The mutation rate and cancer

DNA Vaccines: Their Formulations, Engineering and Delivery

The concept of DNA vaccination was introduced in the early 1990s. Since then, advancements in the augmentation of the immunogenicity of DNA vaccines have brought this technology to the market, especially in veterinary medicine, to prevent many diseases. Along with the successful COVID mRNA vaccines, the first DNA vaccine for human use, the Indian ZyCovD vaccine against SARS-CoV-2, was approved in 2021. In the current review, we first give an overview of the DNA vaccine focusing on the science, including adjuvants and delivery methods. We then cover some of the emerging science in the field of DNA vaccines, notably efforts to optimize delivery systems, better engineer delivery apparatuses, identify optimal delivery sites, personalize cancer immunotherapy through DNA vaccination, enhance adjuvant science through gene adjuvants, enhance off-target and heritable immunity through epigenetic modification, and predict epitopes with bioinformatic approaches. We also discuss the major limitations of DNA vaccines and we aim to address many theoretical concerns.

Molecular cartography uncovers evolutionary and microenvironmental dynamics in sporadic colorectal tumors

Colorectal cancer exhibits dynamic cellular and genetic heterogeneity during progression from precursor lesions toward malignancy. Analysis of spatial multi-omic data from 31 human colorectal specimens enabled phylogeographic mapping of tumor evolution that revealed individualized progression trajectories and accompanying microenvironmental and clonal alterations. Phylogeographic mapping ordered genetic events, classified tumors by their evolutionary dynamics, and placed clonal regions along global pseudotemporal progression trajectories encompassing the chromosomal instability (CIN+) and hypermutated (HM) pathways. Integrated single-cell and spatial transcriptomic data revealed recurring epithelial programs and infiltrating immune states along progression pseudotime. We discovered an immune exclusion signature (IEX), consisting of extracellular matrix regulators DDR1, TGFBI, PAK4, and DPEP1, that charts with CIN+ tumor progression, is associated with reduced cytotoxic cell infiltration, and shows prognostic value in independent cohorts. This spatial multi-omic atlas provides insights into colorectal tumor-microenvironment co-evolution, serving as a resource for stratification and targeted treatments.

NMR structure verifies the eponymous zinc finger domain of transcription factor ZNF750

ZNF750 is a nuclear transcription factor that activates skin differentiation and has tumor suppressor roles in several cancers. Unusually, ZNF750 has only a single zinc-finger (ZNF) domain, Z*, with an amino acid sequence that differs markedly from the CCHH family consensus. Because of its sequence differences Z* is classified as degenerate, presumed to have lost the ability to bind the zinc ion required for folding. AlphaFold predicts an irregular structure for Z* with low confidence. Low confidence predictions are often inferred to be intrinsically disordered regions of proteins, which would be the case if Z* did not bind Zn2+. We use NMR and CD spectroscopy to show that a 25-51 segment of ZNF750 corresponding to the Z* domain folds into a well-defined antiparallel ββα tertiary structure with a pM dissociation constant for Zn2+ and a thermal stability >80 °C. Of three alternative Zn2+ ligand sets, Z* uses a CCHC rather than the expected CCHH ligating motif. The switch in the last ligand maintains the folding topology and hydrophobic core of the classical ZNF motif. CCHC ZNFs are typically associated with protein-protein interactions, raising the possibility that ZNF750 interacts with DNA through other proteins rather than directly. The structure of Z* provides context for understanding the function of the domain and its cancer-associated mutations. We expect other ZNFs currently classified as degenerate could be CCHC-type structures like Z*.

WITHDRAWN: NMR structure verifies the eponymous zinc finger domain of transcription factor ZNF750

This article was initially published in the Journal of Structural Biology, instead of the Journal of Structural Biology: X, due to a publisher error. We regret the inconvenience. The link to the article published in Journal of Structural Biology: X is presented below: https://www.sciencedirect.com/science/article/pii/S2590152423000090. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/policies/article-withdrawal.

KIF11 As a Potential Pan-Cancer Immunological Biomarker Encompassing the Disease Staging, Prognoses, Tumor Microenvironment, and Therapeutic Responses

KIF11 is one of the 45 family members of kinesin superfamily proteins that functions as a motor protein in mitosis. Emerging evidence revealed that KIF11 plays pivotal roles in cancer initiation, development, and progression. However, the prognostic, oncological, and immunological values of KIF11 have not been comprehensively explored in pan-cancer. In present study, we comprehensively interrogated the role of KIF11 in tumor progression, tumor stemness, genomic heterogeneity, tumor immune infiltration, immune evasion, therapy response, and prognosis of cohorts from various cancer types. In general, KIF11 was significantly upregulated in tumors compared with paired normal tissues. KIF11 showed strong relationships with pathological stage, prognosis, tumor stemness, genomic heterogeneity, neoantigens, ESTIMATE, immune checkpoint, and drug sensitivity. The methylation level of KIF11 decreased in most cancers and was correlated with the survival probability in different human cancers. The expression of KIF11 was diverse in different molecular and immune subtypes and remarkably correlated with immune cell infiltration in the tumor microenvironment. Comparative study revealed that KIF11 was a powerful biomarker and associated with immune, targeted, and chemotherapeutic outcomes in various cancers. In addition, KIF11 interaction and coexpression networks mainly participated in the regulation of cell cycle, cell division, p53 signaling pathway, DNA repair and recombination, chromatin organization, antigen processing and presentation, and drug resistance. Our pan-cancer analysis provides a comprehensive understanding of the functions of KIF11 in oncogenesis, progression, and therapy in different cancers. KIF11 may serve as a potential prognostic and immunological pan-cancer biomarker. Moreover, KIF11 could be a novel target for tumor immunotherapy.

Disrupting autorepression circuitry generates "open-loop lethality" to yield escape-resistant antiviral agents

Across biological scales, gene-regulatory networks employ autorepression (negative feedback) to maintain homeostasis and minimize failure from aberrant expression. Here, we present a proof of concept that disrupting transcriptional negative feedback dysregulates viral gene expression to therapeutically inhibit replication and confers a high evolutionary barrier to resistance. We find that nucleic-acid decoys mimicking cis-regulatory sites act as "feedback disruptors," break homeostasis, and increase viral transcription factors to cytotoxic levels (termed "open-loop lethality"). Feedback disruptors against herpesviruses reduced viral replication >2-logs without activating innate immunity, showed sub-nM IC50, synergized with standard-of-care antivirals, and inhibited virus replication in mice. In contrast to approved antivirals where resistance rapidly emerged, no feedback-disruptor escape mutants evolved in long-term cultures. For SARS-CoV-2, disruption of a putative feedback circuit also generated open-loop lethality, reducing viral titers by >1-log. These results demonstrate that generating open-loop lethality, via negative-feedback disruption, may yield a class of antimicrobials with a high genetic barrier to resistance.

Circulating Tumor Cells in Breast Cancer Patients: A Balancing Act between Stemness, EMT Features and DNA Damage Responses

Circulating tumor cells (CTCs) traverse vessels to travel from the primary tumor to distant organs where they adhere, transmigrate, and seed metastases. To cope with these challenges, CTCs have reached maximal flexibility to change their differentiation status, morphology, migratory capacity, and their responses to genotoxic stress caused by metabolic changes, hormones, the inflammatory environment, or cytostatic treatment. A significant percentage of breast cancer cells are defective in homologous recombination repair and other mechanisms that protect the integrity of the replication fork. To prevent cell death caused by broken forks, alternative, mutagenic repair, and bypass pathways are engaged but these increase genomic instability. CTCs, arising from such breast tumors, are endowed with an even larger toolbox of escape mechanisms that can be switched on and off at different stages during their journey according to the stress stimulus. Accumulating evidence suggests that DNA damage responses, DNA repair, and replication are integral parts of a regulatory network orchestrating the plasticity of stemness features and transitions between epithelial and mesenchymal states in CTCs. This review summarizes the published information on these regulatory circuits of relevance for the design of biomarkers reflecting CTC functions in real-time to monitor therapeutic responses and detect evolving chemoresistance mechanisms.

Inverse problem approaches for mutation laws in heterogeneous tumours with local and nonlocal dynamics

Cancer cell mutations occur when cells undergo multiple cell divisions, and these mutations can be spontaneous or environmentally-induced. The mechanisms that promote and sustain these mutations are still not fully understood. This study deals with the identification (or reconstruction) of the usually unknown cancer cell mutation law, which lead to the transformation of a primary tumour cell population into a secondary, more aggressive cell population. We focus on local and nonlocal mathematical models for cell dynamics and movement, and identify these mutation laws from macroscopic tumour snapshot data collected at some later stage in the tumour evolution. In a local cancer invasion model, we first reconstruct the mutation law when we assume that the mutations depend only on the surrounding cancer cells (i.e., the ECM plays no role in mutations). Second, we assume that the mutations depend on the ECM only, and we reconstruct the mutation law in this case. Third, we reconstruct the mutation when we assume that there is no prior knowledge about the mutations. Finally, for the nonlocal cancer invasion model, we reconstruct the mutation law that depends on the cancer cells and on the ECM. For these numerical reconstructions, our approximations are based on the finite difference method combined with the finite elements method. As the inverse problem is ill-posed, we use the Tikhonov regularisation technique in order to regularise the solution. Stability of the solution is examined by adding additive noise into the measurements.

The modification of cancer risk by chemicals

Advances in understanding of the process of carcinogenesis have undermined the concept of chemicals being classifiable as either carcinogens or non-carcinogens. Elements of carcinogenesis are happening all the time and a proportion of cancers cannot be prevented, the 'bad luck hypothesis'. Although the proportion that can be prevented is disputed, it is important to continue efforts to reduce it. Factors that increase cancer risk have been grouped into intrinsic factors that cannot be modified, and endogenous and exogenous factors that can be modified. Chemicals are exogenous factors that can be modified by risk management measures. Chemicals can alter three key rates that influence cancer risk: cell division, mutation rate per cell division, transformation rate of mutated cells to cancer. These rates can form the basis of a dynamic cancer risk model, a generic, adverse outcome pathway for carcinogenesis where chemicals are considered for their ability to modify cancer risk rather than simply whether they are classed as carcinogens or non-carcinogens. This allows the development of different strategies for assessing cancer risk that use a range of data sources and are not dependent on using long-term bioassays and epidemiology to identify carcinogens. The framework will also allow difficult questions such as the effect of less than lifetime exposures and the effect of exposures to more than one chemical to be addressed.

Evolution of cancer stem cell lineage involving feedback regulation

Tumor emergence and progression is a complex phenomenon that assumes special molecular and cellular interactions. The hierarchical structuring and communication via feedback signaling of different cell types, which are categorized as the stem, progenitor, and differentiated cells in dependence of their maturity level, plays an important role. Under healthy conditions, these cells build a dynamical system that is responsible for facilitating the homeostatic regulation of the tissue. Generally, in this hierarchical setting, stem and progenitor cells are yet likely to undergo a mutation, when a cell divides into two daughter cells. This may lead to the development of abnormal characteristics, i.e. mutation in the cell, yielding an unrestrained number of cells. Therefore, the regulation of a stem cell’s proliferation and differentiation rate is crucial for maintaining the balance in the overall cell population. In this paper, a maturity based mathematical model with feedback regulation is formulated for healthy and mutated cell lineages. It is given in the form of coupled ordinary and partial differential equations. The focus is laid on the dynamical effects resulting from acquiring a mutation in the hierarchical structure of stem, progenitor and fully differentiated cells. Additionally, the effects of nonlinear feedback regulation from mature cells into both stem and progenitor cell populations have been inspected. The steady-state solutions of the model are derived analytically. Numerical simulations and results based on a finite volume scheme underpin various expected behavioral patterns of the homeostatic regulation and cancer evolution. For instance, it has been found that the mutated cells can experience significant growth even with a single somatic mutation, but under homeostatic regulation acquire a steady-state and thus, ensuing healthy cell population to either a steady-state or a lower cell concentration. Furthermore, the model behavior has been validated with different experimentally measured tumor values from the literature.

The Prognostic Impact of HER2 Genetic and Protein Expression in Pancreatic Carcinoma-HER2 Protein and Gene in Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is a lethal and clinically heterogeneous disease with a limited benefit from human epidermal growth factor receptor 2 (HER2)-targeted therapy. Recently, some studies have addressed the antitumoral effect of novel anti-HER2 drugs in HER2 low-expressing tumors. However, there have been few studies on the significance of low HER2 expression and genetic heterogeneity in PDAC. Using immunohistochemistry and dual-color silver-enhanced in situ hybridization based on the Trastuzumab for a gastric cancer scoring scheme, we evaluated HER2 protein expression, gene amplification, and genetic heterogeneity in three groups (HER2-neg, HER2-low, HER2-pos) of 55 patients. Among the 55 cases, 41.8% (23/55) showed HER2 expression of any intensity. HER2 amplification independent of HER2 expression was 25.5% (14/55). Patients in both these groups had a shorter overall survival than did patients in the HER2-neg group. HER2 genetic heterogeneity was identified in 37 (70.9%) of the 55 cases, mainly in HER2-neg and HER2-low groups. HER2 genetic heterogeneity significantly correlated with worse survival in the HER2-low and HER2-neg groups of PDAC. These findings support the hypothesis that low-level HER2 expression and heterogeneity have significant clinical implications in PDAC. HER2 heterogeneity might indicate the best strategies of combination therapies to prevent the development of subdominant clones with resistance potential.

Antibodies Targeting the Transferrin Receptor 1 (TfR1) as Direct Anti-cancer Agents

The transferrin receptor 1 (TfR1), also known as cluster of differentiation 71 (CD71), is a type II transmembrane glycoprotein that binds transferrin (Tf) and performs a critical role in cellular iron uptake through the interaction with iron-bound Tf. Iron is required for multiple cellular processes and is essential for DNA synthesis and, thus, cellular proliferation. Due to its central role in cancer cell pathology, malignant cells often overexpress TfR1 and this increased expression can be associated with poor prognosis in different types of cancer. The elevated levels of TfR1 expression on malignant cells, together with its extracellular accessibility, ability to internalize, and central role in cancer cell pathology make this receptor an attractive target for antibody-mediated therapy. The TfR1 can be targeted by antibodies for cancer therapy in two distinct ways: (1) indirectly through the use of antibodies conjugated to anti-cancer agents that are internalized by receptor-mediated endocytosis or (2) directly through the use of antibodies that disrupt the function of the receptor and/or induce Fc effector functions, such as antibody-dependent cell-mediated cytotoxicity (ADCC), antibody-dependent cell-mediated phagocytosis (ADCP), or complement-dependent cytotoxicity (CDC). Although TfR1 has been used extensively as a target for antibody-mediated cancer therapy over the years, interest continues to increase for both targeting the receptor for delivery purposes and for its use as direct anti-cancer agents. This review focuses on the developments in the use of antibodies targeting TfR1 as direct anti-tumor agents.

Rate volatility and asymmetric segregation diversify mutation burden in cells with mutator alleles

Mutations that compromise mismatch repair (MMR) or DNA polymerase ε or δ exonuclease domains produce mutator phenotypes capable of fueling cancer evolution. Here, we investigate how combined defects in these pathways expands genetic heterogeneity in cells of the budding yeast, Saccharomyces cerevisiae, using a single-cell resolution approach that tallies all mutations arising from individual divisions. The distribution of replication errors present in mother cells after the initial S-phase was broader than expected for a single uniform mutation rate across all cell divisions, consistent with volatility of the mutator phenotype. The number of mismatches that then segregated to the mother and daughter cells co-varied, suggesting that each division is governed by a different underlying genome-wide mutation rate. The distribution of mutations that individual cells inherit after the second S-phase is further broadened by the sequential actions of semiconservative replication and mitotic segregation of chromosomes. Modeling suggests that this asymmetric segregation may diversify mutation burden in mutator-driven tumors.

Dowsett et al use a single-cell resolution approach to analyse the distribution of mutations across several divisions in yeast diploid strains mutated in mismatch repair and polymerase delta proofreading. They find that the underlying mutation rate varies from one division to another, and that new mutations segregate unequally between sister chromatids at each division, expanding genetic heterogeneity in the population.

FastClone is a probabilistic tool for deconvoluting tumor heterogeneity in bulk-sequencing samples

Dissecting tumor heterogeneity is a key to understanding the complex mechanisms underlying drug resistance in cancers. The rich literature of pioneering studies on tumor heterogeneity analysis spurred a recent community-wide benchmark study that compares diverse modeling algorithms. Here we present FastClone, a top-performing algorithm in accuracy in this benchmark. FastClone improves over existing methods by allowing the deconvolution of subclones that have independent copy number variation events within the same chromosome regions. We characterize the behavior of FastClone in identifying subclones using stage III colon cancer primary tumor samples as well as simulated data. It achieves approximately 100-fold acceleration in computation for both simulated and patient data. The efficacy of FastClone will allow its application to large-scale data and clinical data, and facilitate personalized medicine in cancers.

Cellular effects and clinical implications of SLC2A3 copy number variation

SLC2A3 encodes the predominantly neuronal glucose transporter 3 (GLUT3), which facilitates diffusion of glucose across plasma membranes. The human brain depends on a steady glucose supply for ATP generation, which consequently fuels critical biochemical processes, such as axonal transport and neurotransmitter release. Besides its role in the central nervous system, GLUT3 is also expressed in nonneural organs, such as the heart and white blood cells, where it is equally involved in energy metabolism. In cancer cells, GLUT3 overexpression contributes to the Warburg effect by answering the cell's increased glycolytic demands. The SLC2A3 gene locus at chromosome 12p13.31 is unstable and prone to non-allelic homologous recombination events, generating multiple copy number variants (CNVs) of SLC2A3 which account for alterations in SLC2A3 expression. Recent associations of SLC2A3 CNVs with different clinical phenotypes warrant investigation of the potential influence of these structural variants on pathomechanisms of neuropsychiatric, cardiovascular, and immune diseases. In this review, we accumulate and discuss the evidence how SLC2A3 gene dosage may exert diverse protective or detrimental effects depending on the pathological condition. Cellular states which lead to increased energetic demand, such as organ development, proliferation, and cellular degeneration, appear particularly susceptible to alterations in SLC2A3 copy number. We conclude that better understanding of the impact of SLC2A3 variation on disease etiology may potentially provide novel therapeutic approaches specifically targeting this GLUT.

Association of Uveal Melanoma Metastatic Rate With Stochastic Mutation Rate and Type of Mutation

Importance

It is necessary to understand the mechanisms of metastasis of uveal melanoma to advise patients and develop treatments for this tumor.

Objective

To examine the stochastic properties of primary uveal melanoma including the mutation rate as a function of tumor size and metastatic rate relative to the type of mutation.

Design, Setting, and Participants

We computed the mutation rate in different sized uveal melanomas using previously published large data sets. Tumor volume was estimated using the spherical cap method. We also calculated the metastatic rate using an updated data set of patients with uveal melanoma with known mutations in BAP1, SF3B1, and EIF1AX provided by the Rotterdam Ocular Melanoma Study Group. Data were analyzed from 2 studies, one taking place from August 25, 1970, to August 27, 2008, and the other taking place between 1993 and 2013. Data were analyzed between 2016 and 2017.

Main Outcomes and Measures

Mutation rates and metastic rates.

Results

Based on the 5-year metastatic rates, mutation rates ranged from 1.09 × 10-8 to 7.86 × 10-7 per cell division, using our calculation algorithm. A higher mutation rate was found for tumors with smaller thicknesses. EIF1AX mutations were not exclusive of other mutations because 2 cases with EIF1AX mutations and metastasis also had BAP1 mutations. None of the tumors with only an EIF1AX mutation metastasized. After plotting the yearly metastatic rate vs time after treatment, we observed a small peak at 1 year and a large peak at 3.5 years after treatment for BAP1 mutations, with peaks between 2 and 3 years and at 7 years for SF3B1 mutations.

Conclusions and Relevance

We observed a higher mutation rate for smaller tumors, which may be explained by a greater number of cell divisions occurring during the expansion phase of smaller uveal melanomas. Regarding time to clinically detected metastases, the first 2 peaks appear to be associated with BAP1-mutated tumors and the late peak to SF3B1-mutated tumors.

Natural killer cells and cancer therapy, what we know and where we are going

Natural killer (NK) cells are among the significant components of innate immune system and they have come to the first line of defense against tumor cells developing inside the body. CD56lo/CD16+ NK cells are highly cytotoxic and CD56hi NK cells can produce cytokines and perform a regulatory function. Specific features of NK cells have made them a unique choice for cancer immunotherapy. Simple interventions like cytokine-injection to boost the internal NK cells were the first trials to target these cells. Nowadays, many other types of intervention are under investigation, such as adoptive NK cell immunotherapy. In this paper, we will discuss the biology and function of NK cells in cancer immunosurveillance and therapeutic approaches against cancer via using NK cells.

Proteogenomic Analysis of Protein Sequence Alterations in Breast Cancer Cells

Cancer evolves as a result of an accumulation of mutations and chromosomal aberrations. Developments in sequencing technologies have enabled the discovery and cataloguing of millions of such mutations. The identification of protein-level alterations, typically by using reversed-phase protein arrays or mass spectrometry, has lagged, however, behind gene and transcript-level observations. In this study, we report the use of mass spectrometry for detecting the presence of mutations-missense, indels and frame shifts-in MCF7 and SKBR3 breast cancer, and non-tumorigenic MCF10A cells. The mutations were identified by expanding the database search process of raw mass spectrometry files by including an in-house built database of mutated peptides (XMAn-v1) that complemented a minimally redundant, canonical database of Homo sapiens proteins. The work resulted in the identification of nearly 300 mutated peptide sequences, of which ~50 were characterized by quality tandem mass spectra. We describe the criteria that were used to select the mutated peptide sequences, evaluate the parameters that characterized these peptides, and assess the artifacts that could have led to false peptide identifications. Further, we discuss the functional domains and biological processes that may be impacted by the observed peptide alterations, and how protein-level detection can support the efforts of identifying cancer driving mutations and genes. Mass spectrometry data are available via ProteomeXchange with identifier PXD014458.

Mutational signatures and mutable motifs in cancer genomes

Cancer is a genetic disorder, meaning that a plethora of different mutations, whether somatic or germ line, underlie the etiology of the 'Emperor of Maladies'. Point mutations, chromosomal rearrangements and copy number changes, whether they have occurred spontaneously in predisposed individuals or have been induced by intrinsic or extrinsic (environmental) mutagens, lead to the activation of oncogenes and inactivation of tumor suppressor genes, thereby promoting malignancy. This scenario has now been recognized and experimentally confirmed in a wide range of different contexts. Over the past decade, a surge in available sequencing technologies has allowed the sequencing of whole genomes from liquid malignancies and solid tumors belonging to different types and stages of cancer, giving birth to the new field of cancer genomics. One of the most striking discoveries has been that cancer genomes are highly enriched with mutations of specific kinds. It has been suggested that these mutations can be classified into 'families' based on their mutational signatures. A mutational signature may be regarded as a type of base substitution (e.g. C:G to T:A) within a particular context of neighboring nucleotide sequence (the bases upstream and/or downstream of the mutation). These mutational signatures, supplemented by mutable motifs (a wider mutational context), promise to help us to understand the nature of the mutational processes that operate during tumor evolution because they represent the footprints of interactions between DNA, mutagens and the enzymes of the repair/replication/modification pathways.

Modeling dynamics for oncogenesis encompassing mutations and genetic instability

Tumorigenesis has been described as a multistep process, where each step is associated with a genetic alteration, in the direction to progressively transform a normal cell and its descendants into a malignant tumour. Into this work, we propose a mathematical model for cancer onset and development, considering three populations: normal, premalignant and cancer cells. The model takes into account three hallmarks of cancer: self-sufficiency on growth signals, insensibility to anti-growth signals and evading apoptosis. By using a nonlinear expression to describe the mutation from premalignant to cancer cells, the model includes genetic instability as an enabling characteristic of tumour progression. Mathematical analysis was performed in detail. Results indicate that apoptosis and tissue repair system are the first barriers against tumour progression. One of these mechanisms must be corrupted for cancer to develop from a single mutant cell. The results also show that the presence of aggressive cancer cells opens way to survival of less adapted premalignant cells. Numerical simulations were performed with parameter values based on experimental data of breast cancer, and the necessary time taken for cancer to reach a detectable size from a single mutant cell was estimated with respect to some parameters. We find that the rates of apoptosis and mutations have a large influence on the pace of tumour progression and on the time it takes to become clinically detectable.

Cooperation between non-essential DNA polymerases contributes to genome stability in Saccharomyces cerevisiae

DNA polymerases influence genome stability through their involvement in DNA replication, response to DNA damage, and DNA repair processes. Saccharomyces cerevisiae possess four non-essential DNA polymerases, Pol λ, Pol η, Pol ζ, and Rev1, which have varying roles in genome stability. In order to assess the contribution of the non-essential DNA polymerases in genome stability, we analyzed the pol4Δ rev1Δ rev3Δ rad30Δ quadruple mutant in microhomology mediated repair, due to recent studies linking some of these DNA polymerases to this repair pathway. Our results suggest that the length and quality of microhomology influence both the overall efficiency of repair and the involvement of DNA polymerases. Furthermore, the non-essential DNA polymerases demonstrate overlapping and redundant functions when repairing double-strand breaks using short microhomologies containing mismatches. Then, we examined genome-wide mutation accumulation in the pol4Δ rev1Δ rev3Δ rad30Δ quadruple mutant compared to wild type cells. We found a significant decrease in the overall rate of mutation accumulation in the quadruple mutant cells compared to wildtype, but an increase in frameshift mutations and a shift towards transversion base-substitution with a preference for G:C to T:A or C:G. Thus, the non-essential DNA polymerases have an impact on the nature of the mutational spectrum. The sequence and functional homology shared between human and S. cerevisiae non-essential DNA polymerases suggest these DNA polymerases may have a similar role in human cells.

Competition and niche construction in a model of cancer metastasis

Niche construction theory states that not only does the environment act on populations to generate Darwinian selection, but organisms reciprocally modify the environment and the sources of natural selection. Cancer cells participate in niche construction as they alter their microenvironments and create pre-metastatic niches; in fact, metastasis is a product of niche construction. Here, we present a mathematical model of niche construction and metastasis. Our model contains producers, which pay a cost to contribute to niche construction that benefits all tumor cells, and cheaters, which reap the benefits without paying the cost. We derive expressions for the conditions necessary for metastasis, showing that the establishment of a mutant lineage that promotes metastasis depends on niche construction specificity and strength of interclonal competition. We identify a tension between the arrival and invasion of metastasis-promoting mutants, where tumors composed only of cheaters remain small but are susceptible to invasion whereas larger tumors containing producers may be unable to facilitate metastasis depending on the level of niche construction specificity. Our results indicate that even if metastatic subclones arise through mutation, metastasis may be hindered by interclonal competition, providing a potential explanation for recent surprising findings that most metastases are derived from early mutants in primary tumors.

A Simple 3-Parameter Model for Cancer Incidences

We propose a simple 3-parameter model that provides very good fits for incidence curves of 18 common solid cancers even when variations due to different locations, races, or periods are taken into account. From a data perspective, we use model selection (Akaike information criterion) to show that this model, which is based on the Weibull distribution, outperforms other simple models like the Gamma distribution. From a modeling perspective, the Weibull distribution can be justified as modeling the accumulation of driver events, which establishes a link to stem cell division based cancer development models and a connection to a recursion formula for intrinsic cancer risk published by Wu et al. For the recursion formula a closed form solution is given, which will help to simplify future analyses. Additionally, we perform a sensitivity analysis for the parameters, showing that two of the three parameters can vary over several orders of magnitude. However, the shape parameter of the Weibull distribution, which corresponds to the number of driver mutations required for cancer onset, can be robustly estimated from epidemiological data.

Mathematical modelling as a proof of concept for MPNs as a human inflammation model for cancer development

The chronic Philadelphia-negative myeloproliferative neoplasms (MPNs) are acquired stem cell neoplasms which ultimately may transform to acute myelogenous leukemia. Most recently, chronic inflammation has been described as an important factor for the development and progression of MPNs in the biological continuum from early cancer stage to the advanced myelofibrosis stage, the MPNs being described as "A Human Inflammation Model for Cancer Development". This novel concept has been built upon clinical, experimental, genomic, immunological and not least epidemiological studies. Only a few studies have described the development of MPNs by mathematical models, and none have addressed the role of inflammation for clonal evolution and disease progression. Herein, we aim at using mathematical modelling to substantiate the concept of chronic inflammation as an important trigger and driver of MPNs.The basics of the model describe the proliferation from stem cells to mature cells including mutations of healthy stem cells to become malignant stem cells. We include a simple inflammatory coupling coping with cell death and affecting the basic model beneath. First, we describe the system without feedbacks or regulatory interactions. Next, we introduce inflammatory feedback into the system. Finally, we include other feedbacks and regulatory interactions forming the inflammatory-MPN model. Using mathematical modeling, we add further proof to the concept that chronic inflammation may be both a trigger of clonal evolution and an important driving force for MPN disease progression. Our findings support intervention at the earliest stage of cancer development to target the malignant clone and dampen concomitant inflammation.

The Damaging Effect of Passenger Mutations on Cancer Progression

Genomic instability and high mutation rates cause cancer to acquire numerous mutations and chromosomal alterations during its somatic evolution; most are termed passengers because they do not confer cancer phenotypes. Evolutionary simulations and cancer genomic studies suggest that mildly deleterious passengers accumulate and can collectively slow cancer progression. Clinical data also suggest an association between passenger load and response to therapeutics, yet no causal link between the effects of passengers and cancer progression has been established. To assess this, we introduced increasing passenger loads into human cell lines and immunocompromised mouse models. We found that passengers dramatically reduced proliferative fitness (∼3% per Mb), slowed tumor growth, and reduced metastatic progression. We developed new genomic measures of damaging passenger load that can accurately predict the fitness costs of passengers in cell lines and in human breast cancers. We conclude that genomic instability and an elevated load of DNA alterations in cancer is a double-edged sword: it accelerates the accumulation of adaptive drivers, but incurs a harmful passenger load that can outweigh driver benefit. The effects of passenger alterations on cancer fitness were unrelated to enhanced immunity, as our tests were performed either in cell culture or in immunocompromised animals. Our findings refute traditional paradigms of passengers as neutral events, suggesting that passenger load reduces the fitness of cancer cells and slows or prevents progression of both primary and metastatic disease. The antitumor effects of chemotherapies can in part be due to the induction of genomic instability and increased passenger load. Cancer Res; 77(18); 4763-72. ©2017 AACR.

Progression inference for somatic mutations in cancer

Computational methods were employed to determine progression inference of genomic alterations in commonly occurring cancers. Using cross-sectional TCGA data, we computed evolutionary trajectories involving selectivity relationships among pairs of gene-specific genomic alterations such as somatic mutations, deletions, amplifications, downregulation, and upregulation among the top 20 driver genes associated with each cancer. Results indicate that the majority of hierarchies involved TP53, PIK3CA, ERBB2, APC, KRAS, EGFR, IDH1, VHL, etc. Research into the order and accumulation of genomic alterations among cancer driver genes will ever-increase as the costs of nextgen sequencing subside, and personalized/precision medicine incorporates whole-genome scans into the diagnosis and treatment of cancer.

In vivo maturation of human embryonic stem cell-derived teratoma over time

Transformation of human embryonic stem cells (hESC) is of interest to scientists who use them as a raw material for cell-processed therapeutic products. However, the WHO and ICH guidelines provide only study design advice and general principles for tumorigenicity tests. In this study, we performed in vivo tumorigenicity tests (teratoma formation) and genome-wide sequencing analysis of undifferentiated hESCs i.e. SEES-1, -2 and -3 cells. We followed up with teratoma formation histopathologically after subcutaneous injection of SEES cells into immunodeficient mice in a qualitative manner and investigated the transforming potential of the teratomas. Maturity of SEES-teratomas perceptibly increased after long-term implantation, while areas of each tissue component remained unchanged. We found neither atypical cells/structures nor cancer in the teratomas even after long-term implantation. The teratomas generated by SEES cells matured histologically over time and did not increase in size. We also analyzed genomic structures and sequences of SEES cells during cultivation by SNP bead arrays and next-generation sequencing, respectively. The nucleotide substitution rate was 3.1 × 10-9, 4.0 × 10-9, and 4.6 × 10-9 per each division in SEES-1, SEES-2, and SEES-3 cells, respectively. Heterozygous single-nucleotide variations were detected, but no significant homologous mutations were found. Taken together, these results imply that SEES-1, -2, and -3 cells do not exhibit in vivo transformation and in vitro genomic instability.

DNA damage regulation and its role in drug-related phenotypes in the malaria parasites

DNA of malaria parasites, Plasmodium falciparum, is subjected to extraordinary high levels of genotoxic insults during its complex life cycle within both the mosquito and human host. Accordingly, most of the components of DNA repair machinery are conserved in the parasite genome. Here, we investigated the genome-wide responses of P. falciparum to DNA damaging agents and provided transcriptional evidence of the existence of the double strand break and excision repair system. We also showed that acetylation at H3K9, H4K8, and H3K56 play a role in the direct and indirect response to DNA damage induced by an alkylating agent, methyl methanesulphonate (MMS). Artemisinin, the first line antimalarial chemotherapeutics elicits a similar response compared to MMS which suggests its activity as a DNA damaging agent. Moreover, in contrast to the wild-type P. falciparum, two strains (Dd2 and W2) previously shown to exhibit a mutator phenotype, fail to induce their DNA repair upon MMS-induced DNA damage. Genome sequencing of the two mutator strains identified point mutations in 18 DNA repair genes which may contribute to this phenomenon.

Methylation of MGMT and ADAMTS14 in normal colon mucosa: biomarkers of a field defect for cancerization preferentially targeting elder African-Americans

Somatic hypermethylation of the O⁶-methylguanine-DNA methyltransferase gene (MGMT) was previously associated with G > A transition mutations in KRAS and TP53 in colorectal cancer (CRC). We tested the association of MGMT methylation with G > A mutations in KRAS and TP53 in 261 CRCs. Sixteen cases, with and without MGMT hypermethylation, were further analyzed by exome sequencing. No significant association of MGMT methylation with G > A mutations in KRAS, TP53 or in the whole exome was found (p > 0.5 in all comparisons). The result was validated by in silico comparison with 302 CRCs from The Cancer Genome Atlas (TCGA) consortium dataset. Transcriptional silencing associated with hypermethylation and stratified into monoallelic and biallelic. We also found a significant clustering (p = 0.001) of aberrant hypermethylation of MGMT and the matrix metalloproteinase gene ADAMTS14 in normal colonic mucosa of CRC patients. This suggested the existence of an epigenetic field defect for cancerization disrupting the methylation patterns of several loci, including MGMT or ADAMTS14, that may lead to predictive biomarkers for CRC. Methylation of these loci in normal mucosa was more frequent in elder (p = 0.001) patients, and particularly in African Americans (p = 1 × 10⁻⁵), thus providing a possible mechanistic link between somatic epigenetic alterations and CRC racial disparities in North America.

Regulation of mismatch repair by histone code and posttranslational modifications in eukaryotic cells

DNA mismatch repair (MMR) protects genome integrity by correcting DNA replication-associated mispairs, modulating DNA damage-induced cell cycle checkpoints and regulating homeologous recombination. Loss of MMR function leads to cancer development. This review describes progress in understanding how MMR is carried out in the context of chromatin and how chromatin organization/compaction, epigenetic mechanisms and posttranslational modifications of MMR proteins influence and regulate MMR in eukaryotic cells.

Can Peto's paradox be used as the null hypothesis to identify the role of evolution in natural resistance to cancer? A critical review

BACKGROUND

Carcinogenesis affects not only humans but almost all metazoan species. Understanding the rules driving the occurrence of cancers in the wild is currently expected to provide crucial insights into identifying how some species may have evolved efficient cancer resistance mechanisms. Recently the absence of correlation across species between cancer prevalence and body size (coined as Peto's paradox) has attracted a lot of attention. Indeed, the disparity between this null hypothesis, where every cell is assumed to have an identical probability to undergo malignant transformation, and empirical observations is particularly important to understand, due to the fact that it could facilitate the identification of animal species that are more resistant to carcinogenesis than expected. Moreover it would open up ways to identify the selective pressures that may be involved in cancer resistance. However, Peto's paradox relies on several questionable assumptions, complicating the interpretation of the divergence between expected and observed cancer incidences.

DISCUSSIONS

Here we review and challenge the different hypotheses on which this paradox relies on with the aim of identifying how this null hypothesis could be better estimated in order to provide a standard protocol to study the deviation between theoretical/theoretically predicted and observed cancer incidence. We show that due to the disproportion and restricted nature of available data on animal cancers, applying Peto's hypotheses at species level could result in erroneous conclusions, and actually assume the existence of a paradox. Instead of using species level comparisons, we propose an organ level approach to be a more accurate test of Peto's assumptions.

SUMMARY

The accuracy of Peto's paradox assumptions are rarely valid and/or quantifiable, suggesting the need to reconsider the use of Peto's paradox as a null hypothesis in identifying the influence of natural selection on cancer resistance mechanisms.

Tumor evolution. High burden and pervasive positive selection of somatic mutations in normal human skin

How somatic mutations accumulate in normal cells is central to understanding cancer development but is poorly understood. We performed ultradeep sequencing of 74 cancer genes in small (0.8 to 4.7 square millimeters) biopsies of normal skin. Across 234 biopsies of sun-exposed eyelid epidermis from four individuals, the burden of somatic mutations averaged two to six mutations per megabase per cell, similar to that seen in many cancers, and exhibited characteristic signatures of exposure to ultraviolet light. Remarkably, multiple cancer genes are under strong positive selection even in physiologically normal skin, including most of the key drivers of cutaneous squamous cell carcinomas. Positively selected mutations were found in 18 to 32% of normal skin cells at a density of ~140 driver mutations per square centimeter. We observed variability in the driver landscape among individuals and variability in the sizes of clonal expansions across genes. Thus, aged sun-exposed skin is a patchwork of thousands of evolving clones with over a quarter of cells carrying cancer-causing mutations while maintaining the physiological functions of epidermis.

Stress-induced mutagenesis and complex adaptation

Because mutations are mostly deleterious, mutation rates should be reduced by natural selection. However, mutations also provide the raw material for adaptation. Therefore, evolutionary theory suggests that the mutation rate must balance between adaptability-the ability to adapt-and adaptedness-the ability to remain adapted. We model an asexual population crossing a fitness valley and analyse the rate of complex adaptation with and without stress-induced mutagenesis (SIM)-the increase of mutation rates in response to stress or maladaptation. We show that SIM increases the rate of complex adaptation without reducing the population mean fitness, thus breaking the evolutionary trade-off between adaptability and adaptedness. Our theoretical results support the hypothesis that SIM promotes adaptation and provide quantitative predictions of the rate of complex adaptation with different mutational strategies.

Protein domain-level landscape of cancer-type-specific somatic mutations

Identifying driver mutations and their functional consequences is critical to our understanding of cancer. Towards this goal, and because domains are the functional units of a protein, we explored the protein domain-level landscape of cancer-type-specific somatic mutations. Specifically, we systematically examined tumor genomes from 21 cancer types to identify domains with high mutational density in specific tissues, the positions of mutational hotspots within these domains, and the functional and structural context where possible. While hotspots corresponding to specific gain-of-function mutations are expected for oncoproteins, we found that tumor suppressor proteins also exhibit strong biases toward being mutated in particular domains. Within domains, however, we observed the expected patterns of mutation, with recurrently mutated positions for oncogenes and evenly distributed mutations for tumor suppressors. For example, we identified both known and new endometrial cancer hotspots in the tyrosine kinase domain of the FGFR2 protein, one of which is also a hotspot in breast cancer, and found new two hotspots in the Immunoglobulin I-set domain in colon cancer. Thus, to prioritize cancer mutations for further functional studies aimed at more precise cancer treatments, we have systematically correlated mutations and cancer types at the protein domain level.

Within-host stochastic emergence dynamics of immune-escape mutants

Predicting the emergence of new pathogenic strains is a key goal of evolutionary epidemiology. However, the majority of existing studies have focussed on emergence at the population level, and not within a host. In particular, the coexistence of pre-existing and mutated strains triggers a heightened immune response due to the larger total pathogen population; this feedback can smother mutated strains before they reach an ample size and establish. Here, we extend previous work for measuring emergence probabilities in non-equilibrium populations, to within-host models of acute infections. We create a mathematical model to investigate the emergence probability of a fitter strain if it mutates from a self-limiting strain that is guaranteed to go extinct in the long-term. We show that ongoing immune cell proliferation during the initial stages of infection causes a drastic reduction in the probability of emergence of mutated strains; we further outline how this effect can be accurately measured. Further analysis of the model shows that, in the short-term, mutant strains that enlarge their replication rate due to evolving an increased growth rate are more favoured than strains that suffer a lower immune-mediated death rate (‘immune tolerance’), as the latter does not completely evade ongoing immune proliferation due to inter-parasitic competition. We end by discussing the model in relation to within-host evolution of human pathogens (including HIV, hepatitis C virus, and cancer), and how ongoing immune growth can affect their evolutionary dynamics.

The ongoing evolution of infectious diseases provides a constant health threat. This evolution can either result in the production of new pathogens, or new strains of existing pathogens that escape prevailing drug treatments or immune responses. The latter process, also known as immune escape, is a predominant reason for the persistence of several viruses, including HIV and hepatitis C virus (HCV), in their human host. As a consequence, the within-host emergence of new strains has been the intense focus of modelling studies. However, existing models have neglected important feedbacks that affects this emergence probability. Specifically, once a mutated pathogen arises that spreads more quickly than the initial (resident) strain, it potentially triggers a heightened immune response that can eliminate the mutated strain before it spreads. Our study outlines novel mathematical modelling techniques that accurately quantify how ongoing immune growth reduces the emergence probability of mutated pathogenic strains over the course of an infection. Analysis of this model suggests that, in order to enlarge its emergence probability, it is evolutionary beneficial for a mutated strain to increase its growth rate rather than tolerate immunity by having a lower immune-mediated death-rate. Our model can be readily applied to existing within-host data, as demonstrated with application to HIV, HCV, and cancer dynamics.

The unforeseen challenge: from genotype-to-phenotype in cell populations

Biological cells present a paradox, in that they show simultaneous stability and flexibility, allowing them to adapt to new environments and to evolve over time. The emergence of stable cell states depends on genotype-to-phenotype associations, which essentially reflect the organization of gene regulatory modes. The view taken here is that cell-state organization is a dynamical process in which the molecular disorder manifests itself in a macroscopic order. The genome does not determine the ordered cell state; rather, it participates in this process by providing a set of constraints on the spectrum of regulatory modes, analogous to boundary conditions in physical dynamical systems. We have developed an experimental framework, in which cell populations are exposed to unforeseen challenges; novel perturbations they had not encountered before along their evolutionary history. This approach allows an unbiased view of cell dynamics, uncovering the potential of cells to evolve and develop adapted stable states. In the last decade, our experiments have revealed a coherent set of observations within this framework, painting a picture of the living cell that in many ways is not aligned with the conventional one. Of particular importance here, is our finding that adaptation of cell-state organization is essentially an efficient exploratory dynamical process rather than one founded on random mutations. Based on our framework, a set of concepts underlying cell-state organization-exploration evolving by global, non-specific, dynamics of gene activity-is presented here. These concepts have significant consequences for our understanding of the emergence and stabilization of a cell phenotype in diverse biological contexts. Their implications are discussed for three major areas of biological inquiry: evolution, cell differentiation and cancer. There is currently no unified theoretical framework encompassing the emergence of order, a stable state, in the living cell. Hopefully, the integrated picture described here will provide a modest contribution towards a physics theory of the cell.

Rapid and convergent evolution in the glioblastoma multiforme genome

Determining which mutations drive tumor progression is a defining question in cancer genomics. We analyzed sequence evolution in Glioblastoma multiforme (GBM) by computing the number of parallel mutations and by estimating ω=dN/dS, a measure of the strength and direction of selection. The ω values of almost all 7617 mutated genes in GBM are much higher than in germline genes. We identified only 21 genes under significant positive selection in GBM, as well as 29 genes under significant purifying selection, including several zinc finger proteins. Therefore, most of the high ω values in the GBM genome are due to weaker purifying selection rather than positive selection. We also found multiple recurrent mutations in GBM, several of which are associated with patient survival time. Our results suggest that convergence and neutral evolution play a significant role in GBM, and that sites with recurrent mutations can serve as molecular diagnostics of the clinical course of GBM tumors.

Only three driver gene mutations are required for the development of lung and colorectal cancers

Cancer arises through the sequential accumulation of mutations in oncogenes and tumor suppressor genes. However, how many such mutations are required for a normal human cell to progress to an advanced cancer? The best estimates for this number have been provided by mathematical models based on the relation between age and incidence. For example, the classic studies of Nordling [Nordling CO (1953) Br J Cancer 7(1):68-72] and Armitage and Doll [Armitage P, Doll R (1954) Br J Cancer 8(1):1-12] suggest that six or seven sequential mutations are required. Here, we describe a different approach to derive this estimate that combines conventional epidemiologic studies with genome-wide sequencing data: incidence data for different groups of patients with the same cancer type were compared with respect to their somatic mutation rates. In two well-documented cancer types (lung and colon adenocarcinomas), we find that only three sequential mutations are required to develop cancer. This conclusion deepens our understanding of the process of carcinogenesis and has important implications for the design of future cancer genome-sequencing efforts.

Evolutionary triage governs fitness in driver and passenger mutations and suggests targeting never mutations

Genetic and epigenetic changes in cancer cells are typically divided into “drivers” and “passengers”. Drug development strategies target driver mutations, but inter- and intra-tumoral heterogeneity usually results in emergence of resistance. Here we model intratumoral evolution in the context of a fecundity/survivorship trade-off. Simulations demonstrate the fitness value, of any genetic change is not fixed but dependent on evolutionary triage governed by initial cell properties, current selection forces, and prior genotypic/phenotypic trajectories. We demonstrate spatial variations in molecular properties of tumor cells are the result of changes in environmental selection forces such as blood flow. Simulated therapies targeting fitness-increasing (driver) mutations usually decrease the tumor burden but almost inevitably fail due to population heterogeneity. An alternative strategy targets gene mutations that are never observed. Because up or down regulation of these genes unconditionally reduces cellular fitness, they are eliminated by evolutionary triage but can be exploited for targeted therapy.

A new model of time scheme for progression of colorectal cancer

BACKGROUND

tumourigenesis can be regarded as an evolutionary process, in which the transformation of a normal cell into a tumour cell involves a number of limiting genetic and epigenetic events. To study the progression process, time schemes have been proposed for studying the process of colorectal cancer based on extensive clinical investigations. Moreover, a number of mathematical models have been designed to describe this evolutionary process. These models assumed that the mutation rate of genes is constant during different stages. However, it has been pointed that the subsequent driver mutations appear faster than the previous ones and the cumulative time to have more driver mutations grows with the growing number of gene mutations. Thus it is still a challenge to calculate the time when the first mutation occurs and to determine the influence of tumour size on the mutation rate.

RESULTS

In this work we present a general framework to remedy the shortcoming of existing models. Rather than considering the information of gene mutations based on a population of patients, we for the first time determine the values of the selective advantage of cancer cells and initial mutation rate for individual patients. The averaged values of doubling time and selective advantage coefficient determined by our model are consistent with the predictions made by the published models. Our calculation showed that the values of biological parameters, such as the selective advantage coefficient, initial mutation rate and cell doubling time diversely depend on individuals. Our model has successfully predicted the values of several important parameters in cancer progression, such as the selective advantage coefficient, initial mutation rate and cell doubling time. In addition, experimental data validated our predicted initial mutation rate and cell doubling time.

CONCLUSIONS

The introduced new parameter makes our proposed model more flexible to fix various types of information based on different patients in cancer progression.

Catastrophic shifts and lethal thresholds in a propagating front model of unstable tumor progression

Unstable dynamics characterizes the evolution of most solid tumors. Because of an increased failure of maintaining genome integrity, a cumulative increase in the levels of gene mutation and loss is observed. Previous work suggests that instability thresholds to cancer progression exist, defining phase transition phenomena separating tumor-winning scenarios from tumor extinction or coexistence phases. Here we present an integral equation approach to the quasispecies dynamics of unstable cancer. The model exhibits two main phases, characterized by either the success or failure of cancer tissue. Moreover, the model predicts that tumor failure can be due to either a reduced selective advantage over healthy cells or excessive instability. We also derive an approximate, analytical solution that predicts the front speed of aggressive tumor populations on the instability space.

Turning ecology and evolution against cancer

The fight against cancer has drawn researchers from a wide variety of disciplines, ranging from molecular biology to physics, but the perspective of an ecological theorist has been mostly overlooked. By thinking about the cells that make up a tumour as an endangered species, cancer vulnerabilities become more apparent. Studies in conservation biology and microbial experiments indicate that extinction is a complex phenomenon, which is often driven by the interaction of ecological and evolutionary processes. Recent advances in cancer research have shown that tumours, like species striving for survival, harbour intricate population dynamics, which suggests the possibility to exploit the ecology of tumours for treatment.

Mosaic KCNJ2 mutation in Andersen-Tawil syndrome: targeted deep sequencing is useful for the detection of mosaicism

Andersen-Tawil syndrome (ATS) is an inherited disease characterized by ventricular arrhythmias, periodic paralysis, and dysmorphic features. It results from a heterozygous mutation of KCNJ2, but little is known about mosaicism in ATS. We performed genetic analysis of KCNJ2 in 32 ATS probands and their family members and identified KCNJ2 mutations in 25 probands, 20 families who underwent extensive genetic testing. These tests revealed that seven probands carried de novo mutations while 13 carried inherited mutations from their parents. We then specifically assessed a single proband and the respective family. The proband was a 9 year old girl who fulfilled the ATS triad and carried an insertion mutation (p.75_76insThr). We determined that the proband's mother carried a somatic mosaicism and that the proband's younger brother also carried the ATS phenotype with the same insertion mutation. The mother, who exhibited mosaicism, was asymptomatic, although she exhibited Q(T)U prolongation. Mutant allele frequency was 11% as per TA cloning and 17.3% as per targeted deep sequencing. Our observations suggest that targeted deep sequencing is useful for the detection of mosaicism and that the detection of mosaic mutations in parents of apparently sporadic ATS patients can help in the process of genetic counseling.

Genetic damage in patients with chronic kidney disease, peritoneal dialysis and haemodialysis: a comparative study

Patients with chronic kidney disease (CKD) have signs of genomic instability and, as a consequence, extensive genetic damage, possibly due to accumulation of uraemic toxins, oxidative stress mediators and other endogenous substances with genotoxic properties. We explored factors associated with the presence and background levels of genetic damage in CKD. A cross-sectional study was performed in 91 CKD patients including pre-dialysis (CKD patients; n = 23) and patients undergoing peritoneal dialysis (PD; n = 33) or haemodialysis (HD; n = 35) and with 61 healthy subjects, divided into two subgroups with the older group being in the age range of the patients, serving as controls. Alkaline comet assay and cytokinesis-block micronucleus assay in peripheral blood lymphocytes were used to determine DNA and chromosome damage, respectively, present in CKD. Markers of oxidative stress [malondialdehyde (MDA), advanced glycation end products (AGEs), thiols, advanced oxidation protein products and 8-hydroxy-2'-deoxyguanosine] and markers of inflammation (C-reactive protein, interleukin-6 and tumour necrosis factor alpha) were also measured. Micronucleus (MN) frequency was significantly higher (P < 0.05) in the CKD group (46±4‰) when compared with the older control (oC) group (27.7±14). A significant increase in MN frequency (P < 0.05) was also seen in PD patients (41.9±14‰) versus the oC group. There was no statistically significant difference for the HD group (29.7±15.6‰; P = NS) versus the oC group. Comet assay data showed a significant increase (P < 0.001) of tail DNA intensity in cells of patients with CKD (15.6±7%) with respect to the total control (TC) group (11±1%). PD patients (14.8±7%) also have a significant increase (P < 0.001) versus the TC group. Again, there was no statistically significant difference for the HD group (12.5±3%) compared with the TC group. Patients with MN values in the upper quartile had increased cholesterol, triglycerides, AGEs and MDA levels and lower albumin levels. Multiple logistic regression analysis showed that male gender, diabetes and treatment modality were independently associated with higher levels of DNA damage. Our results suggest that oxidative stress, diabetes, gender and dialysis modality in CKD patients increased DNA and chromosome damage. To confirm these data, prospective clinical trials need to be performed.

Quantum biology at the cellular level--elements of the research program

Quantum biology is emerging as a new field at the intersection between fundamental physics and biology, promising novel insights into the nature and origin of biological order. We discuss several elements of QBCL (quantum biology at cellular level) - a research program designed to extend the reach of quantum concepts to higher than molecular levels of biological organization. We propose a new general way to address the issue of environmentally induced decoherence and macroscopic superpositions in biological systems, emphasizing the 'basis-dependent' nature of these concepts. We introduce the notion of 'formal superposition' and distinguish it from that of Schroedinger's cat (i.e., a superposition of macroscopically distinct states). Whereas the latter notion presents a genuine foundational problem, the former one contradicts neither common sense nor observation, and may be used to describe cellular 'decision-making' and adaptation. We stress that the interpretation of the notion of 'formal superposition' should involve non-classical correlations between molecular events in a cell. Further, we describe how better understanding of the physics of Life can shed new light on the mechanism driving evolutionary adaptation (viz., 'Basis-Dependent Selection', BDS). Experimental tests of BDS and the potential role of synthetic biology in closing the 'evolvability mechanism' loophole are also discussed.

Modulation of mutagenesis in eukaryotes by DNA replication fork dynamics and quality of nucleotide pools

The rate of mutations in eukaryotes depends on a plethora of factors and is not immediately derived from the fidelity of DNA polymerases (Pols). Replication of chromosomes containing the anti-parallel strands of duplex DNA occurs through the copying of leading and lagging strand templates by a trio of Pols α, δ and ϵ, with the assistance of Pol ζ and Y-family Pols at difficult DNA template structures or sites of DNA damage. The parameters of the synthesis at a given location are dictated by the quality and quantity of nucleotides in the pools, replication fork architecture, transcription status, regulation of Pol switches, and structure of chromatin. The result of these transactions is a subject of survey and editing by DNA repair.

Preliminary evidence of different selection pressures on cancer cells as compared to normal tissues

BACKGROUND

Cancer is characterized by both a high mutation rate as well as high rates of cell division and cell death. We postulate that these conditions will result in the eventual mutational inactivation of genes not essential to the survival of the cancer cell, while mutations in essential genes will be eliminated by natural selection leaving molecular signatures of selection in genes required for survival and reproduction. By looking for signatures of natural selection in the genomes of cancer cells, it should therefore be possible to determine which genes have been essential for the development of a particular cancer.

METHODS

We provide a proof of principle test of this idea by applying a test of neutrality (Nei-Gojobori Z-test of selection) to 139 cancer-related nucleotide sequences obtained from GenBank representing 46 cancer-derived genes.

RESULTS

Among cancer associated sequences, 10 genes showed molecular evidence of selection. Of these 10 genes, four showed molecular evidence of selection in non-cancer transcripts. Among non-cancer associated sequences, eight genes showed molecular evidence of selection, with four of these also showing selection in the cancer associated sequences.

CONCLUSIONS

These results provide preliminary evidence that the same genes may experience different selection pressures within normal and cancer tissues. Application of this technique could identify genes under unique selection pressure in cancer tissues and thereby indicate possible targets for therapeutic intervention.