From mass cytometry to cancer prognosis

Journal name:
Nature Biotechnology
Volume:
33,
Pages:
931–932
Year published:
DOI:
doi:10.1038/nbt.3346
Published online

Applying social network algorithms to mass cytometry data from single cancer cells leads to more accurate predictions of patient outcomes.

References

  1. Levine, J.H. et al. Cell 162, 184197 (2015).
  2. Etzrodt, M., Endele, M. & Schroeder, T. Cell Stem Cell 15, 546558 (2014).
  3. Patel, J.P. et al. N. Engl. J. Med. 366, 10791089 (2012).
  4. Levis, M. et al. Blood 117, 32943301 (2011).
  5. Dombret, H. et al. Blood 126, 291299 (2015).
  6. Ferrara, F. & Schiffer, C.A. Lancet 381, 484495 (2013).
  7. Ding, L. et al. Nature 481, 506510 (2012).
  8. Eppert, K. et al. Nat. Med. 17, 10861093 (2011).
  9. Taussig, D.C. et al. Blood 115, 19761984 (2010).
  10. Jaitin, D.A. et al. Science 343, 776779 (2014).
  11. Zeisel, A. et al. Science 347, 11381142 (2015).

Download references

Author information

Affiliations

  1. Deborah R. Winter, Guy Ledergor & Ido Amit are in the Department of Immunology, Weizmann Institute of Science, Rehovot, Israel.

Competing financial interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to:

Author details

Additional data