using logistic regression adjusted for age and family history of PrCa. RESULTS: The mean age of PrCa cases at diagnosis and age of controls at testing/last clinic visit was 59.5 ± 7.2 and 57.2 ± 13.0 years, respectively. Among 1740 cases with pathology data, 57.4% had Gleason score ≤ 6, while 42.6% had Gleason score ≥ 8. In addition, 39.6% cases and 20.1% controls had a family history of PrCa. The PRS was significantly higher in cases than controls (mean ± SD: 1.42 ± 1.11 vs 1.02 ± 0.76; P < .0001). Compared with men in the 1st quartile of age-adjusted PRS, those in the 2nd, 3rd, and 4th quartile were 1.58 (95% confidence interval [CI]: 1.31-1.90), 2.36 (95% CI: 1.96-2.84), and 3.98 (95% CI: 3.29-4.82) times as likely to have PrCa (all P < .0001). Adjustment for family history yielded similar results. PRS predictive performance was consistent with prior literature (area under the receiver operating curve = 0.64; 95% CI: 0.62-0.66). CONCLUSIONS: These data suggest that a 72-SNP PRS is predictive of PrCa, supporting its potential use in clinical risk assessment.

Detection of structural variation using target captured next-generation sequencing data for genetic diagnostic testing.

PURPOSE: Structural variation (SV) is associated with inherited diseases. Next-generation sequencing (NGS) is an efficient method for SV detection because of its high-throughput, low cost, and base-pair resolution. However, due to lack of standard NGS protocols and a limited number of clinical samples with pathogenic SVs, comprehensive standards for SV detection, interpretation, and reporting are to be established. METHODS: We performed SV assessment on 60,000 clinical samples tested with hereditary cancer NGS panels spanning 48 genes. To evaluate NGS results, NGS and orthogonal methods were used separately in a blinded fashion for SV detection in all samples. RESULTS: A total of 1,037 SVs in coding sequence (CDS) or untranslated regions (UTRs) and 30,847 SVs in introns were detected and validated. Across all variant types, NGS shows 100% sensitivity and 99.9% specificity. Overall, 64% of CDS/UTR SVs were classified as pathogenic/likely pathogenic, and five deletions/duplications were reclassified as pathogenic using breakpoint information from NGS. CONCLUSION: The SVs presented here can be used as a valuable resource for clinical research and diagnostics. The data illustrate NGS as a powerful tool for SV detection. Application of NGS and confirmation technologies in genetic testing ensures delivering accurate and reliable results for diagnosis and patient care.

Correction: Candidate-gene criteria for clinical reporting: diagnostic exome sequencing identifies altered candidate genes among 8% of patients with undiagnosed diseases.

In the published version of this paper, some of the columns in the last three rows of Table 3 were mistakenly transposed. The corrected table appears below. In col. 6 of the row for DNMT3A, "S3" was published in the original article. However, in the revised table for the corrigendum, it has been corrected to "S1". In col. 6 of the row for SON, "S3" was published in the original article. However, in the revised table for the corrigendum, it has been corrected to "S2".

Candidate-gene criteria for clinical reporting: diagnostic exome sequencing identifies altered candidate genes among 8% of patients with undiagnosed diseases.

PURPOSE: Diagnostic exome sequencing (DES) is now a commonly ordered test for individuals with undiagnosed genetic disorders. In addition to providing a diagnosis for characterized diseases, exome sequencing has the capacity to uncover novel candidate genes for disease. METHODS: Family-based DES included analysis of both characterized and novel genetic etiologies. To evaluate candidate genes for disease in the clinical setting, we developed a systematic, rule-based classification schema. RESULTS: Testing identified a candidate gene among 7.7% (72/934) of patients referred for DES; 37 (4.0%) and 35 (3.7%) of the genes received evidence scores of "candidate" and "suspected candidate," respectively. A total of 71 independent candidate genes were reported among the 72 patients, and 38% (27/71) were subsequently corroborated in the peer-reviewed literature. This rate of corroboration increased to 51.9% (27/52) among patients whose gene was reported at least 12 months previously. CONCLUSIONS: Herein, we provide transparent, comprehensive, and standardized scoring criteria for the clinical reporting of candidate genes. These results demonstrate that DES is an integral tool for genetic diagnosis, especially for elucidating the molecular basis for both characterized and novel candidate genetic etiologies. Gene discoveries also advance the understanding of normal human biology and more common diseases.Genet Med 19 2, 224-235.

A recurrent mutation in KCNA2 as a novel cause of hereditary spastic paraplegia and ataxia.

The hereditary spastic paraplegias (HSPs) are heterogeneous neurodegenerative disorders with over 50 known causative genes. We identified a recurrent mutation in KCNA2 (c.881G>A, p.R294H), encoding the voltage-gated K(+) -channel, KV 1.2, in two unrelated families with HSP, intellectual disability (ID), and ataxia. Follow-up analysis of > 2,000 patients with various neurological phenotypes identified a de novo p.R294H mutation in a proband with ataxia and ID. Two-electrode voltage-clamp recordings of Xenopus laevis oocytes expressing mutant KV 1.2 channels showed loss of function with a dominant-negative effect. Our findings highlight the phenotypic spectrum of a recurrent KCNA2 mutation, implicating ion channel dysfunction as a novel HSP disease mechanism. Ann Neurol 2016.

Enhanced utility of family-centered diagnostic exome sequencing with inheritance model-based analysis: results from 500 unselected families with undiagnosed genetic conditions.

PURPOSE: Diagnostic exome sequencing was immediately successful in diagnosing patients in whom traditional technologies were uninformative. Herein, we provide the results from the first 500 probands referred to a clinical laboratory for diagnostic exome sequencing. METHODS: Family-based exome sequencing included whole-exome sequencing followed by family inheritance-based model filtering, comprehensive medical review, familial cosegregation analysis, and analysis of novel genes. RESULTS: A positive or likely positive result in a characterized gene was identified in 30% of patients (152/500). A novel gene finding was identified in 7.5% of patients (31/416). The highest diagnostic rates were observed among patients with ataxia, multiple congenital anomalies, and epilepsy (44, 36, and 35%, respectively). Twenty-three percent of positive findings were within genes characterized within the past 2 years. The diagnostic rate was significantly higher among families undergoing a trio (37%) as compared with a singleton (21%) whole-exome testing strategy. CONCLUSION: Overall, we present results from the largest clinical cohort of diagnostic exome sequencing cases to date. These data demonstrate the utility of family-based exome sequencing and analysis to obtain the highest reported detection rate in an unselected clinical cohort, illustrating the utility of diagnostic exome sequencing as a transformative technology for the molecular diagnosis of genetic disease.

Germline activating MTOR mutation arising through gonadal mosaicism in two brothers with megalencephaly and neurodevelopmental abnormalities.

BACKGROUND: In humans, Mammalian Target of Rapamycin (MTOR) encodes a 300 kDa serine/ threonine protein kinase that is ubiquitously expressed, particularly at high levels in brain. MTOR functions as an integrator of multiple cellular processes, and in so doing either directly or indirectly regulates the phosphorylation of at least 800 proteins. While somatic MTOR mutations have been recognized in tumors for many years, and more recently in hemimegalencephaly, germline MTOR mutations have rarely been described. CASE PRESENTATION: We report the successful application of family-trio Diagnostic Exome Sequencing (DES) to identify the underlying molecular etiology in two brothers with multiple neurological and developmental lesions, and for whom previous testing was non-diagnostic. The affected brothers, who were 6 and 23 years of age at the time of DES, presented symptoms including but not limited to mild Autism Spectrum Disorder (ASD), megalencephaly, gross motor skill delay, cryptorchidism and bilateral iris coloboma. Importantly, we determined that each affected brother harbored the MTOR missense alteration p.E1799K (c.5395G>A). This exact variant has been previously identified in multiple independent human somatic cancer samples and has been shown to result in increased MTOR activation. Further, recent independent reports describe two unrelated families in whom p.E1799K co-segregated with megalencephaly and intellectual disability (ID); in both cases, p.E1799K was shown to have originated due to germline mosaicism. In the case of the family reported herein, the absence of p.E1799K in genomic DNA extracted from the blood of either parent suggests that this alteration most likely arose due to gonadal mosaicism. Further, the p.E1799K variant exerts its effect by a gain-of-function (GOF), autosomal dominant mechanism. CONCLUSION: Herein, we describe the use of DES to uncover an activating MTOR missense alteration of gonadal mosaic origin that is likely to be the causative mutation in two brothers who present multiple neurological and developmental abnormalities. Our report brings the total number of families who harbor MTOR p.E1799K in association with megalencephaly and ID to three. In each case, evidence suggests that p.E1799K arose in the affected individuals due to gonadal mosaicism. Thus, MTOR p.E1799K can now be classified as a pathogenic GOF mutation that causes megalencephaly and cognitive impairment in humans.

ELP2 is a novel gene implicated in neurodevelopmental disabilities.

Elongator is a multi-subunit protein complex essential to transcription elongation, histone acetylation, and tRNA modification. The complex consists of six highly conserved protein subunits, called Elongator Proteins (ELP) 1-6. Apart from an association with intellectual disability (ID), there is limited clinical information about patients with ELP2 variants. Here we report on two brothers with severe ID, spastic diplegia, and self-injury whose presentation eluded a diagnosis for over 20 years. In both brothers, whole exome sequencing revealed a likely pathogenic, compound heterozygous missense variant in ELP2. We describe the phenotype and natural history of the ELP2-related disorder in these brothers.

Probability landscape of heritable and robust epigenetic state of lysogeny in phage lambda.

Computational studies of biological networks can help to identify components and wirings responsible for observed phenotypes. However, studying stochastic networks controlling many biological processes is challenging. Similar to Schrödinger's equation in quantum mechanics, the chemical master equation (CME) provides a basic framework for understanding stochastic networks. However, except for simple problems, the CME cannot be solved analytically. Here we use a method called discrete chemical master equation (dCME) to compute directly the full steady-state probability landscape of the lysogeny maintenance network in phage lambda from its CME. Results show that wild-type phage lambda can maintain a constant level of repressor over a wide range of repressor degradation rate and is stable against UV irradiation, ensuring heritability of the lysogenic state. Furthermore, it can switch efficiently to the lytic state once repressor degradation increases past a high threshold by a small amount. We find that beyond bistability and nonlinear dimerization, cooperativity between repressors bound to O(R)1 and O(R)2 is required for stable and heritable epigenetic state of lysogeny that can switch efficiently. Mutants of phage lambda lack stability and do not possess a high threshold. Instead, they are leaky and respond to gradual changes in degradation rate. Our computation faithfully reproduces the hair triggers for UV-induced lysis observed in mutants and the limitation in robustness against mutations. The landscape approach computed from dCME is general and can be applied to study broad issues in systems biology.

Serial changes in liquid biopsy-derived variant allele frequency predict immune checkpoint inhibitor responsiveness in the pan-cancer setting.

Major immunotherapy challenges include a limited number of predictive biomarkers and the unusual imaging features post-therapy, such as pseudo-progression, which denote immune infiltrate-mediated tumor enlargement. Such phenomena confound clinical decision-making, since the cancer may eventually regress, and the patient should stay on treatment. We prospectively evaluated serial, blood-derived cell-free DNA (cfDNA) (baseline and 2-3 weeks post-immune checkpoint inhibitors [ICIs]) for variant allele frequency (VAF) and blood tumor mutation burden (bTMB) changes (next-generation sequencing) (N = 84 evaluable patients, diverse cancers). Low vs. high cfDNA-derived average adjusted ΔVAF (calculated by a machine-learning model) was an independent predictor of higher clinical benefit rate (stable disease ≥6 months/complete/partial response) (69.2% vs. 22.5%), and longer median progression-free (10.1 vs. 2.25 months) and overall survival (not reached vs. 6.1 months) (all P < .001, multivariate). bTMB changes did not correlate with outcomes. Therefore, early dynamic changes in cfDNA-derived VAF were a powerful predictor of pan-cancer immunotherapy outcomes.

Constrained proper sampling of conformations of transition state ensemble of protein folding.

Characterizing the conformations of protein in the transition state ensemble (TSE) is important for studying protein folding. A promising approach pioneered by Vendruscolo et al. [Nature (London) 409, 641 (2001)] to study TSE is to generate conformations that satisfy all constraints imposed by the experimentally measured φ values that provide information about the native likeness of the transition states. Faísca et al. [J. Chem. Phys. 129, 095108 (2008)] generated conformations of TSE based on the criterion that, starting from a TS conformation, the probabilities of folding and unfolding are about equal through Markov Chain Monte Carlo (MCMC) simulations. In this study, we use the technique of constrained sequential Monte Carlo method [Lin et al., J. Chem. Phys. 129, 094101 (2008); Zhang et al. Proteins 66, 61 (2007)] to generate TSE conformations of acylphosphatase of 98 residues that satisfy the φ-value constraints, as well as the criterion that each conformation has a folding probability of 0.5 by Monte Carlo simulations. We adopt a two stage process and first generate 5000 contact maps satisfying the φ-value constraints. Each contact map is then used to generate 1000 properly weighted conformations. After clustering similar conformations, we obtain a set of properly weighted samples of 4185 candidate clusters. Representative conformation of each of these cluster is then selected and 50 runs of Markov chain Monte Carlo (MCMC) simulation are carried using a regrowth move set. We then select a subset of 1501 conformations that have equal probabilities to fold and to unfold as the set of TSE. These 1501 samples characterize well the distribution of transition state ensemble conformations of acylphosphatase. Compared with previous studies, our approach can access much wider conformational space and can objectively generate conformations that satisfy the φ-value constraints and the criterion of 0.5 folding probability without bias. In contrast to previous studies, our results show that transition state conformations are very diverse and are far from nativelike when measured in cartesian root-mean-square deviation (cRMSD): the average cRMSD between TSE conformations and the native structure is 9.4 Å for this short protein, instead of 6 Å reported in previous studies. In addition, we found that the average fraction of native contacts in the TSE is 0.37, with enrichment in native-like ß-sheets and a shortage of long range contacts, suggesting such contacts form at a later stage of folding. We further calculate the first passage time of folding of TSE conformations through calculation of physical time associated with the regrowth moves in MCMC simulation through mapping such moves to a Markovian state model, whose transition time was obtained by Langevin dynamics simulations. Our results indicate that despite the large structural diversity of the TSE, they are characterized by similar folding time. Our approach is general and can be used to study TSE in other macromolecules.

Perturbation-based Markovian transmission model for probing allosteric dynamics of large macromolecular assembling: a study of GroEL-GroES.

Large macromolecular assemblies are often important for biological processes in cells. Allosteric communications between different parts of these molecular machines play critical roles in cellular signaling. Although studies of the topology and fluctuation dynamics of coarse-grained residue networks can yield important insights, they do not provide characterization of the time-dependent dynamic behavior of these macromolecular assemblies. Here we develop a novel approach called Perturbation-based Markovian Transmission (PMT) model to study globally the dynamic responses of the macromolecular assemblies. By monitoring simultaneous responses of all residues (>8,000) across many (>6) decades of time spanning from the initial perturbation until reaching equilibrium using a Krylov subspace projection method, we show that this approach can yield rich information. With criteria based on quantitative measurements of relaxation half-time, flow amplitude change, and oscillation dynamics, this approach can identify pivot residues that are important for macromolecular movement, messenger residues that are key to signal mediating, and anchor residues important for binding interactions. Based on a detailed analysis of the GroEL-GroES chaperone system, we found that our predictions have an accuracy of 71-84% judged by independent experimental studies reported in the literature. This approach is general and can be applied to other large macromolecular machineries such as the virus capsid and ribosomal complex.

Splicing profile by capture RNA-seq identifies pathogenic germline variants in tumor suppressor genes.

Germline variants in tumor suppressor genes (TSGs) can result in RNA mis-splicing and predisposition to cancer. However, identification of variants that impact splicing remains a challenge, contributing to a substantial proportion of patients with suspected hereditary cancer syndromes remaining without a molecular diagnosis. To address this, we used capture RNA-sequencing (RNA-seq) to generate a splicing profile of 18 TSGs (APC, ATM, BRCA1, BRCA2, BRIP1, CDH1, CHEK2, MLH1, MSH2, MSH6, MUTYH, NF1, PALB2, PMS2, PTEN, RAD51C, RAD51D, and TP53) in 345 whole-blood samples from healthy donors. We subsequently demonstrated that this approach can detect mis-splicing by comparing splicing profiles from the control dataset to profiles generated from whole blood of individuals previously identified with pathogenic germline splicing variants in these genes. To assess the utility of our TSG splicing profile to prospectively identify pathogenic splicing variants, we performed concurrent capture DNA and RNA-seq in a cohort of 1000 patients with suspected hereditary cancer syndromes. This approach improved the diagnostic yield in this cohort, resulting in a 9.1% relative increase in the detection of pathogenic variants, demonstrating the utility of performing simultaneous DNA and RNA genetic testing in a clinical context.

REVEL and BayesDel outperform other in silico meta-predictors for clinical variant classification.

Many in silico predictors of genetic variant pathogenicity have been previously developed, but there is currently no standard application of these algorithms for variant assessment. Using 4,094 ClinVar-curated missense variants in clinically actionable genes, we evaluated the accuracy and yield of benign and deleterious evidence in 5 in silico meta-predictors, as well as agreement of SIFT and PolyPhen2, and report the derived thresholds for the best performing predictor(s). REVEL and BayesDel outperformed all other meta-predictors (CADD, MetaSVM, Eigen), with higher positive predictive value, comparable negative predictive value, higher yield, and greater overall prediction performance. Agreement of SIFT and PolyPhen2 resulted in slightly higher yield but lower overall prediction performance than REVEL or BayesDel. Our results support the use of gene-level rather than generalized thresholds, when gene-level thresholds can be estimated. Our results also support the use of 2-sided thresholds, which allow for uncertainty, rather than a single, binary cut-point for assigning benign and deleterious evidence. The gene-level 2-sided thresholds we derived for REVEL or BayesDel can be used to assess in silico evidence for missense variants in accordance with current classification guidelines.

Association of Breast and Ovarian Cancers With Predisposition Genes Identified by Large-Scale Sequencing.

Importance: Since the discovery of BRCA1 and BRCA2, multiple high- and moderate-penetrance genes have been reported as risk factors for hereditary breast cancer, ovarian cancer, or both; however, it is unclear whether these findings represent the complete genetic landscape of these cancers. Systematic investigation of the genetic contributions to breast and ovarian cancers is needed to confirm these findings and explore potentially new associations. Objective: To confirm reported and identify additional predisposition genes for breast or ovarian cancer. Design, Setting, and Participants: In this sample of 11 416 patients with clinical features of breast cancer, ovarian cancer, or both who were referred for genetic testing from 1200 hospitals and clinics across the United States and of 3988 controls who were referred for genetic testing for noncancer conditions between 2014 and 2015, whole-exome sequencing was conducted and gene-phenotype associations were examined. Case-control analyses using the Genome Aggregation Database as a set of reference controls were also conducted. Main Outcomes and Measures: Breast cancer risk associated with pathogenic variants among 625 cancer predisposition genes; association of identified predisposition breast or ovarian cancer genes with the breast cancer subtypes invasive ductal, invasive lobular, hormone receptor-positive, hormone receptor-negative, and male, and with early-onset disease. Results: Of 9639 patients with breast cancer, 3960 (41.1%) were early-onset cases (≤45 years at diagnosis) and 123 (1.3%) were male, with men having an older age at diagnosis than women (mean [SD] age, 61.8 [12.8] vs 48.6 [11.4] years). Of 2051 women with ovarian cancer, 445 (21.7%) received a diagnosis at 45 years or younger. Enrichment of pathogenic variants were identified in 4 non-BRCA genes associated with breast cancer risk: ATM (odds ratio [OR], 2.97; 95% CI, 1.67-5.68), CHEK2 (OR, 2.19; 95% CI, 1.40-3.56), PALB2 (OR, 5.53; 95% CI, 2.24-17.65), and MSH6 (OR, 2.59; 95% CI, 1.35-5.44). Increased risk for ovarian cancer was associated with 4 genes: MSH6 (OR, 4.16; 95% CI, 1.95-9.47), RAD51C (OR, not estimable; false-discovery rate-corrected P = .004), TP53 (OR, 18.50; 95% CI, 2.56-808.10), and ATM (OR, 2.85; 95% CI, 1.30-6.32). Neither the MRN complex genes nor CDKN2A was associated with increased breast or ovarian cancer risk. The findings also do not support previously reported breast cancer associations with the ovarian cancer susceptibility genes BRIP1, RAD51C, and RAD51D, or mismatch repair genes MSH2 and PMS2. Conclusions and Relevance: The results of this large-scale exome sequencing of patients and controls shed light on both well-established and controversial non-BRCA predisposition gene associations with breast or ovarian cancer reported to date and may implicate additional breast or ovarian cancer susceptibility gene candidates involved in DNA repair and genomic maintenance.

Comprehensive Paired Tumor/Germline Testing for Lynch Syndrome: Bringing Resolution to the Diagnostic Process.

PURPOSE: The current diagnostic testing algorithm for Lynch syndrome (LS) is complex and often involves multiple follow-up germline and somatic tests. We aimed to describe the results of paired tumor/germline testing performed on a large cohort of patients with colorectal cancer (CRC) and endometrial cancer (EC) to better determine the utility of this novel testing methodology. MATERIALS AND METHODS: We retrospectively reviewed a consecutive series of patients with CRC and EC undergoing paired tumor/germline analysis of the LS genes at a clinical diagnostic laboratory (N = 702). Microsatellite instability, MLH1 promoter hypermethylation, and germline testing of additional genes were performed if ordered. Patients were assigned to one of five groups on the basis of prior tumor screening and germline testing outcomes. Results for each group are described. RESULTS: Overall results were informative regarding an LS diagnosis for 76.1% and 60.8% of patients with mismatch-repair-deficient (MMRd) CRC and EC without and with prior germline testing, respectively. LS germline mutations were identified in 24.8% of patients in the group without prior germline testing, and interestingly, in 9.5% of patients with previous germline testing; four of these were discordant with prior tumor screening. Upon excluding patients with MLH1 promoter hypermethylation and germline mutations, biallelic somatic inactivation was seen in approximately 50% of patients with MMRd tumors across groups. CONCLUSION: Paired testing identified a cause for MMRd tumors in 76% and 61% of patients without and with prior LS germline testing, respectively. Findings support inclusion of tumor sequencing as well as comprehensive LS germline testing in the LS testing algorithm. Paired testing offers a complete, convenient evaluation for LS with high diagnostic resolution.

A model study of protein nascent chain and cotranslational folding using hydrophobic-polar residues.

To study protein nascent chain folding during biosynthesis, we investigate the folding behavior of models of hydrophobic and polar (HP) chains at growing length using both two-dimensional square lattice model and an optimized three-dimensional 4-state discrete off-lattice model. After enumerating all possible sequences and conformations of HP heteropolymers up to length N = 18 and N = 15 in two and three-dimensional space, respectively, we examine changes in adopted structure, stability, and tolerance to single point mutation as the nascent chain grows. In both models, we find that stable model proteins have fewer folded nascent chains during growth, and often will only fold after reaching full length. For the few occasions where partial chains of stable proteins fold, these partial conformations on average are very similar to the corresponding parts of the final conformations at full length. Conversely, we find that sequences with fewer stable nascent chains and sequences with native-like folded nascent chains are more stable. In addition, these stable sequences in general can have many more point mutations and still fold into the same conformation as the wild type sequence. Our results suggest that stable proteins are less likely to be trapped in metastable conformations during biosynthesis, and are more resistant to point-mutations. Our results also imply that less stable proteins will require the assistance of chaperone and other factors during nascent chain folding. Taken together with other reported studies, it seems that cotranslational folding may not be a general mechanism of in vivo protein folding for small proteins, and in vitro folding studies are still relevant for understanding how proteins fold biologically.

Generating properly weighted ensemble of conformations of proteins from sparse or indirect distance constraints.

Inferring three-dimensional structural information of biomacromolecules such as proteins from limited experimental data is an important and challenging task. Nuclear Overhauser effect measurements based on nucleic magnetic resonance, disulfide linking, and electron paramagnetic resonance labeling studies can all provide useful partial distance constraint characteristic of the conformations of proteins. In this study, we describe a general approach for reconstructing conformations of biomolecules that are consistent with given distance constraints. Such constraints can be in the form of upper bounds and lower bounds of distances between residue pairs, contact maps based on specific contact distance cutoff values, or indirect distance constraints such as experimental phi-value measurement. Our approach is based on the framework of sequential Monte Carlo method, a chain growth-based method. We have developed a novel growth potential function to guide the generation of conformations that satisfy given distance constraints. This potential function incorporates not only the distance information of current residue during growth but also the distance information of future residues by introducing global distance upper bounds between residue pairs and the placement of reference points. To obtain protein conformations from indirect distance constraints in the form of experimental phi-values, we first generate properly weighted contact maps satisfying phi-value constraints, we then generate conformations from these contact maps. We show that our approach can faithfully generate conformations that satisfy the given constraints, which approach the native structures when distance constraints for all residue pairs are given.