Computational analysis reveals histotype-dependent molecular profile and actionable mutation effects across cancers

Daniel Heim, Grégoire Montavon, Peter Hufnagl, Klaus Robert Müller, Frederick Klauschen

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


Background: Comprehensive mutational profiling data now available on all major cancers have led to proposals of novel molecular tumor classifications that modify or replace the established organ- and tissue-based tumor typing. The rationale behind such molecular reclassifications is that genetic alterations underlying cancer pathology predict response to therapy and may therefore offer a more precise view on cancer than histology. The use of individual actionable mutations to select cancers for treatment across histotypes is already being tested in the so-called basket trials with variable success rates. Here, we present a computational approach that facilitates the systematic analysis of the histological context dependency of mutational effects by integrating genomic and proteomic tumor profiles across cancers. Methods: To determine effects of oncogenic mutations on protein profiles, we used the energy distance, which compares the Euclidean distances of protein profiles in tumors with an oncogenic mutation (inner distance) to that in tumors without the mutation (outer distance) and performed Monte Carlo simulations for the significance analysis. Finally, the proteins were ranked by their contribution to profile differences to identify proteins characteristic of oncogenic mutation effects across cancers. Results: We apply our approach to four current proposals of molecular tumor classifications and major therapeutically relevant actionable genes. All 12 actionable genes evaluated show effects on the protein level in the corresponding tumor type and showed additional mutation-related protein profiles in 21 tumor types. Moreover, our analysis identifies consistent cross-cancer effects for 4 genes (FGFR1, ERRB2, IDH1, KRAS/NRAS) in 14 tumor types. We further use cell line drug response data to validate our findings. Conclusions: This computational approach can be used to identify mutational signatures that have protein-level effects and can therefore contribute to preclinical in silico tests of the efficacy of molecular classifications as well as the druggability of individual mutations. It thus supports the identification of novel targeted therapies effective across cancers and guides efficient basket trial designs.

Original languageEnglish
Article number83
JournalGenome Medicine
Issue number1
Publication statusPublished - 2018 Nov 15

Bibliographical note

Funding Information:
Funding was provided by the Einstein Foundation Berlin and the Charité Universitätsmedizin Berlin. This research was partly supported by the Institute for Information & Communications Technology Promotion and funded by the Korean Government (MSIT) (No. 2017-0-00451, No. 2017-0-01779) and by BMBF (BZML, BBDC). We acknowledge support from the German Research Foundation (DFG) and the Open Access Publication Fund of Charité – Uni-versitätsmedizin Berlin.

Publisher Copyright:
© 2018 The Author(s).


  • Cancer
  • Genomics
  • Pan-cancer analysis
  • Proteomics
  • Targeted cancer therapy

ASJC Scopus subject areas

  • Molecular Medicine
  • Molecular Biology
  • Genetics
  • Genetics(clinical)


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