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340 Sashana Dixon et al.
3.1 Transcriptional signatures: Responsiveness to IL2-based therapy
Over a decade ago, metastatic melanoma patients treated with IL-2 were the first patients
to undergo gene expression profile studies for cancer immunotherapy. Since then the
global transcriptome analysis has been employed to identify pre-treatment markers of
responsiveness in cancer patients innumerous studies, including patients treated with
MAGE-A3 vaccination [74, 93]. In this study, a set of 84 genes could not only differentiate responder and non-responder patients but also correlated with prolonged survival in
these patients. These genes reflected a Th1 polarized expression of chemokines (CXCL9,
CXCL10, and CCL5) and T cell -surface markers (CD3D, CD8A, and IL2RG), T cellactivation markers (ICOS and CD86) and IFN-stimulated genes (STAT1, IRF1, JAK2,
PSMB9, GBP1, GBP5, and FAM26F) [74, 93]. Such gene panel was then used to predict
efficacy of adjuvant MAGE-A3 administration in early stage non-small lung cancer
patients [93]. Moreover, such gene signatures also overlapped with signature from
immune-checkpoint blockade studies. Sensitive melanoma tumors react to IL-2 stimulation and shift towards an acute Th 1 inflammatory status [79, 94]. Similarly, ipilimumab
administration results in elevated chemokine gene expression which is much higher in
responders vs non-responder patients [95].
3.2 Transcriptional signatures: Responsiveness to checkpoint inhibitors
While there are several studies and numerous review articles on the nucleic acid biomarkers for the responsiveness to immune checkpoint inhibitors, we have tried to
maintain a balance between different topics. We encourage the readers to read these
comprehensive and in-depth articles
Molecular analyses and clinical data following immune-checkpoint inhibitors indicate that the expression of IDO and/or FOXP3 inhibitory molecules does not directly
provide immune resistance to cancer cells. Moreover, inflammatory phenotype in tumor
has underlying activation of immune-suppressive pathways [96, 97]. For example, a pos-
itive correlation is observed between clinical outcome in melanoma patients treated with
ipilimumab and pre-treatment levels of FOXP3 (marker of T regulatory cells) as well as
IDO expression in tumor infiltrating immune-cells [70]. Pre-treatment IDO1 gene
expression levels also correlated to efficacy of ipilimumab and anti-PDL1 [95, 98]. This
is not surprising since IFN-γ can induce IDO1 [97], and hence over expression of IDO1
could be because underlying excessive levels of IFN-γ resulting from Th-1 infiltration.
However, immune-favorable gene-expression phenotype characterized by B cell signature like immunoglobulin genes (e.g., IGKC) and surface markers (i.e., CD19), and these
are associated with good prognosis in breast [99–101], colon [102], and non-small lung
cancer [103], as well as responsiveness to IL-2 and ipilimumab in melanoma [104].
FOXP3 also has an association between the infiltration of colon cancer by FOXP3
T-cell and favorable outcome following from primary tumor excision or after
chemo- or immune-therapy [105, 106]. Several studies across many cancers have
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reported a positive correlation between PD-L1 expression and the likelihood to response
to Anti- PD-1/PD-L1 therapy treatment [98, 107]. PD-L1 binds to PD-1 expressed by
activated T cells, triggers downstream inhibitory signaling of the T cell receptor (TCR)
resulting in decreased effector functions. Interestingly, there is a direct correlation
between the expression of PD-L1 by tumor cells and levels of T cells infiltration as well
as with the PD-1 expression by tumor infiltrating lymphocytes (TILs) [107]. Interestingly, a high clonality of the TCR is associated with PD-1 blockade treatment in melanoma patients independent of TILs levels. However, no response was observed in all
patients with low density of TILs and low TCR clonality [108]. As previously mentioned, CTLA4 expression is also associated with anti-PDL1 therapy response [98].
Overall, these studies regarding checkpoint inhibitors demonstrate decisively that
responsive tumors have an underlying inflammatory status coupled with the activation
of immune-suppressive mechanisms. Hence, subsequently, tumors that do not have these
two characteristics are insensitive to immunotherapy (Table 2) adopted from D. Bedognetti et al. The current understanding of the clinical response to ICIs-treatment suggests that any single biomarker can not effectively identify the benefit populations. The
specificity and efficacy of prediction will be greatly improved when combination of multiple factors is used as a composite variable to capture immune status.treatment.
341Nucleic acid biomarker technology for cancer immunotherapy
3.3 Roles of DNA methylation and hydroxymethylation as epigenetic
predictors of ICB response
The role of the 5mC and 5hmC landscape in drug therapy has created a field of
pharmacoepigenetics. Specifically, for tumor cells there are substantial changes in the epigenomic patterns resulting in the increased identification and compilation of epigenomic
biomarkers. The purpose of this book chapter to point out the association and use of such
predictive epigenomic signature for immunotherapy; however, the authors encourage
everyone to read these comprehensive reviews on the compilation of epigenomic marks
in cancer [113–116]. Specifically, we would like to focus on monitoring clinical benefit
of treatment by combining epigenetic changes associated with ICB response. The majority of current well-characterized cytosine methylation biomarkers are limited to the
DNA methylation since only recently the technology to discriminate 5mC from
5hmC was developed [117,118]. A recent study reported the association of methylation
status of about 300 CpGs sites creating an “EPIMMUNE” signature with overall and PFS
to anti-PD-1 treatment in NSCLC patients [119]. This was one of the first studies to
report an association of epigenetic changes to the clinical benefit of the ICB, but similar
studies to identify a specific 5hmC biomarker in association with response to cancer therapy is not validated. However, there are several studies suggesting an involvement of the
TET enzymes associated with response to the cancer therapy; for example, TET1 expression is elevated in lung cancer cells that are responsive to EGFR inhibitor therapy [120].
Table 2 Responsiveness to immunotherapy and association with intratumoral features before
ImmuneGene expression of immunologic constant
of rejection molecules
STAT1/
IRF1/
IFNG-SG
pathway
CXCR3/
CXCL 9–11
pathway
CCR5/
CCL3-5
pathway
Granzyme/
perforin/
granulysin /TIA1
pathway
suppressive
molecules T cells
IDO (gene
expression
or IHC)
PDL1
(IHC)
T-cell receptor
clonality
(sequencing)
CD8 T cell or
T cell
density (IHC)
IL-2-based treatment/
vaccination (metastatic
++ Wang et al.
[109]
melanoma)
MAGE-A3 vaccination
(metastatic melanoma and
NSCLC, adjuvant)
++++ Ulloa-
Montoya
et al. [93]
IL-2 (metastatic melanoma) + Weiss et al.
[79]
Adoptive therapy and IL-2
(metastatic melanoma)
IL-12-based vaccination
(melanoma)
+ Bedognetti
et al. [110]
+ Gajewski
ASCO
(2007) [111]
Anti-CTLA4 (metastatic
++++ + Ji et al. [95]
melanoma)
Anti-PDL1 (metastatic
tumors)
Anti-PD1 a (metastatic
tumors)
Anti-PDL1 (metastatic
tumors)
Anti-PD1 (metastatic
melanoma)
++ + ++ Herbst et al.
[98]
+ Taube et al.
[107]
++ Soria et al.
[112]
++ + Tumeh et al.
[108]
Epigenetic remodeling of 5mC and 5hmC signatures play an underlying role to support
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the innate and acquired resistance to ICB. For example, expression of PD-1, PD-L1, and
CTLA-4 is regulated by DNA methylation resulting in decreased antigen presentation
and subsequent cytotoxic effects of immune system [121,122]. A differential DNA methylation pattern of neuronal development and differentiation is observed between
responders and non-responder patients with metastatic melanoma treated with
CTLA-4 blockers [123]. Stepwise hypermethylation has also been related to the
facilitation of tumor escape by repressing expression of the IFN regulator IRF8 [124].
Importantly, epigenetic regulation is also considered an useful tool to sensitize patients
to anti-PD-L1 ICB [125,126]. Moreover, de-methylation triggers the type
I interferon signaling pathway, sensitizing mouse ovarian cancer to anti-CTLA4 therapy
[127]. Similarly, upregulation of MHC class I components by combination therapy of
azacytidine and CTLA-4 mAb therapy can repress tumor growth stronger as compared
to individual therapies [128]. It is noteworthy to mention that an inverse association
between epigenetic pathways and immune signaling pathways are also observed in cancer; for example, in breast cancer cells, NF-kβ interacts with TET1 and mediates its own
downregulation [129]. Such strong pre-clinical and clinical evidence has supported the
expanding application of combining epigenetic reprogramming therapy to combat drug
resistance and achieve maximal drug response in refractory tumors resulting in increased
number of clinical trials that explore the synergy of epidrug combination therapies
[115,130] and anti-PD-1 drugs and histone deacetylases are the major combination ther-
apy. Several mechanisms can play a role in this synergistic effect for increased efficacy and
decreased relapse, and some of these include upregulation of CD80 and CD86 in the
context of anti-CTLA-4 treatment as well as the regulation of immune checkpoint
ligands. Accordingly, most of these combination strategies aim at the upregulation of
tumor antigens and the PD-L1 downregulation. Lastly, recent study also showed that
BET/bromodomain 4 inhibitors can decrease MDSCs in the tumor microenvironment
by increasing depolarization of macrophages towards immunostimulatory profile [131].
343Nucleic acid biomarker technology for cancer immunotherapy
4. Single cell
Recent advances in technology, computing power and capabilities, robotics and
automation, artificial intelligence, next-generation sequencing as well as molecular techniques like antibody engineering in the biopharmaceutical industry are having radically
progressed our ability to develop targeted small molecules/antibody-based therapeutics.
However, current immunotherapy focuses on simple molecular and cellular readouts as
well as the functional outcome of the process. Unfortunately, the more detailed understanding of pathways and cells targeted by agents of immunotherapy is lacking, something
that contributes to the technique’s relative failure in clinical trials [132]. Since tumors are
complex and heterogeneous bionetworks with different cell types such as tumor,
344 Sashana Dixon et al.
immune and stromal cells, the response to immunotherapy will also be different. As mentioned above gene expression differs between these cell types as well and hence, they
respond differently to the targeting agent, and any resulting improvement from the therapy gets diluted. Single-cell analysis of TILs and stromal components both before treatment and after treatment is recently starting to be used as a tool for mapping new potential
target molecules as well as the effect of checkpoint inhibitors on tumor cells and the
tumor microenvironment [133].
Molecular identification at the single-cell level can create synergistic, or antagonistic
based combination immunotherapy. For example, one study used single cell RNA-seq
and CyTOF analysis to assess the efficacy of single therapy of anti-CTLA-4 or anti-PD-1
versus combination therapy in mouse tumor models. And the combination therapy as
compared to the single drug treatment resulted in increased expansion of specific
+
CD4
T cell and monocyte as well as decreased levels of specific types of macrophages
[133]. This study also suggests that combination therapy might also have underlying addi-
tional mechanism via crosstalk between different cell populations affected by individuals’
drugs independently but collectively have a synergistic effect. While the single cell technologies are still emerging and hence, we will not go into detail in this topic for the matter
of this chapter. But with increasingly more powerful single-cell tools identifying and
selecting genomic signature-based beneficial combination immunotherapies,
Fc-engineered antibodies and bi-specific antibodies can be highly optimized and personalized. Moreover, mechanistic understanding provided by single-cell assessment can
support better predictive outcomes leading to successful immunotherapy treatments.
Hence, we believe single-cell technology combined with understanding nucleic acid
signatures will become a highly critical tool in the near future not only for predicting
outcomes but also for driving drug development for immunotherapies.
5. CRISPR based
Cancer cells sometimes become resistant to certain types of immunotherapy via
“antigen escape” [134]. Clinical trials using B-cell maturation antigen (BCMA)-targeted
CAR T cells have uncovered a potential process that might be related to therapy resistance: reduced BMCA levels at the cell surface [135,136]. The reason why this might be a
mechanism of such resistance, however, is not yet known. CRISPR-based genetic
screens offer the opportunity to change that by defining the mechanism in question as
it relates to various immunotherapies, creating and developing strategies to avoid or overcome resistance, offering a better understanding of immune check-point regulation, and
identifying various novel immunotherapy target antigens [137– 139]. CRISPRinterference/CRISPR-activation (CRISPRi/CRISPRa) functional genomics platform
can help identify mechanisms that control the response of cancer cells to immunotherapy
and support potential pharmacological drug development for immunotherapy [140].
6. Current challenges in immunogenomics
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Immunotherapy approaches are currently designed to help restore T cell function
as they can become fully or partially dysfunctional, in a process called T cell exhaustion, in
the immunosuppressive cancer environment [141]. Unfortunately, these approaches are
only successful when attempting to restore a partially exhausted T cell. When the T cell is
fully depleted, current immunotherapy strategies are generally ineffective [142]. More
notable is the fact that, when generated from potentially exhausted T cells, CAR
T cells can actually cause the further exhaustion of T cells [143,144]. It is possible that
this known complication is responsible for the limited success rate of immunotherapy.
If this is the case, focusing on novel solutions to T cell exhaustion based on experimental
techniques and genomics is likely the best way forward in this area.
One way to improve the efficacy and understanding of immunotherapy, for example,
is to focus on CAR T cells and cytokine release syndrome (CRS) [145]. In order to avoid
inducing CRS, a novel solution might be to increase the efficiency of the CAR T cells
themselves in order to demand fewer of them in the body. This can perhaps be done by
taking advantage of the opportunity presented by CRISPR/Cas9 genome editing, with
the opportunity to engineer “batches” of CAR T cells designed for the patient in question seeming particularly promising [146]. There is a basis to believe this approach might
be effective. Studies have shown that CAR T cells edited by CRISPR/Cas9 have successfully reduced T cell exhaustion when compared to convention CAR T cell therapy
[146]. This approach will require more testing, however, as the potential for undesirable
side effects limiting the practical use of CRISPR/Cas9-edited CAR T cells in human
cancer patients.
345Nucleic acid biomarker technology for cancer immunotherapy
7. Conclusion
In order to continue to develop immunotherapy techniques and determine their
best use in human cancer patients, additional studies are required. The future of immunotherapy seems bright with the increasing accuracy and decreasing cost of NGS applications like WES, WGS, ChIP-seq, and RNA-seq at the single cell and bulk tissue level.
As our understanding of the tumor and immune cell interaction expands via the use of
these tools, personalized medicine draws ever closer. There are already developments that
are quite promising, such as NICHE-seq, which offers the opportunity to add spatial
information to RNA-seq data at the singe cell level [147]. As the rapid increase in
NGS technology and use continues, high-performance computation algorithms and
methods will need to similarly evolve in order to synthesize the information into translationally applicable and experimentally testable knowledge.
346 Sashana Dixon et al.
Acknowledgment
We acknowledge the help and support of Dr. Jianan Dong and Ms. Natasha Rose in preparing this book
chapter.
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349Nucleic acid biomarker technology for cancer immunotherapy
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