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Characterizing genomic alterations in cancer by complementary functional associations

Nature Biotechnology volume 34, pages 539–546 (2016)Cite this article

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Abstract

Systematic efforts to sequence the cancer genome have identified large numbers of mutations and copy number alterations in human cancers. However, elucidating the functional consequences of these variants, and their interactions to drive or maintain oncogenic states, remains a challenge in cancer research. We developed REVEALER, a computational method that identifies combinations of mutually exclusive genomic alterations correlated with functional phenotypes, such as the activation or gene dependency of oncogenic pathways or sensitivity to a drug treatment. We used REVEALER to uncover complementary genomic alterations associated with the transcriptional activation of β-catenin and NRF2, MEK-inhibitor sensitivity, and KRAS dependency. REVEALER successfully identified both known and new associations, demonstrating the power of combining functional profiles with extensive characterization of genomic alterations in cancer genomes.

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Figure 1: REVEALER information-based metrics.
Figure 2: REVEALER results for transcriptional activation of β-catenin in cancer.
Figure 3: REVEALER results for transcriptional NRF2 activation in lung cancer.
Figure 4: REVEALER results for the drug sensitivity to a MEK-inhibitor example.
Figure 5: REVEALER results for KRAS-dependency.
Figure 6: Simulated data results.

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Acknowledgements

This project was supported in part by US National Institutes of Health grants R01 CA154480, R01 CA121941, U01 CA176058, R01 CA109467 and U01 CA184898-02.

Author information

Author notes

  1. Jong Wook Kim and Olga B Botvinnik: These authors contributed equally to this work.

Authors and Affiliations

  1. Eli and Edythe Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Jong Wook Kim, Olga B Botvinnik, Omar Abudayyeh, Chet Birger, Joseph Rosenbluh, Yashaswi Shrestha, Mohamed E Abazeed, Peter S Hammerman, Daniel DiCara, David J Konieczkowski, Cory M Johannessen, Arthur Liberzon, Gabriela Alexe, Andrew Aguirre, Mahmoud Ghandi, Heidi Greulich, Francisca Vazquez, Barbara A Weir, Eliezer M Van Allen, Aviad Tsherniak, Diane D Shao, Travis I Zack, Michael Noble, Gad Getz, Rameen Beroukhim, Levi A Garraway, David A Barbie, Jesse S Boehm, William C Hahn, Jill P Mesirov & Pablo Tamayo

  2. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA

    Jong Wook Kim, Omar Abudayyeh, Joseph Rosenbluh, Yashaswi Shrestha, David J Konieczkowski, Cory M Johannessen, Andrew Aguirre, Heidi Greulich, Francisca Vazquez, Eliezer M Van Allen, Diane D Shao, Rameen Beroukhim, Levi A Garraway, David A Barbie & William C Hahn

  3. Bioinformatics and Systems Biology Program, University of California at San Diego, La Jolla, California, USA

    Olga B Botvinnik

  4. Department of Cellular and Molecular Medicine, University of California at San Diego, La Jolla, California, USA

    Olga B Botvinnik

  5. Stem Cell Program and Institute for Genomic Medicine, University of California at San Diego, La Jolla, California, USA

    Olga B Botvinnik

  6. Harvard Medical School, Boston, Massachusetts, USA

    Omar Abudayyeh & Peter S Hammerman

  7. Department of Radiation Oncology, Cleveland Clinic, Cleveland, Ohio, USA

    Mohamed E Abazeed

  8. Department of Medicine, Dana Farber Cancer Institute, Boston, Massachusetts, USA

    Peter S Hammerman

  9. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada

    Amir Reza Alizad-Rahvar & Masoud Ardakani

  10. Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA

    Gabriela Alexe

  11. Boston Children's Hospital, Boston, Massachusetts, USA

    Gabriela Alexe

  12. Bioinformatics Graduate Program, Boston University, Boston, Massachusetts, USA

    Gabriela Alexe

  13. Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA

    Heidi Greulich, Rameen Beroukhim, Levi A Garraway & William C Hahn

  14. Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA

    Travis I Zack

  15. Program in Biophysics, Harvard University, Boston, Massachusetts, USA

    Travis I Zack & Rameen Beroukhim

  16. Department of Biology, University of Padova, Padova, Italy

    Chiara Romualdi & Gabriele Sales

  17. Center for Cancer Genome Discovery, Dana-Farber Cancer Institute, Boston, Massachusetts, USA

    William C Hahn

  18. Department of Medicine, University of California San Diego, La Jolla, California, USA

    Jill P Mesirov & Pablo Tamayo

  19. Moores Cancer Center, University of California San Diego, La Jolla, California, USA

    Jill P Mesirov & Pablo Tamayo

Authors
  1. Jong Wook Kim
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  2. Olga B Botvinnik
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  3. Omar Abudayyeh
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  4. Chet Birger
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  11. Cory M Johannessen
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Contributions

J.W.K., O.B.B., J.P.M., J.S.B., W.C.H. and P.T. designed and conceptualized the method. O.B.B., O.A., C.B. and P.T. implemented the algorithm. J.W.K., D.A.B., J.R., Y.S., M.E.A., P.S.H., A.A., H.G., F.V., B.A.W., E.M.V.A., D.D.S., T.I.Z., R.B., L.A.G., C.M.J., D.J.K, J.P.M. and P.T. analyzed and interpreted results. A.R.A.-R., M.A., C.R., G.S., D.D., G.G., M.G., G.A., M.N., A.L., A.T. and P.T. provided expertise or work on specific issues regarding algorithmic approaches, data analysis, data preparation, data resources, benchmarking, validation datasets and method comparisons. J.W.K., O.B.B., M.E.A., J.R., J.P.M. and P.T. wrote the manuscript.

Corresponding author

Correspondence to Pablo Tamayo.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 First iteration of REVEALER for the β-catenin activation example

A) The target profile is a β-catenin/TCF4 reporter and the seed feature is the mutation status of β –catenin (CTNNB1.MC_MUT). REVEALER finds APC mutation as the top hit according to the Conditional Information Coefficient (CIC = 0.49). B) The clustered version of the same top scoring features shown in A

Supplementary Figure 2 Second iteration of REVEALER for the β-catenin activation in cancer example.

A) Conditioning to the summary feature after the first iteration, consisting of the seed feature (β-catenin mutation: CTNNB1.MC_MUT) and APC mutation (APC.MC_MUT), REVEALER finds amplifications in 13q33 (feature ITGBL1_AMP) as the best match. B) The clustered version of the same top scoring features shown in A.

Supplementary Figure 3 First iteration of REVEALER for the transcriptional NRF2 activation in lung cancer example.

The target profile is a profile of transcriptional activation of NRF2 and the seed feature is the NRF2 mutation and amplification status. A) REVEALER finds KEAP1 mutation as the top hit according to the Conditional Information Coefficient (ICI=0.53). B) The clustered version of the same top scoring features shown in A.

Supplementary Figure 4 Second iteration of REVEALER for the transcriptional NRF2 activation in lung cancer example.

A) Conditioning to the summary feature after the first iteration, consisting of the seed feature (NFE2L2_MUT + NFE2L2_AMP) and KEAP1 mutations, REVEALER finds amplifications in chr15q22/26 (feature OR4F13P_AMP) as the top hit. B) The clustered version of the same top scoring features shown in A.

Supplementary Figure 5 Assessment of the REVEALER features for the transcriptional NRF2 activation in lung cancer example in an independent test dataset.

The dataset is from The Cancer Genome Atlas (TCGA) consisting of 153 tumor samples (adenocarcinomas and squamous lung cancers). As can be seen in the figure the 4 features (NFE2L2_MUT, NFE2L2_AMP, KEAP1_MUT, and NOX5_AMP (15q23)) appear to generalize well to primary tumors and explain a significant number of samples with NRF2 activation in tumors.

Supplementary Figure 6 First iteration of REVEALER for the drug sensitivity to a MEK-inhibitor example.

A) The target is the MEK-inhibitor PD0325901 sensitivity profile and there is no seed feature (NULLSEED). REVEALER identifies BRAF mutation as the top hit. B) The clustered version of the same top scoring features shown in A.

Supplementary Figure 7 Second iteration of REVEALER for the drug sensitivity to a MEK-inhibitor example.

A) Conditional to the summary feature after the first iteration, consisting of BRAF mutation, REVEALER finds KRAS mutations as the top hit. B) The clustered version of the same top scoring features shown in A.

Supplementary Figure 8 Third iteration of REVEALER for the drug sensitivity to a MEK-inhibitor example.

A) Conditional to the summary feature after the second iteration, consisting of BRAF and KRAS mutations, REVEALER finds NRAS mutations as the top hit. B) The clustered version of the same top scoring features shown in A.

Supplementary Figure 9 First iteration of REVEALER for the KRAS-dependency example.

A) The target profile is a profile relative KRAS-dependence and the seed feature is the mutation status of KRAS. REVEALER identifies a copy number gain across a region on chromosome 8q23-24 (feature NSMCE2_AMP) as the most complementary genomic alteration to KRAS mutation. B) The clustered version of the same top scoring features shown in A.

Supplementary Figure 10 Second iteration of REVEALER for the KRAS-dependency example.

A) REVEALER identifies 9p21.2 amplification (feature LINGO2_AMP). B) The clustered version of the same top scoring features shown in A.

Supplementary Figure 11 Third iteration of REVEALER for the KRAS-dependency example.

A) REVEALER identifies 9p12 deletion (feature FAM74A4). B) The clustered version of the same top scoring features shown in A.

Supplementary Figure 12 Fourth iteration of REVEALER for the KRAS-dependency example.

A) REVEALER identifies 12p12.1 amplification (feature LINC00477). B) The clustered version of the same top scoring features shown in A.

Supplementary Figure 13 Empirical histograms and fitted skew-t distributions for the target profiles of the NRF2 and MEK-inhibition examples.

Supplementary Figure 14 Simulated benchmark results.

A) Simulated generation of target, seed and complementary feature. B) Four examples of simulated iteration instances showing the target, seed complementary feature and the best of the random features. C) Empirical histogram and skew-t distribution fit to the genomic alterations frequencies in the revealer input feature dataset. D) Plot of the IC/CIC (left) and COR-PCOR (right) association metric values for each simulated instance in the benchmark. E) Plot of the probability of detecting the signal (complementary feature) vs. the noise (best random feature) as provided by a logistic model as a function of the CIC values of the complementary feature. F) Signal to noise ratios of the CIC/information-based (y-axis) vs. the PCOR/correlation-based (x-axis) metrics.

Supplementary Figure 15 Comparative results for different algorithms.

A) Comparative heatmaps of the top features results in the β-catenin activation example using REVEALER, the ElasticNet and Dendrix. B) Comparative heatmaps of the top features results in the NRF2 activation example using REVEALER, the ElasticNet and Dendrix. C) Comparative heatmaps of the top features results in the MEK-Inhibition example using REVEALER, the ElasticNet and Dendrix. D) Comparative heatmaps of the top features results in the KRAS-dependency example using REVEALER, the ElasticNet and Dendrix. E) Summary ROC curves for the simulated data benchmark using the Elastic Net and mRMR feature selection methods including all cases regardless of the seed being identified as one of the two top features.

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Supplementary Code: REVEALER (ZIP 498 kb)

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Kim, J., Botvinnik, O., Abudayyeh, O. et al. Characterizing genomic alterations in cancer by complementary functional associations. Nat Biotechnol 34, 539–546 (2016). https://doi.org/10.1038/nbt.3527

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