Pan-cancer image-based detection of clinically actionable genetic alterations-Reference-Cited by-同舟云学术

Pan-cancer image-based detection of clinically actionable genetic alterations

Published:2020-07-27 Issue:8 Volume:1 Page:789-799
ISSN:2662-1347
Container-title:Nature Cancer
language:en
Short-container-title:Nat Cancer

Author:

Kather Jakob Nikolas^ORCID,Heij Lara R.,Grabsch Heike I.^ORCID,Loeffler Chiara,Echle Amelie,Muti Hannah Sophie,Krause Jeremias,Niehues Jan M.,Sommer Kai A. J.,Bankhead Peter,Kooreman Loes F. S.,Schulte Jefree J.^ORCID,Cipriani Nicole A.^ORCID,Buelow Roman D.^ORCID,Boor Peter,Ortiz-Brüchle Nadina^ORCID,Hanby Andrew M.,Speirs Valerie^ORCID,Kochanny Sara,Patnaik Akash,Srisuwananukorn Andrew,Brenner Hermann,Hoffmeister Michael,van den Brandt Piet A.^ORCID,Jäger Dirk,Trautwein Christian,Pearson Alexander T.^ORCID,Luedde Tom^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Cancer Research,Oncology

Link

http://www.nature.com/articles/s43018-020-0087-6.pdf

Reference52 articles.

1. Cheng, M. L., Berger, M. F., Hyman, D. M. & Solit, D. B. Clinical tumour sequencing for precision oncology: time for a universal strategy. Nat. Rev. Cancer 18, 527–528 (2018).

2. Rusch, M. et al. Clinical cancer genomic profiling by three-platform sequencing of whole genome, whole exome and transcriptome. Nat. Commun. 9, 3962 (2018).

3. Kather, J. N., Halama, N. & Jaeger, D. Genomics and emerging biomarkers for immunotherapy of colorectal cancer. Semin. Cancer Biol. 52, 189–197 (2018).

4. Guinney, J. et al. The consensus molecular subtypes of colorectal cancer. Nat. Med. 21, 1350–1356 (2015).

5. Fontana, E., Eason, K., Cervantes, A., Salazar, R. & Sadanandam, A. Context matters—consensus molecular subtypes of colorectal cancer as biomarkers for clinical trials. Ann. Oncol. 30, 520–527 (2019).

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