Proton therapy re-irradiation outcomes and genomic landscape of patients with recurrent head and neck cancer.

INTRODUCTION: Re-irradiation (re-RT) for recurrent head and neck cancer (rHNC) is challenging. We describe clinical outcomes and toxicity of proton therapy (PT) for recurrent HNC, and report genomic alterations associated with patterns of failure. MATERIALS & METHODS: We performed a retrospective analysis of rHNC patients treated with PT. Outcomes were estimated using the Kaplan-Meier method. Univariate (UVA) and multivariate analyses (MVA) were performed to assess multiple patient factors. Next-generation sequencing and genomic analyses were performed on available samples. RESULTS: Eighty-nine patients treated with PBS-PT for rHNC with a median follow-up of 12 mo (0-71 mo) were included. The 1- and 2-y local control (LC) rates were 80.8 % (95 % CI: 70.8-90.8) and 66.2 % (95 % CI: 50.7-81.7), and 1- and 2-y distant metastasis-free survival (DMFS) were 41.0 % (95 % CI: 30.0-52.0) and 26.3 % (95 % CI: 15.7-36.9). The median overall survival (OS) was 13 mo (95 % CI: 9.3-16.7). On UVA and MVA, smaller gross tumor volume (GTV) was associated with improved OS (HR 1.002, P = 0.004), DMFS (HR 1.002, P = 0.004), and PFS (HR 1.002, P = 0.014). There were 35 late Gr3 + toxicity events (30.3 %). Patients with higher candidate gene-specific mutation burden (genes with [OR] > 2, P < 0.05) had inferior PFS. TP53, NOTCH4, and ARID1B mutations were associated with inferior DMFS (OR > 2, P < 0.05). CONCLUSIONS: PBS-PT is effective at achieving LC for rHNC with favorable toxicity. Distant metastases are common, and associated with TP53, NOTCH4, and ARID1B mutations. Inclusion of genomic alterations in the clinical decision process may be warranted.

Revisiting Cardiac Biology in the Era of Single Cell and Spatial Omics.

Throughout our lifetime, each beat of the heart requires the coordinated action of multiple cardiac cell types. Understanding cardiac cell biology, its intricate microenvironments, and the mechanisms that govern their function in health and disease are crucial to designing novel therapeutical and behavioral interventions. Recent advances in single-cell and spatial omics technologies have significantly propelled this understanding, offering novel insights into the cellular diversity and function and the complex interactions of cardiac tissue. This review provides a comprehensive overview of the cellular landscape of the heart, bridging the gap between suspension-based and emerging in situ approaches, focusing on the experimental and computational challenges, comparative analyses of mouse and human cardiac systems, and the rising contextualization of cardiac cells within their niches. As we explore the heart at this unprecedented resolution, integrating insights from both mouse and human studies will pave the way for novel diagnostic tools and therapeutic interventions, ultimately improving outcomes for patients with cardiovascular diseases.

Molecular profiling of pediatric and young adult colorectal cancer reveals a distinct genomic landscapes and potential therapeutic avenues.

Colorectal cancer (CRC) is a global health concern, and the incidence of early onset (EO) CRC, has an upward trend. This study delves into the genomic landscape of EO-CRC, specifically focusing on pediatric (PED) and young adult (YA) patients, comparing them with adult (AD) CRC. In this retrospective monocentric investigation, we performed targeted next-generation sequencing to compare the mutational profile of 38 EO-CRCs patients (eight PED and 30 YA) to those of a 'control group' consisting of 56 AD-CRCs. Our findings reveal distinct molecular profiles in EO-CRC, notably in the WNT and PI3K-AKT pathways. In pediatrics, we observed a significantly higher frequency of RNF43 mutations, whereas APC mutations were more prevalent in adult cases. These observations suggest age-related differences in the activation of the WNT pathway. Pathway and copy number variation analysis reveal that AD-CRC and YA-CRC have more similarities than the pediatric patients. PED shows a peculiar profile with CDK6 amplification and the enrichment of lysine degradation pathway. These findings may open doors for personalized therapies, such as PI3K-AKT pathway inhibitors or CDK6 inhibitors for pediatric patients. Additionally, the distinct molecular signatures of EO-CRC underscore the need for age-specific treatment strategies and precision medicine. This study emphasizes the importance of comprehensive molecular investigations in EO-CRCs, which can potentially improve diagnostic accuracy, prognosis, and therapeutic decisions for these patients. Collaboration between the pediatric and adult oncology community is fundamental to improve oncological outcomes for this rare and challenging pediatric tumor.

Genomic exploration of the fermented meat isolate Staphylococcus shinii IMDO-S216 with a focus on competitiveness-enhancing secondary metabolites.

BACKGROUND: Staphylococcus shinii appears as an umbrella species encompassing several strains of Staphylococcus pseudoxylosus and Staphylococcus xylosus. Given its phylogenetic closeness to S. xylosus, S. shinii can be found in similar ecological niches, including the microbiota of fermented meats where the species may contribute to colour and flavour development. In addition to these conventional functionalities, a biopreservation potential based on the production of antagonistic compounds may be available. Such potential, however, remains largely unexplored in contrast to the large body of research that is available on the biopreservative properties of lactic acid bacteria. The present study outlines the exploration of the genetic basis of competitiveness and antimicrobial activity of a fermented meat isolate, S. shinii IMDO-S216. To this end, its genome was sequenced, de novo assembled, and annotated. RESULTS: The genome contained a single circular chromosome and eight plasmid replicons. Focus of the genomic exploration was on secondary metabolite biosynthetic gene clusters coding for ribosomally synthesized and posttranslationally modified peptides. One complete cluster was coding for a bacteriocin, namely lactococcin 972; the genes coding for the pre-bacteriocin, the ATP-binding cassette transporter, and the immunity protein were also identified. Five other complete clusters were identified, possibly functioning as competitiveness factors. These clusters were found to be involved in various responses such as membrane fluidity, iron intake from the medium, a quorum sensing system, and decreased sensitivity to antimicrobial peptides and competing microorganisms. The presence of these clusters was equally studied among a selection of multiple Staphylococcus species to assess their prevalence in closely-related organisms. CONCLUSIONS: Such factors possibly translate in an improved adaptation and competitiveness of S. shinii IMDO-S216 which are, in turn, likely to improve its fitness in a fermented meat matrix.

Prospective of Mitochondrial DNA Variations in Cancer on Genomic Medicine.

Mitochondrial DNA (mtDNA) has emerged as a pivotal component in understanding the etiology and susceptibility of cancer. A recent study by Chen and colleagues delineated the germline genetic effect of mtDNA single-nucleotide polymorphisms (SNP) and haplogroups across pan-cancer risk. They identified a subset of mtSNPs and the corresponding risk score, as well as haplogroups A and M7 alongside their genetic interactions, conferring a protective effect against various cancers. These findings underscored the value of mtDNA variations as biomarkers for cancer etiology and as tools for cancer risk stratification. Future investigations are encouraged to integrate comprehensive omics data of genomics, transcriptomics, proteomics, and metabolomics, etc., from nuclear DNA with mtDNA variations, alongside single-cell and spatial technologies, to unravel the tumor mechanism and identify the drug targets. Moreover, the incorporation of polygenic risk score, that included mtDNA variations with both rare and common frequencies, and liquid biopsy-based biomarkers would enhance the predictive performance of cancer risk assessment and refine the risk stratification of population-based cancer screening. This commentary advocates for the validation across diverse populations to harness the full potential of mitochondrial genomics, and ultimately paves the prospective way for advancements in personalized cancer therapeutics and prevention strategies. See related article by Chen and colleagues, Cancer Epidemiol Biomarkers Prev 2024;33:381-8.

Accelerating 3D genomics data analysis with Microcket.

The three-dimensional (3D) organization of genome is fundamental to cell biology. To explore 3D genome, emerging high-throughput approaches have produced billions of sequencing reads, which is challenging and time-consuming to analyze. Here we present Microcket, a package for mapping and extracting interacting pairs from 3D genomics data, including Hi-C, Micro-C, and derivant protocols. Microcket utilizes a unique read-stitch strategy that takes advantage of the long read cycles in modern DNA sequencers; benchmark evaluations reveal that Microcket runs much faster than the current tools along with improved mapping efficiency, and thus shows high potential in accelerating and enhancing the biological investigations into 3D genome. Microcket is freely available at https://github.com/hellosunking/Microcket .

Genomic and taxonomic evaluation of 38 Treponema prophage sequences.

BACKGROUND: Despite Spirochetales being a ubiquitous and medically important order of bacteria infecting both humans and animals, there is extremely limited information regarding their bacteriophages. Of the genus Treponema, there is just a single reported characterised prophage. RESULTS: We applied a bioinformatic approach on 24 previously published Treponema genomes to identify and characterise putative treponemal prophages. Thirteen of the genomes did not contain any detectable prophage regions. The remaining eleven contained 38 prophage sequences, with between one and eight putative prophages in each bacterial genome. The prophage regions ranged from 12.4 to 75.1 kb, with between 27 and 171 protein coding sequences. Phylogenetic analysis revealed that 24 of the prophages formed three distinct sequence clusters, identifying putative myoviral and siphoviral morphology. ViPTree analysis demonstrated that the identified sequences were novel when compared to known double stranded DNA bacteriophage genomes. CONCLUSIONS: In this study, we have started to address the knowledge gap on treponeme bacteriophages by characterising 38 prophage sequences in 24 treponeme genomes. Using bioinformatic approaches, we have been able to identify and compare the prophage-like elements with respect to other bacteriophages, their gene content, and their potential to be a functional and inducible bacteriophage, which in turn can help focus our attention on specific prophages to investigate further.

Transcriptomic and genomic characteristics of intrahepatic metastases of primary liver cancer.

BACKGROUND: Patients with primary multifocal hepatocellular carcinoma (HCC) have a poor prognosis and often experience a high rate of treatment failure. Multifocal HCC is mainly caused by intrahepatic metastasis (IM), and though portal vein tumor thrombosis (PVTT) is considered a hallmark of IM, the molecular mechanism by which primary HCC cells invade the portal veins remains unclear. Therefore, it is necessary to recognize the early signs of metastasis of HCC to arrange better treatment for patients. RESULTS: To determine the differential molecular features between primary HCC with and without phenotype of metastasis, we used the CIBERSORTx software to deconvolute cell types from bulk RNA-Seq based on a single-cell transcriptomic dataset. According to the relative abundance of tumorigenic and metastatic hepatoma cells, VEGFA+ macrophages, effector memory T cells, and natural killer cells, HCC samples were divided into five groups: Pro-T, Mix, Pro-Meta, NKC, and MemT, and the transcriptomic and genomic features of the first three groups were analyzed. We found that the Pro-T group appeared to retain native hepatic metabolic activity, whereas the Pro-Meta group underwent dedifferentiation. Genes highly expressed in the group Pro-Meta often signify a worse outcome. CONCLUSIONS: The HCC cohort can be well-typed and prognosis predicted according to tumor microenvironment components. Primary hepatocellular carcinoma may have obtained corresponding molecular features before metastasis occurred.

A versatile, rapid Agrobacterium-mediated transient expression system for functional genomics studies in cannabis seedling.

MAIN CONCLUSION: We have developed and optimized a rapid, versatile Agrobacterium-mediated transient expression system for cannabis seedlings that can be used in functional genomics studies of both hemp-type and drug-type cannabis. Cannabis (Cannabis sativa L.) holds great promise in the medical and food industries due to its diverse chemical composition, including specialized cannabinoids. However, the study of key genes involved in various biological processes, including secondary metabolite biosynthesis, has been hampered by the lack of efficient in vivo functional analysis methods. Here, we present a novel, short-cycle, high-efficiency transformation method for cannabis seedlings using Agrobacterium tumefaciens. We used the RUBY reporter system to monitor transformation results without the need for chemical treatments or specialized equipment. Four strains of A. tumefaciens (GV3101, EHA105, LBA4404, and AGL1) were evaluated for transformation efficiency, with LBA4404 and AGL1 showing superior performance. The versatility of the system was further demonstrated by successful transformation with GFP and GUS reporter genes. In addition, syringe infiltration was explored as an alternative to vacuum infiltration, offering simplicity and efficiency for high-throughput applications. Our method allows rapid and efficient in vivo transformation of cannabis seedlings, facilitating large-scale protein expression and high-throughput characterization studies.

Data Harmonization Guidelines to Combine Multi-platform Genomic Data from Admixed Populations and Boost Power in Genome-Wide Association Studies.

Data harmonization involves combining data from multiple independent sources and processing the data to produce one uniform dataset. Merging separate genotypes or whole-genome sequencing datasets has been proposed as a strategy to increase the statistical power of association tests by increasing the effective sample size. However, data harmonization is not a widely adopted strategy due to the difficulties with merging data (including confounding produced by batch effects and population stratification). Detailed data harmonization protocols are scarce and are often conflicting. Moreover, data harmonization protocols that accommodate samples of admixed ancestry are practically non-existent. Existing data harmonization procedures must be modified to ensure the heterogeneous ancestry of admixed individuals is incorporated into additional downstream analyses without confounding results. Here, we propose a set of guidelines for merging multi-platform genetic data from admixed samples that can be adopted by any investigator with elementary bioinformatics experience. We have applied these guidelines to aggregate 1544 tuberculosis (TB) case-control samples from six separate in-house datasets and conducted a genome-wide association study (GWAS) of TB susceptibility. The GWAS performed on the merged dataset had improved power over analyzing the datasets individually and produced summary statistics free from bias introduced by batch effects and population stratification. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Processing separate datasets comprising array genotype data Alternate Protocol 1: Processing separate datasets comprising array genotype and whole-genome sequencing data Alternate Protocol 2: Performing imputation using a local reference panel Basic Protocol 2: Merging separate datasets Basic Protocol 3: Ancestry inference using ADMIXTURE and RFMix Basic Protocol 4: Batch effect correction using pseudo-case-control comparisons.

Genomic profiling informs therapies and prognosis for patients with hepatocellular carcinoma in clinical practice.

Hepatocellular carcinoma (HCC) genomic research has discovered actionable genetic changes that might guide treatment decisions and clinical trials. Nonetheless, due to a lack of large-scale multicenter clinical validation, these putative targets have not been converted into patient survival advantages. So, it's crucial to ascertain whether genetic analysis is clinically feasible, useful, and whether it can be advantageous for patients. We sequenced tumour tissue and blood samples (as normal controls) from 111 Chinese HCC patients at Qingdao University Hospital using the 508-gene panel and the 688-gene panel, respectively. Approximately 95% of patients had gene variations related to targeted treatment, with 50% having clinically actionable mutations that offered significant information for targeted therapy. Immune cell infiltration was enhanced in individuals with TP53 mutations but decreased in patients with CTNNB1 and KMT2D mutations. More notably, we discovered that SPEN, EPPK1, and BRCA2 mutations were related to decreased median overall survival, although MUC16 mutations were not. Furthermore, we found mutant MUC16 as an independent protective factor for the prognosis of HCC patients after curative hepatectomy. In conclusion, this study connects genetic abnormalities to clinical practice and potentially identifies individuals with poor prognoses who may benefit from targeted treatment or immunotherapy.

How low can you go? Short-read polishing of Oxford Nanopore bacterial genome assemblies.

It is now possible to assemble near-perfect bacterial genomes using Oxford Nanopore Technologies (ONT) long reads, but short-read polishing is usually required for perfection. However, the effect of short-read depth on polishing performance is not well understood. Here, we introduce Pypolca (with default and careful parameters) and Polypolish v0.6.0 (with a new careful parameter). We then show that: (1) all polishers other than Pypolca-careful, Polypolish-default and Polypolish-careful commonly introduce false-positive errors at low read depth; (2) most of the benefit of short-read polishing occurs by 25× depth; (3) Polypolish-careful almost never introduces false-positive errors at any depth; and (4) Pypolca-careful is the single most effective polisher. Overall, we recommend the following polishing strategies: Polypolish-careful alone when depth is very low (<5×), Polypolish-careful and Pypolca-careful when depth is low (5-25×), and Polypolish-default and Pypolca-careful when depth is sufficient (>25×).

An interactive atlas of genomic, proteomic, and metabolomic biomarkers promotes the potential of proteins to predict complex diseases.

Multiomics analyses have identified multiple potential biomarkers of the incidence and prevalence of complex diseases. However, it is not known which type of biomarker is optimal for clinical purposes. Here, we make a systematic comparison of 90 million genetic variants, 1453 proteins, and 325 metabolites from 500,000 individuals with complex diseases from the UK Biobank. A machine learning pipeline consisting of data cleaning, data imputation, feature selection, and model training using cross-validation and comparison of the results on holdout test sets showed that proteins were most predictive, followed by metabolites, and genetic variants. Only five proteins per disease resulted in median (min-max) areas under the receiver operating characteristic curves for incidence of 0.79 (0.65-0.86) and 0.84 (0.70-0.91) for prevalence. In summary, our work suggests the potential of predicting complex diseases based on a limited number of proteins. We provide an interactive atlas (macd.shinyapps.io/ShinyApp/) to find genomic, proteomic, or metabolomic biomarkers for different complex diseases.

A comparative plastome approach enhances the assessment of genetic variation in the Melilotus genus.

BACKGROUND: Melilotus, a member of the Fabaceae family, is a pivotal forage crop that is extensively cultivated in livestock regions globally due to its notable productivity and ability to withstand abiotic stress. However, the genetic attributes of the chloroplast genome and the evolutionary connections among different Melilotus species remain unresolved. RESULTS: In this study, we compiled the chloroplast genomes of 18 Melilotus species and performed a comprehensive comparative analysis. Through the examination of protein-coding genes, we successfully established a robust phylogenetic tree for these species. This conclusion is further supported by the phylogeny derived from single-nucleotide polymorphisms (SNPs) across the entire chloroplast genome. Notably, our findings revealed that M. infestus, M. siculus, M. sulcatus, and M. speciosus formed a distinct subgroup within the phylogenetic tree. Additionally, the chloroplast genomes of these four species exhibit two shared inversions. Moreover, inverted repeats were observed to have reemerged in six species within the IRLC. The distribution patterns of single-nucleotide polymorphisms (SNPs) and insertions/deletions (InDels) within protein-coding genes indicated that ycf1 and ycf2 accumulated nonconservative alterations during evolutionary development. Furthermore, an examination of the evolutionary rate of protein-coding genes revealed that rps18, rps7, and rpl16 underwent positive selection specifically in Melilotus. CONCLUSIONS: We present a comparative analysis of the complete chloroplast genomes of Melilotus species. This study represents the most thorough and detailed exploration of the evolution and variability within the genus Melilotus to date. Our study provides valuable chloroplast genomic information for improving phylogenetic reconstructions and making biogeographic inferences about Melilotus and other Papilionoideae species.

RicePilaf: a post-GWAS/QTL dashboard to integrate pangenomic, coexpression, regulatory, epigenomic, ontology, pathway, and text-mining information to provide functional insights into rice QTLs and GWAS loci.

BACKGROUND: As the number of genome-wide association study (GWAS) and quantitative trait locus (QTL) mappings in rice continues to grow, so does the already long list of genomic loci associated with important agronomic traits. Typically, loci implicated by GWAS/QTL analysis contain tens to hundreds to thousands of single-nucleotide polmorphisms (SNPs)/genes, not all of which are causal and many of which are in noncoding regions. Unraveling the biological mechanisms that tie the GWAS regions and QTLs to the trait of interest is challenging, especially since it requires collating functional genomics information about the loci from multiple, disparate data sources. RESULTS: We present RicePilaf, a web app for post-GWAS/QTL analysis, that performs a slew of novel bioinformatics analyses to cross-reference GWAS results and QTL mappings with a host of publicly available rice databases. In particular, it integrates (i) pangenomic information from high-quality genome builds of multiple rice varieties, (ii) coexpression information from genome-scale coexpression networks, (iii) ontology and pathway information, (iv) regulatory information from rice transcription factor databases, (v) epigenomic information from multiple high-throughput epigenetic experiments, and (vi) text-mining information extracted from scientific abstracts linking genes and traits. We demonstrate the utility of RicePilaf by applying it to analyze GWAS peaks of preharvest sprouting and genes underlying yield-under-drought QTLs. CONCLUSIONS: RicePilaf enables rice scientists and breeders to shed functional light on their GWAS regions and QTLs, and it provides them with a means to prioritize SNPs/genes for further experiments. The source code, a Docker image, and a demo version of RicePilaf are publicly available at https://github.com/bioinfodlsu/rice-pilaf.

Statistical and computational methods for integrating microbiome, host genomics, and metabolomics data.

Large-scale microbiome studies are progressively utilizing multiomics designs, which include the collection of microbiome samples together with host genomics and metabolomics data. Despite the increasing number of data sources, there remains a bottleneck in understanding the relationships between different data modalities due to the limited number of statistical and computational methods for analyzing such data. Furthermore, little is known about the portability of general methods to the metagenomic setting and few specialized techniques have been developed. In this review, we summarize and implement some of the commonly used methods. We apply these methods to real data sets where shotgun metagenomic sequencing and metabolomics data are available for microbiome multiomics data integration analysis. We compare results across methods, highlight strengths and limitations of each, and discuss areas where statistical and computational innovation is needed.

Prognostic value of genomic mutation signature associated with immune microenvironment in southern Chinese patients with esophageal squamous cell carcinoma.

The genomic landscape of esophageal squamous cell cancer (ESCC), as well as its impact on the regulation of immune microenvironment, is not well understood. Thus, tumor samples from 92 patients were collected from two centers and subjected to targeted-gene sequencing. We identified frequently mutated genes, including TP53, KMT2C, KMT2D, LRP1B, and FAT1. The most frequent mutation sites were ALOX12B (c.1565C > T), SLX4 (c.2786C > T), LRIG1 (c.746A > G), and SPEN (c.6915_6917del) (6.5%). Pathway analysis revealed dysregulation of cell cycle regulation, epigenetic regulation, PI3K/AKT signaling, and NOTCH signaling. A 17-mutated gene-related risk model was constructed using random survival forest analysis and showed significant prognostic value in both our cohort and the validation cohort. Based on the Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression (ESTIMATE) algorithm, the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm, and the MCPcounter algorithm, we found that the risk score calculated by the risk model was significantly correlated with stimulatory immune checkpoints (TNFSF4, ITGB2, CXCL10, CXCL9, and BTN3A1; p < 0.05). Additionally, it was significantly associated with markers that are important in predicting response to immunotherapy (CD274, IFNG, and TAMM2; p < 0.05). Furthermore, the results of immunofluorescence double staining showed that patients with high risk scores had a significantly higher level of M2 macrophage than those with low risk scores (p < 0.05). In conclusion, our study provides insights into the genomic landscape of ESCC and highlights the prognostic value of a genomic mutation signature associated with the immune microenvironment in southern Chinese patients with ESCC.

Genome-wide analysis reveals genomic diversity and signatures of selection in Qinchuan beef cattle.

BACKGROUND: Indigenous Chinese cattle have abundant genetic diversity and a long history of artificial selection, giving local breeds advantages in adaptability, forage tolerance and resistance. The detection of selective sweeps and comparative genome analysis of selected breeds and ancestral populations provide a basis for understanding differences among breeds and for the identification and utilization of candidate genes. We investigated genetic diversity, population structure, and signatures of selection using genome-wide sequencing data for a new breed of Qinchuan cattle (QNC, n = 21), ancestral Qinchuan cattle (QCC, n = 20), and Zaosheng cattle (ZSC, n = 19). RESULTS: A population structure analysis showed that the ancestry components of QNC and ZSC were similar. In addition, the QNC and ZSC groups showed higher proportions of European taurine ancestry than that of QCC, and this may explain the larger body size of QNC, approaching that of European cattle under long-term domestication and selection. A neighbor-joining tree revealed that QCC individuals were closely related, whereas QNC formed a distinct group. To search for signatures of selection in the QNC genome, we evaluated nucleotide diversity (θπ), the fixation index (FST) and Tajima's D. Overlapping selective sweeps were enriched for one KEGG pathway, the apelin signaling pathway, and included five candidate genes (MEF2A, SMAD2, CAMK4, RPS6, and PIK3CG). We performed a comprehensive review of genomic variants in QNC, QCC, and ZSC using whole-genome sequencing data. QCC was rich in novel genetic diversity, while diversity in QNC and ZSC cattle was reduced due to strong artificial selection, with divergence from the original cattle. CONCLUSIONS: We identified candidate genes associated with production traits. These results support the success of selective breeding and can guide further breeding and resource conservation of Qinchuan cattle.

NIPT-PG: empowering non-invasive prenatal testing to learn from population genomics through an incremental pan-genomic approach.

Non-invasive prenatal testing (NIPT) is a quite popular approach for detecting fetal genomic aneuploidies. However, due to the limitations on sequencing read length and coverage, NIPT suffers a bottleneck on further improving performance and conducting earlier detection. The errors mainly come from reference biases and population polymorphism. To break this bottleneck, we proposed NIPT-PG, which enables the NIPT algorithm to learn from population data. A pan-genome model is introduced to incorporate variant and polymorphic loci information from tested population. Subsequently, we proposed a sequence-to-graph alignment method, which considers the read mis-match rates during the mapping process, and an indexing method using hash indexing and adjacency lists to accelerate the read alignment process. Finally, by integrating multi-source aligned read and polymorphic sites across the pan-genome, NIPT-PG obtains a more accurate z-score, thereby improving the accuracy of chromosomal aneuploidy detection. We tested NIPT-PG on two simulated datasets and 745 real-world cell-free DNA sequencing data sets from pregnant women. Results demonstrate that NIPT-PG outperforms the standard z-score test. Furthermore, combining experimental and theoretical analyses, we demonstrate the probably approximately correct learnability of NIPT-PG. In summary, NIPT-PG provides a new perspective for fetal chromosomal aneuploidies detection. NIPT-PG may have broad applications in clinical testing, and its detection results can serve as a reference for false positive samples approaching the critical threshold.

Deciphering lung adenocarcinoma evolution: Integrative single-cell genomics identifies the prognostic lung progression associated signature.

We employed single-cell analysis techniques, specifically the inferCNV method, to dissect the complex progression of lung adenocarcinoma (LUAD) from adenocarcinoma in situ (AIS) through minimally invasive adenocarcinoma (MIA) to invasive adenocarcinoma (IAC). This approach enabled the identification of Cluster 6, which was significantly associated with LUAD progression. Our comprehensive analysis included intercellular interaction, transcription factor regulatory networks, trajectory analysis, and gene set variation analysis (GSVA), leading to the development of the lung progression associated signature (LPAS). Interestingly, we discovered that the LPAS not only accurately predicts the prognosis of LUAD patients but also forecasts genomic alterations, distinguishes between 'cold' and 'hot' tumours, and identifies potential candidates suitable for immunotherapy. PSMB1, identified within Cluster 6, was experimentally shown to significantly enhance cancer cell invasion and migration, highlighting the clinical relevance of LPAS in predicting LUAD progression and providing a potential target for therapeutic intervention. Our findings suggest that LPAS offers a novel biomarker for LUAD patient stratification, with significant implications for improving prognostic accuracy and guiding treatment decisions.