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A comparative analysis of network mutation burdens across 21 tumor types augments discovery from cancer genomes

Heiko Horn, Michael S. Lawrence, Jessica Xin Hu, Elizabeth Worstell, Nina Ilic, Yashaswi Shrestha, Eejung Kim, Atanas Kamburov, Alireza Kashani, William C. Hahn, Jesse S. Boehm, Gad Getz, Kasper Lage
doi: https://doi.org/10.1101/025445
Heiko Horn
1Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
2Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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Michael S. Lawrence
2Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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Jessica Xin Hu
1Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
2Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
3Center for Biological Sequence Analysis, Technical University of Denmark, DK-2800 Copenhagen, Denmark
4NNF Center for Protein Research, University of Copenhagen, DK-2200 Copenhagen, Denmark
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Elizabeth Worstell
1Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
2Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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Nina Ilic
2Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
5Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
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Yashaswi Shrestha
2Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
5Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
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Eejung Kim
2Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
5Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
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Atanas Kamburov
2Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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Alireza Kashani
1Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
2Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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William C. Hahn
2Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
5Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
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Jesse S. Boehm
2Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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Gad Getz
2Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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Kasper Lage
1Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
2Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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ABSTRACT

Heterogeneity across cancer makes it difficult to find driver genes with intermediate (2-20%) and low frequency (<2%) mutations1, and we are potentially missing entire classes of networks (or pathways) of biological and therapeutic value. Here, we quantify the extent to which cancer genes across 21 tumor types have an increased burden of mutations in their immediate gene network derived from functional genomics data. We formalize a classifier that accurately calculates the significance level of a gene’s network mutation burden (NMB) and show it can accurately predict known cancer genes and recently proposed driver genes in the majority of tested tumours. Our approach predicts 62 putative cancer genes, including 35 with clear connection to cancer and 27 genes, which point to new cancer biology. NMB identifies proportionally more (4x) low-frequency mutated genes as putative cancer genes than gene-based tests, and provides molecular clues in patients without established driver mutations. Our quantitative and comparative analysis of pan-cancer networks across 21 tumour types gives new insights into the biological and genetic architecture of cancers and enables additional discovery from existing cancer genomes. The framework we present here should become increasingly useful with more sequencing data in the future.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted August 25, 2015.
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A comparative analysis of network mutation burdens across 21 tumor types augments discovery from cancer genomes
Heiko Horn, Michael S. Lawrence, Jessica Xin Hu, Elizabeth Worstell, Nina Ilic, Yashaswi Shrestha, Eejung Kim, Atanas Kamburov, Alireza Kashani, William C. Hahn, Jesse S. Boehm, Gad Getz, Kasper Lage
bioRxiv 025445; doi: https://doi.org/10.1101/025445
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A comparative analysis of network mutation burdens across 21 tumor types augments discovery from cancer genomes
Heiko Horn, Michael S. Lawrence, Jessica Xin Hu, Elizabeth Worstell, Nina Ilic, Yashaswi Shrestha, Eejung Kim, Atanas Kamburov, Alireza Kashani, William C. Hahn, Jesse S. Boehm, Gad Getz, Kasper Lage
bioRxiv 025445; doi: https://doi.org/10.1101/025445

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