Literature DB >> 32302568

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

Orit Rozenblatt-Rosen1, Aviv Regev2, Philipp Oberdoerffer3, Tal Nawy4, Anna Hupalowska1, Jennifer E Rood1, Orr Ashenberg1, Ethan Cerami5, Robert J Coffey6, Emek Demir7, Li Ding8, Edward D Esplin9, James M Ford10, Jeremy Goecks11, Sharmistha Ghosh12, Joe W Gray13, Justin Guinney14, Sean E Hanlon15, Shannon K Hughes3, E Shelley Hwang16, Christine A Iacobuzio-Donahue17, Judit Jané-Valbuena1, Bruce E Johnson18, Ken S Lau6, Tracy Lively19, Sarah A Mazzilli20, Dana Pe'er4, Sandro Santagata21, Alex K Shalek22, Denis Schapiro23, Michael P Snyder9, Peter K Sorger24, Avrum E Spira25, Sudhir Srivastava12, Kai Tan26, Robert B West27, Elizabeth H Williams28.   

Abstract

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.
Copyright © 2020. Published by Elsevier Inc.

Entities:  

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

Mesh:

Year:  2020        PMID: 32302568      PMCID: PMC7376497          DOI: 10.1016/j.cell.2020.03.053

Source DB:  PubMed          Journal:  Cell        ISSN: 0092-8674            Impact factor:   66.850


  64 in total

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  92 in total

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