Oncogenic NRAS signaling differentially regulates survival and proliferation in melanoma

Journal name:
Nature Medicine
Volume:
18,
Pages:
1503–1510
Year published:
DOI:
doi:10.1038/nm.2941
Received
Accepted
Published online
Corrected online

Abstract

The discovery of potent inhibitors of the BRAF proto-oncogene has revolutionized therapy for melanoma harboring mutations in BRAF, yet NRAS-mutant melanoma remains without an effective therapy. Because direct pharmacological inhibition of the RAS proto-oncogene has thus far been unsuccessful, we explored systems biology approaches to identify synergistic drug combination(s) that can mimic RAS inhibition. Here, leveraging an inducible mouse model of NRAS-mutant melanoma, we show that pharmacological inhibition of mitogen-activated protein kinase kinase (MEK) activates apoptosis but not cell-cycle arrest, which is in contrast to complete genetic neuroblastoma RAS homolog (NRAS) extinction, which triggers both of these effects. Network modeling pinpointed cyclin-dependent kinase 4 (CDK4) as a key driver of this differential phenotype. Accordingly, combined pharmacological inhibition of MEK and CDK4 in vivo led to substantial synergy in therapeutic efficacy. We suggest a gradient model of oncogenic NRAS signaling in which the output is gated, resulting in the decoupling of discrete downstream biological phenotypes as a result of incomplete inhibition. Such a gated signaling model offers a new framework to identify nonobvious coextinction target(s) for combined pharmacological inhibition in NRAS-mutant melanomas.

At a glance

Figures

  1. Characterization of the iNRAS mouse melanoma model and experimental design.
    Figure 1: Characterization of the iNRAS mouse melanoma model and experimental design.

    (a) Transgene mRNA levels after 4 d of doxycycline withdrawal in iNRAS melanomas as determined by RT-PCR. –NRAS indicates doxycycline withdrawal for the number of days indicated. Data are means ± s.e.m. n = 5 tumors per cohort. (b) Western blot of pAkt, Akt, pErk, Erk and Bim from iNRAS cell line 413. Heat shock protein 70 (Hsp70) was used as a loading control. Veh, vehicle. (c) Tumor volumes from four independent iNRAS primary tumors (from one male and three female mice). Arrows indicate the start of doxycycline withdrawal. Different colors are shown for clarity to identify individual mice. (d) The effect of two different MEK inhibitors and doxycycline withdrawal on allograft tumor growth from iNRAS cell line 475 (n = 6 tumors per cohort). QD, daily. Data are means ± s.e.m. (e) Flow chart of the experimental design. Transcriptome data comparing genetic NRASQ61K extinction and pharmacological MEK inhibition is processed through statistical and network analyses to generate RSM genes, pathways and, ultimately, pathway regulators. The tumor growth chart is taken from Figure 5a.

  2. RAS-specific module (RSM) genes are highly enriched for cell-cycle functions.
    Figure 2: RAS-specific module (RSM) genes are highly enriched for cell-cycle functions.

    (a) Microarray data. The plot shows log2 fold change values of doxycycline withdrawal (–NRAS) and selumetinib treatment (MEKi) after 4 d compared to vehicle treatment in iNRAS cell line 475 allografts. Each point represents an average value (n = 6 tumors per cohort). RSM genes with Fisher combined P < 10−5 are plotted in red, whereas non-RSM genes (P > 10−5) are shown in blue. (b) Venn diagram showing the overlap in the number of differentially expressed genes shared by both MEKi and NRASQ61K extinction. (c) The top ten RSM pathways defined by GSEA analysis. Results from the Metacore analyses are shown in Supplementary Table 4. (d) Microarray heatmap of RSM genes from significant GSEA cell-cycle pathways with unsupervised hierarchical clustering.

  3. Cellular proliferation is inhibited by NRASQ61K extinction but not MEK inhibition.
    Figure 3: Cellular proliferation is inhibited by NRASQ61K extinction but not MEK inhibition.

    (a) Representative immunofluorescence staining of iNRAS cell line 413 melanoma allografts to measure proliferation by pH3 staining and apoptosis by TUNEL staining. Scale bars, 100 μm. See also Supplementary Figure 2a. (b,c) Quantification of pH3 (b) and TUNEL (c) positivity in iNRAS 413 allografts. (d,e) Quantification of pH3 (d) and TUNEL (e) positivity in iNRAS 475 allografts. Two-tailed Student's t tests were used to calculate the P values shown. Data are means ± s.e.m.

  4. The Cdk4-Rb axis regulates the RAS-specific pathways.
    Figure 4: The Cdk4-Rb axis regulates the RAS-specific pathways.

    (a) Reverse-phase protein array data. The plot shows log2 fold change values of doxycycline withdrawal (–NRAS) compared to selumetinib treatment (MEKi) after 4 d compared to vehicle in two independent iNRAS allograft cohorts, cell lines 413 and 475. Each point represents an average value (n = 6 tumors cohort, except n = 4 tumors for the iNRAS 475 vehicle-treated group). (b) RPPA heatmap of the top ten RSM proteins, including the RSM P values (Fisher combined P values, see Online Methods) and averages (avge). Vehicle represents the control. (c) TRAP network overlaid with microarray and GSEA data and colored according to z score. The yellow highlighted nodes represent the 41 significantly downregulated pathways identified through GSEA and the 55 first-neighbor regulator genes. The enlargement shows all 55 regulators connected to a generic node representing the downregulated pathways. See Supplementary Table 3 for a list of all pathways. The regulators have been ordered clockwise based on the number of edges they share with the pathways, which is reflected in the edge thickness. (d) Top ten pathway regulators ranked by gain in degree centrality, which is a measurement of regulator strength that takes into account both weighted edge strength and the normalized connectivity of the regulators in the subnetwork of RSM pathways compared to all pathways. See Supplementary Table 7 for the full ranked list of 55 regulators.

  5. The combination MEK and CDK4/6 inhibition is synergistic in vivo.
    Figure 5: The combination MEK and CDK4/6 inhibition is synergistic in vivo.

    (a) Mouse cell line iNRAS 475 allografts treated with PD-0332991 (PD) and GSK1120212 (GSK) singly or in combination. Doxycycline withdrawal (–NRAS) is shown for comparison. QD, daily; QOD, every other day; CR, complete response. All data in this figure are means ± s.e.m. (b) Human NRASQ61K cell line SB-2 xenografts treated with PD-0332991 and GSK1120212 singly or in combination. (c,d) Quantification of pH3 (c) and TUNEL (d) positivity in SB-2 tumors after 4 d of treatment. Two-tailed Student's t tests were used to calculate the P values shown. (e) Representative BrdU and cleaved caspase-3 (Casp3) immunohistochemistry from treated EVOC tumor slices after 3 d of treatment. (f,g) Quantification of BrdU (f) and caspase-3 (g) positivity in EVOC tumor slices. Scale bars, 200 μm.

  6. NRAS-MAPK activity differentially regulates apoptosis and proliferation.
    Figure 6: NRAS-MAPK activity differentially regulates apoptosis and proliferation.

    (a) A model of oncogenic NRAS signaling as a gradient with gated phenotypic outputs. As NRAS-MAPK activity is inhibited, apoptosis and cell-cycle arrest are triggered at different amounts of activity. Complete RAS extinction (purple) or combined inhibition of MEK (blue) and CDK4 (red) complementarily trigger both phenotypes, fulfilling the dual input into the AND gate required for efficient tumor regression. In logic, AND gates require simultaneous truth statements to produce an output. (b) Comparison of the average NRAS-MAPK activity in the gene set at various time points after doxycycline withdrawal compared to the activity resulting from MEKi treatment. P values for these comparisons are indicated above each bar. A paired Student's t test of the unlogged fold-change values was used to calculate the P values. (c) Unsupervised hierarchical clustering of RPPA data from iNRAS 413 tumors treated with vehicle, MEKi or 2 d or 4 d of doxycycline withdrawal.

Accession codes

Referenced accessions

Gene Expression Omnibus

Change history

Corrected online 20 November 2012

In the version of this article initially published, the formula to calculate the mutual information matrix, which appears in the last page of the Online Methods, was missing a log multiplier. The error has been corrected in the HTML and PDF versions of the article.

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Author information

Affiliations

  1. Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA.

    • Lawrence N Kwong,
    • Huiyun Liu,
    • Shan Jiang,
    • Aliete E Langsdorf,
    • David Jakubosky,
    • Giannicola Genovese,
    • Florian L Muller,
    • Joseph H Jeong,
    • Ryan P Bender,
    • Gerald C Chu &
    • Lynda Chin
  2. Department of Genomic Medicine, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

    • Lawrence N Kwong,
    • Shan Jiang,
    • Timothy L Helms,
    • Giannicola Genovese,
    • Florian L Muller &
    • Lynda Chin
  3. Howard Hughes Medical Institute, Department of Biomedical Engineering and Center for BioDynamics, Boston University, Boston, Massachusetts, USA.

    • James C Costello &
    • James J Collins
  4. Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts, USA.

    • Gerald C Chu
  5. Division of Medical Oncology, Massachusetts General Hospital, Boston, Massachusetts, USA.

    • Keith T Flaherty
  6. Division of Surgical Oncology, Massachusetts General Hospital, Boston, Massachusetts, USA.

    • Jennifer A Wargo
  7. Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts, USA.

    • James J Collins
  8. Institute for Applied Cancer Science, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

    • Lynda Chin

Contributions

L.N.K. performed the majority of experiments described in the manuscript. J.C.C. performed GSEA and TRAP analyses. L.N.K. and J.C.C. jointly generated the remaining statistics. H.L. and T.L.H. helped maintain nude mouse colonies and provided technical assistance in many experiments. S.J. maintained the iNRAS GEM model colony and measured tumor volumes for primary melanomas. A.E.L., D.J. and G.C.C. performed the EVOC work, and the biopsy was supplied by J.A.W. and K.T.F. G.G. and F.L.M. performed some western blotting. J.H.J. generated the iNRAS mouse. R.P.B. assisted in generating the iNRAS time course microarray data. J.J.C. oversaw TRAP and statistical analyses. L.N.K. and L.C. conceived of the study. L.N.K., J.C.C. and L.C. wrote the paper.

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The authors declare no competing financial interests.

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