









































GSR Journal
 

Georgetown Scientific Research Journal

Volume 1 | Edition 2
July 31, 2021

Danya Adams
         ISSN 2767-6420   





Cancer Models to Defeat Therapy 
Resistance in Pancreatic Ductal 

Adenocarcinoma  
Britney He

Volume Two 
Edition One 
July 2021 

 
GEORGETOWN SCIENTIFIC
RESEARCH JOURNAL

Volume One 
Edition Two
Spring 2021

 
GEORGETOWN SCIENTIFIC
RESEARCH JOURNAL

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Cancer	Models	to	Defeat	Therapy	Resistance	in	
Pancreatic	Ductal	Adenocarcinoma	
	
Britney He 
 
Department of Oncology, Georgetown University Medical Center, Washington D.C.  
E-mail: bkh40@georgetown.edu 
https://doi.org/10.48091/gsr.v1i2.21  

Abstract 

One of the largest hurdles to the efficacy of cancer therapeutics, and a main cause of relapse, is therapy 
resistance. In response, researchers have developed model systems to better understand therapy resistance. 
Cancer research employs several model systems that reflect the biology of actual human tumors: in vitro 
models (2D, 3D cell cultures), in vivo models (PDX, GEMMS, transgenic), proteomic models, and 
computational or mathematical models. One cancer that has been extensively modeled is pancreatic ductal 
adenocarcinoma (PDAC). PDAC is the third most common cause of annual cancer deaths in developed 
countries; as its incidence and mortality rates continue to increase, PDAC is projected to be the second 
leading cause of cancer deaths by 2030. Although chemotherapy is a pillar of clinical PDAC treatment, 
its outcome typically leads to multi-drug resistance, drastically restricting the curative effect of drugs for a 
variety of tumors. Elucidating the underlying mechanisms for resistance through different models is 
essential for the development of new strategies and therapies. This review provides insight into the range 
of in vitro and in vivo models of pancreatic cancer used in preclinical research. This paper provides an 
overview of platforms for cancer research with a focus on those devoted to resistance mechanisms in 
PDAC and to the primary therapeutic intervention for PDAC, gemcitabine (GEM). 

Keywords: therapy resistance, pancreatic cancer, in vivo models, in vitro models, computational, 
proteomic, gemcitabine, PDAC

1. Introduction 
 One of the most studied cancers is pancreatic 
ductal adenocarcinoma (PDAC) due to its high 
stemness and tumorigenicity, making it a major 
health concern that warrants greater efforts 
towards earlier detection and improved treatment. 
There are several models for PDAC on cancer 
resistance due to the extremely deadly nature of       
PDAC. Some of the newest and acclaimed models 
are for PDAC cells. This review will highlight the 
models used for examining drug resistance in 
PDAC.  
 PDAC is the third most common cause of 
annual cancer deaths in developed countries, and 
the incidence and mortality rates of this disease are 

climbing. This disease is projected to be the second 
leading cause of cancer deaths by 2030.67 Despite 
increased survival resulting from various 
multidisciplinary curative and palliative treatment 
options, including surgical removal, stent 
placement, and nonsteroidal anti-inflammatory 
drugs, the disease outcome is most often fatal. The 
most recent advances in chemotherapeutic 
treatment extend the average survival to 8.5-9.4 
months.68,69 Currently, the 5-year survival rate for 
PDAC for all stages is the lowest among all the 
cancers at 9%, which drops to 3% for patients 
diagnosed with distant or metastatic PDAC.70 The 
lack of effective treatment options for PDAC 
contributes to the low survival rate, pointing to the 

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need for improved treatment and earlier 
detection.67 
 Medical and surgical treatments for this highly 
lethal disease are limited and often ineffective. For 
patients with unresectable PDAC, therapy is 
restricted to chemotherapy, for which most 
eventually develop resistance. Gemcitabine 
(GEM) emerged in 1997 as an alternative for 5-
Fluorouracil and ultimately improved survival by a 
few weeks. The introduction of the 
FOLFIRINOX treatment scheme (5-fluorouracil, 
leucovorin, oxaliplatin, and irinotecan) also 
contributed to a small improvement in survival for 
patients with an advantaged stage of the disease.55 
Currently, the most effective and tolerated drug is 
nab-paclitaxel (n-PTX) and, when used with 
GEM, has modestly prolonged median overall 
survival.54,69 
 The poor clinical situation and the fact that 
only three improvements have been introduced 
over the last 20 years underscore how effective 
therapeutic strategies for patients with PDAC 
have been difficult to identify. Despite numerous 
preclinical investigations and clinical trials, only 
moderate progress has been made in improving 
therapeutic strategies.55 Thus, there is a desperate 
need for novel drugs, improved radiation 
protocols, and increased avenues for second- and 
third-line therapies.55 
 Although chemotherapy is one of the pillars of 
clinical cancer treatment, its outcome typically 
leads to multidrug resistance, drastically restricting 
the curative effect of drugs for a variety of tumors, 
such as in pancreatic cancer patients.7,71 Many 
cancer types that are initially susceptible to 
treatment often develop therapeutic resistance over 
the course of the therapeutic regimen. Resistance 
is due to several intracellular factors, including 
genetic and epigenetic changes in signaling 
pathways, drug-metabolizing enzymes, and drug 
efflux pump mechanisms.7,71 Pancreatic tumors are 
especially characterized by genetic instability, 
intra-tumoral heterogeneity, and distinct 
desmoplastic stroma that makes it difficult to 
effectively develop therapeutic strategies for 
PDAC. The lack of innovative approaches to 

treating PDAC stems from the high degree of 
heterogeneity of this tumor with several different 
histopathological subtypes and limited knowledge 
on the molecular mechanisms behind tumor 
development and progression.  
 The development of chemotherapeutic 
resistance in cancer patients poses a major clinical 
problem for chemotherapeutic treatment. The 
elucidation of underlying mechanisms for 
resistance through different models is essential for 
the development of new strategies and 
therapies.7,71 Therapeutic resistance in PDAC has 
been explored through various cell culture and 
animal model systems. Cell cultures include two-
dimensional (2D) culture conditions and, most 
notably, three-dimensional (3D) culture strategies, 
such as organoids and spheroids.36 3D culture 
models have incorporated pancreatic stellate cells 
(PSC) to investigate the prominent 
desmoplastic/stromal reaction in PDAC.62 
Moreover, cells grown in 3D models showed 
resistance to GEM and n-PTX, drugs frequently 
used for PDAC treatment.36,62 Animal model 
systems include patient-derived xenografts 
(PDXs) and genetically engineered mouse models 
(GEMMs).36 
 This review provides an overview of the range 
of in vitro and in vivo models of pancreatic cancer 
that are being used in preclinical research. It 
considers an overview of platforms for cancer 
research with a focus on those devoted to resistance 
mechanisms in PDAC and the therapeutic 
intervention gemcitabine. The goals of the paper 
are to examine the impact of each tumor resistance 
model for PDAC.  

2. Background 
 Therapeutic resistance mechanisms can be 
tumor cell-intrinsic (present before treatment)1, 
acquired during treatment by various therapy-
induced adaptive responses,2 or mediated by the 
tumor microenvironment (TME).3,4,5,6 Tumor 
molecular and genetic heterogeneity is the main 
reason for the failure of conventional cancer 
therapy as resistance can arise from the positive 
selection of a drug-resistant tumor subpopulation.5 

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The high adaptability of tumors through activation 
of pro-survival signaling pathways and the 
inactivation of downstream death signaling 
pathways can lead to drug resistance.2 The 
activation of epidermal growth factor receptor 
(EGFR) also serves as a resistance mechanism 
against chemotherapies, including treatments for 
pancreatic cancer, such as 5-fluorouracil, 
irinotecan, and nanoparticle albumin-bound 
paclitaxel (nab-paclitaxel).5 

 Mechanisms of resistance to cytotoxic and 
targeted chemotherapeutics include an increased 
rate of drug efflux, alterations in the drug target, 
activation of pro-survival compensatory signaling 
pathways, and ineffective induction of cell death.5 
Cell plasticity also facilitates adaptive cellular 
reprogramming to drive acquired drug resistance.2 
 Drug-resistance mechanisms are regulated by 
the TME, epithelial-mesenchymal transitions 
(EMTs), and microRNA.7 Excellent reviews on 
the contents of the TME have been published 
previously4,5,10,13,17,20,21 and from these works, it is 
clear that the TME consists of the extracellular 
matrix, cancer-associated fibroblasts, immune and 
inflammatory cells, tumor-associated macrophages 
(TAM), and blood vessels that provide refuge for 
cancer cells from cytotoxic agents. Additionally, 
epithelial cells can undergo a transition to become 
mesenchymal cells by losing their polarized 
organization and tight cell-cell junctions to change 
cell shape and develop a fibroblast-like 
morphology.5 Studies have found a correlation 
between chemotherapeutic resistance and the 
EMT.23,24 Similarly, microRNAs (miRNAs), a 
class of small non-coding RNAs that negatively 
regulate genes at the post-transcriptional level, 
have also been shown to affect drug resistance.7 
Recent studies have found a correlation between 
miRNA expression and resistance towards 
chemotherapeutic targets.7 

 

 Cancer research typically involves these drug-
resistance mechanisms and relies on model 
systems, which reflect the biology of human 
tumors to a certain extent.25 Models throughout 
cancer research history have addressed all stages of 
drug discovery, including target identification, 
toxicity, and individual patient prediction. Initial 
molecular biology models have increased 
understanding of tumor cell biology, while new 
model systems simulate functional processes 
related to the development and growth of cancer. 
Cell cultures, namely those derived from a cervical 
cancer patient Henrietta Lacks (HeLa), became 
the first laboratory model for cancer research in 
understanding tumor biology, drug identification, 
and drug development.25 In addition to 2D cell 
cultures, other models currently in use include in 
vitro conditionally reprogrammed cell (CRC) 
lines, 3D cell cultures, organoids, spheroids, and 
tumor-on-the-chip models, in vivo zebrafish 
models, patient-derived xenograft (PDX) mouse 
models, genetically engineered mouse models 
(GEMMs), and transgenic mouse models, as well 
as computational or mathematical models. 
Considering how mechanisms for tumor hypoxia, 
senescence, and cytoskeletal organization vary, a      
current challenge in cancer research is selecting the 
model that best reflects the given tumor entity. 
Thus, the roles of cellular senescence, dormancy in 
tumor formation, and therapy resistance have 
become increasingly important and relevant in 
cancer research.26 This paper will survey these 
topics and investigate their utility in various 
applications. 

3. Models in Cancer Research 
 This section describes each of four categories 
of cancer research model systems: in vitro models 
(2D and 3D cell cultures), in vivo models (PDX, 
GEMMS, transgenic), proteomic models, and 
computational or mathematical models (Figure 1). 

Figure 1. Models in Cancer Research 

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3.1 In Vitro Models 

3.1.1 2D Cultures 

 The use of in vitro models has allowed for 
preclinical and translational studies of tumor 
mutations, aberrations, and responses to 
therapeutic agents.27 2D cell culture models are 
inexpensive and easy to generate and maintain, 
making them the mainstay for cancer research.      
Still, these models have limitations, including 
induced alterations in cell morphology that 
translate to changes in gene and protein 
expression. 3D cell cultures have partially 
overcome this shortcoming.27 
 
 Conditionally reprogrammed cells  (CRCs) 
overcome senescence to produce immortalized 
primary cell cultures through a method described 
by Liu et al. (the Georgetown method), which 
combines irradiated mouse fibroblasts as a feeder 
cell layer with the use of a Rho kinase (ROCK) 
inhibitor.28 Cells can be derived from both normal 
and cancer tissues and grown indefinitely under 
these conditions while maintaining a normal 
phenotype.25 The irradiated mouse fibroblasts 
maintain telomerase reverse transcriptase 
(hTERT) expression to prevent chromosome 
shortening and tumorigenic cellular 
transformation of normal cells. Furthermore, the 
use of ROCK inhibitors maintains the 
undifferentiated and proliferative state of epithelial 
cells. In combination with the feeder cells, ROCK 
inhibitors prevent the transformation or 
senescence of cultured cells.25 Thus, Liu et al. 
demonstrated that irradiated murine fibroblasts 
and a ROCK inhibitor are essential for both initial 
survival, unlimited expansion, and senescence 
prevention.28 

 Yuan et al. were one of the first to use CRCs 
to generate cell cultures from the patient’s normal 
and tumorous lung tissue and study tumor 
progression in recurrent respiratory 
papillomatosis.29 By co-culturing the primary cells 
with J2 murine fibroblast cells and a medium 
containing ROCK inhibitor Y-27632, they were 
able to detect a mutation of HPV-11 in the CRCs 

that may contribute to aggressive clinical behavior. 
Furthermore, the CRCs were used in screening 
potential drug therapies, which allowed for the 
identification of vorinostat as a potential therapy 
for patients with recurrent respiratory 
papillomatosis. Thus, this method of generating 
cell cultures from many epithelia can be used in 
personalized medicine and to study other cancers 
and diseases. CRCs have also been used in 
regenerative medicine, drug sensitivity testing, 
gene expression profiling, and xenograft 
studies.29,30,31,32 

 Recent models with CRCs include those for 
prostate cancer, which aimed to define potential 
new therapies and observe drug sensitivity and 
resistance. Naeem et al. modeled prostate cancer 
using CRCs of normal and prostate cancer (PCa) 
cells derived from treatment-naive patients with 
primary PCa.31 Using an integration of an in silico 
proteochemometric network pharmacology 
platform and in vitro methods with CRCs, the 
researchers examined drug response in sensitivity 
assays on PCa CRCs and predicted novel 
applications for PCa chemotherapies, including 
broad applicability to rapidly identity and test 
approved drugs. Additionally, Tricoli et al. 
presented a 3D non-spheroid model using CRCs 
for normal and tumor-derived PCa CRCs to 
describe the combined effects of a multi-
dimensional transwell platform and define culture 
media on PCa cellular proliferation, 
differentiation, and signaling.33 The use of a 
transwell-dish culture method (TDCM) enables 
multi-dimensional culturing of PCa CRCs, 
allowing for a more mature, stratified prostate 
epithelial phenotype that can be used for basic and 
translational studies of PCa.34 

3.1.2 3D Cultures 

 While 2D in vitro cell culture models are 
widely used for studying the basic biology and 
tumorigenesis of various cancers, 3D models more 
accurately mimic the native cancer tissue by 
preserving cellular heterogeneity and replicating 
some of the specific biochemical and 
morphological features of the corresponding tissue 

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in vivo. This similarity to in vivo tissue provides an 
advantage in the model for examining 
morphology, tumor microenvironment, invasion, 
metabolism, and cell-environment crosstalk that 
influences gene expression and cell behavior. 
Furthermore, 3D cell cultures serve as a model for 
experimental therapy studies using radiotherapy, 
chemotherapy, and cell- and antibody-based 
immunotherapy.26 Several reports demonstrate 
that cancer cells can grow in non-adherent 
conditions, forming 3D structures, or spheroids, 
that show increased resistance to drugs frequently 
used for cancer treatment.27 Due to their flexibility 
in mimicking tumor microenvironments through 
modifying cell culture conditions, 3D culture 
models are useful tools for studying cancer 
development and potential targets for therapeutic 
intervention.  
 The most innovative and promising approach 
for in vitro modeling employs tumor tissue 
organoids, which are self-organizing, multicellular 
structures derived from primary tissue and grown 
in well-defined conditions. Several methods for 
developing organoid lines and biobanks have been 
used for prostate cancer,35 pancreatic cancer,36 and 
colorectal cancer37 for in vitro drug testing. While 
drug sensitivity is usually exploited using rapid in 
vitro screening through a short-term culture of 
tumor sections and in vivo screening through 
xenotransplantation of the tumor into 
immunodeficient mice, organoid technology 
bridges these two approaches to produce a feasible 
medium-throughput drug screen on patient-
derived organoids (PDOs).35,37 These 3D 
organoids can maintain their complex architecture 
and reproduce their marker expression, allowing 
several research groups to study tumor 
development and test for drug efficacy.6,27,36,37,38,39,40 
Organoid cultures typically use Matrigel or 
collagen and support the growth of both normal 
and cancer tissue such that cultures can be 
manipulated at the genetic level by transfection.27 
 When cultured with stroma and fibroblasts, 
organoids have the potential to simulate the full 
spectrum of patient cancer progression and study 
normal cells, preinvasive carcinomas, and 

metastatic cells.6,41,42 Furthermore, PDO co-
cultures with immune cells,43,44 cancer-associated 
fibroblasts,45 and stellate cells42 can identify tumor 
microenvironment characteristics and determine 
mechanisms for resistance to traditional and 
investigational drugs.6 

 Organoids are generated rapidly and reliably, 
especially with endoscopic ultrasound (EUS)-
guided tissue acquisition at the time of initial 
diagnosis,41 making them more usable for patients 
who need targeted treatment as quickly as possible. 
The development of PDO biobanks also greatly 
expands the type of patient samples that can be 
propagated and studied, and these samples can 
then be used to accurately predict drug responses 
in a personalized treatment setting.6 

 Tumor- or cancer-on-a-chip (CoC) models 
have been used more recently to dissect the role of 
tumor microenvironment cues and their role in 
metastasis.22 These microfluidic chip device 
models enable control over local gradients, fluid 
flow,46 tissue mechanisms111, and composition of 
the local environment through micrometer- to 
millimeter-sized compartments and 
microchannels.22 The small chamber for cell 
culture creates a niche in which tumors can grow, 
develop, and interact in a specified 
microenvironment.22 

 By making it possible to manipulate the 
variables listed above, CoC models overcome some 
of the limitations of 2D or 3D cell cultures and 
animal models. The CoC model used depends 
upon the particular tumor microenvironment cues 
that researchers aim to understand. The many 
different types of CoC models include 2D chips, 
lumen chips for CAFs,47 compartmentalized chips 
for TAMs,48,49,50 CAFs,32,51 Y chips for CAFs,52 
and membrane chips. Many models have a gel-
fluid interface lined with endothelial cells.48,53 CoC 
models have helped better understand the 
invasion-related interactions between CAFs and 
cancer cells, as well as how activation of TAMs 
enhances cancer invasion through live observation 
of microenvironmental dynamics. 
 

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3.2 In Vivo Models 
 An in vivo model, zebrafish, serves as an 
intermediate of cell culture models and 
mammalian models. This zebrafish model allows 
for extracorporeal fertilization and has a well-
researched translucent embryo, short generation 
time, as well as a well-developed genetical toolbox, 
which makes the zebrafish ideal for studying 
development.26 Zebrafish melanoma models have 
shown that the cause of cancer formation is the de-
differentiation of epithelial cells to form embryonic 
neural crest cells.26 Moreover, zebrafish xenograft 
models can also be used to determine the tumor-
forming capacity of PDAC CRCs and assess 
whether chemotherapeutic resistance was retained 
in vivo.54 
 Therapy-resistance in vivo animal models 
mainly use mice due to optimal short generation 
time (10 weeks), average life expectancy (2.5 
years), the possibilities for reverse genetics, and the 
frequent occurrence of cancer in the absence of 
oncogenic agents. The use of mouse models dates 
to the late 1960s and the development of immune-
deficient mouse strains.26  
 Patient-derived xenotransplants (PDX) use 
chemotherapy-naive tissue obtained from surgery 
or biopsies and transplant them into immune-
deficient mouse strains, like nude or severe 
combined immunodeficiency (SCID). 26,55,56,57,58,59,6 

Immunocompetent and immunodeficient mice 
with xenografted tumors are traditionally 
transplanted subcutaneously or orthotopically.61,62 
These models are widely used due to their 
availability, low cost, and ability to mimic 
attributes of human malignancies by recapitulating 
neoplastic cell architecture and conserving genetic 
and phenotypic biology at the histological and 
molecular models.55 This makes PDX models the 
favored method of identifying drugs that 
significantly inhibit tumor growth, validating 
tumor biomarkers, and predicting treatment 
outcomes.55  
 Highly sophisticated transgenic mouse models 
allow researchers to look at the early stages of 
tumor development and constitutively or 
conditionally induce the expression of an 

oncogenic mutation at a specific time and in a 
specific organ using conventional methods, such as 
retroviral infection, microinjection of DNA 
constructs, and the “gene-targeted transgene” 
approach.26,63,64 The use of transgenic models has 
been important in studies evaluating the 
development of resistance to therapy.63 Knockout 
transgenic mice, in which the gene is depleted or 
silenced to cause a loss of gene function, are a 
powerful tool for assessing the potential validity of 
a targeted therapy because the targets can be 
precisely inactivated in the developing or 
developed tumor.63 Recently, the use of 
CRISPR/Cas9-based transgenic models has 
allowed for more effective systems to study human 
cancers.63 Due to advances in immunotherapy that 
have illuminated the importance of immune 
response in tumor progression and treatment, new 
PDX models, namely those that interact with the 
human immune system, are necessary.  
 Humanized mouse xenograft models replace 
the mouse immune system with a human immune 
system.  These models transplant CD34+ human 
hematopoietic stem cells (HSCs) into mice to 
produce human blood cells. They are potentially 
valuable models for new immunotherapies because 
they mimic tumor heterogeneity, the tumor 
microenvironment, and crosstalk between the 
tumor and stromal/immune cells.63  
 Wang et al. developed human hematopoietic 
and immune systems in mice transplanted with 
human (h)CD34+ hematopoietic progenitor and 
stem cells.65 After implanting the PDX of non-
small cell lung cancer (NSCLC), sarcoma, bladder, 
cancer, and triple-negative breast cancer into the 
humanized NSG (huNSG) mice, the researchers 
discovered that tumor growth curves in the 
humanized mice were similar to those in non-
human immune cell-engrafted NSG mice. 
Additionally, treatment with pembrolizumab, 
which targets programmed cell death protein 1, 
produced significant growth inhibition in PDX 
tumors in huNSG mice but not in NSG mice. 
These results suggest that tumor-bearing huNSG 
mice can serve as a novel platform for testing the 
efficacy of immunotherapies. Future data collected 

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from these humanized mouse models will enable 
the development of predictive cancer biomarkers 
of response to chemotherapies. Chang et al. 
described a novel orthotopic renal cell carcinoma 
(RCC) xenograft humanized mouse model as an 
improved model to evaluate in vivo anti-tumor 
capabilities of fully human monoclonal antibodies 
for RCC therapy.66,55 

 Genetically engineered mouse models 
(GEMM), which are produced by modifying 
specific genes associated with cancer, provide an 
authentic, bridging model to patients, as the 
tumors created are aggressive, heterogeneous, and 
stromal (desmoplastic) in nature.55 In addition to 
sharing similar genetic, phenotypic, and 
physiological characteristics with humans, 
GEMMs also suffer from typical cancer symptoms 
(bodyweight loss, cachexia, etc.) and the 
spontaneous formation of distant metastasis.55 The 
development of these symptoms makes it possible 
to simulate different stages of tumorigenesis. 

3.3 Computational Models 
 Therapy resistance has also been studied 
through computational, theoretical, and 
mathematical models. The possibility of using 
bioinformatics models to create personalized 
medicine applies datasets to tumor material, the 
genome of the cancer patient, and metabolic 
pathways. Mathematical models based on partial 
differential equations often deal with the growth of 
cancer cell lines in vitro while considering 
parameters like initial cell density and 
concentrations of cell cycle inhibitors.26 These 
simulations can predict the conditions in which the 
tumor cells will die out.  

3.4 Gemcitabine Resistance and PDAC models 
 GEM is a nucleoside analog used in 
chemotherapy for non-small cell lung, pancreatic, 
bladder, and breast cancers.72 Since its approval by 
the FDA in 1996, GEM has been used as a first-
line treatment for patients with locally advanced 
(nonresectable Stage I/II) or metastatic (Stage IV) 
PDAC and remains the first-choice treatment for 
PDAC. GEM inhibits DNA synthesis, acting as 

an analog of cytidine to prevent chain elongation, 
and further induces apoptosis in cancer cells via 
caspase signaling.73 However, GEM treatment 
resistance along with the poor pharmacokinetic 
profile of GEM (8-12 mins in humans due to rapid 
metabolism) has resulted in poor treatment 
outcomes and drug resistance development over 
time.71,74 

4. Gemcitabine Resistance and In Vitro Models 

4.1 Gemcitabine Resistance and 2D Cultures 
 Panc-1, MiaPaCa-2, SW1990, and Capan-2 
are 2D cell cultures that remain platforms used to 
study GEM resistance mechanisms and improve 
the efficacy of GEM in combination with other 
therapies.55,72 These cancer cells have the capability 
to generate high-fidelity in vitro models to explore 
the efficacy of anticancer drugs. Pancreatic cell 
lines (Capan-1, T3M4, MiaPaCa-2) with 
acquired GEM resistance (GEM-R) have 
elucidated resistance mechanisms that include 
signaling crosstalk to increases glucose uptake75 
and kinase inhibitors capable of inhibiting the 
growth of GEM-resistant MiaPaCa-2 cells.76 

 Affram et al. investigated the cytotoxic effects 
of an alternative drug delivery system in the form 
of GEM-loaded solid lipid nanoparticle (GEM-
SLN) on patient-derived primary pancreatic cell 
lines (PPCL-46) and MiaPaCa-2 pancreatic 
cancer cells.72 Solid lipid nanoparticles (SLNs) are 
nanocarriers that can be used as an alternative drug 
delivery system to improve therapeutic 
effectiveness for drugs, like GEM, with a short 
half-life that requires continuous parenteral 
administration. The researchers’      cytotoxicity 
studies found a greater cytotoxic effect of GEM-
SLN treated PPCL-46 than of GEM 
hydrochloride (GEM-HCl) treated PPCL-46 
cultures. A similar trend of higher GEM-SLN 
inhibition was found in MiaPaCa-2 cultures as 
well. These results indicate the potential for 
enhanced GEM delivery and improved anticancer 
activity through GEM-SLN. Additionally, 
GEM-SLN is effective on both PPCL-46 cultures 
and established commercially available cell lines, 

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indicating moderate effectiveness in addressing the 
heterogeneity of pancreatic cancer cells. 

4.1.1 Gemcitabine Resistance and 2D Co-Cultures 

 Pancreatic stellate cells (PSC) are often used in 
co-culture experiments with PDAC cells to 
illuminate microenvironmental issues and provide 
a more accurate model than a single epithelial 
monolayer model.77,78,79,80 PDAC often displays a 
dense desmoplastic stroma, which has been 
associated with chemoresistance and inhibition of 
drug penetration.81 3D matrices of PSC cells 
stimulate life-like settings and studies on these 
matrices reveal that additional tumor stroma 
components (fibroblasts, macrophages, immune 
cells, and endothelial cells) play an important role 
in therapy resistance.55 

 Karnevi et al. highlighted the role of PSCs in 
the epithelial-mesenchymal transition (EMT) as 
an intrinsic part of cancer progression that 
downregulates epithelial phenotype and cell-cell 
adhesions.24 Co-cultures of immortalized primary 
PSCs with Panc-1, MiaPaCa-2, and BxPC-3 
revealed down-regulated E-cadherin levels and 
increased expression of vimentin, both of which 
indicate the role of PSCs in modulating the 
epithelial-mesenchymal transition (EMT).  
 More recently, Xiao et al. found increased 
expression of Yes-associated protein 1 (YAP1), a 
protein known to induce cancer-associated 
fibroblast activation in liver and breast tissues, in 
PSCs.82 A co-culture with MiaPaCa-2 and human 
PSCs from residual surgical specimens revealed 
that YAP1 may play a critical role in the regulation 
of PSC activation, indicating a novel rationale for 
targeting YAP1 to reprogram the PDAC 
microenvironment.82 In addition to YAP1, it is 
speculated that secreted protein acidic and 
cysteine-rich (SPARC), a matricellular 
glycoprotein used in ECM assembly and cell-
matrix communication during tumor progression, 
may also be related to PSC activation. High levels 
of SPARC expression in stromal cells indicated 
poor prognosis of PDAC patients and acted as a 
negative predictive biomarker in patients treated 
with GEM based chemotherapy.81 

 
 New cell lines, of human and murine origin, 
are continually being established and characterized 
for use in screening novel drug candidates and 
elucidating signaling pathways or (epi)genetic 
events involved in tumor development, 
progression, and the outcomes of therapy.55 

4.1.2 Gemcitabine Resistance and Conditionally 
Reprogrammed Cell Lines 

 Studies on PDAC CRCs have been 
investigated with nab-paclitaxel, but not GEM.34 
Drug sensitivity screens were conducted for 
cultures of muscle-invasive bladder cancer that 
revealed sensitivity to GEM. These results showed 
that CRCs are a feasible platform for personalized 
drug sensitivity testing for bladder cancer.83 

4.2 Gemcitabine Resistance and 3D models 
 The application of 3D models has been a 
growing trend in PDAC studies, especially with 
developing models for drug screening. Oftentimes, 
chemotherapeutics that were effective in 2D 
models do not remain effective in 3D models. 
Different IC50 values between 2D and 3D models 
point to the clear discrepancy between the 
commonly used 2D drug screening versus the more 
complex 3D and co-culture models. Furthermore, 
2D cell culture is known to not fully recapitulate 
tumor biology.73 3D culture models better reflect 
actual tumor drug responses and aid in the 
identification of novel compounds that are more 
effective.76 Differences between pancreatic cell 
lines in the morphology of both 2D and 3D 
cultures can be attributed to differences in      
origination site. BxPC-3 obtained from PDAC 
lacks metastatic potential, while MiaPaCa-2 and 
PANC-1, both of which are also derived from 
PDAC, demonstrate a predisposition to 
metastasis.84 COLO-357 and AsPC are obtained 
from metastatic sites, while T3M4 cells are derived 
from the lymph node metastatic mass and 
resemble BxPC-3.84  
 Many models are based on co-cultures with 
other cell types or cells believed to contribute to the 
transformed phenotype and invasiveness of the 

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cells. Notably, stromal-tumor cell interactions are 
actively studied in PDAC drug resistance.72 Recent 
research considers the tumor microenvironment 
that has been associated with metastatic 
progression and vascularization. These complex 
systems are easier to model using 3D structures, 
such as multicellular tumor spheroids (MCTS) 84 
and organoids.6 While spheroids and organoids 
both form 3D models, they differ in their 
morphology. Derived from tissue or cancer stem 
cells, spheroids grow in a minimum serum-free 
medium.  They trigger an oxygen and nutrient 
gradient that leads to massive cell death in the 
center of the structure, which is why they cannot 
form tissue-like structures. In contrast, organoids 
require stem cell niche factors and extracellular 
matrices, which allow the organoids to 
differentiate and self-organize. Additionally, 
organoids do not exhibit a hypoxic or nutrient 
gradient.85 

4.2.1 Gemcitabine Resistance and Tumor Spheroids 

 MCTS serve as models of PDAC tumors 
superior to flat cell monolayers (2D cultures) due 
to different geometry that leads to changes in 
nutrient and oxygen turnover as well as cell 
crosstalk. Current MCTS models are derived from 
several epithelial cell lines, including AsPC-1, 
BxPC-3, Capan-1, MiaPaCa-2, and PANC-1.84 
Svirshchevskaya et al. identified three types of 
MCTS: while Type I, BxPC-3, and T3M4      
formed a small number of large and dense 
spheroids, Type II, COLO-357, and AsPC-1, 
generated by E-cadherin contacts, formed 
multiple and loose MCTS of different sizes84. 
Type III, MiaPaCa-2, and PANC-1 cells grew as 
floating monolayer films as they were unable to 
form MCTS. Cell growth for these 3D cultures 
and monolayers revealed a dramatic (2 order) 
reduction in cell proliferation for 3D type I cell line 
cultures treated with GEM. Drug resistance in the 
type I 3D cultures was found to be associated with 
a quiescent state (decreased proliferation) and a 
high level of spontaneous apoptosis in cells. 
Meanwhile, type II and III MCTS had 
comparable sensitivity to the antitumor drugs. 

 Ware et al. describe the generation of a 3D 
PDAC in vitro micro-tumor model that 
encompasses a stromal component using PSCs, 
which are myofibroblast-like cells located in the 
exocrine areas of the pancreas.62 PSCs play a role 
in normal pancreatic architecture as they secrete 
extracellular matrix (ECM) components and are 
the principal source of fibrosis in the stroma. 
Additionally, sequestration of chemotherapeutic 
agents, such as GEM, occurs in the tumor stroma, 
effectively reducing the amount of drug that can 
reach cancer cells. Their PDAC stroma spheroids 
model presented decreased cytostaticity of GEM 
when compared with spheroids grown without 
PSCs. A study by Lee et al. corroborates these 
findings.86 This model will allow for improved 
knowledge of PDAC biology and can be used to 
investigate pathways that can be therapeutically 
targeted to inhibit PSC activation and subsequent 
development of fibrosis in PDAC. 

4.2.2 Gemcitabine Resistance and Organoids 

 Pancreatic ductal organoids are ex-vivo models 
that can be established using very small biopsies, 
such as fine-needle aspirates     36,41 and allow      for 
the study of localized, advanced, and metastatic 
patients. Boj et al. established organoid models 
from normal and malignant murine and human 
pancreas tissues to investigate the pathogenesis 
and address the deficiency in a comprehensive 3D 
cell culture model of murine and human PDAC 
progression.36 This model was used to identify 
genetic drivers, therapeutic targets, diagnostics, 
and progression for PDAC. 
 Tiriac et al. used EUS fine-needle biopsy 
(EUS-FNB) sampling to rapidly establish human 
PDAC organoids within 2 weeks of the EUS 
procedure and assessed the feasibility of this model 
in creating personalized treatment strategies at the 
time of initial tumor diagnosis and over the course 
of a patient’s treatment. PDAC patients are ideal 
for EUS-FNB derived organoids as most patients 
will not undergo surgery, all patients need a tissue 
diagnosis before therapies are initiated, and only a 
small amount of tissue is needed for organoid 
creation. Successful organoid generation is 

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necessary for developing personalized medicine 
platforms for PDAC patients.41 

 These 3D primary ex vivo culture systems 
model a spectrum of tumor stages and have 
elucidated important disease progression findings. 
Profiling with next-generation sequencing of 
DNA and RNA in combination with 
pharmacotyping can be used to predict responses 
in PDAC patients and provide a pathway for 
prioritizing therapy.87 

4.2.3 Gemcitabine Resistance and Tumor-on-a-chip 

 To overcome limitations of traditional disease 
model systems, organ- or tumor-on-a-chip 
systems aim to fully recapitulate the physiology 
and microenvironment of tissues through spatial 
and fluid control of tissue architecture. 
Kramer et al. examined the effect of interstitial 
flow on GEM resistance in PDAC using a 3D 
microfluidic platform. Interstitial pressure and 
flow are hallmarks of PDAC pathogenesis.73 The 
study used 3D cultures of S2-028 cells, a non-
metastatic pancreatic cancer line, and found an 
increase in mRNA expression of 5 multidrug 
resistance proteins (MRPs) when the PDAC cells 
were subjected to interstitial flow. This flow-
induced MRP expression hints towards another 
factor that contributes to GEM resistance via 
elevation of drug efflux transport.  
 
5. Gemcitabine Resistance and In Vivo Models 

5.1 Gemcitabine Resistance and Xenograft 
Models 
 Cell line-derived xenograft models, such as 
BxPC-3, MiaPaCa-2, and Panc-1 xenografts, are 
popular for drug screening and resistance studies in 
PDAC that can be applied to increasing patient 
survival. Novel tumor growth-inhibiting 
compounds derived from gemcitabine have been 
applied to Panc-1 and MiaPaCa-2 bearing mice.88 
 Xenograft tumor assays have demonstrated 
that the tetracyclic diterpenoid compound      
Ordonin overcomes gemcitabine resistance in 
gemcitabine-resistant Panc-1 cells (PANC-
1/GEM) through suppressing tumorigenicity in 

nude mice. Additionally, a combination treatment 
of oridonin and gemcitabine decreased tumor 
growth.88 Gemcitabine resistance is mediated by a 
special AT-rich sequence binding protein 1 
(SATB-1), a chromatin organizer that is secreted 
by cancer-associated fibroblasts (CAFs).89 SATB-
1 plays a vital role in the proliferation capacity of 
SW1990 tumor cells in mouse xenograft models.      
SATB-1 has been associated with poor prognosis 
and tumorigenesis in pancreatic cancer.89,90,91  
 Xenograft models have also revealed the role of 
Prolactin receptors in suppressing tumor growth. 
Dandawate et al. studied the role of prolactin 
receptors in PDAC through the injection of a 
diphenylbutylpiperidine-class antipsychotic drug, 
penfluridol, which binds to prolactin receptors.92 
Penfluridol slowed the growth and volume of 
tumors in Panc-1 xenografts in athymic nude mice 
and PDX in immunodeficient NSG. Western blot 
analyses suggested that penfluridol induces 
autophagy-related proteins p62, ATG-5, ATG-7, 
ATG-12, LC3B, and beclin-1 to suppress PDAC 
tumor growth.92 Additionally, cysteine transport, 
xCT, is key to tumor growth. Tumor xenograft 
growth of genomic knockouts of xCT was delayed 
but not suppressed, indicating the key role of xCT 
and the presence of additional mechanisms for 
cysteine homeostasis in vivo.93 
 Patient-derived xenograft (PDX) models are 
widely used for various solid tumors. Typically, 
chemotherapy-naive tumor tissue obtained from 
surgery or biopsies is transplanted directly into 
immuno-deficient mice.55 PDX models for 
pancreatic cancer are used to identify drugs that 
significantly inhibit tumor growth or to validate 
prognostic biomarkers, as these models      are 
highly representative of their respective tumors due 
to a high degree of genetic stability observed by 
short tandem repeat (STR) profiling and mutation 
analysis.94 Well-defined PDX collections can be 
used to associate biomarkers with drug sensitivity 
and resistance to facilitate precision cancer 
medicine. Most publications on pancreatic PDX 
models describe the establishment, 
characterization, and preclinical application of 

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Georgetown	Scientific	Research	Journal	
 

 

PDXs, but they have yet to be applied towards 
clinical studies.  
 Tang et al. used GEM treated PDX models to 
show that m6A demethylase ALKBH5 is 
downregulated. Overexpression of this 
demethylase sensitizes PDAC cells to 
chemotherapy. Thus, lower levels of ALKBH5 
predict poor clinical outcomes in PDAC and other 
cancers.95 Wei et al. used in vivo CRISPR gene 
knockout screening in PDX mice to identify 
effective lethal drug combinations that synergize 
with GEM for treating PDAC.96 Using a clinically 
relevant PDX model of PDAC with a patient 
tumor being propagated within the pancreas of 
athymic nude mice, they screened for chromatin 
regulators whose depletion may create conditional 
lethality with GEM. They found that inhibition of 
the protein PRMT5 led to synergistic vulnerability 
of PDAC cells to GEM. PRMT5 has been 
considered as a critical driver of cancer progression 
for multiple advanced-stage cancers. This study 
suggests that GEM treatment combined with 
inhibition of PRMT5 will have stronger effects 
and selectivity towards PDAC.96  

5.2 Gemcitabine Resistance and Genetically 
Engineered/Transgenic Models 
 A major drawback of PDX models is the use of 
immunodeficient mice that lack a competent 
immune system to investigate 
immunotherapeutics. Tumor cells used in PDX 
have also been passaged extensively in vitro, 
limiting tumor cell heterogeneity and potential 
biological relevance. Thus, genetically engineered 
mice that spontaneously develop PDAC are 
appealing for drug discovery, especially since 
tumors arise in competent and fully intact immune 
systems.  
 GEMMs are similar to humans in terms of 
genetic, phenotypic, and physiological 
characteristics. Models include KC (KrasLSL.G12D/+ 
and PdxCre) mice and KPC mice that have been 
used to investigate the influence of Kras.55 KC 
mice have normal pancreatic organogenesis and 
develop intraepithelial neoplasia (PanIN) that 
eventually progress to PDAC. KPC (KrasLSL.G12D/+, 

p53R172H/+, and PdxCre) mice have a conditional 
expression of the R72H mutation in the p53 gene 
in the KrasG12D context. KPC is the most 
extensively studied genetic model of PDAC for the 
evaluation of immunotherapy.97 At least 40 
GEMM have been generated for analysis of gene 
function in PDAC.98,99 

 Tadros et al. observed an increase in fatty acid 
synthase expression that corresponded with 
increased disease progression in PDAC 
GEMMs.100 Based on analysis and identification 
of the lipid metabolism pathway to be the most 
significantly enriched in tumors from patients with 
PDAC, they manipulated the fatty acid 
biosynthesis pathway. Fatty acid synthesis is also 
regulated by multiple transcriptional regulators, 
including c-MYC, which is significantly amplified 
in PDAC.54 Through treatment with orlistat, the 
researchers demonstrated a way to overcome 
GEM resistance in pancreatic cancer by regulating 
endoplasmic reticulum stress and stemness. 
 Buccholz et al. showed that depletion of 
pharmacological tumor-associated macrophages 
(TAMs) improves therapeutic response to GEM 
in KPC mice.101 Macrophages are abundant in 
fibro-inflammatory TME of KC and KPC mice, 
which has been shown to correlate with a worse 
prognosis in PDAC.102 Following enrollment of 
KPC mice that had developed pancreatic tumors 
with liposomal clodronate to deplete intratumoral 
TAMS and GEM, they demonstrated improved 
efficacy of GEM in the KPC. These results point 
to another instance of the role of the TME in 
GEM resistance. 
 Principe et al. evaluated the effects of 
prolonged GEM treatment using KPC mice and 
found increased CCL/CXCL 
cytokine/chemokine secretion and upregulation of 
immune surface proteins, including transforming 
growth factor β (TGFβ)-associated signals, in the 
tumor stroma.103 TGFβ-associated signals confer 
drug-resistant phenotypes to neighboring stromal 
cells and further enhance      the production of 
inflammatory cytokines/chemokines. 
 Halbrook et al. examined how tumor-
associated macrophages (TAMs) drive resistance 

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Georgetown	Scientific	Research	Journal	
 

 

to GEM in PDAC cell lines. They found that 
TAMs release a spectrum of pyrimidine species, 
such as deoxycytidine, that inhibit GEM through 
direct competition, hindering drug efficacy. KPC 
mice treated with GEM combination treatments 
had prolonged survival compared to control mice, 
indicating that inhibiting macrophage recruitment 
has the potential to improve current PDAC 
therapies, as seen with FOLFIRINOX.104 
 Özdemir et al. used transgenic 
(Ptf1acre/+;LSL-KrasG12D/+;Tgfbr2flox/flox) 
mice with deleted αSMA+ myofibroblasts in 
pancreatic cancer. Myofibroblast depleted tumors 
did not respond to GEM and resulted in multiple 
adverse outcomes. Their results suggest that 
fibrosis associated with myofibroblasts and type I 
collagen constitutes a protective response from the 
host rather than offering an oncogenic supportive 
role.105 
 
6. Gemcitabine Resistance and Proteomic and 
Computational Models 
 
 GEM resistance has also been investigated 
using proteomic and computational models. While 
biological processes of drug resistance have been 
described before, proteomics serves as a powerful 
tool for better understanding molecular 
mechanisms of GEM resistance.106 Proteomics 
investigates proteins whose expressions differ 
between drug-sensitive and drug-resistance cells. 
This method can provide system-wide views of 
signaling networks to better understand drug 
mechanisms of actions and interactions.106 
Proteomics also provides the knowledge needed to 
identify biomarkers and for targeting specific 
protein pathways.106 
 Chen et al. examined mechanisms associated 
with GEM-induced resistance using 2D-DIGE 
and MALDI-TOF mass spectrometry and 
compared the proteomic alternations of a panel of 
differential GEM-resistant PANC-1 cells and 
GEM-sensitive pancreatic cells. They found that 
33 proteins were differentially expressed between 
GEM-sensitive and GEM-resistant cells.107  

 Zhu et al. studied GEM with birinapant in 
PDAC. They identified 4069 drug-responsive 
proteins and quantified them in a time-series 
proteome analysis to highlight and quantify 
signaling pathways. Pathways related to DNA 
damage response, DNA repair, anti-apoptosis, 
pro-migration/invasion were implicated as 
underlying mechanisms for gemcitabine resistance. 
This study identified promising drug targets for 
future investigation.106  
 Law et al. analyzed clinical PDAC liver 
metastases with quantitative proteomics. Their 
proteomic analysis of molecular signatures unique 
to the disease subtypes identified GEM-induced 
alterations in proteins, such as serine 
hydroxymethyltransferase 1, that are associated 
with drug resistance.108      PDAC subtypes can be 
characterized using proteomics and can be used to 
inform first-line cancer treatment. These efforts 
illustrate the potential of applying proteomics to 
improve PDAC subtype classification and 
therefore early detection and treatment of PDAC. 
 Computational modeling of gemcitabine-
based therapies has also been conducted to 
determine optimal intervention strategies.109 
Furthermore, transcriptomics has      been 
implemented to better under the tumor 
microenvironment and chart changes in the 
fibroblastic landscape in PDAC progression.110 

  
7. Conclusion 
 
 Models are essential to addressing issues of 
drug resistance in cancer phenotypes. While GEM 
resistance in PDAC continues to result in dire 
outcomes for patients, models can serve as a step 
towards elucidating resistance mechanisms to 
improve treatment protocols. The number of 
models currently in use can reproduce a wide range 
of tumor mechanisms to ultimately understand 
factors such as cell-cell interactions and the tumor 
microenvironment. The use of various in vivo, in 
vitro, proteomic, and computational models will 
be crucial in making a clinical impact and 

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benefiting PDAC patients with better platforms 
for treatment and diagnosis.  
 
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Danya Adams
  
      �




	Table of Contents
	Letter From the Editors
	An Investigation Into the Mathematics of DecryptionTechniques in RSA Encryption,With an Implementation in Python
	GPR40 and Postsynaptic NMDA Receptors: A Pair Against Epilepsy
	The Burn Behind the Bullet: Understanding Black Mothers’ Experiences After Losing a Child to Gun Violence in Washington,DC-Baltimore City Metropolitan Region
	Cancer Models to Defeat Therapy Resistance in Pancreatic Ductal Adenocarcinoma
	About the Authors
	Meet the Staff
	Acknowledgements



