The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution

Human Tumor Atlas Network

Research output: Contribution to journalReview articlepeer-review

253 Scopus citations


Crucial transitions in cancer—including tumor initiation, local expansion, metastasis, and therapeutic resistance—involve complex interactions between cells within the dynamic tumor ecosystem. Transformative single-cell genomics technologies and spatial multiplex in situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. This effort complements both ongoing efforts to map healthy organs and previous large-scale cancer genomics approaches focused on bulk sequencing at a single point in time. Generating single-cell, multiparametric, longitudinal atlases and integrating them with clinical outcomes should help identify novel predictive biomarkers and features as well as therapeutically relevant cell types, cell states, and cellular interactions across transitions. The resulting tumor atlases should have a profound impact on our understanding of cancer biology and have the potential to improve cancer detection, prevention, and therapeutic discovery for better precision-medicine treatments of cancer patients and those at risk for cancer. The Human Tumor Atlas Network outlines their ambitious plan to generate 3D, single-cell, multiparametric, and longitudinal maps of diverse tumor types.

Original languageEnglish
Pages (from-to)236-249
Number of pages14
Issue number2
StatePublished - Apr 16 2020


  • AI
  • Cancer Moonshot
  • Human Tumor Atlas
  • cancer transitions
  • data integration
  • data visualization
  • metastasis
  • pre-cancer
  • resistance
  • single-cell genomics
  • spatial genomics
  • spatial imaging
  • tumor


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