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Updated: Aug 21, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
BioPathfinder: Evidence-guided multi-agent platform enables hypothesis discovery for CAR-T engineering
Abstract:
Clinical studies of chimeric antigen receptor (CAR)-T therapy generate diverse molecular and clinical evidence that remains fragmented across publications, public repositories and patient-derived datasets, limiting systematic therapeutic discovery. Here we present BioPathfinder, an evidence-guided multi-agent workflow for closed-loop biomedical discovery. BioPathfinder constructs a provenance-aware knowledge resource linking publications with patient single-cell RNA sequencing (scRNA-seq) datasets and clinical metadata, and uses role-specialized large language model agents to generate, review and prioritize diverse, falsifiable and dataset-aware mechanistic hypotheses for computational and experimental validation. Applied to a curated corpus of CAR-T-treated patient studies and matched scRNA-seq datasets, BioPathfinder identified candidate mechanisms underlying CAR-T persistence, dysfunction and therapeutic resistance. The workflow prioritized the hypothesis that genes associated with an NK-like transition programme could be targeted to reduce CAR-T exhaustion and improve persistence. Analysis of patient scRNA-seq datasets showed enrichment of this programme in exhausted post-infusion CAR-T cells. Virtual perturbation prioritized transition-associated receptor genes, including KLRC1 , KLRD1 and KLRG1 , and expert review selected KLRC1 , encoding NKG2A, for experimental validation. In vitro and in vivo chronic-stimulation models showed that NKG2A marked activated, exhaustion-associated CD8⁺ CAR-T cells, whereas NKG2A blockade enhanced antitumour activity and persistence-associated functional readouts in vivo . BioPathfinder establishes a generalizable framework that transforms fragmented clinical single-cell evidence into experimentally validated therapeutic hypotheses, providing a scalable strategy for AI-guided biomedical discovery.
Highlights:
BioPathfinder integrates fragmented CAR-T clinical studies, patient scRNA-seq datasets and metadata into a provenance-aware evidence resource for AI-guided hypothesis discovery.A multi-agent workflow generates, critiques and prioritizes diverse, falsifiable, dataset-aware mechanisms underlying CAR-T persistence, dysfunction and therapeutic resistance.BioPathfinder prioritizes KLRC1/NKG2A as a therapeutic target, and experimental validation demonstrates that NKG2A blockade enhances CAR-T persistence and antitumour function.
Insights
BioPathfinder, an AI workflow, integrates fragmented CAR-T therapy data to discover new therapeutic targets. It identified NKG2A blockade as a strategy to enhance CAR-T cell persistence and anti-tumor activity.
Area of Science:
- Biomedical discovery
- Immunotherapy research
- Artificial intelligence in medicine
Background:
- Chimeric antigen receptor (CAR)-T therapy research generates fragmented data, hindering systematic discovery.
- Existing data silos across publications, repositories, and patient datasets limit understanding of CAR-T mechanisms.
- There is a need for integrated approaches to leverage diverse evidence for therapeutic advancement.
Purpose of the Study:
- To develop an AI-driven workflow, BioPathfinder, for integrating fragmented evidence to generate and validate therapeutic hypotheses.
- To identify novel mechanisms underlying CAR-T cell persistence, dysfunction, and resistance.
- To discover and experimentally validate new therapeutic targets for improving CAR-T therapy.
Main Methods:
- Constructed a provenance-aware knowledge resource linking publications with single-cell RNA sequencing (scRNA-seq) and clinical metadata.
- Employed role-specialized large language model agents for hypothesis generation, review, and prioritization.
- Applied the workflow to a curated corpus of CAR-T patient studies and scRNA-seq datasets.
- Validated prioritized hypotheses using in vitro and in vivo models.
Main Results:
- BioPathfinder identified candidate mechanisms for CAR-T persistence and dysfunction.
- The workflow prioritized targeting genes in an NK-like transition program to reduce CAR-T exhaustion.
- Analysis revealed enrichment of this program in exhausted CAR-T cells, prioritizing KLRC1 (NKG2A) for validation.
- Experimental validation confirmed NKG2A blockade enhances CAR-T anti-tumor activity and persistence.
Conclusions:
- BioPathfinder provides a generalizable framework for AI-guided biomedical discovery by integrating fragmented evidence.
- The study successfully transformed disparate clinical single-cell evidence into experimentally validated therapeutic hypotheses.
- NKG2A blockade is identified as a promising therapeutic strategy to enhance CAR-T cell function and persistence.
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