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MCAM mediates chemoresistance in small cell lung cancer via the PI3K/AKT/SOX2 signaling pathway
Satyendra C. Tripathi1, Johannes F. Fahrmann1, Muge Celiktas1, Mitzi Aguilar1, Kieren D.
Marini2, Mohit K. Jolly3, Hiroyuki Katayama1, Hong Wang1, Eunice N. Murage1, Jennifer B.
Dennison1, D. Neil Watkins2, Herbert Levine3, Edwin J. Ostrin4, Ayumu Taguchi5 and Samir M.
Hanash1*
Departments of 1Clinical Cancer Prevention, 4Pulmonary Medicine, and 5Translational Molecular
Pathology, University of Texas MD Anderson Cancer Center, Houston, Texas. 2Centre for
Cancer Research, Hudson Institute of Medical Research, Clayton, Victoria, Australia. 3Center
for Theoretical Biological Physics, Rice University, Houston, Texas.
Running title: MCAM modulates SCLC chemoresistance
Funding support: This research was supported through the Rubenstein Family Foundation, the
Lyda Hill Foundation and the MD Anderson Moonshot philanthropy grants (to S.M. Hanash)
*Corresponding: [email protected] (S.M.H.)
Conflicts of interest: The authors report no conflicts of interest.
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Abstract
Despite favorable responses to initial therapy, small cell lung cancer (SCLC) relapse occurs
within a year and exhibits resistance to multiple drugs. Due to limited accessibility of patient
tissues for research purposes, SCLC-patient derived xenografts (PDX) have provided the best
opportunity to address this limitation. Here we sought to identify novel mechanisms involved in
SCLC chemoresistance. Through in-depth proteomic profiling, we identified MCAM as a
markedly upregulated surface receptor in chemoresistant SCLC cell lines and in chemoresistant
PDX compared to matched treatment-naïve tumors. MCAM depletion in chemoresistant cells
reduced cell proliferation and reduced the IC50 inhibitory concentration of chemotherapeutic
drugs in vitro. This MCAM-mediated sensitization to chemotherapy occurred via SOX2-
dependent upregulation of mitochondrial 37S ribosomal protein 1/ATP binding cassette
subfamily C member 1 (MRP1/ABCC1) and the PI3/AKT pathway. Metabolomic profiling
revealed that MCAM modulated lactate production in chemoresistant cells that exhibit a distinct
metabolic phenotype characterized by low oxidative phosphorylation. Our results suggest that
MCAM may serve as a novel therapeutic target to overcome chemoresistance in SCLC.
Keywords: Small cell lung cancer, Chemoresistance, Proteomics, MCAM, EMT, SOX2, PI3K,
AKT, CREB1, patient derived xenograft
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Introduction
Small cell lung cancer (SCLC) is an aggressive and highly metastatic lung cancer
subtype, accounting for about 10–20% of lung cancer cases (1,2). The 5-year survival rate has
remained dismal at 7% and systemic treatment options for patients with SCLC have remained
unchanged (3). Standard first-line treatment of SCLC includes cisplatin or carboplatin in
combination with etoposide, cyclophosphamide, vincristine, or doxorubicin, which leads to
complete remission in a vast majority of patients (4). SCLC is highly responsive to
chemotherapy at the start of treatment. However, relapse and resistance to treatment eventually
contributes strongly to poor prognosis.
Established mechanisms of chemoresistance in cancer include cellular pathways
associated with DNA damage and repair, apoptosis, NOTCH signaling and FGFR signaling
(5,6). Agents targeting these pathways that have shown promise for other tumor types have been
investigated in SCLC without demonstrable clinical benefit (7). Consequently, there is a need to
elucidate novel molecular mechanisms involved in chemoresistance in SCLC. Limited
translational success is largely attributed to the lack of sufficient tumor materials from SCLC
patients. Additionally, data on SCLC in public databases such as TGCA or oncomine is sparse.
SCLC patient derived xenografts (PDXs) have provided the best opportunity to address the
above mentioned limitations as PDXs replicate the biology and clinical properties of the original
patients’ tumors as compared to other animal models (8).
A comprehensive map of the actual proteome in SCLC, in particular chemoresistant
phenotype is still needed. Herein, for the first time we have investigated mechanisms of SCLC
chemoresistance with a focus on the cell surface proteins. We initially compared the proteomic
and metabolomic profiles of SCLC cell lines to identify molecular features associated with
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chemoresistance. MCAM was identified as a markedly overexpressed protein in SCLC
chemoresistants as well as patient tumors. MCAM is a cell adhesion molecule initially identified
as melanoma-specific cell-adhesion molecule (9). MCAM is also involved in several cellular
processes including cell invasion, migration, angiogenesis, epithelial mesenchymal transition,
immune response, and signal transduction (10). Additionally, MCAM has low expression levels
in normal tissue, primarily restricted at intracellular junctions of endothelial cells (10,11).
Previous studies have shown differential expression of MCAM in primary tumors correlated with
metastasis and poor prognosis in several cancers, showing its significant potential in cancer
therapy (12-14). Despite the identification of MCAM expression in the lung adenocarcinoma
(13), its expression and role in SCLC has not been reported yet. We further investigated the
effect of MCAM through knockdown experiments and the consequences of its overexpression on
SCLC chemoresistance and cellular functions.
Materials and Methods
Cell lines and treatment
The human SCLC cell lines H69, H82, DMS79, H209 and H196, H69AR were obtained
from the American Type Culture Collection in 2011 and 2014 respectively. All cells grew in
RPMI 1640 with 10% fetal bovine serum and a 1% penicillin/streptomycin cocktail. For stable
isotope labeling with amino acids in cell culture. Cells were allowed to grow for seven passages
in RPMI 1640 supplemented with 13C-lysine and 10% dialyzed fetal bovine serum according to a
standard protocol (15). Cell lines were cultured continuously for 6 months or less. Cell lines
were validated at the MD Anderson Sequencing and Microarray Facility using short-tandem
repeat DNA fingerprinting and routinely checked for mycoplasma by PCR (PromoKine). Cells
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were treated with 10 nM phosphoinositide 3-kinase (PI3K) inhibitor LYS294002 (Cell Signaling
Technology) for 24 hours. Accumulation of lactate in the cell culture was determined using
lactate assay kit II (Sigma-Aldrich, MAK065) according to the manufacturer's protocol.
Mass spectrometry and metabolomic analysis
Proteomic analysis of total cell extract (TCE) and cell surface proteins from SCLC cell lines was
performed using mass spectrometry as previously described (16). For metabolomic profiling all
samples were run in biological triplicates. Approximately 7.5 x 105 cells were seeded 24 hours
prior to metabolite extraction in 6 cm dishes. On day of extraction, cells were washed twice with
ice-cold 0.9% NaCl, extracted with 1mL of ice-cold 3:1 isopropanol/water mixture containing
50ng/mL internal standard (12-[[(cyclohexylamino)carbonyl]amino]-dodecanoic acid (CUDA),
Cayman Chemical), and dried using a centrifugal evaporator. Upon dryness, samples were
reconstituted in 50:50 methanol/water. Untargeted metabolomics analysis was conducted on a
Waters Acquity™ UPLC system coupled to Xevo G2-XS quadrupole time-of-flight (qTOF)
mass spectrometer. LC-MS and LC-MSe data were processed using Progenesis QI (Nonlinear,
Waters), and values were reported as area units. Details are provided in the Supplementary
Methods.
Patient tumor collection and generation of chemoresistant PDX models
Fresh SCLC cells from endobronchial ultrasound-guided transbronchial needle aspiration
(EBUS-TBNA) were obtained from the patients diagnosed with SCLC in The Kinghorn Cancer
Centre, New South Wales, Australia with informed, written consent in accordance with the
policies of the National Health and Medical Research Council of Australia, Monash Health,
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Melbourne Health and the Declaration of Helsinki. In brief, one million SCLC cells obtained
from each of 11 chemonaïve SCLC patients were centrifuged, washed, and resuspended in 50 μL
of RPMI medium. Cells were then added to 50 μL of Matrigel (BD Biosciences), gently mixed,
and kept on ice. The resulting cell suspension was injected subcutaneously into the right flanks
of nude Balb/c mice to generate PDXs. The mice were monitored daily until palpable tumors
appeared (typically 4 weeks). Tumors were measured daily using digital calipers to record the
longest (l) and shortest (s) dimensions in millimeters. The following equation was used to
calculate the tumor volume: Tumor volume = l x s2 / 2
Once tumors reached 200 mm3, mice were given single intraperitoneal injections of 60
mg/kg carboplatin. SCLC xenografts responded rapidly to this initial carboplatin-based
treatment, becoming barely palpable within 10 days. Relapse of SCLC took 50-81 days. Upon
tumor relapse in two PDX models, an additional 60 mg/kg injection of carboplatin was
administered to both when the tumors reached 200 mm3. Per national health and medical
research council ethical guidelines, a maximum of three doses of carboplatin was injected per
mouse. After the third injection of carboplatin, tumors exhibited little or no shrinkage and minor
growth delays in response to the treatment, suggesting that they were resistant to it. Mice were
then euthanized and harvested tumors were formalin fixed for paraffin embedding.
Proliferation and colony formation assays
For proliferation assays, 8 × 103 SCLC cells were seeded in triplicate and assayed using
MTS reagent (CellTiter 96 Aqueous One Solution Cell Proliferation Assay; Promega). For
colony formation assays, 200 cells were seeded in six-well plates in triplicate and allowed to
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grow for 20 days. Cells were fixed in 6% glutaraldehyde with 0.5% crystal violet and visualized
under the Nikon microscope. Colony areas were measured using the ImageJ software program.
In vitro drug sensitivity assay
SCLC cells were seeded in 96-well plates at a density of 1 × 104 cells per well and treated
in medium with doxorubicin, cisplatin, or etoposide for 24 hours. Cell survival was analyzed
using a CellTiter Glo assay (Promega) according to the manufacturer’s instructions. The range of
drug concentrations was chosen to obtain half-maximal inhibitory concentrations (IC50) values
for SCLC cell lines. After incubation with 100 μL of CellTiter Glo reagent for 10 minutes, the
luminescence was measured. Luminescence reading from the cells incubated without the drugs
were used for 100% survival and to calculate the IC50 of each drug. The data for CellTiter Glo
assay was collected from five technical and three biological replicates for each sample.
Flow cytometric analysis
SCLC cells were treated with doxorubicin, cisplatin, or etoposide for 24 hours and then
collected for apoptosis and cell-cycle analyses. For annexin V analysis, cells were incubated with
annexin V-fluorescein isothiocyanate and propidium iodide (PI) for 15 minutes at room
temperature in the dark. Samples stained with annexin V-fluorescein isothiocyanate and PI were
diluted in 400 μL of annexin V-binding buffer and immediately examined using a fluorescence-
activated cell sorting machine. Stained cells were immediately subjected to flow cytometric
analyses using a Gallios flow cytometer (Beckman Coulter). Early apoptotic cells were defined
as cells with annexin V-positive and PI-negative staining. Late apoptotic and nonviable cells
were defined as having both annexin V-positive and PI-positive staining.
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For cell-cycle analysis, SCLC cells were fixed with 70% (vol/vol) cold ethanol overnight at 4°C.
Cells were then suspended in phosphate-buffered saline buffer containing final concentrations of
20 μg/mL RNase A and 20 μg/mL PI for 20 minutes. The cell-cycle profiles for these cells were
determined using flow cytometry (Gallios; Beckman Coulter) and analyzed using the Kaluza
software program (Beckman Coulter). All samples were assayed in triplicate.
Immunohistochemistry
The paraffin embedded PDX tissues sections (5μm) on glass slides were deparaffinized
and hydrated, and antigen retrieval was performed using a decloaker with a target retrieval
solution (pH, 6.0; Dako). The intrinsic peroxidase activity was blocked using 3% methanol and
hydrogen peroxide for 10 minutes, and a serum-free protein block (Dako) was used for 5 minutes
to block nonspecific antibody binding. The slides were then incubated with antibodies against
human MCAM (ab75769, 1:200 dilution; Abcam), EGFR (ab52894, 1:100, Abcam), EPHA2
(#6997, 1:200, Cell Signaling), ITGB1 (ab52971, 1:250, Abcam) and JAG1 (ab109536, 1:100,
Abcam) overnight at 4°C. After being washed three times in Tris-buffered saline, the slides were
then incubated for 30 minutes with Dako EnVision+ Dual Link at room temperature. Slides were
incubated with Dako chromogen substrate for 5 minutes and counterstained with hematoxylin.
Formalin-fixed, paraffin-embedded, whole-section specimens with the primary antibodies
omitted were used as negative controls.
Mathematical modeling for regulation of MCAM
We developed a mathematical model that incorporates the regulations among MCAM, SOX2,
PI3K, and CREB1 and their association with chemoresistance.
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Oxygen consumption rate measurement
Oligomycin, an inhibitor of ATP synthase, was prepared from 1000× stock at a
concentration of 10 mM in dimethyl sulfoxide (DMSO). FCCP, an ionophore and strong
mitochondrial depolarizer, was prepared from 1,000 × stock at a concentration of 5 mM in
DMSO. Rotenone, a potent inhibitor of mitochondrial complex I, and antimycin A, a strong
suppressor of mitochondrial complex III, were solubilized from 1,000 × stock solutions at
concentrations of 10 mM in DMSO. To measure SCLC-cell oxygen consumption rates (OCRs),
6 × 104 cells from each cell line were seeded into each well of an XF96 microplate 16 hours
before the experiment. Immediately before the OCR measurement, culture medium of the cells
was replaced by an assay medium (low-buffered RPMI containing 25 mM D-glucose, 1 mM
sodium pyruvate, and 1 mM L-glutamine) and incubated for 1 hour at 37°C. The OCR in the
cells were measured using an Extracellular Flux Analyser (Seahorse Biosciences). After baseline
measurements of OCR the inhibitors described above prepared in the assay medium were
sequentially injected into each well to reach the final working 1× concentrations. After 5 minutes
of incubation to expose cancer cells equally to chemical inhibitors, the OCR was measured
again. Data were analyzed using the Seahorse XF software program. The OCR was reported in
pmol min−1, and measurements were normalized according to the final cell number.
Statistical analysis
All bar and line graphs represent means and standard deviations. The error bars in OCR line
graphs are standard deviations. The unpaired t-test was used to compare differences between two
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groups. For comparison of more than two groups, one-way Analysis Of Variance (ANOVA) was
used. A P value less than 0.05 was considered statistically significant. Data were analyzed using
the Prism software program (GraphPad Software) unless otherwise stated. All experiments were
independently repeated at least three times.
Supplementary Data
Supplementary Data includes supplementary methods, five figures, and two tables.
Results
Increased MCAM expression in chemoresistant SCLC
In-depth proteomic multi-compartment profiling was performed to quantify proteomic
changes associated with chemoresistance of SCLC cells (Fig. 1A). We used H69 and H69AR as
a paired SCLC chemosensitive and chemoresistant cell line. Metastasis and chemoresistance in
cancer are linked phenomena (17), hence we used H82 representing a metastatic SCLC cell line.
We also used DMS79, a SCLC cell line established from the tumor cells of a patient who had
undergone chemotherapeutic and radiation treatment, as assumed to be a surviving fraction of
primary SCLC cells and may possess some similarities to chemoresistant cells. In total, we
quantified 3,605, 3,574, 3,205, and 3,211 proteins in total cell extracts (TCE) and 2,852, 1,884,
1,981, and 2,344 proteins on the surface of the H69, H82, DMS79, and H69AR cell lines,
respectively (Supplementary Table 1).
Chemoresistant H69AR exhibited a highly distinct enriched cell surface proteome
relative to other SCLC cell lines (Fig. 1B and Supplementary Table 2), marked by a high
abundance of proteins associated with cytoskeletal reorganization as well as cell adhesion and
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PI3K/AKT-regulated pathways based on Ingenuity Pathway Analysis (Table 1 and
Supplementary Fig. 1A). Differentially expressed surface proteins in H69AR relative to H69
cells were further assessed by immunoblotting (Fig. 1C). To narrow our list of candidate targets,
we focused on cell surface receptors that exhibited the most robust differences between
chemoresistant SCLC cells compared to other cell lines. Five surface membrane receptors
(EGFR, JAG1, ITGB1, EPHA2, and MCAM) were found most highly expressed in
chemoresistant SCLC cells compared to the other cell lines (Fig. 1D). We validated our results
by immunoblotting in these SCLC cell lines also including H196, which has similar
chemoresistant properties as of H69AR, and H209, which is chemosensitive to the
chemotherapeutic drugs. Increased expression of H69AR surface enriched protein on DMS79
and H196 is in concordance to their higher IC50 values for chemotherapeutic drugs compared to
H69, H82 and H209 (Fig. 1C and Supplementary Fig. 1B and 1C). We found that DMS79 and
H196 closely related to H69AR, whereas H82 and H209 resembles H69 in terms of their surface
protein expression. Next, we examined established PDXs derived from SCLC patients for
expression of these receptors. The PDX models replicate the biology and clinical properties of
the original patients’ tumors (8). We observed increased MCAM expression in chemoresistant
tumors compared to matched treatment-naïve tumors (Fig. 1E and Supplementary Fig. 1D). We
also observed increased MCAM expression as early as after two cycles of carboplatin
(Supplementary Fig. 1E) in contrast with EGFR, JAG1, or ITGB1 which did not exhibit any
difference in immunostaining between chemonaïve and chemoresistant tumors. EPHA2 was
undetectable by immunostaining in any of the PDXs (Supplementary Fig. 1F).
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MCAM expression is associated with a mesenchymal phenotype, increased proliferation
and colony formation
MCAM expression was highly correlated with a mesenchymal transition related gene
expression pattern in SCLC cell lines annotated in the Cancer Cell Line Encyclopedia database,
(Supplementary Fig. 2A). Concordantly, we also observed altered expression of multiple
epithelial-mesenchymal transition (EMT) markers, including reduced expression of CDH1 and
NCAM and increased expression of VIM, CDH2, LGALS1, and COL1A1 (Fig. 2A) in H69AR
cells. Interestingly, LGALS1 has also been reported as a ligand for MCAM in melanoma and
endothelial cells (18,19). Ingenuity Pathway Analysis revealed Transforming growth factor β and
ZEB1 as key upstream regulators of altered gene expression in chemoresistant cells
(Supplementary Fig. 2B-D). Higher expression of the EMT regulators ZEB1 and TWIST1 in
H69AR cells compared to H69 cells was confirmed by immunoblotting (Fig. 2B).
Knockdown of MCAM expression in chemoresistant cells using RNA interference had
no effect on mesenchymal-related protein expression (Fig. 2C-E and Supplementary Fig. 3A-C).
However, MCAM knockdown in chemoresistant cells significantly decreased cell proliferation
(p < 0.001, ANOVA) (Fig. 2F and Supplementary Fig. 3D, 3E) and colony-forming rates (p <
0.01, unpaired t-test) (Fig. 2G).
MCAM expression is a determinant of SCLC chemoresistance and sensitivity
To determine the effects of MCAM expression on chemoresistance of SCLC cells, we
treated shMCAM-H69AR cells with varying doses of chemotherapeutic drugs (doxorubicin,
cisplatin, and etoposide). MCAM knockdown in chemoresistant cells increased chemosensitivity,
with significant reductions (p < 0.001, ANOVA) in the IC50 and cell survival (Fig. 3A-3D and
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Supplementary Fig. 3F, 3G). Reduced MCAM expression led to a marked increase in apoptosis
after chemotherapy as determined by flow cytometry (Fig. 3E). Immunoblotting was performed
for proteins in the apoptotic pathway. Of note, expression of p-BAD, a pro-apoptotic protein,
increased after MCAM knockdown. Additionally, cleaved caspase-3 markedly increased during
chemotherapeutic treatment confirming an apoptotic mechanism (Fig. 3F). We also observed
increased cell-cycle arrest at G0/G1 and G2/M after treatment with doxorubicin or cisplatin and
etoposide, respectively, in shMCAM-H69AR compared to H69AR (Supplementary Fig. 4A-C).
To determine whether MCAM expression is sufficient to induce SCLC chemoresistance,
we ectopically expressed MCAM in the chemosensitive H69 cell line. Overexpression of
MCAM resulted in a slight increase in the cell proliferation, however this was not statistically
significant (Fig. 4A–C and Supplementary Fig. 5A). MCAM overexpression resulted in
markedly increased chemotherapeutic IC50 values and cell survival (Fig. 3D and 4D). These
findings were corroborated by reductions in apoptosis and reduced cell cycle arrest at G0/G1 and
G/2M following treatment with doxorubicin or cisplatin and etoposide, respectively (Fig. 4E and
Supplementary Fig. 5B).
Regulation of MCAM via PI3K/AKT pathway in a SOX2/CREB1 dependent manner
PI3K/AKT signaling emerged as an upregulated pathway in chemoresistant SCLC cells
(Table 1). We tested whether MCAM regulates PI3K/AKT activation. MCAM knockdown
considerably reduced the activation state of PI3K/AKT pathway based on phosphoprotein
analysis (Fig. 5A and Supplementary Fig. 4D). Moreover, inhibition of PI3K activity using
LYS29004 markedly decreased MCAM expression suggesting a potential bidirectional
regulation (Fig. 5B). MCAM expression is known to be regulated via CREB1 (20), and CREB1
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activation is dependent on the AKT pathway (21). The gene for the transcription factor SOX2 is
frequently amplified in SCLC (22) and its expression is also regulated via AKT (23). Hence, we
examined the effect of SOX2 knockdown and PI3K inhibition on CREB1 and MCAM protein
expression levels in chemoresistant SCLC cells. Both SOX2 inhibition and LYS29004 treatment
reduced CREB1 and MCAM protein expression (Fig. 5C and 5D). However, CREB1
knockdown reduced MCAM but did not affect SOX2 expression (Fig. 5E).
Our findings led to a mathematical model for a regulatory network mediating
chemoresistance of SCLC that can behave as a bi-stable switch (i.e., cells can attain one of two
stable-steady phenotypes—chemosensitive or chemoresistant, and can transit from one to the
other based on the expression levels of SOX2 and MCAM (Fig. 5F, 5G and Supplementary
data).
Given our observation of a bidirectional regulation of MCAM and PI3K/AKT pathway
activation, we further assessed whether MCAM regulates known PI3K/AKT-regulated targets
that have been associated with chemoresistance. Of particular interest, is the ABCC transporter,
MRP1, which plays a key role in the chemoresistance of several cancers (24,25) and regulated
by PI3K/AKT (26,27). We observed markedly decreased expression of MRP1 protein following
MCAM knockdown, suggesting that MRP1 regulation is associated with MCAM expression in
chemoresistant cells (Fig. 5H).We also observed reduced levels of NRF2, a known modulator of
MRP1 in SCLC (28), in MCAM knockdown cells (Supplementary Figure 4D).
Increased lactate and low oxidative phosphorylation are key features of SCLC chemoresistance
PI3K/AKT activation promotes increased glycolysis rather than oxidative
phosphorylation in cancer (29). MRP1 is known to transport glutathione and glutathione-
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conjugates out of the cell. Thus, the association between MCAM, PI3K/AKT activation and
MRP1 inherently suggests metabolic rewiring, a characteristic that may in part be regulated by
MCAM.
To determine whether H69AR exhibited a unique metabolic profile compared to the
parental H69 cell line, we conducted Ingenuity Pathway Analysis, focusing on proteins that
exhibited at least two-fold changes in TCE and at least 5 MS/MS counts in both cell lines
(Supplementary Table 2). We found that mitochondrial dysfunction was the most altered
pathway (Fig. 6A). Moreover, many of the top perturbed pathways linked to altered metabolism
included oxidative phosphorylation, gluconeogenesis, glycolysis, pentose phosphate pathway,
and mammalian target of rapamycin signaling (Fig. 6A). We thus evaluated the basal OCRs in
the chemoresistant H69AR and other SCLC cell lines (H69, H82, and DMS79) using a Seahorse
assay (Fig. 6B). The basal OCR was significantly lower (p < 0.001, unpaired t-test) in the
chemoresistant cell line compared to the other SCLC cell lines, demonstrating that the basal
metabolic conditions in drug-resistant SCLC cell lines favor elevated aerobic glycolysis and
reduced oxidative phosphorylation. Lactate production rate was significantly higher (p < 0.001,
unpaired t-test) in H69AR cells than in H69 cells (Fig. 6C). These findings are consistent with a
shift towards a glycolytic phenotype associated with PI3K/AKT activation (29). Lactate
production decreased significantly (p < 0.01, unpaired t-test) after MCAM knockdown (Fig. 6D)
suggesting a potential shift away from glycolysis. We also observed reduced levels of
glutathione reductase (GSR), a downstream effector molecule of NRF2 in MCAM knockdown
cells (Supplementary Figure 4D).
Discussion
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The rapid emergence of chemoresistance in SCLC following treatment is a key
contributor to poor survival. Therefore, there is critical clinical need to develop novel strategies
that overcomes chemoresistance and provide significant survival benefits to patients.
Analysis of in depth proteomic profiling for TCE and cell surface enrich proteins of
SCLC cell lines revealed that the chemoresistant cells have marked distinct surface and TCE
profiles. In particular, proteins related to integrin signaling and PI3/AKT signaling such as
ITGB1, ITGB5, ITGA2, ITGA4, VCL, ZYX and CTNNB1, PP2A, GYS respectively, observed
to be upregulated in TCE as well as enriched on surface of chemoresistant cells. The integrin-
ECM interactions are a well-known phenomenon for cell survival and drug resistance in various
cancers including solid and hematological malignancies (30). On the other hand, Akt signaling
can induce transformation and renders tumor cell resistant to chemotherapeutic agent through its
anti-apoptotic activity and induction of cell cycle progression (31-33). We also observed
differential expression of proteins related to cancer stem cell (CD44, ALDH3A2, ALDH7A1,
EpCAM), EMT (CDH1, Vim, CDH2) and receptor signaling (EGFR, LGALS1, EPHA2, JAG1)
that can modulate chemoresistance in cancer cells (34-39).
Our enriched cell surface data identified MCAM among the highly differentially
upregulated surface proteins in chemoresistant compared to other SCLC cells. MCAM (also
designated as CD146 or MUC18) is a marker of endothelial cell lineage(10). One of the
limitation of our study is lack of chemoresistant tissues from SCLC patients. However, SCLC
PDX models can replicate the biology of cancer in patients and hence are superior to traditional
xenograft tumor models. We thus incorporated PDX models as a component for validation of our
proteomics data. We observed markedly increased expression of MCAM protein in
chemoresistant than in chemo-naive SCLC PDXs. Increased expression of MCAM on DMS79
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but not on H82 and on PDX tissues even after two cycles of chemotherapeutic treatment
suggested that the overexpression might be an early event for an acquired chemoresistance in
SCLC cells. MCAM overexpression has been observed in several tumor types including
melanoma, prostate, pancreatic, lung, gastric, breast, and ovarian cancers (10). Altered
expression of MCAM was associated with cell viability and colony forming abilities in SCLC
cells, which is in concordance with the reports of MCAM linked to altered cell proliferation,
angiogenesis, metastasis, cell motility and invasion (10,40,41). The modulation of
chemoresistance through altered MCAM expression is a novel finding, which may be mediated
by regulating the apoptosis related protein expression. We also observed increased LGALS1
protein levels, a known ligand of MCAM (15,16), in chemoresistant cells. MCAM and its ligand
LGALS1 has been reported to regulate apoptosis in cancer cells (18,19,42-44), which further
support our findings.
Given that SCLC is a neuroendocrine tumor type, it is noteworthy that multidrug-
resistance in SCLC was associated with an EMT phenotype suggesting that mesenchymal
transition is a potentially important mechanism of survival for chemoresistant cells in SCLC and
analysis of CCLE (45) data revealed a strong association between MCAM and EMT. Zeng et al.,
(14) demonstrated that high MCAM expression in triple-negative breast cancers induced EMT
and cancer stem cell properties. However, we observed That MCAM modulation has no
significant effect on EMT properties of SCLC cells.
In melanoma, MCAM upregulation is dependent on PI3K/AKT pathway which is a
known contributor of chemoresistance in numerous malignancies (46). However, our results
implicate that in chemoresistant SCLC cells, the relationship between MCAM and PI3K/AKT
activation is bidirectional and postulated to be mediated via SOX2/CREB1 axis. These findings
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are also in concordance with those of recent studies suggesting that SOX2 expression levels in
malignant cells are dependent on AKT regulation (47-49). However, further studies needed to
confirm this potential relationship.
To define the mechanism(s) by which MCAM can modulate chemoresistance, we
evaluated known targets regulated by the PI3K/AKT pathway. Of relevance was MRP1, which
has been previously implicated in promoting chemoresistance in numerous cancer types,
including SCLC (26,50). Knockdown of MCAM reduced MRP1 expression. MRP1 has a high
affinity for exporting glutathione-conjugated metabolites and drugs (51). Multidrug-resistant
H69AR cells exhibited greater Nrf2 activation than H69 cells and its expression is associated
with MRP1 regulation (28). Nrf2 regulates a number of metabolic pathways, including the
induction of antioxidant pathways including the glutathione pathway, as well as NADPH
production through G6PD, the rate-limiting enzyme in the pentose phosphate pathway (52).
Thus, the observed reduction in MRP1, NRF2 and its downstream effector glutathione reductase
due to MCAM knockdown suggests that MCAM works in concert with altered Nrf2 activation, a
notion that is supported by the observation that activation of the PI3K/AKT pathway promotes
Nrf2 activation in other cancer types (53). In the present study, comparison of the proteome of
chemoresistant to other SCLC cell lines revealed perturbations in various metabolic pathways,
highlighted by dysregulation of mitochondrial function, gluconeogenesis, glycolysis, and the
pentose phosphate pathway. Consistently, chemoresistant cells exhibited reduced basal oxygen
consumption, elevated lactate production consistent with increased aerobic glycolysis, and
reduced oxidative phosphorylation. Collectively, these findings implicate a metabolic rewiring of
glucose metabolism and redox status, both of which inherently linked to PI3K/AKT and Nrf2-
activation (29,46,53). Notably, lactate production reduced upon MCAM knockdown implicating
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a reduction in glucose catabolism through the glycolytic pathway. Further studies, including
stable isotope tracer studies, will be required to elucidate the role of MCAM in modulating
cancer cell metabolism; however, these remain outside of the immediate scope of this study.
Our findings point to an important role for MCAM in SCLC chemoresistance. Moreover,
targeting surface MCAM may serve as a novel therapeutic strategy to combat chemoresistance
by modulating the activity and expression of PI3K, Nrf2 and MRP1, well documented and
interconnected contributors of drug resistance.
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Table 1. Dysregulated pathways and their associated proteins in chemoresistant cells.
MAPK, mitogen-activated protein kinase; NF, nuclear factor; TGF, transforming growth factor.
Related pathways Mass Spec Counts
Symbol Gene name Upregulated H69AR H69 H82 DMS79 Fold change
ITGB1 integrin, beta 1 Integrin and PI3/AKT signaling 242 89 86 144 2.73
COL1A1 collagen, type I, alpha 1 PI3/AKT signaling 221 0 0 8 22071.00
EIF4A1 eukaryotic translation initiation factor 4A1 TGF-beta 207 19 8 25 11.11
RPL3 ribosomal protein L3 Infectious diseases 148 0 0 9 14771.00 COL1A2 collagen, type I, alpha 2 PI3/AKT signaling 147 4 0 32 34.09
MCAM melanoma cell adhesion molecule Cell adhesion 135 20 51 107 6.73
ATIC IMP cyclohydrolase Purine biosynthesis 125 12 9 22 10.29
LGALS1 lectin, galactoside-binding, soluble, 1
NF-kappaB and MAPK signaling 116 3 0 3 40.34
JAG1 jagged 1 Notch signaling 114 0 3 0 11406.00
EPHA2 EPH receptor A2 PI3/AKT signaling 98 0 0 3 9752.00
Downregulated
LMNB2 lamin B2 PI3-AKT signaling 24 256 110 210 0.09
NCAM1 neural cell adhesion molecule 1 Cell adhesion 13 342 177 268 0.04
SYNE2 spectrin repeat containing, nuclear envelope 2 Cell cycle 10 115 1 122 0.08
TGM2 transglutaminase 2 Phospholipase-C and CREB 2 111 0 1 0.02
CNTN1 contactin 1 Notch signaling 1 323 88 0 0.00
NCAM2 neural cell adhesion molecule 2 Cell adhesion 0 106 0 6 0.00
FLNB filamin B, beta MAPK signaling 0 156 0 0 0.00 ACTA1 actin, alpha 1, skeletal muscle AKT signaling 0 160 0 0 0.00
CES1 carboxylesterase 1 Xenobiotic metabolism 0 174 0 3 0.00
ALCAM activated leukocyte cell adhesion molecule Cell adhesion 0 191 23 298 0.00
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Figure legends:
Figure 1: MCAM in SCLC chemoresistant cells. A) Schematic diagram of the proteomics
workflow. B) Heatmap cluster of SCLC cell line surface proteins in H69AR compared to other
SCLC cell lines. C) Immunoblot of differentially expressed proteins on cell surface of H69AR
compared to other SCLC cell lines. D) Bar chart of Mass Spec (MS) counts for five most
abundant cell surface receptors on H69AR compared to other SCLC cell lines. E) Schematic
diagram of chemoresistant tumor formation in SCLC PDX models. Highly abundant expression
of MCAM in chemoresistant compared to their respective naïve PDX tissues. Representative
images of MCAM immunohistochemistry at 20X (scale bar is 100μm) and 40X (scale bar is
50μm) magnification.
Figure 2: Effect of MCAM knockdown on SCLC chemoresistant cells with mesenchymal
phenotype. A) Bar chart for Mass Spec (MS) counts of mesenchymal related proteins in SCLC
cell lines. B) Immunoblot for epithelial to mesenchymal transition related proteins and
transcription factors. C and D) Knockdown efficiency of shRNAs for MCAM at transcriptional
(C) and translational (D) levels Data were pooled from three biological replicates (n = 3). E)
Highly efficient reduction of MCAM protein on cell surface after MCAM knockdown was
observed using flow cytometry. F) Stable knockdown of MCAM statistically significantly (p <
0.01, ANOVA) reduced the cell proliferation in chemoresistant H69AR cells Data were pooled
from three biological replicates (n = 3). G) H69AR cells statistically significantly (p < 0.01,
unpaired t-test) lost their colony forming abilities after stable knockdown of MCAM. Colonies
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were stained with crystal violet. Error bars represent means ± SD from three biological replicates
(n = 3). *p < 0.05, **p < 0.01, ***p < 0.001 vs. control shRNA group.
Figure 3: Effect of MCAM knockdown on SCLC chemoresistance. A-C) The cell
proliferation rate were measured using CellTiter Glo assay. Data were pooled from three
biological replicates (n = 3) Values are presented as percentage of cell proliferation in
doxorubicin (A), cisplatin (B) and etoposide (C) treated and untreated cells. *P < 0.001 (one-way
ANOVA; compared with corresponding negative control groups). D) Bar graph representing
effect of MCAM modulation on drug sensitivity of SCLC cell lines. Data were pooled from three
biological replicates (n = 3)). E) MCAM knockdown increased cell apoptosis in SCLC cells after
chemotherapy (n = 3). Representative FACS profiles shown, on which cell population in the
quadrant of Annexin V± DAPI represents apoptotic cells. F) Chemoresistant H69AR cells
(parental and MCAM knockdown clones) were treated with doxorubicin (170 μM) for 24 hours,
and cell lysates were immunoblotted for MCAM, pBAD and cleaved caspase 3.
Figure 4: Effect of MCAM overexpression on SCLC chemoresistance. A and B) Efficient
overexpression of MCAM in H69 cells both at transcriptional (A) and translational (B) level.
Data were pooled from three biological replicates (n = 3) Error bars represent means ± SD; ***p <
0.001. C) Increased MCAM expression on cell surface after MCAM overexpression was
confirmed by flow cytometry. D) The cell proliferation rate were measured using CellTiter Glo
assay. Values are presented as percentage of cell proliferation in doxorubicin, cisplatin,
etoposide-treated and untreated cells (n = 3). E) MCAM overexpression markedly decreased cell
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26
apoptosis in SCLC cells after chemotherapy. Representative FACS profiles are shown, on which
cell population in the quadrant of Annexin V± DAPI represents apoptotic cells.
Figure 5: Regulation of MCAM expression in SCLC chemoresistant cells. A and B) Lysates
from H69AR cells treated with either shMCAM (A) or PI3K inhibitor LYS29004 for 24 hours
(B) were immunoblotted with the indicated antibodies and revealed an inhibited PI3K/AKT
pathway. C-E) Lysates from H69AR cells treated with siSOX2 for 72 hours (C), LYS29004 for
24 hours (D) and siCREB1 for 72 hours (E) were immunoblotted for MCAM, CREB1 and
SOX2. F) Proposed regulatory network for chemoresistance in SCLC. G) Mathematical model of
the network denoting two stable steady states (phenotypes) – chemosensitivity for (low MCAM,
low SOX2) and chemoresistance for (high MCAM, high SOX2) shown by filled circles. The
hollow circle denotes unstable steady state. The red and blue curves are the nullclines denoting
the impact of model variables on MCAM and SOX2 respectively. H) MRP1 expression in
H69AR cells with stable MCAM knockdown.
Figure 6: Effect of MCAM on SCLC metabolism. A) Top perturbed pathways in ingenuity
pathway analysis linked to altered metabolism in SCLC chemoresistant cells. B) OCR
(pmol/min) in SCLC cells measured by a Seahorse Analyser. Basal measurements were
normalized for cell number and protein concentrations. C) Lactate concentration compared
between H69 sensitive and H69AR chemoresistant cells. D) Lactate concentration was assessed
in stable knockdown of MCAM in H69AR chemoresistant cells. Unpaired t-test was used to
calculate significance. Error bars represent means ± SD from triplicates or three independent
experiments. **p < 0.01, ***p < 0.001.
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Figure 1
Negative Control Positive Control
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Published OnlineFirst June 23, 2017.Cancer Res Satyendra C. Tripathi, Johannes F Fahrmann, Muge Celiktas, et al. the PI3K/AKT/SOX2 signaling pathwayMCAM mediates chemoresistance in small cell lung cancer via
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