RefinedScience AML Dataset

RefinedScience

This longitudinal dataset includes 393 newly diagnosed Acute Myeloid Leukemia (AML) patients treated with Venetoclax plus Azacitidine as a first-line therapy. The dataset tracks patients from their diagnosis date (EVENT) through treatment, follow-up, and subsequent therapies. It is derived from the RefinedScience platform, which combines high-fidelity clinical data with single-cell omics to identify novel drug targets and stratify patient risk.


Key features include access to companion analysis tooling for patients who had CITE-seq performed on bone marrow specimens at diagnosis and/or follow-up timepoints. This unique dataset supports precision medicine by linking granular clinical outcomes with deep molecular profiling.

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Datasets

RefinedScience AML Dataset

Data Collection Time Frame

2015 - 2025

Participants

Current: 393 unique patients (3,186 rows of data)

Geographical Coverage

Aurora, Colorado, USA (UCHealth)

Usage examples

  • Drug Discovery & Biomarker Research: Identify and validate novel drug targets or biomarkers for AML patients who develop resistance to standard therapies.
  • Predictive Modeling: Develop machine learning models to predict long-term survival and risk stratification for patients treated with Venetoclax and Azacitidine.
  • Outcomes Analysis: Analyze the impact of evolving toxicities, hospital events, and short-term disease responses on overall survival.
  • Single-Cell Analysis: Utilize the companion analysis tooling to explore gene expression and protein markers (CITE-seq) in bone marrow specimens to understand disease heterogeneity.
  • Molecular Subtype Analysis: Explore mutation-specific and molecular response patterns across treatment and relapse.
  • Translational Trial Design: Inform biomarker enrichment strategies and combination treatment therapies for Venetoclax-based regimens.

Data modalities

EHR data

  • Demographics
  • Lab results
  • Med history
  • Pathology/Molecular reports (available at diagnostic and follow Events)
  • Therapy
  • Social history
  • Procedures
  • Hospitalizations and ICU visits

Derived data

  • Treatment response
  • Risk classifications (eg. ELN)

Omics data

  • CITE-seq (via companion analysis tooling)
  • Integrated molecular profiling, including cytogenic and targeted sequencing data (via companion analysis tooling)