Abstract
Acquired resistance to cyclin-dependent kinases 4 and 6 (CDK4/6)
inhibition in estrogen receptor-positive (ER+) breast cancer remains a
significant clinical challenge. Efforts to uncover the mechanisms
underlying resistance are needed to establish clinically actionable
targets effective against resistant tumors. In this study, we sought to
identify differentially expressed genes (DEGs) associated with acquired
resistance to palbociclib in ER+ breast cancer. We performed
next-generation transcriptomic RNA sequencing (RNA-seq) and pathway
analysis in ER+ MCF7 palbociclib-sensitive (MCF7/pS) and MCF7
palbociclib-resistant (MCF7/pR) cells. We identified 2183 up-regulated
and 1548 down-regulated transcripts in MCF7/pR compared to MCF7/pS
cells. Functional analysis of the DEGs using Gene Ontology (GO) and the
Kyoto Encyclopedia of Genes and Genomes (KEGG) database identified
several pathways associated with breast cancer, including ‘cell cycle’,
‘DNA replication’, ‘DNA repair’ and ‘autophagy’. Additionally,
Ingenuity Pathway Analysis (IPA) revealed that resistance to
palbociclib is closely associated with deregulation of several key
canonical and metabolic pathways. Further studies are needed to
determine the utility of these DEGs and pathways as therapeutics
targets against ER+ palbociclib-resistant breast cancer.
Keywords: palbociclib, estrogen receptor, breast cancer, CDK4/6, CDK4/6
inhibitors, therapy resistance, DNA repair, metabolic rewiring
1. Introduction
Breast cancer is the most frequent malignancy among women, and
approximately 60–70% of cases are estrogen receptor-positive (ER+).
Selective inhibition of cyclin-dependent kinases 4 and 6 (CDK4/6) and
ER signaling is now standard-of-care therapy for ER+ metastatic breast
cancer [[40]1]. Three CDK4/6 inhibitors, palbociclib, ribociclib and
abemaciclib, are currently used in combination with endocrine therapy
given their shown improvement in progression-free survival compared to
endocrine therapy alone in the metastatic setting [[41]2]. Despite the
clear benefit of this combination, approximately 10% of patients remain
insensitive, whereas nearly all patients become resistant within 12 to
36 months of therapy initiation [[42]3]. Therefore, determining the
underlying mechanisms of resistance is required to design novel
treatment strategies that delay or overcome clinical resistance.
Previous studies have shown that resistance to palbociclib is commonly
associated with cyclin E or CDK6 amplification, CDK2 activation and
loss of the retinoblastoma (Rb) protein in ER+ breast cancer cells
[[43]4,[44]5,[45]6]. Analysis of circulating tumor DNA from patients
enrolled in the PALOMA-3 trial (fulvestrant or fulvestrant +
palbociclib) identified an enrichment of Rb mutations, although this
only occurred in 4.5% of the palbociclib-treated cohort [[46]7,[47]8].
Importantly, acquired alterations in ESR and PIK3CA were also observed;
however, these alterations occurred in both treatment arms indicating
distinct events driving resistance to palbociclib versus fulvestrant
[[48]9]. Additional studies have implicated fibroblast growth factor
receptor (FGFR) or aurora kinase A amplifications, enhanced MAPK or AKT
signaling and decreased DNA repair as mechanisms of resistance against
CDK4/6 inhibition [[49]10,[50]11,[51]12,[52]13]. Taken together, these
studies have provided rationale for the testing of CDK4/6 inhibitors in
combination with MEK or PI3K inhibitors [[53]11,[54]14].
The major goal of this study was to identify additional mechanisms of
resistance to palbociclib in ER+ breast cancer cells through
transcriptomic analyses. We previously demonstrated that ER+
palbociclib-resistant cells exhibit a marked decrease in the cellular
antiviral interferon (IFN) response [[55]6], and thus we expected that
other drivers of resistance remained to be identified. Here, we
determined the transcriptional landscape of ER+ MCF7
palbociclib-sensitive (MCF7/pS) and palbociclib-resistant (MCF7/pR)
breast cancer cells via next-generation transcriptomic RNA sequencing
(RNA-seq). Gene expression profile and pathway analysis identified
significant canonical pathways associated with resistance to
palbociclib including cell cycle regulation, immune responses and DNA
damage repair (DDR) among others. Importantly, we identified several
metabolic pathways uniquely enriched in palbociclib-resistant cells
compared to palbociclib-sensitive cells. These studies provide a
mechanistic base for the further validation of these pathways in
mediating resistance to palbociclib.
2. Materials and Methods
2.1. Cell Culture, Generation of Palbociclib-Resistant Cells and Palbociclib
Treatment
MCF7 (HTB-22) cells were purchased from the American Type Culture
Collection (ATCC) and maintained at 37 °C with 5% CO[2]. MCF7 cells
were cultured in IMEM (Corning) supplemented with 10% fetal bovine
serum (FBS, Invitrogen). Drug-resistant MCF7 cells were established by
culturing in media containing palbociclib (0.1–4 μM). Drug was
replenished every 3 days. Cells were subcultured every 1–2 weeks with
25% increments in drug concentration. The resistant cells were
established after 6 months and maintained in the presence of 1 μM
palbociclib. Cells were authenticated by the short tandem repeat (STR)
assay (Genetica).
2.2. RNA Extraction and Next-Generation Sequencing
MCF7/pS and MCF7/pR cells were seeded in 10 cm^2 dishes at a density of
2 × 10^6 cells and allowed to incubate overnight prior to RNA
extraction using the RNeasy kit (Qiagen) for a total of three
independent replicates per cell line. Libraries were prepared
simultaneously for all replicates and cell lines using the TruSeq
Stranded mRNA LT Sample Prep Kit - Set A (Cat# RS-122-2101) with poly-A
enrichment. Sequencing was performed on the University of Louisville
Center for Genetics and Molecular Medicine’s (CGeMM) Illumina NextSeq
500 using the NextSeq 500/550 1 × 75 cycle High Output Kit v2 (Cat#
FC-404-2005). A second run was performed on all samples to achieve an
average of 45 million reads per sample.
2.3. DEG Analysis
The resulting samples were downloaded from Illumina’s BaseSpace
[[56]15] ([57]https://basespace.illumina.com/). Sequences were directly
aligned to the Homo sapiens hg38 reference genome assembly (hg38.fa)
using tophat2 (version 2.0.13), generating alignment files in bam
format. DEGs were identified for the pairwise comparison MCF7/pS versus
MCF7/pR using the tuxedo suite programs including cufflinks-cuffdiff2
(VERSION2.2.1). A total of 60,603 ENSEMBL genes were considered. Of
these, 26,837 showed no gene expression and were excluded. A q-value
cutoff ≤ 0.05 with |log[2]FC|
[MATH: ≥1 :MATH]
and gene expression greater than 1 in at least one replicate was used
to determine differential expression. RNA-seq data are available (GEO
accession number [58]GSE130437). Gene Ontology Biological Processes
(GO:BP) and KEGG pathway analysis was performed by using
CategoryCompare [[59]16].
2.4. In Silico Ingenuity Network Analysis
Pathway and biological processes analysis of all differentially
expressed genes was performed using Ingenuity Pathway Analysis
(Qiagen).
2.5. GFP-LC3 Visualization
Plasmid vector containing green fluorescent protein linked to
microtubule-associated protein 1 LC3 was used to detect autophagosome
formation in MCF7/pS and MCF7/pR cell lines [[60]17]. Cells were
treated with either vehicle control or palbociclib after 24 h of
transfection. The expression of GFP was monitored by fluorescence
microscopy 48 h after treatment. Cells were classified as having a
predominantly diffuse GFP stain or having numerous punctate structures
representing autophagosomes. Images were taken at 40× magnification
with the EVOS FL Imaging System (Thermo Fisher Scientific, Waltham, MA,
USA) under 357/44 and 447/60 nanometers (nm) excitation and emission
visualization, respectively.
3. Results
3.1. RNA-Seq Profiling Reveals a Distinct Transcriptomic Profiling in
Palbociclib-Resistant Cells
To characterize transcriptional alterations driven by acquired
resistance to palbociclib, we performed gene expression profiling in
MCF7/pS and MCF7/pR cells. These cells were developed by our group and
were previously shown to be resistant to palbociclib [[61]6].
Hierarchical clustering based on differentially expressed RNA
transcripts revealed a distinct transcriptomic profile in MCF7/pR cells
compared to MCF7/pS ([62]Figure 1). Using a q-value cutoff ≤ 0.05 with
|log[2]FC|
[MATH: ≥1 :MATH]
, we identified 2183 up-regulated genes and 1548 down-regulated
transcripts in MCF7/pR cells. [63]Table 1 shows the top 20 up-regulated
and down-regulated genes in MCF7/pR cells compared to MCF7/pS cells.
Figure 1.
[64]Figure 1
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Differential expression heatmap of estrogen receptor-positive (ER+)
MCF7 palbociclib-sensitive (MCF7/pS) compared to MCF7
palbociclib-resistant (MCF7/pR) cells. Next-generation transcriptomic
RNA sequencing (RNA-seq) was performed and the raw expression of genes
is shown as a heatmap. Replicate samples are clustered. Red and yellow
indicate lower and higher gene expression, respectively.
Table 1.
Top 20 up-regulated and down-regulated genes between MCF7/pS and
MCF7/pR ranked by p-value (pval ≤ 0.05; qval ≤ 0.05; |log[2]FC| ≥ 1).
Up-Regulated Down-Regulated
Ensembl ID Gene Symbol|Description Log[2]FC p-Value Q-value Ensembl ID
Gene Symbol|Description Log[2]FC p-Value Q-Value
ENSG00000011465 DCN|decorin 18.6778 5 × 10^−5 0.00026 ENSG00000211816
TRAV38-1| −16.6898 5 × 10^−5 0.00026
ENSG00000011677 GABRA3|gamma-aminobutyric acid (GABA) A receptor, alpha
3 18.6778 5 × 10^−5 0.00026 ENSG00000228340 MIR646HG|MIR646 host gene
−16.6898 5 × 10^−5 0.00026
ENSG00000012223 LTF|lactotransferrin 18.6778 5 × 10^−5 0.00026
ENSG00000259761 −16.6898 5 × 10^−5 0.00026
ENSG00000070729 CNGB1|cyclic nucleotide gated channel beta 1 18.6778 5
× 10^−5 0.00026 ENSG00000082482 KCNK2|potassium channel, two pore
domain subfamily K, member 2 −7.09413 5 × 10^−5 0.00026
ENSG00000079689 SCGN|secretagogin, EF-hand calcium binding protein
18.6778 5 × 10^−5 0.00026 ENSG00000182836
PLCXD3|phosphatidylinositol-specific phospholipase C, X domain
containing 3 −6.53477 5 × 10^−5 0.00026
ENSG00000083782 EPYC|epiphycan 18.6778 5 × 10^−5 0.00026
ENSG00000253293 HOXA10|homeobox A10 −6.24454 5 × 10^−5 0.00026
ENSG00000086205 FOLH1|folate hydrolase (prostate-specific membrane
antigen) 1///FOLH1|folate hydrolase 1B 18.6778 5 × 10^−5 0.00026
ENSG00000259527 LINC00052|long intergenic non-protein coding RNA 52
−6.17543 5 × 10^−5 0.00026
ENSG00000101441 CST4|cystatin S 18.6778 5 × 10^−5 0.00026
ENSG00000006747 SCIN|scinderin −5.91229 5 × 10^−5 0.00026
ENSG00000101460 MAP1LC3A|microtubule-associated protein 1 light chain 3
alpha 18.6778 5 × 10^−5 0.00026 ENSG00000110492 MDK|midkine (neurite
growth-promoting factor 2) −5.60036 5 × 10^−5 0.00026
ENSG00000102854 MSLN|mesothelin 18.6778 5 × 10^−5 0.00026
ENSG00000140538 NTRK3|neurotrophic tyrosine kinase, receptor, type 3
−5.39223 5 × 10^−5 0.00026
ENSG00000104313 EYA1|EYA transcriptional coactivator and phosphatase 1
18.6778 5 × 10^−5 0.00026 ENSG00000102271 KLHL4|kelch-like family
member 4 −5.2236 5 × 10^−5 0.00026
ENSG00000109846 CRYAB|crystallin, alpha B 18.6778 5 × 10^−5 0.00026
ENSG00000105974 CAV1|caveolin 1, caveolae protein, 22kDa −5.18256 5 ×
10^−5 0.00026
ENSG00000111863 ADTRP|androgen-dependent TFPI-regulating protein
18.6778 5 × 10^−5 0.00026 ENSG00000112175 BMP5|bone morphogenetic
protein 5 −5.08812 5 × 10^−5 0.00026
ENSG00000113805 CNTN3|contactin 3 (plasmacytoma associated) 18.6778 5 ×
10^−5 0.00026 ENSG00000159618 ADGRG5|adhesion G protein-coupled
receptor G5 −4.97744 5 × 10^−5 0.00026
ENSG00000115009 CCL20|chemokine (C-C motif) ligand 20 18.6778 5 × 10^−5
0.00026 ENSG00000174498 IGDCC3|immunoglobulin superfamily, DCC
subclass, member 3 −4.96488 5 × 10^−5 0.00026
ENSG00000125788 DEFB126|defensin, beta 126 18.6778 5 × 10^−5 0.00026
ENSG00000168269 FOXI1|forkhead box I1 −4.95038 5 × 10^−5 0.00026
ENSG00000131095 GFAP|glial fibrillary acidic protein 18.6778 5 × 10^−5
0.00026 ENSG00000087077 TRIP6|thyroid hormone receptor interactor 6
−4.80366 5 × 10^−5 0.00026
ENSG00000139329 LUM|lumican 18.6778 5 × 10^−5 0.00026 ENSG00000134193
REG4|regenerating islet-derived family, member 4 −4.705 5 × 10^−5
0.00026
ENSG00000140287 HDC|histidine decarboxylase 18.6778 5 × 10^−5 0.00026
ENSG00000003436 TFPI|tissue factor pathway inhibitor
(lipoprotein-associated coagulation inhibitor) −4.62227 5 × 10^−5
0.00026
ENSG00000146233 CYP39A1|cytochrome P450, family 39, subfamily A,
polypeptide 1 18.6778 5 × 10^−5 0.00026 ENSG00000064205 WISP2|WNT1
inducible signaling pathway protein 2 −4.57396 5 × 10^−5 0.00026
[66]Open in a new tab
3.2. KEGG Annotation of DEG and Enriched Biological Processes Analysis
To gain insight into the molecular mechanisms underlying palbociclib
resistance, we performed KEGG pathway analysis of all DEGs identified
using CategoryCompare [[67]16]. [68]Table 2 lists the enriched KEGG
pathways identified in MCF7/pS vs. MCF7/pR cells (false discovery rate
(FDR) ≤ 0.05 and p-value ≤ 0.001). The KEGG terms associated with
resistance to palbociclib included ‘cell cycle’, ‘DNA replication’,
‘mismatch repair’ and ‘phagosome’. Subsequent analysis of GO:BP
identified many enriched biological processes that correlated with
palbociclib resistance ([69]Figure 2). Importantly, we observed
distinct groups of nodes such as DNA replication, cell cycle
transition, mitosis, protein–DNA assembly and organization and response
to virus revealing multiple functional ‘themes’ associated with
resistance to palbociclib.
Table 2.
Top enriched KEGG terms between MCF7/pS and MCF7/pR ranked by p-value.
(pval ≤ 0.05; qval ≤ 0.05; |log[2]FC| ≥ 1).
KEGG ID Description p-value FDR
4110 Cell cycle 8.73 × 10^−8 0
3030 DNA replication 1.20 × 10^−7 0
4114 Oocyte meiosis 6.34 × 10^−5 0
4914 Progesterone-mediated oocyte maturation 8.36 × 10^−5 0
4360 Axon guidance 8.76 × 10^−5 0
3430 Mismatch repair 5.20 × 10^−4 0.01
4115 p53 signaling pathway 1.11 × 10^−3 0.01143
4010 MAPK signaling pathway 2.91 × 10^−3 0.03333
600 Sphingolipid metabolism 3.88 × 10^−3 0.044
4145 Phagosome 4.78 × 10^−3 0.05
[70]Open in a new tab
Figure 2.
[71]Figure 2
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Enriched biological processes (BP) analysis of ER+
palbociclib-resistant breast cancer cells.
3.3. Resistance to Palbociclib Is Associated with Increased Autophagosome
Formation
Characterization of MCF7/pR cells by KEGG pathway analysis revealed an
enrichment in genes associated with phagosomes ([73]Table 2). Given a
previous observation suggesting a crosstalk between phagocytosis and
autophagy, we sought to investigate autophagy levels in the context of
resistance to palbociclib [[74]18]. We performed hierarchical
clustering of autophagy-related genes in MCF7/pS and MCF7/pR cells
([75]Figure 3A). Using a p-value cutoff ≤ 0.05, we identified a
significant number of autophagy-related genes differentially expressed
in MCF7/pR compared to MCF7/pS cells. Next, we measured autophagosome
formation by monitoring the conversion of cytoplasm-diffuse GFP-LC3-I
to punctate forms of membrane-associated GFP-LC3-II, which indicates
LC3-II incorporation into the autophagosomes. We observed that MCF7/pR
cells displayed a significant increase in autophagosome formation
compared to MCF7/pS and that the addition of palbociclib led to a
marked increase in autophagosome formation in both MCF7/pS and MCF7/pR
cells ([76]Figure 3B). These results confirmed an increase in autophagy
in MCF7/pR cells and are in line with previous studies [[77]19].
Numerous studies have demonstrated that autophagy contributes to the
resistance of breast cancer cells to targeted therapies by promoting
tumor cell survival and blocking apoptosis [[78]20,[79]21,[80]22].
Recently, it has been shown that autophagy inhibitors synergize with
palbociclib in ER+ MCF7 and T47D breast cancer cells resulting in a
significant increase in growth inhibition [[81]19]. Our results provide
rationale for the use of autophagy inhibitors to treat
palbociclib-resistant cells in addition to palbociclib-sensitive cells.
Future studies will test the efficacy of this combination against
CDK4/6 inhibition in the resistance setting and determine the molecular
mechanisms driving the increase in autophagy in resistant tumors.
Figure 3.
[82]Figure 3
[83]Open in a new tab
Increased autophagy is associated with palbociclib resistance in ER+
MCF7 cells. (A) Hierarchical clustering of autophagy-related genes
performed by MetaCore analysis. (B) Cells were transfected with a
pEGFP-LC3 plasmid and treated with either vehicle control (0.5% water)
or 500 nM palbociclib for 24 h. Formation of autophagosomes is depicted
by punctate structures (arrows). Images were taken at 40× magnification
with an EVOS microscope.
3.4. Pathway Enrichment Analysis of DEG
To identify potential targetable pathways, all altered transcripts were
mapped to known pathways using Ingenuity Pathway Analysis (IPA). We
observed significant enrichment of several canonical pathways including
four pathways involved in cell cycle regulation (‘Estrogen-mediated
S-phase entry’, ‘Cell cycle control of chromosomal replication’,
‘Mitotic roles of Polo-Like Kinase’ and ‘Role of CHK proteins in cell
cycle checkpoint control’), four involved in DDR (‘ATM signaling’,
‘Role of BRCA in DNA damage response’, ‘Mismatch repair in eukaryotes’
and ‘G2/M DNA damage checkpoint regulation’), eight involved in immune
responses (‘IL-17A signaling’, ‘Interferon signaling’, ‘STAT3 pathway’,
‘April mediated signaling’, ‘Tec Kinase signaling’, ‘Antigen
presentation pathway’, ‘Production of nitric oxide and reactive oxygen
species in macrophages’ and ‘IL-15 production’) among other pathways
([84]Figure 4).
Figure 4.
[85]Figure 4
[86]Open in a new tab
Canonical pathway analysis of ER+ palbociclib-resistant breast cancer
cells. A higher–log(B-H p-value) shown on the left Y axis represents
more significant pathways. The ratio (right Y axis) refers to the
number of genes from the data set that map to the pathway divided by
the total number of genes that map the canonical pathway from the
Ingenuity Pathway Analysis (IPA) database. pval ≤ 0.05; qval ≤ 0.05;
|log[2]FC| ≥ 1.
3.5. Metabolic Pathways Associated with Resistance to Palbociclib
Previous reports have indicated that cellular metabolism is a
downstream target of CDK4/6 inhibition. Specifically, it has been shown
that palbociclib administration increases glucose utilization in
cancer, whereas cyclin D3-CDK6 can directly phosphorylate and inhibit
the activity of two key enzymes in the glycolytic pathway
[[87]23,[88]24]. To identify metabolic pathways associated with
resistance to palbociclib, we performed metabolic pathway analysis of
all DEGs using IPA ([89]Figure 5). We observed an enrichment of several
metabolic pathways including three pathways involved in ribonucleotides
synthesis (‘Pyrimidine deoxyribonucleotides de novo biosynthesis I’,
‘dTMP de novo biosynthesis’ and ‘Salvage pathway of pyrimidine
ribonucleotides’), six pathways involved in inositol metabolism
(‘3-Phosphoinositide biosynthesis’, ‘3-Phosphoinositide degradation’,
‘D-myo-inositol(1,4,5,6)-tetrakisphosphate biosynthesis’,
‘D-myo-inositol-5-phosphate metabolism’ and ‘Superpathway of inositol
phosphate compounds’). Among other pathways, we found
‘Glycerol-3-phosphate shuttle’, ‘Asparagine degradation’ and ‘NAD
biosynthesis II (from tryptophan)’ to be enriched in our dataset. These
results indicate that deregulated metabolism may play an essential role
in mediating resistance to palbociclib.
Figure 5.
[90]Figure 5
[91]Open in a new tab
Metabolic pathway analysis of ER+ palbociclib-resistant breast cancer
cells. A higher–log(p-value) shown on the left Y axis represents more
significant pathways. The ratio (right Y axis) refers to the number of
genes from the dataset that map to the pathway divided by the total
number of genes that map the canonical pathway from the IPA database.
pval ≤ 0.05; qval ≤ 0.05; |log[2]FC| ≥ 1.
4. Discussion
Three orally available inhibitors of CDK4/6 are currently used in
combination with endocrine therapy (ET) as first-line therapy ER+
metastatic breast cancer patients [[92]25]. Although initially
beneficial, resistance to CDK4/6 inhibition arises in almost all
patients within two years thus limiting durable responses. Currently,
there are no biomarkers that can predict treatment response or early
resistance [[93]26]. Here, we identified a number of clinically
relevant pathways that are associated with resistance to palbociclib,
largely focusing on metabolic alterations and oncogenic signaling such
as nucleotide metabolism, inositol metabolism, cell cycle, immune
regulation and DDR.
Previous efforts to identify mechanisms of resistance to CDK4/6
inhibition have found that lack of Rb protein, increased cyclin E
expression, IL6/STAT3 pathway activation and decreased DNA repair are
some of the underlying mechanisms of resistance in ER+ breast cancer
cells [[94]6,[95]13,[96]19,[97]27,[98]28]. Analysis of ctDNA or tumor
mRNA from patients enrolled in the PALOMA-3, NeoPalAna and MONALEESA-3
trials have identified Rb mutations, activating mutations in PIK3CA and
ESR1, increased cyclin E1 and activation of the PDK1-AKT axis as some
of the drivers of resistance [[99]7,[100]9,[101]11]. Consistent with
previous findings, we observed a significant enrichment in pathways
involved in DDR [[102]13]. Furthermore, we observed an increased in
autophagy in MCF7/pR cells which is consistent with the previously
described increase in autophagy driven by CDK4/6 inhibition in
palbociclib-sensitive ER+ breast cancer cells [[103]19]. Previous
studies have shown that resistance to CDK4/6 inhibition is associated
with a loss of ER/progesterone receptor (PR) expression in tumor
biopsies of patients treated with the CDK4/6 inhibitor abemaciclib
[[104]5]. Notably, we observed a significant decrease in PR expression
in palbociclib-resistant cells ([105]Supplementary File 1). This
finding is relevant given that unliganded PR sustains ER expression
levels by maintaining a low methylation status of the ER gene
[[106]29]. Taken together, these observations suggest that PR loss may
drive breast cancer cells to escape CDK4/6 inhibition by altering ER
methylation thereby resulting in the down-regulation of ER expression.
Additionally, our results highlight that ER methylation status can
potentially be used to predict acquired resistance to CDK4/6
inhibition.
While our findings are in line with previously identified mechanisms of
resistance, our analysis uncovered additional potential mechanisms of
resistance such as deregulation of ‘Polo-Like Kinase (PLK)’, ‘April
mediated signaling’ and ‘Tec Kinase signaling’. Of these, targeting
PLK1 is of high relevance due to its role as a master regulator of the
G2-M phase and DNA replication [[107]30,[108]31]. Importantly, PLK1 has
been shown to play a role in mediating tamoxifen resistance in ER+
breast cancer cells, and thus we will conduct additional studies
evaluating the role of PLK1 as a novel target for the ER+ breast cancer
resistant to CDK4/6 inhibition [[109]32]. Importantly, a potent PLK1
inhibitor, volasertib (BI6727), has been recently approved for the
treatment of acute myeloid leukemia and would be a promising
therapeutic agent against palbociclib-resistant breast cancer
[[110]33,[111]34].
Close examination of the DEGs revealed significant expression changes
in many genes involved in tumorigenesis and chemoresistance. For
example, up-regulation of three of the small leucine-rich family of
proteoglycans (SLRP), decorin, epiphycan and lumican, was observed in
our dataset ([112]Table 1). These proteoglycans are known for their
ability to regulate cell signaling, adhesion, migration, proliferation
and apoptosis in many types of cancer [[113]35,[114]36]. Notably,
accumulated evidence supports a role for both decorin and lumican in
mediating drug resistance, and thus our data suggest a potential role
for these proteoglycans in mediating resistance to palbociclib
[[115]37,[116]38,[117]39,[118]40,[119]41]. Other promising genes that
were shown to be up-regulated in our dataset were cystatin S and alpha
B-crystallin. Elevated blood levels of cystatin-C have been detected in
women with breast cancer and are shown to correlate with cancer
progression [[120]42,[121]43]. Alpha B-crystallin expression has been
associated with high metastatic potential, poor clinical outcome and
drug resistance in breast cancer [[122]44,[123]45,[124]46]. Our
findings raise the possibility of the potential use of alpha
B-crystallin and cystatin-C as biomarkers of sensitivity to CDK4/6
inhibition. Of the top 20 down-regulated genes, miR-646 host gene and
homeobox A10 (HOXA10) are of great relevance given their emerging tumor
suppressive functions. Expression of miR-646 has been shown to directly
regulate CDK6 and FOXK1 expression in gastric cancer, suggesting its
utility as a potential therapeutic target [[125]47,[126]48]. A lack of
HOXA10 in breast cancer has been shown to decrease apoptosis and
promote metastasis, and thus the role of HOXA10 in the context of
palbociclib resistance warrants further investigation [[127]49]. A
limitation of our studies is the lack of validation of gene expression
changes by real-time PCR; however, we believe that our initial
profiling will help guide further efforts to better understand the
molecular mechanisms driving drug resistance.
Metabolic reprograming is a well-established oncogenic driver that
allows cells to support the increased bioenergetic and anabolic demands
[[128]50]. Importantly, CDK4/6 are key regulators of metabolic
pathways, and therefore we anticipated that metabolic rewiring will be
observed upon the development of resistance to palbociclib. While a
previous study described an increase in glucose dependence in ER+/Her2-
palbociclib-sensitive compared to palbociclib-resistant cells
[[129]51], to date little is known about global metabolic changes
driving resistance to CDK4/6 inhibition. Our unbiased analysis of DEGs
and metabolic pathways began to define metabolic hubs linked to
palbociclib resistance. Specifically, we observed alterations in
nucleotide metabolism in MCF7/pS vs. MCF7/pR cells. Importantly, these
results are in line with previous reports indicating that increased
expression of thymidine kinase-1 (TK1), an enzyme of the pyrimidine
salvage pathway, correlates with poor prognosis in breast cancer
patients treated with palbociclib [[130]52,[131]53,[132]54,[133]55].
Our findings indicate that inositol metabolism was altered in ER+
palbociclib-resistant cells. Given the role of inositols as essential
membrane components crucial for the generation of secondary messengers,
our results are of high biological significance and provide a direct
link between signal transduction and metabolic alterations contributing
to resistance. Future metabolomic profiling will be needed to confirm
our initial findings and provide further evidence as to how inositol
alteration contributes to resistance to palbociclib.
Collectively, our RNA-seq analysis uncovered key canonical and
metabolic pathways altered in ER+ palbociclib-resistant cells and
provided new insights into the molecular mechanisms and potential
therapeutic targets underlying resistance to CDK4/6 inhibition.
Acknowledgments