Functional Profiling of Live Melanoma Samples Using a
Novel Automated Platform
Adam Schayowitz1, Greg Bertenshaw1, Emiko Jeffries1, Timothy Schatz1, James Cotton1,
Jessie Villanueva2, Meenhard Herlyn2, Clemens Krepler2, Adina Vultur2, Wei Xu3, Gordon H. Yu4,
Lynn Schuchter3, Douglas P. Clark1*
1BioMarker Strategies, Baltimore, Maryland, United States of America, 2Melanoma Research Center, The Wistar Institute, Philadelphia, Pennsylvania, United States of
America, 3Abramson Cancer Center, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, United States of America, 4Department of Pathology and
Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, United States of America
Aims: This proof-of-concept study was designed to determine if functional, pharmacodynamic profiles relevant to targeted
therapy could be derived from live human melanoma samples using a novel automated platform.
Methods: A series of 13 melanoma cell lines was briefly exposed to a BRAF inhibitor (PLX-4720) on a platform employing
automated fluidics for sample processing. Levels of the phosphoprotein p-ERK in the mitogen-activated protein kinase
(MAPK) pathway from treated and untreated sample aliquots were determined using a bead-based immunoassay.
Comparison of these levels provided a determination of the pharmacodynamic effect of the drug on the MAPK pathway. A
similar ex vivo analysis was performed on fine needle aspiration (FNA) biopsy samples from four murine xenograft models of
metastatic melanoma, as well as 12 FNA samples from patients with metastatic melanoma.
Results: Melanoma cell lines with known sensitivity to BRAF inhibitors displayed marked suppression of the MAPK pathway
in this system, while most BRAF inhibitor-resistant cell lines showed intact MAPK pathway activity despite exposure to a
BRAF inhibitor (PLX-4720). FNA samples from melanoma xenografts showed comparable ex vivo MAPK activity as their
respective cell lines in this system. FNA samples from patients with metastatic melanoma successfully yielded three
categories of functional profiles including: MAPK pathway suppression; MAPK pathway reactivation; MAPK pathway
stimulation. These profiles correlated with the anticipated MAPK activity, based on the known BRAF mutation status, as well
as observed clinical responses to BRAF inhibitor therapy.
Conclusion: Pharmacodynamic information regarding the ex vivo effect of BRAF inhibitors on the MAPK pathway in live
human melanoma samples can be reproducibly determined using a novel automated platform. Such information may be
useful in preclinical and clinical drug development, as well as predicting response to targeted therapy in individual patients.
Citation: Schayowitz A, Bertenshaw G, Jeffries E, Schatz T, Cotton J, et al. (2012) Functional Profiling of Live Melanoma Samples Using a Novel Automated
Platform. PLoS ONE 7(12): e52760. doi:10.1371/journal.pone.0052760
Editor: Yiqun G. Shellman, University of Colorado, United States of America
Received September 5, 2012; Accepted November 22, 2012; Published December 28, 2012
Copyright: ? 2012 Schayowitz et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: Authors DPC, AS, GB, EJ, TS, JC and the SnapPathTMplatform are supported by the National Cancer Institute (SBIR Contract ID: HHSN261200900075C
and HHSN261201100112C). Authors JV, AV, CK, WX, LS and MH are supported by NIH PO1 CA114046 - Targeted Therapies in Melanoma. Authors LS, WX and GHY
are supported by Abramson Cancer Center Helbert Institute fund (#461505) and Gift & Endowment funds within the Abramson Cancer Center. The funders had
no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: Authors DPC, AS, GB, EJ, TS, JC are paid employees of BioMarker Strategies, each with equity stake in the private commercial company.
DPC is a co-founder and a member of the Board of Directors of BioMarker Strategies. BioMarker Strategies manufactures the SnapPath platform, a research use
only instrument. The Company is currently engaged in the patent prosecution of three applications (U.S. Serial #: 13/089,219; 12/258,251; and 12/125,790).
Authors LS and WX have received research support from BioMarker Strategies. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing
data and materials.
* E-mail: email@example.com
Molecularly targeted agents (MTAs) that block specific, critical
signal transduction pathways in malignant cells have emerged as
major tools in the treatment of cancer. To be most effective, these
drugs must be paired with a predictive test to match the right
agent with the signaling defect harbored by the patient’s tumor.
However, despite some successes, most cancer patients do not yet
benefit from such personalized cancer care [1–2]. Part of the
reason for this unmet need is due to the fact that the currently
available predictive tests do not provide any direct information
regarding signal transduction in patients’ cancer cells . While
the actual targets of most molecularly targeted agents are elements
of the signal transduction network, most predictive tests only
provide indirect and inferential information about signal trans-
duction. These tests typically consist of DNA analysis for
mutations in genes encoding activated signal transduction proteins
or immunohistochemistry of over-expressed receptor tyrosine
kinase proteins in fixed tissue. In reality, the goal of MTAs is
not only to target individual proteins, but also to control the
dynamic, complex circuitry of signal transduction that leads to
tumor cell survival and proliferation. Such networks are not
PLOS ONE | www.plosone.org1December 2012 | Volume 7 | Issue 12 | e52760
simple, linear pathways, but rather involve complex bypass
mechanisms and feedback loops that are impossible to assess
using genetic analysis . This complexity often undermines
successful MTA development and therapy [5–7].
MTA-related signaling complexities have been highlighted by
the recent challenges posed by the development of BRAF
inhibitors and other MAPK pathway inhibitors for metastatic
melanoma . It is clear that the subset of melanoma patients with
activating mutations in BRAF derive the most benefit from MAPK
pathway inhibitors such as vemurafenib, dabrafenib, and trame-
tinib, but individual patient responses are variable and, more
importantly, transient due to the rapid development of resistance.
Interestingly, this resistance is not due to the emergence of
secondary mutations in the kinase domain of BRAF, but rather
through a variety of other mechanisms that reactivate the MAPK
pathway and/or through the activation of bypass networks such as
those involving the PI3K/AKT pathway [9–14]. Such signal
transduction-mediated resistance is not limited to melanoma since
it was recently found that BRAF-mutant colorectal carcinomas
might subvert BRAF inhibitor monotherapy through a feedback
loop involving activation of EGFR [15–16]. Fortunately, many
approved and emerging drugs already exist to block these feedback
loops and bypass mechanisms, provided that adequate tests are
available to guide the selection of effective combination therapies.
To that end, we have created an automated, robust, reproduc-
ible, and potentially widely disseminated system for processing
unfixed, fresh tumor samples from individual patients. This system
enables ‘‘ex vivo’’ (that which takes place outside an organism)
modulation and subsequent analysis of dynamic signal transduc-
tion networks in tumor biopsy samples to generate what we term a
functional signaling profile . In this proof-of-concept study we
have focused on the ex vivo modulation of the MAPK pathway in
melanoma cells by a BRAF inhibitor; however, the analysis of
other relevant pathways is also possible. We have successfully
generated functional signaling profiles from fine needle aspiration
(FNA) biopsies of preclinical murine xenograft models of
melanoma, as well as FNA biopsies of human metastatic
melanoma. This system provides the unique opportunity to assess
the pharmacodynamic effects of MTAs on individual patient
samples during drug development, and may serve as the
foundation for predictive tests to guide targeted therapy.
Cell Culture and Growth Inhibition Assays
Melanoma cell lines (A2058, COLO 829, Malme-3M, RPMI-
7951, SK-MEL-2, SK-MEL-3, SK-MEL-28 and SK-MEL-31)
were purchased from the American Type Culture Collection
(Manassas, VA) and were maintained per ATCC instructions. The
establishment and maintenance of the 451Lu, 451Lu-R, Mel1617,
Mel1617-R, WM983B and WM983B-R cell lines has been
described previously . For growth inhibition assays, all cell
lines were routinely cultured in the standard growth media
described above. Cell proliferation assays were performed using
the MTT assay (Sigma), to examine the effect of exposure to
increasing concentrations of PLX-4720, a BRAF inhibitor that is
structurally similar to the approved BRAF inhibitor, vemurafenib
(Selleck Chemicals, Houston TX). The results were expressed as a
percentage of the cell number in control wells. The IC50 values for
PLX-4720 were calculated using non-linear regression (sigmoidal
dose response) of the plot of percentage inhibition versus the log of
inhibitor concentration in GraphPad Prism (v5; GraphPad
Software, Inc., La Jolla, CA). Biological triplicates from three
individual experiments were treated for _72_ hours with 3 nM to
30 uM PLX-4720. Error bars represent SEM.
Instrumentation and Sample Processing
Cell lines, xenografts and human samples were processed on
SnapPathTM(BioMarker Strategies, Baltimore MD), an automat-
ed cell and tissue processing platform which evokes functional
signaling profiles from live cell and biopsy samples. Briefly, fresh
cell and biopsy samples, containing a mixture of live and dead
cells, were loaded onto the instrument in 500 ul of supportive
media. Samples were then moved through five distinct processing
steps on the instrument utilizing automated fluidics. Samples were
first dispersed into a more homogeneous cellular suspension using
mechanical shear forces. Next, samples were enriched for tumor
cells by immunodepleting the samples of CD45-positive non-
tumor cells (lymphocytes and macrophages) with a short 2 minute
incubation with anti-CD45 antibodies conjugated to magnetic
Dynal beads (Life Technologies, Carlsbad, CA) then separating
out bead-bound cells by a magnet on the platform. The sample
was then aliquoted into two equally-sized separate temperature-
controlled test chambers and treated with either vehicle (DMSO)
or PLX-4720 (3 uM final concentration) and incubated at 37 C
for 5 min. Samples were then lysed and stabilized using a cell lysis
buffer (20 mM Tris-HCl, pH 7.5, 150 mM NaCl, 1% Triton,
1 mM Na2EDTA, 1 mM EGTA, 2.5 mM sodium pyrophos-
phate, 1 mM beta-glycerophosphate, 1 mM Na3VO4, 1 mg/ml
leupeptin and 1 mM PMSF; Cell Signaling, Boston, MA).
Replicates were generated by additional runs on the instrument.
For cell lines the number of replicates ranged from 4 to 8. For
xenografts, the number of replicates was 4. For clinical human
samples, which unlike cell lines and xenografts cannot be
replenished, each was processed on the instrument once. Once
generated, lysates were analyzed off the instrument.
For each sample, p-ERK was measured in cell lysates from both
the control and PLX-4720 test chamber aliquots on the Bio-
PlexTM200 System using a bead-based immunoassay that detects
both p-ERK1 (Thr202/Tyr204) and p-ERK2 (Thr185/Tyr187)(-
Bio-Rad Laboratories, Hercules, CA). Each sample was analyzed
in triplicate and results mean averaged. Data were acquired using
Bio-Plex ManagerTMInstrument Control (v6), then extracted and
analyzed using GraphPad Prism (v5; GraphPad Software, Inc., La
Jolla, CA). Levels of p-ERK detected in the control test chamber
were compared with those in the PLX-4720 chamber and a
percent inhibition level was determined based on the difference.
For Bio-Plex analysis, inputs were normalized by total protein
concentration using the PierceH 660 nm Protein Assay (Thermo
Scientific, Rockford, IL, USA). For cell lines and xenografts, the
lysate concentration was 200 ug/ml and 140 ug/ml, respectively.
For clinical samples, the lysates were analyzed at the highest
possible concentration with a range of 25–300 ug/ml. For a given
control-PLX-4720 sample pair, the protein concentration was
equalized using lysis buffer.
Western Blot Immunoassays
For SK-MEL-28, SK-MEL-2, A2058 and RPMI-7951 cell
lines, we measured p-ERK1 (Thr202/Tyr204) and p-ERK2
(Thr185/Tyr187) using rabbit anti-p-ERK1/2 (Cell Signaling;
Clone D13.14.4E; 1:500 dilution) and goat anti-rabbit IgG
conjugated with horseradish peroxidase (Bio-Rad; 1:3000). Total
ERK1/2 was measured using rabbit anti-ERK1/2 (Cell Signaling;
Clone 137F5; 1:1000) and goat anti-rabbit IgG conjugated with
horseradish peroxidase. As a loading control, we measured actin
Functional Profiling of Live Melanoma Samples
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using mouse anti-beta-actin (Sigma, St Louis, MO;, A2228;
1:2000) and goat anti-mouse IgG conjugated with horseradish
peroxidase (Bio-Rad: 1:3000). For, p-ERK and total ERK, 10 ug
of each lysate was subjected to reducing and denaturing PAGE
using precast 10% PAGE Tris-HCl gels (Bio-Rad). For beta-actin,
5 ug of each lysate was used. Proteins were then transferred to
nitrocellulose for probing. The Pierce ECL Western Blotting
Substrate (Thermo) was used as recommended by the manufac-
turer. Film was scanned and densitometry conducted using ImageJ
(NIH, Bethesda, MD).
Athymic nu/nu female mice 4–6 weeks of age were obtained
from Harlan Laboratories and were housed in a pathogen-free
environment under controlled conditions of light and humidity
and received food and water ad libitum. The mice were inoculated
with SK-MEL-28, MALME-3M, COLO829 or A2058 cell lines
obtained from ATCC. The cells were grown in regular growth
media as described above. Subconfluent cells were removed from
culture, then resuspended in Matrigel at 26107cells/ml. Each
mouse received a subcutaneous inoculation at one site per flank
with 100 ul of cell suspension. Tumor formation varied between 4
and 8 weeks by cell line. Upon reaching 500 mm3FNA biopsies
were performed. One FNA sample consisted of 4 passes of a 23G,
1’’ needle on a 10cc syringe. Samples were then processed on the
SnapPath as described above. Following biopsy, all mice were
euthanized by CO2and cervical dislocation.
Subjects were enrolled who had a known history of metastatic
melanoma and either subcutaneous metastases amenable to
palpation-guided fine needle aspiration biopsy or who were
undergoing surgical resection of a metastatic lesion for clinical
purposes. The clinical status of each patient at the time of biopsy,
including current therapy and current response to therapy, was
determined by the oncologist based on clinical assessment and
available medical records. Tumor BRAF status was obtained from
the individual patients medical record based on clinical testing.
For the subcutaneous lesions, samples were collected using
standard fine needle aspiration biopsy techniques by either an
experienced cytopathologist or an oncologist who was trained and
experienced in the procedure. For the surgically-excised lesions,
specimens were delivered within 30 min. of excision to the
pathology department where a cytopathologist examined the
specimen and performed the FNA procedure on the specimen.
FNA Samples were delivered within 1 hour to the laboratory for
ex vivo processing on the on SnapPathTMplatform.
This study was carried out in strict accordance with the
recommendations in the Guide for the Care and Use of
Laboratory Animals of the National Institutes of Health. The
protocol was approved by the Committee on the Ethics of Animal
Experiments of Sobran, Inc., Baltimore, MD (Protocol Number:
BIO-001-2010). The human subjects protocol and consent form
were approved by the Institutional Review Boards (IRBs) of the
University of Pennsylvania and Abramson Cancer Center’s
Clinical Trials Scientific Review and Monitoring Committee.
The study was conducted in compliance with regulations of the
Health Insurance Portability and Accountability Act and the
Declaration of Helsinki. Informed written consent was obtained
for the biopsy procedure prior to enrollment.
Automated Platform (SnapPathTM) for ex vivo Generation
of Functional Signaling Profiles
Our goal was to develop a reproducible and potentially
distributable system to profile dynamic signal transduction events
in living tumor cells from individual patients. During the
development process our priorities included: the ability to
accommodate multiple sample types; the inclusion of small
samples from minimally invasive biopsies like FNAs; the ability
to provide relatively rapid results; and automation to provide
robust and reproducible results.
In the resultant system, suspensions of live tumor cells are
placed onto an automated fluidics-based platform where the
samples are dispersed using mechanical shear forces into a more
homogeneous suspension of individual cells and small cell clusters
(Figure 1). The samples are then enriched for tumor cells by
immunodepleting the samples of non-tumor cell types such as
lymphocytes, using antibody-coated magnetic beads. The actual
FNA procurement process enriches for tumor cells, thus samples
contain very few fibroblasts or endothelial cells. The most
common non-tumor cell contaminants are lymphocytes. There-
fore, we developed the capability of the platform to remove these
cells. The instrument has the ability to remove .80% of CD45+
cells. The tumor cell-enriched samples are then aliquoted into
multiple individual test chambers containing reagents to modulate
signal transduction in the tumor cells, such as drugs or growth
factors. After a brief incubation (from 5 min. to 4 hours) with the
modulating agent, the samples are lysed for subsequent analysis of
phosphoproteins within signal transduction pathways of interest.
With a 5 min. modulation step, this entire process requires
approximately 30 min. In order to generate data on numerous
phosphoproteins from small samples, we utilize a sensitive bead-
based immunoassay capable of multiplexing (Bio-Plex). This off-
instrument assay has allowed us to successfully assay as few as
50,000 cells. The functional signaling profile is then derived from a
comparison of phosphoprotein biomarker levels in the control,
unmodulated test chamber to those in the modulated test
chambers. For example, in these studies we compared the levels
of p-ERK in control aliquots to those treated with the BRAF
Growth Inhibition of Melanoma Cell Lines by a BRAF
As a foundation for subsequent studies in murine xenografts and
humans, a series of 13 BRAF V600E mutant melanoma cell lines
was tested for sensitivity to BRAF inhibition by determining the
IC50 values of each cell line to PLX-4720. As anticipated, 8 of the
cell lines showed marked sensitivity to PLX-4720 (IC50,3 uM)
(Figure 2) [18–19]. Despite containing a BRAF V600E mutation,
five cell lines were resistant to PLX-4720 (IC50.3 uM). Included
in these resistant lines were three previously generated cell lines
(MEL1617-R; WM983B-R; 451Lu-R) that had been selected for
resistance by exposure to increasing concentrations of a BRAF
inhibitor (SB-590885) .
Functional Signaling Profiles Uniquely Stratify Melanoma
Next, functional signaling profiles were generated from each of
the cell lines using the previously described automated platform.
Briefly, tumor cells were grown to ,70% confluence on 10 cm
dishes, and then suspensions of tumor cell lines (1–3 million cells)
that had been removed from culture plates were processed on the
Functional Profiling of Live Melanoma Samples
PLOS ONE | www.plosone.org3 December 2012 | Volume 7 | Issue 12 | e52760
automated platform, which included exposure to PLX-4720 for
5 min., then levels of p-ERK were assessed from aliquots that had
been treated or untreated with PLX-4720 using an immunoassay.
For each sample, the level of the p-ERK phosphoprotein in the
untreated aliquot was compared to the level in the PLX-4720-
treated aliquot and a percent inhibition was generated such that
100% inhibition represented complete suppression of the MAPK
pathway marker, p-ERK, and 0% inhibition represented complete
inactivity of PLX-4720 on the MAPK pathway.
In this analysis, 9 of the 14 cell lines displayed substantial ex
vivo PLX-4720 suppression of the MAPK pathway (69–95%
mean p-ERK inhibition) (Figure 3). Eight of these nine lines
displayed sensitivity to PLX-4720 in our previous IC50 assess-
ment. Interestingly, the intrinsically BRAF inhibitor-resistant line,
A2058, displayed functional evidence of MAPK pathway blockade
by PLX-4720 (88% mean inhibition), suggesting an alternate
resistance mechanism. Four of the remaining lines showed intact
activity of the MAPK pathway (4–30% mean p-ERK inhibition)
Figure 1. Overview of the automated system (SnapPathTM) to generate functional signaling profiles (FSP) from live tumor samples.
This system employs the SnapPath instrument, an automated fluidics device, to move a suspension of live tumor cells through various steps to evoke
measurable changes in signal transduction ex vivo. These five sequential steps include: (1) dispersion of the tissue fragments into a homogeneous
suspension using mechanical shear forces; (2) enrichment of the sample for tumor cells by immunodepleting non-tumor cells; (3) distribution of equal
aliquots into multiple test chambers; (4) modulation of signal transduction pathways by exposure to drugs or growth factors; and (5) stabilization of
the sample through cell lysis. Once processed, samples can be analyzed off-platform. In the current use, we used a bead-based immmunoassay to
analyze the phosphoprotein p-ERK1/2 levels in output samples. Finally, an algorithmic analysis of data can be used to generate a functional signaling
profile of the tumor. In the current use, we used a simple cut-off value of percent inhibition of p-ERK1/2.
Figure 2. IC50 Values of a panel of melanoma cell lines exposed to the BRAF inhibitor, PLX-4720. The relative sensitivity of a panel of
melanoma cells lines containing V600E BRAF mutations was determined using the MTT assay to assess cell proliferation in the presence of PLX-4720.
Five cell lines were determined to be resistant (IC50.3 uM). Three of these resistant lines (MEL1617-R, WM983B-R, 451Lu-R) were previously
generated from respective sensitive parental cell lines (MEL1617, WM983B, 451Lu) by exposure to increasing concentrations of a BRAF inhibitor.
Functional Profiling of Live Melanoma Samples
PLOS ONE | www.plosone.org4 December 2012 | Volume 7 | Issue 12 | e52760
despite exposure to PLX-4720 (Figure 3). Each of these cell lines
was determined to be a PLX-4720-resistant line based on our
previous analysis (Figure 2). Finally, a functional profile was also
obtained from a cell line (SKMEL2) with a wild type BRAF gene
and a known NRAS mutation (Q61R) that displays a high level of
resistance to PLX-4720 . Interestingly, in this cell line,
exposure to PLX-4720 increased the activity of the MAPK
pathway (30% mean stimulation). This paradoxical MAPK
pathway up-regulation by BRAF inhibitors in NRAS mutant cells
has been previously described [20–23], but reinforces the validity
of our approach in monitoring MAPK pathway dynamics. Based
on all of these cell line data, we visually established an arbitrary cut
value of 66% inhibition to distinguish BRAF inhibitor sensitive
lines from resistant lines using our functional profiling assay. This
cut value is also consistent with the previous evidence that BRAF
inhibitors must have a significant impact on the MAPK pathway
in order to be clinically effective . In addition, the paradoxical
up-regulation of the MAPK pathway in the setting of NRAS
mutations was also detected in this functional assay, creating three
distinct categories of functional signaling profiles. To verify the
Bio-Plex assay data, we have conducted Western blot analysis. We
utilized 4 cell lines that are representative of all lines used in the
study. Namely, the SK-MEL-28 cell line that is sensitive to PLX-
4720 in vivo and ex vivo, SK-MEL-2 that is resistant to PLX-4720
in vivo and ex vivo, A2058 that is resistant to PLX-4720 in vivo
but sensitive ex vivo and finally RPMI-7951 that has intermediate
sensitivity to PLX-4720 in vivo and ex vivo. For all four cell lines,
the Western blot data corroborated the Bio-Plex data (Figures 3
and 4). For A2058 cells, the p-ERK inhibition was 92.1% by
Western, based on densitometry, and 87.0% by Bio-Plex. For SK-
MEL-28, RPMI-7951 and SK-MEL-2 the inhibition was 73.8%
and 68.8%, 37.4% and 40.4%, and 210% and 229.8%,
Functional Signaling Profiling of Melanoma Xenograft
The ultimate value of this system lies in its ability to generate
functional information from relatively small, clinically relevant
samples. In addition, it has the potential to streamline preclinical
studies utilizing human tumor cell-based xenografts or murine
tumorgrafts of patient derived samples. To that end, we have
optimized protocols to generate functional profiles from fine
needle aspiration biopsy (FNA) samples. Murine models of
metastatic melanoma resemble human subcutaneous melanoma
metastases, displaying similar biology, histopathology, and FNA
cytopathologic features . Four of the previously characterized
Figure 3. Functional signaling profiles of a panel of melanoma cell lines exposed to the BRAF inhibitor, PLX-4720. A panel of fourteen
melanoma cell lines was exposed to PLX-4720 using the automated platform. Cell lines previously determined to be resistant to BRAF inhibition are
indicated by grey shaded columns. All cell lines contain a BRAF V600E mutation except SKMEL2, which is wild type for BRAF but contains an NRAS
mutation. The percent inhibition of p-ERK levels relative to a control untreated aliquot is indicated for each sample, with each dot representing an
individual replicate. A dotted line at 66% inhibition indicates a cut value that stratifies most BRAF inhibitor sensitive cell lines from resistant lines. In
the SKMEL2 sample, p-ERK levels were increased by exposure to PLX-4720, relative to the untreated aliquot.
Figure 4. Western Blot analysis of melanoma cell lines exposed
to the BRAF inhibitor, PLX-4720. A panel of four melanoma cell
lines was exposed to PLX-4720 using the automated platform and then
lysates were analyzed by Western Blot analysis. For each lysate, p-ERK,
total ERK and beta-actin levels were determined. Percent inhibition of
p-ERK levels was determined using densitometry by determining the
ratio of the p-ERK signal for a vehicle control sample (C) compared to
the PLX-4720-treated sample (P).
Functional Profiling of Live Melanoma Samples
PLOS ONE | www.plosone.org5 December 2012 | Volume 7 | Issue 12 | e52760
cell lines were propagated as xenografted tumors in mice,
including three PLX-4720-sensitive lines (SKMEL28, MAL-
ME3M; COLO829) and one PLX-4720-resistant line (A2058).
FNA biopsies were performed on each model.
Ex vivo functional profiling was then performed on the FNA-
procured tumor cell suspensions from each xenograft model, using
the automated platform, as previously described for the cell lines.
Despite variable cell counts (0.60 to 196106) and tumor cell
viability (14.6–51.0%) between FNA biopsy samples, successful
functional profiles were obtained from most samples. The mean
percent inhibition of these samples following PLX-4720 treatment
ranged from 66–96% (Figure 5). Within each xenograft model, the
inter-sample and inter-tumor variability was low, with standard
deviations of p-ERK inhibition of 0.5% in COLO-829 to 8.62%
in A2058. The mean percent p-ERK inhibition was similar
between the previous cell line analysis (Figure 3) and the
corresponding xenograft FNA samples (SKMEL28:69 vs. 89%;
MALME3M: 80 vs. 96%; COLO829:95 vs. 90%; A2058:88 vs.
66%)(Figure3 and 5). While some differences exist between the cell
lines and the xenograft data, this may be due to different biology
between the 2-dimensional cultures and the xenografted cell line
tumors. Despite these differences, the cell lines and respective
tumors still fall within the same functional category of MAPK
activity. These studies indicate that functional profiling can be
performed on preclinical xenograft models of melanoma and
suggest that similar studies could be performed on human samples.
Functional Signaling Profiles Generated from Human
Metastatic Melanoma Biopsy Samples
Functional profiling was then performed on a set of human
samples derived from patients with metastatic melanoma. These
samples were procured by FNA, either directly from subcutaneous
metastatic lesions (n=6) or from surgically-excised metastases
(n=6). Cell numbers in the samples ranged from 0.228–5.956106
for subcutaneous metastases and 5.70–1836106for surgically-
excised samples. As in the previous preclinical studies, the
functional profiling of these samples involved brief exposure to
PLX-4720 on the automated platform followed by stabilization
and subsequent analysis of p-ERK in untreated and treated
aliquots of the sample to assess the ex vivo effect of the drug on the
MAPK pathway. Of the twelve samples, two (#1 and 2) displayed
substantial ex vivo PLX-4720 suppression of the MAPK pathway
(69 and 71% inhibition), seven (#3–9) showed intact MAPK
pathway activity (18–56% inhibition), and three (#10–12) showed
MAPK pathway activation (21–235% stimulation) upon ex vivo
PLX-4720 exposure (Figure 6). Two samples (#8 and 9) were
from the same patient and the same subcutaneous flank lesion,
procured several days apart via FNA and the surgically-excised
specimen, respectively. These samples showed similar levels of
MAPK inhibition (41 and 56%).
Correlation of these functional profiles with the known tumor
BRAF genotype and clinical response to BRAF inhibitors revealed
that the two patients with substantial ex vivo PLX-4720
suppression of the MAPK pathway (#1 and 2) were the only
two patients whose tumors contained the BRAF V600E genotype
and who showed at least a partial response to BRAF inhibitor
therapy. Of the seven samples with intact MAPK pathway activity,
three were from patients that were wild type for BRAF (#7–9),
which would be anticipated based on the known intrinsic
resistance of BRAF wild type tumors to BRAF inhibition .
Interestingly, the remaining four samples that showed intact
activity of the MAPK pathway despite ex vivo exposure to PLX-
4720 (#3–6) were BRAF V600E/K patients who were not
responding to BRAF or BRAF plus MEK inhibitors at the time of
the biopsy. Of the three patients that showed MAPK pathway
activation, two were wild type for BRAF (#11–12), and a third
(#10) had the V600E mutation and was not responding to BRAF
inhibitor therapy. In these three patients, the MAPK pathway
activation suggests that these patients may have signal transduc-
tion network alterations that lead to paradoxical MAPK activation
upon BRAF inhibition. Taken together, these results indicate that
functional profiles can be generated from FNA biopsy samples
from patients or surgically-excised tumors. In addition, these
functional profiles generate novel information about individual
patient tumor signal transduction circuitry that correlates with
known genetics, biology and individual patient responses to drug
In this study we have described a novel, automated system for
the ex vivo pharmacodynamic analysis of human melanoma
samples. We have termed the results of such analyses functional
signaling profiles. Our preliminary studies with melanoma cell
lines confirmed that functional signaling profiles from this ex vivo
system accurately predicted BRAF inhibitor sensitivity in virtually
all cell lines, based on their genotype, known biology, and IC50
values. These studies were extended to a clinically-relevant model
system, using fine needle aspiration biopsy samples of human
melanoma xenografts, which resemble human FNAs of metastatic
melanoma in their cell composition and procurement method
. These xenograft studies confirmed that functional signaling
profiles can be generated from such samples and that these profiles
resemble those found in the preliminary cell line work. Finally, we
generated functional signaling profiles from a set of samples
derived from patients, including FNAs of subcutaneous melanoma
metastases, as well as FNAs of surgically-excised metastatic
Figure 5. Functional Signaling profiles of melanoma xenograft
FNA samples exposed to the BRAF inhibitor, PLX-4720. FNA
samples were procured from a set of four different melanoma xenograft
tumor models (SKMEL28, MALME3M, COLO829, A2058) and ex vivo
functional profiles were obtained using the automated platform. The
percent inhibition of p-ERK levels relative to a control untreated aliquot
is indicated for each sample, with each dot representing an individual
FNA sample from a unique tumor. A dotted line at 66% inhibition
indicates a cut value that stratifies most BRAF inhibitor sensitive cell
lines from resistant lines.
Functional Profiling of Live Melanoma Samples
PLOS ONE | www.plosone.org6 December 2012 | Volume 7 | Issue 12 | e52760
melanoma lesions. These profiles were consistent with the known
melanoma genotype, therapeutic regimen, and clinical status on
Due to the prevalence of BRAF mutations in melanoma, the
MAPK pathway has emerged as a prime target of molecularly
targeted agents for this disease, including an approved inhibitor of
BRAF (vemurafenib) and additional emerging inhibitors of BRAF
and MEK [27–31]. Despite success in extending overall survival in
patients with BRAF V600E mutations, a spectrum of responses,
ranging from no response to complete response, occurs even in this
highly selected population. In addition, virtually all patients
develop resistance to BRAF therapy, often through re-activation of
the MAPK pathway. This suggests that many additional factors
are important in the modulation and pharmacodynamics of
MAPK pathway activity in these patients.
In our cell line work, the functional signaling profiles of the
MAPK pathway identified ex vivo pathway blockade in every
sensitive line, identified the known paradoxical pathway activation
in an NRAS mutant line, and identified MAPK pathway
reactivation in the known BRAF inhibitor resistant lines, with
one interesting exception. The A2058 cell line contains a V600E
mutation, displays ex vivo MAPK inhibition by PLX-4720, but is
intrinsically resistant to BRAF inhibitors. While the exact
mechanism of this resistance, despite pathway inhibition, is still
under investigation, one study has suggested that it could be due to
bypass pathway activation via PTEN loss and RB loss . These
findings are clinically relevant, since they suggest that in such
patients, combination therapy should include continued use of the
BRAF inhibitor, with addition of a PI3K pathway inhibitor, for
example. Conversely, patients with ex vivo MAPK reactivation in
the presence of a BRAF inhibitor will likely require a different
MAPK inhibitor, such as a MEK inhibitor, and possibly a PI3K
pathway inhibitor as has been previously proposed  [33–34].
The functional signaling profiles generated in this study stratify
human samples into three groups that display the following key
features in the ex vivo presence of BRAF inhibition: MAPK
suppression; MAPK reactivation; and MAPK stimulation. Because
of the enrollment criteria and the practice patterns at the referral
center where the study was conducted, most of the patients in the
study were either BRAF wild type or BRAF V600E patients who
were clinically progressing on a MAPK inhibitor regimen (BRAF
or BRAF+MEK inhibitors). Most of these patients displayed a
MAPK reactivation profile, confirming the previous evidence that
MAPK reactivation plays a major role in BRAF inhibitor
resistance. Interestingly, several patients displayed a MAPK
activation profile, suggesting the acquisition of a previously
described MAPK activation mechanism , or a novel pathway
activation mechanism. Finally, in this cohort there were two
patients who displayed a MAPK suppression profile. Interestingly,
these were the only patients who possessed a BRAF mutation,
were receiving a BRAF inhibitor, and showed clinical signs of
In summary, in this proof-of-concept study we have successfully
generated pharmacodynamic data from human melanoma sam-
ples using a novel, automated ex vivo platform. This study focused
on the MAPK pathway in metastatic melanoma, but this system is
applicable to any solid tumor and multiple other molecularly
targeted agents and signaling pathways. This system has potential
applications in the efficient performance of preclinical drug studies
in murine xenograft or human tumorgraft models. Also, it
represents a novel tool for early clinical drug development in the
assessment of pharmacodynamic responses to investigational
drugs. Finally, it also has the potential to serve as a powerful
predictive test to stratify patients in late stage clinical trials, and
ultimately to guide targeted therapy.
Figure 6. Functional Signaling profiles of human melanoma patient samples. Twelve human melanoma samples derived from FNA samples
of subcutaneous lesions (#3, 4, 5, 7, 8, 10) or FNA samples of surgically-excised lesions (#1, 2, 6, 9, 11, 12) were exposed to PLX-4720 ex vivo using
the automated platform. The percent inhibition of p-ERK levels relative to a control untreated aliquot is indicated for each sample. A dotted line at
66% inhibition was previously found to stratify BRAF inhibitor sensitive cell lines from resistant lines. The grey shaded columns indicate tumors that
are known to be BRAF mutant (all V600E, except #6 which was V600K); un-shaded columns are BRAF wild type. Two samples showed a substantial
decrease of p-ERK levels by PLX-4720 (#1 and 2), consistent with MAPK pathway suppression. Seven samples (#3–9) showed sustained p-ERK levels
in the presence of PLX-4720, consistent with persistent MAPK activity. In three samples (#10, 11 and 12) the p-ERK levels were paradoxically
increased by exposure to PLX-4720. The clinical response to BRAF inhibitor therapy at the time of biopsy is noted; patients with no history of BRAF
inhibitor therapy are also indicated.
Functional Profiling of Live Melanoma Samples
PLOS ONE | www.plosone.org7 December 2012 | Volume 7 | Issue 12 | e52760
Author Contributions Download full-text
Conceived and designed the experiments: DPC AS GB LS GHY MH JV.
Performed the experiments: EJ TS JC. Analyzed the data: DPC AS GB EJ
TS JC JV AV CK MH WX GHY LS. Contributed reagents/materials/
analysis tools: AS GB EJ TS JC JV AV CK MH WX GHY LS. Wrote the
1. Sawyers CL (2008) The cancer biomarker problem. Nature 452: 548–552.
2. Sikorski R, Yao B (2010) Visualizing the landscape of selection biomarkers in
current phase III oncology clinical trials. Sci Transl Med 2: 34ps27.
3. Irish JM, Kotecha N, Nolan GP (2006) Mapping normal and cancer cell
signalling networks: towards single-cell proteomics. Nat Rev Cancer 6: 146–155.
4. Avraham R, Yarden Y (2011) Feedback regulation of EGFR signalling: decision
making by early and delayed loops. Nat Rev Mol Cell Biol 12: 104–117.
5. Parkinson DR, Cesano A (2009) Patient-specific classifications of human
malignant disease. Curr Opin Mol Ther 11: 252–259.
6. Fojo T, Parkinson DR (2010) Biologically targeted cancer therapy and marginal
benefits: are we making too much of too little or are we achieving too little by
giving too much? Clin Cancer Res 16: 5972–5980.
7. Poste G (2011) Bring on the biomarkers. Nature 469: 156–157.
8. Flaherty KT, Hodi FS, Fisher DE (2012) From genes to drugs: targeted strategies
for melanoma. Nat Rev Cancer 12: 349–361.
9. Villanueva J, Vultur A, Lee JT, Somasundaram R, Fukunaga-Kalabis M, et al.
(2010) Acquired resistance to BRAF inhibitors mediated by a RAF kinase switch
in melanoma can be overcome by cotargeting MEK and IGF-1R/PI3K. Cancer
Cell 18: 683–695.
10. Johannessen CM, Boehm JS, Kim SY, Thomas SR, Wardwell L, et al. (2010)
COT drives resistance to RAF inhibition through MAP kinase pathway
reactivation. Nature 468: 968–972.
11. Nazarian R, Shi H, Wang Q, Kong X, Koya RC, et al. (2010) Melanomas
acquire resistance to B-RAF(V600E) inhibition by RTK or N-RAS upregula-
tion. Nature 468: 973–977.
12. Emery CM, Vijayendran KG, Zipser MC, Sawyer AM, Niu L, et al. (2009)
MEK1 mutations confer resistance to MEK and B-RAF inhibition. Proceedings
of the National Academy of Sciences of the United States of America 106:
13. Poulikakos PI, Persaud Y, Janakiraman M, Kong X, Ng C, et al. (2011) RAF
inhibitor resistance is mediated by dimerization of aberrantly spliced
BRAF(V600E). Nature 480: 387–390.
14. Shi H, Moriceau G, Kong X, Lee MK, Lee H, et al. (2012) Melanoma whole-
exome sequencing identifies (V600E)B-RAF amplification-mediated acquired B-
RAF inhibitor resistance. Nat Commun 3: 724.
15. Corcoran RB, Ebi H, Turke AB, Coffee EM, Nishino M, et al. (2012) EGFR-
mediated re-activation of MAPK signaling contributes to insensitivity of BRAF
mutant colorectal cancers to RAF inhibition with vemurafenib. Cancer Discov 2:
16. Prahallad A, Sun C, Huang S, Di Nicolantonio F, Salazar R, et al. (2012)
Unresponsiveness of colon cancer to BRAF(V600E) inhibition through feedback
activation of EGFR. Nature 483: 100–103.
17. Clark DP (2009) Ex vivo biomarkers: functional tools to guide targeted drug
development and therapy. Expert Rev Mol Diagn 9: 787–794.
18. Tsai J, Lee JT, Wang W, Zhang J, Cho H, et al. (2008) Discovery of a selective
inhibitor of oncogenic B-Raf kinase with potent antimelanoma activity.
Proceedings of the National Academy of Sciences of the United States of
America 105: 3041–3046.
19. Yang H, Higgins B, Kolinsky K, Packman K, Go Z, et al. (2010) RG7204
(PLX4032), a selective BRAFV600E inhibitor, displays potent antitumor activity
in preclinical melanoma models. Cancer Res 70: 5518–5527.
20. Poulikakos PI, Zhang C, Bollag G, Shokat KM, Rosen N (2010) RAF inhibitors
transactivate RAF dimers and ERK signalling in cells with wild-type BRAF.
Nature 464: 427–430.
21. Hatzivassiliou G, Song K, Yen I, Brandhuber BJ, Anderson DJ, et al. (2010)
RAF inhibitors prime wild-type RAF to activate the MAPK pathway and
enhance growth. Nature 464: 431–435.
22. Heidorn SJ, Milagre C, Whittaker S, Nourry A, Niculescu-Duvas I, et al. (2010)
Kinase-dead BRAF and oncogenic RAS cooperate to drive tumor progression
through CRAF. Cell 140: 209–221.
23. Kaplan FM, Shao Y, Mayberry MM, Aplin AE (2011) Hyperactivation of
MEK-ERK1/2 signaling and resistance to apoptosis induced by the oncogenic
B-RAF inhibitor, PLX4720, in mutant N-RAS melanoma cells. Oncogene 30:
24. Bollag G, Hirth P, Tsai J, Zhang J, Ibrahim PN, et al. (2010) Clinical efficacy of
a RAF inhibitor needs broad target blockade in BRAF-mutant melanoma.
Nature 467: 596–599.
25. Herlyn M, Fukunaga-Kalabis M (2010) What is a good model for melanoma?
J Invest Dermatol 130: 911–912.
26. Clark DP (2009) Seize the opportunity: underutilization of fine-needle aspiration
biopsy to inform targeted cancer therapy decisions. Cancer Cytopathol 117:
27. Chapman PB, Hauschild A, Robert C, Haanen JB, Ascierto P, et al. (2011)
Improved Survival with Vemurafenib in Melanoma with BRAF V600E
Mutation. N Engl J Med 364: 2507–2516.
28. Sosman JA, Kim KB, Schuchter L, Gonzalez R, Pavlick AC, et al. (2012)
Survival in BRAF V600-mutant advanced melanoma treated with vemurafenib.
N Engl J Med 366: 707–714.
29. Flaherty KT, Robert C, Hersey P, Nathan P, Garbe C, et al. (2012) Improved
survival with MEK inhibition in BRAF-mutated melanoma. N Engl J Med 367:
30. Kirkwood JM, Bastholt L, Robert C, Sosman J, Larkin J, et al. (2012) Phase II,
open-label, randomized trial of the MEK1/2 inhibitor selumetinib as
monotherapy versus temozolomide in patients with advanced melanoma. Clin
Cancer Res 18: 555–567.
31. Hauschild A, Grob JJ, Demidov LV, Jouary T, Gutzmer R, et al. (2012)
Dabrafenib in BRAF-mutated metastatic melanoma: a multicentre, open-label,
phase 3 randomised controlled trial. Lancet 380: 358–365.
32. Paraiso KH, Xiang Y, Rebecca VW, Abel EV, Chen YA, et al. (2011) PTEN
loss confers BRAF inhibitor resistance to melanoma cells through the
suppression of BIM expression. Cancer Res 71: 2750–2760.
33. Shi H, Kong X, Ribas A, Lo RS (2011) Combinatorial treatments that overcome
PDGFRbeta-driven resistance of melanoma cells to V600EB-RAF inhibition.
Cancer Res 71: 5067–5074.
34. Greger JG, Eastman SD, Zhang V, Bleam MR, Hughes AM, et al. (2012)
Combinations of BRAF, MEK, and PI3K/mTOR inhibitors overcome
acquired resistance to the BRAF inhibitor GSK2118436 dabrafenib, mediated
by NRAS or MEK mutations. Mol Cancer Ther 11: 909–920.
Functional Profiling of Live Melanoma Samples
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