Rare cancer research generates heterogeneous data across genomic sequencing pipelines, clinical trial registries, biomarker repositories, and peer-reviewed literature. Integrating these sources for a single investigation typically requires custom ETL pipelines, manual schema reconciliation, and iterative querying across disconnected systems—a process that can take weeks before any analysis begins.

Amazon Quick Research addresses this integration challenge by providing a unified research environment. It ingests structured and unstructured data from multiple sources, including publicly available biomedical databases such as PubMed, and applies large language model (LLM)-driven synthesis to generate cited, versioned research reports.

In this post, we walk through how to use Amazon Quick Research to integrate biomedical data sources for rare cancer research. The walkthrough uses pediatric sarcoma as the research domain and draws on publicly available datasets from PubMed and other open biomedical repositories. It covers the end-to-end workflow: defining a research objective, configuring data sources, reviewing the AI-generated research plan, running the investigation, and iterating on results using the revision and versioning system.