Leveraging Single-Cell Sequencing to Classify and Characterize Tumor Subgroups in Bulk RNA-Sequencing Data
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE264680 | GEO | 2024/04/23
REPOSITORIES: GEO
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Project description:Leveraging Single-Cell Sequencing to Classify and Characterize Tumor Subgroups in Bulk RNA-Sequencing Data
| PRJNA1103795 | ENA
Project description:Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease, commonly described by cell-of-origin (COO) molecular subtypes. We sought to identify novel patient subgroups through an unsupervised analysis of a large public dataset of gene expression profiles from newly diagnosed de novo DLBCL patients, yielding 2 biologically distinct subgroups characterized by differences in the tumor microenvironment. Pathway analysis and immune deconvolution algorithms identified higher B-cell content and a strong proliferative signal in subgroup A and enriched T-cell, macrophage, and immune/inflammatory signals in subgroup B, reflecting similar biology to published DLBCL stratification research. A gene expression classifier, featuring 26 gene expression scores, was derived from the public dataset to discriminate subgroup A (classifier-negative, immune-low) and subgroup B (classifier-positive, immune-high) patients. Subsequent application to an independent series of diagnostic biopsies replicated the subgroups, with immune cell composition confirmed via immunohistochemistry. Avadomide, a CRL4CRBN E3 ubiquitin ligase modulator, demonstrated clinical activity in relapsed/refractory DLBCL patients, independent of COO subtypes. Given the immunomodulatory activity of avadomide and the need for a patient-selection strategy, we applied the gene expression classifier to pretreatment biopsies from relapsed/refractory DLBCL patients receiving avadomide (NCT01421524). Classifier-positive patients exhibited an enrichment in response rate and progression-free survival of 44% and 6.2 months vs 19% and 1.6 months for classifier-negative patients (hazard ratio, 0.49; 95% confidence interval, 0.280-0.86; P = .0096). The classifier was not prognostic for rituximab, cyclophosphamide, doxorubicin, vincristine, prednisone or salvage immunochemotherapy. The classifier described here discriminates DLBCL tumors based on tumor and nontumor composition and has potential utility to enrich for clinical response to immunomodulatory agents, including avadomide.
| S-EPMC7099333 | biostudies-literature
Project description:Single cell technologies are rapidly generating large amounts of data that enables us to understand biological systems at single-cell resolution. However, joint analysis of datasets generated by independent labs remains challenging due to a lack of consistent terminology to describe cell types. Here, we present OnClass, an algorithm and accompanying software for automatically classifying cells into cell types that are part of the controlled vocabulary that forms the Cell Ontology. A key advantage of OnClass is its capability to classify cells into cell types not present in the training data because it uses the Cell Ontology graph to infer cell type relationships. Furthermore, OnClass can be used to identify marker genes for all the cell ontology categories, regardless of whether the cell types are present or absent in the training data, suggesting that OnClass goes beyond a simple annotation tool for single cell datasets, being the first algorithm capable to identify marker genes specific to all terms of the Cell Ontology and offering the possibility of refining the Cell Ontology using a data-centric approach.
| S-EPMC8455606 | biostudies-literature
Project description:Gastric cancer is a significant global health concern with complex molecular underpinnings influencing disease progression and patient outcomes. Various molecular drivers were reported, and these studies offered potential avenues for targeted therapies, biomarker discovery, and the development of precision medicine strategies. However, it was posed that the heterogeneity of the disease and the complexity of the molecular interactions are still challenging. By seamlessly integrating data from single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq), we embarked on characterizing molecular signatures and establishing a prognostic signature for this complex malignancy. We offered a holistic view of gene expression landscapes in gastric cancer, identified 226 candidate marker genes from 3 different dimensions, and unraveled key players' risk stratification and treatment decision-making. The convergence of molecular insights in gastric cancer progression occurs at multiple biological scales simultaneously. The focal point of this study lies in developing a prognostic model, and we amalgamated four molecular signatures (COL4A1, FKBP10, RNASE1, SNCG) and three clinical parameters using advanced machine-learning techniques. The model showed high predictive accuracy, with the potential to revolutionize patient care by using clinical variables. This will strengthen the reliability of the model and enable personalized therapeutic strategies based on each patient's unique molecular profile. In summary, our research sheds light on the molecular underpinnings of gastric cancer, culminating in a powerful prognostic tool for gastric cancer. With a firm foundation in biological insights and clinical implications, our study paves the way for future validations and underscores the potential of integrated molecular analysis in advancing precision oncology.
| S-EPMC11266334 | biostudies-literature
Project description:BackgroundPrior studies indicate that lactylation regulates various biological mechanisms within cancer. However, lactylation-related genes (LRGs) have been found to have limited value in predicting the prognosis of hepatocellular carcinoma (HCC). The aim of this study was to review HCC LRGs using data from The Cancer Genome Atlas (TCGA).MethodsThe RNA sequencing data and related clinical information of patients with HCC patients were collected from the TCGA database. A total of 20 LRGs were selected and bioinformatics analysis was performed. A consistency cluster analysis was conducted to classify the HCC tumors. Using a lactylation-related model of HCC, prognosis, immune cell infiltration, and immunotherapy was evaluated.ResultsA total of 4,378 genes were associated with prognosis. Twenty LRGs (i.e., ACIN1, RAN, PPP1CB, ALDOB, SUMO2, THOC2, HDAC1, SF3A1, SF3B1, HNRNPM, PPP1CC, SRRM1, PRPF6, HDAC2, H2AFV, ALYREF, H2AFZ, H2AFX, HNRNPK, and MAGOH) were identified. The 20 LRGs were used to divide TCGA-HCC patients into low-risk (G1) and high-risk (G2) categories. The upregulated genes in the G1 group primarily participate in the p53 signaling pathway, focal adhesion, extracellular matrix (ECM)-receptor interaction, and cell cycle, while the downregulated genes primarily participate in the glycolysis/gluconeogenesis, carbon metabolism, and biosynthesis of amino acids. The box plots showed a significant difference in the immune cell populations, with a higher abundance of B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and myeloid dendritic cells in the G1 than the G2 HCC samples. Further, the box plots showed higher expression levels of seven of the eight immune checkpoint inhibitor (ICI)-related genes in the G1 HCC samples than the G2 samples. There was a significant disparity in the cancer stem cell (CSC) scores between the G1 and G2 TCGA-HCC patients. Additionally, the G1 TCGA-HCC patients had higher tumor immune dysfunction and exclusion (TIDE) scores than the G2 TCGA-HCC patients. The prognosis of the HCC patients was also predicted using a six-LRG model, comprising HDAC2, SRRM1, SF3B1, HDAC1, THOC2, and PPP1CB.ConclusionsStrong correlation between LRGs and tumor classification as well as immunity in patients with HCC was identified. LRG signatures serve as reliable prognostic markers for HCC.
| S-EPMC11399878 | biostudies-literature
Project description:The purpose of this paper is to describe ongoing research on appropriate instrumentation and analysis techniques to characterize postural stability, postural agility, and dynamic stability, which collectively comprise the postural control spectrum. This study had a specific focus on using emerging sensors to develop protocols suitable for use outside laboratory or clinical settings. First, we examined the optimal number and placement of wearable accelerometers for assessing postural stability. Next, we proposed metrics and protocols for assessing postural agility with the use of a custom force plate-controlled video game. Finally, we proposed a method to quantify dynamic stability during walking tasks using novel frequency-domain metrics extracted from acceleration data obtained with a single body-worn IMU. In each of the three studies, a surrogate for instability was introduced, and the sensors and metrics discussed in this paper show promise for differentiating these trials from stable condition trials. Next steps for this work include expanding the tested population size and refining the methods to even more reliably and unobtrusively characterize postural control status in a variety of scenarios.
| S-EPMC11644092 | biostudies-literature
Project description:Despite recent progress on estimating the heritability explained by genotyped SNPs (h(2)g), a large gap between h(2)g and estimates of total narrow-sense heritability (h(2)) remains. Explanations for this gap include rare variants or upward bias in family-based estimates of h(2) due to shared environment or epistasis. We estimate h(2) from unrelated individuals in admixed populations by first estimating the heritability explained by local ancestry (h(2)γ). We show that h(2)γ = 2FSTCθ(1 - θ)h(2), where FSTC measures frequency differences between populations at causal loci and θ is the genome-wide ancestry proportion. Our approach is not susceptible to biases caused by epistasis or shared environment. We applied this approach to the analysis of 13 phenotypes in 21,497 African-American individuals from 3 cohorts. For height and body mass index (BMI), we obtained h(2) estimates of 0.55 ± 0.09 and 0.23 ± 0.06, respectively, which are larger than estimates of h(2)g in these and other data but smaller than family-based estimates of h(2).
| S-EPMC4244251 | biostudies-literature
Project description:BackgroundThe Cancer Genome Atlas identified four molecular subgroups of endometrial cancer with survival differences based on whole genome, transcriptomic, and proteomic characterization. Clinically accessible algorithms that reproduce this data are needed. Our aim was to determine if targeted sequencing alone allowed for molecular classification of endometrial cancer.MethodsUsing a custom-designed 156 gene panel, we analyzed 47 endometrial cancers and matching non-tumor tissue. Variants were annotated for pathogenicity and medical records were reviewed for the clinicopathologic variables. Using molecular characteristics, tumors were classified into four subgroups. Group 1 included patients with > 570 unfiltered somatic variants, > 9 cytosine to adenine nucleotide substitutions per sample, and < 1 cytosine to guanine nucleotide substitution per sample. Group 2 included patients with any somatic mutation in MSH2, MSH6, MLH1, PMS2. Group 3 included patients with TP53 mutations without mutation in mismatch repair genes. Remaining patients were classified as group 4. Analyses were performed using SAS 9.4 (SAS Institute Inc., Cary, North Carolina, USA).ResultsEndometrioid endometrial cancers had more candidate variants of potential pathogenic interest (median 6 IQR 4.13 vs. 2 IQR 2.3; p < 0.01) than uterine serous cancers. PTEN (82% vs. 15%, p < 0.01) and PIK3CA (74% vs. 23%, p < 0.01) mutations were more frequent in endometrioid than serous carcinomas. TP53 (18% vs. 77%, p < 0.01) mutations were more frequent in serous carcinomas. Visual inspection of the number of unfiltered somatic variants per sample identified six grade 3 endometrioid samples with high tumor mutational burden, all of which demonstrated POLE mutations, most commonly P286R and V411L. Of the grade 3 endometrioid carcinomas, those with POLE mutations were less likely to have risk factors necessitating adjuvant treatment than those with low tumor mutational burden. Targeted sequencing was unable to assign samples to microsatellite unstable, copy number low, and copy number high subgroups.ConclusionsTargeted sequencing can predict the presence of POLE mutations based on the tumor mutational burden. However, targeted sequencing alone is inadequate to classify endometrial cancers into molecular subgroups identified by The Cancer Genome Atlas.
| S-EPMC7706212 | biostudies-literature
Project description:Copy number variants (CNVs) are currently defined as genomic sequences that are polymorphic in copy number and range in length from 1,000 to several million base pairs. Among current array-based CNV detection platforms, long-oligonucleotide arrays promise the highest resolution. However, the performance of currently available analytical tools suffers when applied to these data because of the lower signal:noise ratio inherent in oligonucleotide-based hybridization assays. We have developed wuHMM, an algorithm for mapping CNVs from array comparative genomic hybridization (aCGH) platforms comprised of 385,000 to more than 3 million probes. wuHMM is unique in that it can utilize sequence divergence information to reduce the false positive rate (FPR). We apply wuHMM to 385K-aCGH, 2.1M-aCGH, and 3.1M-aCGH experiments comparing the 129X1/SvJ and C57BL/6J inbred mouse genomes. We assess wuHMM’s performance on the 385K platform by comparison to the higher resolution platforms and we independently validate 10 CNVs. The method requires no training data and is robust with respect to changes in algorithm parameters. At a FPR of less than 10%, the algorithm can detect CNVs with five probes on the 385K platform and three on the 2.1M and 3.1M platforms, resulting in effective resolutions of 24 kb, 2-5 kb, and 1 kb, respectively. Keywords: CNV detection algorithm development and assessment
2008-03-12 | GSE10511 | GEO
Project description:The availability of reliable socioeconomic data is critical for the design of urban policies and the implementation of location-based services; however, often, their temporal and geographical coverage remain scarce. We explore the potential for insurance customers data to predict socioeconomic indicators of Swiss municipalities. First, we define a features space by aggregating at city-level individual customer data along several behavioral and user profile dimensions. Second, we collect official statistics shared by the Swiss authorities on a wide spectrum of categories: Population, Transportation, Work, Space and Territory, Housing, and Economy. Third, we adopt two spatial regression models exploring both global and local geographical dependencies to investigate their predictability. Results show consistently a correlation between insurance customer characteristics and official socioeconomic indexes. Performance fluctuates depending on the category, with values of R2 > 0.6 for several target variables using a 5-fold cross validation. As a case study, we focus on predicting the percentage of the population using public transportation and we discuss the implications on a regional scope. We believe that this methodology can support official statistical offices and it could open up new opportunities for the characterization of socioeconomic traits at highly-granular spatial and temporal scales.
| S-EPMC7928527 | biostudies-literature