An approach to suppress the evolution of resistance in BRAFV600E-mutant cancer

Journal name:
Nature Medicine
Year published:
Published online


The principles that govern the evolution of tumors exposed to targeted therapy are poorly understood. Here we modeled the selection and propagation of an amplification in the BRAF oncogene (BRAFamp) in patient-derived tumor xenografts (PDXs) that were treated with a direct inhibitor of the kinase ERK, either alone or in combination with other ERK signaling inhibitors. Single-cell sequencing and multiplex fluorescence in situ hybridization analyses mapped the emergence of extra-chromosomal amplification in parallel evolutionary trajectories that arose in the same tumor shortly after treatment. The evolutionary selection of BRAFamp was determined by the fitness threshold, the barrier that subclonal populations need to overcome to regain fitness in the presence of therapy. This differed for inhibitors of ERK signaling, suggesting that sequential monotherapy is ineffective and selects for a progressively higher BRAF copy number. Concurrent targeting of the RAF, MEK and ERK kinases, however, imposed a sufficiently high fitness threshold to prevent the propagation of subclones with high-level BRAFamp. When administered on an intermittent schedule, this treatment inhibited tumor growth in 11/11 PDXs of lung cancer or melanoma without apparent toxicity in mice. Thus, gene amplification can be acquired and expanded through parallel evolution, enabling tumors to adapt while maintaining their intratumoral heterogeneity. Treatments that impose the highest fitness threshold will likely prevent the evolution of resistance-causing alterations and, thus, merit testing in patients.

At a glance


  1. ERK-inhibitor-resistant populations with extrachromosomal BRAF amplification.
    Figure 1: ERK-inhibitor-resistant populations with extrachromosomal BRAF amplification.

    (a) Effect of the ERKi SCH984 on tumor volumes in mice harboring PDXs derived from patients with BRAFV600E-mutant lung cancer or melanoma (n = 5 mice per group). Data are mean ± s.e.m. (b) Hematoxylin and eosin (H&E)-stained sections of the PDX models before (left) and after (right) ERKi treatment. Scale bars, 500 μm and 25 mm (inset). (c) FACS analysis of single nuclei extracted from PDX1D tumors. A human diploid cell line was used as a control. (d) CN profiles of 69 single cells that were derived from parental (Par; n = 36) and ERKi-resistant (EiR; n = 33) PDX1D tumors. (e) Projection of single cells into the top three principal components. (f) Subclonal distribution of parental and resistant tumors. (g) Segment values spanning the BRAF locus in tumor and stromal cells (n = 24 per group). For stromal cells, sequenced reads were mapped to the mouse genome (Supplementary Fig. 1e). (h) Representative images of fluorescence in situ hybridization (FISH) analysis of PDX1D and PDX1E tumors with probes specific for BRAF or chromosome 7 centromere in red or green, respectively (n = 5 fields). (i) Probes were quantified for PDX1D and PDX1D-EiR tumors by manual counting (n = 100 cells, all data are shown). (j) Representative image showing the extrachromosomal localization of the BRAF gene (arrowheads) in a PDX1D-EiR cell undergoing metaphase. Scale bar, 5 μm. (k) The expression of BRAFV600E protein in matched PDX1D and PDX1E tumor sets, as determined by mass spectrometry (n = 3 sections). Actin and tubulin were used as controls. Data are mean ± s.e.m. Throughout, P values were determined by an unpaired t-test; n.s., not significant.

  2. BRAFamp emerges in parallel evolutionary tracts.
    Figure 2: BRAFamp emerges in parallel evolutionary tracts.

    (a) The clonal relationship of single cells (n = 69), as inferred by Manhattan–Ward clustering of integer CN values. The bar graph shows the BRAF CN value. (b,c) Single cells derived from parental (b) or resistant (c) tumors were subjected to hierarchical clustering and subclonal analysis independent of each other. Phylogenetic inference was established using a heuristic maximal parsimony approach. Subclones A and B were subdivided on the basis of additional CN alterations and their inferred phylogeny. (d) Distribution of single cells based on their subclone classification and BRAF segment value. (e) The CN state of select chromosomal regions with heterogeneous profiles. Note the emergence of BRAFamp cells in three distinct evolutionary trajectories depending on co-occurring losses in chromosomes 2p, 11q and/or 13 (arrows). (f,g) Multiplex FISH with probes targeting the indicated chromosomal regions in PDX1D-EiR. Manual quantification (f) and representative images (g) are shown. Scale bar, 5 μm.

  3. BRAFamp is sufficient to confer a selective advantage in the presence of ERKi treatment.
    Figure 3: BRAFamp is sufficient to confer a selective advantage in the presence of ERKi treatment.

    (a) Representative immunoblot analysis (n = 2 independent experiments) of cell lines derived from parental (1D) or ERK inhibitor-resistant (1D-EiR) PDX. (b) Representative immunoblot analysis (n = 2 independent experiments) of signaling intermediates in 1D and 1D-EiR cells that were treated for 1 h with SCH984. (c) Cell viability of 1D and 1D-EiR cells at 72 h after treatment with varying concentrations of SCH984 (n = 3 experimental replicates). Data are mean ± s.e.m. (d) Cell viability of 1D-EiR cells after transfection with BRAF-specific or control siRNAs followed by drug treatment as in c (n = 3 experimental replicates). Data are mean ± s.e.m. (e,f) A375 cells, engineered to express BRAFV600E under a Dox-induced promoter, were treated as shown (Dox, 2 μg/ml; SCH984, 500 nM) to determine the effect on signaling by immunoblotting (e) or viability (n = 3 experimental replicates) (f). Data are mean ± s.e.m. Withdrawal of Dox after a 6-week stimulation restored sensitivity to the ERKi.

  4. Fitness threshold model.
    Figure 4: Fitness threshold model.

    (a) BRAF mRNA expression as a function of CN in 145 untreated BRAFV600E-mutant melanomas. The data were obtained from TCGA. The dotted area represents tumors at risk for selective propagation of BRAFamp during drug treatment. (b,c) A375 cells were stimulated with increasing concentrations of Dox (for 24 h), followed by treatment with ERK signaling inhibitors (RAFi: vemurafenib, 1 μM; MEKi: trametinib, 25 nM; or ERKi: SCH984, 500 nM) for 1 h (b) or 72 h (c), to determine the effect on signaling (b, quantification of representative immunoblots from two independent experiments) or relative fitness (c; n = 3 experimental replicates; data are mean ± s.e.m.) or ERKi: SCH984, 500 nM) for 1 h (b) or 72 h (c). The effect on signaling was adjusted for the effect of Dox treatment alone. Relative fitness is the change in log(IC50) with increasing concentrations of Dox. (d) A schematic representation of the fitness threshold model. Sequential monotherapy is predicted to impose a selective gradient for the propagation of high-level BRAFamp. In contrast, combination therapy is predicted to maximally elevate the fitness threshold, thus suppressing the selection and propagation of BRAFamp subclones. (e) BRAF CN after sequencing of genomic DNA that was extracted from patient biopsies before and after RAFi treatment (pre, post) or their derivative PDX models, before and after exposure to the ERKi. Diploid A375 cells were used as a control. (f) The duration of ERKi treatment response in patients who were either targeted therapy-naive (pt. A and B) or pretreated with a RAFi and MEKi combination (pts. C–E). See also Supplementary Table 2. (g) The duration of treatment response in patients with lung cancer who were treated first with a RAFi (left) followed by the addition of a MEKi (right). By comparison, combination therapy with the RAFi and MEKi showed a ~60% response rate in treatment-native patients with lung cancer9. PR, partial response; SD, stable disease; PD, progressive disease.

  5. Identification of a treatment to suppress the evolution of BRAF-amplified clones.
    Figure 5: Identification of a treatment to suppress the evolution of BRAF-amplified clones.

    (a) Immunoblot analysis of Dox-induced A375 cells that were treated as shown (doses as in Fig. 4b) for 24 h to determine the effect on ERK signaling intermediates. A representative of two independent experiments is shown. (b) As in a, but cells were treated for 72 h to determine the effect on viability (n = 3). Data are mean ± s.e.m. (c,d) Tumor growth in mice bearing PDX1D (c) or PDX1E (d) that were treated with dabrafenib (RAFi), trametinib and/or SCH984 daily for 14 d followed by discontinuation of treatment (n = 5 mice per group). Data are mean ± s.e.m. A vemurafenib analog (PLX4720), alone or in combination, had a similar effect as that with dabrafenib (see below). Mice that were treated with the MEKi and ERKi combination experienced significant toxicity, leading to discontinuation of the experiment in d. (e) Tumors that regrew after discontinuation of drug treatment in c were analyzed to determine BRAF CN by sequencing (top) and BRAF protein expression by immunoblot analysis (bottom).

  6. An intermittent combination treatment inhibits tumor growth in BRAFV600E PDX models for lung cancer and melanoma.
    Figure 6: An intermittent combination treatment inhibits tumor growth in BRAFV600E PDX models for lung cancer and melanoma.

    (a,b) A schematic representation (a) of several three-drug combination treatment schedules and their effect on the growth of PDX1D tumors in athymic mice (n = 5 mice) (b). Data are mean ± s.e.m. RAFi, vemurafenib analog PLX4270; MEKi, trametinib; ERKi, SCH984. Treatment-related toxicity was determined by monitoring mouse weight (wg) or mortality. Mice that were treated as described in schedule 5 remained free of tumors for up to 180 d after drug discontinuation. (c) Additional optimization of the off-drug interval to minimize toxicity while retaining maximal tumor growth inhibition (n = 5 mice per group). Asterisk indicates no death. Data are mean ± s.e.m. (d) Expression of total BRAF in the PDX models, as determined by mass spectrometry (n = 3). Data are mean ± s.e.m. (e) The profile of genetic alterations in the BRAFV600E PDX models used in this study. (f) Effect of the intermittent regimen on tumor growth in a model with de novo insensitivity to ERKi treatment (n = 5 mice per group). Data are mean ± s.e.m. *P < 0.002 for MEKi–ERKi versus int. RAFi–MEKi–ERKi by unpaired t-test. (g) The effect of intermittent treatment with a three-drug combination (administered on a 3/7 schedule) in PDX models of lung cancer and melanoma (n = 5 mice, for each untreated or treated arm). Data are mean ± range. n.s., P > 0.05; *P < 0.01; by unpaired t-test. Primary data are shown in Supplementary Figure 6.


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Author information

  1. Present address: School of Clinical Sciences, Monash University, Clayton, Victoria, Australia.

    • Luciano Martelotto
  2. These authors contributed equally to this work.

    • Yaohua Xue &
    • Luciano Martelotto


  1. Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center (MSKCC), New York, New York, USA.

    • Yaohua Xue,
    • Alberto Vides,
    • Martha Solomon,
    • Trang Thi Mai,
    • Neelam Chaudhary &
    • Piro Lito
  2. Weill Cornell–Rockefeller–Sloan Kettering Tri-institutional MD–PhD Program, New York, New York, USA.

    • Yaohua Xue
  3. Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

    • Luciano Martelotto,
    • Michael F Berger &
    • Jorge S Reis-Filho
  4. Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

    • Timour Baslan &
    • Scott W Lowe
  5. Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

    • Greg J Riely,
    • Bob T Li &
    • Piro Lito
  6. NantOmics, Rockville, Maryland, USA.

    • Kerry Scott,
    • Fabiola Cechhi,
    • Sarit Schwartz &
    • Todd Hembrough
  7. Sahlgrenska Translational Melanoma Group, Sahlgrenska Cancer Center, University of Gothenburg, Gothenburg, Sweden.

    • Ulrika Stierner &
    • Jonas Nilsson
  8. Molecular Cytogenetics Core Facility, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

    • Kalyani Chadalavada &
    • Gouri Nanjangud
  9. Antitumor Assessment Core Facility, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

    • Elisa de Stanchina
  10. Weill Cornell Medical College, Cornell University, New York, New York, USA.

    • Michael F Berger &
    • Piro Lito
  11. Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

    • Neal Rosen


Y.X., L.M., A.V., M.S., T.T.M. and N.C. performed experiments; L.M., T.B. and J.S.R.-F. performed, analyzed and helped interpret the single-cell sequencing experiments; M.F.B. analyzed the bulk-sequencing data; G.J.R. and B.T.L. provided the patient samples; J.N. and U.S. provided the PDX models; E.d.S. performed the animal experiments; K.S., F.C., T.H. and S.S. performed the mass spectrometry experiments; K.C. performed the FISH experiments, and G.N. analyzed the results. N.R. and S.W.L. provided key scientific insights and reagents; P.L. conceived and supervised the study, designed and performed experiments, and interpreted data; Y.X. and P.L. were the principal writers of the manuscript; and all of the authors reviewed the manuscript and contributed in writing.

Competing financial interests

P.L. is listed as an inventor on a patent application filed by MSKCC that incorporates discoveries described in the manuscript. N.R. is on the scientific advisory board, and has received grant support from, Chugai Pharmaceutical and is on the SAB of Astra-Zeneca, Beigene and Kura. S.S., T.H., K.S. and F.C. are employees of NantOmics.

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