The Rembrandt brain cancer dataset includes 671 patients collected from 14 contributing institutions from 2004-2006. It is accessible for conducting clinical translational research using the open ...access Georgetown Database of Cancer (G-DOC) platform. In addition, the raw and processed genomics and transcriptomics data have also been made available via the public NCBI GEO repository as a super series GSE108476. Such combined datasets would provide researchers with a unique opportunity to conduct integrative analysis of gene expression and copy number changes in patients alongside clinical outcomes (overall survival) using this large brain cancer study.
Stem cell antigen-1 (Sca-1) is used to isolate and characterize tumor initiating cell populations from tumors of various murine models 1. Sca-1 induced disruption of TGF-β signaling is required in ...vivo tumorigenesis in breast cancer models 2, 3-5. The role of human Ly6 gene family is only beginning to be appreciated in recent literature 6-9. To study the significance of Ly6 gene family members, we have visualized one hundred thirty gene expression omnibus (GEO) dataset using Oncomine (Invitrogen) and Georgetown Database of Cancer (G-DOC). This analysis showed that four different members Ly6D, Ly6E, Ly6H or Ly6K have increased gene expressed in bladder, brain and CNS, breast, colorectal, cervical, ovarian, lung, head and neck, pancreatic and prostate cancer than their normal counter part tissues. Increased expression of Ly6D, Ly6E, Ly6H or Ly6K was observed in sub-set of cancer type. The increased expression of Ly6D, Ly6E, Ly6H and Ly6K was found to be associated with poor outcome in ovarian, colorectal, gastric, breast, lung, bladder or brain and CNS as observed by KM plotter and PROGgeneV2 platform. The remarkable findings of increased expression of Ly6 family members and its positive correlation with poor outcome on patient survival in multiple cancer type indicate that Ly6 family members Ly6D, Ly6E, Ly6K and Ly6H will be an important targets in clinical practice as marker of poor prognosis and for developing novel therapeutics in multiple cancer type.
MicroRNA (miRNA) is a new class of small, noncoding RNA. The purpose of this study was to determine if miRNAs are differentially expressed in hepatocellular carcinoma (HCC).
More than 200 precursor ...and mature miRNAs were profiled by real-time PCR in 43 and 28 pairs of HCC and adjacent benign liver, respectively, and in normal liver specimens.
Several miRNAs including miR-199a, miR-21, and miR-301 were differentially expressed in the tumor compared with adjacent benign liver. A large number of mature and precursor miRNAs were up-regulated in the adjacent benign liver specimens that were both cirrhotic and hepatitis-positive compared with the uninfected, noncirrhotic specimens (P < 0.01). Interestingly, all of the miRNAs in this comparison had increased expression and none were decreased. The expression of 95 randomly selected mRNAs was not significantly altered in the cirrhotic and hepatitis-positive specimens, suggesting a preferential increase in the transcription of miRNA. Comparing the miRNA expression in the HCC tumors with patient's survival time revealed two groups of patients; those with predominantly lower miRNA expression and poor survival and those with predominantly higher miRNA expression and good survival (P < 0.05). A set of 19 miRNAs significantly correlated with disease outcome. A number of biological processes including cell division, mitosis, and G(1)-S transition were predicted to be targets of the 19 miRNAs in this group.
We show that a global increase in the transcription of miRNA genes occurs in cirrhotic and hepatitis-positive livers and that miRNA expression may prognosticate disease outcome in HCC.
Thymic carcinoma is rare and has a poorer prognosis than thymomas. The treatment options are limited after failure of platinum-based chemotherapy. We previously performed a single-center phase II ...study of pembrolizumab in patients with advanced thymic carcinoma, showing a 22.5% response rate. Here, we characterize the genomic and transcriptomic profile of thymic carcinoma samples from 10 patients (5 non-responders versus 5 responders) in this cohort, with the main aim of identifying potential predictors of response to immunotherapy. We find that expression of PDL1 and alterations in genes or pathways that correlated with PD-L1 expression (CYLD and BAP1) could be potential predictors for response or resistance to immunotherapy in patients with advanced thymic carcinoma. Our study provides insights into potential predictive markers/pathways to select patients with thymic carcinoma for anti-PD-1 immunotherapy.
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Expression of PDL1 is a predictor for response to immunotherapy in thymic carcinomaAlterations of CYLD are exclusively found in tumor samples of the respondersAlterations of BAP1 are exclusively found in tumor samples of the non-responders
He et al. characterize the genomic and transcriptomic profile of thymic carcinoma samples from 10 patients treated with pembrolizumab. They find that expression of PDL1 and alterations in genes or pathways that correlated with PD-L1 expression could be potential predictors for response to immunotherapy in patients with advanced thymic carcinoma.
The coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-CoV-2 virus has affected over 700 million people, and caused over 7 million deaths throughout the world as of April 2024, and ...continues to affect people through seasonal waves. While over 675 million people have recovered from this disease globally, the lingering effects of the disease are still under study. Long term effects of SARS-CoV-2 infection, known as 'long COVID,' include a wide range of symptoms including fatigue, chest pain, cellular damage, along with a strong innate immune response characterized by inflammatory cytokine production. Three years after the pandemic, data about long covid studies are finally emerging. More clinical studies and clinical trials are needed to understand and determine the factors that predispose individuals to these long-term side effects.
In this methodology paper, our goal was to apply data driven approaches in order to explore the multidimensional landscape of infected lung tissue microenvironment to better understand complex interactions between viral infection, immune response and the lung microbiome of patients with (a) SARS-CoV-2 virus and (b) NL63 coronavirus. The samples were analyzed with several machine learning tools allowing simultaneous detection and quantification of viral RNA amount at genome and gene level; human gene expression and fractions of major types of immune cells, as well as metagenomic analysis of bacterial and viral abundance. To contrast and compare specific viral response to SARS-COV-2, we analyzed deep sequencing data from additional cohort of patients infected with NL63 strain of corona virus.
Our correlation analysis of three types of RNA-seq based measurements in patients i.e. fraction of viral RNA (at genome and gene level), Human RNA (transcripts and gene level) and bacterial RNA (metagenomic analysis), showed significant correlation between viral load as well as level of specific viral gene expression with the fractions of immune cells present in lung lavage as well as with abundance of major fractions of lung microbiome in COVID-19 patients.
Our methodology-based proof-of-concept study has provided novel insights into complex regulatory signaling interactions and correlative patterns between the viral infection, inhibition of innate and adaptive immune response as well as microbiome landscape of the lung tissue. These initial findings could provide better understanding of the diverse dynamics of immune response and the side effects of the SARS-CoV-2 infection and demonstrates the possibilities of the various types of analyses that could be performed from this type of data.
An estimated 17% of cancers worldwide are associated with infectious causes. The extent and biological significance of viral presence/infection in actual tumor samples is generally unknown but could ...be measured using human transcriptome (RNA-seq) data from tumor samples. We present an open source bioinformatics pipeline viGEN, which allows for not only the detection and quantification of viral RNA, but also variants in the viral transcripts. The pipeline includes 4 major modules: The first module aligns and filter out human RNA sequences; the second module maps and count (remaining un-aligned) reads against reference genomes of all known and sequenced human viruses; the third module quantifies read counts at the individual viral-gene level thus allowing for downstream differential expression analysis of viral genes between case and controls groups. The fourth module calls variants in these viruses. To the best of our knowledge, there are no publicly available pipelines or packages that would provide this type of complete analysis in one open source package. In this paper, we applied the viGEN pipeline to two case studies. We first demonstrate the working of our pipeline on a large public dataset, the TCGA cervical cancer cohort. In the second case study, we performed an in-depth analysis on a small focused study of TCGA liver cancer patients. In the latter cohort, we performed viral-gene quantification, viral-variant extraction and survival analysis. This allowed us to find differentially expressed viral-transcripts and viral-variants between the groups of patients, and connect them to clinical outcome. From our analyses, we show that we were able to successfully detect the human papilloma virus among the TCGA cervical cancer patients. We compared the viGEN pipeline with two metagenomics tools and demonstrate similar sensitivity/specificity. We were also able to quantify viral-transcripts and extract viral-variants using the liver cancer dataset. The results presented corresponded with published literature in terms of rate of detection, and impact of several known variants of HBV genome. This pipeline is generalizable, and can be used to provide novel biological insights into microbial infections in complex diseases and tumorigeneses. Our viral pipeline could be used in conjunction with additional type of immuno-oncology analysis based on RNA-seq data of host RNA for cancer immunology applications. The source code, with example data and tutorial is available at: https://github.com/ICBI/viGEN/.
Very little is known regarding regulation of microRNA (miRNA) biogenesis in normal tissues, tumors, and cell lines. Here, we profiled the expression of 225 precursor and mature miRNAs using real-time ...PCR and compared the expression levels to determine the processing patterns. RNA from 22 different human tissues, 37 human cancer cell lines, and 16 pancreas and liver tissues/tumors was profiled. The relationship between precursor and mature miRNA expression fell into the following four categories: (1) a direct correlation exists between the precursor and mature miRNA expression in all cells/tissues studied; (2) direct correlation of the precursor and mature miRNA exists, yet the expression is restricted to specific cell lines or tissues; (3) there is detectable expression of mature miRNA in certain cells and tissues while the precursor is expressed in all or most cells/tissues; or (4) both precursor and mature miRNA are not expressed. Pearson correlation between the precursor and mature miRNA expression was closer to one for the tissues but was closer to zero for the cell lines, suggesting that processing of precursor miRNAs is reduced in cancer cell lines. By using Northern blotting, we show that many of these miRNAs (e.g., miR-31, miR-105 and miR-128a) are processed to the precursor, but in situ hybridization analysis demonstrates that these miRNA precursors are retained in the nucleus. We provide a database of the levels of precursor and mature miRNA in a variety of cell types. Our data demonstrate that a large number of miRNAs are transcribed but are not processed to the mature miRNA.
Identifying targets of dysregulated microRNAs (miRNAs) will enhance our understanding of how altered miRNA expression contributes to the malignant phenotype of breast cancer. The expression of ...miR-205 was reduced in four breast cancer cell lines compared to the normal-like epithelial cell line MCF10A and in tumor and metastatic tissues compared to adjacent benign breast tissue. Two predicted binding sites for miR-205 were identified in the 3' untranslated region of the high mobility group box 3 gene, HMGB3. Both dual-luciferase reporter assay and Western blotting confirmed that miR-205 binds to and regulates HMGB3. To further explore miR-205 targeting of HMGB3, WST-1 proliferation and in vitro invasion assays were performed in MDA-MB-231 and BT549 cells transiently transfected with precursor miR-205 oligonucleotide or HMGB3 small interfering RNA (siRNA). Both treatments reduced the proliferation and invasion of the cancer cells. The mRNA and protein levels of HMGB3 were higher in the tumor compared to adjacent benign specimens and there was an indirect correlation between the expression of HMGB3 mRNA and patient survival. Treatment of breast cancer cells with 5-Aza/TSA derepressed miR-205 and reduced HMGB3 mRNA while knockdown of the transcriptional repressor NRSF/REST, reduced miR-205 and increased HMGB3. In conclusion, regulation of HMGB3 by miR-205 reduced both proliferation and invasion of breast cancer cells. Our findings suggest that modulating miR-205 and/or targeting HMGB3 are potential therapies for advanced breast cancer.
Osteosarcoma is the most common malignant bone tumor in children. Survival remains poor among histologically poor responders, and there is a need to identify them at diagnosis to avoid delivering ...ineffective therapy. Genetic variation contributes to a wide range of response and toxicity related to chemotherapy. The aim of this study is to use sequencing of blood cells to identify germline haplotypes strongly associated with drug resistance in osteosarcoma patients.
We used sequencing data from two patient datasets, from Inova Hospital and the NCI TARGET. We explored the effect of mutation hotspots, in the form of haplotypes, associated with relapse outcome. We then mapped the single nucleotide polymorphisms (SNPs) in these haplotypes to genes and pathways. We also performed a targeted analysis of mutations in Drug Metabolizing Enzymes and Transporter (DMET) genes associated with tumor necrosis and survival.
We found intronic and intergenic hotspot regions from 26 genes common to both the TARGET and INOVA datasets significantly associated with relapse outcome. Among significant results were mutations in genes belonging to AKR enzyme family, cell-cell adhesion biological process and the PI3K pathways; as well as variants in SLC22 family associated with both tumor necrosis and overall survival. The SNPs from our results were confirmed using Sanger sequencing. Our results included known as well as novel SNPs and haplotypes in genes associated with drug resistance.
We show that combining next generation sequencing data from multiple datasets and defined clinical data can better identify relevant pathway associations and clinically actionable variants, as well as provide insights into drug response mechanisms.
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