Establishing an EGFR mutation screening service for non-small cell lung cancer - sample quality criteria and candidate histological predictors.

INTRODUCTION: EGFR screening requires good quality tissue, sensitivity and turn-around time (TAT). We report our experience of routine screening, describing sample type, TAT, specimen quality (cellularity and DNA yield), histopathological description, mutation result and clinical outcome. METHODS: Non-small cell lung cancer (NSCLC) sections were screened for EGFR mutations (M+) in exons 18-21. Clinical, pathological and screening outcome data were collected for year 1 of testing. Screening outcome alone was collected for year 2. RESULTS: In year 1, 152 samples were tested, most (72%) were diagnostic. TAT was 4.9 days (95%confidence interval (CI)=4.5-5.5). EGFR-M+ prevalence was 11% and higher (20%) among never-smoking women with adenocarcinomas (ADCs), but 30% of mutations occurred in current/ex-smoking men. EGFR-M+ tumours were non-mucinous ADCs and 100% thyroid transcription factor (TTF1+). No mutations were detected in poorly differentiated NSCLC-not otherwise specified (NOS). There was a trend for improved overall survival (OS) among EGFR-M+ versus EGFR-M- patients (median OS=78 versus 17 months). In year 1, test failure rate was 19%, and associated with scant cellularity and low DNA concentrations. However 75% of samples with poor cellularity but representative of tumour were informative and mutation prevalence was 9%. In year 2, 755 samples were tested; mutation prevalence was 13% and test failure only 5.4%. Although samples with low DNA concentration (2.2 ng/μL), the mutation rate was 9.2%. CONCLUSION: Routine epidermal growth factor receptor (EGFR) screening using diagnostic samples is fast and feasible even on samples with poor cellularity and DNA content. Mutations tend to occur in better-differentiated non-mucinous TTF1+ ADCs. Whether these histological criteria may be useful to select patients for EGFR testing merits further investigation.

HIV-1 Mutation and Recombination Rates Are Different in Macrophages and T-cells

High rates of mutation and recombination help human immunodeficiency virus (HIV) to evade the immune system and develop resistance to antiretroviral therapy. Macrophages and T-cells are the natural target cells of HIV-1 infection. A consensus has not been reached as to whether HIV replication results in differential recombination between primary T-cells and macrophages. Here, we used HIV with silent mutation markers along with next generation sequencing to compare the mutation and the recombination rates of HIV directly in T lymphocytes and macrophages. We observed a more than four-fold higher recombination rate of HIV in macrophages compared to T-cells (p < 0.001) and demonstrated that this difference is not due to different reliance on C-X-C chemokine receptor type 4 (CXCR4) and C-C chemokine receptor type 5 (CCR5) co-receptors between T-cells and macrophages. We also found that the pattern of recombination across the HIV genome (hot and cold spots) remains constant between T-cells and macrophages despite a three-fold increase in the overall recombination rate. This indicates that the difference in rates is a general feature of HIV DNA synthesis during macrophage infection. In contrast to HIV recombination, we found that T-cells have a 30% higher mutation rate than macrophages (p < 0.001) and that the mutational profile is similar between these cell types. Unexpectedly, we found no association between mutation and recombination in macrophages, in contrast to T-cells. Our data highlights some of the fundamental difference of HIV recombination and mutation amongst these two major target cells of infection. Understanding these differences will provide invaluable insights toward HIV evolution and how the virus evades immune surveillance and anti-retroviral therapeutics.

Time-dependent rates of molecular evolution

For over half a century, it has been known that the rate of morphological evolution appears to vary with the time frame of measurement. Rates of microevolutionary change, measured between successive generations, were found to be far higher than rates of macroevolutionary change inferred from the fossil record. More recently, it has been suggested that rates of molecular evolution are also time dependent, with the estimated rate depending on the timescale of measurement. This followed surprising observations that estimates of mutation rates, obtained in studies of pedigrees and laboratory mutation-accumulation lines, exceeded long-term substitution rates by an order of magnitude or more. Although a range of studies have provided evidence for such a pattern, the hypothesis remains relatively contentious. Furthermore, there is ongoing discussion about the factors that can cause molecular rate estimates to be dependent on time. Here we present an overview of our current understanding of time-dependent rates. We provide a summary of the evidence for time-dependent rates in animals, bacteria and viruses. We review the various biological and methodological factors that can cause rates to be time dependent, including the effects of natural selection, calibration errors, model misspecification and other artefacts. We also describe the challenges in calibrating estimates of molecular rates, particularly on the intermediate timescales that are critical for an accurate characterization of time-dependent rates. This has important consequences for the use of molecular-clock methods to estimate timescales of recent evolutionary events.

B-lactamase-mediated resistance to antimicrobials : the relationship between genotype and phenotype

This thesis examined the ability to predict the emergence of bacteria resistant to antibiotics using genetic markers in the bacteria. Bacteria containing the genetic markers were able to become resistant to antibiotics, whereas bacteria that did not have the genetic markers remained susceptible. Existing techniques can identify the presence of resistance by looking at the characteristics of the bacteria during growth. However, having the ability to predict antibiotic resistance before it emerges could improve the preservation of currently available antibiotics and minimise treatment failure.

Adaptive aneuploidy protects against thiol peroxidase deficiency by increasing respiration via key mitochondrial proteins

Aerobic respiration is a fundamental energy-generating process; however, there is cost associated with living in an oxygen-rich environment, because partially reduced oxygen species can damage cellular components. Organisms evolved enzymes that alleviate this damage and protect the intracellular milieu, most notably thiol peroxidases, which are abundant and conserved enzymes that mediate hydrogen peroxide signaling and act as the first line of defense against oxidants in nearly all living organisms. Deletion of all eight thiol peroxidase genes in yeast (∆8 strain) is not lethal, but results in slow growth and a high mutation rate. Here we characterized mechanisms that allow yeast cells to survive under conditions of thiol peroxidase deficiency. Two independent ∆8 strains increased mitochondrial content, altered mitochondrial distribution, and became dependent on respiration for growth but they were not hypersensitive to H2O2. In addition, both strains independently acquired a second copy of chromosome XI and increased expression of genes encoded by it. Survival of ∆8 cells was dependent on mitochondrial cytochrome-c peroxidase (CCP1) and UTH1, present on chromosome XI. Coexpression of these genes in ∆8 cells led to the elimination of the extra copy of chromosome XI and improved cell growth, whereas deletion of either gene was lethal. Thus, thiol peroxidase deficiency requires dosage compensation of CCP1 and UTH1 via chromosome XI aneuploidy, wherein these proteins support hydroperoxide removal with the reducing equivalents generated by the electron transport chain. To our knowledge, this is the first evidence of adaptive aneuploidy counteracting oxidative stress.

Comparative analysis of genome maintenance genes in naked mole rat, mouse, and human

Genome maintenance (GM) is an essential defense system against aging and cancer, as both are characterized by increased genome instability. Here, we compared the copy number variation and mutation rate of 518 GM-associated genes in the naked mole rat (NMR), mouse, and human genomes. GM genes appeared to be strongly conserved, with copy number variation in only four genes. Interestingly, we found NMR to have a higher copy number of CEBPG, a regulator of DNA repair, and TINF2, a protector of telomere integrity. NMR, as well as human, was also found to have a lower rate of germline nucleotide substitution than the mouse. Together, the data suggest that the long-lived NMR, as well as human, has more robust GM than mouse and identifies new targets for the analysis of the exceptional longevity of the NMR.

Genetic alterations in microsatellite-unstable colorectal cancer

Colorectal cancer (CRC) is the third most common cancer in Finland. Of all CRC tumors, 15% display microsatellite-instability (MSI) caused by defective cellular mismatch repair. Cells displaying MSI accumulate a high number of mutations genome-wide, especially in short repeat areas, microsatellites. When targeting genes essential for cell growth or death, MSI can promote tumorigenesis. In non-coding areas, microsatellite mutations are generally considered as passenger events. Since the discovery of MSI and its linkage to cancer, more that 200 genes have been investigated for a role in MSI tumorigenesis. Although various criteria have been suggested for MSI target gene identification, the challenge has been to distinguish driver mutations from passenger mutations. This study aimed to clarify these key issues in the research field of MSI cancer. Prior to this, background mutation rate in MSI cancer has not been studied in a large-scale. We investigated the background mutation rate in MSI CRC by analyzing the spectrum of microsatellite mutations in non-coding areas. First, semenogelin I was studied for a possible role in MSI carcinogenesis. The intronic T9 repeat of semenogelin I was frequently mutated but no evidence for selection during tumorigenesis was obtained. Second, a sequencing approach was utilized to evaluate the general background mutation rate in MSI CRC. Both intronic and intergenic repeats harbored extremely high mutation rates of ≤ 87% and intergenic repeats were more unstable than the intronic repeats. As mutation rates of presumably neutral microsatellites can be high in MSI CRC in the absence of apparent selection pressure, high mutation frequency alone is not sufficient evidence for identification of driver MSI target genes. Next, an unbiased approach was designed to identify the mutatome of MSI CRC. By combining expression array data and a database search we identified novel genes possibly related to MSI CRC carcinogenesis. One of the genes was studied further. In the functional analysis this gene was observed to cause an abnormal cancer-prone cellular phenotype, possibly through altered responses to DNA damage. In our recent study, smooth muscle myosin heavy chain 11 (MYH11) was identified as a novel MSI CRC gene. Additionally, MYH11 has a well established role in acute myeloid leukemia (AML) through an oncogenic fusion protein CBFB-MYH11. We investigated further the role of MYH11 in AML by sequencing. Three novel missense variants of MYH11 were identified. None of the variants were present in the population-based control material. One of the identified variants, V71A, lies in the N-terminal SH3-like domain of MYH11 of unknown function. The other two variants, K1059E and R1792Q are located in the coil-coiled myosin rod essential for the regulation and filament formation of MYH11. The variant K1059E lies in the close proximity of the K1044N that has been functionally assessed in our earlier work of CRC and has been reported to cause total loss of MYH11 protein regulation. As the functional significance of the three novel variants examined in this work remains unknown, future studies should clarify the further role of MYH11 in AML leukaemogenesis and in other malignancies.

Genetic Mechanisms of Tumourigenesis in von Hippel-Lindau-Associated Tumours with the Emphasis on Capillary Hemangioblastoma

The von Hippel-lindau (VHL) disease is a dominantly inherited neoplastic disorder which predisposes patients to multiple tumours including capillary haemangioblastomas (CHBs), pheochromocytomas (PCCs), renal cell carcinomas (RCCs). CHBs are the most common manifestations of VHL disease, occurring sporadically or as a manifestation of VHL disease. Inactivation of the VHL gene at 3p25-26 is believed to cause both familial and sporadic VHL-associated tumours and germ-line mutation of the VHL gene have been detected in 100% of the CHBs studied. However, a limited number of sporadic CHBs, PCCs display VHL inactivation. Other molecular alterations involved in tumourigenesis of sporadic CHBs, PCCs remain largely unknown. The purpose of the present work was to search for genetic alterations, or other mechanisms of inactivation, in addition to the VHL gene, that may be important in the development of VHL-associated tumours. Though less satisfactory than cure, prevention and early detection are the most promising and feasible means reducing cancer morbidity and mortality. This work is based on the view that increasing knowledge about the molecular events underlying tumour development will eventually aid in early detection and lead to improved treatment. We evaluated a large set of VHL-associated patients, searched for a clinical and radiologic signs of the disease. We succesfully performed a germ-line mutation analysis and characterised three patient groups, VHL, suspect VHL and sporadic, a germ-line mutation analysis revealed a 50% mutation rate only in the VHL groups, no sporadic or suspect cases displayed any mutation. We also utilized comparative genomic hybridization (CGH) to screen for DNA copy number changes in both sporadic and VHL-associated CHB. Our analysis revealed (27%) DNA copy number losses. The most common finding was loss of chromosomal arm 6q, seen in (23%) cases, No differences were noted between VHL-associated and sporadic tumours. Furthermore a loss of heterozygosity (LOH) study on chromosome 3p and 6q was done with the purpose to determine allele losses not observable by CGH, and to uncover the location of putative tumour suppressor genes important in CHB and PCC tumourigenesis. We identified loss of chromosome 6q and a minimal deleted area at 6q23-24 in CHBs. We also showed LOH at 6q23-24 in PCCs and identified the ZAC1 (6q24-25) as a candidate gene, ZAC1 is a maternally imprinted tumour suppressor gene with anti proliferative properties. To study further the role of ZAC inactivation in CHBs, we investigated LOH, promoter hypermethylation and expression status of the ZAC1 gene in mainly sporadic CHBs. Our LOH analysis revealed that the majority of the tumours with allele loss. The gene promoter methylation analysis similarly detected predominance of the methylated ZAC sequence in almost all tumours. Immunohistochemistry exhibited a strongly reduced expression of ZAC in stromal cells of all CHBs studied. Our current results indicate that the absence of the unmethylated, ZAC1 promoter sequence was highly concurrent with LOH for the ZAC1 region or 6q loss. This observation together with lack of ZAC expression, points to preferential loss of the non imprinted, expressed ZAC allele in CHB, in summary, our series of studies reveal a new chromosomal region 6q, emphasizes the importance of ZAC1 gene in the development of CHB and PCC, particularly in non-VHL associated cases.

Effects of environmental variation on ecological and evolutionary dynamics

Environmental variation is a fact of life for all the species on earth: for any population of any particular species, the local environmental conditions are liable to vary in both time and space. In today's world, anthropogenic activity is causing habitat loss and fragmentation for many species, which may profoundly alter the characteristics of environmental variation in remaining habitat. Previous research indicates that, as habitat is lost, the spatial configuration of remaining habitat will increasingly affect the dynamics by which populations are governed. Through the use of mathematical models, this thesis asks how environmental variation interacts with species properties to influence population dynamics, local adaptation, and dispersal evolution. More specifically, we couple continuous-time continuous-space stochastic population dynamic models to landscape models. We manipulate environmental variation via parameters such as mean patch size, patch density, and patch longevity. Among other findings, we show that a mixture of high and low quality habitat is commonly better for a population than uniformly mediocre habitat. This conclusion is justified by purely ecological arguments, yet the positive effects of landscape heterogeneity may be enhanced further by local adaptation, and by the evolution of short-ranged dispersal. The predicted evolutionary responses to environmental variation are complex, however, since they involve numerous conflicting factors. We discuss why the species that have high levels of local adaptation within their ranges may not be the same species that benefit from local adaptation during range expansion. We show how habitat loss can lead to either increased or decreased selection for dispersal depending on the type of habitat and the manner in which it is lost. To study the models, we develop a recent analytical method, Perturbation expansion, to enable the incorporation of environmental variation. Within this context, we use two methods to address evolutionary dynamics: Adaptive dynamics, which assumes mutations occur infrequently so that the ecological and evolutionary timescales can be separated, and via Genotype distributions, which assume mutations are more frequent. The two approaches generally lead to similar predictions yet, exceptionally, we show how the evolutionary response of dispersal behaviour to habitat turnover may qualitatively depend on the mutation rate.

A self-adaptive migration model genetic algorithm for data mining applications

Data mining involves nontrivial process of extracting knowledge or patterns from large databases. Genetic Algorithms are efficient and robust searching and optimization methods that are used in data mining. In this paper we propose a Self-Adaptive Migration Model GA (SAMGA), where parameters of population size, the number of points of crossover and mutation rate for each population are adaptively fixed. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions and a set of actual classification datamining problems. Michigan style of classifier was used to build the classifier and the system was tested with machine learning databases of Pima Indian Diabetes database, Wisconsin Breast Cancer database and few others. The performance of our algorithm is better than others.

A Dynamic Migration Model for Self-adaptive Genetic Algorithms

In this paper, we propose a self Adaptive Migration Model for Genetic Algorithms, where parameters of population size, the number of points of crossover and mutation rate for each population are fixed adaptively. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions, when compared with Island model GA(IGA) and Simple GA(SGA).

A Dynamic Migration Model for self-adaptive Genetic Algorithms

In this paper, we propose a self Adaptive Migration Model for Genetic Algorithms, where parameters of population size, the number of points of crossover and mutation rate for each population are fixed adaptively. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions, when compared with Island model GA(IGA) and Simple GA(SGA).

Taking Multiple Infections of Cells and Recombination into Account Leads to Small Within-Host Effective-Population-Size Estimates of HIV-1

Whether HIV-1 evolution in infected individuals is dominated by deterministic or stochastic effects remains unclear because current estimates of the effective population size of HIV-1 in vivo, N-e, are widely varying. Models assuming HIV-1 evolution to be neutral estimate N-e similar to 10(2)-10(4), smaller than the inverse mutation rate of HIV-1 (similar to 10(5)), implying the predominance of stochastic forces. In contrast, a model that includes selection estimates N-e>10(5), suggesting that deterministic forces would hold sway. The consequent uncertainty in the nature of HIV-1 evolution compromises our ability to describe disease progression and outcomes of therapy. We perform detailed bit-string simulations of viral evolution that consider large genome lengths and incorporate the key evolutionary processes underlying the genomic diversification of HIV-1 in infected individuals, namely, mutation, multiple infections of cells, recombination, selection, and epistatic interactions between multiple loci. Our simulations describe quantitatively the evolution of HIV-1 diversity and divergence in patients. From comparisons of our simulations with patient data, we estimate N-e similar to 10(3)-10(4), implying predominantly stochastic evolution. Interestingly, we find that N-e and the viral generation time are correlated with the disease progression time, presenting a route to a priori prediction of disease progression in patients. Further, we show that the previous estimate of N-e>10(5) reduces as the frequencies of multiple infections of cells and recombination assumed increase. Our simulations with N-e similar to 10(3)-10(4) may be employed to estimate markers of disease progression and outcomes of therapy that depend on the evolution of viral diversity and divergence.

Stochastic Simulations Suggest that HIV-1 Survives Close to Its Error Threshold

The use of mutagenic drugs to drive HIV-1 past its error threshold presents a novel intervention strategy, as suggested by the quasispecies theory, that may be less susceptible to failure via viral mutation-induced emergence of drug resistance than current strategies. The error threshold of HIV-1, mu(c), however, is not known. Application of the quasispecies theory to determine mu(c) poses significant challenges: Whereas the quasispecies theory considers the asexual reproduction of an infinitely large population of haploid individuals, HIV-1 is diploid, undergoes recombination, and is estimated to have a small effective population size in vivo. We performed population genetics-based stochastic simulations of the within-host evolution of HIV-1 and estimated the structure of the HIV-1 quasispecies and mu(c). We found that with small mutation rates, the quasispecies was dominated by genomes with few mutations. Upon increasing the mutation rate, a sharp error catastrophe occurred where the quasispecies became delocalized in sequence space. Using parameter values that quantitatively captured data of viral diversification in HIV-1 patients, we estimated mu(c) to be 7 x 10(-5) -1 x 10(-4) substitutions/site/replication, similar to 2-6 fold higher than the natural mutation rate of HIV-1, suggesting that HIV-1 survives close to its error threshold and may be readily susceptible to mutagenic drugs. The latter estimate was weakly dependent on the within-host effective population size of HIV-1. With large population sizes and in the absence of recombination, our simulations converged to the quasispecies theory, bridging the gap between quasispecies theory and population genetics-based approaches to describing HIV-1 evolution. Further, mu(c) increased with the recombination rate, rendering HIV-1 less susceptible to error catastrophe, thus elucidating an added benefit of recombination to HIV-1. Our estimate of mu(c) may serve as a quantitative guideline for the use of mutagenic drugs against HIV-1.

Scaling law characterizing the dynamics of the transition of HIV-1 to error catastrophe

Increasing the mutation rate, mu, of viruses above a threshold, mu(c), has been predicted to trigger a catastrophic loss of viral genetic information and is being explored as a novel intervention strategy. Here, we examine the dynamics of this transition using stochastic simulations mimicking within-host HIV-1 evolution. We find a scaling law governing the characteristic time of the transition: tau approximate to 0.6/(mu - mu(c)). The law is robust to variations in underlying evolutionary forces and presents guidelines for treatment of HIV-1 infection with mutagens. We estimate that many years of treatment would be required before HIV-1 can suffer an error catastrophe.