Source 1primary paper2025MED
Toolkit/SCAN-ACT
SCAN-ACT
Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
We developed a comprehensive computational pipeline called SCAN-ACT that leverages single-cell RNA sequencing and multi-omics data from tumor and normal tissues to nominate and prioritize putative targets for both chimeric antigen receptor (CAR)- and TCR-T cells.
Usefulness & Problems
No literature-backed usefulness or problem-fit explainer has been materialized for this record yet.
Published Workflows
Objective: Nominate and prioritize adoptive T cell therapy targets for CAR-T and TCR-T cells in solid tumors using tumor and normal tissue single-cell and multi-omics data.
Why it works: The workflow is described as leveraging single-cell RNA sequencing and multi-omics data from both tumor and normal tissues, which is intended to identify tumor-associated targets while filtering against normal tissue expression.
surface membrane antigen targetingbispecific Boolean logic-gated CAR target pairingintracellular peptide presentation by HLA for TCR-T targetingsingle-cell RNA sequencing analysismulti-omics integrationtarget nominationtarget prioritization
Stages
- 1.Integrative target nomination and prioritization from tumor and normal tissue data(in_silico_filter)
This stage addresses the bottleneck of finding tumor-associated targets without expression in normal tissues.
Selection: single-cell RNA sequencing and multi-omics data from tumor and normal tissues were used to nominate and prioritize putative ACT targets.
- 2.Surface membrane target and target-pair proposal for CAR-T(hit_picking)
This stage converts nominated surface targets into CAR-relevant outputs, including target pairs for logic-gated designs.
Selection: surface membrane targets were separated into monospecific targets and potential target pairs for bispecific Boolean logic-gated CAR T cells.
- 3.Peptide-MHC target proposal for TCR-T(hit_picking)
This stage generates TCR-T-relevant targets from intracellular peptide presentation rather than surface membrane antigens.
Selection: intracellular peptides bound to a diverse set of human leukocyte antigens were proposed as peptide-MHC targets for TCR-T cells.
- 4.Experimental validation of selected targets(confirmatory_validation)
This stage provides experimental confirmation for selected computationally nominated targets.
Selection: selected targets were tested experimentally by protein expression and peptide-MHC binding.
Steps
- 1.Integrate single-cell RNA-seq and multi-omics data from tumor and normal tissuescomputational pipeline
To nominate and prioritize putative ACT targets using tumor and normal tissue evidence.
The workflow begins with data integration because the paper presents SCAN-ACT as a computational nomination and prioritization pipeline before any experimental validation.
- 2.Propose monospecific surface targets and target pairs for bispecific Boolean logic-gated CAR T cellscomputational pipeline
To generate CAR-T-relevant outputs from surface membrane target candidates.
After general target nomination, the workflow branches into a surface-target module for CAR-T applications.
- 3.Propose intracellular peptide-MHC targets across diverse HLA moleculescomputational pipeline
To generate TCR-T-relevant targets based on intracellular peptides presented by HLA.
This step follows nomination because the workflow separately reports a peptide-MHC branch for TCR-T applications distinct from the surface-target CAR-T branch.
- 4.Experimentally validate selected targets by protein expression and peptide-MHC bindingsource of selected targets
To confirm selected computationally nominated targets with experimental evidence.
Experimental validation is performed after computational nomination and prioritization because only selected targets are taken forward for protein expression and peptide-MHC binding tests.
Taxonomy & Function
Primary hierarchy
Technique Branch
Method: A concrete computational method used to design, rank, or analyze an engineered system.
Mechanisms
boolean logic-gated target-pair selection for bispecific car-t designcomputational target nomination and prioritizationpeptide-mhc target identification across diverse hla moleculesTarget processes
No target processes tagged yet.
Validation
Cell-freeBacteriaMammalianMouseHumanTherapeuticIndep. Replication
Supporting Sources
Ranked Claims
Applied to soft tissue sarcoma, SCAN-ACT analyzed 986,749 single cells and identified and prioritized 395 monospecific CAR-T targets, 14,192 bispecific CAR-T targets, and 5020 peptide-MHC targets for TCR-T cells.
We applied the SCAN-ACT pipeline to soft tissue sarcoma (STS), analyzing 986,749 single cells to identify and prioritize 395 monospecific CAR-T targets, 14,192 bispecific CAR-T targets, and 5020 peptide-MHC targets for TCR-T cells.
bispecific CAR-T targets identified and prioritized 14192monospecific CAR-T targets identified and prioritized 395peptide-MHC targets for TCR-T cells identified and prioritized 5020single cells analyzed 986749
SCAN-ACT was further validated in glioblastoma, indicating versatility beyond soft tissue sarcoma.
We further validated SCAN-ACT in glioblastoma revealing its versatility.
For peptide-MHC targets, SCAN-ACT proposes intracellular peptides bound to a diverse set of human leukocyte antigens for TCR-T applications.
For peptide-MHC targets, SCAN-ACT proposes intracellular peptides bound to a diverse set of human leukocyte antigens.
For surface membrane targets, SCAN-ACT proposes monospecific targets and target pairs for bispecific Boolean logic-gated CAR T cells.
For surface membrane targets, SCAN-ACT proposes monospecific targets and potential target pairs for bispecific Boolean logic-gated CAR T cells.
SCAN-ACT is a computational pipeline that uses single-cell RNA sequencing and multi-omics data from tumor and normal tissues to nominate and prioritize putative targets for CAR-T and TCR-T cells.
We developed a comprehensive computational pipeline called SCAN-ACT that leverages single-cell RNA sequencing and multi-omics data from tumor and normal tissues to nominate and prioritize putative targets for both chimeric antigen receptor (CAR)- and TCR-T cells.
Selected SCAN-ACT targets were experimentally validated by protein expression and peptide-MHC binding assays.
Selected targets were validated experimentally by protein expression and for peptide-MHC binding.
Approval Evidence
1 source6 linked approval claimsfirst-pass slug scan-act
We developed a comprehensive computational pipeline called SCAN-ACT that leverages single-cell RNA sequencing and multi-omics data from tumor and normal tissues to nominate and prioritize putative targets for both chimeric antigen receptor (CAR)- and TCR-T cells.
Source:
application resultsupports
Applied to soft tissue sarcoma, SCAN-ACT analyzed 986,749 single cells and identified and prioritized 395 monospecific CAR-T targets, 14,192 bispecific CAR-T targets, and 5020 peptide-MHC targets for TCR-T cells.
We applied the SCAN-ACT pipeline to soft tissue sarcoma (STS), analyzing 986,749 single cells to identify and prioritize 395 monospecific CAR-T targets, 14,192 bispecific CAR-T targets, and 5020 peptide-MHC targets for TCR-T cells.
Source:
generalizationsupports
SCAN-ACT was further validated in glioblastoma, indicating versatility beyond soft tissue sarcoma.
We further validated SCAN-ACT in glioblastoma revealing its versatility.
Source:
method capabilitysupports
For peptide-MHC targets, SCAN-ACT proposes intracellular peptides bound to a diverse set of human leukocyte antigens for TCR-T applications.
For peptide-MHC targets, SCAN-ACT proposes intracellular peptides bound to a diverse set of human leukocyte antigens.
Source:
method capabilitysupports
For surface membrane targets, SCAN-ACT proposes monospecific targets and target pairs for bispecific Boolean logic-gated CAR T cells.
For surface membrane targets, SCAN-ACT proposes monospecific targets and potential target pairs for bispecific Boolean logic-gated CAR T cells.
Source:
method capabilitysupports
SCAN-ACT is a computational pipeline that uses single-cell RNA sequencing and multi-omics data from tumor and normal tissues to nominate and prioritize putative targets for CAR-T and TCR-T cells.
We developed a comprehensive computational pipeline called SCAN-ACT that leverages single-cell RNA sequencing and multi-omics data from tumor and normal tissues to nominate and prioritize putative targets for both chimeric antigen receptor (CAR)- and TCR-T cells.
Source:
validation resultsupports
Selected SCAN-ACT targets were experimentally validated by protein expression and peptide-MHC binding assays.
Selected targets were validated experimentally by protein expression and for peptide-MHC binding.
Source:
Comparisons
No literature-backed comparison notes have been materialized for this record yet.
Ranked Citations
- 1.