Analysis Of Genomic Data Research Articles

Extensive cytonuclear discordance revealed by phylogenomic analyses suggests complex evolutionary history in the holly genus Ilex (Aquifoliaceae)

Ilex L., the exclusive genus of Aquifoliaceae, encompasses over 600 dioecious wood species with a highly irregular distribution, predominantly found in South America and Asia. The phylogeny and classification of this genus remain enigmatic due to significant early extinctions, constrained morphological diversity, recent hybridization/introgression, and conflicting signals from previously utilized markers. This study presents phylogenetic reconstructions based on complete chloroplast genome sequences and single nucleotide polymorphisms (SNPs) derived from genome resequencing data. A total of 116 accessions of Ilex, representing approximately 108 taxa, were included as the ingroup, with five accessions of two species serving as outgroups. Analysis of the chloroplast genome and nuclear SNP data individually resulted in two robust phylogenetic trees, revealing substantial discrepancies between the chloroplast genome and nuclear SNP phylogenies at both the species and clade levels. The chloroplast genome sequences successfully resolved relationships within this genus into eight strongly supported major clades, while the nuclear SNPs resolved relationships into seven highly supported major clades. Our nuclear SNP phylogenetic tree, in comparison to the chloroplast genome tree, aligns more closely with the recently updated classification of Ilex in multiple instances. The extensive cytonuclear discordance identified may be attributed to recent hybridization events and incomplete lineage sorting (ILS).

PhyloForge: Unifying Micro- and Macroevolution With Comprehensive Genomic Signals.

The dimensions of phylogenetic research have expanded to encompass the study of large-scale populations at the microevolutionary level and comparisons between different species or taxonomic units at the macroevolutionary level. Traditional phylogenetic tools often struggle to handle the diverse and complex data required for these different evolutionary scales. In response to this challenge, we introduce PhyloForge, a robust tool designed to seamlessly integrate the demands of both micro- and macroevolution, comprehensively utilising diverse phylogenomic signals, such as genes, SNPs, and structural variations, as well as mitochondrial and chloroplast genomes. PhyloForge's innovation lies in its capability to seamlessly integrate multiple phylogenomic signals, enabling the unified analysis of multidimensional genomic data. This unique feature empowers researchers to gain a more comprehensive understanding of diverse aspects of biological evolution. PhyloForge not only provides highly customisable analysis tools for experienced researchers but also features an intuitively designed interface, facilitating effortless phylogenetic analysis for beginners. Extensive testing across various domains, including animals, plants and fungi, attests to its broad applicability in the field of phylogenetics. In summary, PhyloForge has significant potential in the era of large-scale genomics, offering a new perspective and toolset for a deeper understanding of the evolution of life. PhyloForge codes could be found in GitHub (https://github.com/wangyayaya/PhyloForge/), and the program could be installed in Conda (https://anaconda.org/wangxiaobei/phyloforge).

Integrated analyses of multi-omic data derived from paired primary lung cancer and brain metastasis reveal the metabolic vulnerability as a novel therapeutic target

BackgroundLung cancer brain metastases (LC-BrMs) are frequently associated with dismal mortality rates in patients with lung cancer; however, standard of care therapies for LC-BrMs are still limited in their efficacy. A deep understanding of molecular mechanisms and tumor microenvironment of LC-BrMs will provide us with new insights into developing novel therapeutics for treating patients with LC-BrMs.MethodsHere, we performed integrated analyses of genomic, transcriptomic, proteomic, metabolomic, and single-cell RNA sequencing data which were derived from a total number of 154 patients with paired and unpaired primary lung cancer and LC-BrM, spanning four published and two newly generated patient cohorts on both bulk and single cell levels.ResultsWe uncovered that LC-BrMs exhibited a significantly greater intra-tumor heterogeneity. We also observed that mutations in a subset of genes were almost always shared by both primary lung cancers and LC-BrM lesions, including TTN, TP53, MUC16, LRP1B, RYR2, and EGFR. In addition, the genome-wide landscape of somatic copy number alterations was similar between primary lung cancers and LC-BrM lesions. Nevertheless, several regions of focal amplification were significantly enriched in LC-BrMs, including 5p15.33 and 20q13.33. Intriguingly, integrated analyses of transcriptomic, proteomic, and metabolomic data revealed mitochondrial-specific metabolism was activated but tumor immune microenvironment was suppressed in LC-BrMs. Subsequently, we validated our results by conducting real-time quantitative reverse transcription PCR experiments, immunohistochemistry, and multiplexed immunofluorescence staining of patients’ paired tumor specimens. Therapeutically, targeting oxidative phosphorylation with gamitrinib in patient-derived organoids of LC-BrMs induced apoptosis and inhibited cell proliferation. The combination of gamitrinib plus anti-PD-1 immunotherapy significantly improved survival of mice bearing LC-BrMs. Patients with a higher expression of mitochondrial metabolism genes but a lower expression of immune genes in their LC-BrM lesions tended to have a worse survival outcome.ConclusionsIn conclusion, our findings not only provide comprehensive and integrated perspectives of molecular underpinnings of LC-BrMs but also contribute to the development of a potential, rationale-based combinatorial therapeutic strategy with the goal of translating it into clinical trials for patients with LC-BrMs.

Comorbidity alters the genetic relationship between anxiety disorders and major depression.

There is extensive comorbidity between anxiety disorders (ANX) and major depression (MD). Most studies on the genetics of ANX do not exclude comorbid cases of MD, and vice versa, therefore confounding genetic association analyses. Disorder-specific analysis of genomic data may reveal more precise biological pathways and causal relationships. To investigate the genetic relationship between disorder-specific ANX and MD compared to samples with comorbidity, including their causal relationship. Data from UK Biobank was used to perform genome-wide association studies (GWAS) of ANX-only and MD-only, and generate disorder-specific polygenic risk scores (PRS). The Norwegian Mother, Father, and Child Cohort (MoBa) was used to test the associations of PRS with diagnosis and symptoms. MD and ANX GWAS data including comorbidities (MD-co and ANX-co) were used as comparators. Genetic correlation was assessed using LDSC, and Mendelian randomization was employed to infer causal relationships. GWAS of ICD-10 diagnoses of ANX, MD, or both. Genetic correlations between pairs of ANX and MD phenotypes. PRS associations with diagnoses of ANX, MD, and their comorbid states, and anxiety or depressive symptoms. The GWAS of ANX-only (9,980 cases and 179,442 controls) and MD-only (15,301 cases and 179,038 controls) showed a lower genetic correlation (0.53) than the one between ANX-co and MD-co (0.90). ANX-only showed a causal relationship with MD-only (P FDR =1.5e-02), but not vice versa , while comorbid cases showed a significant bidirectional causal relationship (P FDR =2.9e-12, P FDR =9.3e-06). The PRS-MD-only were differentially associated with MD-only compared to ANX-only cases (β= -0.08; 95%CI: -0.11, -0.03); however, this differential association was not observed for the PRS-MD-co. A similar pattern of differential association with anxiety and depressive symptoms was observed for PRS-ANX-only, but not for PRS-MD-co. The genetics and underlying biology of ANX and MD are more distinct when comorbid cases are excluded from analyses and reveals that ANX may be causal for MD. This confounding of genetic relationships as a result of comorbidity is likely to apply to other psychiatric disorders. Disorder-specific genetic studies may help uncover more relevant biological mechanisms and guide more targeted clinical interventions. Question: Is the high genetic correlation between anxiety disorders (ANX) and major depression (MD) due to phenotypic overlap (comorbidity) and will disorder-specific (non-comorbid) analysis provide insight into their underlying causal factors?Findings: Applying statistical genetics tools in two large population samples (UK Biobank, n=500,000; and MoBa, n= 130,000), we found a smaller genetic correlation between MD and ANX when comorbid cases were excluded. Additionally, after excluding comorbid cases, Mendelian randomization revealed that genetic liability to ANX is causally linked to MD but not vice versa . Meaning: The genetic overlap between ANX and MD may be driven by phenotypic comorbidity, which inflates their genetic correlation. The results support that ANX has a causal link to MD, suggesting that early interventions for ANX may prevent subsequent development of MD. Further, investigating the genetic underpinnings of ANX and MD, while excluding their comorbid states, may help advance precision medicine and uncover novel biological mechanisms.

Closha 2.0: a bio-workflow design system for massive genome data analysis on high performance cluster infrastructure

BackgroundThe explosive growth of next-generation sequencing data has resulted in ultra-large-scale datasets and significant computational challenges. As the cost of next-generation sequencing (NGS) has decreased, the amount of genomic data has surged globally. However, the cost and complexity of the computational resources required continue to be substantial barriers to leveraging big data. A promising solution to these computational challenges is cloud computing, which provides researchers with the necessary CPUs, memory, storage, and software tools.ResultsHere, we present Closha 2.0, a cloud computing service that offers a user-friendly platform for analyzing massive genomic datasets. Closha 2.0 is designed to provide a cloud-based environment that enables all genomic researchers, including those with limited or no programming experience, to easily analyze their genomic data. The new 2.0 version of Closha has more user-friendly features than the previous 1.0 version. Firstly, the workbench features a script editor that supports Python, R, and shell script programming, enabling users to write scripts and integrate them into their pipelines. This functionality is particularly useful for downstream analysis. Second, Closha 2.0 runs on containers, which execute each tool in an independent environment. This provides a stable environment and prevents dependency issues and version conflicts among tools. Additionally, users can execute each step of a pipeline individually, allowing them to test applications at each stage and adjust parameters to achieve the desired results. We also updated a high-speed data transmission tool called GBox that facilitates the rapid transfer of large datasets.ConclusionsThe analysis pipelines on Closha 2.0 are reproducible, with all analysis parameters and inputs being permanently recorded. Closha 2.0 simplifies multi-step analysis with drag-and-drop functionality and provides a user-friendly interface for genomic scientists to obtain accurate results from NGS data. Closha 2.0 is freely available at https://www.kobic.re.kr/closha2.

Identification of Picea mongolica LEA Gene Family Implicates PmLEA25 in Drought Resistance

Picea mongolica is a rare and valuable tree species in China, having high tolerance for drought, cold, and sand burial. The late embryogenesis abundant protein (LEA protein) is a crucial transcription factor that plays a key role in both plant embryonic development and stress response. LEA genes have, however, not yet been reported in P. mongolica. In this study, through the analysis of genome data from Picea abies and transcriptome data from P. mongolica, a total of 49 PmLEAs were discovered and categorized into eight subfamilies based on their Pfam domain and phylogenetic relationship. RNA-Seq research revealed that 37 PmLEAs were differentially expressed at various stages of embryonic development. Using qRT-PCR, we found that most PmLEAs responded strongly to drought stress, with genes in the same subfamily exhibiting identical expression patterns. In particular, PmLEA25 is the most highly induced by drought treatment. Furthermore, we heterologously transformed PmLEA25 into Arabidopsis. The overexpression of PmLEA25 remarkably increased the germination rate, root length, and antioxidant capacity in Arabidopsis under drought treatment, compared with WT. The results serve as a point of reference for gaining a deeper comprehension of the function of PmLEA25 in the molecular process of stress resistance in P. mongolica. Additionally, they offer significant genetic materials for the purpose of breeding stress-resistant spruce species.

Study of the Genetic Mechanisms of Siberian Stone Pine (Pinus sibirica Du Tour) Adaptation to the Climatic and Pest Outbreak Stresses Using Dendrogenomic Approach.

A joint analysis of dendrochronological and genomic data was performed to identify genetic mechanisms of adaptation and assess the adaptive genetic potential of Siberian stone pine (Pinus sibirica Du Tour) populations. The data obtained are necessary for predicting the effect of climate change and mitigating its negative consequences. Presented are the results of an association analysis of the variation of 84,853 genetic markers (single nucleotide polymorphisms-SNPs) obtained by double digest restriction-site associated DNA sequencing (ddRADseq) and 110 individual phenotypic traits, including dendrophenotypes based on the dynamics of tree-ring widths (TRWs) of 234 individual trees in six natural populations of Siberian stone pine, which have a history of extreme climatic stresses (e.g., droughts) and outbreaks of defoliators (e.g., pine sawfly [Neodiprion sertifer Geoff.]). The genetic structure of studied populations was relatively weak; samples are poorly differentiated and belong to genetically similar populations. Genotype-dendrophenotype associations were analyzed using three different approaches and corresponding models: General Linear Model (GLM), Bayesian Sparse Linear Mixed Model (BSLMM), and Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK), respectively. Thirty SNPs were detected by at least two different approaches, and two SNPs by all three. In addition, three SNPs associated with mean values of recovery dendrophenotype (Rc) averaged across multiple years of climatic stresses were also found by all three methods. The sequences containing these SNPs were annotated using genome annotation of a very closely related species, whitebark pine (P. albicaulis Engelm.). We found that most of the SNPs with supposedly adaptive variation were located in intergenic regions. Three dendrophenotype-associated SNPs were located within the 10 Kbp regions and one in the intron of the genes encoding proteins that play a crucial role in ensuring the integrity of the plant's genetic information, particularly under environmental stress conditions that can induce DNA damage. In addition, we found a correlation of individual heterozygosity with some dendrophenotypes. Heterosis was observed in most of these statistically significant cases; signs of homeostasis were also detected. Although most of the identified SNPs were not assigned to a particular gene, their high polymorphism and association with adaptive traits likely indicate high adaptive potential that can facilitate adaptation of Siberian stone pine populations to the climatic stresses and climate change.

In silico framework for genome analysis

Genomes hold the complete genetic information of an organism. Examining and analyzing genomic data plays a critical role in properly understanding an organism, particularly the main characteristics, functionalities, and evolving nature of harmful viruses. However, the rapid increase in genomic data poses new challenges and demands for extracting meaningful and valuable insights from large and complex genomic datasets. In this paper, a novel Framework for Genome Data Analysis (F4GDA), is developed that offers various methods for the analysis of viral genomic data in various forms. The framework’s methods can not only analyze the changes in genomes but also various genome contents. As a case study, the genomes of five SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) VoC (variants of concern), which are divided into three types/groups on the basis of geographical locations, are analyzed using this framework to investigate (1) the nucleotides, amino acids and synonymous codon changes in the whole genomes of VoC as well as in the Spike (S) protein, (2) whether different environments affect the rate of changes in genomes, (3) the variations in nucleotide bases, amino acids, and codon base compositions in VoC genomes, and (4) to compare VoC genomes with the reference genome sequence of SARS-CoV-2.

Microevolution of longan flowering behaviors from primitive to KClO3 induction is driven by artificial selection for agricultural benefit

Dimocarpus longan is an economically important fruit tree primarily cultivated in various Asian countries and the Indochina region encompassing Thailand, China, Vietnam, Laos and Cambodia. Here, we report an extensive dataset of 606.72Gb of the whole-genome resequencing data obtained from 31 Dimocarpus longan and 1 Dimocarpus obtusus samples performed on Illumina HiSeq PE150, providing the coverage over 30-fold of the reference genome (averaging 15Gb per sample) with an addition of 12 longan accessions from China. A phylogenomic tree inferred from this analyzed dataset (19,270,513 SNPs) was constructed and results indicated that there are three separate monophyletic clades of China-USA (1), Thai (2) and Vietnam longan (3). Interestingly, however, Thai and Vietnam clades appear to be closely linked in genetic relationship based on analyses of karyotype, genomic data and certain morphological characteristics. De novo transcriptome assembly further revealed variations in gene sequences, divergent gene expression and candidate genes associated with different flowering behaviors. The greatest change in differential gene expression (51.87%) observed between natural independent and seasonal flowering implied the microevolutionary shift observed in longan is influenced by artificial selection, resulting in gradual changes in flowering behaviors from natural independent flowering to artificial flowering induction using potassium chlorate (KClO3). In summary, the data obtained from this study serves as the essential evidence for elucidating the microevolution of longan and shedding new light on an agronomical application of artificial flowering induction via modulation of KClO3 responsive genes in longan or other fruits in the future.

Source attribution of Listeria monocytogenes in the Netherlands

The aim of this study was to determine the relative contributions of various potential food sources of human listeriosis and to identify source-specific risk factors, at exposure level, for human Listeria monocytogenes (Lm) infection. To achieve this, available Lm isolates from human cases (n = 756) and food/animal sources (n = 950) from national surveillance systems in the Netherlands (2010−2020) were whole genome sequenced. Additionally, questionnaire-based exposure data for human cases was collected. Source attribution analysis was performed using a Random Forest model based on core-genome multilocus sequence typing (cgMLST). Risk factors for human Lm infection of cattle, chicken and seafood origin were determined using beta regression analysis on the cgMLST-based attribution estimates. Results indicated that the 756 human Lm isolates were mainly attributed to cattle (62.3 %), chicken (19.4 %), and seafood (16.9 %). Specifically, fresh meat (86.2 %), including fresh bovine meat (43.7 %) and fresh chicken meat (39.3 %), accounted for most cases. These attributions stemmed from Lm contamination of either the food products or their production environments. Consumption of steak tartare and smoked salmon was associated with an increased risk of human Lm infections attributed to cattle and seafood, respectively, while no specific risk factors for chicken-borne listeriosis were identified. This study indicated that Lm isolates of cattle origin, particularly those from fresh bovine meat and associated production environments, are estimated to be the primary cause of human listeriosis in the Netherlands. This aligns with several other European source attribution studies on Lm. Moreover, the identified risk factors for human Lm infection from cattle (i.e. steak tartare) and seafood (i.e. smoked salmon) clearly indicated their attributable sources. This joint analysis of core genome and epidemiological data provided novel insights into the origins and transmission pathways of human listeriosis.

The UCSC Genome Browser database: 2025 update.

The UCSC Genome Browser (https://genome.ucsc.edu) is a widely utilized web-based tool for visualization and analysis of genomic data, encompassing over 4000 assemblies from diverse organisms. Since its release in 2001, it has become an essential resource for genomics and bioinformatics research. Annotation data available on Genome Browser includes both internally created and maintained tracks as well as custom tracks and track hubs provided by the research community. This last year's updates include over 25 new annotation tracks such as the gnomAD 4.1 track on the human GRCh38/hg38 assembly, the addition of three new public hubs, and significant expansions to the Genome Archive[GenArk) system for interacting with the enormous variety of assemblies. We have also made improvements to our interface, including updates to the browser graphic page, such as a new popup dialog feature that now displays item details without requiring navigation away from the main Genome Browser page. GenePred tracks have been upgraded with right-click options for zooming and precise navigation, along with enhanced mouseOver functions. Additional improvements include a new grouping feature for track hubs and hub description info links. A new tutorial focusing on Clinical Genetics has also been added to the UCSC Genome Browser.

Efficient Storage and Analysis of Genomic Data: A k-mer Frequency Mapping and Image Representation Method.

k-mer frequencies are crucial for understanding DNA sequence patterns and structure, with applications in motif discovery, genome classification, and short read assembly. However, the exponential increase in the dimension of frequency tables with increasing k-mer length poses storage challenges. In this study, we present a novel method for compressing k-mer data without information loss, aiming to optimize storage and analysis processes. We employed Chaos Game Representation (CGR) to map k-mers to coordinates and used these components to generate raster images of k-mers. The CGR maps were partitioned and labeled based on substrings, with each substring mapped to a subframe, creating a fractal-like structure. The entire k-mer frequency set of each genomic sequence was represented as a single image, with each pixel corresponding to a specific k-mer and its occurrence. This approach reduced file size by up to 16-fold compared to plain text and 3-fold compared to binary format. Furthermore, we demonstrated the feasibility of performing alignment-free similarity analyses on images derived from k-mer frequencies of whole genome sequences from 14 plant species. Our results highlight the potential of this method as a fast and efficient tool for accessing, processing, and analyzing large biological sequence datasets, enabling the retrieval of k-mer frequencies and image reconstruction.

Synergistic effect of reinforced cellulose nanofibrils/polyethylene glycol embedded particles in ammonia nitrogen wastewater: An in-depth microbial denitrification analysis

The encapsulation and immobilization technology provide a good growth environment for bacteria, strong protection ability, improved survival rate, and resistance to adverse environmental factors. Cellulose nanofibrils (CNF) with polyhydroxyl structure improve the elasticity, tensile properties, mechanical strength and gel strength of embedded particles through non-covalent forces. Polyethylene glycol (PEG) is a water-soluble polymer with unique carbon‑oxygen chain as the core skeleton. The ether bonds present in each unit give PEG extremely strong hydrophilicity and solubility. CNF and PEG to reinforce SA (sodium alginate) and PVA (polyvinyl alcohol)-SA embedded particle were adopted to treat ammonia nitrogen wastewater. The ammonia diffusion coefficients and oxygen diffusion coefficients of the CNF/PEG/SA and CNF/PEG/PVA-SA were 0.454 × 10−9, 0.286 × 10−9, 0.672 × 10−9 and 0.493 × 10−9 m2/s, respectively. The physicochemical properties of embedded particles were characterized by mass transfer characterization, SEM, XRD, FTIR, and BET analysis. The specific surface areas of CNF/PEG/SA and CNF/PEG/PVA-SA increased to 2.583 m2/g and 3.962 m2/g, respectively. The removal efficiency of NH4+-N, COD and TN in the CNF/PEG/PVA-SA system was 90 %, 74 % and 80 % at 30 °C. By HRT 8 h, the removal efficiency of NH4+-N, chemical oxygen demand (COD)and total nitrogen (TN) by the reinforced system was 94.35 %, 63.07 % and 73.84 %, respectively. The neutral to weakly alkaline pH range showed the highest removal efficiencies of NH4+-N, COD, and TN, at 88.51 %, 74.95 %, and 77.56 %, respectively. The half-saturation constant K was 82.98 mg/L, and the maximum ammonia oxidation rate (Vmax) was 358.92 mgN/(L-particles-h). The reinforced system showed zero-order reaction kinetics. Nonlinear fitting of each initial NH4+-N concentration and ammonia oxidation rate was carried out using the Michaelis-Menten equation. In reinforced embedded particles, microbial genomic data (KEGG; MetaCyc database annotation) analysis was conducted. The reinforced system demonstrated enhanced strength, specific surface area, mass transfer properties, resistance to adverse external environmental factors, and microbial survival rate for the effective treatment of NH4+-N wastewater.

PyRforest: a comprehensive R package for genomic data analysis featuring scikit-learn Random Forests in R.

Random Forest models are widely used in genomic data analysis and can offer insights into complex biological mechanisms, particularly when features influence the target in interactive, nonlinear, or nonadditive ways. Currently, some of the most efficient Random Forest methods in terms of computational speed are implemented in Python. However, many biologists use R for genomic data analysis, as R offers a unified platform for performing additional statistical analysis and visualization. Here, we present an R package, pyRforest, which integrates Python scikit-learn "RandomForestClassifier" algorithms into the R environment. pyRforest inherits the efficient memory management and parallelization of Python, and is optimized for classification tasks on large genomic datasets, such as those from RNA-seq. pyRforest offers several additional capabilities, including a novel rank-based permutation method for biomarker identification. This method can be used to estimate and visualize P-values for individual features, allowing the researcher to identify a subset of features for which there is robust statistical evidence of an effect. In addition, pyRforest includes methods for the calculation and visualization of SHapley Additive exPlanations values. Finally, pyRforest includes support for comprehensive downstream analysis for gene ontology and pathway enrichment. pyRforest thus improves the implementation and interpretability of Random Forest models for genomic data analysis by merging the strengths of Python with R. pyRforest can be downloaded at: https://www.github.com/tkolisnik/pyRforest with an associated vignette at https://github.com/tkolisnik/pyRforest/blob/main/vignettes/pyRforest-vignette.pdf.

Machine Learning for Genomic Expression Classification-Based Phenotype Prediction in Topological Data Analysis

Genomic data has become more prevalent due to sequencing and Machine Learning (ML) innovations, which have increased the biological genomics study. The multidimensional nature of this data provides challenges to phenotype prediction, which is required for individualized health care and the research investigation of genetic problems; nevertheless, it holds tremendous potential for understanding the association between genes and physical features. The authors of this paper introduce a new technique for symptom prediction from data from genomes, which combines Topological Data Analysis (TDA), Graph Convolutional Networks (GCN), and Support Vector Machines (SVM). The proposed method aims to address these challenges. By using TDA for multifaceted feature extraction, GCN to analyze gene interaction networks, and SVM for reliable classification in high-dimensional spaces, the above technique overcomes the drawbacks of conventional approaches. This TDA-GCN-SVM model has been demonstrated to be implemented in a method that is superior to conventional methods on distinct tumor datasets in terms of accuracy and additional measures. A novel method for genomic study and a more significant comprehension of genomic data analysis are both caused by this innovation, which is an enormous achievement in precision healthcare.

Deciphering resistance mechanisms in cancer: final report of MATCH-R study with a focus on molecular drivers and PDX development

BackgroundUnderstanding the resistance mechanisms of tumor is crucial for advancing cancer therapies. The prospective MATCH-R trial (NCT02517892), led by Gustave Roussy, aimed to characterize resistance mechanisms to cancer treatments through molecular analysis of fresh tumor biopsies. This report presents the genomic data analysis of the MATCH-R study conducted from 2015 to 2022 and focuses on targeted therapies.MethodsThe study included resistant metastatic patients (pts) who accepted an image-guided tumor biopsy. After evaluation of tumor content (TC) in frozen tissue biopsies, targeted NGS (10 < TC < 30%) or Whole Exome Sequencing and RNA sequencing (TC > 30%) were performed before and/or after the anticancer therapy.Patient-derived xenografts (PDX) were established by implanting tumor fragments into NOD scid gamma mice and amplified up to five passages.ResultsA total of 1,120 biopsies were collected from 857 pts with the most frequent tumor types being lung (38.8%), digestive (16.3%) and prostate (14.1%) cancer. Molecular targetable driver were identified in 30.9% (n = 265/857) of the patients, with EGFR (41.5%), FGFR2/3 (15.5%), ALK (11.7%), BRAF (6.8%), and KRAS (5.7%) being the most common altered genes. Furthermore, 66.0% (n = 175/265) had a biopsy at progression on targeted therapy. Among resistant cases, 41.1% (n = 72/175) had no identified molecular mechanism, 32.0% (n = 56/175) showed on-target resistance, and 25.1% (n = 44/175) exhibited a by-pass resistance mechanism. Molecular profiling of the 44 patients with by-pass resistance identified 51 variants, with KRAS (13.7%), PIK3CA (11.8%), PTEN (11.8%), NF2 (7.8%), AKT1 (5.9%), and NF1 (5.9%) being the most altered genes. Treatment was tailored for 45% of the patients with a resistance mechanism identified leading to an 11 months median extension of clinical benefit.A total of 341 biopsies were implanted in mice, successfully establishing 136 PDX models achieving a 39.9% success rate. PDX models are available for EGFR (n = 31), FGFR2/3 (n = 26), KRAS (n = 18), ALK (n = 16), BRAF (n = 6) and NTRK (n = 2) driven cancers. These models closely recapitulate the biology of the original tumors in term of molecular alterations and pharmacological status, and served as valuable models to validate overcoming treatment strategies.ConclusionThe MATCH-R study highlights the feasibility of on purpose image guided tumor biopsies and PDX establishment to characterize resistance mechanisms and guide personalized therapies to improve outcomes in pre-treated metastatic patients.Graphical

Molecular characterization of a novel putative pathogen, Streptococcus nakanoensis sp. nov., isolated from sputum culture.

The genus Streptococcus encompasses a wide range of bacteria with more than 60 species. Recently, there has been a notable increase in reports of novel species of α-Streptococcus based on genomic analysis data. However, limited information exists regarding the pathogenicity of these species. In this study, a quinolone-resistant α-hemolytic Streptococcus strain, MTG105, was isolated from a patient with pneumonia. Genetic analysis revealed that this species was a novel species closely related to S. pseudopneumoniae. In an infection assay using organotypic lung tissue models, MTG105 induced epithelial damage comparable to that caused by S. pneumoniae and S. pseudopneumoniae, strongly suggesting its potential as a pathogenic α-Streptococcus. The natural transformation abilities of Streptococcus species facilitate gene exchange within the same genus, leading to the emergence of species with increasingly diverse genome structures. Therefore, the identification of this species underscores the importance of monitoring the emergence of novel species exhibiting virulence and/or multidrug resistance.

Seamless, rapid, and accurate analyses of outbreak genomic data using split k-mer analysis.

Sequence variation observed in populations of pathogens can be used for important public health and evolutionary genomic analyses, especially outbreak analysis and transmission reconstruction. Identifying this variation is typically achieved by aligning sequence reads to a reference genome, but this approach is susceptible to reference biases and requires careful filtering of called genotypes. There is a need for tools that can process this growing volume of bacterial genome data, providing rapid results, but that remain simple so they can be used without highly trained bioinformaticians, expensive data analysis, and long-term storage and processing of large files. Here we describe split k-mer analysis (SKA2), a method that supports both reference-free and reference-based mapping to quickly and accurately genotype populations of bacteria using sequencing reads or genome assemblies. SKA2 is highly accurate for closely related samples, and in outbreak simulations, we show superior variant recall compared with reference-based methods, with no false positives. SKA2 can also accurately map variants to a reference and be used with recombination detection methods to rapidly reconstruct vertical evolutionary history. SKA2 is many times faster than comparable methods and can be used to add new genomes to an existing call set, allowing sequential use without the need to reanalyze entire collections. With an inherent absence of reference bias, high accuracy, and a robust implementation, SKA2 has the potential to become the tool of choice for genotyping bacteria. SKA2 is implemented in Rust and is freely available as open-source software.

Exploring the molecular landscape of cancer of unknown primary: A comparative analysis with other metastatic cancers.

Cancer of unknown primary (CUP) tumors are biologically very heterogeneous, which complicates stratification of patients for treatment. Consequently, these patients face limited treatment options and a poor prognosis. With this study, we aim to expand on the current knowledge of CUP biology by analyzing two cohorts: a well-characterized cohort of 44 CUP patients, and 213 metastatic patients with known primary. These cohorts were treated at the same institution and characterized by identical molecular assessments. Through comparative analysis of genomic and transcriptomic data, we found that CUP tumors were characterized by high expression of immune-related genes and pathways compared to other metastatic tumors. Moreover, CUP tumors uniformly demonstrated high levels of tumor-infiltrating leukocytes and circulating Tcells, indicating a strong immune response. Finally, the genetic landscape of CUP tumors resembled that of other metastatic cancers and demonstrated mutations in established cancer genes. In conclusion, CUP tumors possess a distinct immunophenotype that distinguishes them from other metastatic cancers. These results may suggest an immune response in CUP that facilitates metastatic tumor growth while limiting growth of the primary tumor.

Pathomics signatures and cuproptosis-related genes signatures for prediction of prognosis in patients with hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a common malignant tumor with high heterogeneity and poor prognosis, so early prediction and treatment are still difficult. Cuproptosis is a newly discovered type of programmed cell death that has been shown to be closely related to the occurrence and progression of HCC. Cancer morphology is influenced by genetic drivers, and computational pathology methods typically use tissue images such as entire slide images as input to predict clinical or genetic features. Therefore, the comprehensive analysis of pathological features and genomic data provides a feasible way to explore the potential mechanism of the tumor. The objective of this study was to develop a prediction model for HCC prognosis based on the pathomics signatures (PS) and the genomics signatures (GS). A dataset comprising 315 HCC patients was randomly divided into a training set (n=200) and a validation set (n=115). Prognostic models related to PS and GS were constructed by univariate and multivariate Cox regression analyses and least absolute shrinkage and selection operator (LASSO) regression analysis. Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curve, univariate and multivariate Cox analyses, and nomogram were used to evaluate the predictive performance of the prognostic model. The prognostic value of the model was internally validated. A prognostic model incorporating clinical features, PS, and GS was developed using Cox regression analysis and LASSO regression analyses. Kaplan-Meier survival analysis revealed statistically significant differences in survival time between high-risk and low-risk subgroups in both the training and validation datasets (PS: P=0.003 and <0.001, respectively; GS: P=0.008 and 0.004, respectively). The time-dependent ROC curve showed favorable predictive value for survival in both the training and validation sets. The area under the ROC curves at 1, 3, and 5 years was 0.750, 0.830, and 0.870 in the training set, and 0.780, 0.810, and 0.760 in the validation set, respectively. A nomogram model based on the risk model score could effectively predict the survival probability of HCC patients. The calibration curves further demonstrated the good predictive capability of the nomogram model. The prognostic model incorporating PS and GS could effectively predict the prognosis of HCC patients.