



































Berkeley
Pharma Tech
Journal of Medicine

Correspondence: 
gonzaga.patriciaangelica@gmail.com               

Keywords:
Tumor Heterogeneity, 
Immunotherapy, Cancer, Dual/
Multi Ex Vivo Armed T Cells 
(EATs), NanoRNPS, CAR-T cell 
Therapy  

Submitted August 11, 2023
Accepted April 5, 2024
Published June 28, 2024

Full Open Access

Creative Commons Attribution 
License 4.0

Abstract
Tumor heterogeneity refers to the phenomenon when tumors possess a medley of cell 
types, and it is one of the central challenges facing cancer treatment as it is associated 
with drug resistance and worse prognosis. Each cell type responds to treatment 
in a unique way, thus tumors with high levels of heterogeneity are not often fully 
treated by typical cancer therapies. There are a variety of novel treatments being 
developed that aim to eliminate the obstacle that heterogeneity poses by utilizing 
more personalized approaches. This review assesses immunotherapy, combination 
therapy, dual- or multi-ex vivo armed T cells, and a nano-Cas9 ribonucleoprotein 
system as treatment strategies. Although further research is required to ensure 
their clinical safety, these treatments show their potential to overcome the challenges 
posed by tumor heterogeneity in cancer development. This paper also discusses 
circulating tumor cells as a way to test therapeutic drugs and determine treatment 
progress. 

Tumor Heterogeneity and Therapeutic 
Challenges: Exploring Approaches and 
Future Directions
By: Patricia Gonzaga, Ammabel Tukiman, Noelie MacMullan, Jason Park, M.S.



1. Introduction

Cancer, a disease in which cells grow uncontrollably, was one of the three 
leading causes of death in 2022, with 607,790 deaths in the U.S.1 In 2023, an 
estimated 609,820 out of roughly 2 million people in the U.S. were 
predicted to die of cancer.2 However, treating cancer is rather complicated. 
Cancer is not merely one disease but rather a group of diseases that consist 
of various classi�cations, with each cancer generally named after the body 
part where the abnormal cell growth begins. The various types of cancers 
share certain hallmarks, such as invasion and metastasis, tumoral 
angiogenesis, and the evasion of apoptosis.

The most common cancers in the U.S. are breast, prostate, and lung cancer, 
but it can also occur in skin cells or bone tissues.3 The problem is that no 
two cancers are exactly alike, even if they are classi�ed as the same type or 
occur in the same body tissue. Each individual’s cancer contains unique 
combinations of genetic changes, which can a�ect a patient’s response to 
speci�c treatments.4 Researchers from the Wellcome Trust Sanger Institute 
analyzed 4,938,362 mutations from 7,042 cancers, investigated the genomic 
dynamics of tumors undergoing exogenous and endogenous mutational 
processes, and extracted more than 20 distinct mutational signatures.5 These 
statistics highlight the vast possibilities of genetic alterations a cancer cell 
could undergo, leading to more variation between and within tumors. 
Factors such as family history and lifestyle habits, especially smoking, also 
contribute to the uniqueness of each individual’s tumor. Hence, this 
individuality of cancer, also known as tumor heterogeneity, complicates its 
treatment.

Tumor heterogeneity refers to the disparities between tumors of the same 
type in di�erent patients, between cancer cells within a single tumor, or 
between primary and secondary tumors. Intratumor heterogeneity (ITH), 
which refers to the presence of a diverse cell population within a single 
tumor, poses the greatest challenge to cancer treatment today. It can be 
further distinguished by genetic and phenotypic properties, which vary and 
a�ect the behaviors of di�erent tumor cell populations. One system of 
tumor cell analysis is the Clustering, Classi�cation, and Sorting Tree 
(CCAST), which aims to target speci�c characteristics that can vary widely

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within malignant cells. It analyzes the genetic and phenotypic variations 
within tumor cells by distinguishing homogeneous subpopulations within a 
mixed group of single cells. For example, CCAST was applied to a breast 
cancer cell line and identi�ed at least �ve distinct cell types, which helped 
elucidate which tumor cell subpopulations warranted further investigation.6 

Thus, utilizing such diagnostic guidelines to identify variations in genetic 
and phenotypic properties is bene�cial as a prognostic indicator to better 
predict tumor response and guide therapeutic precision strategies. 
Associated with poor prognosis, outcome, and overall survival, ITH is 
thought to be a signi�cant factor in causing therapeutic resistance and 
treatment failure.7 Tailoring e�ective cancer treatments is also challenging 
due to the unique response patterns exhibited by individual patients. Thus, 
understanding tumor heterogeneity is crucial in treating cancer and 
overcoming therapeutic resistance.

Initially, the primary source of tumor heterogeneity was thought to be 
genetic or epigenetic alterations as scientists were beginning to understand 
the impact of tumor heterogeneity.8  While this understanding holds some 
truth, more studies in recent years have observed tumor heterogeneity of 
varying types, such as metabolic, cellular, spatial, and more. However, 
despite the e�orts of current common cancer treatments to address tumor 
heterogeneity, there is still a signi�cant risk of cancer cells resisting 
treatments and therapies through mutations, selective pressure, and other 
adaptation processes, setting back the development of more e�ective cancer 
treatments.

The latest endeavors have focused on developing novel strategies to 
overcome the challenges posed by tumor heterogeneity and combat 
treatment resistance and disease progression. Some of these treatments 
target unique features of the tumor, allowing for a more precise and 
personalized approach. We reviewed recent scienti�c literature for an 
overview of tumor heterogeneity, along with current and potential cancer 
treatments that surmount its challenges. In addition, we examined recent 
and ongoing clinical trials to analyze the purpose and design of the studies 
and gain more information on the signi�cance of their results. Through this 
review, we hope to provide insight into the causes and negative e�ects of

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tumor heterogeneity on individual cancer treatments, along with potential
strategies that could overcome such negative implications.

2. Tumor Heterogeneity

2.1 Overview of Tumor Heterogeneity

Tumor heterogeneity has countless causes that complicates its prevention
methods and treatment, with two of its leading causes being genetic and
environmental factors. Table 1 expands on the causes of tumor
heterogeneity, including genetic mutations, clonal evolution,
microenvironmental factors, and phenotypic plasticity.

Factors
Genomic
Instability

Clonal
Evolution

Microenvironmental
Factors

Phenotypic
Plasticity

De�nition

The higher
tendency of
cells to obtain
genetic
alterations9

The process by
which di�erent
subclones
within a tumor
change over
time11

Factors (e.g. oxygen,
nutrient availability,
immune cell
in�ltration) and
interactions with
surrounding stromal
cells in the tumor
microenvironment

The ability of
one genotype
to cause
di�erent
phenotypes to
arise in
response to
di�erent
environments15

Causes

Anything that
causes more
genetic
mutations
(e.g. limitless
replicative
potential10,
DNA repair
defects9)

- Typical
evolutionary
processes12

- In�uenced by
many factors,
such as the
tumor
microenviron-
ment,
metabolism,
growth factors,
and mutation

- Collection of tumor
cells that combine to
create the tumor
microenvironment13

- Consists of the
extracellular matrix,
immune cells, and
stromal cells13

- Epithelial-to-
mesenchymal
transition
(EMT) and
certain
transcription
factors inducing
EMT (e.g. Zeb1,
Twist)15

- Genetic
mutations and
epigenetic

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rate12 modi�cations

E�ects

- Results in
genetically
distinct
subpopulatio
ns of di�erent
cells →
Genetic
heterogeneity

- Leads to the
expansion of
speci�c
populations of
tumor cells11→
Cellular
heterogeneity

- Promotes angiogenesis
and an environment for
cancer cells to grow14

- Prevents immune cells
from in�ltrating the
tumor14

- In�uences
surrounding cells and
cancerous cells14

- Protects
metastasized
cancer cells from
ferroptosis, a
type of cell
death16

- Give rise to
subpopulations
of tumor cells
with distinct
functional
properties16→
Cellular
heterogeneity

Table 1. Factors contributing to tumor heterogeneity. This table summarizes the 
de�nition, causes, and e�ects of the di�erent factors that contribute to tumor 
heterogeneity–from left to right: genomic instability, clonal evolution, microenvironmental 
factors, and phenotypic plasticity.

There are four types of tumor heterogeneity: intratumor, intermetastatic, 
intrametastatic, and interpatient. All four types cause unique complications 
in the clinical process. This review focuses on intratumoral heterogeneity, 
which refers to phenotypic or genetic variation in cells within a single 
tumor. The other three types of tumor heterogeneity o�er additional 
context to the complexities of tumor heterogeneity. For instance, 
intermetastatic heterogeneity is the variety between two metastatic tumors 
within the same patient; metastatic tumors are two or more tumors that 
di�er from one another. Intrametastatic heterogeneity is ITH within a 
metastasis – there is variation within that single lesion. Lastly, interpatient 
heterogeneity is the di�erence in tumors between di�erent patients 
possessing the same type of cancer. Interpatient heterogeneity is the main 
reason for the need of personalized treatments, regardless of the level of 
variation observed within a single tumor.17

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Figure 1. Types of tumor heterogeneity. The di�erent-colored dots represent di�erent
subpopulations of cells in a tumor – in other words, tumor heterogeneity. The top left
circle represents interpatient tumor heterogeneity between two patients with tumors in
their liver, where despite having the same cancer, their tumors have unique subpopulations
of cancer cells. The top right circle shows intermetastatic heterogeneity in a single patient.
The diagram illustrates how di�erent subpopulations of cancer cells from the liver
metastasize to the lung and the brain, resulting in the presence of tumor heterogeneity
between the di�erent metastatic lesions. The bottom left circle displays intrametastatic
heterogeneity, where di�erent subpopulations of cancer cells are present within a lung
metastasis. Lastly, the bottom right circle shows intratumor heterogeneity in the liver of a
single patient, where the primary tumor contains di�erent subpopulations of cancer cells.

2.2 Intratumoral Heterogeneity Across Di�erent Types of
Cancers

Intratumoral heterogeneity can re�ect genetic alterations in the tumor,
which can a�ect disease progression and treatment response. For example,
mutations in genes like BRCA1, BRCA2, TP53, and HER2 in breast
cancer were identi�ed as risk factors in cancer development and can impact
treatment outcomes.18Additionally, in lung cancer, mutations in epidermal
growth factor receptor (EGFR) and KRAS, along with ALK and ROS1

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rearrangements, are signi�cant determinants of tumor behavior and
therapeutic choices.19,20 Identifying these genetic variations allows physicians
to accommodate treatment plans to tumor-particular genetic structures,
resulting in more e�ective treatments and improved patient outcomes. One
method that targets these genetic mutations is CRISPR-Cas9-mediated
genome editing, which can be utilized to target speci�c alleles, such as the
TP53 gene in KHOS and KHOSR2 cell lines of osteosarcoma. This
technology can hinder tumor proliferation and migration, as well as modify
drug sensitivity in cancer treatment.21

Cellular heterogeneity in ITH refers to the numerous subpopulations of
cells inside a tumor, each with precise traits and sets of behaviors. For
instance, cell types in breast cancer include luminal cells, basal-like cells, and
HER2-positive cells, all of which impact general tumor behavior due to
di�erent signaling pathways and levels of receptor expression. 22,23

Understanding cellular heterogeneity helps researchers and clinicians
identify potential targets for therapy since di�erent cell populations may
respond di�erently to various treatments. Targeting particular cellular
subpopulations would disrupt a tumor’s growth and development more
e�ectively. Table 2 provides a comprehensive overview of the genetic and
cellular heterogeneity observed in key cancer types.

Cancer Type Genetic Heterogeneity Cellular Heterogeneity

Breast Cancer
BRCA1, BRCA2, TP53,
HER2/neu mutations18

Luminal cells, basal-like cells,
HER2-positive cells22,23

Lung Cancer
EGFRmutations, KRAS
mutations, ALK rearrangements,
ROS1 rearrangements19,20

Adenocarcinoma cells,
squamous cell carcinoma
cells24

Colorectal
Cancer

APC, KRAS, TP53, BRAF
mutations25

Stem-like cells, di�erentiated
cells, tumor-in�ltrating
lymphocytes26

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Prostate Cancer PTEN, TP53, AR alterations27

Adenocarcinoma cells,

neuroendocrine cells, cancer
stem cells28,29

Bone Cancer
(Osteosarcoma)

TP53, RB1, p16INK4a

variations30

Osteoblastic cells,
chondroblastic cells,
�broblastic cells31

Table 2. Genetic and cellular heterogeneity of di�erent types of cancers. This table
illustrates genetic and cellular heterogeneity of di�erent types of cancers. Column 1
identi�es cancer types, column 2 indicates genetic heterogeneity, and the last column shows
cellular heterogeneity.

The presence of ITH across these di�erent types of cancers emphasizes the
signi�cance of personalized treatment approaches. Understanding a tumor’s
unique genetic and cellular landscapes can guide the selection of
appropriate therapies that target speci�c cell populations or overcome
resistance mechanisms. Integrating technologies like single-cell analysis and
spatial pro�ling techniques allows for a comprehensive assessment of ITH
and aids in the development of tailored treatment strategies.37

2.3 Determining the Degree of Tumor Heterogeneity

A tumor’s degree of heterogeneity is correlated with tumor prognosis,
genomic instability, tumor advancement, and immunosuppression. Higher
levels of heterogeneity (i.e. a tumor with a greater cell diversity) suggests that
the patient is more likely to experience worse treatment outcomes and drug
resistance. Knowing the level of heterogeneity a tumor possesses permits
researchers to begin planning specialized treatments since more diversity in
the cell population demands a more personalized treatment. This
knowledge also permits studies to be done on groups with high and low
levels of heterogeneity in order to determine the impact of ITH on a speci�c
treatment.36

Research has determined that three methods are the most accurate and
e�ective regarding how closely correlated they are with heterogeneity

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outcomes: DEPTH38 , DEPTH239 , and tITH40 , which all have comparable
performances. These newer algorithms di�er from those used in previous
studies in that they implement RNA sequencing as opposed to DNA
sequencing. Experiments using DEPTH, which stands for Deviating Gene
Expression Pro�ling Tumor Heterogeneity, were conducted for over 25
cancer types alongside 10,000 samples of TCGA pan-cancer. DEPTH
showed stronger correlations between tumor prognosis and anti-tumor
heterogeneity than DNA-based algorithms such as ABSOLUTE,
EXPANDS, MATH, and phyloWGS.38 DEPTH2 showed similar
correlations to the original DEPTH program, but it had the advantage of
being applicable to more gene expression pro�les as it does not reference
normal controls like other mRNA or DNA-based algorithms.39DEPTH2
was used in a recent 2022 study to separate groups into high and low
intratumor heterogeneity groups. These groups were then compared in a
study to determine a correlation between heterogeneity and chemotherapy
response in patients with colon adenocarcinoma.41 Finally,
transcriptome-based ITH (tITH) involves de�ning a network and
determining the distance between typical genetic sequences and cancer
sequences. Pathway-tITH is de�ned using the genes from one speci�ed
pathway. One study demonstrated that in 255 out of 291 pathways,
genomic ITH was strongly correlated with pathway-tITH, supporting
earlier �ndings that overall genetic diversity impacts variation in certain
pathways.40 Table 3 below displays information on the DNA-based
algorithms used in earlier studies, as well as the more recent RNA-based
algorithms primarily discussed in the paper.

Algorithm
Name

Description Features
Year

Created

ABSOLUTE42

Pro�les DNA from
heterogeneous cell populations
to determine cellular copy
number and identify variant
alleles

- Identi�es alterations
in cancer cells

- Evaluates tumor
ploidy estimates

2012

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MATH43

Measures intratumor genetic
heterogeneity based on
mutant-allele fraction

- Correlates ITHwith
mutations in TP53 and
HPV status

2013

EXPANDS44

Characterizes coexisting tumor
subpopulations using copy
number and allele frequencies

- Estimates tumor
purity and predicts
clonal subpopulations

- Quanti�es genetic
ITH

2014

PhyloWGS45

Combines somatic mutation
and copy number information
for subclonal reconstruction

- Provides complete

subclonal
reconstruction

2015

tITH40

Models gene relationships and
measures network disruptions
to assess ITH

- Shows positive
correlation with tumor
progression and worse
survival

2016

DEPTH38

Calculates ITH based on gene
expression pro�les from RNA
sequencing data

- Is associated with
genomic instability,
worse survival, and
decreased antitumor
immunity

2020

DEPTH239

Calculates ITH based on
disruptions of gene expression
pro�les without reference
controls

- Is associated with
worse survival and
more aggressive cancer
subtypes

2022

Table 3. Algorithms used to assess tumor heterogeneity. This table lists the algorithms
that are used to assess tumor heterogeneity in order of the year it was created. It also
explains how each algorithm works and what it assesses.

Single-cell RNA sequencing (scRNA-seq) has also shown promise in
helping develop personalized treatment plans. ScRNA-seq works by
isolating the tumor cells and running mRNA reverse transcription and

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cDNA ampli�cation before sequencing. As recent as 2020, single-cell RNA
sequencing e�ciently analyzed thousands of cells at once, making it more
useful for treatment than in previous years. A study found that scRNA-seq
could adequately determine clusters of cells associated with poor clinical
outcomes and �nd targets for immunotherapy treatments in an analysis of
triple-negative breast cancer (TNBC). Knowing the di�erent types of cells
in a tumor will become essential in the personalized treatment for
high-degree heterogeneous tumors, which will need a combination of
therapies in order to be e�ectively treated. There are, however, some
limitations to this technology, including cell integrity and viability, its
relatively high cost, and its integration with other genomic and protein
information. Further research to advance scienti�c and technological
developments, such as gentle extraction and data analysis methods, is
expected to overcome these challenges.46

2.4 Complications of Tumor Heterogeneity

Di�erent cell types respond to di�erent treatments, which is the reason
behind the utilization of combination therapies for tumors with high
degrees of heterogeneity. Using a single treatment can lead to a relapse of
cells that are more resistant to typical therapies. The �rst round of treatment
might e�ectively eliminate one type of cell, making the tumor appear
smaller. However, over time, the cells that were unresponsive to the
treatment will become the dominant cell type, which makes the tumor
drug-resistant and more challenging to treat further. The term for this
phenomenon is called selective pressure.10

A study observed 20 patients with TNBC treated with neoadjuvant
chemotherapy (NAC) to combat highly heterogeneous cells, a characteristic
of TNBC. They determined that this type of resistance can be acquired and
adapted. Additionally, they found evidence that the patients who
experienced relapses, as opposed to cancer elimination, had cells with
genetic markers for that chemoresistance. The researchers were able to
determine potential treatments for the cells that had genetic resistance using
single-cell RNA and DNA sequencing. These treatments included EMT
signaling, P13K inhibitors, and hypoxia inhibition using HIF-1 inhibitors.47,
48

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Another study investigated the role of tumor heterogeneity in the resistance
to EGFR-targeted treatment in colorectal cancer cells. Three
cetuximab-resistant derivatives of LIM1215, OXCO-2, and DiFi cell
lineages were utilized in next-generation sequencing,
immunohistochemistry, and proliferation assays to identify the mechanisms
of drug resistance in tumor cells. The results in the cell proliferation assays
showed that colorectal cancer cells with developed resistance to cetuximab
and panitumumab secrete transforming growth factor alpha (TGF-α) and
amphiregulin. These secreted growth factors protect the encompassing
sensitive cells from EGFR blockade by sustaining EGFR/ERK signaling in
sensitive cells. The results showed that TGF-α and amphiregulin binding to
EGFR caused a longer retention time of the receptor on the surface of the
plasma membrane and redirected EGFR to the recycling pathway rather
than to proteasomal degradation. This can potentially enhance the
pro-proliferating e�ect of the protective microenvironment.49

3. Treatments of Interest

3.1 Immunotherapy

Cancer immunotherapy utilizes the body’s immune system against cancer.
Some patients possess immune system components that naturally �ght the
cancer cells, called tumor-in�ltrating lymphocytes (TILs), while others do
not. Despite a patient possessing TILs, the immune system still has
di�culty �ghting the cancer cells since they are, by de�nition, abnormal and
lack certain processes that normal cells should have, such as missing
proteins.49 Immunotherapy includes various treatment options, including
immune checkpoint inhibitors, adoptive cellular therapy (ACT),
monoclonal antibodies, treatment vaccines, and immune system
modulators.50,51

One type of immunotherapy that has shown great promise in recent years is
chimeric antigen receptor (CAR)-T cell therapy, a type of ACT.
Unfortunately, it is only approved for the treatment of blood cancers, and
research into its e�ects on solid tumors is ongoing. CAR-T cell therapy uses
either analogous or allogeneic donated T cells that can be found as part of
the immune system. These cells are then genetically modi�ed to express

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CARs that target a speci�c antigen found on the surface of the intended
cancer cells. This therapy is designed to boost the immune system and
provide a speci�c target. Figure 2 summarizes the steps of CAR-T cell
therapy. A disadvantage of this therapy is that it can lead to selective pressure
since T cells only target one antigen at a time. Selective pressure refers to the
eventual resistance of a heterogeneous tumor with multiple antigens to
CAR-T cell therapy. This process is due to the elimination of all of the cells
within the target antigen, leaving only the cancer cells that do not have the
target antigen and therefore do not respond to the therapy.51Elimination of
the target antigen is also known as antigen loss or escape.

Many ongoing clinical trials seek to determine if CAR-T cells are e�ective
treatments for cancers with solid tumors, given that it is an approved
treatment for blood cancers. Trials, even those without reliable results, are
relevant to conversations about treatments of interest because it
demonstrates the researchers’ belief that these therapies have great potential
to be e�ective. Until current research shows promising results, the success of
the treatment will remain unknown.

One clinical trial run by Fred Hutchinson Cancer Center from 2016-2021
investigated the use of modi�ed CAR-T cells to �nd ROR1 proteins on
cancer cells from di�erent cancer types, including but not limited to
non-small cell lung cancer (NSCLC) and TNBC, both of which present as
solid tumors. Participants with either NSCLC or TNBC had unsuccessfully
undergone chemotherapy and other traditional treatments. Upon
undergoing the clinical trial, all patients displayed adverse e�ects with mixed
responses to the therapy. One out of three patients in dosage level 2
experienced either complete or partial remission, one out of six patients in
dosage level 3 experienced progression-free survival after one year, and,
across all dosage levels, there was a 38.89% overall survival rate.52,53

A second study funded by the National Natural Science Foundation of
China and conducted at Nanjing Normal University analyzed ways to
enhance CAR-T cell therapy. Knowing that TIGIT was a suppressor of
anti-tumor processes and that MSLN was highly expressed in breast,
prostate, and ovarian cancers, researchers combined an anti-ɑ-TIGIT with
MLSN CAR-T cells as a treatment. This combination signi�cantly

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enhanced the anti-tumor properties of MLSNCAR-T cells. The number of
CAR-T cells positive for TIGIT was initially 18.3% but dropped to 1.81%
after the addition of anti-ɑ-TIGIT, which led to a more e�cient MLSN
CAR-T cell. 53Additionally, a third Phase I trial is ongoing to treat prostate
cancer with CAR-T cells modi�ed for PSCA. The City of Hope Medical
Center began this study in 2019 and it is estimated to complete in late
2023/early 2024. No current results are available.53,55

Figure 2. CAR-T cell therapy procedure. In CAR-T cell therapy, blood is �rst removed
from the patient to obtain their T cells. CAR-T cells are then engineered and grown in the
laboratory before being infused back into the patient. In the patient, the CAR-T cells
would target and bind to speci�c antigens present on the tumor cells, killing them. In the
long term, however, the tumor could acquire resistance to this therapy through antigen
loss.

A more common type of immunotherapy treatment is immune checkpoint
inhibitors (ICIs). These checkpoints are part of the immune system and
prevent immune cells from reacting too aggressively and attacking bene�cial
cells.50 However, these checkpoints can also prevent the immune system
from e�ectively dealing with cancer cells. Hence, by blocking these
checkpoints, scientists are able to permit the immune system to start

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treating the cancer cells fully. The most common immune checkpoints
targeted are cytotoxic-T-lymphocytes-associated proteins (CTLA-4),
programmed cell death 1 (PD-1), and programmed cell death ligand 1
(PD-L1).56Figure 3 shows the mechanism of this checkpoint blockade.

There are a variety of clinical trials that involve ICIs. Many of them use
combination therapy by combining one of the seven FDA-approved ICI
treatments with either another approved ICI treatment or with another
therapy like chemotherapy.56 A clinical trial run by Bristol-Myers Squibb
from 2016-2023 tested the combination of ipilimumab and nivolumab
(treatment A, immunotherapy) against pemetrexed and cisplatin or
carboplatin (treatment B, type of chemotherapy) in malignant pleural
mesothelioma (MPM). Treatment A had an overall survival that was, on
average, four months longer than treatment B. The median disease control
rates for treatment A and treatment B were 76.6% and 85.1%, respectively.57

In a three-year minimum follow-up, the trial showed overall survival rates of
23% and 15% for treatment A and treatment B respectively. Moreover, at
three years, 28% of patients had an ongoing response to treatment A while
treatment B had 0% patients with ongoing response.58 These results
demonstrate how the combination of nivolumab and ipilimumab
continued to provide long-term survival bene�t over chemotherapy,
supporting this combination of ICIs as a �rst-line treatment for
unresectable MPM.

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Figure 3. Mechanism of immune checkpoint inhibitors. (a) An antigen-presenting cell
(APC) displays an antigen bound by the major histocompatibility complex (MHC). T cell
receptors (TCR) recognize the antigen, causing interaction between the T cell and the APC
or tumor cell. A co-stimulatory signal caused by CD80/86 binding to CD28 results in T
cell activation and proliferation, allowing the T cell to kill the cancer cell. (b) When
CTLA-4 is present on a T cell, it binds to CD80/86 in place of CD28. PD-1 on T cells also
bind to PD-L1 that are present on tumor cells. Both of these interactions lead to an
inhibitory signal that blocks the T cell from killing the cancer cell. (c) Anti-CTLA-4 binds
to CTLA-4, blocking its interaction with CD80/86. This allows for binding of CD28 to
CD80/86, producing a co-stimulatory signal. Similarly, anti-PD-1 binds to PD-1 while
anti-PD-L1 binds to PD-L1, blocking PD-1 and PD-L1 interactions. Thus, through
immune checkpoint inhibition, T cells are reactivated and can kill tumor cells.

3.2 Combination Therapy

Combination therapy involves using multiple types of treatment. It can be a
combination of drugs, immunotherapies, chemotherapy, radiation, and
other cancer treatments. Since cells respond to di�erent therapies, a highly
heterogeneous tumor likely needs multiple treatments in order to eliminate

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all tumor cells from the body. However, too many drugs can strain the body
and be highly toxic. Combination therapy thus seeks to maximize e�ciency
while minimizing toxicity. By using existing treatments, it can also be
cheaper and more time-e�ective to research than developing a new drug or
treatment.59

One study on mice, published in 2018 and funded by the Canadian Cancer
Society, used oncolytic viruses and the HDAC inhibitor MS-275 to prevent
relapse from ACT. The CD8+ T cells targeted a speci�c antigen on the
tumor. With only ACT, there was a signi�cant improvement in tumor size;
however, selective pressure had occurred until only antigen-negative tumor
cells were remaining, signifying that the T cells could no longer target those
tumor cells. The use of oncolytic viruses andMS-275 was shown to prevent
any relapse and change tumor-in�ltrating myeloid cells into
pro-in�ammatory cells, allowing for better recovery.60 In a Phase II clinical
trial funded by Bristol Myers Squibb that ended in 2018, researchers found
that patients with melanoma responded favorably to a combination of
melphalan (chemotherapy) and the approved CTLA-4 blocker ipilimumab
(immunotherapy). Based on the data provided, 85% of patients had
observable responses to the treatment, and there was a 58% progression-free
survival (PFS) rate after one year, with no increase in toxicity at the site of
the treatment.61

An ongoing clinical trial (2018-2024 estimate) by MedImmune LLC is
testing various dosages of oleclumab and osimertinib in the treatment of
NSCLC. The percentage of patients with disease control (complete
response, partial response, or stable) and overall survival for each dose is as
follows:

oleclumab 1 + osimertinib 1: 80%, 21.9 months

oleclumab 2 + osimertinib 1: 81%, 24.8 months

None of the patients that received the above doses had any dose-limiting
toxicities within twenty-eight days of the �rst treatment. However, within
ninety days of the last dose, all patients had adverse e�ects, such as infections
and infestations like cystitis and pneumonia, as well as nervous system
disorders like cerebral infarction and spinal cord compression. Even so, only

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one patient who received the oleclumab 1 + osimertinib 1 doses had
abnormal vital signs that were considered severe.62 This shows the
importance of �nding the right amount of dose to give to the patient,
especially in the case of combination therapy.

3.3 Dual/Multi Ex vivo Armed T Cells (EATs)

Dual- or multi-EATs are T cells armed with two or more bispeci�c
antibodies (BsAbs). Generally, T-cell-engaging bispeci�c antibodies
(T-BsAbs) bind speci�cally to a tumor-associated antigen (TAA) and a CD3
subunit that forms a complex with the tumor cell receptor (TCR). T-BsAbs
can thus link tumor cells and T cells together, activating T cells and leading
to tumor death. Moreover, CD3 engagement stimulates the T cells’ immune
response, which redirects host immunity toward tumors. Hence, T-BsAbs
are a promising antibody therapy for various cancers.64A Phase I/II clinical
trial was conducted on epcoritamab, a T-BsAb that targets CD3 and CD20.
This targeting redirects and activates T cells to kill CD20-expressing
malignant cells in relapsed or refractory Large B-Cell Lymphoma (LBCL).
Among 157 patients, the overall response rate (ORR), de�ned as the
proportion of patients who have a partial or complete response to therapy,
was found to be 63.1%.64An ORR value greater than 60% is Grade 3 and is
considered a high value, showing the high e�cacy of epcoritamab.65 The
complete response (CR) rate was 38.9%, with the median time to CR being
2.7 months.

Responses with epcoritamab were also shown to have transitioned from
partial response (PR) to CR at the later assessments in nine patients. These
results suggest there is an added bene�t in certain patients with continuous
treatment using this T-BsAb.64Another clinical trial found that ABBV-383,
a B-cell maturation antigen x CD3 T-BsAb, could treat patients with
relapsed or refractory multiple myeloma with an ORR of 68% at ≥ 40 mg
dosage, showing the T-BsAb’s promise in treating already heavily-treated
patients at that dosage amount.66

EATs are similar to T-BsAbs in that they are also able to crosslink tumor
cells and T cells together, activating the subsequent immune response.
However, in EATs, the BsAbs are already attached to the T cells, which
makes them similar to CAR-T cell therapy. The only di�erence is that they

Berkeley Pharma Tech Journal of Medicine | 100



are armed with BsAbs instead of CARs. With multiple BsAbs attached,
dual- or multi-EATs can target a wider variety of TAAs, thus helping to
overcome tumor heterogeneity.

A study by Park and Cheung tested the e�cacy of dual-antigen targeting
strategies using di�erent kinds of EATs, including pooled-EATs (EATs with
unique speci�city administered simultaneously), alternate-EATs (EATs with
unique speci�city administered in an alternating schedule), dual-EATs,
TriAb-EATs (T cells armed with a BsAb speci�c for two targets besides
CD3), and multi-EATs, with GD2 and HER2 as target antigens. Among
these, they found that dual- and multi-EATs had the most potential in
overcoming tumor heterogeneity and target antigen loss, both of which are
challenges to current T cell immunotherapies. Dual-EATs, armed with
GD2- and HER2-BsAbs, and multi-EATs, armed with GD2-, HER2-,
CD33-, PSMA-, and STEAP1-BsAbs, had induced stronger cytotoxicity
against a mixed lineage of cancer cells than mono-EATs armed with only one
type of BsAb. This stronger cytotoxicity resulted in a more potent
anti-tumor response and dual- and multi-EATs exceeded the e�cacy of
mono-EATs, signi�cantly improving tumor-free survival. They, along with
alternate-EATs, were also successful in inducing tumor regression, giving
rise to long-term survival. Furthermore, dual- and multi-EATs exerted a
synergistic anti-tumor e�ect when they encountered multiple antigens
simultaneously, which played a signi�cant role in preventing antigen loss.67

However, experiments have only been done in mouse models. Even though
no additional toxicities that could cause serious or fatal e�ects upon
infusion of CAR-T cells or BsAbs were observed, the same results might not
be reproduced in humans. The BsAbs also hold speci�city for human
antigens, not mouse antigens, so using a mouse model fails to mimic human
diseases and their therapeutics perfectly. Although the T cells, tumors, and
BsAbs were of human origin, the tumor microenvironment contained cells
of mouse origin, which included tumor-in�ltrating myeloid cells,
�broblasts, vasculature, and lymphatics. These could interact with one
another and a�ect tumorigenesis and anti-tumor response. Despite these
limitations, dual- and multi-EATs have potential to overcome tumor
heterogeneity and cancer resistance.67 Moreover, they could potentially be

Berkeley Pharma Tech Journal of Medicine | 101



used in more targeted and personalized treatments by arming the T cells
with BsAbs that target speci�c TAAs found in a patient’s tumor.

3.4 NanoRNPs with a Combination of Single Guide RNAs
(sgRNAs)

In the early stages of integrating nanotechnology into cancer therapy,
profound strides were made in improving existing therapies. One such
advancement has been the development of nanotechnology-mediated drug
delivery systems to enhance their delivery to tumor sites and reduce systemic
toxicity.68 Meanwhile, nano-Cas9 ribonucleoprotein (nanoRNP) with a
combination of sgRNAs harnesses the full potency of nanotechnology and
gene editing for precision treatment.

In a study by Liu et al., a nanoRNP system that could carry any
combination of sgRNAs was demonstrated to achieve targeted gene
disruption and e�ective suppression of heterogeneous tumors. NanoRNP
has a core-shell structure linked by CA that degrades under acidic
conditions. This way, it maintains a stable structure in blood circulation and
normal organs but detaches its shell when in the acidic tumor
microenvironment. This action facilitates tumor accumulation, cell
internalization, and eventual gene editing by the Cas9/sgRNA complex in
its core. With Cas9, nanoRNPs can disrupt the targeted gene sequence
under acidic conditions, signi�cantly downregulating the expression of the
target genes. When nanoRNPs carry a combination of sgRNAs, they
simultaneously disrupt the expression of multiple target genes, and this
could potentially overcome the genetic heterogeneity that causes treatment
resistance in cancer.69

In a heterogeneous tumor model at pH 6.5, Liu et al. expressed the target
genes STAT3, which increases tumor cell proliferation, survival, and
invasion while suppressing immunity towards tumors, and RUNX1, whose
increased levels correlate with cancer cell proliferation, tumoral
angiogenesis, and metastasis. The nanoRNP carrying a combination of
sgRNAs, nanoRNP-STAT3+RUNX1, disrupted the expression of both
genes, inhibiting the proliferation of the tumor cells. It also increasingly
induced cell apoptosis in the tumor. In contrast, nanoRNPs carrying a

Berkeley Pharma Tech Journal of Medicine | 102



single type of sgRNA led to the reduced expression of only one of the genes,
resulting in partial growth inhibition in the heterogeneous tumor. However,
through an analysis of gene disruption on STAT3 and RUNX1, it was
discovered that complete reduction of the target genes could not be
achieved by the nanoRNP, even if it carried multiple di�erent sgRNAs.
Even so, nanoRNPs carrying a combination of sgRNAs could
simultaneously suppress the proliferation of multiple tumor cell
subpopulations, showing their potential to overcome tumor
heterogeneity.69 Similar to dual- or multi-EATs, this treatment strategy can
be used in a more personalized approach, wherein the nanoRNPs could
carry the sgRNAs required to disrupt the speci�c target genes expressed in a
patient’s tumor.

3.5 Circulating Tumor Cells (CTCs)

CTCs, rather than a treatment, are better described as a research
methodology. They are cells in the blood that come from a tumor, and they
have the potential to become the primary way to test and develop new
drugs, as well as to test for the progression of cancer. More traditional
methods have various problems. For instance, 2D cultures lack the
complexity of a tumor structurally on the genetic and physical level but can
be tested in high volumes at high speeds. Patient-derived xenografts �x the
structural problems of 2D cultures, but they are unable to be used in
high-throughput screenings, which signi�cantly reduces research speed.
CTCs maintain the level of heterogeneity and the tumor’s structure, which
allows for high-throughput screenings. These advantages allow for new and
more personalized treatments as these therapies can be tested on an accurate
model that poses zero risk for the patient.70

The greatest challenge to utilizing CTCs is accurately isolating them from
other cells in the blood since CTCs are very rare in the blood and little is
known about their genetic structure.71,72 CTCs can be isolated based on
di�erences in their physical properties, such as density, size, deformability,
and electrical properties. However, these methods are very ine�cient as they
lack purity and speci�city. For this reason, researchers usually use
CTC-related technologies based on biological properties, particularly

Berkeley Pharma Tech Journal of Medicine | 103



techniques dependent on the epithelial cell adhesion molecule (EpCAM), a
marker positively enriched in CTCs.72

CTCs are also indicators of prognosis and a non-invasive method of
determining whether a treatment is working. Through liquid biopsies70 ,
doctors can determine the CTC count in a patient’s blood.70 In a study that
analyzed blood samples from 59 patients with esophageal squamous cell
carcinoma, CTC levels were found to be correlated with overall survival
(OS) and PFS rates. Researchers found that the overall and progression-free
survival rates were signi�cantly better for patients with a CTC count of less
than three. The mortality rates for the patients with either >0, >5, and >7
CTCs per 7.5 mL were 65.2%, 78.4%, and 87.5% respectively.73 A lower
CTC count after treatment is also associated with an excellent prognosis.71,72

There are also limited studies that suggest CTCs can be used for early cancer
detection, although this has only been shown in mouse models.71

4. Future Directions

Dual- or multi-EATs and nanoRNPs with a combination of sgRNAs have
strong potential to confront therapeutic resistance, making them promising
improved treatment strategies for cancer. These strategies would also
provide more insight into the design of more advanced and e�ective cancer
therapies. However, they have only been studied in mouse models and
heterogeneous tumor models respectively, both of which may not represent
a human system perfectly. Hence, before these treatments can undergo
clinical trials and be used as personalized approaches for tumor
heterogeneity, more research must be conducted to ensure that side e�ects,
such as o�-target toxicities, are minimized.67,69 On another note, further
research on optimizing models to better imitate human diseases would
undoubtedly be useful for preclinical testing of the safety and e�cacy of
drugs and treatment strategies.

CTCs, in turn, are promising as a way to determine the progress and clinical
e�ciency of a treatment. The CTC count can be used as an indicator that a
treatment is no longer e�ective, triggering a change in the type of therapy a
patient is receiving. Since a key challenge is the rarity of CTCs, more
research has to be done to �nd ways to identify and isolate CTCs. This

Berkeley Pharma Tech Journal of Medicine | 104



could then contribute to a larger sample size that can be used in studies,
improving their reliability.70

The emerging technologies and methodologies for assessing tumor
heterogeneity are clear implications of the advancement of personalized
treatment and precision targeting for heterogeneous tumors. Currently,
novel targets and therapeutic strategies for overcoming tumor heterogeneity
are critical in cancer research. Cancer research is extremely complicated and
unique because each type of cancer for every individual is di�erent, one
factor of which is tumor heterogeneity. This poses a major barrier to
producing a generalized approach and treatment for all individuals su�ering
from this disease. Because tumors possess the capability to overcome
therapeutics and treatments, scientists and medical researchers are
constantly faced with new challenges in producing a cure for cancer. On the
other hand, novel techniques that analyze individual cells and their
distribution, such as single-cell sequencing and spatial transcriptomics, have
transformed our predictive value and comprehension of the complex
cellular organization.74,75 All of the mentioned novel technologies and
ongoing clinical trials pave a clearer path toward �nding a more
target-speci�c treatment for cancer. Once tumor heterogeneity is addressed,
research can focus on other complex aspects of cancer treatment.

5. Conclusion

Tumor heterogeneity is the largest barrier in developing ground-breaking
and target-speci�c cancer treatments. With therapeutic resistance forming
among all forms of cancer, tumor heterogeneity highlights the need for
novel treatments that will overcome this barrier. Current therapies that can
limit cancer disease progression include immunotherapy, particularly
CAR-T cell therapy, and combination therapy. There are also novel
strategies, such as dual- or multi-EATs, nanoRNPs with a combination of
sgRNAs, and CTCs. Each strategy presents their own respective challenges
that limit them to mouse model studies or insu�cient target gene
reduction. Ongoing clinical trials exist to reduce such limitations and
challenges and discover crucial information for the treatment of humans.
Since no single treatment has yet been discovered to completely overcome

Berkeley Pharma Tech Journal of Medicine | 105



tumor heterogeneity, medical research in developing personalized treatment
and precision targeting is thus vital in saving lives.

Berkeley Pharma Tech Journal of Medicine | 106



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