The Human Tumor Atlas Network: Charting tumor transitions across space and time at single-cell resolution Editorial


Authors: Rozenblatt-Rosen, O.; Regev, A.; Oberdoerffer, P.; Nawy, T.; Hupalowska, A.; Rood, J. E.; Ashenberg, O.; Cerami, E.; Coffey, R. J.; Demir, E.; Ding, L.; Esplin, E. D.; Ford, J. M.; Goecks, J.; Ghosh, S.; Gray, J. W.; Guinney, J.; Hanlon, S. E.; Hughes, S. K.; Hwang, E. S.; Iacobuzio-Donahue, C. A.; Jané-Valbuena, J.; Johnson, B. E.; Lau, K. S.; Lively, T.; Mazzilli, S. A.; Pe'er, D.; Santagata, S.; Shalek, A. K.; Schapiro, D.; Snyder, M. P.; Sorger, P. K.; Spira, A. E.; Srivastava, S.; Tan, K.; West, R. B.; Williams, E. H.; and the Human Tumor Atlas Network; Schultz, N.; de Bruijn, I.; Gao, J.; Rudin, C. M.; Hollmann, T.; Massague, J.; Mazutis, L.; Boire, A.; Allaj, V.; Bott, M.; Chan, J. M.; Chaudhary, O.; Chun, J.; Egger, J.; Gan, S.; Gao, V. R.; Hayashi, A.; Laughney, A. M.; Masilionis, I.; Mattar, M.; Offin, M.; Pe'er, I.; Quintanal-Villalonga, A.; Sen, T.; Xie, Y.; Zhang, M.
Title: The Human Tumor Atlas Network: Charting tumor transitions across space and time at single-cell resolution
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. The Human Tumor Atlas Network outlines their ambitious plan to generate 3D, single-cell, multiparametric, and longitudinal maps of diverse tumor types. © 2020
Keywords: sequence analysis; review; cancer patient; cancer diagnosis; cancer prevention; metastasis; cancer research; cell specificity; health care quality; high risk population; tumor; factual database; cell interaction; national health organization; resistance; personalized medicine; tumor microenvironment; data visualization; single cell analysis; clinical outcome; oncological parameters; three-dimensional imaging; human; priority journal; data integration; oncogenomics; ai; cancer moonshot; cancer transitions; human tumor atlas; pre-cancer; single-cell genomics; spatial genomics; spatial imaging; human tumor atlas network; tumor transition
Journal Title: Cell
Volume: 181
Issue: 2
ISSN: 0092-8674
Publisher: Cell Press  
Date Published: 2020-04-16
Start Page: 236
End Page: 249
Language: English
DOI: 10.1016/j.cell.2020.03.053
PUBMED: 32302568
PROVIDER: scopus
PMCID: PMC7376497
DOI/URL:
Notes: Review -- Source: Scopus
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  1. Joan Massague
    389 Massague
  2. Matthew Bott
    135 Bott
  3. Jianjiong Gao
    132 Gao
  4. Nikolaus D Schultz
    487 Schultz
  5. Adrienne Boire
    106 Boire
  6. Travis Jason Hollmann
    126 Hollmann
  7. Charles Rudin
    489 Rudin
  8. Joseph Minhow Chan
    48 Chan
  9. Viola   Allaj
    29 Allaj
  10. Marissa   Mattar
    57 Mattar
  11. Michael David Offin
    170 Offin
  12. Dana Pe'er
    110 Pe'er
  13. Linas Mazutis
    34 Mazutis
  14. Jaeyoung Chun
    5 Chun
  15. Jacklynn V Egger
    68 Egger
  16. Triparna Sen
    56 Sen
  17. Mianlei Zhang
    4 Zhang
  18. Yubin Xie
    11 Xie
  19. Tal Nawy
    15 Nawy
  20. Siting Gan
    3 Gan
  21. Vianne Ran Gao
    12 Gao
  22. Akimasa Hayashi
    13 Hayashi