Human epidermal growth factor receptor-2 expression and subsequent dynamic changes in patients with ovarian cancer.

Human epidermal growth factor receptor-2 (HER2)-targeting drugs are increasingly being incorporated into therapeutic paradigms for non-breast cancers, yet studies on HER2 expression in ovarian cancer (OC) are inadequate. Here, we studied the HER2 status and dynamic changes in OC by reviewing the records of patients who underwent HER2 testing at a single institution. Clinical parameters, including histology, BRCA status, and immunohistochemistry (IHC), were evaluated alongside HER2 expression, timing, and anatomical location. Among 200 patients, 28% and 6% exhibited expression scores of 2+ and 3+, respectively. HER2 3+ scores were observed in 23%, 11%, 9%, and 5% of mucinous, endometrioid, clear cell, and high-grade serous tumors, respectively, and were exclusively identified in BRCA-wildtype, mismatch repair-proficient, or PD-L1-low-expressing tumors. The TP53 mutation rate was low, whereas ARID1A, KRAS, and PIK3CA mutations were relatively more prevalent with HER2 scores of 2+ or 3+ than with 0 or 1+. Four of the five tumors with an HER2 3+ score exhibited ERBB2 amplification. Among 19 patients who underwent multiple time-lagged biopsies, 11 showed increased HER2 expression in subsequent biopsies. Patients with HER2-overexpressing OC exhibited distinct histological, IHC, and genomic profiles. HER2-targeting agents are potential options for BRCA-wildtype patients, particularly as later lines of treatment.

Evolutionary graph theory beyond pairwise interactions: Higher-order network motifs shape times to fixation in structured populations.

To design population topologies that can accelerate rates of solution discovery in directed evolution problems or for evolutionary optimization applications, we must first systematically understand how population structure shapes evolutionary outcome. Using the mathematical formalism of evolutionary graph theory, recent studies have shown how to topologically build networks of population interaction that increase probabilities of fixation of beneficial mutations, at the expense, however, of longer fixation times, which can slow down rates of evolution, under elevated mutation rate. Here we find that moving beyond dyadic interactions in population graphs is fundamental to explain the trade-offs between probabilities and times to fixation of new mutants in the population. We show that higher-order motifs, and in particular three-node structures, allow the tuning of times to fixation, without changes in probabilities of fixation. This gives a near-continuous control over achieving solutions that allow for a wide range of times to fixation. We apply our algorithms and analytic results to two evolutionary optimization problems and show that the rate of solution discovery can be tuned near continuously by adjusting the higher-order topology of the population. We show that the effects of population structure on the rate of evolution critically depend on the optimization landscape and find that decelerators, with longer times to fixation of new mutants, are able to reach the optimal solutions faster than accelerators in complex solution spaces. Our results highlight that no one population topology fits all optimization applications, and we provide analytic and computational tools that allow for the design of networks suitable for each specific task.

Self-replicating artificial neural networks give rise to universal evolutionary dynamics.

In evolutionary models, mutations are exogenously introduced by the modeler, rather than endogenously introduced by the replicator itself. We present a new deep-learning based computational model, the self-replicating artificial neural network (SeRANN). We train it to (i) copy its own genotype, like a biological organism, which introduces endogenous spontaneous mutations; and (ii) simultaneously perform a classification task that determines its fertility. Evolving 1,000 SeRANNs for 6,000 generations, we observed various evolutionary phenomena such as adaptation, clonal interference, epistasis, and evolution of both the mutation rate and the distribution of fitness effects of new mutations. Our results demonstrate that universal evolutionary phenomena can naturally emerge in a self-replicator model when both selection and mutation are implicit and endogenous. We therefore suggest that SeRANN can be applied to explore and test various evolutionary dynamics and hypotheses.

Increased genomic instability and reshaping of tissue microenvironment underlie oncogenic properties of Arid1a mutations.

Oncogenic mutations accumulating in many chromatin-associated proteins have been identified in different tumor types. With a mutation rate from 10 to 57%, ARID1A has been widely considered a tumor suppressor gene. However, whether this role is mainly due to its transcriptional-related activities or its ability to preserve genome integrity is still a matter of intense debate. Here, we show that ARID1A is largely dispensable for preserving enhancer-dependent transcriptional regulation, being ARID1B sufficient and required to compensate for ARID1A loss. We provide in vivo evidence that ARID1A is mainly required to preserve genomic integrity in adult tissues. ARID1A loss primarily results in DNA damage accumulation, interferon type I response activation, and chronic inflammation leading to tumor formation. Our data suggest that in healthy tissues, the increased genomic instability that follows ARID1A mutations and the selective pressure imposed by the microenvironment might result in the emergence of aggressive, possibly immune-resistant, tumors.

Unravelling the link between SARS-CoV-2 mutation frequencies, patient comorbidities, and structural dynamics.

Genomic surveillance is crucial for tracking emergence and spread of novel variants of pathogens, such as SARS-CoV-2, to inform public health interventions and to enforce control measures. However, in some settings especially in low- and middle- income counties, where sequencing platforms are limited, only certain patients get to be selected for sequencing surveillance. Here, we show that patients with multiple comorbidities potentially harbour SARS-CoV-2 with higher mutation rates and thus deserve more attention for genomic surveillance. The relationship between the patient comorbidities, and type of amino acid mutations was assessed. Correlation analysis showed that there was a significant tendency for mutations to occur within the ORF1a region for patients with higher number of comorbidities. Frequency analysis of the amino acid substitution within ORF1a showed that nsp3 P822L of the PLpro protease was one of the highest occurring mutations. Using molecular dynamics, we simulated that the P822L mutation in PLpro represents a system with lower Root Mean Square Deviation (RMSD) fluctuations, and consistent Radius of gyration (Rg), Solvent Accessible Surface Area (SASA) values-indicate a much stabler protein than the wildtype. The outcome of this study will help determine the relationship between the clinical status of a patient and the mutations of the infecting SARS-CoV-2 virus.

The evolutionary safety of mutagenic drugs should be assessed before drug approval.

Some drugs increase the mutation rate of their target pathogen, a potentially concerning mechanism as the pathogen might evolve faster toward an undesired phenotype. We suggest a four-step assessment of evolutionary safety for the approval of such treatments.

Low-frequency somatic mutations are heritable in tropical trees Dicorynia guianensis and Sextonia rubra.

Somatic mutations potentially play a role in plant evolution, but common expectations pertaining to plant somatic mutations remain insufficiently tested. Unlike in most animals, the plant germline is assumed to be set aside late in development, leading to the expectation that plants accumulate somatic mutations along growth. Therefore, several predictions were made on the fate of somatic mutations: mutations have generally low frequency in plant tissues; mutations at high frequency have a higher chance of intergenerational transmission; branching topology of the tree dictates mutation distribution; and exposure to UV (ultraviolet) radiation increases mutagenesis. To provide insights into mutation accumulation and transmission in plants, we produced two high-quality reference genomes and a unique dataset of 60 high-coverage whole-genome sequences of two tropical tree species, Dicorynia guianensis (Fabaceae) and Sextonia rubra (Lauraceae). We identified 15,066 de novo somatic mutations in D. guianensis and 3,208 in S. rubra, surprisingly almost all found at low frequency. We demonstrate that 1) low-frequency mutations can be transmitted to the next generation; 2) mutation phylogenies deviate from the branching topology of the tree; and 3) mutation rates and mutation spectra are not demonstrably affected by differences in UV exposure. Altogether, our results suggest far more complex links between plant growth, aging, UV exposure, and mutation rates than commonly thought.

N-Acetylcysteine Alters Disease Progression and Increases Janus Kinase Mutation Frequency in a Mouse Model of Precursor B-Cell Acute Lymphoblastic Leukemia.

B-cell acute lymphoblastic leukemia (B-ALL) is the most prevalent type of cancer in young children and is associated with high levels of reactive oxygen species (ROS). The antioxidant N-acetylcysteine (NAC) was tested for its ability to alter disease progression in a mouse model of B-ALL. Mb1-CreΔPB mice have deletions in genes encoding PU.1 and Spi-B in B cells and develop B-ALL at 100% incidence. Treatment of Mb1-CreΔPB mice with NAC in drinking water significantly reduced the frequency of CD19+ pre-B-ALL cells infiltrating the thymus at 11 weeks of age. However, treatment with NAC did not reduce leukemia progression or increase survival by a median 16 weeks of age. NAC significantly altered gene expression in leukemias in treated mice. Mice treated with NAC had increased frequencies of activating mutations in genes encoding Janus kinases 1 and 3. In particular, frequencies of Jak3 R653H mutations were increased in mice treated with NAC compared with control drinking water. NAC opposed oxidization of PTEN protein ROS in cultured leukemia cells. These results show that NAC alters leukemia progression in this mouse model, ultimately selecting for leukemias with high Jak3 R653H mutation frequencies. SIGNIFICANCE STATEMENT: In a mouse model of precursor B-cell acute lymphoblastic leukemia associated with high levels of reactive oxygen species, treatment with N-acetylcysteine did not delay disease progression but instead selected for leukemic clones with activating R653H mutations in Janus kinase 3.

Geometric analysis of SARS-CoV-2 variants.

SARS-CoV-2 as a severe respiratory disease has been prevalent around the world since its first discovery in 2019.As a single-stranded RNA virus, its high mutation rate makes its variants manifold and enables some of them to have high pathogenicity, such as Omicron variant, the most prevalent virus now. Research on the relationship of these SARS-CoV-2 variants, especially exploring their difference is a hot issue. In this study, we constructed a geometric space to represent all SARS-CoV-2 sequences of different variants. An alignment-free method: natural vector method was utilized to establish genome space. The genome space of SARS-CoV-2 was constructed based on the 24-dimensional natural vector and the appropriate metric was determined through performing phylogenetic analysises. Phylogenetic trees of different lineages constructed under the selected natural vector and metric coincided with the lineage naming standards, which means lineages with same alphabetical prefix cluster in phylogenetic trees. Furthermore, the relationships between the various GISAID clades as depicted by the natural graph primarily matched the description provided in the GISAID clade naming.The validity of our geometric space was demonstrated by these phylogenetic analysis results. So in this research, we constructed a geometry space for the genomes of the novel coronavirus SARS-CoV-2, which allows us to compare the different variants. Our geometric space is valuable for resolving the issues insides the virus.

Emergence of a multilocus mutator genotype in mutator Escherichia coli experimental populations under repeated lethal selection.

Mutator alleles, which confer increased mutation rates, are known to spontaneously emerge and "hitchhike" to fixation in evolving asexual populations. Theory predicts that in an evolving asexual mutator population, a second mutator allele may spontaneously arise and hitchhike to fixation. Here, we describe an empirical test of the hypothesis of repeated hitchhiking. The starting population was a clonal strain of mutL-Escherichia coli whose mutation rate was 100-fold higher than wild type. We exposed the mutL- strain to a series of three antibiotics in increasing order of selective strength: fosfomycin, rifampicin, and streptomycin. Two independent replicates of the experiment were performed. As predicted, elevated mutation rates and enrichment for multilocus mutators (which bear more than one mutator allele) were observed in the end point populations of both experiments. DNA sequencing revealed an identical spontaneous 1-bp insertion in the mutator gene mutT in both end point populations. In the multilocus mutators, the causal relationship between the mutT- mutations and the increase in mutation rate was supported with mutT+ plasmid complementation tests. Surprisingly, when the experiment was repeated with the antibiotics deployed in decreasing order of selective strength, enrichment for multilocus mutators was not observed. Our data support the likelihood that the mutT- mutations rose to fixation in both populations, consistent with the hypothesis of repeated mutator hitchhiking. The escalation of mutation rates in asexual populations is relevant to multiple biological scenarios, including antibiotic resistance, host-pathogen interactions, and carcinogenesis.

Application of next-generation sequencing in the detection of low-abundance mutations.

Mutation accumulation in somatic cells contributes to cancer development, aging and many non-malignant diseases. The true mutation frequency in normal cells is extremely low, which presents a challenge in detecting these mutations at such low frequencies. The emergence of next-generation sequencing (NGS) technology enables direct detection of rare mutations across the entire genome of any species. This breakthrough overcomes numerous limitations of traditional mutation detection techniques that rely on specific detection models and sites. However, conventional NGS is limited in its application for detecting low-frequency mutations due to its high sequencing error rate. To address this challenge, high-accuracy NGS sequencing techniques based on molecular consensus sequencing strategies have been developed. These techniques have the ability to correct sequencing errors, resulting in error rates lower than 10-7, are expected to serve as effective tools for low-frequency mutation detection. Error-corrected NGS (ecNGS) techniques hold great potential in various areas, including safety evaluation and research on environmental mutagens, risk assessment of cell and gene therapy drugs, population health risk monitoring, and fundamental research in life sciences. This review highlights a comprehensive review of the research progress in low-frequency mutation detection techniques based on NGS, and provides a glimpse into their potential applications. It also offers an outlook on the potential applications of these techniques, thereby providing valuable insights for further development, research, and application of this technology in relevant fields.

High germline mutation rates, but not extreme population outbreaks, influence genetic diversity in a keystone coral predator.

Lewontin's paradox, the observation that levels of genetic diversity (π) do not scale linearly with census population size (Nc) variation, is an evolutionary conundrum. The most extreme mismatches between π and Nc are found for highly abundant marine invertebrates. Yet, the influences of new mutations on π relative to extrinsic processes such as Nc fluctuations are unknown. Here, we provide the first germline mutation rate (µ) estimate for a marine invertebrate in corallivorous crown-of-thorns sea stars (Acanthaster cf. solaris). We use high-coverage whole-genome sequencing of 14 parent-offspring trios alongside empirical estimates of Nc in Australia's Great Barrier Reef to jointly examine the determinants of π in populations undergoing extreme Nc fluctuations. The A. cf. solaris mean µ was 9.13 x 10-09 mutations per-site per-generation (95% CI: 6.51 x 10-09 to 1.18 x 10-08), exceeding estimates for other invertebrates and showing greater concordance with vertebrate mutation rates. Lower-than-expected Ne (~70,000-180,000) and low Ne/Nc values (0.0047-0.048) indicated weak influences of population outbreaks on long-term π. Our findings are consistent with elevated µ evolving in response to reduced Ne and generation time length, with important implications for explaining high mutational loads and the determinants of genetic diversity in marine invertebrate taxa.

Male Pedigree Toolbox: A Versatile Software for Y-STR Data Analyses.

Y-chromosomal short tandem repeats (Y-STRs) are widely used in forensic, genealogical, and population genetics. With the recent increase in the number of rapidly mutating (RM) Y-STRs, an unprecedented level of male differentiation can be achieved, widening and improving the applications of Y-STRs in various fields, including forensics. The growing complexity of Y-STR data increases the need for automated data analyses, but dedicated software tools are scarce. To address this, we present the Male Pedigree Toolbox (MPT), a software tool for the automated analysis of Y-STR data in the context of patrilineal genealogical relationships. The MPT can estimate mutation rates and male relative differentiation rates from input Y-STR pedigree data. It can aid in determining ancestral haplotypes within a pedigree and visualize the genetic variation within pedigrees in all branches of family trees. Additionally, it can provide probabilistic classifications using machine learning, helping to establish or prove the structure of the pedigree and the level of relatedness between males, even for closely related individuals with highly similar haplotypes. The tool is flexible and easy to use and can be adjusted to any set of Y-STR markers by modifying the intuitive input file formats. We introduce the MPT software tool v1.0 and make it publicly available with the goal of encouraging and supporting forensic, genealogical, and other geneticists in utilizing the full potential of Y-STRs for both research purposes and practical applications, including criminal casework.

Effects of parental age and polymer composition on short tandem repeat de novo mutation rates.

Short tandem repeats (STRs) are hotspots of genomic variability in the human germline because of their high mutation rates, which have long been attributed largely to polymerase slippage during DNA replication. This model suggests that STR mutation rates should scale linearly with a father's age, as progenitor cells continually divide after puberty. In contrast, it suggests that STR mutation rates should not scale with a mother's age at her child's conception, since oocytes spend a mother's reproductive years arrested in meiosis II and undergo a fixed number of cell divisions that are independent of the age at ovulation. Yet, mirroring recent findings, we find that STR mutation rates covary with paternal and maternal age, implying that some STR mutations are caused by DNA damage in quiescent cells rather than polymerase slippage in replicating progenitor cells. These results echo the recent finding that DNA damage in oocytes is a significant source of de novo single nucleotide variants and corroborate evidence of STR expansion in postmitotic cells. However, we find that the maternal age effect is not confined to known hotspots of oocyte mutagenesis, nor are postzygotic mutations likely to contribute significantly. STR nucleotide composition demonstrates divergent effects on de novo mutation (DNM) rates between sexes. Unlike the paternal lineage, maternally derived DNMs at A/T STRs display a significantly greater association with maternal age than DNMs at G/C-containing STRs. These observations may suggest the mechanism and developmental timing of certain STR mutations and contradict prior attribution of replication slippage as the primary mechanism of STR mutagenesis.

Hotspot propensity across mutational processes.

The sparsity of mutations observed across tumours hinders our ability to study mutation rate variability at nucleotide resolution. To circumvent this, here we investigated the propensity of mutational processes to form mutational hotspots as a readout of their mutation rate variability at single base resolution. Mutational signatures 1 and 17 have the highest hotspot propensity (5-78 times higher than other processes). After accounting for trinucleotide mutational probabilities, sequence composition and mutational heterogeneity at 10 Kbp, most (94-95%) signature 17 hotspots remain unexplained, suggesting a significant role of local genomic features. For signature 1, the inclusion of genome-wide distribution of methylated CpG sites into models can explain most (80-100%) of the hotspot propensity. There is an increased hotspot propensity of signature 1 in normal tissues and de novo germline mutations. We demonstrate that hotspot propensity is a useful readout to assess the accuracy of mutation rate models at nucleotide resolution. This new approach and the findings derived from it open up new avenues for a range of somatic and germline studies investigating and modelling mutagenesis.

Mutation and evolution: Conceptual possibilities.

Although random mutation is central to models of evolutionary change, a lack of clarity remains regarding the conceptual possibilities for thinking about the nature and role of mutation in evolution. We distinguish several claims at the intersection of mutation, evolution, and directionality and then characterize a previously unrecognized category: complex conditioned mutation. Empirical evidence in support of this category suggests that the historically famous fluctuation test should be revisited, and new experiments should be undertaken with emerging experimental techniques to facilitate detecting mutation rates within specific loci at an ultra-high, individual base pair resolution.

Within-host genomic evolution of methicillin-resistant Staphylococcus aureus in long-term carriers.

Assessing the genomic evolution of Staphylococcus aureus can help us understand how the bacteria adapt to its environment. In this study, we aimed to assess the mutation rate within 144 methicillin-resistant Staphylococcus aureus (MRSA) carriers with a carriage time from 4 to 11 years, including some carriers who belonged to the same households. We found that 23 of the 144 individuals had completely different MRSA types over time and were therefore not long-term carriers of the same MRSA. From the remaining 121 individuals, we performed whole-genome sequencing (WGS) on 424 isolates and then compared these pairwise using core genome multilocus sequence typing (cgMLST) and single-nucleotide polymorphism (SNP) analyses. We found a median within-host mutation rate in long-term MRSA carriers of 4.9 (3.4-6.9) SNPs/genome/year and 2.7 (1.8-4.2) allelic differences/genome/year, when excluding presumed recombination. Furthermore, we stratified the cohort into subgroups and found no significant difference between the median mutation rate of members of households, individuals with presumed continued exposure, e.g., from travel and persons without known continued exposure. Finally, we found that SNPs occurred at random within the genes in our cohort. KEY POINTS: • Median mutation rate within long-term MRSA carriers of 4.9 (3.4-6.9) SNPs/genome/year • Similar median mutation rates in subgroups (households, travelers) • No hotspots for SNPs within the genome.

A dynamical limit to evolutionary adaptation.

Natural selection makes evolutionary adaptation possible even if the overwhelming majority of new mutations are deleterious. However, in rapidly evolving populations where numerous linked mutations occur and segregate simultaneously, clonal interference and genetic hitchhiking can limit the efficiency of selection, allowing deleterious mutations to accumulate over time. This can in principle overwhelm the fitness increases provided by beneficial mutations, leading to an overall fitness decline. Here, we analyze the conditions under which evolution will tend to drive populations to higher versus lower fitness. Our analysis focuses on quantifying the boundary between these two regimes, as a function of parameters such as population size, mutation rates, and selection pressures. This boundary represents a state in which adaptation is precisely balanced by Muller's ratchet, and we show that it can be characterized by rapid molecular evolution without any net fitness change. Finally, we consider the implications of global fitness-mediated epistasis and find that under some circumstances, this can drive populations toward the boundary state, which can thus represent a long-term evolutionary attractor.

Tissue-Predisposition to Cancer Driver Mutations.

Driver mutations are considered the cornerstone of cancer initiation. They are defined as mutations that convey a competitive fitness advantage, and hence, their mutation frequency in premalignant tissue is expected to exceed the basal mutation rate. In old terms, that translates to "the survival of the fittest" and implies that a selective process underlies the frequency of cancer driver mutations. In that sense, each tissue is its own niche that creates a molecular selective pressure that may favor the propagation of a mutation or not. At the heart of this stands one of the biggest riddles in cancer biology: the tissue-predisposition to cancer driver mutations. The frequency of cancer driver mutations among tissues is non-uniform: for instance, mutations in APC are particularly frequent in colorectal cancer, and 99% of chronic myeloid leukemia patients harbor the driver BCR-ABL1 fusion mutation, which is rarely found in solid tumors. Here, we provide a mechanistic framework that aims to explain how tissue-specific features, ranging from epigenetic underpinnings to the expression of viral transposable elements, establish a molecular basis for selecting cancer driver mutations in a tissue-specific manner.

[Mutation characteristics of angioimmunoblastic T-cell lymphoma: an analysis of 75 cases].

Objective: To investigate the characteristics of gene mutations in angioimmunoblastic T-cell lymphoma (AITL). Methods: Seventy-five AITL cases diagnosed at the Department of Pathology, Ruijin Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China from June 2021 to June 2023 were included. Their formalin-fixed and paraffin-embedded or fresh tissues were subject to targeted next generation sequencing (NGS). The sequencing data was collected, and the distribution and type of gene mutations were analyzed. Results: 492 potential driver mutations were identified in 74 out of the 84 genes. Targeted sequencing data for the 75 AITL patients showed that the genes with mutation frequencies of ≥10% were TET2 (89.3%), RHOA (57.3%), IDH2 (37.3%), DNMT3A (36.0%), KMT2C (21.3%), PLCG1 (12.0%), and KDM6B (10.7%). There were significant co-occurrence relationships between TET2 and RHOA, TET2 and IDH2, and RHOA and IDH2 gene mutations (P<0.05), respectively, while TET2 and KDM6B gene mutations were mutually exclusive (P<0.05). Conclusions: The study reveals the mutational characteristics of AITL patients using NGS technology, which would provide insights for molecular diagnosis and targeted therapy of AITL.