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Series GSE228560 Query DataSets for GSE228560
Status Public on Feb 15, 2024
Title Artificial Intelligence-powered Drug Discovery against CTLA-4 in Cancer
Organism Mus musculus
Experiment type Expression profiling by high throughput sequencing
Other
Summary Cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) plays a pivotal role in preventing autoimmunity and fostering anticancer immunity by interacting with B7 proteins CD80 and CD86. CTLA-4 is the first immune checkpoint targeted with a monoclonal antibody inhibitor. Checkpoint inhibitors have generated durable responses in many cancer patients, representing a revolutionary milestone in cancer immunotherapy. However, therapeutic efficacy is limited to a small portion of patients, and immune-related adverse events are noteworthy, especially for monoclonal antibodies directed against CTLA-4. Previously, small molecules have been developed to impair the CTLA-4: CD80 interaction; however, they directly targeted CD80 and not CTLA-4. In this study, we performed artificial intelligence (AI)-powered virtual screening of approximately ten million compounds to target CTLA-4. We validated primary hits with biochemical, biophysical, immunological, and experimental animal assays. We then optimized lead compounds and obtained inhibitors with an inhibitory concentration of 1 micromole in disrupting the interaction between CTLA-4 and CD80. Unlike ipilimumab, these small molecules did not degrade CTLA-4. Several compounds inhibited tumor development prophylactically and therapeutically in syngeneic and CTLA-4-humanized mice. This project supports an AI-based framework in designing small molecules targeting immune checkpoints for cancer therapy.
 
Overall design Tumors from CTLA-4-humanized MC38 syngeneic mice (n = 3 - 5 mice) were extracted for single cell suspension using a gentleMACSTM Octo Dissociator. The cells were stained with anti-mouse CD45 antibody (PE-Cy7, Clone 30-F11, Cat. 103114, Lot: B354212) and sorted with a BD FACSAriaII Cell Sorting Flow Cytometer. Library preparation of CD45+ sorted cells for single-cell- RNA sequencing (scRNA-seq) was performed using the 10X Genomics Chromium Single Cell 5’ Library & Gel Bead reagent kit. For TCR sequencing, the Chromium Single Cell V(D)J Enrichment kit was used. The acquired scRNA-seq reads from the 10X Genomics platform were aligned using Cell Ranger (V.7.1, 10 X Genomics) to a reference genome (mm10) using default parameters. R studio Seurat package was used for analyses.
 
Contributor(s) Sobhani N, Li Y
Citation(s) 38312352
Submission date Mar 30, 2023
Last update date Feb 16, 2024
Contact name Navid Sobhani
E-mail(s) navidsobhani19@gmail.com, navid.sobhani@cantab.net
Phone 3467750094
Organization name Baylor College of Medicine
Department Medicine, Epidemiology and Population Sciences
Lab N720
Street address 1 BAYLOR PLAZA
City HOUSTON
State/province TEXAS
ZIP/Postal code 77030
Country USA
 
Platforms (1)
GPL19057 Illumina NextSeq 500 (Mus musculus)
Samples (10)
GSM7124072 PD-1
GSM7124073 PD-1_mTCR
GSM7124074 Ipilimumab
Relations
BioProject PRJNA950363

Download family Format
SOFT formatted family file(s) SOFTHelp
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Supplementary file Size Download File type/resource
GSE228560_RAW.tar 122.1 Mb (http)(custom) TAR (of H5, TAR)
SRA Run SelectorHelp
Raw data are available in SRA
Processed data provided as supplementary file

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