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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
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
Phone 3467750094
Organization name Baylor College of Medicine
Department Medicine, Epidemiology and Population Sciences
Lab N720
Street address 1 BAYLOR PLAZA
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
BioProject PRJNA950363

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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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