Genetic Signatures Research Articles

Characterization of genomic instability-related genes predicts survival and therapeutic response in lung adenocarcinoma

BackgroundLung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer (NSCLC) and is the leading cause of cancer death worldwide. Its progression is characterized by genomic instability. In turn, the level of genomic instability affects the prognosis and immune status of patients with LUAD. However, the impact of molecular features associated with genomic instability on the tumor microenvironment (TME) has not been well characterized. In addition, the effect of the genes related to genomic instability in LUAD on individualized treatment of LUAD is unknown.MethodsThe RNA-Sequencing, somatic mutation, and clinical data of LUAD patients were downloaded from publicly available databases. A genetic signature associated with genomic instability (GSAGI) was constructed by univariate Cox regression, Lasso regression, and multivariate Cox regression analysis. Bioinformatics analysis investigated the differences in prognosis, immune characteristics, and the most appropriate treatment strategy among different subtypes of LUAD patients. CCK-8 and colony formation verified the various effects of Etoposide on different subtypes of LUAD cell lines. Cell-to-cell communication analysis was performed using the “CellChat” R package. The expression of the risk factors in the GSAGI was verified using real-time quantitative PCR (qRT-PCR) and Immunohistochemistry (IHC).ResultsWe constructed and validated the GSAGI, consisting of five genes: ANLN, RHOV, KRT6A, SIGLEC6, and KLRG2. The GSAGI was an independent prognostic factor for LUAD patients. Patients in the high-risk group distinguished by the GSAGI are more suitable for chemotherapy. More immune cells are infiltrating the tumor microenvironment of patients in the low-risk group, especially B cells. Low-risk group patients are more suitable for receiving immunotherapy. The single-cell level analysis confirmed the influence of the GSAGI on TME and revealed the Mode of action between tumor cells and other types of cells. qRT-PCR and IHC showed increased ANLN, RHOV, and KRT6A expression in the LUAD cells and tumor tissues.ConclusionThis study confirms that genes related to genomic instability can affect the prognosis and immune status of LUAD patients. The GSAGI we identified has the potential to guide clinicians in predicting clinical outcomes, assessing immunological status, and even developing personalized treatment plans for LUAD patients.

Disentangling archaic introgression and genomic signatures of selection at human immunity genes

Pathogens and infectious diseases have imposed exceptionally strong selective pressure on ancient and modern human genomes and contributed to the current variation in many genes. There is evidence that modern humans acquired immune variants through interbreeding with ancient hominins, but the impact of such variants on human traits is not fully understood. The main objectives of this research were to infer the genetic signatures of positive selection that may be involved in adaptation to infectious diseases and to investigate the function of Neanderthal alleles identified within a set of 50 Lithuanian genomes. Introgressed regions were identified using the machine learning tool ArchIE. Recent positive selection signatures were analysed using iHS. We detected high-scoring signals of positive selection at innate immunity genes (EMB, PARP8, HLAC, and CDSN) and evaluated their interactions with the structural proteins of pathogens. Interactions with human immunodeficiency virus (HIV) 1 and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were identified. Overall, genomic regions introgressed from Neanderthals were shown to be enriched in genes related to immunity, keratinocyte differentiation, and sensory perception.

Epigenetic alterations in urothelial bladder cancer associated with disease outcomes.

Bladder cancer (BLCA) is a molecular heterogeneous disease with known genetic distinctive signatures. However, DNA methylation is highly prevalent across a wide range of tumors, suggesting its potential in oncogenesis. Here, we aimed to interrogate the role of nine epigenetic alterations as diagnostic and prognostic markers in BLCA. DNA methylation, gene expression, and clinicopathological information were retrieved from The Cancer Genome Atlas data portal. Methylation values and gene expression were assessed to determine their association with normal and malignant tissue. Additionally, we studied the association between methylation values and clinicopathological variables. For the prognostic model, Kaplan-Meier Survival curves were generated. Lastly, univariate and multivariate analysis were performed to evaluate the simultaneous impact of methylation and clinicopathological variables on the risk of tumor progression and survival. Nine CpG sites' methylation -values involved in our study demonstrated different methylation signatures between normal and malignant urothelium. Hypermethylated CpGs were overrepresented in tumor tissue (p < 0.0001). Opposingly, 4 CpG sites showed lower methylation values in tumor samples (p < 0.0001). Cg12743248high and cg17192862low are risk factors for progression-free survival, whereas cg12374721high (HR:3.003 (1.283-7.030)) also demonstrated to be the most valuable independent risk factor for disease progression and a risk factor for overall survival. We have identified that methylated cg12374721 shows promise as a diagnostic and independent prognostic marker in BLCA progression.

Genetic Diversity and Signatures of Selection in the Roughskin Sculpin (Trachidermus fasciatus) Revealed by Whole Genome Sequencing.

The roughskin sculpin (Trachidermus fasciatus) is an endangered fish species in China. In recent years, artificial breeding technology has made significant progress, and the population of roughskin sculpin has recovered in the natural environment through enhancement programs and the release of juveniles. However, the effects of released roughskin sculpin on the genetic structure and diversity of wild populations remain unclear. Studies on genetic diversity analysis based on different types and numbers of molecular markers have yielded inconsistent results. In this study, we obtained 2,610,157 high-quality SNPs and 494,698 InDels through whole-genome resequencing of two farmed populations and one wild population. Both farmed populations showed consistent levels of genomic polymorphism and a slight increase in linkage compared with wild populations. The population structure of the two farmed populations was distinct from that of the wild population, but the degree of genetic differentiation was low (overall average Fst = 0.015). Selective sweep analysis showed that 523,529 genes were selected in the two farmed populations, and KEGG enrichment analysis showed that the selected genes were related to amino acid metabolism, which might be caused by artificial feeding. The findings of this study provide valuable additions to the existing genomic resources to help conserve roughskin sculpin populations.

Sphingolipids in Childhood Asthma and Obesity (SOAP Study): A Protocol of a Cross-Sectional Study.

Asthma and obesity are two of the most common chronic conditions in children and adolescents. There is increasing evidence that sphingolipid metabolism is altered in childhood asthma and is linked to airway hyperreactivity. Dysregulated sphingolipid metabolism is also reported in obesity. However, the functional link between sphingolipid metabolism, asthma, and obesity is not completely understood. This paper describes the protocol of an ongoing study on sphingolipids that aims to examine the pathophysiology of sphingolipids in childhood asthma and obesity. In addition, this study aims to explore the novel biomarkers through a comprehensive multi-omics approach including genomics, genome-wide DNA methylation, RNA-Seq, microRNA (miRNA) profiling, lipidomics, metabolomics, and cytokine profiling. This is a cross-sectional study aiming to recruit 440 children from different groups: children with asthma and normal weight (n = 100), asthma with overweight or obesity (n = 100), overweight or obesity (n = 100), normal weight (n = 70), and siblings of asthmatic children with normal weight, overweight, or obesity (n = 70). These participants will be recruited from the pediatric pulmonology, pediatric endocrinology, and general pediatric outpatient clinics at Sidra Medicine, Doha, Qatar. Information will be obtained from self-reported questionnaires on asthma, quality of life, food frequency (FFQ), and a 3-day food diary that are completed by the children and their parents. Clinical measurements will include anthropometry, blood pressure, biochemistry, bioelectrical impedance, and pulmonary function tests. Blood samples will be obtained for sphingolipid analysis, serine palmitoyltransferase (SPT) assay, whole-genome sequencing (WGS), genome-wide DNA methylation study, RNA-Seq, miRNA profiling, metabolomics, lipidomics, and cytokine analysis. Group comparisons of continuous outcome variables will be carried out by a one-way analysis of variance or the Kruskal-Wallis test using an appropriate pairwise multiple comparison test. The chi-squared test or a Fisher's exact test will be used to test the associations between categorical variables. Finally, multivariate analysis will be carried out to integrate the clinical data with multi-omics data. This study will help us to understand the role of dysregulated sphingolipid metabolism in obesity and asthma. In addition, the multi-omics data from the study will help to identify novel genetic and epigenetic signatures, inflammatory markers, and mechanistic pathways that link asthma and obesity in children. Furthermore, the integration of clinical and multi-omics data will help us to uncover the potential interactions between these diseases and to offer a new paradigm for the treatment of pediatric obesity-associated asthma.

MODL-33. PATIENT-DERIVED GLIOBLASTOMA MINIATURE AND CANCER STEM CELL SUICIDE-INDUCING NANO-AWAKENER BASED ON IT

Abstract Patient-specific anti-tumor therapies can evolve by vitalizing the mother tissue-like cancer niche, cellular profile, genetic signature, and drug responsiveness. This evolution elucidated a key mechanism along with the development of the cancer therapy. The present study aimed to meet these clinical demands by approaching the direct culture of GBM patient tissues with a 1 mm post-punching diameter in a 3D microchannel network chip (“GBM miniature”). Hence, we directly cultured of GBM patient tissue (1 mm diameter) in a microchannel network chip (GBM miniature) to preserve mother tissue-like characteristic signatures and microenvironment. When chemoradiation therapy were administered within 1 day, the responsiveness of the tissue in the chip mirroring the clinical outcomes. The characteristics of migrating cells from GBM miniature were analyzed, and peptides that target these migrating CSCs were searched for. And, by attaching this CSC peptide to the nanovesicle, the therapeutic effect was confirmed in GBM miniature. Upon testing within 1 day, the tissue chip reflected patient drug responsiveness as seen in the clinic. As the tissue chip culture was continued, the intact GBM signature was getting lost, and the outward migration of stem cells from the tissue origin increased, indicating a leaving-home effect on the family dismantle. Nanovesicle production using GBM stem cells enabled self-chasing of the cells that escaped the temozolomide effect owing to quiescence. The anti-PTPRZ1 peptide display and temozolomide loading to nanovesicles awaked stem cells from the quiescent stage to death. This research proposes a GBM patient avatar platform and mechanism-learned nanotherapy for translation.

Eukaryotic genomes from a global metagenomic data set illuminate trophic modes and biogeography of ocean plankton.

Single-celled eukaryotes play ecologically significant roles in the marine environment, yet fundamental questions about their biodiversity, ecological function, and interactions remain. Environmental sequencing enables researchers to document naturally occurring protistan communities, without culturing bias, yet metagenomic and metatranscriptomic sequencing approaches cannot separate individual species from communities. To more completely capture the genomic content of mixed protistan populations, we can create bins of sequences that represent the same organism (metagenome-assembled genomes [MAGs]). We developed the EukHeist pipeline, which automates the binning of population-level eukaryotic and prokaryotic genomes from metagenomic reads. We show exciting insight into what protistan communities are present and their trophic roles in the ocean. Scalable computational tools, like EukHeist, may accelerate the identification of meaningful genetic signatures from large data sets and complement researchers' efforts to leverage MAG databases for addressing ecological questions, resolving evolutionary relationships, and discovering potentially novel biodiversity.

Bone Allograft Acid Lysates Change the Genetic Signature of Gingival Fibroblasts.

Bone allografts are widely used as osteoconductive support to guide bone regrowth. Bone allografts are more than a scaffold for the immigrating cells as they maintain some bioactivity of the original bone matrix. Yet, it remains unclear how immigrating cells respond to bone allografts. To this end, we have evaluated the response of mesenchymal cells exposed to acid lysates of bone allografts (ALBA). RNAseq revealed that ALBA has a strong impact on the genetic signature of gingival fibroblasts, indicated by the increased expression of IL11, AREG, C11orf96, STC1, and GK-as confirmed by RT-PCR, and for IL11 and STC1 by immunoassays. Considering that transforming growth factor-β (TGF-β) is stored in the bone matrix and may have caused the expression changes, we performed a proteomics analysis, TGF-β immunoassay, and smad2/3 nuclear translocation. ALBA neither showed detectable TGF-β nor was the lysate able to induce smad2/3 translocation. Nevertheless, the TGF-β receptor type I kinase inhibitor SB431542 significantly decreased the expression of IL11, AREG, and C11orf96, suggesting that other agonists than TGF-β are responsible for the robust cell response. The findings suggest that IL11, AREG, and C11orf96 expression in mesenchymal cells can serve as a bioassay reflecting the bioactivity of the bone allografts.

Genetic diversity, population structure, and selective signature of sheep in the northeastern Tarim Basin.

Local sheep in the northeastern Tarim Basin can adapt to dry and low-rainfall regional environments. In this study, three local sheep breeds in the northeastern Tarim Basin, LOP (LOP) sheep, Bayinbuluke (BYK) sheep, and Kunlun (KUN, also known as the Qiemo sheep) sheep, and three introduced sheep breeds, Suffolk (SUF) sheep, Dorset (APD) sheep, and Texel (TEX) sheep, were analyzed for genetic diversity, population structure, and selective signature using the Illumina OvineSNP50K BeadChip. We found that LOP, BYK, and KUN had lower observed heterozygosity and expected heterozygosity than TEX, SUF, and ADP, which were differentiated based on geographic distribution. We performed fixation index (FST) analysis on three local sheep breeds in the northeastern Tarim Basin (LOP, BYK, and KUN) and introduced sheep breeds (TEX, SUF, and ADP) to measure genetic differentiation. Nucleotide diversity (PI) analysis was performed on single-nucleotide polymorphism (SNP) data of LOP, BYK, and KUN. A total of 493 candidate genes were obtained by taking the intersection at a threshold of 5%. Among them, SMAD2, ESR2, and HAS2 were related to reproductive traits. PCDH15, TLE4, and TFAP2B were related to growth traits. SOD1, TSHR, and DNAJB5 were related to desert environmental adaptation. Analyzing the genetic patterns of local sheep in the northeastern Tarim Basin can protect the germplasm resources of local sheep and promote the development and utilization of sheep genetic resources.

Hematopoietic loss of Y-chromosome in monocytes from heart failure patients associates with cardiac fibrosis signaling

Abstract Background Hematopoietic mosaic loss of Y chromosome (LOY) increases with age and associates with increased cardiovascular disease incidence. Furthermore, recent murine studies suggest that LOY could potentially promote cardiac fibrosis by stimulating the infiltration and activation of bone marrow-derived macrophages. Yet, specific signatures of hematopoietic LOY cells in patients with heart failure (HF) are missing. Methods and results To decode the genetic signature of circulating hematopoietic cells and assess the effects of LOY in human leukocytes, we performed single-cell RNA-sequencing (scRNA-seq) of immune cells from 10 male patients with chronic HF (mean age 67.3 years, ejection fraction 34.4%). We sorted immune cells by presence or absence of Y-chromosome-associated genes (figure 1A). Relative distribution of LOY was assessed, with monocytes having the greatest absolute count of LOY cells with no significant change in distribution among cell types. Given the abundance of monocytic LOY cells we investigated LOY monocytic cells transcriptional signatures (29810 Y-carrier monocytes, 2205 LOY monocytes) in an unbiased manner. Using a patient-specific, paired analysis, LOY monocytes showed significantly reduced expression of TGF-ß inhibiting genes (SMAD7, TGIF2) in LOY cells versus Y-carrier cells. Opposingly, from 193 upregulated genes, LOY cells showed elevated markers associated with monocyte/macrophage-mediated tissue damage and pro-fibrotic cardiac remodelling (S100A8, CLEC4D, TLR2). Gene ontology terms derived from upregulated genes in LOY cells further supported a mechanistic link to cardiac fibrosis by calling terms associated with TLR signaling, which are known to enhance cardiac fibrosis. (figure 1B). Conclusion This is the first study to decipher the genetic signature of LOY monocytes in HF patients. Our results considerably extend very recent experimental findings in mice that LOY sensitizes macrophages to enhanced profibrotic signaling. Diminished TGF-ß inhibitory molecules and enhanced TLR signaling may contribute to the aggravation of heart failure by LOY in patients with HF.HF mLOY scRNA-seq

Identification of cell-intrinsic effects of mutated circulating cells due to clonal hematopoiesis in patients with heart failure

Abstract Clonal hematopoiesis of indeterminate potential (CHIP) is caused by somatic mutations in hematopoietic stem cells and is associated with worse prognosis in patients with heart failure with reduced ejection fraction (HFrEF). Circulating immune cells derived from patients with HFrEF harboring CHIP mutations show enhanced proinflammatory signatures. However, it is unclear, whether these signatures are derived from the relatively low number of cells harboring CHIP-driver mutations, or are indicators of a systemic pro-inflammatory activation associated with CHIP. Methods and Results We assessed the cell-intrinsic effects of the most prevalent CHIP-driver gene (DNMT3A) mutant-carrying circulating immune cells in a total of 5 patients with HFrEF. Using an improved protocol to detect mutant cells on a single cell level (MutDetect-Seq), as illustrated in figure 1A, we show that CHIP mutations can be detected in all major immune cell types (e.g. T cells, monocytes, B cells, NK cells). DNMT3A mutant monocytes demonstrated the highest number of significantly upregulated genes relative to other cell types. Specifically, DNMT3A mutant monocytes exhibit altered gene expression profiles associated with inflammation and phagolysosome function (figure 1B). In addition, DNMT3A mutant carrying CD4+, but not CD8+ T cells, showed significant changes in gene expression patterns towards effector cells and away from a naïve T cell state. Finally, increased paracrine signaling pathways involved in amplifying inflammatory circuits are predicted between mutated and non-mutated monocytes. Conclusion This is the first study to decipher the genetic signature of DNMT3A-CHIP-driver mutation carrying circulating immune cells, thereby providing first evidence for the cell intrinsic effects of DNMT3A mutations on peripheral blood cell subsets in patients with HFrEF. The altered gene expression profiles associated with inflammation and phagolysosome function of mutant monocyte as well as the skewing of mutated T cells towards effector cells provide novel insights into how CHIP might promote worse prognosis in heart failure patients.MutDetect-seq

Silicon versus Superbug: Assessing Machine Learning's Role in the Fight against Antimicrobial Resistance.

In his 1945 Nobel Prize acceptance speech, Sir Alexander Fleming warned of antimicrobial resistance (AMR) if the necessary precautions were not taken diligently. As the growing threat of AMR continues to loom over humanity, we must look forward to alternative diagnostic tools and preventive measures to thwart looming economic collapse and untold mortality worldwide. The integration of machine learning (ML) methodologies within the framework of such tools/pipelines presents a promising avenue, offering unprecedented insights into the underlying mechanisms of resistance and enabling the development of more targeted and effective treatments. This paper explores the applications of ML in predicting and understanding AMR, highlighting its potential in revolutionizing healthcare practices. From the utilization of supervised-learning approaches to analyze genetic signatures of antibiotic resistance to the development of tools and databases, such as the Comprehensive Antibiotic Resistance Database (CARD), ML is actively shaping the future of AMR research. However, the successful implementation of ML in this domain is not without challenges. The dependence on high-quality data, the risk of overfitting, model selection, and potential bias in training data are issues that must be systematically addressed. Despite these challenges, the synergy between ML and biomedical research shows great promise in combating the growing menace of antibiotic resistance.

Use of comprehensive genomic profiling for biomarker discovery for the management of non-small cell lung cancer brain metastases.

Clinical biomarkers for brain metastases remain elusive. Increased availability of genomic profiling has brought discovery of these biomarkers to the forefront of research interests. In this single institution retrospective series, 130 patients presenting with brain metastasis secondary to Non-Small Cell Lung Cancer (NSCLC) underwent comprehensive genomic profiling conducted using next generation circulating tumor deoxyribonucleic acid (DNA) (Guardant Health, Redwood City, CA). A total of 77 genetic mutation identified and correlated with nine clinical outcomes using appropriate statistical tests (general linear models, Mantel-Haenzel Chi Square test, and Cox proportional hazard regression models). For each outcome, a genetic signature composite score was created by summing the total genes wherein genes predictive of a clinically unfavorable outcome assigned a positive score, and genes with favorable clinical outcome assigned negative score. Seventy-two genes appeared in at least one gene signature including: 14 genes had only unfavorable associations, 36 genes had only favorable associations, and 22 genes had mixed effects. Statistically significant associated signatures were found for the clinical endpoints of brain metastasis velocity, time to distant brain failure, lowest radiosurgery dose, extent of extracranial metastatic disease, concurrent diagnosis of brain metastasis and NSCLC, number of brain metastases at diagnosis as well as distant brain failure. Some genes were solely associated with multiple favorable or unfavorable outcomes. Genetic signatures were derived that showed strong associations with different clinical outcomes in NSCLC brain metastases patients. While these data remain to be validated, they may have prognostic and/or therapeutic impact in the future. Using Liquid biopsy in NSCLC brain metastases patients, the genetic signatures identified in this series are associated with multiple clinical outcomes particularly these ones that lead to early or more numerous metastases. These findings can be reverse-translated in laboratory studies to determine if they are part of the genetic pathway leading to brain metastasis formation.

Peripheral Blood IL5RA Gene Expression as a Diagnostic Biomarker for Eosinophilic Esophagitis

Eosinophilic esophagitis (EoE) is an allergic inflammatory condition of the esophagus, often diagnosed late because of its challenging symptoms and costly and invasive diagnostic methods.1,2 To address the need for more accessible biomarkers in EoE,3 we aimed to investigate the potential of whole-blood RNA expression as a noninvasive biomarker for diagnosing and monitoring EoE, hypothesizing that genetic signatures in blood could distinguish EoE cases, correlate with disease activity, and predict treatment responses.

Analysis of CD74 Occurrence in Oncogenic Fusion Proteins.

CD74 is a type II cell surface receptor found to be highly expressed in several hematological and solid cancers, due to its ability to activate pathways associated with tumor cell survival and proliferation. Over the past 16 years, CD74 has emerged as a commonly detected fusion partner in multiple oncogenic fusion proteins. Studies have found CD74 fusion proteins in a range of cancers, including lung adenocarcinoma, inflammatory breast cancer, and pediatric acute lymphoblastic leukemia. To date, there are five known CD74 fusion proteins, CD74-ROS1, CD74-NTRK1, CD74-NRG1, CD74-NRG2α, and CD74-PDGFRB, with a total of 16 different variants, each with unique genetic signatures. Importantly, the occurrence of CD74 in the formation of fusion proteins has not been well explored despite the fact that ROS1 and NRG1 families utilize CD74 as the primary partner for the formation of oncogenic fusions. Fusion proteins known to be oncogenic drivers, including those of CD74, are typically detected and targeted after standard chemotherapeutic plans fail and the disease relapses. The analysis reported herein provides insights into the early intervention of CD74 fusions and highlights the need for improved routine assessment methods so that targeted therapies can be applied while they are most effective.

Impact of “Cell-of-Origin” on Outcome after Axicabtagene-Ciloleucel CAR-T Cell Therapy in Relapsed/Refractory Aggressive Non-Hodgkin B-Cell Lymphomas

Introduction: Despite significant advances in characterizing aggressive B-cell non-Hodgkin lymphomas (aNHL) based on molecular and genetic signatures, the Hans algorithm, determined by immunohistochemistry, remains the most commonly used approach to differentiate aNHLs based on the cell-of-origin (COO) into germinal center B-cell (GCB) and non-GCB types. Although the Hans algorithm has demonstrated reliability as a predictor of better outcomes in newly diagnosed GCB cases, conflicting evidence has emerged, particularly in the relapsed/refractory (R/R) setting, and there are no definitive data about the impact of COO in patients (pts) receiving CAR-T cells. Thus, we conducted a retrospective analysis to evaluate the impact of COO extended to R/R aNHLs who underwent treatment with axicabtagene-ciloleucel (axi-cel). Methods: We retrospectively collected data from pts with R/R aNHLs treated with axi-cel at Fred Hutchinson Cancer Center between February 2018 and August 2022. The Hans algorithm, using CD10, BCL6 and MUM1 markers, was employed to stratify the pts into two cohorts: GCB and non-GCB. Paired biopsies of diagnosis and relapse before CAR-T eligibility were reviewed at our institution. Response to CAR-T was assessed at one month per Lugano PET/CT criteria. Univariate cox proportional hazard regression was used to estimate associations between baseline factors and outcomes. Statistically significant factors ( p&amp;lt;.05) in univariate analysis were further evaluated by multivariate analysis. Continuous variables were compared by Mann-Whitney U test and categorical variables by Fischer's exact test. Progression-free survival (PFS) was calculated from the date of CAR-T cell infusion to the date of death from any cause, disease progression or relapse. Overall survival (OS) was calculated from the date of CAR-T cell infusion to the date of death from any cause. Results: Sixty-four pts were included in this analysis, with an equal distribution of pts in the two cohorts: 32 with GCB subtype and 32 with non-GCB subtype. Baseline pts' characteristics and histology subtypes are listed in Table 1. Transformed follicular lymphoma and primary mediastinal B-cell lymphoma were not included in this analysis. The presence of bulky disease (defined as &amp;gt;5cm diameter) was higher in the GCB subtype (56% vs 22%, p&amp;lt;.001). Although primary refractory cases were more frequent in the GCB cohort (87.5 vs 66%), statistical significance was not reached (NR)( p=.07). The same proportion of pts received bridging therapy in the two groups (53% vs 53%, p=.99). Pts evaluable for response assessment were 62. Median time to best response was 29 days (range, 21-186) for GCB pts and 31 days (range, 22-182) for non-GCB pts ( p=.91). The overall response rate was 70% for GCBs (34.5% complete response [CR], 34.5% partial response [PR]) and 75% for non-GCBs (44% CR, 31% PR). After a median follow-up of 24.5 months (range, 9-121), comparable outcomes were observed for both the COO groups: despite the estimated median OS of 14 months (95% CI, 4.73-NR) for GCB vs 40 months (95% CI, 12.55-67.46) for non-GCB, we did not find any significant differences ( p=.73) (Figure 1A). Estimated median PFS was 10 months (95% confidence interval [CI], 3.5-16) for GCB and 11 months (95% CI, 0-24) for non-GCB ( p=.57) (Figure 1B). The 2-year OS and PFS were: GCB, 45% (95% CI, 30-69) and 44% (95% CI, 29-67) respectively; non-GCB, 55% (95% CI, 38-79) and 29% (95% CI, 16-55), respectively. Treatment-related toxicities did not differ between GCB and non-GCB, with cytokine-release syndrome rates of 97% vs 94% ( p=.99) and neurotoxicity rates of 53% vs 62.5% ( p=.61), respectively. The incidence of infectious complications was 25% vs 19% ( p=.76). On univariate analysis, the impact of COO on OS (HR=0.82, 95% CI 0.39-1.73, p=.60) and PFS (HR=1.18, 95% CI 0.61-2.28, p=.62) was undetermined. After adjusting for bulky disease, elevated LDH, absolute lymphocyte count≤1000/uL, performance status≥2, &amp;gt;2 lines of therapy and response status before axi-cel infusion, the impact of COO on OS (adjusted HR [aHR]=1.73, 95% CI 0.71-4.22, p=.23) and PFS (aHR=2.01, 95% CI 0.93-4.35, p=.08), remained undetermined. Conclusion: We could not confirm an association between COO, as determined by Hans algorithm, and outcomes in R/R aNHLs who received axi-cel at our center. Prospective analyses on larger datasets and other types of CAR-T products are needed.

Adaptive evolution of mitochondrial genomes in Triplophysa cavefishes

Extreme conditions in caves pose survival challenges for cave dwellers, who gradually develop adaptive survival features. Cavefishes are one of the most successful animals among cave dwellers. Triplophysa cavefishes are an important group of cavefishes, and they show remarkable adaptability to the extreme environments of caves. However, there is a limited understanding of their adaptation mechanisms. In this study, eight complete mitochondrial genomes of Triplophysa cavefishes were newly obtained, and their genomic characteristics, including the base composition, base bias, and codon usage, were analyzed. Phylogenetic analysis was carried out based on 13 mitochondrial protein-coding genes from 44 Nemacheilidae species. This showed that Triplophysa cavefishes and non-cavefishes separate into two reciprocally monophyletic clades, suggesting a single origin of the cave phenotype. Positive selection analysis strongly suggested that the selection pressure in cavefishes is higher than that in non-cavefishes. Furthermore, the ND5 gene in cavefishes showed evidence of positive selection, which suggests that the gene may play an important role in the adaptation of cavefishes to the cave environment. Protein structure analysis of the ND5 subunit implied that the sites of positive selection in cavefishes might allow them to acquire lower ND5 protein stability, compared to that in non-cavefishes, which might help the accumulation of nonsynonymous (mildly deleterious) mutations. Together, our study revealed the genetic signatures of cave adaptation in Triplophysa cavefishes from the perspective of energy metabolism.

Cancer signature ensemble integrating cfDNA methylation, copy number, and fragmentation facilitates multi-cancer early detection

Cell-free DNA (cfDNA) sequencing has demonstrated great potential for early cancer detection. However, most large-scale studies have focused only on either targeted methylation sites or whole-genome sequencing, limiting comprehensive analysis that integrates both epigenetic and genetic signatures. In this study, we present a platform that enables simultaneous analysis of whole-genome methylation, copy number, and fragmentomic patterns of cfDNA in a single assay. Using a total of 950 plasma (361 healthy and 589 cancer) and 240 tissue samples, we demonstrate that a multifeature cancer signature ensemble (CSE) classifier integrating all features outperforms single-feature classifiers. At 95.2% specificity, the cancer detection sensitivity with methylation, copy number, and fragmentomic models was 77.2%, 61.4%, and 60.5%, respectively, but sensitivity was significantly increased to 88.9% with the CSE classifier (p value < 0.0001). For tissue of origin, the CSE classifier enhanced the accuracy beyond the methylation classifier, from 74.3% to 76.4%. Overall, this work proves the utility of a signature ensemble integrating epigenetic and genetic information for accurate cancer detection.

Genetic signatures of lineage fusion closely resemble population decline.

Accurate interpretation of the genetic signatures of past demographic events is crucial for reconstructing evolutionary history. Lineage fusion (complete merging, resulting in a single panmictic population) is a special case of secondary contact that is seldom considered. Here, the circumstances under which lineage fusion can be distinguished from population size constancy, growth, bottleneck, and decline were investigated. Multi-locus haplotype data were simulated under models of lineage fusion with different divergence versus sampling lag times (D:L ratios). These pseudo-observed datasets also differed in their allocation of a fixed amount of sequencing resources (number of sampled alleles, haplotype length, number of loci). Distinguishability of lineage fusion versus each of 10 untrue non-fusion scenarios was quantified based on six summary statistics (neutrality tests). Some datasets were also analyzed using extended Bayesian skyline plots. Results showed that signatures of lineage fusion very closely resemble those of decline-high distinguishability was generally limited to the most favorable scenario (D:L = 9), using the most sensitive summary statistics (F S and Z nS), coupled with the optimal sequencing resource allocation (maximizing number of loci). Also, extended Bayesian skyline plots often erroneously inferred population decline. Awareness of the potential for lineage fusion to carry the hallmarks of population decline is critical.

Genetic risk factors for severe and fatigue dominant long COVID and commonalities with ME/CFS identified by combinatorial analysis

BackgroundLong COVID is a debilitating chronic condition that has affected over 100 million people globally. It is characterized by a diverse array of symptoms, including fatigue, cognitive dysfunction and respiratory problems. Studies have so far largely failed to identify genetic associations, the mechanisms behind the disease, or any common pathophysiology with other conditions such as myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) that present with similar symptoms.MethodsWe used a combinatorial analysis approach to identify combinations of genetic variants significantly associated with the development of long COVID and to examine the biological mechanisms underpinning its various symptoms. We compared two subpopulations of long COVID patients from Sano Genetics’ Long COVID GOLD study cohort, focusing on patients with severe or fatigue dominant phenotypes. We evaluated the genetic signatures previously identified in an ME/CFS population against this long COVID population to understand similarities with other fatigue disorders that may be triggered by a prior viral infection. Finally, we also compared the output of this long COVID analysis against known genetic associations in other chronic diseases, including a range of metabolic and neurological disorders, to understand the overlap of pathophysiological mechanisms.ResultsCombinatorial analysis identified 73 genes that were highly associated with at least one of the long COVID populations included in this analysis. Of these, 9 genes have prior associations with acute COVID-19, and 14 were differentially expressed in a transcriptomic analysis of long COVID patients. A pathway enrichment analysis revealed that the biological pathways most significantly associated with the 73 long COVID genes were mainly aligned with neurological and cardiometabolic diseases.Expanded genotype analysis suggests that specific SNX9 genotypes are a significant contributor to the risk of or protection against severe long COVID infection, but that the gene-disease relationship is context dependent and mediated by interactions with KLF15 and RYR3.Comparison of the genes uniquely associated with the Severe and Fatigue Dominant long COVID patients revealed significant differences between the pathways enriched in each subgroup. The genes unique to Severe long COVID patients were associated with immune pathways such as myeloid differentiation and macrophage foam cells. Genes unique to the Fatigue Dominant subgroup were enriched in metabolic pathways such as MAPK/JNK signaling. We also identified overlap in the genes associated with Fatigue Dominant long COVID and ME/CFS, including several involved in circadian rhythm regulation and insulin regulation. Overall, 39 SNPs associated in this study with long COVID can be linked to 9 genes identified in a recent combinatorial analysis of ME/CFS patient from UK Biobank.Among the 73 genes associated with long COVID, 42 are potentially tractable for novel drug discovery approaches, with 13 of these already targeted by drugs in clinical development pipelines. From this analysis for example, we identified TLR4 antagonists as repurposing candidates with potential to protect against long term cognitive impairment pathology caused by SARS-CoV-2. We are currently evaluating the repurposing potential of these drug targets for use in treating long COVID and/or ME/CFS.ConclusionThis study demonstrates the power of combinatorial analytics for stratifying heterogeneous populations in complex diseases that do not have simple monogenic etiologies. These results build upon the genetic findings from combinatorial analyses of severe acute COVID-19 patients and an ME/CFS population and we expect that access to additional independent, larger patient datasets will further improve the disease insights and validate potential treatment options in long COVID.