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Cancer Therapy: Clinical

Identification of Multiple Mechanisms of Resistance to Vemurafenib in a Patient with BRAFV600E-Mutated Cutaneous Melanoma Successfully Rechallenged after Progression

Emanuela Romano, Sylvain Pradervand, Alexandra Paillusson, Johann Weber, Keith Harshman, Katja Muehlethaler, Daniel Speiser, Solange Peters, Donata Rimoldi and Olivier Michielin
Emanuela Romano
1Department of Oncology, University Hospital of Lausanne; 2Genomic Technologies Facility (GTF), Center for Integrative Genomics, University of Lausanne; 3Vital-IT, Swiss Institute of Bioinformatics, and the 4Ludwig Center for Cancer Research of the University of Lausanne, Lausanne, Switzerland
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Sylvain Pradervand
1Department of Oncology, University Hospital of Lausanne; 2Genomic Technologies Facility (GTF), Center for Integrative Genomics, University of Lausanne; 3Vital-IT, Swiss Institute of Bioinformatics, and the 4Ludwig Center for Cancer Research of the University of Lausanne, Lausanne, Switzerland
1Department of Oncology, University Hospital of Lausanne; 2Genomic Technologies Facility (GTF), Center for Integrative Genomics, University of Lausanne; 3Vital-IT, Swiss Institute of Bioinformatics, and the 4Ludwig Center for Cancer Research of the University of Lausanne, Lausanne, Switzerland
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Alexandra Paillusson
1Department of Oncology, University Hospital of Lausanne; 2Genomic Technologies Facility (GTF), Center for Integrative Genomics, University of Lausanne; 3Vital-IT, Swiss Institute of Bioinformatics, and the 4Ludwig Center for Cancer Research of the University of Lausanne, Lausanne, Switzerland
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Johann Weber
1Department of Oncology, University Hospital of Lausanne; 2Genomic Technologies Facility (GTF), Center for Integrative Genomics, University of Lausanne; 3Vital-IT, Swiss Institute of Bioinformatics, and the 4Ludwig Center for Cancer Research of the University of Lausanne, Lausanne, Switzerland
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Keith Harshman
1Department of Oncology, University Hospital of Lausanne; 2Genomic Technologies Facility (GTF), Center for Integrative Genomics, University of Lausanne; 3Vital-IT, Swiss Institute of Bioinformatics, and the 4Ludwig Center for Cancer Research of the University of Lausanne, Lausanne, Switzerland
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Katja Muehlethaler
1Department of Oncology, University Hospital of Lausanne; 2Genomic Technologies Facility (GTF), Center for Integrative Genomics, University of Lausanne; 3Vital-IT, Swiss Institute of Bioinformatics, and the 4Ludwig Center for Cancer Research of the University of Lausanne, Lausanne, Switzerland
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Daniel Speiser
1Department of Oncology, University Hospital of Lausanne; 2Genomic Technologies Facility (GTF), Center for Integrative Genomics, University of Lausanne; 3Vital-IT, Swiss Institute of Bioinformatics, and the 4Ludwig Center for Cancer Research of the University of Lausanne, Lausanne, Switzerland
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Solange Peters
1Department of Oncology, University Hospital of Lausanne; 2Genomic Technologies Facility (GTF), Center for Integrative Genomics, University of Lausanne; 3Vital-IT, Swiss Institute of Bioinformatics, and the 4Ludwig Center for Cancer Research of the University of Lausanne, Lausanne, Switzerland
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Donata Rimoldi
1Department of Oncology, University Hospital of Lausanne; 2Genomic Technologies Facility (GTF), Center for Integrative Genomics, University of Lausanne; 3Vital-IT, Swiss Institute of Bioinformatics, and the 4Ludwig Center for Cancer Research of the University of Lausanne, Lausanne, Switzerland
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Olivier Michielin
1Department of Oncology, University Hospital of Lausanne; 2Genomic Technologies Facility (GTF), Center for Integrative Genomics, University of Lausanne; 3Vital-IT, Swiss Institute of Bioinformatics, and the 4Ludwig Center for Cancer Research of the University of Lausanne, Lausanne, Switzerland
1Department of Oncology, University Hospital of Lausanne; 2Genomic Technologies Facility (GTF), Center for Integrative Genomics, University of Lausanne; 3Vital-IT, Swiss Institute of Bioinformatics, and the 4Ludwig Center for Cancer Research of the University of Lausanne, Lausanne, Switzerland
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DOI: 10.1158/1078-0432.CCR-13-0661 Published October 2013
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    Figure 1.

    A, Thorax-abdomen CT scan at baseline, 2 and 6 weeks after reintroduction of vemurafenib. B, Brain MRI at baseline, 2 and 6 weeks after reintroduction of vemurafenib.

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    Figure 2.

    Schematic representation of the tumor samples analyzed. Pre, regional lymph node metastasis; PV1, metastasis that was present at baseline, completely responded to first vemurafenib treatment, then reappeared and progressed during chemotherapy, responded again (partially) with second vemurafenib treatment, then progressed; PV2, metastasis that occurred de novo during second vemurafenib treatment. Ipi, ipilimumab; chem., chemotherapy; Vem., vemurafenib.

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    Figure 3.

    Somatic single-nucleotide substitutions of Pre, PV1, and PV2 samples. A and B, the number and spectrum, respectively, of exonic mutations in the three metastasis Pre, PV1, and PV2 identified by whole-exome sequencing using the Illumina HiSeq platform. C, the number and type of mutations common to the three samples (COM), found in one or two samples only (not common, NOT), or private to individual samples (PREpr, PV1pr, and PV2pr). The majority of C>A in the pool of not common mutations were validated with Ion Torrent sequencing using an enzymatic DNA fragmentation and had frequencies higher than 0.2 (Supplementary Table S2), indicating that they were accumulated by the tumor and were not technical artifacts (35). D, number and type of private mutations further subclassified as clonal (pr-clon) or subclonal (pr-sub) using frequencies obtained by the somatic variant caller MuTect (36) and taking into account normal tissue contamination.

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    Figure 4.

    Characterization of somatic mutations. A, mutant allele frequencies of validated mutations in Pre, PV1, and PV2 samples. The points on or near the axis were classified as Pre, PV1, and PV2 specific. The two clusters and outliers are clonal mutations and were defined by fitting a three-component mixture of trivariate Gaussian distributions on the other points using the expectation maximization algorithm implemented in the Statistics Toolbox of MATLAB. Mutations in cluster 2 lay on the minor allele of trisomic chromosomes 1q, 6p, and 20p. Most of the outliers lay in regions with CNA. B, compares the B-allele frequencies (BAF) of germline heterozygous SNPs located on chromosome 7q of samples PV1 and PV2. The 7q amplification involves different haplotypes. The canonical BRAF mutation V600E is indicated in red. Its location just outside the main cluster is due to 20% to 30% contamination with normal tissue. C, sampling of 100 reads that map to NRAS Q61 codon in PV1 and PV1-derived cell line. The two NRAS mutations Q61K (C>A) and Q61R (A>G) have different frequencies and are mutually exclusive on sequencing reads. Base substitutions are indicated in red for A, blue for G, and green for T. Bases identical to hg19 are indicated in gray.

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    Figure 5.

    Sensitivity of PV1-derived single-cell clones carrying the PIK3CAH1047R mutation to RAF, PI3K, and MEK inhibitors. A, assays were conducted with the indicated drugs and PIK3CAH1047R-mutant clones (PI3K Cl 5/10/11), along with the uncloned parental population and two NRAS-mutant clones. Top, relative growth of cells (average and SD of three replicates) after 4 days of incubation with drugs. Bottom, IC50 concentrations (average and range) calculated from two independent experiments. B, effect of PLX4032 (PLX, 1 μmol/L) and GDC-0941 (GDC, 1 μmol/L) alone or in combination on cell growth. Results show average and SD of three independent experiments.

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    Figure 6.

    Detection of alternatively spliced BRAF transcripts. A, BRAF splice junctions found by RNA-seq in samples PV1, PV2, and in the cell line derived from PV1. BRAF exons and introns are indicated at the top. Splice junctions are represented by an arc from the beginning to the end of the junction. The thickness of the arc is proportional to the number of reads covering the junction. The canonical junctions are represented below and the alternative junctions are represented above the line. Exons 4–10 are aberrantly spliced-out in sample PV2. B, the results of reverse transcriptase PCR (RT-PCR) conducted with primers located in exon 3 and 11 (top) on the indicated samples. Alternatively spliced BRAF (97 bp product) is preferentially amplified over the longer (907 bp) canonical form in PV2, but is undetectable in PV1 and Pre samples. Bottom, amplification of a cDNA fragment (exon 15) similar in size to the alternatively spliced BRAF product as control for RNA quality for the FFPE sample (Pre). PCR was conducted for 35 or 40 cycles (left and right, respectively). Lack of amplification in RT− samples shows specificity for cDNA.

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    • Supplementary Methods - Supplementary Methods - PDF file 55K, This file contains Supplementary Methods.
    • Supplementary Figures - Supplementary Figures - PDF file 828K, This file contains Supplementary Figures: This file contains supplementary Figures: Suppl. Figure 1. CT scan at baseline and after 3 months since first introduction of vemurafenib. Suppl. Figure 2. Copy number alteration (CNA) from whole exome sequencing data. Suppl. Figure 3. B Allele Frequencies (BAF) of germline heterozygous SNPs located in CNA regions. Suppl. Figure 4. Sensitivity of a PV1-derived cell line to BRAF and MEK inhibitors. Suppl. Figure 5. Sanger sequencing of PI3KCA and NRAS amplified cDNA fragments from single cell clones isolated from the T1407A short-time culture (PV1 metastasis). Suppl. Figure 6. Sensitivity of PIK3CA mutated clones to combinations of MEK/PI3K and RAF/MEK inhibitors. Suppl. Figure 7. Confirmation of the splice junction and presence of cT1799A/V600E mutation in the alternatively spliced BRAF sequence. Suppl. Figure 8. Comparison of gene expression levels in PV1, PV2 and vemurafenib-naive metastasis
    • Supplementary Tables - Supplementary Tables - PDF file 61K, This file contains Supplementary Tables: Suppl. Table 1. Somatic single nucleotide substitution spectrum. Suppl. Table 2. Somatic single nucleotide substitution frequencies. Suppl. Table 3. Cancer genes in aneuploidy regions. Suppl. Table 4. RNA-seq log2 normalized gene counts for genes belonging to tyrosine kinase receptor family, RAS/MAPK and AKT/mTOR signaling pathways
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Clinical Cancer Research: 19 (20)
October 2013
Volume 19, Issue 20
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Identification of Multiple Mechanisms of Resistance to Vemurafenib in a Patient with BRAFV600E-Mutated Cutaneous Melanoma Successfully Rechallenged after Progression
Emanuela Romano, Sylvain Pradervand, Alexandra Paillusson, Johann Weber, Keith Harshman, Katja Muehlethaler, Daniel Speiser, Solange Peters, Donata Rimoldi and Olivier Michielin
Clin Cancer Res October 15 2013 (19) (20) 5749-5757; DOI: 10.1158/1078-0432.CCR-13-0661

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Identification of Multiple Mechanisms of Resistance to Vemurafenib in a Patient with BRAFV600E-Mutated Cutaneous Melanoma Successfully Rechallenged after Progression
Emanuela Romano, Sylvain Pradervand, Alexandra Paillusson, Johann Weber, Keith Harshman, Katja Muehlethaler, Daniel Speiser, Solange Peters, Donata Rimoldi and Olivier Michielin
Clin Cancer Res October 15 2013 (19) (20) 5749-5757; DOI: 10.1158/1078-0432.CCR-13-0661
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