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329Spatial mapping of the tumor immune microenvironment
CHAPTER TEN
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Nucleic acid biomarker technology
for cancer immunotherapy
Sashana Dixon, Alice Tran, Matthew Schrier, and Malav Trivedi
Department of Pharmaceutical Sciences, College of Pharmacy, Nova Southeastern University, Fort Lauderdale, FL, United States
Contents
1. Introduction 331
2. NGS and cancer 332
2.1 Precision medicine in cancer treatment: NGS-guided immunotherapy 333
2.2 Genetic drivers of tumor immune responsiveness 334
2.3 Genetics of the host 336
2.4 CTLA4 polymorphisms 337
2.5 CCR5 and CXCR3 polymorphisms 337
2.6 CAR T cell therapy and NGS 339
3. Transcriptional signatures 339
3.1 Transcriptional signatures: Responsiveness to IL2-based therapy 340
3.2 Transcriptional signatures: Responsiveness to checkpoint inhibitors 340
3.3 Roles of DNA methylation and hydroxymethylation as epigenetic predictors
of ICB response 341
4. Single cell 343
5. CRISPR based 344
6. Current challenges in immunogenomics 345
7. Conclusion 345
Acknowledgment 346
References 346
1. Introduction
Cancer cells present with genetic mutations and epigenetic modifications that
allow them to evade the immune system [1]. Such cellular complexities also contribute
to increased tumor heterogeneity resulting in less effective immunotherapeutic
approaches as the disease progresses [2]. However, recent studies identifying cellular
and molecular tumor immunology over the past two decades have enabled much better
identification of new ways to manipulate the immune response against cancer to counteract immunosuppressive mechanisms that evolve during tumor progression [3]. This
Engineering Technologies and Clinical Translation Copyright © 2022 Elsevier Inc.
All rights reserved.https://doi.org/10.1016/B978-0-323-90949-5.00010-3
331
332 Sashana Dixon et al.
has led to rapid expansion of modern cancer immunotherapy with dramatic improvement in patient survival and sustained remission for otherwise refractory malignancies.
Advanced malignancies, such as stage IV melanoma, lung cancer, and relapsed/refractory
acute lymphoblastic leukemia (ALL), have shown dramatic improvements in overall survival (OS), progression-free survival (PFS), and complete response (CR) with the introduction of therapies such as immune checkpoint inhibition (ICI), bispecific antibodies,
and chimeric antigen receptor (CAR) T cells. However, a significant limitation in these
current treatment modalities is an irregularity in clinical response, and patients tend to
respond to therapies, differently from one another. Furthermore, this unpredictability
also leads to significant side effects, financial costs, and health care burden, but more
importantly contributes to discrepancies and unsatisfactory clinical benefit in the majority
of treated patients. This highlights the special need for a predictive biomarker in
immuno-oncology and emphasizes that assessing individual patient cancer characteristics
is vital to creating a treatment plan with the greatest effectivity possible [4]. Although such
ongoing studies and trials investigate the use of multiple biomarkers predictive of patient
response or harm, none of these are comprehensive in predicting potential benefit. This
unmet need for validated biomarkers is largely secondary to a prohibitive complexity
within tumor parenchyma and the microenvironment, dynamic clonal and proteomic
changes to therapy, heterogenous host immune defects, and varied standardization
among sample preparation and reporting. Herein, we discuss current advantages of
nucleic acid predictive biomarkers with recent advances in molecular profiling of circulating tumor cells and host cells using next-generation sequencing which has dramatically
expanded the pool of potentially useful predictive biomarkers. Overall, as immunotherapy moves toward personalized medicine, a composite panel of both genomic and proteomic biomarkers will have enormous utility in therapeutic decision-making.
2. NGS and cancer
New developments in next-generation sequencing (NGS) and bioinformatics
offers a promising new avenue of treatment for patients who are unresponsive to conventional treatment approaches. One recent study found that immunotherapy in breast
cancer patients unresponsive to chemotherapy was effective, for example, resulting in the
complete disappearance of the patients’ tumors [5]. NGS technology has also been used
to identify a significant amount of genomic data from patients with acute myeloid leukemia, information that was subsequently expanded to multiple solid tumors in The Cancer Genome Atlas (TCGA) project [6, 7]. The NGS data were then used to develop a
molecular classification system for cancers like glioblastoma and lung cancer to complement more conventional histology-based classification.
Profiles acquired from the NGS analysis of various cancers has led to the increasingly
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widespread use of targeted therapy designed to identify specific mutations in signaling
pathways in order to block them with drugs, be they existing or newly developed.
As a form of high-throughput parallel sequencing, NGS technology allows scientists
to collect a large amount of information from genomes. Shortly after the human genome
project was completed, 33 types of cancer were sequenced in order to identify the mutations specific to each kind along with their copy number variations. The gathered information was made public via the TCGA Project and the International Cancer Genome
Consortium (ICGC) [8, 9]. Along with RNA-seq, the NGS approach to chromatin
immunoprecipitation (ChIP-seq) has analyzed this data in order to determine how
transcription-regulated gene expression is triggered and expressed in cancer [10].
The integration of various datasets like Catalogue of Somatic Mutations in Cancer
(COSMIC) and SNP500 from NGS techniques used to profile cancer gene expression
and epigenome, cancer genetic sequence, and the ways in which cancer changes in
response to drugs or small molecules, promotes rational drug design that targets multiple
different aspects of cancer biology. Other important datasets include NCI-60 and CellMinerCDB, both resources that offer comprehensive information about cancer mutation, genomewide RNA expression, drug interaction data, and enzyme activity for a
total of around 1000 cell lines [11, 12]. One recent study used these databases to advance
computation tools designed to predict the responses of drug combinations in cancer
treatment—important information given the lack of experimental data in the area
[13]. This highlights the importance of public NGS datasets as well as high-throughput
NGS methods themselves, to the study and understanding of cancer and its extensive
molecular complexities when determining new precision medicine and therapies.
333Nucleic acid biomarker technology for cancer immunotherapy
2.1 Precision medicine in cancer treatment: NGS-guided immunotherapy
In addition to providing insight about cancer, NGS techniques can also provide important information about the immune system and how it reacts to the disease [14]. Immunotherapy is a treatment strategy used to potentiate and restore the body’s immune system
to allow it to recognize, remove, and attack cancer cells, a functionality that is missing
once the disease takes hold [15]. When combined with other conventional cancer treatments like radiotherapy, which damages cancer cells, immunotherapy can significantly
improve treatment effectivity [16–18]. NGS-guided immunotherapy can also be used
in the case of surgery to remove cancerous growths, a process that has a chance of allowing the cancer cells that remain into the bloodstream after the surgery is completed
[19]. It is hypothesized that using immunotherapy after surgery might be an effective
strategy to destroy the remaining cancer cells [20].
One challenge that the application of immunotherapy in a widespread clinical setting
faces is the sometimes inconsistent results of the therapy [21]. More specifically,
334 Sashana Dixon et al.
immunotherapy doesn’t yield the same results for all patients, perhaps due to the heterogeneity in both cancer cells and T cells as well as the complicated interactions between
them in the microenvironment of the tumor [22]. In response to this concern, a new field
in cancer research known as immunogenomics seeks to utilize NGS to obtain the genomic profile of both immune cells and cancer cells. This is made easier thanks to recent
advancements in ChIP-seq and single-cell RNA-seq, improvements that promise to
uncover the transcriptomic and epigenetic heterogeneity of cancer cells at the single-cell
level [23, 24]. These genomic profiles can improve the efficiency of immunotherapy
including the administration cytokines for systemic modulation of the patient’s immune
system [25]. In addition to cytokines, whose role has been limited in cancer treatment due
to their role as both immune response repressors and activators, monoclonal antibodies
have been successfully used as targeted therapy to block certain abnormal proteins that are
expressed by cancer cells [26–28].
The idea of immune system manipulation to inhibit cancer growth or elicit tumor
rejection is not a new one. The concept was one that was well explored a century
ago, when William Coley discovered that injecting certain bacterial substances into a
patient’s body could lead to significant tumor regression [29]. This was the beginning
of immunology, although it wouldn’t be such named until the 1990s, and some of
the first rigorous clinical trials designed to investigate cytokines that were proinflammatory and their impact upon the immune system were conducted [30]. These
trials found that while provoking antigen-specifical cellular responses using the cytokines
interferon-(IF) and interleukin-(IL)-2 was very effective, the process was rarely followed
by a significant instance of tumor rejection [31]. This indicated that a piece of the puzzle
was still missing, and it seems as though NGS might be the solution.
It should be noted that NGS serves an important role in quantifying as well as identifying host immune response biomarkers in FFPE (formalin-fixed, paraffin-embedded)
tumor specimens. It can help characterize the immunologic tumor microenvironment in
order to allow clinicians a stable base upon which to base therapeutic decisions. This concept has been the subject of recent research published in The Journal of Molecular Diagnosis.
The study sought to determine the utility of the assay as it pertains to clinical scenarios
while utilizing a standardized NGS workflow to make the sequencing process as controlled and efficient as possible.
2.2 Genetic drivers of tumor immune responsiveness
While the transcriptional profile of the whole tumor tissue is highly informative, it cannot
precisely indicate the source of the immune genes provide the immune-favorable cancer
phenotype. Development of the desirable immune phenotype in tumor is collectively
provided by underlying cancer cells, stromal cells, and different subsets of immune cells
interacting with each other [32, 33]. For example, Zeimet et al. reported that number of
T cells in ovarian cancer correlated with IRF1 expression in the tumor; whereas only the
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cancer cells in the tumor stained positively for IRF1, and IRF1 was not all expressed by
stromal cells [34]. Similarly, cancer cells that expresses HLA class II molecules IRF1 in
turn is correlated with the levels of CD8 T cells [35], and cultured tumor cells can secrete
chemokines to recruit T cells [36]. It is important to note that, it is not clear from these
studies whether the cancer cells drive these immune signatures or whether this immune
system responses are a result of the release of IFN-γ and other proinflammatory molecules
by activated T cells against cancer cells [37]. Based on emerging evidence, it is believed
that the origin of the immune signature in some tumors is a complex, non-linear, multifactorial and dynamic in vivo phenomenon, but also governed by the cancer cells’ inherent biology. For example, a study identified a list of 968 genes (genomic delegates) that
display correlation between copy number and gene expression in melanoma tumor cell
lines which strongly correlated with gene expression profile of the parental tumor metastases [38]. These genomic delegates were segregated into two different categories when
the tumor metastases were reanalyzed. While one class had transcripts previously associated to more aggressive cancer [39–41], the second class had genes associated with melanoma signaling and classically associated with better prognosis and likelihood to respond
to Immunotherapy [33, 42]. Similar previous study in breast cancer cells has showed that
copy number landscape associated with enriched ICR genes have favorable prognosis,
suggesting the driving role of cancer-cell genetics in determining the in vivo
immune-phenotype [43]. Another study compared expression of genes for melanoma
metastases and when segregated these genes based on the presence or absence of BRAF
and NRAS mutations, they observed 112 BRAF-specific transcripts classified in two different immune phenotypes (Th-1 and Th-17) [44]. This observation indicates that
pathways related to driver oncogenes can also develop cancer phenotype that is not
immune-supportive. Recently, it has been shown that IFN-α responsiveness of peripheral blood mononuclear cells is impaired by overexpression of NOS, with underlying
genomic amplification of NOS1 locus within segment 12q22-24. In the same study with
113 metastatic melanoma patients, the baseline expression of NOS1 in tumor metastases
was negatively correlated with adoptive cell therapy response linking genetic of tumors to
specific immune-dysfunctions [45], building upon other studies that indicate that the
immune-favorable cancer phenotype is driven by tumor genetics. Public database such
as TCGA melanoma datasets have been analyzed in numerous studies to identify defined
sets of genes, and one of these studies defined sets of co-regulated immune genes that are
associated with prolonged survival [46]. Another study identified that CXCL13 was correlated with B, T follicular helper, T helper 1, and cytotoxic T cells, and patients who had
CXCL13 deletions experienced a shorter but disease-free survival [39]. Such correlation
between CXCL13, T follicular helper cells and patient prognosis is also found in breast
cancer [47, 48]. In a separate study, such correlation was measured for gain of TNF, IFN,
IL, and TGF family genes in colorectal cancer [49]. The highest risk of relapse was
335Nucleic acid biomarker technology for cancer immunotherapy
336 Sashana Dixon et al.
associated with deletion of IL15, IL21, and IL2. Moreover, IL-15 levels correlated with
cytotoxic T, activated T/NK, T helper 1, and memory T cells levels and significantly
higher levels of proliferating T and B cells in patients with high level of IL15 [49].
Hoadley et al., conducted a deep analysis of TCGA genomic and proteomic database with
12 different cancer types, and reported a pan-cancer genomic classification with several
subtypes with specific molecular signatures [50]. TP53 alteration, amplification and overexpression of immune genes were some of the few underlying characteristics for lung
squamous, head and neck, and a subset of bladder cancers. Moreover, the study also
emphasized the prognostic value of immune signatures of cancers originating from different tissues, since the underlying pathways like PD-1 and CTLA4 which are related to
T cell activity strongly correlated to increased OS [50]. Overall, all these studies highlight
the analogies between prognostic and predictive cancer genome signatures and the implication for cancer immunotherapy.
Recent studies have reported a direct correlation between somatic mutations and
response to immunotherapy via exome-wide study of melanoma metastases in the context of anti-CTLA4 therapy. For example, Snyder et al. reported a significant correlation
between the mutational load with increased OS [51]. They also reported the set of
mutated antigens mostly common in all responder patients, and this signature set could
precisely predict the prolonged survival in these patients in response to CTLA-4 blockade
[51]. It is important to note that this study also reinforced the findings that epitopes with
mutation can still be recognized by T cells and can also be used for therapeutic targeting
[52, 53]. Lastly, it also indicates that genetic mutations in proteins currently not directly
involved or implicated in cancer progression might still play a contributing role and are
called a passenger mutations since they may still contribute towards final cancer phenotype [51]. However, it is important to note that despite such great progress in this field, it
is still not clear how and to what extent do such genomic alterations in tumor as well as
immune system genes may have an overall impact on tumor responsiveness.
2.3 Genetics of the host
Over 85 different loci contribute to susceptibility to autoimmune diseases as identified by
several different Genome-wide association studies (GWAS) [54]. ADA2 [55], PLCG2
[56, 57], HOIL1 [58], and PIK3CD [59] are few of the critical genes for immune system
homeostasis identified via studies characterizing inherited immune-related diseases
through Next-gen exome sequencing. However, their contribution in pathology of cancer is only being understood recently [60]. Orru and coworkers reported the relationship
between genetic makeup and variability of immune-cell populations by studying levels of
immune-cell population (i.e., 272 immune traits) in 1500 individuals. Interestingly, the
strongest heritability estimates were identified in T regulatory cells with the strongest
association observed for CD8A/CD8B, HLAs, IL2RA, NCAM1, CD4, TNFS13B,
and SLFN clusters. Few of these variants also overlapped with those identified by GWAS
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in auto-immunity disease [60]. Overall, this is highly significant, since it indicates that
individuals have differences in stable baseline immune-cells traits and these can play a role
in responding to immune perturbations [61]. For example, several GWAS studies have
identified that IL28B gene is strongly correlated with response to IFN-α in chronic hepatitis C (HCV) infection [62–64], promoting the use of IFN-α as the gold standard for the
treatment of HCV infection. However, such strong GWAS studies at such a larger scale
are still not conducted investigating cancer immunotherapy, since, immunotherapy was
only recently approved by the FDA. Hence, the results, correlative analysis and predictive
biomarker identification mostly rely on gathering evidence from a few studies with
smaller sample sizes, which introduce numerous confounders. Hence, researchers have
focused on studying specific genes such as CTLA4, IRF5, and CCR5 and HLAs. For
example, one study described a significant association between HLA-DRB1*15,
Cw6, Cw7, and B44 and survival in melanoma patients treated with adjuvant IFN-α
(P ¼ 0.028, 0.029, 0.030, and 0.040, respectively) [65, 66]. For the purpose of the current
chapter, we have described studies focused on CTLA4 and CCR5 polymorphisms
below.
2.4 CTLA4 polymorphisms
CTLA4 shapes the T cell response during their initial response to antigen exposure, and is
an important modulator for immune tolerance. It also plays an important role in regulating T-cell-mediated antitumor immune response. CTLA-4 is expressed by activated
CD8 + effector T cells, but it is critical in regulating CD4 + T cells via downregulation of
helper T-cell function as well as upregulation of regulatory T-cell immunosuppressive
activity [67, 68]. Three studies focused on anti-CTLA4 mAbs treatment of individuals
with metastatic melanoma identified 12 CTLA4 single nucleotide polymorphisms
(SNPs) [69–71], however the comparison between the different studies is overall inconclusive due to insufficient data as well as lack of consistency in analytical assessment or
underlying specific SNPs. A summary of these studies is reported in Table 1.
337Nucleic acid biomarker technology for cancer immunotherapy
2.5 CCR5 and CXCR3 polymorphisms
Activated Th-1, cytotoxic T, and NK cells, all express the CC chemokine receptor
C (CCR5) together with CXC chemokine receptor 3 (CXCR3). Ligands acting on
CXCR3 and CCR5 recruit activated T lymphocytes and support immune-mediated
tumor rejection [42, 72–75]. In contrast, IL-2 induces inflammation within tumors
and can lead to secretion of CXCR3 and CCR5 ligands ([76, 77],p.5;[72, 78, 79]).
Hence, it is believed that any polymorphisms and/or elevated expression and/or activation of CXCR3 and CCR5 can affect chemokine receptor expression subsequently leading to immune-mediated tumor rejection. The CCR5Δ32 mutation/polymorphism
338 Sashana Dixon et al.
Table 1 Response to immunotherapy in association with CTLA-4 SNPs.
Tested CTLA4
References
SNPs Setting Findings
Hamid et al. [70] rs11571317
rs3087243
rs4553808
rs1863800
Breunis et al. [69] rs4553808
rs11571317
rs231775
rs5742909
rs7565213
rs733618
Queirolo et al.
[71]
rs5742909
rs231775
rs3087243
rs4553808
rs11571317
rs11571316
Metastatic Melanoma
treated with Ipilimumab
(N ¼ 55–57)
Metastatic melanoma
treated with ipilimumab
(N ¼ 152)
Metastatic melanoma
treated with ipilimumab
or tremelimumab (N ¼
14)
No significant association, but a
significant trend was observed
rs4553808 (1660 G vs A allele);
rs11571317 (657 T vs G allele),
and rs231775 (49 A vs G allele)
were associated with the overall
response
rs11571316 was associated with
clinical benefit (1577 G/A vs
G/G; P value: 0.041); a trend was
observed for rs3087243 (CT60
G/A vs G/G; P value: 0.072);
both polymorphisms were
associated with overall survival (P
value: <0.006)
consists of a 32-base deletion resulting in a protein that is not expressed on the surface of
the cells. While homozygous carriers of this mutation are resistant to HIV-1 [77]; in contrast, in the field of immunotherapy, reports focusing on metastatic melanoma are conflicting. Few studies report decreased survival of patients carrying this polymorphism
(either in heterozygous or homozygous state) when treated with immunotherapy or
immunochemotherapy ([80], p. 5); however, few other studies have found no association
between CCR5Δ32 or CCR5 rs1799987 polymorphisms and responsiveness to
ipilimumab [70]. One recent study has also reported the association of CCR5Δ32
and CXCR3 rs2280964 polymorphism in the context of metastatic melanoma patients
treated with adoptive cell therapy and high dose IL-2 [81]. Recent study also reported
that down-regulation of CXCR3 and CCR5 receptor and related gene expression in
lymphocytes due presence of CCR5Δ32 mutation, strongly correlated with the degree
of response in these individuals ([82],p.3)(P< 0.001). The TIL migration to the tumor
following IL-2 administration does not follow a linear kinetic [83] but the levels of
CCR5 and CXCR3 ligands (e.g., CCL3, CCL4, CXCL9, CXCL10 and CXCL11)
increase immediately after IL-2 administration [78]. Other treatments (e.g., combination
of immunochemotherapy ([80], p. 5), or vaccine therapy), may also induce a proportional
induction of the CXCR3 and CCR5 ligands following treatment similar to the administration of high-dose IL-2, resulting in different kinetic modulation of T cells. Although
ATCT studies in mouse models have reported a key role of upregulated CCR5 levels in
TILs towards mediating tumor rejection, a maximal expression of CCR5 (and CXCR3)
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by TILs is not a critical factor in ATCT, and hence the complete role of ATCT in immunotherapy needs to be further investigated
2.6 CAR T cell therapy and NGS
The immune system is stimulated to produce antibodies inside the body when vaccines
for immunotherapy are administered, whereas in ATCT, the T cells are isolated from the
body, multiplied and then stimulated outside of the body before they are injected back to
the patient [84]. One of the types of ATCT, with heightened T cell immune response is
accomplished by genetic modification of T cells with CAR-T cells [85]. The T cells are
altered outside of the body to express a novel protein or a CAR which is directed to the
antigen expressed on the cancer cell surface. This CAR protein fused with an extracellular single-chain fragment variable monoclonal antibody-derived domain, with an intracellular TCR-derived domain, resulting in heightened T cell antigen recognition and
binding [86]. These modified T cells when injected into the patients, recognize surface
antigen specific on the surface of cancer cells or secreted from the cancer cells, and attacks
the tumor. Clinical trials of CAR T cell therapy show high effectiveness in leukemia
patients and are in trials for other cancer types [87, 88]. However, the limited information
on cancer-specific target antigens is a major challenge similar to any other immunotherapy. Moreover, the T cell heterogeneity resulting from differences in genomic sequences
of TCRs also limits the CAR T cell immunotherapy. However, advances in technology
such as single-cell RNA-seq has allowed to understand the T cell heterogeneity and
TCR repertoire and demonstrated at least 11 types of T cells in liver cancer with distinct
underlying molecular and functional properties [89]. Such studies characterizing the
T cell heterogeneity and TCR repertoire combined with advances in NGS and bioinformatics algorithms will definitely further support and progress analyze T cells, cancer
cells, and progress effective T cell-based immunotherapies.
339Nucleic acid biomarker technology for cancer immunotherapy
3. Transcriptional signatures
Tumor gene expression signature or transcriptome profile has been widely studied
via (1) excised primary tumor, (2) biopsies of primary or metastatic tumors from patients
before receiving chemotherapy or immunotherapy, as well as (3) post-treatment tumor
biopsies from patients. Overall, these studies have contributed in understanding (1) prognostic signatures (2) predictive signatures (likelihood of treatment effectiveness) (3)
mechanistic signatures. Moreover, it is also identified that these different signatures overlap with each other and molecular pathways captured with in these signatures in turn have
a lot of overlap with pathways contributing to the development of other forms of
immune-mediated tissue damage as well as autoimmune responses [73, 90–92]. Below
we provide an overview of signatures associated with immunotherapy responsiveness.
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