Kinetics of cancer: a method to test hypotheses of genetic causation

BACKGROUND: Mouse studies have recently compared the age-onset patterns of cancer between different genotypes. Genes associated with earlier onset are tentatively assigned a causal role in carcinogenesis. These standard analyses ignore the great amount of information about kinetics contained in age-onset curves. We present a method for analyzing kinetics that measures quantitatively the causal role of candidate genes in cancer progression. We use our method to demonstrate a clear association between somatic mutation rates of different DNA mismatch repair (MMR) genotypes and the kinetics of cancer progression. METHODS: Most experimental studies report age-onset curves as the fraction diagnosed with tumors at each age for each group. We use such data to estimate smoothed survival curves, then measure incidence rates at each age by the slope of the fitted curve divided by the fraction of mice that remain undiagnosed for tumors at that age. With the estimated incidence curves, we compare between different genotypes the median age of cancer onset and the acceleration of cancer, which is the rate of increase in incidence with age. RESULTS: The direction of change in somatic mutation rate between MMR genotypes predicts the direction of change in the acceleration of cancer onset in all 7 cases (p ˜ 0.008), with the same result for the association between mutation rate and the median age of onset. CONCLUSION: Many animal experiments compare qualitatively the onset curves for different genotypes. If such experiments were designed to analyze kinetics, the research could move to the next stage in which the mechanistic consequences of particular genetic pathways are related to the dynamics of carcinogenesis. The data we analyzed here were not collected to test mechanistic and quantitative hypotheses about kinetics. Even so, a simple reanalysis revealed significant insights about how DNA repair genotypes affect separately the age of onset and the acceleration of cancer. Our method of comparing genotypes provides good statistical tests even with small samples for each genotype

nature biotechnology VOLUME

To better understand the molecular mechanisms and genetic basis of human disease, we systematically examine relationships between 3,949 genes, 62,663 mutations and 3,453 associated disorders by generating a three-dimensional, structurally resolved human interactome. This network consists of 4,222 high-quality binary protein-protein interactions with their atomic-resolution interfaces. We find that in-frame mutations (missense point mutations and in-frame insertions and deletions) are enriched on the interaction interfaces of proteins associated with the corresponding disorders, and that the disease specificity for different mutations of the same gene can be explained by their location within an interface. We also predict 292 candidate genes for 694 unknown disease-to-gene associations with proposed molecular mechanism hypotheses. This work indicates that knowledge of how in-frame disease mutations alter specific interactions is critical to understanding pathogenesis. Structurally resolved interaction networks should be valuable tools for interpreting the wealth of data being generated by large-scale structural genomics and disease association studies. Over the past few decades, a tremendous amount of resources and effort have been invested in mapping human disease loci genetically and later physically 1 . Since the completion of the human genome sequence, especially with advances in genome-wide association studies and ongoing cancer genome sequencing projects, an impressive list of disease-associated genes and their mutations have been produced 2 . However, it has rarely been possible to translate this wealth of information on individual mutations and their association with disease into biological or therapeutic insights 3 . Most of the drugs approved by the US Food and Drug Administration today are palliative 4 -they merely treat symptoms, rather than targeting specific genes or pathways responsible, even if associated genes are known. One main reason for this lack of success is the complex genotype-tophenotype relationships among diseases and their associated genes and mutations. In particular, (i) the same gene can be associated with multiple disorders (gene pleiotropy); and (ii) mutations in any one of many genes can cause the same clinical disorder (locus heterogeneity). For example, mutations in TP53 are linked to 32 clinically distinguishable forms of cancer and cancer-related disorders, whereas mutations in any of at least 12 different genes can lead to long QT syndrome. With the publication of several large-scale protein-protein interaction networks in human 5-8 , researchers have recently begun to use complex cellular networks to explore these genotype-to-phenotype relationships 2,9 , on the basis that many proteins function by interacting with other proteins. However, most analyses model proteins as graph-theoretical nodes, ignoring the structural details of individual proteins and the spatial constraints of their interactions. Here, we investigate on a large-scale the underlying molecular mechanisms for the complex genotype-to-phenotype relationships by integrating three-dimensional (3D) atomic-level protein structure information with high-quality large-scale protein-protein interaction data. Within the framework of this structurally resolved protein interactome, we examine the relationships among human diseases and their associated genes and mutations. RESULTS Structurally resolved protein interactome for human disease We first combined 12,577 reliable literature-curated binary interactions filtered from six widely used databases 10-15 (Online Methods) and 8,173 well-verified, high-throughput, yeast two-hybrid (Y2H) interactions Next, we structurally resolved the interfaces of these interactions using a homology modeling approach 16 . We used both iPfam 17 and 3did 18 to identify the interfaces of two interacting proteins by mapping them to known atomic-resolution 3D structures of interactions in the Protein Data Bank (PDB) Finally, to compile a comprehensive list of disease-associated genes and their mutations, we combined information from both Online Mendelian Inheritance in Man (OMIM

Antibodies to Enteroviruses in Cerebrospinal Fluid of Patients with Acute Flaccid Myelitis.

Acute flaccid myelitis (AFM) has caused motor paralysis in >560 children in the United States since 2014. The temporal association of enterovirus (EV) outbreaks with increases in AFM cases and reports of fever, respiratory, or gastrointestinal illness prior to AFM in >90% of cases suggest a role for infectious agents. Cerebrospinal fluid (CSF) from 14 AFM and 5 non-AFM patients with central nervous system (CNS) diseases in 2018 were investigated by viral-capture high-throughput sequencing (VirCapSeq-VERT system). These CSF and serum samples, as well as multiple controls, were tested for antibodies to human EVs using peptide microarrays. EV RNA was confirmed in CSF from only 1 adult AFM case and 1 non-AFM case. In contrast, antibodies to EV peptides were present in CSF of 11 of 14 AFM patients (79%), significantly higher than controls, including non-AFM patients (1/5 [20%]), children with Kawasaki disease (0/10), and adults with non-AFM CNS diseases (2/11 [18%]) (P = 0.023, 0.0001, and 0.0028, respectively). Six of 14 CSF samples (43%) and 8 of 11 sera (73%) from AFM patients were immunoreactive to an EV-D68-specific peptide, whereas the three control groups were not immunoreactive in either CSF (0/5, 0/10, and 0/11; P = 0.008, 0.0003, and 0.035, respectively) or sera (0/2, 0/8, and 0/5; P = 0.139, 0.002, and 0.009, respectively).IMPORTANCE The presence in cerebrospinal fluid of antibodies to EV peptides at higher levels than non-AFM controls supports the plausibility of a link between EV infection and AFM that warrants further investigation and has the potential to lead to strategies for diagnosis and prevention of disease

Complex Langevin Equation and the Many-Fermion Problem

We study the utility of a complex Langevin (CL) equation as an alternative for the Monte Carlo (MC) procedure in the evaluation of expectation values occurring in fermionic many-body problems. We find that a CL approach is natural in cases where non-positive definite probability measures occur, and remains accurate even when the corresponding MC calculation develops a severe ``sign problem''. While the convergence of CL averages cannot be guaranteed in principle, we show how convergent results can be obtained in three examples ranging from simple one-dimensional integrals over quantum mechanical models to a schematic shell model path integral.Comment: 19 pages, 10 PS figures embedded in tex

Localization of MMR proteins on meiotic chromosomes in mice indicates distinct functions during prophase I

Mammalian MutL homologues function in DNA mismatch repair (MMR) after replication errors and in meiotic recombination. Both functions are initiated by a heterodimer of MutS homologues specific to either MMR (MSH2–MSH3 or MSH2–MSH6) or crossing over (MSH4–MSH5). Mutations of three of the four MutL homologues (Mlh1, Mlh3, and Pms2) result in meiotic defects. We show herein that two distinct complexes involving MLH3 are formed during murine meiosis. The first is a stable association between MLH3 and MLH1 and is involved in promoting crossing over in conjunction with MSH4–MSH5. The second complex involves MLH3 together with MSH2–MSH3 and localizes to repetitive sequences at centromeres and the Y chromosome. This complex is up-regulated in Pms2−/− males, but not females, providing an explanation for the sexual dimorphism seen in Pms2−/− mice. The association of MLH3 with repetitive DNA sequences is coincident with MSH2–MSH3 and is decreased in Msh2−/− and Msh3−/− mice, suggesting a novel role for the MMR family in the maintenance of repeat unit integrity during mammalian meiosis

Inhibition of colorectal cancer genomic copy number alterations and chromosomal fragile site tumor suppressor FHIT and WWOX deletions by DNA mismatch repair

Homologous recombination (HR) enables precise DNA repair after DNA double strand breaks (DSBs) using identical sequence templates, whereas homeologous recombination (HeR) uses only partially homologous sequences. Homeologous recombination introduces mutations through gene conversion and genomic deletions through single-strand annealing (SSA). DNA mismatch repair (MMR) inhibits HeR, but the roles of mammalian MMR MutL homologues (MLH1, PMS2 and MLH3) proteins in HeR suppression are poorly characterized. Here, we demonstrate that mouse embryonic fibroblasts (MEFs) carrying Mlh1, Pms2, and Mlh3 mutations have higher HeR rates, by using 7,863 uniquely mapping paired direct repeat sequences (DRs) in the mouse genome as endogenous gene conversion and SSA reporters. Additionally, when DSBs are induced by gamma-radiation, Mlh1, Pms2 and Mlh3 mutant MEFs have higher DR copy number alterations (CNAs), including DR CNA hotspots previously identified in mouse MMR-deficient colorectal cancer (dMMR CRC). Analysis of The Cancer Genome Atlas CRC data revealed that dMMR CRCs have higher genome-wide DR HeR rates than MMR proficient CRCs, and that dMMR CRCs have deletion hotspots in tumor suppressors FHIT/WWOX at chromosomal fragile sites FRA3B and FRA16D (which have elevated DSB rates) flanked by paired homologous DRs and inverted repeats (IR). Overall, these data provide novel insights into the MMR-dependent HeR inhibition mechanism and its role in tumor suppression

miR-1269 promotes metastasis and forms a positive feedback loop with TGF-β

As patient survival drops precipitously from early-stage cancers to late-stage and metastatic cancers, microRNAs that promote relapse and metastasis can serve as prognostic and predictive markers as well as therapeutic targets for chemoprevention. Here we show that miR-1269a promotes colorectal cancer (CRC) metastasis and forms a positive feedback loop with TGF-β signalling. miR-1269a is upregulated in late-stage CRCs, and long-term monitoring of 100 stage II CRC patients revealed that miR-1269a expression in their surgically removed primary tumours is strongly associated with risk of CRC relapse and metastasis. Consistent with clinical observations, miR-1269a significantly increases the ability of CRC cells to invade and metastasize in vivo. TGF-β activates miR-1269 via Sox4, while miR-1269a enhances TGF-β signalling by targeting Smad7 and HOXD10, hence forming a positive feedback loop. Our findings suggest that miR-1269a is a potential marker to inform adjuvant chemotherapy decisions for CRC patients and a potential therapeutic target to deter metastasis

Frequency Domain Analysis Reveals External Periodic Fluctuations Can Generate Sustained p53 Oscillation

p53 is a well-known tumor suppressor protein that regulates many pathways, such as ones involved in cell cycle and apoptosis. The p53 levels are known to oscillate without damping after DNA damage, which has been a focus of many recent studies. A negative feedback loop involving p53 and MDM2 has been reported to be responsible for this oscillatory behavior, but questions remain as how the dynamics of this loop alter in order to initiate and maintain the sustained or undamped p53 oscillation. Our frequency domain analysis suggests that the sustained p53 oscillation is not completely dictated by the negative feedback loop; instead, it is likely to be also modulated by periodic DNA repair-related fluctuations that are triggered by DNA damage. According to our analysis, the p53-MDM2 feedback mechanism exhibits adaptability in different cellular contexts. It normally filters noise and fluctuations exerted on p53, but upon DNA damage, it stops performing the filtering function so that DNA repair-related oscillatory signals can modulate the p53 oscillation. Furthermore, it is shown that the p53-MDM2 feedback loop increases its damping ratio allowing p53 to oscillate at a frequency more synchronized with the other cellular efforts to repair the damaged DNA, while suppressing its inherent oscillation-generating capability. Our analysis suggests that the overexpression of MDM2, observed in many types of cancer, can disrupt the operation of this adaptive mechanism by making it less responsive to the modulating signals after DNA damage occurs