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Mouse models of thyroid cancer: Bridging pathogenesis and novel therapeutics
Journal Pre-proof Mouse Models of Thyroid Cancer: Bridging Pathogenesis and Novel Therapeutics Yuchen Jin, Min Liu, Ri Sa, Hao Fu, Lin Cheng, Libo Chen PII:
S0304-3835(19)30489-6
DOI:
https://doi.org/10.1016/j.canlet.2019.09.017
Reference:
CAN 114507
To appear in:
Cancer Letters
Received Date: 22 July 2019 Revised Date:
25 September 2019
Accepted Date: 30 September 2019
Please cite this article as: Y. Jin, M. Liu, R. Sa, H. Fu, L. Cheng, L. Chen, Mouse Models of Thyroid Cancer: Bridging Pathogenesis and Novel Therapeutics, Cancer Letters, https://doi.org/10.1016/ j.canlet.2019.09.017. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Elsevier B.V. All rights reserved.
Abstract Due to a global increase in the incidence of thyroid cancer, numerous novel mouse models were established to reveal thyroid cancer pathogenesis and test promising therapeutic strategies, necessitating a comprehensive review of translational medicine that covers (i) the role of mouse models in the research of thyroid cancer pathogenesis, and (ii) preclinical testing of potential anti-thyroid cancer therapeutics. The present review article aims to: (i) describe the current approaches for mouse modeling of thyroid cancer, (ii) provide insight into the biology and genetics of thyroid cancers, and (iii) offer guidance on the use of mouse models for testing potential therapeutics in preclinical settings. Based on research with mouse models of thyroid cancer pathogenesis involving the RTK, RAS/RAF/MEK/ERK, PI3K/AKT/mTOR, SRC, and JAK-STAT signaling pathways, inhibitors of VEGFR, MEK, mTOR, SRC, and STAT3 have been developed as anti-thyroid cancer drugs for “bench-to-bedside” translation. In the future, mouse models of thyroid cancer will be designed to be ‘‘humanized” and “patient-like,” offering opportunities to: (i) investigate the pathogenesis of thyroid cancer through target screening based on the CRISPR/Cas system, (ii) test drugs based on new mouse models, and (iii) explore the underlying mechanisms based on multi-omics.
1
1
Mouse Models of Thyroid Cancer: Bridging
2
Pathogenesis and Novel Therapeutics
3
Yuchen Jin1*, Min Liu1, 2*, Ri Sa1, Hao Fu1, Lin Cheng1, and Libo Chen1
4
1
5
Shanghai 200233, People’s Republic of China.
6
2
7
200032, China.
8
*
9
E-mail addresses for authors:
Department of Nuclear Medicine, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital,
Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Rd, Shanghai,
Yuchen Jin and Min Liu were the co-first authors.
10
[email protected] (Yuchen Jin)
11
[email protected] (Ri Sa)
12
[email protected] (Hao Fu)
13
[email protected] (Lin Cheng)
14
[email protected] (Min Liu)
15
[email protected] (Libo Chen)
16 17
Addresses for correspondence:
18
Professor Libo Chen, MD & PhD, Department of Nuclear Medicine, Shanghai Jiao Tong University
19
Affiliated Sixth People’s Hospital, Shanghai 200233, People’s Republic of China.
20
Telephone: +86-21-64369181
21
Fax: +86-21-64844183
22
E-mail: [email protected]
23 1
24
Financial support and conflicts of interest: This study was sponsored by the National Natural
25
Science Foundation of China (Grant Nos. 81671711 and 81701731) and Shanghai Key Discipline of
26
Medical Imaging (Grant No. 2017ZZ02005). None of the authors has any conflict of interest to
27
declare.
28 29
Manuscript length: 4408 words (main text)
30
Abstract: 199 words
31
References: 259
32
Figures: 4
33
Tables: 5
34
Abstract
35
Due to a global increase in the incidence of thyroid cancer, numerous novel mouse models were established to
36
reveal thyroid cancer pathogenesis and test promising therapeutic strategies, necessitating a comprehensive review
37
of translational medicine that covers (i) the role of mouse models in the research of thyroid cancer pathogenesis,
38
and (ii) preclinical testing of potential anti-thyroid cancer therapeutics. The present review article aims to: (i)
39
describe the current approaches for mouse modeling of thyroid cancer, (ii) provide insight into the biology and
40
genetics of thyroid cancers, and (iii) offer guidance on the use of mouse models for testing potential therapeutics in
41
preclinical settings. Based on research with mouse models of thyroid cancer pathogenesis involving the RTK,
42
RAS/RAF/MEK/ERK, PI3K/AKT/mTOR, SRC, and JAK-STAT signaling pathways, inhibitors of VEGFR, MEK,
43
mTOR, SRC, and STAT3 have been developed as anti-thyroid cancer drugs for “bench-to-bedside” translation. In
44
the future, mouse models of thyroid cancer will be designed to be ‘‘humanized” and “patient-like,” offering
45
opportunities to: (i) investigate the pathogenesis of thyroid cancer through target screening based on the
46
CRISPR/Cas system, (ii) test drugs based on new mouse models, and (iii) explore the underlying mechanisms
47
based on multi-omics.
48 49
Keywords: Thyroid cancer; Mouse model; Preclinical testing; Anti-cancer therapeutics 2
50
Introduction
51
Thyroid cancers are endocrine-related tumors, and their incidence is increasing globally [1].
52
Surveillance and Epidemiology and End Results (SEER) indicated that the numbers of new cases of
53
thyroid cancer and thyroid cancer-related deaths in the USA in 2019 were expected to reach 52,070
54
and 2,170, respectively [2]. In China, there were about 90,000 new cases and 6,800 deaths in 2015
55
[3]. By 2030, the incidence of thyroid cancer is expected to rank second among all tumors in women
56
and ninth in men [4]. The most prevalent type of thyroid cancer, differentiated thyroid cancer (DTC),
57
originates in follicular cells and accounts for 90,800 of the total number of cases [1]. Within 10 years
58
after an initial operation, local recurrence and distant metastases occur in about 20% and 10% of
59
DTC cases, respectively. Despite the availability of I-131 therapy, metastasectomy, and radiotherapy,
60
only one-third of patients are identified as having a “complete response”; the remaining patients are
61
refractory to I-131 and have a poor prognosis [5]. Medullary thyroid cancer (MTC) and anaplastic
62
thyroid cancer (ATC) are rare forms that account for less than 2% of all thyroid cancers. However,
63
50% to 80% of MTC patients have metastatic disease at the time of diagnosis, with a five-year
64
survival of <50%; ATC is an extremely aggressive disease with a median overall survival of <1 year
65
[6]. Therefore, preclinical translational studies on its pathogenesis and novel therapeutics using
66
animal models of thyroid cancer are urgently needed.
67
In the past decade, spontaneous and transplantation mouse models have been used to explore the
68
biomedical features of thyroid cancer. These models produce thyroid tumors that resemble their
69
counterparts in humans, both histologically and genetically [7]. Sequencing results for the mouse and
70
human genomes have revealed the extent of their cross-species genomic similarity, indicating the
71
value of mouse models in thyroid cancer research [8]. Currently, genetic manipulation techniques,
72
such as conditional genetic recombination and the introduction of severe immunodeficiency, have
73
allowed for the development of new mouse models of thyroid cancer to mimic complicated clinical
74
settings, tumor heterogeneity, and disease status in thyroid cancer patients. Interestingly, such models 3
75
provide insight into thyroid cancer pathogenesis and facilitate preclinical testing of anti-cancer drugs.
76
Furthermore, mouse models are indispensable for elucidating the interaction of anti-cancer drugs
77
with thyroid cancer cells, the screening of drugs before clinical trials, and the interpretation of drug
78
efficacy and safety results for thyroid cancer patients in clinical trials [9].
79
Given the increasing number of newly established thyroid cancer mouse models, recently
80
discovered anti-thyroid cancer mechanisms, and potential anti-cancer therapeutics, we performed a
81
review of the currently available literature, including established thyroid cancer mouse models,
82
thyroid cancer pathogenesis, and new drugs for thyroid cancer therapy. We also discuss perspectives
83
for further applications of thyroid cancer mouse models, highlighting techniques for such modeling,
84
and provide guidance for the translation of anti-cancer drugs as individualized thyroid cancer
85
therapeutics.
86
Modeling
87
Anti-cancer investigations can be performed by injecting cultured cell-derived xenografts into
88
immunodeficient mice. Considerable work on drug testing relies on the use of these cell line-derived
89
xenograft models (CDXMs). CDXMs, as well as patient-derived xenograft models (PDXMs) and
90
genetically engineered models (GEMs), provide a basis to investigate thyroid cancer pathogenesis
91
and the pharmacological mechanisms of anti-thyroid cancer therapeutics. These three models are
92
described below.
93
CDXMs
94
The establishment of CDXMs is based on xenotransplants. Tumor cells can be engrafted
95
subcutaneously, orthotopically, or metastatically. Such thyroid cancer models commonly have been
96
built with authenticated cell lines (e.g., 8505C, TPC-1, FTC133) to investigate thyroid cancer-related
97
phenomena, including invasion, metastasis, and angiogenesis.
98
Subcutaneous tumor models are established by inoculating Matrigel-suspended thyroid tumor cells
99
into immunodeficient mice [10]. Although the progression of tumors can be monitored easily, the 4
100
models fail to imitate the microenvironment, invasional type, and metastatic patterns of thyroid
101
cancer, and thus may not be adequate for the prediction of clinical anti-cancer responses [11]. In fact,
102
in immunodeficient mice, subcutaneously transplanted thyroid cancer cells rarely metastasize, failing
103
to mimic the thyroid tumor microenvironment [12,13]. In contrast, thyroid cancer models involving
104
orthotropic transplants simulate the microenvironment, morphology, growth, and metastatic patterns,
105
thereby reflecting the clinical spectrum of thyroid cancer. Orthotropic transplants are established by
106
injecting thyroid tumor cells into the thyroid glands of immunodeficient mice under surgery, with or
107
without ultrasound examination [14]. Mouse models of metastatic thyroid cancer, established in
108
severely immunodeficient mice, mimic the metastatic patterns of thyroid cancers in human patients.
109
After intravenous or intraventricular injection with as few as 30,000 thyroid cancer cells, metastases
110
develop rapidly in the lungs and bones of these mice [15].
111
Approaches involving cell lines result in rapid development of thyroid tumors, generating cohorts
112
for preclinical investigation. However, CDXMs suffer from several drawbacks. First, cell lines may
113
have incurred changes in their microenvironment, especially after many passages, which may alter
114
the DNA structure, RNA/protein expression, and the status of thyroid cancer differentiation, thereby
115
decreasing the reliability of the experiments [16]. Second, in CDXMs, the tumor microenvironment,
116
including stroma and the immune system, is lacking [17]. Finally, tumor cell implantation is
117
accomplished through a wound and therefore may not accurately mimic local invasion through the
118
thyroid capsule [18]. In this respect, in preclinical experiments, their resemblance to thyroid cancers
119
in patients may not be adequate. However, because their experimental repeatability is excellent,
120
CDXMs have been widely used.
121
PDXMs
122
PDXMs are established by transplanting tissue or cells from tumors of patients into immunodeficient
123
mice. The growth of patient-derived tumor fragments creates a stroma-based tumor environment,
124
which is of value for investigating thyroid cancer pathogenesis, evaluating drug efficacy and safety,
5
125
and exploring tumor heterogeneity; for thyroid cancer patients, they allow individualization of drug
126
management [16].
127
For
establishing
thyroid
cancer
PDXMs,
NOD/Shi-scid/IL-2Rγnull
and
128
NOD.Cg-PrkdcscidIl-2rgtm1Wjl/SzJ mice are more commonly used than nude mice [12].
129
Patient-derived thyroid tumor fragments are required for the establishment of PDXMs. After
130
resection of thyroid tumors is performed, tumors are divided into several fragments. Next, the
131
fragments are digested into single-cell suspensions and injected into immunodeficient mice [19]. In
132
these fragments, cell–cell interactions and some tissue architecture of the original thyroid cancer
133
remain; therefore, these models mimic the thyroid tumor microenvironment [20].
134
PDXMs allow the real-time investigation of novel anti-thyroid cancer drugs [21] (Figure 1). Up to
135
date, multiple tumor PDXMs have been established and tested in preclinical trials, yielding
136
promising results [22–24]; Yet none of thyroid cancer PDXM has been applied in clinical trials,
137
which should be regarded as a gateway, inspiring the translational studies. So far, only vemurafenib
138
[25], obatoclax [25], LOXO-292 [26], Sorafeinib [27], Lenvatinib [27], PLX51107 [28], PD0325901
139
[28], and cabozantinib [29] were tested in thyroid cancer PDXM-based preclinical trials. Although
140
no evidence showed therapeutic drugs effective in thyroid cancer PDXMs could be also effective in
141
corresponding patients; According to stably maintained pathology [30], and genome [31] of patient
142
tumors in thyroid cancer PDXMs, PDXM-based pre-clinical trials may still show meaningful
143
benefits for finding and testing potential anti-thyroid cancer drugs. Based on the individualized
144
molecular pathogenesis of thyroid cancer, the application of PDXM may precisely reflect biological
145
characteristics of the disease in patient individuals and firmly accelerate the clinical translation
146
process of anti-thyroid cancer therapeutics. One minor problem——despite PDXMs overcome the
147
lack of a tumor microenvironment in CDXMs, implantation is still accomplished through a wound.
148
GEMs
6
149
Differing from mouse models involving grafts, GEMs are established by genome alterations using
150
genetic engineering tools [32]. GEMs are increasingly utilized for thyroid cancer investigations, for
151
these models offer the possibility to generate gene mutations, amplifications, deletions, and
152
translocations, allowing researchers to turn on/off oncogenes and tumor suppressor genes [7].
153
To evade normal cellular control systems, GEMs target genes that can be altered by Cre-mediated
154
gene recombination [33]. The expression of oncogenes caused by mutations, amplifications,
155
deletions, and translocations can be temporally or spatially controlled with promoters specific for
156
thyroid tissue (e.g., promoters for thyroglobulin, thyroid peroxidase, or calcitonin) (Figure 2)
157
[33–35].
158
Two important factors may influence the choice for the use of thyroid cancer GEMs. First, GEMs,
159
which exhibit the characteristic development of a thyroid tumor, circumvent most problems
160
associated with grafted models and meet the need for exploring the interactions between tumor cells,
161
the tumor microenvironment, and the immune system [36]. Second, however, the microenvironment
162
and the immune system remain based on mouse DNA, RNA, and proteins, and the thyroid cancer
163
phenotype may be different from that of thyroid cancer patients [37].
164
Translation
165
By use of the above mouse models, various biological systems and events that are involved in
166
thyroid cancer dedifferentiation, proliferation, and metastasis have been described, providing
167
potential targets for anti-thyroid cancer targeted therapy (Figure 3). To lay a foundation for potential
168
bench-to-bedside translation of targeted therapy, thyroid cancer mouse models are of value to
169
understand the specific mechanisms how thyroid cancers derived pathologically. Identification of the
170
genetic and molecular alterations in thyroid cancer cells has advanced our knowledge about thyroid
171
cancer pathogenesis, which involves the RTK, RAS/RAF/MEK/ERK, PI3K/AKT/mTOR, SRC, and
172
JAK-STAT signaling pathways. Activation of RAS/RAF/MEK/ERK pathway are tend to transfer
173
follicular cells into PTC; Upregulated PI3K/AKT/mTOR changes the follicular cells into FTC; And 7
174
dysfunctional RTK pathway are prone to be observed in MTC, indicating that thyroid cancer patients
175
should be personally treated with specific anti-pathway drugs [38,39]. Necessarily, these potential
176
drugs should be tested in mouse models to evaluate the efficacy and safety of anti-thyroid cancer
177
therapeutics.
178
RTK Signaling Pathway
179
RTKs include 20 different surface receptor families, i.e., those of EGF, insulin, PDGF, VEGF, FGF,
180
CCK, NGF, HGF, Eph, AXL, TIE, RYK, DDR, RET, ROS, LTK, ROR, MuSK, LMR, and an
181
undetermined family, which act through growth factors, hormones, and other extracellular molecules
182
[40,41]. Mutations in RTKs are often found in thyroid cancers of patients. RET mutations are
183
commonly present in MTCs, including sporadic MTCs, familial MTCs, and multiple endocrine
184
neoplasia 2 (MEN2) syndromes [38,42]. RET/PTC, a rearranged form of RET, was the first genetic
185
alteration identified in papillary thyroid carcinoma (PTC) [43].
186
Thyroid cancer models with RET-PTC1 and RET-PTC3 gene rearrangements have been created by
187
using the Cre–loxP system. In GEMs, tumors with RET-PTC1 and RET-PTC3 develop into
188
non-invasive PTCs. RET/PTC rearrangements are early events in thyroid carcinogenesis and are
189
specific for dedifferentiation [44]. Based on the corresponding thyroid cancer mouse models, the
190
RTK signaling pathway has been identified as a controller of angiogenesis, proliferation, and
191
metastasis. In the process of thyroid cancer progression, RTKs, activated after ligand binding with
192
extracellular molecules, phosphorylate PI3K and MAPK, leading to activation of the
193
PI3K/AKT/mTOR and RAS/RAF/MEK/ERK pathways. The RTK signaling pathway and
194
subsequently the phenotypes of cell proliferation, dedifferentiation, survival, migration, and tumor
195
angiogenesis in thyroid tumors have been validated with both CDXMs and GEMs [43,45,46].
196
RTK inhibitors, including cabozantinib, gefitinib, imatinib, lenvatinib, motesanib, pazopanib,
197
sorafenib, sunitinib, and vandetanib, have been evaluated in preclinical studies and clinical trials,
198
which have yielded some promising results (Table 1). Some recently developed RTK inhibitors,
8
199
including anlotinib, apatinib, nintedanib, ponatinib, and regorafenib, which inhibit the
200
anti-angiogenesis-related targets of VEGFR, PDGFR, FGFR, KIT, and RET, have been tested in
201
mice and are being translated into clinical settings (Table 2). Other preclinically tested RTK
202
inhibitors, such as LIF, AEE788, CUDC-101, PD173074, tetraiodothyroacetic acid, CLM3,
203
withaferin A, Pulsatilla koreana extract, and crizotinib, have been tested in mouse models as
204
potential candidates for anti-thyroid cancer therapy (Table 3). Targeting RET and E2F1, LIF shows a
205
capacity for decreasing tumor burdens; tumor weights are reduced by 50–70% compared to the
206
control group, showing potential value for translation. AEE788 and CUDC-101, which inhibit EGFR,
207
show anti-proliferative activity in ATC/FTC-derived CDXMs [47–49]. Agents targeting RTKs have
208
been applied for redifferentiation of radioiodine-refractory DTCs. There is an inverse relationship
209
between PDGFRα activation and the transcriptional activity of thyroid transcription factor-1 (TTF1).
210
PDGFRα blockade promotes expression of sodium/iodide symporter (NIS), restoring iodine
211
transport in a thyroid cancer mouse model [50]. VEGF inhibitors include antibodies against VEGF
212
(bevacizumab) and VEGFR-2 (ramucirumab) and recombinant fusion protein against VEGF-A
213
(aflibercept). However, despite the positive results obtained with thyroid cancer CDXMs, for many
214
patients, VEGF-targeted therapy fails to show appreciable clinical benefit [27,51]. The observation
215
that sorafenib and lenvatinib are effective inhibitors of radioiodine-refractory PTCs in individual, but
216
not all, mice indicates that individualized therapeutics are of high value [27].
217
RAS/RAF/MEK/ERK Signaling Pathway
218
Components of the RAS/RAF/MEK/ERK signaling pathway are RAS and the downstream kinases
219
RAF, MEK, and ERK. RET, RAS, BRAF, and TERT mutations are mainly related to the Ras signaling
220
pathway [52]. The prevalence of RAS mutations is 18–55% in PDTCs, 45% in FTCs, 35% in
221
follicular variant-PTCs, and 4–60% in ATCs [53]. BRAFV600E is commonly detected in thyroid
222
cancers, especially in PTCs; the incidence of BRAFV600E varies from 29 to 70% in PTCs [38]. TERT
223
mutations usually accompany BRAFV600E mutations; most are found in aggressive, dedifferentiated
9
224
thyroid cancers, accounting for about 7–10% in PTCs [54].
225
K-RasG12D, N-RASQ61K, RET-PTC1, RET-PTC3, and BRAFV600E mice have been created using the
226
Cre–loxP system. In these models, H-RAS and K-RAS are responsible for hyperplasia [55,56]. In
227
GEMs, tumors with RET-PTC1, RET-PTC3, and BRAFV600E develop into non-invasive PTCs.
228
RET/PTC rearrangements are early events in thyroid carcinogenesis and are specific for
229
dedifferentiation [57]. In K-RASG12D, N-RASQ61K, and BRAFV600E GEMs, RAS mutations and
230
BRAFV600E lead to constitutive activation of RAS and BRAF, inducing thyroid cancer
231
dedifferentiation and proliferation [58]. In general, RAS mutations are associated with thyroid tumor
232
progression. However, thyroid carcinogenesis and dedifferentiation are not necessarily only driven
233
by RAS mutations; the combination of BRAF mutations and RAS mutations may be involved in
234
thyroid cancer formation and progression. The activated RAS/RAF/MEK/ERK signaling pathway
235
induces matrix degrading proteases, including MMP-1, -2, -3, and -9, and overexpression of
236
urokinase plasminogen activator (uPa). RAS (H-, K-, and N-RAS) and its downstream targets
237
promote migration of thyroid tumor cells [38,46]. Moreover, BRAFV600E-induced dedifferentiation is
238
associated with dysfunction of NIS. BRAFV600E causes poor radioiodine uptake in thyroid tumors and
239
may lead to therapeutic resistance to radioiodine therapy, which has been observed in thyroid cancer
240
GEMs exposed to radioiodine [59,60].
241
Some molecular therapeutics target the RAS/RAF/MEK/ERK signaling cascade. Inhibitors of the
242
RAS/RAF/MEK/ERK signaling pathway, including vemurafenib, selumetinib, dabrafenib, and
243
trametinib, have been investigated in thyroid cancer clinical trials, revealing promising results [9,61]
244
(Table 1). The allosteric MEK inhibitor CH5126766 is a candidate for clinical translation. In the
245
BRAFV600E GEM, CH5126766 induces a five-fold increase in NIS expression in thyroid cancer cells,
246
and reuptake radioiodine accumulation is twice as high as that observed after selumetinib treatment.
247
In addition, CH5126766 treatment prior to radioiodine delivery decreases thyroid tumor size
248
compared to radioiodine delivery without pretreatment [59].
10
249
PI3K/AKT/mTOR Signaling Pathway
250
The PI3K/AKT/mTOR signaling pathway is involved in thyroid cancer proliferation and in survival
251
and angiogenesis of tumor cells [62]. In thyroid cancer cells, PIK3CA mutations commonly exist in
252
FTCs, PDTCs, and ATCs [36].
253
In GEMs, activation of PIK3CA may lead to poor differentiation and rapid progression. In
254
addition, in FTC GEMs, amplification or mutation of PIK3CA, which encodes the catalytic subunit
255
of the PI3K converting PIP2 to PIP3, activates mTOR [38,63]. Higher mTOR levels lead to
256
downstream activation of S6K1 and 4E-BP1, which induce migration and invasion of thyroid tumor
257
cells. The matrix remodeling enzymes MMP-2/-9, uPa, and plasminogen activator inhibitor-1 (PAI-1)
258
are upregulated, resulting in invasiveness [64]. If mTOR is inhibited by stress signals, such as low
259
oxygen tension or low glucose in a low-pH extracellular setting, the beclin 1/PI3K complex is
260
activated, which enhances autophagy to overcome the hostile microenvironment and sustain tumor
261
proliferation [65]. In addition, with a transgenic mouse model of thyroid cancer, PI3K acts as a
262
negative controller of NIS, and PTEN as a positive regulator of TTF1 and NIS expression, balancing
263
differentiation and dedifferentiation [66].
264
The
PI3K/AKT/mTOR
pathway
contains
a
variety
of
therapeutic
targets.
Several
265
PI3K/AKT/mTOR pathway inhibitors, including everolimus, temsirolimus, torin2, CUDC-907,
266
PP121, GDC-0941, LY294002, IC87114, and AZD8055, have been tested preclinically and/or
267
clinically (Tables 1 and 3). Torin2, discovered through screening of compound libraries (3282 drugs),
268
suppresses thyroid tumor cell proliferation and, in CDXMs, inhibits thyroid tumor growth and
269
metastasis [67].
270
SRC Signaling Pathway
271
SRC family kinases (SFKs), which include SRC, FYN, YES, BLK, FGR, HCK, LCK, YRK, and
272
LYN, are non-receptor tyrosine kinases. Tyrosine kinase, G protein-coupled receptors, steroid
273
receptors, and Signal Transducers and Activators of Transcription (STAT) are activators of SFK,
11
274
which is involved in cell proliferation, motility, invasion, and angiogenesis [68].
275
Only a few studies have focused on the SRC pathway in thyroid cancer cells or in mouse models
276
of thyroid cancer. In CDXMs with a RET/PTC1 rearrangement, the SRC inhibitor dasatinib
277
suppresses tumor volume, suggesting that the SRC pathway is involved in regulating growth of
278
thyroid cancers [69]. In mice with ThrbPV/PVPten+/- thyroid cancer, SKI-606, an SRC/ABL dual
279
inhibitor, reduces tumor growth, invasion, and metastasis, effects that are related to inhibition of the
280
SRC pathway and the epithelial–mesenchymal transition [70]. These results suggest that the SRC
281
pathway influences the invasion and metastasis of thyroid cancer and that the activated SRC pathway
282
subsequently activates the MAPK, PI3K, FAK, and STAT pathways, promoting the progression of
283
thyroid cancers [69][69–72].
284
JAK-STAT Signaling Pathway
285
The JAK family includes JAK1, JAK2, JAK3, and TYK2, and the STAT family includes STAT1,
286
STAT2, STAT3, STAT4, STAT5a, STAT5b, and STAT6. JAK is activated by a combination of
287
cytokines or growth factors and their corresponding receptors, and activated JAK in turn activates
288
STAT, which can interact with DNA and regulate translation of various genes [73,74].
289
A high-fat diet induces the expression of STAT3, cyclin D1, and phosphorylated retinoblastoma
290
protein through the JAK2-STAT3 pathway, which leads to the development of ATCs in mice with
291
ThrbPV/PVPten+/- thyroid cancer. This suggests that, in these mice, STAT3 boosts the effects of a
292
high-fat diet by inducing development and progression of thyroid cancers [75]. Despite preclinical
293
results showing that the efficacies of cucurbitacin I, metformin, and S3I-201 are limited, the response
294
to the JAK1/2 inhibitor AZD1480 in PTC CDXMs is extensive (Table 3). These results indicate that
295
the JAK-STAT3 pathway may be involved in the development, progression, and metastasis of
296
thyroid tumors. However, other researchers have obtained opposite results, i.e., that STAT3 may be a
297
negative regulator of thyroid tumor growth, suggesting that the JAK-STAT3 pathway may inhibit
298
rather than promote thyroid cancer [76,77].
12
299
Other Signaling Pathways
300
Other signaling pathways may be related to the progression of thyroid tumors. The
301
RASSF1-MST1-FOXO3 pathway may be regulated by BRAFV600E gene mutations [78]; the activated
302
C-MET pathway may promote the growth, invasion, and metastasis of thyroid tumors [79,80]; and
303
the hedgehog pathway may be associated with the progression and metastasis of thyroid cancer
304
[38,81]. The corresponding therapeutics tested in preclinical settings are listed in Table 3.
305
Perspectives
306
Despite broad use of mouse models in thyroid cancer research, a certain amount of skepticism about
307
their relevance with human thyroid cancer and their value for clinical translation is presented in the
308
field of thyroid cancer research [11]. This is likely due to the differences between mice and humans,
309
but also to the fact that some clinical trials still fail despite the use of mouse models in the preceding
310
discovery research phases. According to Tables 1–4, the translational rate was only about 4–5%,
311
raising the awareness that thyroid cancer mouse models need to be further refined. Given the
312
increasing kinds of PDXMs and humanized PDXMs for thyroid cancer research, these best
313
approaches may facilitate in vivo drug tests and consequently accelerate the bench to bedside
314
translation. In the future, the ever-evolving establishment of thyroid cancer mouse models may solve
315
the significant issue that some therapeutic drug effective in mouse models but invalid in thyroid
316
cancer patients.
317
The dawn of a new century has witnessed not only the establishment of CDXMs, PDXMs, and
318
GEMs of thyroid cancer, but also the increasing applications of these models. To better translate
319
novel therapeutics into clinical settings, further advanced studies are essential in the areas of mouse
320
modeling, drug testing, target screening, and mechanism identification.
321
Mouse Modeling Based on Humanization
322
Humanized mice are now used as PDXMs in biological and medical research on thyroid cancer.
323
Severe combined immunodeficient mice that are highly deficient in T, B, and NK cells support the 13
324
engraftment of functional human immune cells [17,37]. These cells migrate into thyroid tumors and
325
replicate their natural microenvironment [37]. With the ongoing progress in immunology, scientists
326
are increasingly aware that the therapeutic effects of anti-cancer therapeutics are inseparable from
327
the role of the anti-tumor immune system. Anti-thyroid therapeutics applied systemically, especially
328
MEK inhibitors, CDK inhibitors, and PD1/PDL1 inhibitors, influence the immune system.
329
Consequently, the role and pharmacological mechanism of anti-apoptosis therapy, redifferentiation
330
therapy, and immunotherapy should be tested in the environment of a humanized immune system.
331
The humanized mouse model may lead to a revolution in mouse modeling. The newly established
332
mouse models may be “patient-like” and “humanized,” and may provide new, efficacious ways for
333
testing drugs for treatment of thyroid cancers.
334
Drug Testing Based on New Mouse Models
335
Dedifferentiated and highly aggressive thyroid cancers present challenges in the translation of
336
therapeutics into clinical applications. As shown in Table 1, most results of clinical trials match the
337
conclusions derived from preclinical studies, showing that the efficacy and safety of anti-thyroid
338
cancer drugs may be predicted from the data derived in preclinical studies. In clinical trials,
339
promising drugs, such as anlotinib, apatinib, nintedanib, ponatinib, regorafenib, and tipifarnib, may
340
be translated into clinical applications (Table 2). Future studies, many involving the use of thyroid
341
cancer mouse models, will continue to focus on “bench-to-bedside” translation. To date, numerous
342
drugs have been designed and tested in mouse models; promising drugs are listed in Table 3.
343
Additionally, drug combinations are of interest for the treatment of thyroid cancer; these are listed in
344
Table 4.
345
Target Screening Based on the CRISPR/Cas System
346
CRISPR is a method for gene editing derived from the bacterial anti-viral defense system. With the
347
intracellular delivery of Cas9 nuclease and synthetic guide RNA (sgRNA), genes can be cut at
348
desired positions [82]. The designed sgRNAs can be included in sgRNA libraries by
14
349
adeno-associated virus (AAV), which offers an alternative approach to screen anti-thyroid cancer
350
targets. For lung cancers and glioblastomas, AAV encoding sgRNA libraries are delivered to the
351
lungs and brains of mice expressing Cas9. As a result, the pathogenesis of cancers and anti-tumor
352
targets can be analyzed [83,84]. Target screens by delivery of sgRNA libraries can be performed with
353
thyroid tissue and could clarify the role of genes associated with thyroid cancer.
354
Mechanism Exploring Based on Multi-Omics
355
Omics methods involve high-throughput investigations of the genome, epigenome, transcriptome,
356
proteome, and metabolome. These methods are used to understand thyroid cancer pathogenesis, to
357
find the underlying molecular characteristics of thyroid tumors, and to reveal the phenotypes of
358
thyroid cancers [85]. With multi-omics analysis of thyroid cancer mouse models, the link between
359
genomic alterations and mRNAs, proteins, and metabolites in relation to thyroid cancer may be
360
revealed. Based on mouse models of thyroid cancer, the next phase of multi-omics analysis will
361
focus on dealing with thousands of mRNAs and proteins to reveal changes in the thyroid cancer
362
omics in a dynamic manner. For the biologic analysis of thyroid cancer, it will be highly desirable to
363
develop PDXMs that are useful in the development of drugs for precision therapy based on
364
personalized multi-omics data. Moreover, driven by such data, analysis of thyroid cancer GEMs is
365
likely to make large contributions to the research on thyroid cancer pathogenesis and to the
366
development of novel drugs [86].
367
Summary
368
Mouse models of thyroid cancer are of high importance for the development of therapeutic
369
interventions. In our opinion, CDXMs, PDXMs, and GEMs are all effective approaches for the
370
analysis of pathogenesis and for drug evaluations. CDXMs, as a flexible tool, have mostly been
371
utilized in thyroid cancer research, especially in tests of anti-thyroid cancer drugs. With CDXMs,
372
mechanisms of tumor differentiation, vascularization, proliferation, and metastasis have been
373
explored. GEMs have mainly been used for investigating effects of gene alterations, tumorigenesis, 15
374
confirming anti-thyroid cancer targets, and for investigating pharmacological mechanisms of
375
anti-thyroid cancer drugs. They have yielded specific targets and mechanism-based drugs for clinical
376
translation. Differing from CDXMs and GEMs, PDXMs are used in preclinical trials and for
377
screening drugs for individual patients. However, the development of PDXMs for thyroid cancer
378
investigations remains insufficient. Ideal thyroid cancer models will mimic thyroid cancer biology
379
and facilitate drug translation in cancer therapy. Therefore, advanced mouse models will be
380
necessary in cross-linking basic researches on thyroid cancer mechanisms and clinical studies on
381
potential anti-thyroid cancer therapeutics (Figure 4). The literature reveals that thyroid cancer mouse
382
models tend to be ‘‘humanized” and “patient-like.” They will offer opportunities to investigate
383
thyroid cancer mechanisms and to optimize anti-cancer drugs for “bench-to-bedside” translation.
384
Studies using thyroid cancer mouse models have provided information on the pathogenesis and
385
tumorigenesis of thyroid cancer and have been useful for both fundamental research and clinical
386
studies. The roles of signaling pathways, such as RAS/RAF/MEK/ERK and PI3K/AKT/mTOR, in
387
thyroid tumors have been analyzed using mouse models. In thyroid cancer GEMs, several gene
388
alterations, including RET/PTC rearrangements and the BRAFV600E mutation, have proved to be
389
oncogenic, validating the relevance of MAPK signaling pathways in PTC. In addition, RAS
390
mutations are involved in the development of PTCs and FTCs. Mice with overactivation of the
391
PI3K/AKT/mTOR signaling pathway develop FTCs. Other signaling pathways, including the SRC,
392
JAK-STAT, RASSF1-MST1-FOXO3, C-MET, and sonic hedgehog pathways, are studied as
393
potential targets for anti-thyroid cancer drugs. These studies have revealed mechanisms of thyroid
394
tumorigenesis and progression and have led to the development, testing, and translation of
395
anti-thyroid cancer drugs into clinical settings. Mouse models have confirmed the pathogenesis of
396
thyroid cancer and have led to the availability of new drugs for the treatment of thyroid cancer.
397
Consistent with the results on tumor pathogenesis, drugs such as MLN8054 (aurora kinase inhibitor),
398
radicicol (HSP90 inhibitor), HNHA (HDAC inhibitor), and PD-L1 antibody have been tested in
16
399
mouse models.
400
There remains a need for new screening methods to evaluate potential thyroid cancer therapeutics.
401
The development of mouse models, the understanding of the pathogenesis of thyroid cancer, and the
402
discovery of anti-thyroid cancer therapeutics will help in establishing individualized therapy. The
403
next step will be the discovery of drug response-related biomarkers, which will aid in the prediction
404
of the results of anti-tumor therapy and help in the management of thyroid cancer.
405
Abbreviations
406
See Table 5.
407
Acknowledgments
408
This study was supported by the National Natural Science Foundation of China (Grant Nos.
409
81671711 and 81701731) and the Shanghai Key Discipline of Medical Imaging (Grant No.
410
2017ZZ02005). We thank LetPub (www.letpub.com) for their linguistic assistance during the
411
preparation of this manuscript.
412
Conflict of interest
413
The authors have no conflicts of interest to declare.
17
414 415
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56
1382
Legends
1383
Figure 1. Establishment and testing of drugs in PDXMs. Tumor fragments were obtained from
1384
thyroid cancer patients. The clinical-histopathologic parameters and genomic profiling by the
1385
next-generation sequencing of these specimens were analyzed for drug candidates. Non-necrotic
1386
areas of these tumors were incubated as organoids and implanted subcutaneously into
1387
immunodeficient mice. Genome and pathology of thyroid tumor cells from patients remain stable in
1388
PDXMs and patient-derived organoid. After tumor establishment and growth, biological assays of
1389
drug efficacy and biomarker prediction for targeted therapies were conducted. If the drugs showed
1390
efficacy in PDXMs, they could be developed for patients. PDXM, patient-derived xenograft model.
1391 1392
Figure 2. Examples of constructed GEMs with a thyroid-specific promoter and Cre recombinase.
1393
The oncogene could be placed downstream of a stop signal gene that is inserted between two loxP
1394
sites. The stop cassette prevents oncogene expression, and thyroid-specific promoter activates Cre
1395
recombinase targets the stop cassette to allow oncogene gene expression only in the thyroid.
1396
Similarly, under control of a thyroid-specific promoter, the Cre recombinase also targets the stop
1397
cassette, resulting in knock-out of tumor suppressor genes in the thyroid. Consequently, the
1398
recombinated gene alterations facilitate the carcinogenesis in thyroid. KI, knock in; KO, knock out;
1399
PTC, papillary thyroid cancer; FTC, follicular thyroid cancer; ATC, anaplastic thyroid cancer. Red
1400
arrow-head, loxp position; Green arrow, gene expression.
1401 1402
Figure 3. Primary signaling pathways in thyroid cancer and molecular targeted agents tested in
1403
mouse models. Ligand-mediated activation of RTK results in activation of the PI3K/AKT/mTOR
1404
and RAS/RAF/MAPK/ERK pathways [38,87]. This leads to initiation of tumor dedifferentiation,
1405
migration, survival, proliferation, and angiogenesis. Moreover, molecular inhibitors and their targets
1406
of action are shown. PI3K, phosphatidylinositol 3-kinase; PIP2, phosphatidylinositol 4,5-biphosphate; 57
1407
PIP3, phosphatidylinositol 3,4,5-triphosphate; PTEN, phosphatase and tensin homologue; PDK-1,
1408
3-Phosphoinositide Dependent Protein Kinase 1; AKT, V-Akt Murine Thymoma Viral Oncogene
1409
Homolog 1; STAT3, Signal Transducer and Activator of Transcription 3; MSK, mitogen- and
1410
stress-activated protein kinase; mTOR, mammalian target of rapamycin; S6K, Ribosomal Protein S6
1411
Kinase I; CDK, Cyclin Dependent Kinase; RAS, rat sarcoma viral oncogene homologue; NF1,
1412
neurofribomin 1; ARAF, V-Raf Murine Sarcoma 3611 Viral Oncogene Homolog 1; BRAF, V-Raf
1413
Murine Sarcoma Viral Oncogene Homolog B; CRAF, V-Raf-1 Murine Leukemia Viral Oncogene
1414
Homolog 1; MEK, mitogen-activated protein kinase; ERK, extracellular signal-regulated kinase;
1415
Vinexin, Sorbin and SH3 Domain Containing 3; ETS1, ETS Proto-Oncogene 1; MMP2, Matrix
1416
Metallopeptidase 2.
1417
1418
Figure 4. An example of mouse models bridging bench and bedside via translational research of
1419
thyroid cancer treatment. During the process of Sorafenib translation, the drug was initially tested in
1420
mouse model trials, and the preclinical results were well interpreted on basic aspect and in the field
1421
of potential clinical application. The interpretation on pharmacology, pharmacokinetics, and
1422
pharmacodynamics of Sorafenib built the footstone for clinical trials and the probability for potential
1423
translation. After phase II and III trials, the efficacy and safety of Sorafenib was confirmed and then
1424
the drug was approved for clinical application. In the stage of post-market, amelioration around
1425
Sorafenib have been still simultaneously researching in thyroid cancer mouse models and real-world
1426
clinical settings. As the results, drug resistant mechanisms and drug combinations were obtained,
1427
inspiring the future translational studies. DTC, differentiated thyroid cancer; ATC, anaplastic thyroid
1428
cancer; RRDTC, radioiodine refractory thyroid cancer. CDXMs, cell line-derived xenograft models;
1429
PDXM, patient-derived xenograft model.
58
Table 1. Outcomes of thyroid cancer mouse model-based preclinical studies on agents with completed clinical trials. Drug Targets Pre-clinical study Clinical trial Ref. Tumor Mice Survival Tumor Body Arm, phase Tumor Type Burden* Weight (Reference) Type Cabozantinib VEGFR, [42] MTC CDXM NR ↓50 – 80% Stable RCT, III [88] MTC PDGFR, RET PDXM NR ↓55 – 60% NR Everolimus
mTOR
[65,89,90]
DTC
GEM
Gefitinib Imatinib
EGFR [92,93] ABL, c-kit, [94,95] PDGF-R VEGFR, [27,97,98] FGFR, c-Kit, RET, PDGFR VEGFR, RET, [100] PDGFR, c-Kit VEGFR, [102,103] PDGFR, c-Kit
ATC ATC ATC
Selumetinib
MEK1
Sorafenib
VEGFR, [30,108–115] PDGFR, c-Kit, RET, RAF VEGFR, [103,117–121] PDGFR, c-Kit, RET, FLT3 VEGFR, [30,32,47,124–1 EGFR, RET 30]
Lenvatinib
Motesanib Pazopanib
Sunitinib
Vandetanib
Vemurafenib Pan-Raf
Depsipeptide HDAC1, 2
[59]
↑ 28%
Enrolled Median Median CR,% PR,% n OS, mo PFS, mo 330 26.6 11.2 ORR: 28%
SD,%
PD,%
Main adverse events (Grade ≥ 3)
NR
NR
Diarrhea (21.5%), HFSR (12.6%), hypocalcemia (10.7%), decreased weight (9.8%), fatigue (9.8%), hypertension (8.9%), asthenia (6.5%) Mucositis (15%), diarrhea (10%), neutropenia (5%), hypertriglyceridemia (5%) Anorexia (11%), diarrhea (4%), rash (7%) Lymphopenia (45.5%), edema (25%), anemia (18.2%), fatigue (12.5%), hyponatremia (12.5%) Hypertension (10-41.8%), diarrhea (8-10%), fatigue or asthenia (9.2%), decreased weight (9.6-12%), proteinuria (10%) Hypertension (25%), diarrhea (13%), weight loss (5%), abdominal pain (5%) Hypertension (13%), pharyngo-laryngeal pain (13%), raised ALT concentration (10.8%), lower-gastrointestinal haemorrhage (8.1%) Rash (18%), fatigue (8%), diarrhea (5%), peripheral edema (5%) HFSR (16-44.1%), weight loss (8.4%-11.5%), hypertension (9.7-16.1%), rash (16.1%)
↓50 – 75% Stable
Single, II [91] RRDTC 28
NR
11.8
0
10
66
18
CDXM NR CDXM NR
↓30–>90% NR ↓12 – 25% Stable
Single, II [81] RRDTC 27 Single, II [96] ATC 11
27.4 NR
3.9 NR
0 0
12 25
NR 50
38 12.5
CDXM NR PDXM NR
↓60–>90% Stable ↓50 – 60% ↓ 5–10%
RCT, III [99]
NE
18.3
1.5
63.2
23
6.9
MTC CDXM ↑ 33%
↓30 – 50% Stable
Single, II [101] RRDTC 93
NE
9.2
0
14
67
8
DTC ATC
GEM ↑ 21% CDXM NR
↓ 20% ↓ 5% ↓25 – 50% Stable
ATC
CDXM NR
↓30 – 70% NR
Single, II [104] Single, II [105] Single, II [106] Single, II [107]
NE 3.7 19.9 NR
11.7 2.1 9.4 7.4
0 NE 0 0
49 NE 14.3 3
NR NE 57.1 54
NR NE 17.1 28
ATC
CDXM ↑ 18% GEM NR PDXM NR
↓20 – 30% Stable ↓ 66% NR ↓50 – 60% ↓ 5–10%
RCT, III [116] RRDTC 417
NE
10.8
NR
12.2
41.8
NR
↓ 10% ↓ 10% ↓10 – 50% Stable
Single, II [122] RRDTC 35 Single, II [123] RRDTC 23
NE 56.5
NR 8
1 0
28 26
46 57
17 4
Neutropenia (34%), leukopenia (13-31%), diarrhea (17%), HFSR (17%), hypertension (13%), fatigue (11%)
↓50 – 80% ↓ 5% ↓ 5-30% Stable ↓50 – >80% ↓ 17% ↓30 – 50% NR No effect NR ↓50% NR ↓80 – 90% NR ↓25 – 30% NR
RCT, II [131] RRDTC 145 RCT, III [132] MTC 331
NR NR
11.1 19.3
NR 0
1 45
54 42
44 13
Diarrhea (10%), asthenia (7%), fatigue (5%).
DTC GEM ↑ 33% MTC CDXM NR
DTC ATC MTC [15,25,133–138] PTC ATC ATC PTC [139–141] MTC
PDXM ↑ 15% CDXM NR CDXM NR GEMs NR GEMs ↑ 75% CDXM NR PDXM NR GEM NR
RRDTC 382
RRDTC ATC MTC RRDTC
39 15 35 39
Single, II [102] RRDTC 51 (C1: NE 26, C2: (C1) 25) NE (C2) Single, II [142] RRDTC 20 33.2
15.1 (C1) 0 (C1) 38.5 (C1) 57.7 (C1) 3.8 (C1) Weight loss (5%), abdominal pain (5%) 8.9 (C2) 0 (C2) 27.3 (C2) 63.6 (C2) 4.5 (C2)
NR
0
0
65
35
Thrombosis/thrombus/embolism (5%), fatigue (5%), dysphagia (5%), dyspnea (5%), lymphopenia (5%), prolonged QTc interval (5%) *Tumor burden was evaluated according to the tumor weight/volume/activity data at day 10 and/or day 20 post drug delivery; NR, not reported; NE, not estimable; CI, confidence interval; CR, complete response; OS, overall survival; PD, progressive disease; PFS, progression-free survival; PR, partial response; DTC, differentiated thyroid cancer; PTC, papillary thyroid cancer; ATC, anaplastic thyroid cancer; MTC, medullary thyroid cancer; RRDTC, radioiodine refractory thyroid cancer; CDXM, cell line-derived xenograft model; PDXM, patient-derived xenograft model; GEM, genetically engineered model; RCT, randomized controlled trial; RECIST, response evaluation criteria in solid tumors; SD, stable disease; FGFR, fibroblast growth factor receptor; FLT3, Fms-like tyrosine kinase 3; MEK1, MAPK/ERK kinase 1; mTOR, mammalian target of rapamycin; PDGFR, platelet-derived growth factor receptor; RAF, rapidly accelerating fibrosarcoma; RET, glial cell line-derived neurotrophic factor receptor; VEGFR, vascular endothelial growth factor receptor; c-kit, stem cell factor receptor; EGFR, epidermal growth factor receptor; HFSR, hand-foot-skin reaction; HDAC, histone deacetylase. C1: Cohort 1, patients never received multikinase inhibitors; C2: Cohort 2 patients previously received multikinase inhibitors.
Table 2. Outcomes of thyroid cancer mouse model-based preclinical studies on agents with ongoing clinical trials. Drug
Action
Pre-clinical study Ref.
Tumor
Mice
Survival
Apatinib
Nintedanib
VEGFR2
VEGFR2
RTK
[143]
[144]
[145]
Phase
Tumor Type
Status
Body *
Type Anlotinib
Tumor
Clinical Trial No. Burden
Weight
ATC
CDXM
NR
↓ 60 – >90%
Stable
NCT02586337
II/ III
DTC
Recruiting
MTC
CDXM
NR
↓ 50 – 60%
Stable
NCT02586350
II/ III
MTC
Recruiting
ATC
CDXM
NR
↓ 10 – 60%
NR
NCT03199677
II
TC
Recruiting
NCT03167385
II
DTC
Recruiting
NCT03048877
III
DTC
Recruiting
NCT03300765
II
TC
Recruiting
NCT01788982
II
MTC/DTC
Active,
MTC
GEM
NR
↓ 50 – 60%
NR
not
recruiting Ponatinib
RTK
[146]
MTC
CDXM
NR
↓ > 90%
↓ < 10%
NCT03838692
II
MTC
Not recruiting
Regorafenib
RTK
[147]
ATC
GEM
NR
↓ 65%
NR
NCT02657551
II
TC
Recruiting
Tipifarnib
FTase
[46]
DTC
GEM
↑ 200%
↓ 50%
↓ < 10%
NCT02383927
II
TC
Recruiting
*Tumor burden was evaluated according to the tumor weight/volume/activity data on day 10 and/or day 20 post drug delivery; NR, not reported; VEGFR2, vascular endothelial growth factor receptor 2; RTK, receptor tyrosine kinase; Ftase, farnesyltransferase; DTC, differentiated thyroid cancer; PTC, papillary thyroid cancer; ATC, anaplastic thyroid cancer; MTC, medullary thyroid cancer; TC, thyroid cancer; CDXM, cell line-derived xenograft model; GEM, genetically engineered model.
yet
Table 3. Thyroid cancer mouse model-based preclinical studies on single-drug therapeutics with potential translation into clinical trials. No. Drug Target Tumor Mice Survival Tumor Body Weight No. Drug Target Type Burden* Chemotherapy NF-κB signaling pathway inhibitors 1 Docetaxel [94,148] Microtubule assembly ATC CDXM NR ↓ 30 – 50% Stable 1 DHMEQ [149,150] NF-κB 2 Cisplatin [121] DNA crosslinking ATC CDXM NR ↓ 20 – 50% Stable 2 Triptolide [151,152] NF-κB 3 TSH-SiO2-Doxorubicin [153] DNA topoisomerase II FTC CDXM NR ↓ 50 – 75% Stable JAK/STAT signaling pathway inhibitors 4 5FU [154] DNA synthesis MTC CDXM NR ↓ 20 – 40% NR 1 Cucurbitacin I [155] STAT3 RTK signaling pathway inhibitors 2 Metformin [156–158] STAT3
Mice
Survival Tumor Burden*
ATC ATC
CDXM NR CDXM NR
↓70–80% ↓ 50%
Stable NR
ATC FTC ATC FTC PTC
CDXM GEM CDXM GEM CDXM
↓5– 30% No effect ↓25-50% ↓5– 10% ↓ > 80%
NR Stable NR NR NR
ATC PTC
CDXM NR CDXM NR PDXM NR
↓ 5-30% ↓50–60% NR
Stable Stable NR
FTC PTC ATC
GEM ↑ 40% CDXM NR CDXM NR
↓ 30% ↓30–40% ↓20–30%
Stable NR NR
↑ 25-35% NE NR ↑ 20% NR
Body Weight
1 2 3 4 5
LIF [127,159] AEE788 [48,49,161] CUDC-101 [163,164] PD173074 [165,166] Tetraiodothyroacetic Acid [168]
RET, E2F1 EGFR/VEGFR EGFR and HDAC FGFR VEGF
MTC ATC/FTC ATC DTC/ATC ATC
CDXM CDXM CDXM CDXM CDXM
↓ 50 – 70% ↓ 50 – 80% ↓ 50 – 60% ↓ 50% ↓ 50 – 70%
↓ 9% Stable Stable Stable NR
3 S3I-201 [160] STAT3 4 AZD1480 [162] JAK1/2 Apoptosis signaling pathway inhibitors 1 Reversine [167] Apoptosis pathway 2 Obatoclax [169–171] Bcl-2
6 8 9
CLM3 [172,173] Withaferin A [174,175] Pulsatilla koreana extract [176]
Angiogenesis RET Angiogenesis
ATC MTC ATC
CDXM NR ↓ 40 – 50% CDXM ↑ > 30% ↓ 50 – 60% CDXM NR ↓ 50 – 60%
Stable ↑ 10% Stable
SRC signaling pathway inhibitors 1 SKI-606 [70] SRC 2 Dasatinib [69,71,72] SRC
c-MET
ATC
CDXM NR
↓ 5 – 20%
NR
BRAF MEK RAF MEK MEK MEK
ATC ATC PTC ATC ATC PTC
↑ 40% NR NR ↑ 80% NR ↑ 50% NR NR NR NR
↓ 50 – 70% ↓ 50 – 70% ↓ 50 – 85% ↓ 95% ↓ 25% ↓ 60 – 90% ↓ 80 – 90% ↓ 50% ↓ 50% ↓ 10 – 30%
↑ 50% NR Stable NR Stable NR NR NR Stable NR
Proteosome PPARγ COX-2 P2R Carcinoembryonic antigen Aurora kinase PLK1
ATC DTC MTC PTC MTC ATC ATC
CDXM GEM CDXM CDXM CDXM CDXM CDXM
↑ 25% NR NR NR NR NR NR
↓20–30% ↓50–>90% ↓ 50% ↓50–80% ↓50–80% ↓50 –80% ↓60–70%
Stable NR NR Stable ↓ < 8% Stable ↓ 7%
ATC ATC
CDXM CDXM CDXM CDXM CDXM CDXM PDXM GEM CDXM CDXM
Other drugs 1 Carfilzomib [163,179] 2 Rosiglitazone [190,191] 3 Celecoxib [193,194] 4 Clodronate [195,196] 5 I-131-F6 [197] 6 MLN8054 [30,198,199] 7 GSK461364A [201]
PTC ATC FTC ATC FTC FTC PTC PTC FTC ATC ATC
CDXM CDXM CDXM CDXM CDXM CDXM CDXM CDXM CDXM CDXM CDXM
NR ↑ 16% NR NR NR NR NR NR NR NR NR
↓ 30 – 50% ↓ 50% ↓ 50 – 70% ↓ 25 – 60% ↓ 30 – 40% ↓ 60% ↓ 20% ↓ 50% ↓ 80 – 85% ↓ 30% ↓ 30 – 70%
NR Stable Stable Stable Stable NR NR NR NR NR NR
8 9 10 11
Oct-CL [203] Thiostrepton [205] 17-AAG [206] PLX6134 [208]
p70S6K and TOPO 1 FoxM1 HIF-1a and HSP90 c-FMS/CSF1-R kinase
MTC PTC PTC PTC
CDXM CDXM CDXM GEM
↑ 50% NR NR NR
↓ > 70% ↓50–75% ↓80% ↓15%
Stable NR NR NR
12 13 14 15
Tolfenamic acid [210] Reparixin [148] Dinaciclib [214] Quinacrine [110]
NAG-1 CXCR1 and CXCR2 CDK Phospholipase A2
ATC ATC ATC ATC
CDXM CDXM CDXM CDXM
NR NR NR ↑ 45%
↓50-60% ↓60–70% ↓30–50% NE
NR Stable ↓ 7-8.1% NR
16 cD-tboc [218] 17 Ganetespib [221]
Estrogen pathway HSP90
18 YM155 [223,224] 19 AG-IR820 [225] 20 Lexatumumab [182]
Survivin and claspin Glucose metabolism TRAIL-R2
ATC ATC MTC ATC MTC PTC
CDXM CDXM CDXM CDXM CDXM CDXM
NR NR NR ↑ 119.0% NR NR
↓50-60% ↓ 60% ↓50–60% ↓ > 90% ↓60–70% ↓ 70%
Stable ↓ 4.5% ↓ 9.5% Stable Stable Stable
10 Crizotinib [177,178] MAPK signaling pathway inhibitors 1 PLX4720 [180–189] 2 Trametinib [192] 3 LY3009120 [169] 4 PA-L1/LF [115] 5 SL327 [117] 6 PD0325901 [28,181,200]
7 Colchicine [202] MEK1/2 and JNK 8 Salirasib [204] RAS mTOR signaling pathway inhibitors 1 Torin2 [65,67,207] mTOR 2 3 4 5
CUDC-907 [164,209] PP121 [211] GDC-0941 [212,213] LY294002 [182,215,216]
PI3K and HDAC PI3K/Akt PI3K, mTOR and HIF-1α PI3K
7 8
IC87114 [217] Temsirolimus [219,220]
PI3K mTOR
9 AZD8055 [222] mTOR HDAC signaling pathway inhibitors 1 HNHA [226,227] HDAC
NR NR NR NR NR
Tumor Type
PTC CDXM ↑ 175% ↓ 50% Stable ATC CDXM ↑ 186% ↓ 50% Stable 2 PXD101 [228] HDAC ATC CDXM NR ↓ 30 – 50% Stable 21 PF-271 [229] FAK PTC CDXM NR ↓ 50% NR 3 SAHA [230] HDAC FTC GEM NR ↑ 61.8% NR 22 JQ1 [157,231,232] BET FTC GEM ↑ 50% ↓ 60% Stable PTC CDXM ↑ 50% ↓ 30% Stable ATC CDXM ↑ 115% ↓ 30% Stable 4 Panobinostat [233] HDAC ATC CDXM NR ↓ 50 – 60% ↓5–13% *Tumor burden was evaluated according to the tumor weight/volume/activity data on day 10 and/or day 20 post drug delivery; NR, not reported; NE, not estimable; PTC, papillary thyroid cancer; ATC, anaplastic thyroid cancer; MTC, medullary thyroid cancer; CDXM, cell line-derived xenograft model; PDXM, patient-derived xenograft model; GEM, genetically engineered model; SD, stable disease; MEK, MAPK/ERK kinase; mTOR, mammalian target of rapamycin; RAF, rapidly accelerating fibrosarcoma; RET, glial cell line-derived neurotrophic factor receptor; VEGFR, vascular endothelial growth factor receptor; c-kit, stem cell factor receptor; EGFR, epidermal growth factor receptor; HDAC, histone deacetylase; NF-κB, nuclear factor kappa B; STAT3, Signal Transducer And Activator Of Transcription 3; JAK1/2, Janus Kinase 1/2; Bcl-2, B-Cell CLL/Lymphoma 2; SRC, V-Src Avian Sarcoma (Schmidt-Ruppin A-2) Viral Oncogene Homolog; PPARγ, Peroxisome proliferator-activated receptor gamma; COX-2, cyclooxygenase-2; P2R, P2 receptor; PLK1, Polo Like Kinase 1; p70S6K, 70 KDa Ribosomal Protein S6 Kinase 1; TOPO 1, Topoisomerase I; FoxM1, Forkhead Box M1; HIF-1a, Hypoxia Inducible Factor 1 Subunit Alpha; c-FMS/CSF1-R, Colony Stimulating Factor 1 Receptor; NAG-1, Non-Steroidal Anti-Inflammatory Drug-Activated Gene-1; CXCR1, C-X-C Motif Chemokine Receptor 1; CXCR2, C-X-C Motif Chemokine Receptor 2; CDK, Cyclin Dependent Kinase; HSP90, Heat Shock Protein 90; TRAIL-R2, TNF-Related Apoptosis-Inducing Ligand Receptor 2; FAK, Focal Adhesion Kinase; BET, bromodomain and extra terminal; SAHA, Suberoylanilide Hydroxamic Acid; DHMEQ, Dehydroxymethylepoxyquinomicin.
Table 4. Thyroid cancer mouse model-based preclinical studies on combined therapeutics with potential translation into clinical trials. No Drug Target Tumor Type Mice Survival Tumor Burden* Body Weight . 1 Imatinib and docetaxel [94] RTK, microtubule assembly ATC CDXM NR ↓ 60-70% Stable 2 MV-NIS and I-131 [234] NIS ATC CDXM NR ↓ 50-60% NR 3 Bay 11-7082 and I-131 [235] NF-κB and isotope therapy PTC CDXM NR ↓ 40-50% NR 4 Dasatinib and Trametinib [236] SRC and MEK ATC CDXM NR ↓ 50-70% NR 5 TRAIL and Bortezomib [237] ROS PTC CDXM NR ↓ 30-50% NR 6 TRAIL and Valproic Acid [138] ROS, HDAC PTC CDXM ↑ 45% ↓ > 90% NR 7 Vemurafenib and Bortezomib [137] BRAF and proteasome PTC CDXM NR ↓ 70% NR 8 Ribavirin and Paclitaxel [238] eIF4E-β-catenin axis ATC CDXM NR ↓ 70% Stable 9 Sorafenib and quinacrine [110] RTK and phospholipase A2 ATC CDXM ↑ 243.2% NE ↓ 15% 10 Embelin and LY294002 [216] PI3K, XIAP PTC CDXM NR ↓ 30-60% NR 11 SL327 and Sunitinib [117] MEK, RTK ATC CDXM NR ↓ 50% Stable 12 2ME2 and Cabozantinib [29] HIF, VEGFR MTC CDXM NR ↓ 60-80% Stable PDXM NR ↓ 60-80% Stable 13 PLX4032 and PHA665752 [80] c-MET ATC CDXM NR ↓ 80% Stable 14 RDEA119 and Temsirolimus [229] MEK and mTOR FTC CDXM NR ↓ 90% NR 15 Sorafenib and CQ [204] RTK and autophagy ATC CDXM NR ↓ 50-80% NR FTC CDXM NR ↓ 60-80% NR 16 PD-325901 and GDC-0941 [212] MEK and Pi3K PTC GEM NR ↓ 30-50% NR 17 10F.9G2 and PLX4720 [239] PDL-1 and BRAF ATC CDXM NR ↓ 80% NR 18 AZD8055 and AZD6244 [222] mTOR MEK ATC CDXM NR ↓ 60-90% NR 19 dl922-947 and Olaparib [240] PARP/pRb ATC CDXM NR ↓ 40-60% Stable 20 HCQ and Vemurafenib [133] Autophagy, BRAF ATC CDXM NR ↓ 60% Stable 21 Neoadjuvant therapy with PLX4720 [241] BRAF ATC CDXM ↑ > 40% ↓ > 90% ↓ 5-30% *Tumor burden was evaluated according to the tumor weight/volume/activity data on day 10 and/or day 20 post drug delivery; MV-NIS, measles virus encoding the human thyroidal sodium iodide symporter; TRAIL, tumor necrosis factor–related apoptosis-inducing ligand; 2ME2, 2-methoxyestradiol; CQ, chloroquine; HCQ, hydroxychloroquine; RTK, receptor tyrosine kinase; NIS, Na+/I− Symporter; NF-κB, nuclear factor kappa-light-chain-enhancer of activated B cells; SRC, Rous sarcoma oncogene; MEK, MAP kinase-ERK kinase; ROS, transcriptional regulator; HDAC, histone deacetylase; BRAF, v-Raf murine sarcoma viral oncogene homolog B; mTOR, mammalian target of rapamycin; PI3K, Phosphoinositide 3-kinases; XIAP, X-linked inhibitor of apoptosis protein; HIF, hypoxia inducible factor; VEGFR, vascular endothelial growth factor receptor; EGFR, epidermal growth factor receptor, PDL-1, programmed death-ligand 1; PRPP, poly (ADP-ribose) polymerase; ATC, anaplastic thyroid cancer; PTC, papillary thyroid cancer; MTC, medullary thyroid cancer; FTC, follicular thyroid cancer; CDXM, cell line-derived xenograft model; GEM, genetically engineered model; NR, not reported; NE, not estimable.
Table 5. List of abbreviations. Abbreviation
Full Name
ABL
Abelson Tyrosine-Protein Kinase 1
AKT
Rac-Alpha Serine/Threonine-Protein Kinase
ATC
Anaplastic Thyroid Cancer
AXL
Tyrosine-Protein Kinase Receptor UFO
BRAF
V-Raf Murine Sarcoma Viral Oncogene Homolog B
c-kit
Stem Cell Factor Receptor
CCK
Cholecystokinin
CCND1
Cyclin D1
CDK
Cyclin Dependent Kinase
CDXM
Cell Line-Derived Xenograft Model
CI
Confidence Interval
C-MET
Hepatocyte Growth Factor Receptor
CR
Complete Response
Cre
Cyclization Recombination Enzyme
loxP
Locus of X-Over P1 Bacteriophage; The Recognition Sites for Cre Recombinase
DDR
Discoidin Domain Receptor Tyrosine Kinase
DNA
Deoxyribonucleic Acid
DTC
Differentiated Thyroid Cancer
EGF
Epidermal Growth Factor
EGFR
Epidermal Growth Factor Receptor
Eph
Ephrin
ERK
Mitogen-Activated Protein Kinase 1
FAK
Protein Tyrosine Kinase 2
FGF
Fibroblast Growth Factor
FGFR
Fibroblast Growth Factor Receptor
FLT3
Fms-Like Tyrosine Kinase 3
FOXO3
Forkhead Box O3
GEM
Genetically Engineered Model
HDAC
Histone Deacetylase
HGF
Hepatocyte Growth Factor
HNHA
High Mobility Group Box 1
HSP90
Heat Shock Protein 90 Alpha Family Class A Member 1
IL-2Rγ
Interleukin 2 Receptor Subunit Gamma
JAK
Janus Kinase
KIT
Tyrosine-Protein Kinase Kit
LMR
Lemur Tyrosine Kinase
LTK
Leukocyte Receptor Tyrosine Kinase
MEK
Mitogen-Activated Protein Kinase Kinase 1
MEN2
Multiple Endocrine Neoplasia 2
MMP
Matrix Metallopeptidase
mRNA
Messenger RNA
MST1
Macrophage Stimulating 1
MTC
Medullary Thyroid Cancer
mTOR
Mechanistic Target of Rapamycin Kinase
MuSK
Muscle Associated Receptor Tyrosine Kinase
NE
Not Estimable
NGF
Nerve Growth Factor
NIS
Sodium/Iodide Symporter
NOD
Non Obese Diabetic Mouse Model
NR
Not Reported
ORR
Overall Response Rate
OS
Overall Survival
PAI-1
Plasminogen Activator Inhibitor-1
PD
Progressive Disease
PD1
Programmed Cell Death 1
PDGF
Platelet-Derived Growth Factor
PDGFR
Platelet Derived Growth Factor Receptor
PDGFRα
Platelet Derived Growth Factor Receptor Alpha
PDL1
Programmed Cell Death 1 Ligand 1
PDTC
Poorly Differentiated Thyroid Cancer
PDXM
Patient-Derived Xenograft Model
PFS
Progression-Free Survival
pH
Potential of Hydrogen
PI3K
Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Delta
PIK3CA
Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Alpha
PIP2
Phosphatidylinositol 4,5-Biphosphate
PIP3
Phosphatidylinositol 3,4,5-Trisphosphate
PR
Partial Response
PTC
Papillary Thyroid Carcinoma
PTC1
Protein Patched Homolog 1
PTC3
Protein Patched Homolog 3
Pten
Phosphatase and Tensin Homolog
QTc
Corrected QT Interval
RAF
Murine Leukemia Viral Oncogene Homolog
RAS
Rat Sarcoma Viral Oncogene Homolog
RASSF1
Ras Association Domain Family Member 1
RCT
Randomized Controlled Trial
RECIST
Response Evaluation Criteria In Solid Tumors
RET
Glial Cell Line-Derived Neurotrophic Factor Receptor;
RNA
Ribonucleic Acid
ROR
Rar Related Orphan Receptor
ROS
Transmembrane Tyrosine-Specific Protein Kinase
RRDTC
Radioiodine Refractory Thyroid Cancer
RTK
Receptor Tyrosine Kinase
RYK
Receptor Like Tyrosine Kinase
S6K1
Ribosomal Protein S6 Kinase B1
scid
The Severe Combined Immunodeficiency
SD
Stable Disease
SEER
Surveillance and Epidemiology and End Results
SFK
Src Family Kinases
sgRNA
Single Guide RNA
SRC
V-Src Avian Sarcoma (Schmidt-Ruppin A-2) Viral Oncogene Homolog
STAT
Signal Transducers and Activators of Transcription
TERT
Telomerase Reverse Transcriptase
TIE
Tyrosine Kinase With Immunoglobulin Like and EGF Like Domains
TTF1
Thyroid Transcription Factor-1
TYK2
Tyrosine Kinase
uPa
Urokinase Plasminogen Activator
VEGF
Vascular Endothelial Growth Factor
VEGFR
Vascular Endothelial Growth Factor Receptor
Highlights 1. Traditional cell line-derived xenograft models and genetically engineered models have been modified, and innovative patient-derived xenograft models (PDXMs), including humanized PDXMs, have been established to accelerate the translation of anti-thyroid cancer therapeutics.
2. Experiments in thyroid cancer mouse models have dynamically revealed the ever-changing thyroid cancer
pathogenesis-related
signaling
pathways,
including
the
RAS/RAF/MEK/ERK,
PI3K/AKT/mTOR, SRC, and JAK-STAT pathways.
3. Novel inhibitors of VEGFR, MEK, mTOR, SRC, and STAT3 have been discovered and tested in multiple thyroid cancer mouse models, showing the potential for “bench-to-bedside” translation.
4. The future establishment of thyroid cancer mouse models may lay the foundation for comprehensive investigations in the field of thyroid cancer research through CRISPR/Cas-mediated target screening, humanized model-based drug testing, and multi-omics-associated mechanism exploring.
Conflict of Interest Statement The authors have no conflicts of interest to declare.
S0304-3835(19)30489-6
DOI:
https://doi.org/10.1016/j.canlet.2019.09.017
Reference:
CAN 114507
To appear in:
Cancer Letters
Received Date: 22 July 2019 Revised Date:
25 September 2019
Accepted Date: 30 September 2019
Please cite this article as: Y. Jin, M. Liu, R. Sa, H. Fu, L. Cheng, L. Chen, Mouse Models of Thyroid Cancer: Bridging Pathogenesis and Novel Therapeutics, Cancer Letters, https://doi.org/10.1016/ j.canlet.2019.09.017. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Elsevier B.V. All rights reserved.
Abstract Due to a global increase in the incidence of thyroid cancer, numerous novel mouse models were established to reveal thyroid cancer pathogenesis and test promising therapeutic strategies, necessitating a comprehensive review of translational medicine that covers (i) the role of mouse models in the research of thyroid cancer pathogenesis, and (ii) preclinical testing of potential anti-thyroid cancer therapeutics. The present review article aims to: (i) describe the current approaches for mouse modeling of thyroid cancer, (ii) provide insight into the biology and genetics of thyroid cancers, and (iii) offer guidance on the use of mouse models for testing potential therapeutics in preclinical settings. Based on research with mouse models of thyroid cancer pathogenesis involving the RTK, RAS/RAF/MEK/ERK, PI3K/AKT/mTOR, SRC, and JAK-STAT signaling pathways, inhibitors of VEGFR, MEK, mTOR, SRC, and STAT3 have been developed as anti-thyroid cancer drugs for “bench-to-bedside” translation. In the future, mouse models of thyroid cancer will be designed to be ‘‘humanized” and “patient-like,” offering opportunities to: (i) investigate the pathogenesis of thyroid cancer through target screening based on the CRISPR/Cas system, (ii) test drugs based on new mouse models, and (iii) explore the underlying mechanisms based on multi-omics.
1
1
Mouse Models of Thyroid Cancer: Bridging
2
Pathogenesis and Novel Therapeutics
3
Yuchen Jin1*, Min Liu1, 2*, Ri Sa1, Hao Fu1, Lin Cheng1, and Libo Chen1
4
1
5
Shanghai 200233, People’s Republic of China.
6
2
7
200032, China.
8
*
9
E-mail addresses for authors:
Department of Nuclear Medicine, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital,
Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Rd, Shanghai,
Yuchen Jin and Min Liu were the co-first authors.
10
[email protected] (Yuchen Jin)
11
[email protected] (Ri Sa)
12
[email protected] (Hao Fu)
13
[email protected] (Lin Cheng)
14
[email protected] (Min Liu)
15
[email protected] (Libo Chen)
16 17
Addresses for correspondence:
18
Professor Libo Chen, MD & PhD, Department of Nuclear Medicine, Shanghai Jiao Tong University
19
Affiliated Sixth People’s Hospital, Shanghai 200233, People’s Republic of China.
20
Telephone: +86-21-64369181
21
Fax: +86-21-64844183
22
E-mail: [email protected]
23 1
24
Financial support and conflicts of interest: This study was sponsored by the National Natural
25
Science Foundation of China (Grant Nos. 81671711 and 81701731) and Shanghai Key Discipline of
26
Medical Imaging (Grant No. 2017ZZ02005). None of the authors has any conflict of interest to
27
declare.
28 29
Manuscript length: 4408 words (main text)
30
Abstract: 199 words
31
References: 259
32
Figures: 4
33
Tables: 5
34
Abstract
35
Due to a global increase in the incidence of thyroid cancer, numerous novel mouse models were established to
36
reveal thyroid cancer pathogenesis and test promising therapeutic strategies, necessitating a comprehensive review
37
of translational medicine that covers (i) the role of mouse models in the research of thyroid cancer pathogenesis,
38
and (ii) preclinical testing of potential anti-thyroid cancer therapeutics. The present review article aims to: (i)
39
describe the current approaches for mouse modeling of thyroid cancer, (ii) provide insight into the biology and
40
genetics of thyroid cancers, and (iii) offer guidance on the use of mouse models for testing potential therapeutics in
41
preclinical settings. Based on research with mouse models of thyroid cancer pathogenesis involving the RTK,
42
RAS/RAF/MEK/ERK, PI3K/AKT/mTOR, SRC, and JAK-STAT signaling pathways, inhibitors of VEGFR, MEK,
43
mTOR, SRC, and STAT3 have been developed as anti-thyroid cancer drugs for “bench-to-bedside” translation. In
44
the future, mouse models of thyroid cancer will be designed to be ‘‘humanized” and “patient-like,” offering
45
opportunities to: (i) investigate the pathogenesis of thyroid cancer through target screening based on the
46
CRISPR/Cas system, (ii) test drugs based on new mouse models, and (iii) explore the underlying mechanisms
47
based on multi-omics.
48 49
Keywords: Thyroid cancer; Mouse model; Preclinical testing; Anti-cancer therapeutics 2
50
Introduction
51
Thyroid cancers are endocrine-related tumors, and their incidence is increasing globally [1].
52
Surveillance and Epidemiology and End Results (SEER) indicated that the numbers of new cases of
53
thyroid cancer and thyroid cancer-related deaths in the USA in 2019 were expected to reach 52,070
54
and 2,170, respectively [2]. In China, there were about 90,000 new cases and 6,800 deaths in 2015
55
[3]. By 2030, the incidence of thyroid cancer is expected to rank second among all tumors in women
56
and ninth in men [4]. The most prevalent type of thyroid cancer, differentiated thyroid cancer (DTC),
57
originates in follicular cells and accounts for 90,800 of the total number of cases [1]. Within 10 years
58
after an initial operation, local recurrence and distant metastases occur in about 20% and 10% of
59
DTC cases, respectively. Despite the availability of I-131 therapy, metastasectomy, and radiotherapy,
60
only one-third of patients are identified as having a “complete response”; the remaining patients are
61
refractory to I-131 and have a poor prognosis [5]. Medullary thyroid cancer (MTC) and anaplastic
62
thyroid cancer (ATC) are rare forms that account for less than 2% of all thyroid cancers. However,
63
50% to 80% of MTC patients have metastatic disease at the time of diagnosis, with a five-year
64
survival of <50%; ATC is an extremely aggressive disease with a median overall survival of <1 year
65
[6]. Therefore, preclinical translational studies on its pathogenesis and novel therapeutics using
66
animal models of thyroid cancer are urgently needed.
67
In the past decade, spontaneous and transplantation mouse models have been used to explore the
68
biomedical features of thyroid cancer. These models produce thyroid tumors that resemble their
69
counterparts in humans, both histologically and genetically [7]. Sequencing results for the mouse and
70
human genomes have revealed the extent of their cross-species genomic similarity, indicating the
71
value of mouse models in thyroid cancer research [8]. Currently, genetic manipulation techniques,
72
such as conditional genetic recombination and the introduction of severe immunodeficiency, have
73
allowed for the development of new mouse models of thyroid cancer to mimic complicated clinical
74
settings, tumor heterogeneity, and disease status in thyroid cancer patients. Interestingly, such models 3
75
provide insight into thyroid cancer pathogenesis and facilitate preclinical testing of anti-cancer drugs.
76
Furthermore, mouse models are indispensable for elucidating the interaction of anti-cancer drugs
77
with thyroid cancer cells, the screening of drugs before clinical trials, and the interpretation of drug
78
efficacy and safety results for thyroid cancer patients in clinical trials [9].
79
Given the increasing number of newly established thyroid cancer mouse models, recently
80
discovered anti-thyroid cancer mechanisms, and potential anti-cancer therapeutics, we performed a
81
review of the currently available literature, including established thyroid cancer mouse models,
82
thyroid cancer pathogenesis, and new drugs for thyroid cancer therapy. We also discuss perspectives
83
for further applications of thyroid cancer mouse models, highlighting techniques for such modeling,
84
and provide guidance for the translation of anti-cancer drugs as individualized thyroid cancer
85
therapeutics.
86
Modeling
87
Anti-cancer investigations can be performed by injecting cultured cell-derived xenografts into
88
immunodeficient mice. Considerable work on drug testing relies on the use of these cell line-derived
89
xenograft models (CDXMs). CDXMs, as well as patient-derived xenograft models (PDXMs) and
90
genetically engineered models (GEMs), provide a basis to investigate thyroid cancer pathogenesis
91
and the pharmacological mechanisms of anti-thyroid cancer therapeutics. These three models are
92
described below.
93
CDXMs
94
The establishment of CDXMs is based on xenotransplants. Tumor cells can be engrafted
95
subcutaneously, orthotopically, or metastatically. Such thyroid cancer models commonly have been
96
built with authenticated cell lines (e.g., 8505C, TPC-1, FTC133) to investigate thyroid cancer-related
97
phenomena, including invasion, metastasis, and angiogenesis.
98
Subcutaneous tumor models are established by inoculating Matrigel-suspended thyroid tumor cells
99
into immunodeficient mice [10]. Although the progression of tumors can be monitored easily, the 4
100
models fail to imitate the microenvironment, invasional type, and metastatic patterns of thyroid
101
cancer, and thus may not be adequate for the prediction of clinical anti-cancer responses [11]. In fact,
102
in immunodeficient mice, subcutaneously transplanted thyroid cancer cells rarely metastasize, failing
103
to mimic the thyroid tumor microenvironment [12,13]. In contrast, thyroid cancer models involving
104
orthotropic transplants simulate the microenvironment, morphology, growth, and metastatic patterns,
105
thereby reflecting the clinical spectrum of thyroid cancer. Orthotropic transplants are established by
106
injecting thyroid tumor cells into the thyroid glands of immunodeficient mice under surgery, with or
107
without ultrasound examination [14]. Mouse models of metastatic thyroid cancer, established in
108
severely immunodeficient mice, mimic the metastatic patterns of thyroid cancers in human patients.
109
After intravenous or intraventricular injection with as few as 30,000 thyroid cancer cells, metastases
110
develop rapidly in the lungs and bones of these mice [15].
111
Approaches involving cell lines result in rapid development of thyroid tumors, generating cohorts
112
for preclinical investigation. However, CDXMs suffer from several drawbacks. First, cell lines may
113
have incurred changes in their microenvironment, especially after many passages, which may alter
114
the DNA structure, RNA/protein expression, and the status of thyroid cancer differentiation, thereby
115
decreasing the reliability of the experiments [16]. Second, in CDXMs, the tumor microenvironment,
116
including stroma and the immune system, is lacking [17]. Finally, tumor cell implantation is
117
accomplished through a wound and therefore may not accurately mimic local invasion through the
118
thyroid capsule [18]. In this respect, in preclinical experiments, their resemblance to thyroid cancers
119
in patients may not be adequate. However, because their experimental repeatability is excellent,
120
CDXMs have been widely used.
121
PDXMs
122
PDXMs are established by transplanting tissue or cells from tumors of patients into immunodeficient
123
mice. The growth of patient-derived tumor fragments creates a stroma-based tumor environment,
124
which is of value for investigating thyroid cancer pathogenesis, evaluating drug efficacy and safety,
5
125
and exploring tumor heterogeneity; for thyroid cancer patients, they allow individualization of drug
126
management [16].
127
For
establishing
thyroid
cancer
PDXMs,
NOD/Shi-scid/IL-2Rγnull
and
128
NOD.Cg-PrkdcscidIl-2rgtm1Wjl/SzJ mice are more commonly used than nude mice [12].
129
Patient-derived thyroid tumor fragments are required for the establishment of PDXMs. After
130
resection of thyroid tumors is performed, tumors are divided into several fragments. Next, the
131
fragments are digested into single-cell suspensions and injected into immunodeficient mice [19]. In
132
these fragments, cell–cell interactions and some tissue architecture of the original thyroid cancer
133
remain; therefore, these models mimic the thyroid tumor microenvironment [20].
134
PDXMs allow the real-time investigation of novel anti-thyroid cancer drugs [21] (Figure 1). Up to
135
date, multiple tumor PDXMs have been established and tested in preclinical trials, yielding
136
promising results [22–24]; Yet none of thyroid cancer PDXM has been applied in clinical trials,
137
which should be regarded as a gateway, inspiring the translational studies. So far, only vemurafenib
138
[25], obatoclax [25], LOXO-292 [26], Sorafeinib [27], Lenvatinib [27], PLX51107 [28], PD0325901
139
[28], and cabozantinib [29] were tested in thyroid cancer PDXM-based preclinical trials. Although
140
no evidence showed therapeutic drugs effective in thyroid cancer PDXMs could be also effective in
141
corresponding patients; According to stably maintained pathology [30], and genome [31] of patient
142
tumors in thyroid cancer PDXMs, PDXM-based pre-clinical trials may still show meaningful
143
benefits for finding and testing potential anti-thyroid cancer drugs. Based on the individualized
144
molecular pathogenesis of thyroid cancer, the application of PDXM may precisely reflect biological
145
characteristics of the disease in patient individuals and firmly accelerate the clinical translation
146
process of anti-thyroid cancer therapeutics. One minor problem——despite PDXMs overcome the
147
lack of a tumor microenvironment in CDXMs, implantation is still accomplished through a wound.
148
GEMs
6
149
Differing from mouse models involving grafts, GEMs are established by genome alterations using
150
genetic engineering tools [32]. GEMs are increasingly utilized for thyroid cancer investigations, for
151
these models offer the possibility to generate gene mutations, amplifications, deletions, and
152
translocations, allowing researchers to turn on/off oncogenes and tumor suppressor genes [7].
153
To evade normal cellular control systems, GEMs target genes that can be altered by Cre-mediated
154
gene recombination [33]. The expression of oncogenes caused by mutations, amplifications,
155
deletions, and translocations can be temporally or spatially controlled with promoters specific for
156
thyroid tissue (e.g., promoters for thyroglobulin, thyroid peroxidase, or calcitonin) (Figure 2)
157
[33–35].
158
Two important factors may influence the choice for the use of thyroid cancer GEMs. First, GEMs,
159
which exhibit the characteristic development of a thyroid tumor, circumvent most problems
160
associated with grafted models and meet the need for exploring the interactions between tumor cells,
161
the tumor microenvironment, and the immune system [36]. Second, however, the microenvironment
162
and the immune system remain based on mouse DNA, RNA, and proteins, and the thyroid cancer
163
phenotype may be different from that of thyroid cancer patients [37].
164
Translation
165
By use of the above mouse models, various biological systems and events that are involved in
166
thyroid cancer dedifferentiation, proliferation, and metastasis have been described, providing
167
potential targets for anti-thyroid cancer targeted therapy (Figure 3). To lay a foundation for potential
168
bench-to-bedside translation of targeted therapy, thyroid cancer mouse models are of value to
169
understand the specific mechanisms how thyroid cancers derived pathologically. Identification of the
170
genetic and molecular alterations in thyroid cancer cells has advanced our knowledge about thyroid
171
cancer pathogenesis, which involves the RTK, RAS/RAF/MEK/ERK, PI3K/AKT/mTOR, SRC, and
172
JAK-STAT signaling pathways. Activation of RAS/RAF/MEK/ERK pathway are tend to transfer
173
follicular cells into PTC; Upregulated PI3K/AKT/mTOR changes the follicular cells into FTC; And 7
174
dysfunctional RTK pathway are prone to be observed in MTC, indicating that thyroid cancer patients
175
should be personally treated with specific anti-pathway drugs [38,39]. Necessarily, these potential
176
drugs should be tested in mouse models to evaluate the efficacy and safety of anti-thyroid cancer
177
therapeutics.
178
RTK Signaling Pathway
179
RTKs include 20 different surface receptor families, i.e., those of EGF, insulin, PDGF, VEGF, FGF,
180
CCK, NGF, HGF, Eph, AXL, TIE, RYK, DDR, RET, ROS, LTK, ROR, MuSK, LMR, and an
181
undetermined family, which act through growth factors, hormones, and other extracellular molecules
182
[40,41]. Mutations in RTKs are often found in thyroid cancers of patients. RET mutations are
183
commonly present in MTCs, including sporadic MTCs, familial MTCs, and multiple endocrine
184
neoplasia 2 (MEN2) syndromes [38,42]. RET/PTC, a rearranged form of RET, was the first genetic
185
alteration identified in papillary thyroid carcinoma (PTC) [43].
186
Thyroid cancer models with RET-PTC1 and RET-PTC3 gene rearrangements have been created by
187
using the Cre–loxP system. In GEMs, tumors with RET-PTC1 and RET-PTC3 develop into
188
non-invasive PTCs. RET/PTC rearrangements are early events in thyroid carcinogenesis and are
189
specific for dedifferentiation [44]. Based on the corresponding thyroid cancer mouse models, the
190
RTK signaling pathway has been identified as a controller of angiogenesis, proliferation, and
191
metastasis. In the process of thyroid cancer progression, RTKs, activated after ligand binding with
192
extracellular molecules, phosphorylate PI3K and MAPK, leading to activation of the
193
PI3K/AKT/mTOR and RAS/RAF/MEK/ERK pathways. The RTK signaling pathway and
194
subsequently the phenotypes of cell proliferation, dedifferentiation, survival, migration, and tumor
195
angiogenesis in thyroid tumors have been validated with both CDXMs and GEMs [43,45,46].
196
RTK inhibitors, including cabozantinib, gefitinib, imatinib, lenvatinib, motesanib, pazopanib,
197
sorafenib, sunitinib, and vandetanib, have been evaluated in preclinical studies and clinical trials,
198
which have yielded some promising results (Table 1). Some recently developed RTK inhibitors,
8
199
including anlotinib, apatinib, nintedanib, ponatinib, and regorafenib, which inhibit the
200
anti-angiogenesis-related targets of VEGFR, PDGFR, FGFR, KIT, and RET, have been tested in
201
mice and are being translated into clinical settings (Table 2). Other preclinically tested RTK
202
inhibitors, such as LIF, AEE788, CUDC-101, PD173074, tetraiodothyroacetic acid, CLM3,
203
withaferin A, Pulsatilla koreana extract, and crizotinib, have been tested in mouse models as
204
potential candidates for anti-thyroid cancer therapy (Table 3). Targeting RET and E2F1, LIF shows a
205
capacity for decreasing tumor burdens; tumor weights are reduced by 50–70% compared to the
206
control group, showing potential value for translation. AEE788 and CUDC-101, which inhibit EGFR,
207
show anti-proliferative activity in ATC/FTC-derived CDXMs [47–49]. Agents targeting RTKs have
208
been applied for redifferentiation of radioiodine-refractory DTCs. There is an inverse relationship
209
between PDGFRα activation and the transcriptional activity of thyroid transcription factor-1 (TTF1).
210
PDGFRα blockade promotes expression of sodium/iodide symporter (NIS), restoring iodine
211
transport in a thyroid cancer mouse model [50]. VEGF inhibitors include antibodies against VEGF
212
(bevacizumab) and VEGFR-2 (ramucirumab) and recombinant fusion protein against VEGF-A
213
(aflibercept). However, despite the positive results obtained with thyroid cancer CDXMs, for many
214
patients, VEGF-targeted therapy fails to show appreciable clinical benefit [27,51]. The observation
215
that sorafenib and lenvatinib are effective inhibitors of radioiodine-refractory PTCs in individual, but
216
not all, mice indicates that individualized therapeutics are of high value [27].
217
RAS/RAF/MEK/ERK Signaling Pathway
218
Components of the RAS/RAF/MEK/ERK signaling pathway are RAS and the downstream kinases
219
RAF, MEK, and ERK. RET, RAS, BRAF, and TERT mutations are mainly related to the Ras signaling
220
pathway [52]. The prevalence of RAS mutations is 18–55% in PDTCs, 45% in FTCs, 35% in
221
follicular variant-PTCs, and 4–60% in ATCs [53]. BRAFV600E is commonly detected in thyroid
222
cancers, especially in PTCs; the incidence of BRAFV600E varies from 29 to 70% in PTCs [38]. TERT
223
mutations usually accompany BRAFV600E mutations; most are found in aggressive, dedifferentiated
9
224
thyroid cancers, accounting for about 7–10% in PTCs [54].
225
K-RasG12D, N-RASQ61K, RET-PTC1, RET-PTC3, and BRAFV600E mice have been created using the
226
Cre–loxP system. In these models, H-RAS and K-RAS are responsible for hyperplasia [55,56]. In
227
GEMs, tumors with RET-PTC1, RET-PTC3, and BRAFV600E develop into non-invasive PTCs.
228
RET/PTC rearrangements are early events in thyroid carcinogenesis and are specific for
229
dedifferentiation [57]. In K-RASG12D, N-RASQ61K, and BRAFV600E GEMs, RAS mutations and
230
BRAFV600E lead to constitutive activation of RAS and BRAF, inducing thyroid cancer
231
dedifferentiation and proliferation [58]. In general, RAS mutations are associated with thyroid tumor
232
progression. However, thyroid carcinogenesis and dedifferentiation are not necessarily only driven
233
by RAS mutations; the combination of BRAF mutations and RAS mutations may be involved in
234
thyroid cancer formation and progression. The activated RAS/RAF/MEK/ERK signaling pathway
235
induces matrix degrading proteases, including MMP-1, -2, -3, and -9, and overexpression of
236
urokinase plasminogen activator (uPa). RAS (H-, K-, and N-RAS) and its downstream targets
237
promote migration of thyroid tumor cells [38,46]. Moreover, BRAFV600E-induced dedifferentiation is
238
associated with dysfunction of NIS. BRAFV600E causes poor radioiodine uptake in thyroid tumors and
239
may lead to therapeutic resistance to radioiodine therapy, which has been observed in thyroid cancer
240
GEMs exposed to radioiodine [59,60].
241
Some molecular therapeutics target the RAS/RAF/MEK/ERK signaling cascade. Inhibitors of the
242
RAS/RAF/MEK/ERK signaling pathway, including vemurafenib, selumetinib, dabrafenib, and
243
trametinib, have been investigated in thyroid cancer clinical trials, revealing promising results [9,61]
244
(Table 1). The allosteric MEK inhibitor CH5126766 is a candidate for clinical translation. In the
245
BRAFV600E GEM, CH5126766 induces a five-fold increase in NIS expression in thyroid cancer cells,
246
and reuptake radioiodine accumulation is twice as high as that observed after selumetinib treatment.
247
In addition, CH5126766 treatment prior to radioiodine delivery decreases thyroid tumor size
248
compared to radioiodine delivery without pretreatment [59].
10
249
PI3K/AKT/mTOR Signaling Pathway
250
The PI3K/AKT/mTOR signaling pathway is involved in thyroid cancer proliferation and in survival
251
and angiogenesis of tumor cells [62]. In thyroid cancer cells, PIK3CA mutations commonly exist in
252
FTCs, PDTCs, and ATCs [36].
253
In GEMs, activation of PIK3CA may lead to poor differentiation and rapid progression. In
254
addition, in FTC GEMs, amplification or mutation of PIK3CA, which encodes the catalytic subunit
255
of the PI3K converting PIP2 to PIP3, activates mTOR [38,63]. Higher mTOR levels lead to
256
downstream activation of S6K1 and 4E-BP1, which induce migration and invasion of thyroid tumor
257
cells. The matrix remodeling enzymes MMP-2/-9, uPa, and plasminogen activator inhibitor-1 (PAI-1)
258
are upregulated, resulting in invasiveness [64]. If mTOR is inhibited by stress signals, such as low
259
oxygen tension or low glucose in a low-pH extracellular setting, the beclin 1/PI3K complex is
260
activated, which enhances autophagy to overcome the hostile microenvironment and sustain tumor
261
proliferation [65]. In addition, with a transgenic mouse model of thyroid cancer, PI3K acts as a
262
negative controller of NIS, and PTEN as a positive regulator of TTF1 and NIS expression, balancing
263
differentiation and dedifferentiation [66].
264
The
PI3K/AKT/mTOR
pathway
contains
a
variety
of
therapeutic
targets.
Several
265
PI3K/AKT/mTOR pathway inhibitors, including everolimus, temsirolimus, torin2, CUDC-907,
266
PP121, GDC-0941, LY294002, IC87114, and AZD8055, have been tested preclinically and/or
267
clinically (Tables 1 and 3). Torin2, discovered through screening of compound libraries (3282 drugs),
268
suppresses thyroid tumor cell proliferation and, in CDXMs, inhibits thyroid tumor growth and
269
metastasis [67].
270
SRC Signaling Pathway
271
SRC family kinases (SFKs), which include SRC, FYN, YES, BLK, FGR, HCK, LCK, YRK, and
272
LYN, are non-receptor tyrosine kinases. Tyrosine kinase, G protein-coupled receptors, steroid
273
receptors, and Signal Transducers and Activators of Transcription (STAT) are activators of SFK,
11
274
which is involved in cell proliferation, motility, invasion, and angiogenesis [68].
275
Only a few studies have focused on the SRC pathway in thyroid cancer cells or in mouse models
276
of thyroid cancer. In CDXMs with a RET/PTC1 rearrangement, the SRC inhibitor dasatinib
277
suppresses tumor volume, suggesting that the SRC pathway is involved in regulating growth of
278
thyroid cancers [69]. In mice with ThrbPV/PVPten+/- thyroid cancer, SKI-606, an SRC/ABL dual
279
inhibitor, reduces tumor growth, invasion, and metastasis, effects that are related to inhibition of the
280
SRC pathway and the epithelial–mesenchymal transition [70]. These results suggest that the SRC
281
pathway influences the invasion and metastasis of thyroid cancer and that the activated SRC pathway
282
subsequently activates the MAPK, PI3K, FAK, and STAT pathways, promoting the progression of
283
thyroid cancers [69][69–72].
284
JAK-STAT Signaling Pathway
285
The JAK family includes JAK1, JAK2, JAK3, and TYK2, and the STAT family includes STAT1,
286
STAT2, STAT3, STAT4, STAT5a, STAT5b, and STAT6. JAK is activated by a combination of
287
cytokines or growth factors and their corresponding receptors, and activated JAK in turn activates
288
STAT, which can interact with DNA and regulate translation of various genes [73,74].
289
A high-fat diet induces the expression of STAT3, cyclin D1, and phosphorylated retinoblastoma
290
protein through the JAK2-STAT3 pathway, which leads to the development of ATCs in mice with
291
ThrbPV/PVPten+/- thyroid cancer. This suggests that, in these mice, STAT3 boosts the effects of a
292
high-fat diet by inducing development and progression of thyroid cancers [75]. Despite preclinical
293
results showing that the efficacies of cucurbitacin I, metformin, and S3I-201 are limited, the response
294
to the JAK1/2 inhibitor AZD1480 in PTC CDXMs is extensive (Table 3). These results indicate that
295
the JAK-STAT3 pathway may be involved in the development, progression, and metastasis of
296
thyroid tumors. However, other researchers have obtained opposite results, i.e., that STAT3 may be a
297
negative regulator of thyroid tumor growth, suggesting that the JAK-STAT3 pathway may inhibit
298
rather than promote thyroid cancer [76,77].
12
299
Other Signaling Pathways
300
Other signaling pathways may be related to the progression of thyroid tumors. The
301
RASSF1-MST1-FOXO3 pathway may be regulated by BRAFV600E gene mutations [78]; the activated
302
C-MET pathway may promote the growth, invasion, and metastasis of thyroid tumors [79,80]; and
303
the hedgehog pathway may be associated with the progression and metastasis of thyroid cancer
304
[38,81]. The corresponding therapeutics tested in preclinical settings are listed in Table 3.
305
Perspectives
306
Despite broad use of mouse models in thyroid cancer research, a certain amount of skepticism about
307
their relevance with human thyroid cancer and their value for clinical translation is presented in the
308
field of thyroid cancer research [11]. This is likely due to the differences between mice and humans,
309
but also to the fact that some clinical trials still fail despite the use of mouse models in the preceding
310
discovery research phases. According to Tables 1–4, the translational rate was only about 4–5%,
311
raising the awareness that thyroid cancer mouse models need to be further refined. Given the
312
increasing kinds of PDXMs and humanized PDXMs for thyroid cancer research, these best
313
approaches may facilitate in vivo drug tests and consequently accelerate the bench to bedside
314
translation. In the future, the ever-evolving establishment of thyroid cancer mouse models may solve
315
the significant issue that some therapeutic drug effective in mouse models but invalid in thyroid
316
cancer patients.
317
The dawn of a new century has witnessed not only the establishment of CDXMs, PDXMs, and
318
GEMs of thyroid cancer, but also the increasing applications of these models. To better translate
319
novel therapeutics into clinical settings, further advanced studies are essential in the areas of mouse
320
modeling, drug testing, target screening, and mechanism identification.
321
Mouse Modeling Based on Humanization
322
Humanized mice are now used as PDXMs in biological and medical research on thyroid cancer.
323
Severe combined immunodeficient mice that are highly deficient in T, B, and NK cells support the 13
324
engraftment of functional human immune cells [17,37]. These cells migrate into thyroid tumors and
325
replicate their natural microenvironment [37]. With the ongoing progress in immunology, scientists
326
are increasingly aware that the therapeutic effects of anti-cancer therapeutics are inseparable from
327
the role of the anti-tumor immune system. Anti-thyroid therapeutics applied systemically, especially
328
MEK inhibitors, CDK inhibitors, and PD1/PDL1 inhibitors, influence the immune system.
329
Consequently, the role and pharmacological mechanism of anti-apoptosis therapy, redifferentiation
330
therapy, and immunotherapy should be tested in the environment of a humanized immune system.
331
The humanized mouse model may lead to a revolution in mouse modeling. The newly established
332
mouse models may be “patient-like” and “humanized,” and may provide new, efficacious ways for
333
testing drugs for treatment of thyroid cancers.
334
Drug Testing Based on New Mouse Models
335
Dedifferentiated and highly aggressive thyroid cancers present challenges in the translation of
336
therapeutics into clinical applications. As shown in Table 1, most results of clinical trials match the
337
conclusions derived from preclinical studies, showing that the efficacy and safety of anti-thyroid
338
cancer drugs may be predicted from the data derived in preclinical studies. In clinical trials,
339
promising drugs, such as anlotinib, apatinib, nintedanib, ponatinib, regorafenib, and tipifarnib, may
340
be translated into clinical applications (Table 2). Future studies, many involving the use of thyroid
341
cancer mouse models, will continue to focus on “bench-to-bedside” translation. To date, numerous
342
drugs have been designed and tested in mouse models; promising drugs are listed in Table 3.
343
Additionally, drug combinations are of interest for the treatment of thyroid cancer; these are listed in
344
Table 4.
345
Target Screening Based on the CRISPR/Cas System
346
CRISPR is a method for gene editing derived from the bacterial anti-viral defense system. With the
347
intracellular delivery of Cas9 nuclease and synthetic guide RNA (sgRNA), genes can be cut at
348
desired positions [82]. The designed sgRNAs can be included in sgRNA libraries by
14
349
adeno-associated virus (AAV), which offers an alternative approach to screen anti-thyroid cancer
350
targets. For lung cancers and glioblastomas, AAV encoding sgRNA libraries are delivered to the
351
lungs and brains of mice expressing Cas9. As a result, the pathogenesis of cancers and anti-tumor
352
targets can be analyzed [83,84]. Target screens by delivery of sgRNA libraries can be performed with
353
thyroid tissue and could clarify the role of genes associated with thyroid cancer.
354
Mechanism Exploring Based on Multi-Omics
355
Omics methods involve high-throughput investigations of the genome, epigenome, transcriptome,
356
proteome, and metabolome. These methods are used to understand thyroid cancer pathogenesis, to
357
find the underlying molecular characteristics of thyroid tumors, and to reveal the phenotypes of
358
thyroid cancers [85]. With multi-omics analysis of thyroid cancer mouse models, the link between
359
genomic alterations and mRNAs, proteins, and metabolites in relation to thyroid cancer may be
360
revealed. Based on mouse models of thyroid cancer, the next phase of multi-omics analysis will
361
focus on dealing with thousands of mRNAs and proteins to reveal changes in the thyroid cancer
362
omics in a dynamic manner. For the biologic analysis of thyroid cancer, it will be highly desirable to
363
develop PDXMs that are useful in the development of drugs for precision therapy based on
364
personalized multi-omics data. Moreover, driven by such data, analysis of thyroid cancer GEMs is
365
likely to make large contributions to the research on thyroid cancer pathogenesis and to the
366
development of novel drugs [86].
367
Summary
368
Mouse models of thyroid cancer are of high importance for the development of therapeutic
369
interventions. In our opinion, CDXMs, PDXMs, and GEMs are all effective approaches for the
370
analysis of pathogenesis and for drug evaluations. CDXMs, as a flexible tool, have mostly been
371
utilized in thyroid cancer research, especially in tests of anti-thyroid cancer drugs. With CDXMs,
372
mechanisms of tumor differentiation, vascularization, proliferation, and metastasis have been
373
explored. GEMs have mainly been used for investigating effects of gene alterations, tumorigenesis, 15
374
confirming anti-thyroid cancer targets, and for investigating pharmacological mechanisms of
375
anti-thyroid cancer drugs. They have yielded specific targets and mechanism-based drugs for clinical
376
translation. Differing from CDXMs and GEMs, PDXMs are used in preclinical trials and for
377
screening drugs for individual patients. However, the development of PDXMs for thyroid cancer
378
investigations remains insufficient. Ideal thyroid cancer models will mimic thyroid cancer biology
379
and facilitate drug translation in cancer therapy. Therefore, advanced mouse models will be
380
necessary in cross-linking basic researches on thyroid cancer mechanisms and clinical studies on
381
potential anti-thyroid cancer therapeutics (Figure 4). The literature reveals that thyroid cancer mouse
382
models tend to be ‘‘humanized” and “patient-like.” They will offer opportunities to investigate
383
thyroid cancer mechanisms and to optimize anti-cancer drugs for “bench-to-bedside” translation.
384
Studies using thyroid cancer mouse models have provided information on the pathogenesis and
385
tumorigenesis of thyroid cancer and have been useful for both fundamental research and clinical
386
studies. The roles of signaling pathways, such as RAS/RAF/MEK/ERK and PI3K/AKT/mTOR, in
387
thyroid tumors have been analyzed using mouse models. In thyroid cancer GEMs, several gene
388
alterations, including RET/PTC rearrangements and the BRAFV600E mutation, have proved to be
389
oncogenic, validating the relevance of MAPK signaling pathways in PTC. In addition, RAS
390
mutations are involved in the development of PTCs and FTCs. Mice with overactivation of the
391
PI3K/AKT/mTOR signaling pathway develop FTCs. Other signaling pathways, including the SRC,
392
JAK-STAT, RASSF1-MST1-FOXO3, C-MET, and sonic hedgehog pathways, are studied as
393
potential targets for anti-thyroid cancer drugs. These studies have revealed mechanisms of thyroid
394
tumorigenesis and progression and have led to the development, testing, and translation of
395
anti-thyroid cancer drugs into clinical settings. Mouse models have confirmed the pathogenesis of
396
thyroid cancer and have led to the availability of new drugs for the treatment of thyroid cancer.
397
Consistent with the results on tumor pathogenesis, drugs such as MLN8054 (aurora kinase inhibitor),
398
radicicol (HSP90 inhibitor), HNHA (HDAC inhibitor), and PD-L1 antibody have been tested in
16
399
mouse models.
400
There remains a need for new screening methods to evaluate potential thyroid cancer therapeutics.
401
The development of mouse models, the understanding of the pathogenesis of thyroid cancer, and the
402
discovery of anti-thyroid cancer therapeutics will help in establishing individualized therapy. The
403
next step will be the discovery of drug response-related biomarkers, which will aid in the prediction
404
of the results of anti-tumor therapy and help in the management of thyroid cancer.
405
Abbreviations
406
See Table 5.
407
Acknowledgments
408
This study was supported by the National Natural Science Foundation of China (Grant Nos.
409
81671711 and 81701731) and the Shanghai Key Discipline of Medical Imaging (Grant No.
410
2017ZZ02005). We thank LetPub (www.letpub.com) for their linguistic assistance during the
411
preparation of this manuscript.
412
Conflict of interest
413
The authors have no conflicts of interest to declare.
17
414 415
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Mol.
Cancer
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molcanther.0211.2019.
1381
56
1382
Legends
1383
Figure 1. Establishment and testing of drugs in PDXMs. Tumor fragments were obtained from
1384
thyroid cancer patients. The clinical-histopathologic parameters and genomic profiling by the
1385
next-generation sequencing of these specimens were analyzed for drug candidates. Non-necrotic
1386
areas of these tumors were incubated as organoids and implanted subcutaneously into
1387
immunodeficient mice. Genome and pathology of thyroid tumor cells from patients remain stable in
1388
PDXMs and patient-derived organoid. After tumor establishment and growth, biological assays of
1389
drug efficacy and biomarker prediction for targeted therapies were conducted. If the drugs showed
1390
efficacy in PDXMs, they could be developed for patients. PDXM, patient-derived xenograft model.
1391 1392
Figure 2. Examples of constructed GEMs with a thyroid-specific promoter and Cre recombinase.
1393
The oncogene could be placed downstream of a stop signal gene that is inserted between two loxP
1394
sites. The stop cassette prevents oncogene expression, and thyroid-specific promoter activates Cre
1395
recombinase targets the stop cassette to allow oncogene gene expression only in the thyroid.
1396
Similarly, under control of a thyroid-specific promoter, the Cre recombinase also targets the stop
1397
cassette, resulting in knock-out of tumor suppressor genes in the thyroid. Consequently, the
1398
recombinated gene alterations facilitate the carcinogenesis in thyroid. KI, knock in; KO, knock out;
1399
PTC, papillary thyroid cancer; FTC, follicular thyroid cancer; ATC, anaplastic thyroid cancer. Red
1400
arrow-head, loxp position; Green arrow, gene expression.
1401 1402
Figure 3. Primary signaling pathways in thyroid cancer and molecular targeted agents tested in
1403
mouse models. Ligand-mediated activation of RTK results in activation of the PI3K/AKT/mTOR
1404
and RAS/RAF/MAPK/ERK pathways [38,87]. This leads to initiation of tumor dedifferentiation,
1405
migration, survival, proliferation, and angiogenesis. Moreover, molecular inhibitors and their targets
1406
of action are shown. PI3K, phosphatidylinositol 3-kinase; PIP2, phosphatidylinositol 4,5-biphosphate; 57
1407
PIP3, phosphatidylinositol 3,4,5-triphosphate; PTEN, phosphatase and tensin homologue; PDK-1,
1408
3-Phosphoinositide Dependent Protein Kinase 1; AKT, V-Akt Murine Thymoma Viral Oncogene
1409
Homolog 1; STAT3, Signal Transducer and Activator of Transcription 3; MSK, mitogen- and
1410
stress-activated protein kinase; mTOR, mammalian target of rapamycin; S6K, Ribosomal Protein S6
1411
Kinase I; CDK, Cyclin Dependent Kinase; RAS, rat sarcoma viral oncogene homologue; NF1,
1412
neurofribomin 1; ARAF, V-Raf Murine Sarcoma 3611 Viral Oncogene Homolog 1; BRAF, V-Raf
1413
Murine Sarcoma Viral Oncogene Homolog B; CRAF, V-Raf-1 Murine Leukemia Viral Oncogene
1414
Homolog 1; MEK, mitogen-activated protein kinase; ERK, extracellular signal-regulated kinase;
1415
Vinexin, Sorbin and SH3 Domain Containing 3; ETS1, ETS Proto-Oncogene 1; MMP2, Matrix
1416
Metallopeptidase 2.
1417
1418
Figure 4. An example of mouse models bridging bench and bedside via translational research of
1419
thyroid cancer treatment. During the process of Sorafenib translation, the drug was initially tested in
1420
mouse model trials, and the preclinical results were well interpreted on basic aspect and in the field
1421
of potential clinical application. The interpretation on pharmacology, pharmacokinetics, and
1422
pharmacodynamics of Sorafenib built the footstone for clinical trials and the probability for potential
1423
translation. After phase II and III trials, the efficacy and safety of Sorafenib was confirmed and then
1424
the drug was approved for clinical application. In the stage of post-market, amelioration around
1425
Sorafenib have been still simultaneously researching in thyroid cancer mouse models and real-world
1426
clinical settings. As the results, drug resistant mechanisms and drug combinations were obtained,
1427
inspiring the future translational studies. DTC, differentiated thyroid cancer; ATC, anaplastic thyroid
1428
cancer; RRDTC, radioiodine refractory thyroid cancer. CDXMs, cell line-derived xenograft models;
1429
PDXM, patient-derived xenograft model.
58
Table 1. Outcomes of thyroid cancer mouse model-based preclinical studies on agents with completed clinical trials. Drug Targets Pre-clinical study Clinical trial Ref. Tumor Mice Survival Tumor Body Arm, phase Tumor Type Burden* Weight (Reference) Type Cabozantinib VEGFR, [42] MTC CDXM NR ↓50 – 80% Stable RCT, III [88] MTC PDGFR, RET PDXM NR ↓55 – 60% NR Everolimus
mTOR
[65,89,90]
DTC
GEM
Gefitinib Imatinib
EGFR [92,93] ABL, c-kit, [94,95] PDGF-R VEGFR, [27,97,98] FGFR, c-Kit, RET, PDGFR VEGFR, RET, [100] PDGFR, c-Kit VEGFR, [102,103] PDGFR, c-Kit
ATC ATC ATC
Selumetinib
MEK1
Sorafenib
VEGFR, [30,108–115] PDGFR, c-Kit, RET, RAF VEGFR, [103,117–121] PDGFR, c-Kit, RET, FLT3 VEGFR, [30,32,47,124–1 EGFR, RET 30]
Lenvatinib
Motesanib Pazopanib
Sunitinib
Vandetanib
Vemurafenib Pan-Raf
Depsipeptide HDAC1, 2
[59]
↑ 28%
Enrolled Median Median CR,% PR,% n OS, mo PFS, mo 330 26.6 11.2 ORR: 28%
SD,%
PD,%
Main adverse events (Grade ≥ 3)
NR
NR
Diarrhea (21.5%), HFSR (12.6%), hypocalcemia (10.7%), decreased weight (9.8%), fatigue (9.8%), hypertension (8.9%), asthenia (6.5%) Mucositis (15%), diarrhea (10%), neutropenia (5%), hypertriglyceridemia (5%) Anorexia (11%), diarrhea (4%), rash (7%) Lymphopenia (45.5%), edema (25%), anemia (18.2%), fatigue (12.5%), hyponatremia (12.5%) Hypertension (10-41.8%), diarrhea (8-10%), fatigue or asthenia (9.2%), decreased weight (9.6-12%), proteinuria (10%) Hypertension (25%), diarrhea (13%), weight loss (5%), abdominal pain (5%) Hypertension (13%), pharyngo-laryngeal pain (13%), raised ALT concentration (10.8%), lower-gastrointestinal haemorrhage (8.1%) Rash (18%), fatigue (8%), diarrhea (5%), peripheral edema (5%) HFSR (16-44.1%), weight loss (8.4%-11.5%), hypertension (9.7-16.1%), rash (16.1%)
↓50 – 75% Stable
Single, II [91] RRDTC 28
NR
11.8
0
10
66
18
CDXM NR CDXM NR
↓30–>90% NR ↓12 – 25% Stable
Single, II [81] RRDTC 27 Single, II [96] ATC 11
27.4 NR
3.9 NR
0 0
12 25
NR 50
38 12.5
CDXM NR PDXM NR
↓60–>90% Stable ↓50 – 60% ↓ 5–10%
RCT, III [99]
NE
18.3
1.5
63.2
23
6.9
MTC CDXM ↑ 33%
↓30 – 50% Stable
Single, II [101] RRDTC 93
NE
9.2
0
14
67
8
DTC ATC
GEM ↑ 21% CDXM NR
↓ 20% ↓ 5% ↓25 – 50% Stable
ATC
CDXM NR
↓30 – 70% NR
Single, II [104] Single, II [105] Single, II [106] Single, II [107]
NE 3.7 19.9 NR
11.7 2.1 9.4 7.4
0 NE 0 0
49 NE 14.3 3
NR NE 57.1 54
NR NE 17.1 28
ATC
CDXM ↑ 18% GEM NR PDXM NR
↓20 – 30% Stable ↓ 66% NR ↓50 – 60% ↓ 5–10%
RCT, III [116] RRDTC 417
NE
10.8
NR
12.2
41.8
NR
↓ 10% ↓ 10% ↓10 – 50% Stable
Single, II [122] RRDTC 35 Single, II [123] RRDTC 23
NE 56.5
NR 8
1 0
28 26
46 57
17 4
Neutropenia (34%), leukopenia (13-31%), diarrhea (17%), HFSR (17%), hypertension (13%), fatigue (11%)
↓50 – 80% ↓ 5% ↓ 5-30% Stable ↓50 – >80% ↓ 17% ↓30 – 50% NR No effect NR ↓50% NR ↓80 – 90% NR ↓25 – 30% NR
RCT, II [131] RRDTC 145 RCT, III [132] MTC 331
NR NR
11.1 19.3
NR 0
1 45
54 42
44 13
Diarrhea (10%), asthenia (7%), fatigue (5%).
DTC GEM ↑ 33% MTC CDXM NR
DTC ATC MTC [15,25,133–138] PTC ATC ATC PTC [139–141] MTC
PDXM ↑ 15% CDXM NR CDXM NR GEMs NR GEMs ↑ 75% CDXM NR PDXM NR GEM NR
RRDTC 382
RRDTC ATC MTC RRDTC
39 15 35 39
Single, II [102] RRDTC 51 (C1: NE 26, C2: (C1) 25) NE (C2) Single, II [142] RRDTC 20 33.2
15.1 (C1) 0 (C1) 38.5 (C1) 57.7 (C1) 3.8 (C1) Weight loss (5%), abdominal pain (5%) 8.9 (C2) 0 (C2) 27.3 (C2) 63.6 (C2) 4.5 (C2)
NR
0
0
65
35
Thrombosis/thrombus/embolism (5%), fatigue (5%), dysphagia (5%), dyspnea (5%), lymphopenia (5%), prolonged QTc interval (5%) *Tumor burden was evaluated according to the tumor weight/volume/activity data at day 10 and/or day 20 post drug delivery; NR, not reported; NE, not estimable; CI, confidence interval; CR, complete response; OS, overall survival; PD, progressive disease; PFS, progression-free survival; PR, partial response; DTC, differentiated thyroid cancer; PTC, papillary thyroid cancer; ATC, anaplastic thyroid cancer; MTC, medullary thyroid cancer; RRDTC, radioiodine refractory thyroid cancer; CDXM, cell line-derived xenograft model; PDXM, patient-derived xenograft model; GEM, genetically engineered model; RCT, randomized controlled trial; RECIST, response evaluation criteria in solid tumors; SD, stable disease; FGFR, fibroblast growth factor receptor; FLT3, Fms-like tyrosine kinase 3; MEK1, MAPK/ERK kinase 1; mTOR, mammalian target of rapamycin; PDGFR, platelet-derived growth factor receptor; RAF, rapidly accelerating fibrosarcoma; RET, glial cell line-derived neurotrophic factor receptor; VEGFR, vascular endothelial growth factor receptor; c-kit, stem cell factor receptor; EGFR, epidermal growth factor receptor; HFSR, hand-foot-skin reaction; HDAC, histone deacetylase. C1: Cohort 1, patients never received multikinase inhibitors; C2: Cohort 2 patients previously received multikinase inhibitors.
Table 2. Outcomes of thyroid cancer mouse model-based preclinical studies on agents with ongoing clinical trials. Drug
Action
Pre-clinical study Ref.
Tumor
Mice
Survival
Apatinib
Nintedanib
VEGFR2
VEGFR2
RTK
[143]
[144]
[145]
Phase
Tumor Type
Status
Body *
Type Anlotinib
Tumor
Clinical Trial No. Burden
Weight
ATC
CDXM
NR
↓ 60 – >90%
Stable
NCT02586337
II/ III
DTC
Recruiting
MTC
CDXM
NR
↓ 50 – 60%
Stable
NCT02586350
II/ III
MTC
Recruiting
ATC
CDXM
NR
↓ 10 – 60%
NR
NCT03199677
II
TC
Recruiting
NCT03167385
II
DTC
Recruiting
NCT03048877
III
DTC
Recruiting
NCT03300765
II
TC
Recruiting
NCT01788982
II
MTC/DTC
Active,
MTC
GEM
NR
↓ 50 – 60%
NR
not
recruiting Ponatinib
RTK
[146]
MTC
CDXM
NR
↓ > 90%
↓ < 10%
NCT03838692
II
MTC
Not recruiting
Regorafenib
RTK
[147]
ATC
GEM
NR
↓ 65%
NR
NCT02657551
II
TC
Recruiting
Tipifarnib
FTase
[46]
DTC
GEM
↑ 200%
↓ 50%
↓ < 10%
NCT02383927
II
TC
Recruiting
*Tumor burden was evaluated according to the tumor weight/volume/activity data on day 10 and/or day 20 post drug delivery; NR, not reported; VEGFR2, vascular endothelial growth factor receptor 2; RTK, receptor tyrosine kinase; Ftase, farnesyltransferase; DTC, differentiated thyroid cancer; PTC, papillary thyroid cancer; ATC, anaplastic thyroid cancer; MTC, medullary thyroid cancer; TC, thyroid cancer; CDXM, cell line-derived xenograft model; GEM, genetically engineered model.
yet
Table 3. Thyroid cancer mouse model-based preclinical studies on single-drug therapeutics with potential translation into clinical trials. No. Drug Target Tumor Mice Survival Tumor Body Weight No. Drug Target Type Burden* Chemotherapy NF-κB signaling pathway inhibitors 1 Docetaxel [94,148] Microtubule assembly ATC CDXM NR ↓ 30 – 50% Stable 1 DHMEQ [149,150] NF-κB 2 Cisplatin [121] DNA crosslinking ATC CDXM NR ↓ 20 – 50% Stable 2 Triptolide [151,152] NF-κB 3 TSH-SiO2-Doxorubicin [153] DNA topoisomerase II FTC CDXM NR ↓ 50 – 75% Stable JAK/STAT signaling pathway inhibitors 4 5FU [154] DNA synthesis MTC CDXM NR ↓ 20 – 40% NR 1 Cucurbitacin I [155] STAT3 RTK signaling pathway inhibitors 2 Metformin [156–158] STAT3
Mice
Survival Tumor Burden*
ATC ATC
CDXM NR CDXM NR
↓70–80% ↓ 50%
Stable NR
ATC FTC ATC FTC PTC
CDXM GEM CDXM GEM CDXM
↓5– 30% No effect ↓25-50% ↓5– 10% ↓ > 80%
NR Stable NR NR NR
ATC PTC
CDXM NR CDXM NR PDXM NR
↓ 5-30% ↓50–60% NR
Stable Stable NR
FTC PTC ATC
GEM ↑ 40% CDXM NR CDXM NR
↓ 30% ↓30–40% ↓20–30%
Stable NR NR
↑ 25-35% NE NR ↑ 20% NR
Body Weight
1 2 3 4 5
LIF [127,159] AEE788 [48,49,161] CUDC-101 [163,164] PD173074 [165,166] Tetraiodothyroacetic Acid [168]
RET, E2F1 EGFR/VEGFR EGFR and HDAC FGFR VEGF
MTC ATC/FTC ATC DTC/ATC ATC
CDXM CDXM CDXM CDXM CDXM
↓ 50 – 70% ↓ 50 – 80% ↓ 50 – 60% ↓ 50% ↓ 50 – 70%
↓ 9% Stable Stable Stable NR
3 S3I-201 [160] STAT3 4 AZD1480 [162] JAK1/2 Apoptosis signaling pathway inhibitors 1 Reversine [167] Apoptosis pathway 2 Obatoclax [169–171] Bcl-2
6 8 9
CLM3 [172,173] Withaferin A [174,175] Pulsatilla koreana extract [176]
Angiogenesis RET Angiogenesis
ATC MTC ATC
CDXM NR ↓ 40 – 50% CDXM ↑ > 30% ↓ 50 – 60% CDXM NR ↓ 50 – 60%
Stable ↑ 10% Stable
SRC signaling pathway inhibitors 1 SKI-606 [70] SRC 2 Dasatinib [69,71,72] SRC
c-MET
ATC
CDXM NR
↓ 5 – 20%
NR
BRAF MEK RAF MEK MEK MEK
ATC ATC PTC ATC ATC PTC
↑ 40% NR NR ↑ 80% NR ↑ 50% NR NR NR NR
↓ 50 – 70% ↓ 50 – 70% ↓ 50 – 85% ↓ 95% ↓ 25% ↓ 60 – 90% ↓ 80 – 90% ↓ 50% ↓ 50% ↓ 10 – 30%
↑ 50% NR Stable NR Stable NR NR NR Stable NR
Proteosome PPARγ COX-2 P2R Carcinoembryonic antigen Aurora kinase PLK1
ATC DTC MTC PTC MTC ATC ATC
CDXM GEM CDXM CDXM CDXM CDXM CDXM
↑ 25% NR NR NR NR NR NR
↓20–30% ↓50–>90% ↓ 50% ↓50–80% ↓50–80% ↓50 –80% ↓60–70%
Stable NR NR Stable ↓ < 8% Stable ↓ 7%
ATC ATC
CDXM CDXM CDXM CDXM CDXM CDXM PDXM GEM CDXM CDXM
Other drugs 1 Carfilzomib [163,179] 2 Rosiglitazone [190,191] 3 Celecoxib [193,194] 4 Clodronate [195,196] 5 I-131-F6 [197] 6 MLN8054 [30,198,199] 7 GSK461364A [201]
PTC ATC FTC ATC FTC FTC PTC PTC FTC ATC ATC
CDXM CDXM CDXM CDXM CDXM CDXM CDXM CDXM CDXM CDXM CDXM
NR ↑ 16% NR NR NR NR NR NR NR NR NR
↓ 30 – 50% ↓ 50% ↓ 50 – 70% ↓ 25 – 60% ↓ 30 – 40% ↓ 60% ↓ 20% ↓ 50% ↓ 80 – 85% ↓ 30% ↓ 30 – 70%
NR Stable Stable Stable Stable NR NR NR NR NR NR
8 9 10 11
Oct-CL [203] Thiostrepton [205] 17-AAG [206] PLX6134 [208]
p70S6K and TOPO 1 FoxM1 HIF-1a and HSP90 c-FMS/CSF1-R kinase
MTC PTC PTC PTC
CDXM CDXM CDXM GEM
↑ 50% NR NR NR
↓ > 70% ↓50–75% ↓80% ↓15%
Stable NR NR NR
12 13 14 15
Tolfenamic acid [210] Reparixin [148] Dinaciclib [214] Quinacrine [110]
NAG-1 CXCR1 and CXCR2 CDK Phospholipase A2
ATC ATC ATC ATC
CDXM CDXM CDXM CDXM
NR NR NR ↑ 45%
↓50-60% ↓60–70% ↓30–50% NE
NR Stable ↓ 7-8.1% NR
16 cD-tboc [218] 17 Ganetespib [221]
Estrogen pathway HSP90
18 YM155 [223,224] 19 AG-IR820 [225] 20 Lexatumumab [182]
Survivin and claspin Glucose metabolism TRAIL-R2
ATC ATC MTC ATC MTC PTC
CDXM CDXM CDXM CDXM CDXM CDXM
NR NR NR ↑ 119.0% NR NR
↓50-60% ↓ 60% ↓50–60% ↓ > 90% ↓60–70% ↓ 70%
Stable ↓ 4.5% ↓ 9.5% Stable Stable Stable
10 Crizotinib [177,178] MAPK signaling pathway inhibitors 1 PLX4720 [180–189] 2 Trametinib [192] 3 LY3009120 [169] 4 PA-L1/LF [115] 5 SL327 [117] 6 PD0325901 [28,181,200]
7 Colchicine [202] MEK1/2 and JNK 8 Salirasib [204] RAS mTOR signaling pathway inhibitors 1 Torin2 [65,67,207] mTOR 2 3 4 5
CUDC-907 [164,209] PP121 [211] GDC-0941 [212,213] LY294002 [182,215,216]
PI3K and HDAC PI3K/Akt PI3K, mTOR and HIF-1α PI3K
7 8
IC87114 [217] Temsirolimus [219,220]
PI3K mTOR
9 AZD8055 [222] mTOR HDAC signaling pathway inhibitors 1 HNHA [226,227] HDAC
NR NR NR NR NR
Tumor Type
PTC CDXM ↑ 175% ↓ 50% Stable ATC CDXM ↑ 186% ↓ 50% Stable 2 PXD101 [228] HDAC ATC CDXM NR ↓ 30 – 50% Stable 21 PF-271 [229] FAK PTC CDXM NR ↓ 50% NR 3 SAHA [230] HDAC FTC GEM NR ↑ 61.8% NR 22 JQ1 [157,231,232] BET FTC GEM ↑ 50% ↓ 60% Stable PTC CDXM ↑ 50% ↓ 30% Stable ATC CDXM ↑ 115% ↓ 30% Stable 4 Panobinostat [233] HDAC ATC CDXM NR ↓ 50 – 60% ↓5–13% *Tumor burden was evaluated according to the tumor weight/volume/activity data on day 10 and/or day 20 post drug delivery; NR, not reported; NE, not estimable; PTC, papillary thyroid cancer; ATC, anaplastic thyroid cancer; MTC, medullary thyroid cancer; CDXM, cell line-derived xenograft model; PDXM, patient-derived xenograft model; GEM, genetically engineered model; SD, stable disease; MEK, MAPK/ERK kinase; mTOR, mammalian target of rapamycin; RAF, rapidly accelerating fibrosarcoma; RET, glial cell line-derived neurotrophic factor receptor; VEGFR, vascular endothelial growth factor receptor; c-kit, stem cell factor receptor; EGFR, epidermal growth factor receptor; HDAC, histone deacetylase; NF-κB, nuclear factor kappa B; STAT3, Signal Transducer And Activator Of Transcription 3; JAK1/2, Janus Kinase 1/2; Bcl-2, B-Cell CLL/Lymphoma 2; SRC, V-Src Avian Sarcoma (Schmidt-Ruppin A-2) Viral Oncogene Homolog; PPARγ, Peroxisome proliferator-activated receptor gamma; COX-2, cyclooxygenase-2; P2R, P2 receptor; PLK1, Polo Like Kinase 1; p70S6K, 70 KDa Ribosomal Protein S6 Kinase 1; TOPO 1, Topoisomerase I; FoxM1, Forkhead Box M1; HIF-1a, Hypoxia Inducible Factor 1 Subunit Alpha; c-FMS/CSF1-R, Colony Stimulating Factor 1 Receptor; NAG-1, Non-Steroidal Anti-Inflammatory Drug-Activated Gene-1; CXCR1, C-X-C Motif Chemokine Receptor 1; CXCR2, C-X-C Motif Chemokine Receptor 2; CDK, Cyclin Dependent Kinase; HSP90, Heat Shock Protein 90; TRAIL-R2, TNF-Related Apoptosis-Inducing Ligand Receptor 2; FAK, Focal Adhesion Kinase; BET, bromodomain and extra terminal; SAHA, Suberoylanilide Hydroxamic Acid; DHMEQ, Dehydroxymethylepoxyquinomicin.
Table 4. Thyroid cancer mouse model-based preclinical studies on combined therapeutics with potential translation into clinical trials. No Drug Target Tumor Type Mice Survival Tumor Burden* Body Weight . 1 Imatinib and docetaxel [94] RTK, microtubule assembly ATC CDXM NR ↓ 60-70% Stable 2 MV-NIS and I-131 [234] NIS ATC CDXM NR ↓ 50-60% NR 3 Bay 11-7082 and I-131 [235] NF-κB and isotope therapy PTC CDXM NR ↓ 40-50% NR 4 Dasatinib and Trametinib [236] SRC and MEK ATC CDXM NR ↓ 50-70% NR 5 TRAIL and Bortezomib [237] ROS PTC CDXM NR ↓ 30-50% NR 6 TRAIL and Valproic Acid [138] ROS, HDAC PTC CDXM ↑ 45% ↓ > 90% NR 7 Vemurafenib and Bortezomib [137] BRAF and proteasome PTC CDXM NR ↓ 70% NR 8 Ribavirin and Paclitaxel [238] eIF4E-β-catenin axis ATC CDXM NR ↓ 70% Stable 9 Sorafenib and quinacrine [110] RTK and phospholipase A2 ATC CDXM ↑ 243.2% NE ↓ 15% 10 Embelin and LY294002 [216] PI3K, XIAP PTC CDXM NR ↓ 30-60% NR 11 SL327 and Sunitinib [117] MEK, RTK ATC CDXM NR ↓ 50% Stable 12 2ME2 and Cabozantinib [29] HIF, VEGFR MTC CDXM NR ↓ 60-80% Stable PDXM NR ↓ 60-80% Stable 13 PLX4032 and PHA665752 [80] c-MET ATC CDXM NR ↓ 80% Stable 14 RDEA119 and Temsirolimus [229] MEK and mTOR FTC CDXM NR ↓ 90% NR 15 Sorafenib and CQ [204] RTK and autophagy ATC CDXM NR ↓ 50-80% NR FTC CDXM NR ↓ 60-80% NR 16 PD-325901 and GDC-0941 [212] MEK and Pi3K PTC GEM NR ↓ 30-50% NR 17 10F.9G2 and PLX4720 [239] PDL-1 and BRAF ATC CDXM NR ↓ 80% NR 18 AZD8055 and AZD6244 [222] mTOR MEK ATC CDXM NR ↓ 60-90% NR 19 dl922-947 and Olaparib [240] PARP/pRb ATC CDXM NR ↓ 40-60% Stable 20 HCQ and Vemurafenib [133] Autophagy, BRAF ATC CDXM NR ↓ 60% Stable 21 Neoadjuvant therapy with PLX4720 [241] BRAF ATC CDXM ↑ > 40% ↓ > 90% ↓ 5-30% *Tumor burden was evaluated according to the tumor weight/volume/activity data on day 10 and/or day 20 post drug delivery; MV-NIS, measles virus encoding the human thyroidal sodium iodide symporter; TRAIL, tumor necrosis factor–related apoptosis-inducing ligand; 2ME2, 2-methoxyestradiol; CQ, chloroquine; HCQ, hydroxychloroquine; RTK, receptor tyrosine kinase; NIS, Na+/I− Symporter; NF-κB, nuclear factor kappa-light-chain-enhancer of activated B cells; SRC, Rous sarcoma oncogene; MEK, MAP kinase-ERK kinase; ROS, transcriptional regulator; HDAC, histone deacetylase; BRAF, v-Raf murine sarcoma viral oncogene homolog B; mTOR, mammalian target of rapamycin; PI3K, Phosphoinositide 3-kinases; XIAP, X-linked inhibitor of apoptosis protein; HIF, hypoxia inducible factor; VEGFR, vascular endothelial growth factor receptor; EGFR, epidermal growth factor receptor, PDL-1, programmed death-ligand 1; PRPP, poly (ADP-ribose) polymerase; ATC, anaplastic thyroid cancer; PTC, papillary thyroid cancer; MTC, medullary thyroid cancer; FTC, follicular thyroid cancer; CDXM, cell line-derived xenograft model; GEM, genetically engineered model; NR, not reported; NE, not estimable.
Table 5. List of abbreviations. Abbreviation
Full Name
ABL
Abelson Tyrosine-Protein Kinase 1
AKT
Rac-Alpha Serine/Threonine-Protein Kinase
ATC
Anaplastic Thyroid Cancer
AXL
Tyrosine-Protein Kinase Receptor UFO
BRAF
V-Raf Murine Sarcoma Viral Oncogene Homolog B
c-kit
Stem Cell Factor Receptor
CCK
Cholecystokinin
CCND1
Cyclin D1
CDK
Cyclin Dependent Kinase
CDXM
Cell Line-Derived Xenograft Model
CI
Confidence Interval
C-MET
Hepatocyte Growth Factor Receptor
CR
Complete Response
Cre
Cyclization Recombination Enzyme
loxP
Locus of X-Over P1 Bacteriophage; The Recognition Sites for Cre Recombinase
DDR
Discoidin Domain Receptor Tyrosine Kinase
DNA
Deoxyribonucleic Acid
DTC
Differentiated Thyroid Cancer
EGF
Epidermal Growth Factor
EGFR
Epidermal Growth Factor Receptor
Eph
Ephrin
ERK
Mitogen-Activated Protein Kinase 1
FAK
Protein Tyrosine Kinase 2
FGF
Fibroblast Growth Factor
FGFR
Fibroblast Growth Factor Receptor
FLT3
Fms-Like Tyrosine Kinase 3
FOXO3
Forkhead Box O3
GEM
Genetically Engineered Model
HDAC
Histone Deacetylase
HGF
Hepatocyte Growth Factor
HNHA
High Mobility Group Box 1
HSP90
Heat Shock Protein 90 Alpha Family Class A Member 1
IL-2Rγ
Interleukin 2 Receptor Subunit Gamma
JAK
Janus Kinase
KIT
Tyrosine-Protein Kinase Kit
LMR
Lemur Tyrosine Kinase
LTK
Leukocyte Receptor Tyrosine Kinase
MEK
Mitogen-Activated Protein Kinase Kinase 1
MEN2
Multiple Endocrine Neoplasia 2
MMP
Matrix Metallopeptidase
mRNA
Messenger RNA
MST1
Macrophage Stimulating 1
MTC
Medullary Thyroid Cancer
mTOR
Mechanistic Target of Rapamycin Kinase
MuSK
Muscle Associated Receptor Tyrosine Kinase
NE
Not Estimable
NGF
Nerve Growth Factor
NIS
Sodium/Iodide Symporter
NOD
Non Obese Diabetic Mouse Model
NR
Not Reported
ORR
Overall Response Rate
OS
Overall Survival
PAI-1
Plasminogen Activator Inhibitor-1
PD
Progressive Disease
PD1
Programmed Cell Death 1
PDGF
Platelet-Derived Growth Factor
PDGFR
Platelet Derived Growth Factor Receptor
PDGFRα
Platelet Derived Growth Factor Receptor Alpha
PDL1
Programmed Cell Death 1 Ligand 1
PDTC
Poorly Differentiated Thyroid Cancer
PDXM
Patient-Derived Xenograft Model
PFS
Progression-Free Survival
pH
Potential of Hydrogen
PI3K
Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Delta
PIK3CA
Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Alpha
PIP2
Phosphatidylinositol 4,5-Biphosphate
PIP3
Phosphatidylinositol 3,4,5-Trisphosphate
PR
Partial Response
PTC
Papillary Thyroid Carcinoma
PTC1
Protein Patched Homolog 1
PTC3
Protein Patched Homolog 3
Pten
Phosphatase and Tensin Homolog
QTc
Corrected QT Interval
RAF
Murine Leukemia Viral Oncogene Homolog
RAS
Rat Sarcoma Viral Oncogene Homolog
RASSF1
Ras Association Domain Family Member 1
RCT
Randomized Controlled Trial
RECIST
Response Evaluation Criteria In Solid Tumors
RET
Glial Cell Line-Derived Neurotrophic Factor Receptor;
RNA
Ribonucleic Acid
ROR
Rar Related Orphan Receptor
ROS
Transmembrane Tyrosine-Specific Protein Kinase
RRDTC
Radioiodine Refractory Thyroid Cancer
RTK
Receptor Tyrosine Kinase
RYK
Receptor Like Tyrosine Kinase
S6K1
Ribosomal Protein S6 Kinase B1
scid
The Severe Combined Immunodeficiency
SD
Stable Disease
SEER
Surveillance and Epidemiology and End Results
SFK
Src Family Kinases
sgRNA
Single Guide RNA
SRC
V-Src Avian Sarcoma (Schmidt-Ruppin A-2) Viral Oncogene Homolog
STAT
Signal Transducers and Activators of Transcription
TERT
Telomerase Reverse Transcriptase
TIE
Tyrosine Kinase With Immunoglobulin Like and EGF Like Domains
TTF1
Thyroid Transcription Factor-1
TYK2
Tyrosine Kinase
uPa
Urokinase Plasminogen Activator
VEGF
Vascular Endothelial Growth Factor
VEGFR
Vascular Endothelial Growth Factor Receptor
Highlights 1. Traditional cell line-derived xenograft models and genetically engineered models have been modified, and innovative patient-derived xenograft models (PDXMs), including humanized PDXMs, have been established to accelerate the translation of anti-thyroid cancer therapeutics.
2. Experiments in thyroid cancer mouse models have dynamically revealed the ever-changing thyroid cancer
pathogenesis-related
signaling
pathways,
including
the
RAS/RAF/MEK/ERK,
PI3K/AKT/mTOR, SRC, and JAK-STAT pathways.
3. Novel inhibitors of VEGFR, MEK, mTOR, SRC, and STAT3 have been discovered and tested in multiple thyroid cancer mouse models, showing the potential for “bench-to-bedside” translation.
4. The future establishment of thyroid cancer mouse models may lay the foundation for comprehensive investigations in the field of thyroid cancer research through CRISPR/Cas-mediated target screening, humanized model-based drug testing, and multi-omics-associated mechanism exploring.
Conflict of Interest Statement The authors have no conflicts of interest to declare.