Mutation Data Research Articles

Effect of sequencing platforms on the sensitivity of chemical mutation detection using Hawk-Seq™

BackgroundError-corrected next-generation sequencing (ecNGS) technologies have enabled the direct evaluation of genome-wide mutations after exposure to mutagens. Previously, we reported an ecNGS methodology, Hawk-Seq™, and demonstrated its utility in evaluating mutagenicity. The evaluation of technical transferability is essential to further evaluate the reliability of ecNGS-based assays. However, cutting-edge sequencing platforms are continually evolving, which can affect the sensitivity of ecNGS. Therefore, the effect of differences in sequencing instruments on mutation data quality should be evaluated.ResultsWe assessed the performance of four sequencing platforms (HiSeq2500, NovaSeq6000, NextSeq2000, and DNBSEQ-G400) with the Hawk-Seq™ protocol for mutagenicity evaluation using DNA samples from mouse bone marrow exposed to benzo[a]pyrene (BP). The overall mutation (OM) frequencies per 106 bp in vehicle-treated samples were 0.22, 0.36, 0.46, and 0.26 for HiSeq2500, NovaSeq6000, NextSeq2000, and DNBSEQ-G400, respectively. The OM frequency of NextSeq2000 was significantly higher than that of HiSeq2500, suggesting the difference to be based on the platform. The relatively higher value in NextSeq2000 was a consequence of the G:C to C:G mutations in NextSeq2000 data (0.67 per 106 G:C bp), which was higher than the mean of the four platforms by a ca. of 0.25 per 106 G:C bp. A clear dose-dependent increase in G:C to T:A mutation frequencies was observed in all four sequencing platforms after BP exposure. The cosine similarity values of the 96-dimensional trinucleotide mutation patterns between HiSeq and the three other platforms were 0.93, 0.95, and 0.92 for NovaSeq, NextSeq, and DNBSeq, respectively. These results suggest that all platforms can provide equivalent data that reflect the characteristics of the mutagens.ConclusionsAll platforms sensitively detected mutagen-induced mutations using the Hawk-Seq™ analysis. The substitution types and frequencies of the background errors differed depending on the platform. The effects of sequencing platforms on mutagenicity evaluation should be assessed before experimentation.

Host-dependent C-to-U RNA editing in SARS-CoV-2 creates novel viral genes with optimized expressibility.

Rampant C-to-U RNA editing drives the mutation and evolution of SARS-CoV-2. While much attention has been paid to missense mutations, the C-to-U events leading to AUG and thus creating novel ORFs were uninvestigated. By utilizing the public time-course mutation data from the worldwide SARS-CoV-2 population, we systematically identified the "AUG-gain mutations" caused by C-to-U RNA editing. Synonymous mutations were of special focus. A total of 58 synonymous C-to-U sites are able to create out-of-frame AUG in coding sequence (CDS). These 58 synonymous sites showed significantly higher allele frequency (AF) and increasing rate (dAF/dt) than other C-to-U synonymous sites in the SARS-CoV-2 population, suggesting that these 58 AUG-gain events conferred additional benefits to the virus and are subjected to positive selection. The 58 predicted new ORFs created by AUG-gain events showed the following advantages compared to random expectation: they have longer lengths, higher codon adaptation index (CAI), higher Kozak scores, and higher tRNA adaptation index (tAI). The 58 putatively novel ORFs have high expressibility and are very likely to be functional, providing an explanation for the positive selection on the 58 AUG-gain mutations. Our study proposed a possible mechanism of the emergence of de novo genes in SARS-CoV-2. This idea should be helpful in studying the mutation and evolution of SARS-CoV-2.

Pediatric-type follicular lymphoma and pediatric nodal marginal zone lymphoma: additional evidence to support they are a single disease with variation in the histologic spectrum

Pediatric-type follicular lymphoma (PTFL) and pediatric nodal marginal zone lymphoma (PNMZL) are two rare indolent B-cell lymphomas with overlapping features. Recently, cases showing hybridizing features of PTFL and PNMZL have been reported. Herein, we retrospectively analyzed the clinicopathologic features of 59 patients, including 39 with PTFL, 5 with PNMZL, and 15 with mixed-type tumors (MTT). And next-generation sequencing analysis was performed on 3 PTFL, 2 PNMZL, and 2 MTT cases. In addition, previously published mutational data of 96 PTFLs, 25 PNMZLs, and 46 MTTs were also analyzed. There were 52 male and 7 female patients, with a median age of 17 years. Most patients (96.6%) had lymph node involvement in the head and neck region and were diagnosed with stage I disease. Among the 50 patients (85%) with telephone follow-up, 44 (88%) adopted a watch-and-wait strategy after surgical resection of the lesions. Only one PTFL patient experienced a relapse 6 months after diagnosis. Microscopically, not only the MTT cases showed a composite form of enlarged follicles and interfollicular lymphocytic proliferation producing a progressively transformed germinal center (PTGC) pattern, but also focal follicles with a PTGC-like pattern were observed in PTFL cases. Genetically, the most frequently mutated genes were TNFRSF14 (in 3 PTFLs and 2 MTTs), MAP2K1 (in 2 PTFLs, 1 PNMZL and 1 MTT), and IRF8 (in 2 MTTs and 1 PNMZL). Based on the similar or overlapping clinical, pathologic, and genetic features, PTFL and PNMZL are likely to represent two different histologic patterns of the same disease.

Development of a prognostic model related to homologous recombination deficiency in glioma based on multiple machine learning.

Despite advances in neuro-oncology, treatments of glioma and tools for predicting the outcome of patients remain limited. The objective of this research is to construct a prognostic model for glioma using the Homologous Recombination Deficiency (HRD) score and validate its predictive capability for glioma. We consolidated glioma datasets from TCGA, various cancer types for pan-cancer HRD analysis, and two additional glioma RNAseq datasets from GEO and CGGA databases. HRD scores, mutation data, and other genomic indices were calculated. Using machine learning algorithms, we identified signature genes and constructed an HRD-related prognostic risk model. The model's performance was validated across multiple cohorts. We also assessed immune infiltration and conducted molecular docking to identify potential therapeutic agents. Our analysis established a correlation between higher HRD scores and genomic instability in gliomas. The model, based on machine learning algorithms, identified seven key genes, significantly predicting patient prognosis. Moreover, the HRD score prognostic model surpassed other models in terms of prediction efficacy across different cancers. Differential immune cell infiltration patterns were observed between HRD risk groups, with potential implications for immunotherapy. Molecular docking highlighted several compounds, notably Panobinostat, as promising for high-risk patients. The prognostic model based on the HRD score threshold and associated genes in glioma offers new insights into the genomic and immunological landscapes, potentially guiding therapeutic strategies. The differential immune profiles associated with HRD-risk groups could inform immunotherapeutic interventions, with our findings paving the way for personalized medicine in glioma treatment.

Experience with luspatercept therapy in patients with transfusion-dependent low-risk myelodysplastic syndromes in real-world clinical practice: exploring the positive effect of combination with erythropoietin alfa.

Luspatercept, an inhibitor of the transforming growth factor beta (TGF-β) pathway, is a novel treatment for anemic patients with lower-risk myelodysplastic syndromes (MDS) with transfusion dependence (TD) who do not respond to erythropoiesis-stimulating agents (ESA) therapy or are not suitable candidates for this treatment. We present real-world experience with luspatercept therapy from two hematology centers in the Czech Republic. By January 2024, 54 MDS patients (33 men, 21 women) with a median age of 74 years (range, 55-95) were treated with luspatercept ± ESA at two Charles University hematology centers in Prague and Hradec Králové. According to the WHO 2016 classification, the cohort included 32 MDS-RS-MLD, seven MDS-MLD, two patients with 5q- + ring sideroblasts (RS), 12 RARS-T, and 1 patient with CMML-0 + RS. SF3B1 mutation data were available for 45 patients. All patients were in the IPSS-R and IPSS-M lower-risk groups (except four IPSS-M high). The median follow-up was 17 months (range, 1-54). All patients were transfusion-dependent. Thirty-five (64.8%) patients had a high transfusion burden (HTB) with ≥ 4 transfusion units (TU)/8 weeks, and 19 (35.2%) had a low transfusion burden (LTB) (< 4 TU/8 weeks). The median time between diagnosis and initiation of luspatercept was 27 months (range, 4-156). ESA were used prior to luspatercept in 45 patients, and luspatercept was used as first-line treatment in nine patients. Thirty-one (61%) patients were treated simultaneously with ESA. Only patients who received luspatercept for ≥ 8 weeks (51 patients) were assessed. We evaluated the achievement of transfusion independence (TI) lasting 8, 12, 16, and 24 weeks. Thirty-two (62.7%) patients achieved TI for ≥ 8 weeks, 31 (60.7%) for ≥ 12 weeks, 29 (56.8%) for ≥ 16 weeks, and 25 (49%) for ≥ 24 weeks. Hematologic improvement (HI) without TI was achieved in six patients (11.7%). Overall, HI + TI was achieved in 38 patients (74.5%). Epoetin alfa was used simultaneously in 31 patients (60.7%). In 21 (55.2%) of all responding patients, concomitant therapy with epoetin alfa led to an improved response, with 16 reaching TI. Thirteen (25.5%) patients were nonresponders. Eight (21%) patients experienced therapy failure and became transfusion-dependent again. Optimal response required a gradual increase in the luspatercept dose to 1.75 mg/kg in up to 35 patients, with 23 responders (TI + HI). Response rates varied by transfusion burden: 79% in LTB and 50% in HTB reached TI. Of RS+ patients, 70% reached TI, while only one out of five RS- patients achieved TI. Among 39 SF3B1-positive patients, 61.6% achieved TI. In the low and very low IPSS-M groups, 86% of patients responded (TI + HI), compared to 62% in the moderate-low group. Luspatercept was well-tolerated, with no adverse events higher than grade II toxicity. We have demonstrated in real-world clinical practice that luspatercept is a very effective agent, even in an unselected, pretreated, significantly TD MDS population. The effect was particularly high in the IPSS-M low and very low groups. We believe that the relatively high response rate in our patients was influenced by the frequent use of a higher dose (1.75 mg/kg) and especially by adding ESA to luspatercept in poorly responding patients.

Two-step consensus clustering approach to immune cell infiltration: An integrated exploration and validation of prognostic and immune implications in sarcomas

To conduct a comprehensive investigation of the sarcoma immune cell infiltration (ImmCI) patterns and tumoral microenvironment (TME). We utilized transcriptomic, clinical, and mutation data of sarcoma patients (training cohort) obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) server. Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithms were applied to decipher the immune cell infiltration landscape and TME profiles of sarcomas. An unsupervised clustering method was utilized for classifying ImmCI clusters (initial clustering) and ImmCI-based differentially expressed gene-driven clusters (secondary clustering). Mortality rates and immune checkpoint gene levels was analyzed among the identified clusters. We calculated the ImmCI score through principal component analysis. The tumor immune dysfunction evaluation (TIDE) score was also employed to quantify immunotherapy efficacy between two ImmCI score groups. We further validated the biomarkers for ImmCI and gene-driven clusters via experimental verification and the accuracy of the ImmCI score in predicting survival outcomes and immunotherapy efficacy by external validation cohorts (testing cohort). We demonstrated that ImmCI cluster A and gene-driven cluster A, were beneficial prognostic biomarkers and indicators of immune checkpoint blockade response in sarcomas via in-silico and laboratory experiments. Additionally, the ImmCI score exhibited independent prognostic significance and was predictive of immunotherapy response. Our research underscores the clinical significance of ImmCI scores in identifying sarcoma patients likely to respond to immunotherapy.

Prognostic signature and immunotherapeutic relevance of Focal adhesion signaling pathway-related genes in osteosarcoma

BackgroundAs the most common primary malignant bone tumor in children and adolescents, osteosarcoma currently lacks an effective clinical cure. Focal adhesion plays a crucial role in tumor invasion, migration, and drug resistance by mediating communication between the extracellular matrix and tumor cells. This study investigated the prognostic features and immunotherapeutic relevance of focal adhesion pathway-related genes in osteosarcoma to aid in the development of new therapeutic options. MethodsWe obtained mutational, transcriptomic, gene expression, and clinical data of osteosarcoma patients from the Gene Expression Omnibus (GEO) and Therapeutically Applicable Research to Generate Effective (TARGET) databases. Differentially expressed genes were screened, followed by the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses. Kaplan-Meier survival analysis was performed for genes related to the focal adhesion pathway, and multivariate Cox regression analysis was employed to construct a prognostic signature model. Genes such as SIGLEC15, TIGIT, CD274, HAVCR2, PDCD1, CTLA4, and LAG3 were extracted from the TARGET and CCLE databases for osteosarcoma patients and osteosarcoma cell lines, respectively，to observe the expression of immune checkpoint-related genes. Finally, qRT-PCR was used to verify the expression of these immune checkpoint-related genes in osteosarcoma cell lines. ResultsIn our study, 376 samples were analyzed, including 369 osteosarcoma samples and 7 normal tissue samples. We identified 50 up-regulated and 28 down-regulated differentially expressed genes. Among these, 10 Candidate genes relative to focal Adhesion were selected， and CAV1, ZYX, and ITGA5 were found to have a significant prognostic role based on survival analysis of osteosarcoma samples from the TARGET database. A predictive signature model related to the focal adhesion signaling pathway was constructed using these genes, and the AUCs of the 1-year, 3-year, and 5-year ROC curves were 0. 647, 0. 712, and 0. 717, respectively. The overall survival (OS) rate of osteosarcoma patients with high-risk scores was poorer than those with low-risk scores. Then, samples were divided into two subgroups based on the expression of the three genes, revealing significant differences in the expression of certain immune checkpoint-related genes between the subgroups. Additionally, above three genes and immune checkpoint-related genes in osteosarcoma cell lines were extracted from the CCLE database, showing high expression levels in eight osteosarcoma cell lines. We observed that CD274 and PDCD1LG2 were highly expressed in some osteosarcoma cell lines. Finally, the expression of CAV1, ZYX, ITGA5, CD80, CD274, and PDCD1LG2 in osteosarcoma cell lines was verified by qRT-PCR. ConclusionsOur study validated the prognostic role of three focal adhesion pathway-related genes (ZYX, CAV1, and ITGA5) in patients with osteosarcoma and constructed a prognostic signature model associated with the focal adhesion signaling pathway. We identified significant differences in the expression of multiple immune checkpoint-related genes among subgroups defined by the three genes. Additionally, CD274 and PDCD1LG2 showed higher expression in osteosarcoma cell lines characterized by these genes. These findings may aid in the selection of effective immunotherapy for specific osteosarcoma patients.

HATZFS predicts pancreatic cancer driver biomarkers by hierarchical reinforcement learning and zero-forcing set

The search for cancer biomarkers is of great significance for the early diagnosis and clinical treatment planning of pancreatic cancer. RNA transcription networks can effectively represent the interactions between RNA molecules, and the irregular perturbations in these interactions can lead to pathological states, making them commonly used for identifying cancer biomarkers. However, traditional network-based methods for identifying cancer biomarkers often rely on prior information and mutation data. Furthermore, most network controllability-based methods face the limitation of high computational complexity, preventing their integration with deep learning methods. In this paper, we propose a novel deep learning framework, called HATZFS, for identifying driver biomarkers in pancreatic cancer using RNA differential expression data. The proposed model combines hierarchical deep Q-network (HDQN), graph attention network (GAT), and zero-forcing set (ZFS). Firstly, a pancreatic cancer RNA transcription regulatory network is constructed to embed the complex relationship among Long non-coding RNA (lncRNA), microRNA (miRNA) and messenger RNA (mRNA). Then, we transform it into multiple subgraphs using graph sampling method, and apply GAT to learn the node features of the subgraphs. Finally, the HATZFS performs HDQN to select subgraphs and identify important RNA as driver nodes, and determining the controllability of the subgraphs based on ZFS. More important, the paper theoretically proves that when all subgraphs are controllable, the original graph is also controllable, and the union of the driver node sets of all subgraphs is equal to the driver node set of the original graph. To confirm the effectiveness of the proposed model, comprehensive experiments are conducted on the benchmark dataset consisting of 14 databases, comparing it with eight other algorithms. The experimental results demonstrate that the proposed model outperforms other methods. Overall, HATZFS not only provides the importance of all RNA molecules in the pancreatic cancer RNA regulatory network, but also identifies the driver node set of the network as driver biomarkers. Source code can be accessed at https://github.com/HongJieTongXue/HATZFS.

Mechanism of Nucleic Acid Phosphodiester Bond Cleavage by Human Endonuclease V: MD and QM/MM Calculations Reveal a Versatile Metal Dependence.

Human endonuclease V (EndoV) catalytically removes deaminated nucleobases by cleaving the phosphodiester bond as part of RNA metabolism. Despite being implicated in several diseases (cancers, cardiovascular diseases, and neurological disorders) and potentially being a useful tool in biotechnology, details of the human EndoV catalytic pathway remain unclear due to limited experimental information beyond a crystal structure of the apoenzyme and select mutational data. Since a mechanistic understanding is critical for further deciphering the central roles and expanding applications of human EndoV in medicine and biotechnology, molecular dynamics (MD) simulations and quantum mechanics/molecular mechanics (QM/MM) calculations were used to unveil the atomistic details of the catalytic pathway. Due to controversies surrounding the number of metals required for nuclease activity, enzyme-substrate models with different numbers of active site metals and various metal-substrate binding configurations were built based on structural data for other nucleases. Subsequent MD simulations revealed the structure and stability of the human EndoV-substrate complex for a range of active site metal binding architectures. Four unique pathways were then characterized using QM/MM that vary in metal number (one versus two) and modes of substrate coordination [direct versus indirect (water-mediated)], with several mechanisms being fully consistent with experimental structural, kinetic, and mutational data for related nucleases, including members of the EndoV family. Beyond uncovering key roles for several active site amino acids (D240 and K155), our calculations highlight that while one metal is essential for human EndoV activity, the enzyme can benefit from using two metals due to the presence of two suitable metal binding sites. By directly comparing one- versus two-metal-mediated P-O bond cleavage reactions within the confines of the same active site, our work brings a fresh perspective to the "number of metals" controversy.

Mut-Map: Comprehensive Computational Pipeline for Structural Mapping and Analysis of Cancer-Associated Mutations.

Understanding the functional impact of genetic mutations on protein structures is essential for advancing cancer research and developing targeted therapies. The main challenge lies in accurately mapping these mutations to protein structures and analysing their effects on protein function. To address this, Mut-Map (https://genemutation.org/) is a comprehensive computational pipeline designed to integrate mutation data from the Catalogue Of Somatic Mutations In Cancer database with protein structural data from the Protein Data Bank and AlphaFold models. The pipeline begins by taking a UniProt ID and proceeds through mapping corresponding Protein Data Bank structures, renumbering residues, and assessing disorder percentages. It then overlays mutation data, categorizes mutations based on structural context, and visualizes them using advanced tools like MolStar. This approach allows for a detailed analysis of how mutations may disrupt protein function by affecting key regions such as DNA interfaces, ligand-binding sites, and dimer interactions. To validate the pipeline, a case study on the TP53 gene, a critical tumour suppressor often mutated in cancers, was conducted. The analysis highlighted the most frequent mutations occurring at the DNA-binding interface, providing insights into their potential role in cancer progression. Mut-Map offers a powerful resource for elucidating the structural implications of cancer-associated mutations, paving the way for more targeted therapeutic strategies and advancing our understanding of protein structure-function relationships.

Impact of phylogeny on the inference of functional sectors from protein sequence data.

Statistical analysis of multiple sequence alignments of homologous proteins has revealed groups of coevolving amino acids called sectors. These groups of amino-acid sites feature collective correlations in their amino-acid usage, and they are associated to functional properties. Modeling showed that nonlinear selection on an additive functional trait of a protein is generically expected to give rise to a functional sector. These modeling results motivated a principled method, called ICOD, which is designed to identify functional sectors, as well as mutational effects, from sequence data. However, a challenge for all methods aiming to identify sectors from multiple sequence alignments is that correlations in amino-acid usage can also arise from the mere fact that homologous sequences share common ancestry, i.e. from phylogeny. Here, we generate controlled synthetic data from a minimal model comprising both phylogeny and functional sectors. We use this data to dissect the impact of phylogeny on sector identification and on mutational effect inference by different methods. We find that ICOD is most robust to phylogeny, but that conservation is also quite robust. Next, we consider natural multiple sequence alignments of protein families for which deep mutational scan experimental data is available. We show that in this natural data, conservation and ICOD best identify sites with strong functional roles, in agreement with our results on synthetic data. Importantly, these two methods have different premises, since they respectively focus on conservation and on correlations. Thus, their joint use can reveal complementary information.

Engineering bacteriophages through deep mining of metagenomic motifs.

Bacteriophages can adapt to new hosts by altering sequence motifs through recombination or convergent evolution. Where such motifs exist and what fitness advantage they confer remains largely unknown. We report a new method, Metagenomic Sequence Informed Functional Scoring (Meta-SIFT), to discover sequence motifs in metagenomic datasets that can be used to engineer phage activity. Meta-SIFT uses experimental deep mutational scanning data to create sequence profiles to enable deep mining of metagenomes for functional motifs which are otherwise invisible to searches. We experimentally tested over 17,000 Meta-SIFT derived sequence motifs in the receptor-binding protein of the T7 phage. The screen revealed thousands of T7 variants with novel host specificity with functional motifs sourced from distant families. Position, substitution and location preferences dictated specificity across a panel of 20 hosts and conditions. To demonstrate therapeutic utility, we engineered active T7 variants against foodborne pathogen E. coli O121. Meta-SIFT is a powerful tool to unlock the functional potential encoded in phage metagenomes to engineer bacteriophages.

Abstract A014: Protein-informed gene methylation signatures to predict treatment response and outcomes in pancreatic ductal adenocarcinoma

Abstract Pancreatic ductal adenocarcinoma (PDA) is a deadly cancer with dismal outcomes. We need new biomarkers to optimize the limited treatment combinations used in current clinical practice. Many machine-learning models have been presented based on mutational or transcriptome data claiming to predict outcomes. Still, they do not provide actionable targets (genes) that could improve treatment response. Moreover, DNA-methylation changes that influence gene/protein expression have been explored for this purpose. From the literature, we curated a 122-gene panel (January 1970 to April 2024) constructed on the proteins with preclinical (on cell lines or mouse models) or clinical (tissue-based studies) evidence suggesting their individual roles in influencing response to drugs often used in managing PDA such as gemcitabine (Gem), nab-paclitaxel, oxaliplatin, 5-fluorouracil, irinotecan, and cisplatin. We hypothesized that methylation changes in these genes are surrogates for gene/protein expression and tested their clinical significance in The Cancer Genome Atlas (TCGA) PDA population (n=184). The goal was to develop methylation signatures composed of specific cytosines followed by guanine residue (CpG) sites that aid in identifying patients at high risk of treatment failure and poor outcomes. We used elastic net multivariate analysis to calculate survival and plot Kaplan Meier plots based on our gene panel. The alpha (α) used for this script was calculated based on the lowest mean error and the minimum lambda (λ) value accordingly for each iteration (100 iterations to calculate the best values for α and λ). Our analysis yielded 28 methylation signatures that could identify high-risk PDA patients with poor overall survival (OS). The number of CpG sites in these signatures ranged from 1 to 1933. The differences in OS for these signatures were better than those available in the literature. The best signature had 18 CpG sites (17 months (m) vs. not evaluable) followed by a 53-CpG site signature (16 m vs. 67m) and a 6-CpG site signature (17m vs. 67m). A signature with the highest CpG sites (1933) was not better than those with smaller ones (16m vs. 21m). Multiple signatures had &amp;gt;1 CpG sites from one gene. Early growth response 1 (EGR1) coding the KCNH2 gene has featured in most signatures. One CpG site in this gene had a clinically significant prognostic value (15m vs. 30m). Other frequently featured genes were CCDC85A, EGFR, SST, and HMGA1. The main drawbacks for this study were that Gem-related genes (&amp;gt; 60) dominated the panel, and those with Gem treatment dominated the patient population. This panel should be further refined to make it more relevant to current clinical practice. We provide preliminary evidence suggesting the clinical significance of our panel and lay the foundation for future studies that would enable us to personalize prognostication and treatment selection in PDA patients. Our work can provide novel therapeutic targets that, alone or in combination with chemotherapy, can improve the outcomes for PDA. Citation Format: Ashish Manne, Harshitha Puram, Deepak Sherpally, Sravan Jeepalyam, Upender Manne. Protein-informed gene methylation signatures to predict treatment response and outcomes in pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr A014.

Sialylation-associated long non-coding RNA signature predicts the prognosis, tumor microenvironment, and immunotherapy and chemotherapy options in uterine corpus endometrial carcinoma

BackgroundSialylation in uterine corpus endometrial carcinoma (UCEC) differs significantly from apoptotic and ferroptosis pathways. It plays a crucial role in cancer progression and immune response modulation. Exploring how sialylation affects tumor behavior and its link with long non-coding RNAs (lncRNAs) may provide new insights into UCEC prognosis and treatment.MethodsWe obtained RNA transcriptome, clinical, and mutation data of UCEC samples from the TCGA database. Our approach involved developing a risk model based on the co-expression patterns of sialylation genes and lncRNAs. Prognostic lncRNAs were identified through Cox regression and further refined using LASSO analysis. To understand the biological functions and pathways of model-associated differentially expressed genes (MADEGs), we conducted enrichment analyses. We also assessed the immune infiltration status of MADEGs using eight different algorithms, which helped in evaluating the potential for immunotherapy. Additionally, we validated the expression of these lncRNAs in UCEC using cell lines and clinical samples.ResultsWe developed a UCEC risk model using five sialylation-related lncRNAs (AC004884.2, AC026202.2, LINC01579, LINC00942, SLC16A1-AS1). This model, confirmed through Cox analysis and clinical evaluation, effectively predicted patient outcomes. Survival data analysis across entire cohort, as well as within training and test groups, indicated better survival in low-risk UCEC patients. Enrichment analyses linked MADEGs to sialylation functions and cancer pathways. High-risk patients showed increased responsiveness to immune checkpoint inhibitors (ICIs), as indicated by immunological assessments. Subgroup C2 patients showed superior outcomes and a robust response to immunotherapy and chemotherapy. Notably, LINC01579, LINC00942, and SLC16A1-AS1 were significantly overexpressed in UCEC clinical tumor samples as well as in Ishikawa and HEC-1-B cell lines, compared to the normal groups.ConclusionsThis lncRNA signature associated with sialylation could guide prognosis, enhance the understanding of molecular mechanisms, and inform treatment strategies in UCEC. It highlights the potential for the use of ICIs and chemotherapy.

A time-dependent explainable radiomic analysis from the multi-omic cohort of CPTAC-Pancreatic Ductal Adenocarcinoma

Background and ObjectiveIn Pancreatic Ductal Adenocarcinoma (PDA), multi-omic models are emerging to answer unmet clinical needs to derive novel quantitative prognostic factors. We realized a pipeline that relies on survival machine-learning (SML) classifiers and explainability based on patients’ follow-up (FU) to stratify prognosis from the public-available multi-omic datasets of the CPTAC-PDA project. Materials and MethodsAnalyzed datasets included tumor-annotated radiologic images, clinical, and mutational data. A feature selection was based on univariate (UV) and multivariate (MV) survival analyses according to Overall Survival (OS) and recurrence (REC). In this study, we considered seven multi-omic datasets and compared four SML classifiers: Cox, survival random forest, generalized boosted, and support vector machines (SVM). For each classifier, we assessed the concordance (C) index on the validation set. The best classifiers for the validation set on both OS and REC underwent explainability analyses using SurvSHAP(t), which extends SHapley Additive exPlanations (SHAP). ResultsAccording to OS, after UV and MV analyses we selected 18/37 and 10/37 multi-omic features, respectively. According to REC, based on UV and MV analyses we selected 10/35 and 5/35 determinants, respectively. Generally, SML classifiers including radiomics outperformed those modelled on clinical or mutational predictors. For OS, the Cox model encompassing radiomic, clinical, and mutational features reached 75 % of C index, outperforming other classifiers. On the other hand, for REC, the SVM model including only radiomics emerged as the best-performing, with 68 % of C index. For OS, SurvSHAP(t) identified the first order Median Gray Level (GL) intensities, the gender, the tumor grade, the Joint Energy GL Co-occurrence Matrix (GLCM), and the GLCM Informational Measures of Correlations of type 1 as the most important features. For REC, the first order Median GL intensities, the GL size zone matrix Small Area Low GL Emphasis, and first order variance of GL intensities emerged as the most discriminative. ConclusionsIn this work, radiomics showed the potential for improving patients’ risk stratification in PDA. Furthermore, a deeper understanding of how radiomics can contribute to prognosis in PDA was achieved with a time-dependent explainability of the top multi-omic predictors.

Improving Individualized Rhabdomyosarcoma Prognosis Predictions Using Somatic Molecular Biomarkers.

Molecular markers, such as FOXO1 fusion genes and TP53 and MYOD1 mutations, increasingly influence risk-stratified treatment selection for pediatric rhabdomyosarcoma (RMS). This study aims to integrate molecular and clinical data to produce individualized prognosis predictions that can further improve treatment selection. Clinical variables and somatic mutation data for 20 genes from 641 RMS patients in the United Kingdom and the United States were used to develop three Cox proportional hazard models for predicting event-free survival (EFS). The 'Baseline Clinical' (BC) model included treatment location, age, fusion status, and risk group. The 'Gene Enhanced 2' (GE2) model added TP53 and MYOD1 mutations to the BC predictors. The 'Gene Enhanced 6' (GE6) model further included NF1, MET, CDKN2A, and MYCN mutations, selected through LASSO regression. Model performance was assessed using likelihood ratio (LR) tests and optimism-adjusted, bootstrapped validation and calibration metrics. The GE6 model demonstrated superior predictive performance, offering 39% more predictive information than the BC model (LR p<0.001) and 15% more than the GE2 model (LR p<0.001). The GE6 model achieved the highest discrimination with a C-index of 0.7087, a Nagalkerke R2 of 0.205, and appropriate calibration. Mutations in TP53, MYOD1, CDKN2A, MET, and MYCN were associated with higher hazards, while NF1 mutation correlated with lower hazard. Individual prognosis predictions varied between models in ways that may suggest different treatments for the same patient. For example, the 5-year EFS for a 10-year-old patient with high-risk, fusion-negative, NF1-positive disease was 50.0% (95% confidence interval: 39-64%) from BC but 76% (64-90%) from GE6. Incorporating molecular markers into RMS prognosis models improves prognosis predictions. Individualized prognosis predictions may suggest alternative treatment regimens compared to traditional risk-classification schemas. Improved clinical variables and external validation are required prior to implementing these models into clinical practice.

Construction of a cuproptosis‑related lncRNA signature to predict biochemical recurrence of prostate cancer.

Biochemical recurrence (BCR) is common in prostate cancer (PCa), and patients with BCR usually have a poor prognosis. Cuproptosis is a unique type of cell death, and copper homeostasis is crucial to the occurrence and development of malignancies. The present study aimed to explore the prognostic value of cuproptosis-related long non-coding RNAs (lncRNAs; CRLs) in PCa and to develop a predictive signature for forecasting BCR in patients with PCa. Using The Cancer Genome Atlas database, transcriptomic, mutation and clinical data were collected from patients with PCa. A total of 121 CRLs were identified using Pearson's correlation coefficient. Subsequently, a 6-CRL signature consisting of AC087276.2, CNNM3-DT, AC090198.1, AC138207.5, METTL14-DT and LINC01515 was created to predict the BCR of patients with PCa through Cox and least absolute shrinkage and selection operator regression analyses. Kaplan-Meier curve analysis demonstrated that high-risk patients had a low BCR-free survival rate. In addition, there was a substantial difference between the high- and low-risk groups in the immune microenvironment, immune therapy, drug sensitivity and tumor mutational burden. A nomogram integrating the Gleason score, 6-CRL signature and clinical T-stage was established and evaluated. Finally, the expression of signature lncRNAs in PCa cells was verified through reverse transcription-quantitative PCR. In conclusion, the 6-CRL signature may be a potential tool for making predictions regarding BCR in patients with PCa, and the prognostic nomogram may be considered a practical tool for clinical decision-making.

Exploration of Key Genes Combining with Immune Infiltration Level andTumor Mutational Burden in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a lethal malignancy due to its heterogeneity and aggressive behavior. Recently, somatic mutations and tumor cell interactions with the surrounding tumor immune microenvironment (TIME) have been reported to participate in HCC carcinogenesis and predict HCC progression. In this study, we aimed to investigate the association between tumor mutational burden (TMB) and TIME in HCC. Additionally, we sought to identify differentially expressed genes (DEGs) associated with HCC prognosis and progression. The expression, clinical, and mutational data were downloaded from the cancer genome atlas (TCGA) database. The immune infiltration levels and TMB levels of the HCC samples were estimated and the samples were divided into immune cluster (ICR)-1 and 2 based on immune infiltration score and high and low TMB groups based on TMB score. Thereafter, differential gene expression analysis was conducted to identify the DEGs in the ICR1/2 and high/low TMB groups, and the intersecting DEGs were selected. Thereafter, Cox regression analysis was performed on 89 significant DEGs, among which 19 were associated with prognosis. These 19 DEGs were then used to construct a prognostic model based on their expression levels and regression coefficients. Thereafter, we analyzed the DEGs in mutant and wildtype TP53 HCC samples and identified high BCL10 and TRAF3 expression in the mutant TP53 samples. BCL10 and TRAF3 expression was detected by real-time quantitative reverse transcription PCR and immunohistochemistry, and their clinical correlation, biological function, and immune infiltration levels were analyzed by chi-square analyses, Gene Set Enrichment Analysis (GSEA), and "ssGSEA", respectively. The results of our study revealed that immune infiltration level was correlated with TMB and that they synergistically predicted poor prognosis of HCC patients. DEGs enriched in immune-related pathways could serve as indicators of immunotherapy response in HCC. Among these DEGs, BCL10 and TRAF3 were highly expressed in HCC tissues, especially in the mutant TP53 group, and they co-operatively exhibited immunological function, thereby affecting HCC progression and prognosis. In this study, we identified BCL10 and TRAF3 as potential prognostic indicators in HCC patients. Additionally, we found that BCL10 and TRAF3 influence TMB and TIME in HCC patients and can be used for the development of immune-based therapies for improving the long-term survival of HCC patients.

Development of the TP53 mutation associated hypopharyngeal squamous cell carcinoma prognostic model through bulk multi-omics sequencing and single-cell sequencing

ObjectiveThe aim of this study was to construct a prognostic model based on the TP53 mutation to calculate prognostic risk scores of patients with HPSCC. MethodsTP53 mutation and transcriptome data were downloaded from the TCGA databases. Gene expression data from GSE65858, GSE41613, GSE3292, GSE31056, GSE39366, and GSE227156 datasets were downloaded from the GEO database. GSEA, univariate, multivariate Cox analyses, and LASSO analysis were employed to identify key genes and construct the prognostic model. ROC curves were utilized to validate the OS and RFS results obtained from the model. The associations between risk scores with various clinicopathological characteristics and immune scores were analyzed via ggplot2, corrplot package, and GSVA, respectively. Single-cell sequencing data was analyzed via unbiased clustering and SingleR cell annotations. ResultsInitially, two key genes, POLD2 and POLR2G, were identified and utilized to construct the prognostic model. Samples were divided into different risk groups via the risk scores obtained from the model, with high-risk group samples exhibiting poorer prognosis. Furthermore, the risk score exhibited a positive correlation with lymphatic metastasis in patients and the immune scores of CD4+ T, CD8+ T, dendritic cell, macrophage, and neutrophil. The immune responses also exhibited notable disparities between the high- and low-risk groups. The results of single-cell sequencing analysis demonstrated that epithelial cells and macrophages were relatively abundant in HPSCC samples. POLD2 and POLR2G exhibited higher expressions in epithelial cells, with most of the identified pathways also enriched in epithelial cells. ConclusionThe prognostic model exhibited a significant capacity for predicting the prognosis of HSPCC samples based on the TP53 mutation conditions and may also predict the cancer characteristics and immune infiltration scores of samples via different risk scores obtained from the model. Level of evidenceLevel 5.

Anoikis-related subtype and prognosis analyses based on bioinformatics, and an expression verification of ANGPTL4 based on experiments of lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is one of the most common malignant tumors with high mortality. Anoikis resistance is an important mechanism of tumor cell proliferation and migration. Our research is devoted to exploring the role of anoikis in the diagnosis, classification, and prognosis of LUAD. We downloaded the expression profile, mutation, and clinical data of LUAD from The Cancer Genome Atlas (TCGA) database. The "ConsensusClusterPlus" package was then used for the cluster analysis, and least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were used to establish the prognostic model. We verified the reliability of the model using a Gene Expression Omnibus (GEO) data set. A gene set variation analysis (GSVA) was conducted to investigate the functional enrichment differences in the different clusters and risk groups. The CIBERSORT algorithm and a single-sample gene set enrichment analysis (ssGSEA) were used to analyze immune cell infiltration. The tumor mutation burden (TMB) and Tumor Immune Dysfunction and Exclusion (TIDE) scores were used to evaluate the patients' sensitivity to immunotherapy. Immunohistochemical staining of tissue microarrays was used to verify the correlation between ANGPTL4 expression and the clinicopathological characteristics and prognosis of LUAD patients. First, we screened 135 differentially expressed anoikis-related genes (ARGs) and 23 prognosis-related ARGs from TCGA-LUAD data set. Next, 494 LUAD samples were allocated to cluster A and cluster B based on the 23 prognosis-related ARGs. The Kaplan-Meier (K-M) analysis showed the overall survival (OS) of cluster B was better than that of cluster A. The clinicopathological characteristics and functional enrichment analyses revealed significant differences between clusters A and B. The tumor microenvironment (TME) analysis showed that cluster B had more immune cell infiltration and a higher TME score than cluster A. Subsequently, a LASSO Cox regression model of LUAD was constructed with ten ARGs. The K-M analysis showed that the low-risk patients had longer OS than the high-risk patients. The receiver operating characteristic curve, nomogram, and GEO data set verification results showed that the model had high accuracy and reliability. The level of immune cell infiltration and TME score were higher in the low-risk group than the high-risk group. The high-risk group had stronger sensitivity to immune checkpoint block therapy and weaker sensitivity to chemotherapy drugs than the low-risk group. ANGPTL4 expression was correlated with stage, tumor differentiation, tumor size, lymph node metastasis, and OS. We discovered novel molecular subtypes and constructed a novel prognostic model of LUAD. Our findings provide important insights into subtype classification and the accurate survival prediction of LUAD. We also identified ANGPTL4 as a prognostic indicator of LUAD.