5 research outputs found

    Deciphering the Tumor–Immune–Microbe Interactions in HPV-Negative Head and Neck Cancer

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    Patients with human papillomavirus-negative head and neck squamous cell carcinoma (HPV-negative HNSCC) have worse outcomes than HPV-positive HNSCC. In our study, we used a published dataset and investigated the microbes enriched in molecularly classified tumor groups. We showed that microbial signatures could distinguish Hypoxia/Immune phenotypes similar to the gene expression signatures. Furthermore, we identified three highly-correlated microbes with immune processes that are crucial for immunotherapy response. The survival of patients in a molecularly heterogenous group shows significant differences based on the co-abundance of the three microbes. Overall, we present evidence that tumor-associated microbiota are critical components of the tumor ecosystem that may impact tumor microenvironment and immunotherapy response. The results of our study warrant future investigation to experimentally validate the conclusions, which have significant impacts on clinical decision-making, such as treatment selection

    Murine AGM single-cell profiling identifies a continuum of hemogenic endothelium differentiation marked by ACE

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    In vitro generation and expansion of hematopoietic stem cells (HSCs) holds great promise for the treatment of any ailment that relies on bone marrow or blood transplantation. To achieve this, it is essential to resolve the molecular and cellular pathways that govern HSC formation in the embryo. HSCs first emerge in the aorta-gonad-mesonephros (AGM) region, where a rare subset of endothelial cells, hemogenic endothelium (HE), undergoes an endothelial-to-hematopoietic transition (EHT). Here, we present full-length single-cell RNA sequencing (scRNA-seq) of the EHT process with a focus on HE and dorsal aorta niche cells. By using Runx1b and Gfi1/1b transgenic reporter mouse models to isolate HE, we uncovered that the pre-HE to HE continuum is specifically marked by angiotensin-I converting enzyme (ACE) expression. We established that HE cells begin to enter the cell cycle near the time of EHT initiation when their morphology still resembles endothelial cells. We further demonstrated that RUNX1 AGM niche cells consist of vascular smooth muscle cells and PDGFRa(+) mesenchymal cells and can functionally support hematopoiesis. Overall, our study provides new insights into HE differentiation toward HSC and the role of AGM RUNX1(+) niche cells in this process. Our expansive scRNA-seq datasets represents a powerful resource to investigate these processes further

    The 4717C > G polymorphism in periplakin modulates sensitivity to EGFR inhibitors

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    Abstract The use of EGFR inhibitors on oral squamous cell carcinoma (OSCC) as monotherapy yielded modest clinical outcomes and therefore would benefit from biomarkers that could predict which patient subsets are likely to respond. Here, we determined the efficacy of erlotinib in OSCC cell lines, and by comparing sensitive and resistant lines to identify potential biomarkers. We focused on the 4717C > G polymorphism in periplakin (PPL) where the CC genotype was associated with erlotinib resistance. To validate this, erlotinib-resistant cell lines harbouring CC genotype were engineered to overexpress the GG genotype and vice versa. Isogenic cell lines were then studied for their response to erlotinib treatment. We demonstrated that overexpression of the GG genotype in erlotinib-resistant lines sensitized them to erlotinib and inhibition of AKT phosphorylation. Similarly, the expression of the CC genotype conferred resistance to erlotinib with a concomitant increase in AKT phosphorylation. We also demonstrated that cell lines with the CC genotype generally are more resistant to other EGFR inhibitors than those with the GG genotype. Overall, we showed that a specific polymorphism in the PPL gene could confer resistance to erlotinib and other EGFR inhibitors and further work to evaluate these as biomarkers of response is warranted

    GENIPAC: A Genomic Information Portal for Head and Neck Cancer Cell Systems

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    Head and neck cancer (HNC)–derived cell lines represent fundamental models for studying the biological mechanisms underlying cancer development and precision therapies. However, mining the genomic information of HNC cells from available databases requires knowledge on bioinformatics and computational skill sets. Here, we developed a user-friendly web resource for exploring, visualizing, and analyzing genomics information of commonly used HNC cell lines. We populated the current version of GENIPAC with 44 HNC cell lines from 3 studies: ORL Series, OPC-22, and H Series. Specifically, the mRNA expressions for all the 3 studies were derived with RNA-seq. The copy number alterations analysis of ORL Series was performed on the Genome Wide Human Cytoscan HD array, while copy number alterations for OPC-22 were derived from whole exome sequencing. Mutations from ORL Series and H Series were derived from RNA-seq information, while OPC-22 was based on whole exome sequencing. All genomic information was preprocessed with customized scripts and underwent data validation and correction through data set validator tools provided by cBioPortal. The clinical and genomic information of 44 HNC cell lines are easily assessable in GENIPAC. The functional utility of GENIPAC was demonstrated with some of the genomic alterations that are commonly reported in HNC, such as TP53, EGFR, CCND1, and PIK3CA. We showed that these genomic alterations as reported in The Cancer Genome Atlas database were recapitulated in the HNC cell lines in GENIPAC. Importantly, genomic alterations within pathways could be simultaneously visualized. We developed GENIPAC to create access to genomic information on HNC cell lines. This cancer omics initiative will help the research community to accelerate better understanding of HNC and the development of new precision therapeutic options for HNC treatment. GENIPAC is freely available at http://genipac.cancerresearch.my/

    QuickCount ® : A novel automated software for rapid cell detection and quantification

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    We describe a novel automated cell detection and counting software, QuickCount ® (QC), designed for rapid quantification of cells. The Bland–Altman plot and intraclass correlation coefficient (ICC) analyses demonstrated strong agreement between cell counts from QC to manual counts (mean and SD: -3.3 ± 4.5; ICC = 0.95). QC has higher recall in comparison to ImageJ auto , CellProfiler and CellC and the precision of QC, ImageJ auto , CellProfiler and CellC are high and comparable. QC can precisely delineate and count single cells from images of different cell densities with precision and recall above 0.9. QC is unique as it is equipped with real-time preview while optimizing the parameters for accurate cell count and needs minimum hands-on time where hundreds of images can be analyzed automatically in a matter of milliseconds. In conclusion, QC offers a rapid, accurate and versatile solution for large-scale cell quantification and addresses the challenges often faced in cell biology research