MedicinemedRxiv
Heuristic editor, no API keyVerdict: NotableA ReAct Agentic AI System for Natural Language Querying and Statistical Analysis of The Cancer Genome Atlas Clinical Data
The Cancer Genome Atlas (TCGA) holds clinical data for over 11,000 patients across 33 cancer types, but access is hard because of complex file structures, heterogeneous formats, and the need for programming.
Key numbers
- 93.4% overall accuracy
- 100% single-patient lookups
- 99.1% cohort statistics
VerdictWorth a reader's time today.
Abstract
The Cancer Genome Atlas (TCGA) holds clinical data for over 11,000 patients across 33 cancer types, but access is hard because of complex file structures, heterogeneous formats, and the need for programming. We present an agentic system for natural language querying and statistical analysis of TCGA clinical data. The system uses a large language model as an autonomous ReAct agent that selects from eight computational tools, including data extraction, descriptive statistics, Kaplan-Meier survival analysis with log-rank tests, hypothesis testing, and verification against the curated TCGA Pan-Cancer Clinical Data Resource (CDR). The agent reasons about intermediate results, adapts its approach, and returns clinically contextualized responses with source attribution and auditable traces. We introduce TCGA-Agent-Bench, 440 queries across five difficulty tiers with ground truth from the independently curated TCGA-CDR, evaluated with dual metrics of numerical accuracy and clinical completeness. The system achieves 93.4% overall accuracy (100% single-patient lookups, 99.1% cohort statistics, 92.8% comparative analyses), outperforming a fixed rule-based pipeline (87.1%), a single-pass LLM (81.8%), and retrieval-augmented generation (66.9% on a subset). Most of the benchmark is answerable from the CDR alone, so we locate the extraction layer's value in fields the CDR lacks (drug treatments, TNM components, biomarkers, biospecimen metadata): on 26 queries targeting these, the full system answers 100% versus 3.8% for CDR-only. Ablations show the reasoning loop is most impactful (+9.1% accuracy, +22.0 completeness points). A tool-based agentic architecture enables accurate, auditable analysis of clinical repositories, with value driven by tool design and recovered fields rather than model scale.
The editor's rubric
| Dimension | Level | Weight | What that level means |
|---|---|---|---|
| Leverage | ███░░ 3 | 10% | A method or resource many groups across the field will adopt within a year. |
| Magnitude | ███░░ 3 | 20% | Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem. |
| Evidence | ████░ 4 | 32% | Strong: large scale, preregistered, independently replicated, or a well-powered randomized trial. |
| Novelty | ███░░ 3 | 8% | A genuinely new approach to an open problem. |
| Trajectory | ██░░░ 2 | 5% | Some room to improve with obvious engineering. |
| Stakes | ███░░ 3 | 25% | Meaningful benefit to many people within a few years. |
Editor’s rationale
Heuristic triage from title and abstract text only, not a reading of the paper. Cues found: method (we propose); gains (versus baseline, outperforms); novelty (alternative to status quo); verification (ablations, independent replication); scale (scalable); stakes (mortality, major disease, prevention or cure).
How the score was computed
- Merit
- 6.5 / 10
- Adjusted merit
- 5.3 / 10
- Attention
- 0%
- Freshness
- 84%
- Citations0 (reference 20, via openalex, Sep 29, 2026, 23:37 UTC)