Associating somatic mutation with clinical outcomes through kernel regression and optimal transport

Somatic mutations in cancer patients are inherently sparse and potentially high dimensional. Cancer patients may share the same set of deregulated biological processes perturbed by different sets of somatically mutated genes. Therefore, when assessing the associations between somatic mutations and clinical outcomes, gene-by-gene analysis is often under-powered because it does not capture the complex disease mechanisms shared across cancer patients. Rather than testing genes one by one, an intuitive approach is to aggregate somatic mutation data of multiple genes to assess their joint association with clinical outcomes. The challenge is how to aggregate such information. Building on the optimal transport method, we propose a principled approach to estimate the similarity of somatic mutation profiles of multiple genes between tumor samples, while accounting for gene–gene similarities defined by gene annotations or empirical mutational patterns. Using such similarities, we can assess the associations between somatic mutations and clinical outcomes by kernel regression. We have applied our method to analyze somatic mutation data of 17 cancer types and identified at least five cancer types, where somatic mutations are associated with overall survival, progression-free interval, or cytolytic activity.     

E92A是HIV-1整合酶耐药突变N155S的活性回复突变

HIV-1整合酶是目前抗艾滋病药物研发的重要靶点之一,整合酶的耐药突变是导致整合酶抑制剂类药物治疗失败的主要原因,但突变产生耐药性的机理仍不清楚.本工作通过人工构建突变型整合酶,测试其活性和耐药性,对整合酶的耐药机理进行初步探索.构建整合酶的突变型包括E92A、N155S两种单突变及E92A/N155S双突变.通过基因工程操作引入突变、构建质粒、表达纯化得到整合酶蛋白.用基于磁珠的整合酶链转移ELISA测试整合酶的链转移活性,用S-1360和Raltegravir两种抑制剂测试整合酶的耐药性.另外,用Autodock软件做了S-1360和整合酶核心区(包括野生型和突变型)的分子对接.结果表明,N155S突变使整合酶链转移活性下降约80%,而E92A/N155S双突变仅使活性下降约42%,这表明N155S突变基础上的E92A突变可使整合酶的活性大幅回复.E92A和E92A/N155S对不同的抑制剂可产生不同的耐药性,它们对Raltegravir的耐药性强于对S-1360.突变对整合酶活性和耐药性的影响主要是通过改变整合酶活性中心结构实现的,E92A突变可能导致其与周围残基静电相互作用减弱,间接影响到D64和D116残基,产生活性回复作用.     

Combination of Two but Not Three Current Targeted Drugs Can Improve Therapy of Chronic Myeloid Leukemia

Chronic myeloid leukemia (CML) is a cancer of the hematopoietic system and has been treated with the drug Imatinib relatively successfully. Drug resistance, acquired by mutations, is an obstacle to success. Two additional drugs are now considered and could be combined with Imatinib to prevent resistance, Dasatinib and Nilotinib. While most mutations conferring resistance to one drug do not confer resistance to the other drugs, there is one mutation (T315I) that induces resistance against all three drugs. Using computational methods, the combination of two drugs is found to increase the probability of treatment success despite this cross-resistance. Combining more than two drugs, however, does not provide further advantages. We also explore possible combination therapies using drugs currently under development. We conclude that among the targeted drugs currently available for the treamtent of CML, only the two most effective ones should be used in combination for the prevention of drug resistance.     

The molecular mechanism behind resistance of the kinase FLT3 to the inhibitor quizartinib

Fms‐like tyrosine kinase 3 (FLT3) is a receptor tyrosine kinase that is a drug target for leukemias. Several potent inhibitors of FLT3 exist, and bind to the inactive form of the enzyme. Unfortunately, resistance due to mutations in the kinase domain of FLT3 limits the therapeutic effects of these inhibitors. As in many other cases, it is not straightforward to explain why certain mutations lead to drug resistance. Extensive fully atomistic molecular dynamics (MD) simulations of FLT3 were carried out with an inhibited form (FLT‐quizartinib complex), a free (apo) form, and an active conformation. In all cases, both the wild type (wt) proteins and two resistant mutants (D835F and Y842H) were studied. Analysis of the simulations revealed that impairment of protein‐drug interactions cannot explain the resistance mutations in question. Rather, it appears that the active state of the mutant forms is perturbed by the mutations. It is therefore likely that perturbation of deactivation of the protein (which is necessary for drug binding) is responsible for the reduced affinity of the drug to the mutants. Importantly, this study suggests that it is possible to explain the source of resistance by mutations in FLT3 by an analysis of unbiased MD simulations.     

Models and assumptions underlying genetic risk assessment

Various methods employed for estimating the genetic risks of radiation are reviewed. With the doubling-dose method, genetic damage is expressed as an increase in cases of known genetic disease. The actual doubling dose is based on figures obtained with the mouse. There have been no recent data on induced mutation frequencies. Recent results suggest that the prevalence figure for multifactorial disease may be at least one order of magnitude higher than before. Various assumptions underlying the doubling-dose concept are discussed in the light of recent findings on: (1) spontaneous mutations resulting from insertion elements, and (2) the comparability between spontaneous and induced mutations. The so-called direct method makes use of figures for induction of dominant mutations affecting the skeleton and the lens of the eye in the mouse, and of translocation induction in monkeys. Induction rates are converted to overall rates of induced dominant effects in man by applying certain assumptions. The proportionality between dose and effect is the basis for all genetic risk assessments. The possible significance of data on human lymphocytes indicating a threshold below 4 rad and the induction of repair enzymes by low radiation doses is discussed. The parallelogram approach is based on the principle that estimates can be obtained on the amount of genetic damage that cannot always be assessed directly. Thus mutations in mouse germ cells can be predicted by using mutation frequencies in cultured mammalian cells and O6-ethylguanine adducts. Measurement of haemoglobin mutations in human and mouse erythrocytes, and of HPRT-deficient mutations in lymphocytes of man and mouse should make more precise estimates of mutation frequencies in human germ cells possible. The development of a database on mutations in somatic cells of the mouse, their induction frequencies and molecular nature are considered an important priority. Used in combination with mouse germ-cell mutation frequencies, they should enable more precise risk estimates on the basis of mutations in somatic cells of man.     

Allostery and Missense Mutations as Intermittently Linked Promising Aspects of Modern Computational Drug Discovery

Drug research and development is a multidisciplinary field with its own successes. Yet, given the complexity of the process, it also faces challenges over the long development stages and even includes those that develop once a drug is marketed, i.e. drug toxicity and drug resistance. Better success can be achieved via well designed criteria in the early drug development stages. Here, we introduce the concepts of allostery and missense mutations, and argue that incorporation of these two intermittently linked biological phenomena into the early computational drug discovery stages would help to reduce the attrition risk in later stages of the process. We discuss the individual or in concert mechanisms of actions of mutations in allostery. Design of allosteric drugs is challenging compared to orthosteric drugs, yet they have been gaining popularity in recent years as alternative systems for the therapeutic regulation of proteins with an action-at-a-distance mode and non-invasive mechanisms. We propose an easy-to-apply computational allosteric drug discovery protocol which considers the mutation effect, and detail it with three case studies focusing on (1) analysis of effect of an allosteric mutation related to isoniazid drug resistance in tuberculosis; (2) identification of a cryptic pocket in the presence of an allosteric mutation of falcipain-2 as a malarial drug target; and (3) deciphering the effects of SARS-CoV-2 evolutionary mutations on a potential allosteric modulator with changes to allosteric communication paths.     

Integrating external biological knowledge in the construction of regulatory networks from time-series expression data

ABSTRACT: BACKGROUND: Inference about regulatory networks from high-throughput genomics data is of great interest in systems biology. We present a Bayesian approach to infer gene regulatory networks from time series expression data by integrating various types of biological knowledge. RESULTS: We formulate network construction as a series of variable selection problems and use linear regression to model the data. Our method summarizes additional data sources with an informative prior probability distribution over candidate regression models. We extend the Bayesian model averaging (BMA) variable selection method to select regulators in the regression framework. We summarize the external biological knowledge by an informative prior probability distribution over the candidate regression models. CONCLUSIONS: We demonstrate our method on simulated data and a set of time-series microarray experiments measuring the effect of a drug perturbation on gene expression levels, and show that it outperforms leading regression-based methods in the literature.     

A general framework for detecting disease associations with rare variants in sequencing studies

Biological and empirical evidence suggests that rare variants account for a large proportion of the genetic contributions to complex human diseases. Recent technological advances in high-throughput sequencing platforms have made it possible for researchers to generate comprehensive information on rare variants in large samples. We provide a general framework for association testing with rare variants by combining mutation information across multiple variant sites within a gene and relating the enriched genetic information to disease phenotypes through appropriate regression models. Our framework covers all major study designs (i.e., case-control, cross-sectional, cohort and family studies) and all common phenotypes (e.g., binary, quantitative, and age at onset), and it allows arbitrary covariates (e.g., environmental factors and ancestry variables). We derive theoretically optimal procedures for combining rare mutations and construct suitable test statistics for various biological scenarios. The allele-frequency threshold can be fixed or variable. The effects of the combined rare mutations on the phenotype can be in the same direction or different directions. The proposed methods are statistically more powerful and computationally more efficient than existing ones. An application to a deep-resequencing study of drug targets led to a discovery of rare variants associated with total cholesterol. The relevant software is freely available.     

Personalized pathway enrichment map of putative cancer genes from next generation sequencing data

BACKGROUND: Pathway analysis of a set of genes represents an important area in large-scale omic data analysis. However, the application of traditional pathway enrichment methods to next-generation sequencing (NGS) data is prone to several potential biases, including genomic/genetic factors (e.g., the particular disease and gene length) and environmental factors (e.g., personal life-style and frequency and dosage of exposure to mutagens). Therefore, novel methods are urgently needed for these new data types, especially for individual-specific genome data. METHODOLOGY: In this study, we proposed a novel method for the pathway analysis of NGS mutation data by explicitly taking into account the gene-wise mutation rate. We estimated the gene-wise mutation rate based on the individual-specific background mutation rate along with the gene length. Taking the mutation rate as a weight for each gene, our weighted resampling strategy builds the null distribution for each pathway while matching the gene length patterns. The empirical P value obtained then provides an adjusted statistical evaluation. PRINCIPAL FINDINGS/CONCLUSIONS: We demonstrated our weighted resampling method to a lung adenocarcinomas dataset and a glioblastoma dataset, and compared it to other widely applied methods. By explicitly adjusting gene-length, the weighted resampling method performs as well as the standard methods for significant pathways with strong evidence. Importantly, our method could effectively reject many marginally significant pathways detected by standard methods, including several long-gene-based, cancer-unrelated pathways. We further demonstrated that by reducing such biases, pathway crosstalk for each individual and pathway co-mutation map across multiple individuals can be objectively explored and evaluated. This method performs pathway analysis in a sample-centered fashion, and provides an alternative way for accurate analysis of cancer-personalized genomes. It can be extended to other types of genomic data (genotyping and methylation) that have similar bias problems.     

NRMD: Nuclear Receptor Mutation Database

The NRMD is a database for nuclear receptor mutation information. It includes mutation information from SWISS-PROT/TrEMBL, several web-based mutation data resources, and data extracted from the literature in a fully automatic manner. Because it is also possible to add mutations manually, a hundred mutations were added for completeness. At present, the NRMD contains information about 893 mutations in 54 nuclear receptors. A common numbering scheme for all nuclear receptors eases the use of the information for many kinds of studies. The NRMD is freely available to academia and industry as a stand-alone version at: www.receptors.org/NR/.     

Detection of minority point mutations by modified PCR technique: a new approach for a sensitive diagnosis of tumor-progression markers.

The detection of point mutations correlated with diseases, in enzymatically amplified DNA sequences (Polymerase Chain Reaction), is currently performed by digestion of PCR products when an existing restriction site disappears at least in one allele of the amplified mutated sequence or by allele specific radiolabeled probes in all other cases. These methods are the most sensitive but they cannot detect a mutation if it is present in less than 5% of the studied cells. We describe here a method based on the introduction of an artificial restriction site, using a modified primer during the PCR, which creates a RFLP indicative of the studied mutation. This RFLP is detected by a radiolabeled oligonucleotide probe which is not related to the mutation. Our approach multiplies the sensitivity by a factor of 1000 and it is practical for use in screening purposes and the detection, after treatment, of the residual disease in human malignancies. Using this method we detected 20% more mutations at codon 12 in the Ki ras oncogene in DNA from colorectal cancers that were undetectable with all the previous methods.     

Comparison of the EGFR resistance mutation profiles generated by EGFR-targeted tyrosine kinase inhibitors and the impact of drug combinations

Recent clinical data indicates that the emergence of mutant drug-resistant kinase alleles may be particularly relevant for targeted kinase inhibitors. In order to explore how different classes of targeted therapies impact upon resistance mutations, we performed EGFR (epidermal-growth-factor receptor) resistance mutation screens with erlotinib, lapatinib and CI-1033. Distinct mutation spectra were generated with each inhibitor and were reflective of their respective mechanisms of action. Lapatinib yielded the widest variety of mutations, whereas mutational variability was lower in the erlotinib and CI-1033 screens. Lapatinib was uniquely sensitive to mutations of residues located deep within the selectivity pocket, whereas mutation of either Gly(796) or Cys(797) resulted in a dramatic loss of CI-1033 potency. The clinically observed T790M mutation was common to all inhibitors, but occurred with varying frequencies. Importantly, the presence of C797S with T790M in the same EGFR allele conferred complete resistance to erlotinib, lapatinib and CI-1033. The combination of erlotinib and CI-1033 effectively reduced the number of drug-resistant clones, suggesting a possible clinical strategy to overcome drug resistance. Interestingly, our results also indicate that co-expression of ErbB2 (v-erb-b2 erythroblastic leukaemia viral oncogene homologue 2) has an impact upon the EGFR resistance mutations obtained, suggesting that ErbB2 may play an active role in the acquisition of drug-resistant mutations.     

Microsatellite mutations and inferences about human demography

Microsatellites have been widely used as tools for population studies. However, inference about population processes relies on the specification of mutation parameters that are largely unknown and likely to differ across loci. Here, we use data on somatic mutations to investigate the mutation process at 14 tetranucleotide repeats and carry out an advanced multilocus analysis of different demographic scenarios on worldwide population samples. We use a method based on less restrictive assumptions about the mutation process, which is more powerful to detect departures from the null hypothesis of constant population size than other methods previously applied to similar data sets. We detect a signal of population expansion in all samples examined, except for one African sample. As part of this analysis, we identify an "anomalous" locus whose extreme pattern of variation cannot be explained by variability in mutation size. Exaggerated mutation rate is proposed as a possible cause for its unusual variation pattern. We evaluate the effect of using it to infer population histories and show that inferences about demographic histories are markedly affected by its inclusion. In fact, exclusion of the anomalous locus reduces interlocus variability of statistics summarizing population variation and strengthens the evidence in favor of demographic growth.     

The stationary distribution of a continuously varying strategy in a class-structured population under mutation-selection-drift balance

Many traits and/or strategies expressed by organisms are quantitative phenotypes. Because populations are of finite size and genomes are subject to mutations, these continuously varying phenotypes are under the joint pressure of mutation, natural selection and random genetic drift. This article derives the stationary distribution for such a phenotype under a mutation-selection-drift balance in a class-structured population allowing for demographically varying class sizes and/or changing environmental conditions. The salient feature of the stationary distribution is that it can be entirely characterized in terms of the average size of the gene pool and Hamilton's inclusive fitness effect. The exploration of the phenotypic space varies exponentially with the cumulative inclusive fitness effect over state space, which determines an adaptive landscape. The peaks of the landscapes are those phenotypes that are candidate evolutionary stable strategies and can be determined by standard phenotypic selection gradient methods (e.g. evolutionary game theory, kin selection theory, adaptive dynamics). The curvature of the stationary distribution provides a measure of the stability by convergence of candidate evolutionary stable strategies, and it is evaluated explicitly for two biological scenarios: first, a coordination game, which illustrates that, for a multipeaked adaptive landscape, stochastically stable strategies can be singled out by letting the size of the gene pool grow large; second, a sex-allocation game for diploids and haplo-diploids, which suggests that the equilibrium sex ratio follows a Beta distribution with parameters depending on the features of the genetic system.     

乙肝治疗耐药基因突变的焦磷酸测序技术诊断

目的：建立焦磷酸测序技术检测拉米夫定和阿德福韦酯治疗乙肝所致乙肝病毒基因耐药突变的定量检测方法,为临床乙肝耐药诊断和治疗提供依据。方法：针对乙肝病毒DNA聚合酶基因序列上4个常见基因突变位点的6种突变形式,分别克隆构建野生型和突变型质粒作为标准品,应用生物信息学手段设计目标基因通用PCR引物和各突变点的焦磷酸测序引物,建立焦磷酸测序的突变检测方法。对接受拉米夫定、阿德福韦酯治疗的慢性乙型肝炎患者血清标本进行检测。结果：构建了乙肝病毒四种常见耐药性突变的标准株和变异株克隆,建立了分别或同时检测拉米夫定、阿德福韦酯耐药突变的焦磷酸测序方法,对68例临床耐药或疑似耐药的患者血清标本进行检测,双脱氧测序验证,检出拉米夫定耐药突变32例,阿德福韦酯耐药突变5例,其中焦磷酸测序检出20例为混合突变,而双脱氧测序显示为6例。结论：成功建立了焦磷酸测序定量检测拉米夫定、阿德福韦酯耐药基因突变的方法,构建了乙肝病毒耐药基因突变的标准质粒,为临床动态监测乙肝病毒变异病毒株、指导合理用药奠定了基础。     

Automatic extraction of protein point mutations using a graph bigram association

Protein point mutations are an essential component of the evolutionary and experimental analysis of protein structure and function. While many manually curated databases attempt to index point mutations, most experimentally generated point mutations and the biological impacts of the changes are described in the peer-reviewed published literature. We describe an application, Mutation GraB (Graph Bigram), that identifies, extracts, and verifies point mutations from biomedical literature. The principal problem of point mutation extraction is to link the point mutation with its associated protein and organism of origin. Our algorithm uses a graph-based bigram traversal to identify these relevant associations and exploits the Swiss-Prot protein database to verify this information. The graph bigram method is different from other models for point mutation extraction in that it incorporates frequency and positional data of all terms in an article to drive the point mutation–protein association. Our method was tested on 589 articles describing point mutations from the G protein–coupled receptor (GPCR), tyrosine kinase, and ion channel protein families. We evaluated our graph bigram metric against a word-proximity metric for term association on datasets of full-text literature in these three different protein families. Our testing shows that the graph bigram metric achieves a higher F-measure for the GPCRs (0.79 versus 0.76), protein tyrosine kinases (0.72 versus 0.69), and ion channel transporters (0.76 versus 0.74). Importantly, in situations where more than one protein can be assigned to a point mutation and disambiguation is required, the graph bigram metric achieves a precision of 0.84 compared with the word distance metric precision of 0.73. We believe the graph bigram search metric to be a significant improvement over previous search metrics for point mutation extraction and to be applicable to text-mining application requiring the association of words.