OCTAD: an open workspace for virtually screening therapeutics targeting precise cancer patient groups using gene expression features

Zeng, Billy; Glicksberg, Benjamin S.; Newbury, Patrick; Chekalin, Evgeny; Xing, Jing; Liu, Ke; Wen, Anita; Chow, C. W.; Chen, Bin

doi:10.1038/s41596-020-00430-z

Cited by 29 publications

(46 citation statements)

References 54 publications

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“…A larger absolute z -score means a higher magnitude of expression change. The LINCS profiles have been extensively explored for therapeutic discovery in our previous works [ 7 , 8 , 28 , 29 ].…”

Section: Methodsmentioning

confidence: 99%

“…For example, fold change values of all genes were derived by comparing COVID-19 patients aged 50–59 versus healthy donors aged 50–59. The log 2 fold change values were calculated using the diffExp function in the open Cancer TherApeutic discovery (OCTAD) [ 28 ] R package. For better visualization in a heatmap, the log 2 fold change values from different comparisons were converted into gene rankings within a group.…”

Section: Methodsmentioning

confidence: 99%

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Published anti-SARS-CoV-2 in vitro hits share common mechanisms of action that synergize with antivirals

Xing

Paithankar

Liu

et al. 2021

Briefings in Bioinformatics

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The global efforts in the past year have led to the discovery of nearly 200 drug repurposing candidates for COVID-19. Gaining more insights into their mechanisms of action could facilitate a better understanding of infection and the development of therapeutics. Leveraging large-scale drug-induced gene expression profiles, we found 36% of the active compounds regulate genes related to cholesterol homeostasis and microtubule cytoskeleton organization. Following bioinformatics analyses revealed that the expression of these genes is associated with COVID-19 patient severity and has predictive power on anti-SARS-CoV-2 efficacy in vitro. Monensin, a top new compound that regulates these genes, was further confirmed as an inhibitor of SARS-CoV-2 replication in Vero-E6 cells. Interestingly, drugs co-targeting cholesterol homeostasis and microtubule cytoskeleton organization processes more likely present a synergistic effect with antivirals. Therefore, potential therapeutics could be centered around combinations of targeting these processes and viral proteins.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Published anti-SARS-CoV-2 in vitro hits share common mechanisms of action that synergize with antivirals

Xing

Paithankar

Liu

et al. 2021

Briefings in Bioinformatics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Next, we removed probes matching the same gene symbol by selecting the one with the lowest p-value. For the TCGA RNA-Seq dataset, the differential expression analysis between TCGA tumor and GTEx normal samples was performed using EdgeR [48] in the workspace Open Cancer TherApeutic Discovery (OCTAD) [49]. For this analysis, OCTAD uses a deep learning approach to select the top 50 highly correlated normal samples based on their gene expression profiles [50].…”

Section: Generation Of Reca Signaturesmentioning

confidence: 99%

“…The gene expression signature for each dataset was virtually screened for therapeutic targets using the OCTAD tool [49]. This platform matches cancer-specific expression signatures to compound-induced gene expression profiles (66,612 drug-induced gene expression profiles derived from 71 cell lines and 12,442 drugs) of the Library of Integrated Network-based Cellular Signatures (LINCS; L1000 dataset) [28,33,34].…”

Section: Screening Drugs Targeting Reca Using Gene Expression Signaturesmentioning

confidence: 99%

Drug Repositioning Based on the Reversal of Gene Expression Signatures Identifies TOP2A as a Therapeutic Target for Rectal Cancer

Carvalho

Canto

Cury

et al. 2021

Cancers

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Rectal cancer is a common disease with high mortality rates and limited therapeutic options. Here we combined the gene expression signatures of rectal cancer patients with the reverse drug-induced gene-expression profiles to identify drug repositioning candidates for cancer therapy. Among the predicted repurposable drugs, topoisomerase II inhibitors (doxorubicin, teniposide, idarubicin, mitoxantrone, and epirubicin) presented a high potential to reverse rectal cancer gene expression signatures. We showed that these drugs effectively reduced the growth of colorectal cancer cell lines closely representing rectal cancer signatures. We also found a clear correlation between topoisomerase 2A (TOP2A) gene copy number or expression levels with the sensitivity to topoisomerase II inhibitors. Furthermore, CRISPR-Cas9 and shRNA screenings confirmed that loss-of-function of the TOP2A has the highest efficacy in reducing cellular proliferation. Finally, we observed significant TOP2A copy number gains and increased expression in independent cohorts of rectal cancer patients. These findings can be translated into clinical practice to evaluate TOP2A status for targeted and personalized therapies based on topoisomerase II inhibitors in rectal cancer patients.

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“…In addition to the image-based approaches, biological data such as gene expression (Dolezal et al 2020) (Xie et al 2021), assurance of effective therapeutics for cancer treatment (Zeng et al 2021), classification of cancer subtypes (Binder et al 2021, Galili et al 2021, Ahn et al 2018. Ahn et al developed a deep learning algorithm using publicly available gene expression databases to classify the samples as normal or tumor and high predictive scores were obtained.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network Approach to Predict Tumor Samples Using Gene Expression Data

Darendeli¹,

Yılmaz²

2021

Journal of Intelligent Systems: Theory and Applications

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Cancer is threatening millions of people each year and its early diagnosis is still a challenging task. Early diagnosis is one of the major ways to tackle the disease and lower the mortality rate. Advancements in deep learning approaches and the availability of biological data offer applications that can facilitate the diagnosis and characterization of cancer. Here, we aimed to provide a new perspective of cancer diagnosis using a deep learning approach on gene expression data. In this study, RNA-Seq data of approximately 30 different types of cancer patients the Cancer Genome Atlas (TCGA) study, and normal tissue RNA-Seq data from GTEx were used. The input data for the training was transformed to RGB format and the training was carried out with a Convolutional Neural Network (CNN). The trained algorithm is able to predict cancer with 97% accuracy, using gene expression data. In conclusion, our study shows that the deep learning approach and biological data have a huge potential in the diagnosis and identification of tumor samples.

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OCTAD: an open workspace for virtually screening therapeutics targeting precise cancer patient groups using gene expression features

Cited by 29 publications

References 54 publications

Published anti-SARS-CoV-2 in vitro hits share common mechanisms of action that synergize with antivirals

Published anti-SARS-CoV-2 in vitro hits share common mechanisms of action that synergize with antivirals

Drug Repositioning Based on the Reversal of Gene Expression Signatures Identifies TOP2A as a Therapeutic Target for Rectal Cancer

Convolutional Neural Network Approach to Predict Tumor Samples Using Gene Expression Data

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