Uniform Genomic Data Analysis in the NCI Genomic Data Commons

Zhang, Zhenyu; Hernandez, Kyle M.; Savage, Jeremiah; Li, Shenglai; Miller, Dan; Agrawal, Stuti; Ortuño, Francisco; Staudt, Lou; Heath, Allison P.; Grossman, Robert L.

doi:10.1101/788919

Cited by 18 publications

(23 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The regulatory networks were reverse engineered by ARACNe from RNASeq profiles of human cancer tissue from The Cancer Genome Atlas (TCGA), Therapeutically Applicable Research To Generate Effective Treatments (TARGET), and a few other high quality publicly available mRNA datasets (Table S4). The RNASeq level 3 data were downloaded from NCI Genomics Data Commons [48]; raw counts were normalized and the variance was stabilized by fitting the dispersion to a negative-binomial distribution as implemented in the DESeq2 R-package [49]. ARACNe was run with 100 bootstrap iterations using an input set of candidate regulators including: 1,813 transcription factors (genes annotated in Gene Ontology Molecular Function database (GO)55 as GO:0003700-'DNA-binding transcription factor activity', or as GO:0003677-'DNA binding' and GO:0030528-'Transcription regulator activity', or as GO:0003677 and GO:0045449-'Regulation of transcription, DNA-templated'); 969 transcriptional co-factors (a manually curated list, not overlapping with the transcritpion factor list, built upon genes annotated as GO:0003712-'transcription coregulator activity' or GO:0030528 or GO:0045449); and 3,370 signaling pathway related genes (annotated in GO Biological Process database as GO:0007165-'signal transduction' and in GO Cellular Component database as GO:0005622-'intracellular' or GO:0005886-'plasma membrane').…”

Section: Generation Of Gene Regulatory Network: Aracne (Algorithm For the Reconstruction Ofmentioning

confidence: 99%

Pre-clinical validation of an RNA-based precision oncology platform for patient-therapy alignment in a diverse set of human malignancies resistant to standard treatments

Mundi¹,

Cruz²,

Grunn³

et al. 2021

Preprint

View full text Add to dashboard Cite

Predicting tumor sensitivity to antineoplastics remains an elusive challenge, with no methods demonstrating predictive power. Joint analysis of tumors-from patients with distinct malignancies who had progressed on multiple lines of therapy-and drug perturbation transcriptional profiles predicted sensitivity to 28 of 350 drugs, 26 of which (93%) were confirmed in low-passage, patient-derived xenograft (PDX) models. Drugs were prioritized based on their ability to either invert the activity of individual Master Regulator proteins, with available high-affinity inhibitors, or of the modules they comprise (Tumor-Checkpoints), based on de novo mechanism of action analysis. Of 138 PDX mice enrolled in 16 single and 18 multi-protein treatment arms, a disease control rate (DCR) of 68% and 91%, and an objective response rate (ORR) of 12% and 17%, were achieved respectively, compared to 6% and 0% in the negative controls arm, with multi-protein drugs achieving significantly more durable responses. Thus, these approaches may effectively complement and expand current precision oncology approaches, as also illustrated by a case study.

show abstract

Section: Generation Of Gene Regulatory Network: Aracne (Algorithm For the Reconstruction Ofmentioning

confidence: 99%

Pre-clinical validation of an RNA-based precision oncology platform for patient-therapy alignment in a diverse set of human malignancies resistant to standard treatments

Mundi¹,

Cruz²,

Grunn³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Fragments Per Kilobase of transcript per Million mapped reads (FPKM) quantification of gene expression and whole-exome sequencing (WES) Binary Alignment Map (BAM) files were downloaded from GDC. 33 Demographic and clinical information were retrieved from the TCGA Pan-Cancer Clinical Data Resource. 34 BMI and smoking history were retrieved from legacy clinical files.…”

Section: Methodsmentioning

confidence: 99%

ACE2andTMPRSS2expression by clinical, HLA, immune, and microbial correlates across 34 human cancers and matched normal tissues: implications for SARS-CoV-2 COVID-19

Bao

Hernandez

Huang

et al. 2020

J Immunother Cancer

Self Cite

View full text Add to dashboard Cite

BackgroundPandemic COVID-19 by severe acute respiratory syndrome (SARS) coronavirus 2 (SARS-CoV-2) infection is facilitated by the ACE2 receptor and protease TMPRSS2. Modestly sized case series have described clinical factors associated with COVID-19, while ACE2 and TMPRSS2 expression analyses have been described in some cell types. Patients with cancer may have worse outcomes to COVID-19.MethodsWe performed an integrated study of ACE2 and TMPRSS2 gene expression across and within organ systems, by normal versus tumor, across several existing databases (The Cancer Genome Atlas, Census of Immune Single Cell Expression Atlas, The Human Cell Landscape, and more). We correlated gene expression with clinical factors (including but not limited to age, gender, race, body mass index, and smoking history), HLA genotype, immune gene expression patterns, cell subsets, and single-cell sequencing as well as commensal microbiome.ResultsMatched normal tissues generally display higher ACE2 and TMPRSS2 expression compared with cancer, with normal and tumor from digestive organs expressing the highest levels. No clinical factors were consistently identified to be significantly associated with gene expression levels though outlier organ systems were observed for some factors. Similarly, no HLA genotypes were consistently associated with gene expression levels. Strong correlations were observed between ACE2 expression levels and multiple immune gene signatures including interferon-stimulated genes and the T cell-inflamed phenotype as well as inverse associations with angiogenesis and transforming growth factor-β signatures. ACE2 positively correlated with macrophage subsets across tumor types. TMPRSS2 was less associated with immune gene expression but was strongly associated with epithelial cell abundance. Single-cell sequencing analysis across nine independent studies demonstrated little to no ACE2 or TMPRSS2 expression in lymphocytes or macrophages. ACE2 and TMPRSS2 gene expression associated with commensal microbiota in matched normal tissues particularly from colorectal cancers, with distinct bacterial populations showing strong associations.ConclusionsWe performed a large-scale integration of ACE2 and TMPRSS2 gene expression across clinical, genetic, and microbiome domains. We identify novel associations with the microbiota and confirm host immunity associations with gene expression. We suggest caution in interpretation regarding genetic associations with ACE2 expression suggested from smaller case series.

show abstract

“…The standardized, upper-quartile normalized, batch-corrected, and platform-corrected RNAseq expression of 20,531 genes in RSEM (RNA-Seq by Expectation Maximization)-quantified read count estimates were downloaded from PanCancer Atlas consortium studies (https://gdc.cancer.gov/about-data/publications/pancanatlas) and log2-transformed for further analysis. FPKM (Fragments Per Kilobase of transcript per Million mapped reads) quantification of gene expression as well as whole-exome sequencing (WES) BAM files were downloaded from GDC [28]. Demographic and clinical information were retrieved from the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR) [29].…”

Section: Methodsmentioning

confidence: 99%

ACE2 and TMPRSS2 expression by clinical, HLA, immune, and microbial correlates across 34 human cancers and matched normal tissues: implications for SARS-COV-2 COVID-19

Bao

Hernandez

Huang

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Background: Pandemic COVID-19 by SARS-COV-2 infection is facilitated by the ACE2 receptor and protease TMPRSS2. Modestly sized case series have described clinical factors associated with COVID-19, while ACE2 and TMPRSS2 expression analyses have been described in some cell types. Cancer patients may have worse outcomes to COVID-19. Methods:We performed an integrated study of ACE2 and TMPRSS2 gene expression across and within organ systems, by normal versus tumor, across several existing databases (The Cancer Genome Atlas, Census of Immune Single Cell Expression Atlas, The Human Cell Landscape, and more). We correlated gene expression with clinical factors (including but not limited to age, gender, race, BMI and smoking history), HLA genotype, immune gene expression patterns, cell subsets, and single-cell sequencing as well as commensal microbiome.Results: Matched normal tissues generally display higher ACE2 and TMPRSS2 expression compared with cancer, with normal and tumor from digestive organs expressing the highest levels. No clinical factors were consistently identified to be significantly associated with gene expression levels though outlier organ systems were observed for some factors. Similarly, no HLA genotypes were consistently associated with gene expression levels. Strong correlations were observed between ACE2 expression levels and multiple immune gene signatures including interferon-stimulated genes and the T cell-inflamed phenotype as well as inverse associations with angiogenesis and transforming growth factor-β signatures. ACE2 positively correlated with macrophage subsets across tumor types. TMPRSS2 was less associated with immune gene expression but was strongly associated with epithelial cell abundance. Single-cell sequencing analysis across nine independent studies demonstrated little to no ACE2 or

show abstract

Uniform Genomic Data Analysis in the NCI Genomic Data Commons

Cited by 18 publications

References 39 publications

Pre-clinical validation of an RNA-based precision oncology platform for patient-therapy alignment in a diverse set of human malignancies resistant to standard treatments

Pre-clinical validation of an RNA-based precision oncology platform for patient-therapy alignment in a diverse set of human malignancies resistant to standard treatments

ACE2andTMPRSS2expression by clinical, HLA, immune, and microbial correlates across 34 human cancers and matched normal tissues: implications for SARS-CoV-2 COVID-19

ACE2 and TMPRSS2 expression by clinical, HLA, immune, and microbial correlates across 34 human cancers and matched normal tissues: implications for SARS-COV-2 COVID-19

Contact Info

Product

Resources

About