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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5346_Библиотеки_им_академика_М_И_Перельмана
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472 Amit Singh
relevant preclinical models, therefore, are one of the key challenges that can potentially
change the landscape of cancer immunotherapy through better predictability of the therapeutic outcome if addressed through smart design principles [8].
2.2 Identifying dominant drivers of cancer immunity
Cancer is a disease results from series of sequential genetic insults that aid in the cells to
adapt, alter their phenotypic and metabolic identity, and evade immune surveillance to
continue a sustained proliferation. Genomic instability is central to the genetic novelties
that are the hallmark of cancer cells and mutations are the primary drivers of highly adaptive phenotypes with a diverse population that facilitates rapid tumor progression. In
order to design effective immunotherapy against cancer, it is really important to understand the link between genomic instability leading to immune checkpoint inhibition or
adoptive cell transfer, where tumor mutational burden appears to play an important role
[9]. Tumor immunity status is defined by three immunophenotypes, inflamed, immune
excluded, and immune deserted; classified based on the infiltration and distribution of
CD8+ T cells in the TME [10]. The inflamed tumors are characterized by the presence
of IFN-γ producing T cells, tumor-infiltrating lymphocytes (TILs), genomic instability
and high mutational burden, high programmed death-Ligand 1 (PD-L1) expression, and
preexisting immune response. The immune excluded tumors show predominantly
TGFβ signaling, inefficient infiltration of CD8 + T cells, angiogenesis while immune
deserted tumors are completely devoid of any T cells infiltration, have poor immunological representation, low expression of PD-L1, and highly proliferating cells with the low
mutational burden. In general, these tumors create an immunosuppressive microenvironment through the expression of TGFβ which promotes tissue rebuilding by inducing
the expression of extracellular matrix genes and simultaneously inhibiting molecular factors essential for infiltration of immune cells.
A deeper understanding of the cellular diversity, the immunological status of the
tumor, and expression of PD-L1 levels in the TME becomes extremely critical to design
immunotherapy and predict clinical outcome. Clinical evidence clearly suggests that
most checkpoint inhibitor (CPIs) therapies show anticancer activity by reviving the preexisting T cell response and therefore have been most effective in inflamed tumors [11].
Targeting the programmed death-1 (PD-1)/PD-L1 pathway has shown to increase the
population of active CD8+ T cells marked with higher production of IFN-γ, which in
turn induces PD-L1 expression on tumor cells and tumor-infiltrating immune cells as a
negative feedback mechanism. The response is significantly improved in the patients that
have tumors with high baseline T cell infiltration. On the other hand, the patients who do
not respond to the therapy show a depleted CD8+ T cell density, no marked improvement in the T cell infiltration capability and TGFβ signaling pathway. Since this pathway
has direct implication in immune suppression and inhibition of the signaling pathway
results in reversal of excluded tumors to inflamed phenotype, a combination of anti-PD-
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L1 and anti-TGFβ therapy may provide a favorable outcome. Multiple clinical trials are
underway to study the impact of such combination therapies on the clinical outcome of
otherwise immunologically challenged tumors. Therefore, a clear understanding of the
dominant drivers of cancer immunity, characterization of TME biomarkers, and identification of the right patient population is pertinent to devise any successful immunotherapy strategy with favorable possibilities.
2.3 Understanding organ-specific TME
Tumor cells grow in very close association with their microenvironment and as such the
TME has a significant role to play in the disease growth, progression, and metastasis.
A TME is composed of different types of cells of hematopoietic (lymphocytes, myeloid
cells) or mesenchymal (fibroblasts, stem cells, endothelial cells) origin along with the different components of the extracellular matrix. The cells recruited to the TME have a
multifactorial impact on the disease development including sustained proliferative signal,
evading the immune system and growth suppressors, angiogenesis induction, resisting
cell death, and deregulated metabolism [12]. The immunosuppressive cells in the
TME, such as regulatory T cells, myeloid-derived suppressor cells (MDSCs), and
tumor-associated macrophages play a huge role in facilitating tumor growth by providing
an ideal environment. Similarly, cancer stem cells (CSCs) that play a key role in cancer
metastasis and relapse after therapy are favorably supported by the tumor stromal cells in
the TME. The presence of these normal immune regulatory cells and stem cells has a
great impact on a therapeutic outcome of a drug. Depletion of these cells from the
TME has been shown to reduce the progression of the disease in animal models and a
higher infiltration of these cell types in the tumors has been associated with poor therapeutic response.
The TME shows variation depending on the organ and tissue that the tumor resides in
and that in turn influences the therapeutic response. The metastatic urothelial carcinoma
that metastasize to the liver has poor therapeutic efficacy while those that invade into the
lymph node show excellent response to CPIs [13]. The liver more specifically is known
to employ MDSCs, dendritic cells, and Kupffer cells to promote immune-suppressing
pathways through reduced effector T cell activation and subsequent immune tolerance
[14]. This immune tolerance is the prime contributor to the poor response of liver metas-
tases from melanoma or nonsmall cell lung cancer (NSCLC) to pembrolizumab [13].
Therefore, tissue-specific pathways should be studied, understood, and underscored in
the design of a therapeutic approach to get the desired clinical success. Targeting the vascular endothelial growth factor (VEGF) pathway for example leads to a reduction in the
myeloid inflammation along with normalized vasculature and its combination therapy
with CPIs shows improved recovery from hepatocellular carcinoma. However, the
473Clinical translation and challenges in cancer immunotherapies
474 Amit Singh
efficacy of the combination therapy on the liver metastatic tumor is yet to be established,
previous attempts with such combination therapies have not shown particularly encouraging efficacy. There has been a strong correlation between the organ-specific location of
the tumor and treatment prognosis but so far, organ-specific therapeutic approach has
largely been ignored. Understanding the organ-specific TME, its cellular and molecular
composition, and key physiological factors are critical especially for immune targeted
therapy that relies heavily on effector T cell infiltration.
2.4 Understanding drivers of immune evasion
Tumor relapse is the single most challenging aspect in cancer therapy because more often
than not, the relapsed tumor presents a therapy-resistant phenotype with an extremely
poor prognosis. Even in the patient population where the tumor is primed to respond
to CPIs (i.e., characterized for high tumor PD-L1 expression on at least 50% of the cells),
not all patients with NSCLC respond favorably to the Pembrolizumab [15].Asecondary
immune escape has also been observed where patients who have shown positi ve
response to the treatment for many years in the past start to show tumor progression
eventually. The development of multiple CPIs over the years and numerous clinical
trials associated with them has generated a pool of data from patient tissue analysis that
gives insight into the mechanisms that result in immune evasion. It has been es tablished
that urothelial cancer tumors with poor PD-L1 expression (immune excluded) and
increased TGFβ signal ing demonstrates a poor response to CPIs. Tissue analysis from
the tumors reveals a dense collagen-rich stroma that essent ially tra ps the eff ector T c ells
and prevents their activity. In this regard, combinat ion treatment with CPI and antiTGFβ antibodies has shown favorable outcomes preclinically by modifying the
immune composition of the tumors and promoting infiltration of effector T cells to
facilitate antitumor response [16]. Therefore, the i mmune make-up of the tumor plays
a key role in tumor prognosis and certain immune evasion pathways may be directed by
the immune composition of tumors.
Alternate mechanisms of developing resistance to immunotherapy or adopting evasive pathways have been observed in other TMEs. In renal c ell carcinoma, tumors that
are inflamed with low mutational burden initially show response to CPIs but progressively acquire immune suppression thr ough myeloid inflammation and develop
resistance t o monotherapy. However, a combination treatment of CPIs with
anti-VEGF antibody [17] or small molecule inhibitor [ 18] leads to reversal of the resistance through MDSCs reprogramming and shows a marked improvement in the
progression-free survival. Similar to the mechanism of primary resistance to therapy,
secondary resistance is also gaine d through a myriad of diverse pathways that need a
detailed understanding to devise a therapeuti c appr oa ch. It is impo rtant t o appr eciate
that the mechanism of resistance to immunotherapy could be a c ombina tion of
pathways and their interplay and contribution may vary in the spatial distribution of the
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tumor and from tumor to tumor in the same organ. The evolution of these pathways is
driven by the host’s t umor/immune syst em interaction and therefore studying the
mechanism of primary resistance to therapy is much easier than for the secondary
resistance. Studying the development of secondary resistance in the tumor over a
course of immunotherapy is challenging and would require the systematic collection
of tissue samples before, during, and after the completion o f therapy. An elaborate plan
of tissue collection and study will be required to understand the factors driving the
mechanism of secondary resistance and identify the molecular drivers of the pathway
before any suitable course of combination therapy can b e devised t o overcome resistance and inhibit disease progression.
2.5 Harvesting endogenous vs synthetic immunity
The traditional immunotherapy targeting PD-L1/PD-1 receptor largely relies on endogenous immunity through previously primed CD8+ T cells that have capability to recognize, bind and kill the cancer cells but are rendered ineffective due to the immune
evasive mechanisms. Blocking the PD-L1/PD-1 activity on cancer cells subverts
immune evasion, stimulates the tumor-infiltrating T cells to attack the tumor cells,
and activates immune cell population expansion in the TME. This approach has led
to the development of several successful anticancer therapies especially in inflamed
tumors with high expression of the PD-L1/PD-1 as well as a high population of infiltrating CD8+ T cells. However, the efficacy of this treatment has not shown similar success in immune excluded and immune desert tumors where the mechanisms of immune
evasion are different, and the population of the immune cells is significantly lower. More
importantly, not all cancer cells are immunogenic and several others may use downregulate antigen-presenting capability of immune cells as an immune evasion mechanism.
Induction of synthetic immunity is an alternate approach where immune cells are bound
to the cancer cells through external intervention. Two independent approaches have
been successfully developed: chimeric antigen receptor (CAR) T cell therapy [19] and
CD3 bispecific antibody therapy [20]. Adoptive T cell transfer (ACT) has been a revolutionary approach that involves the external manipulation of immune cells from a
patient followed by reinfusion back to mediate antitumor activity. CART T cell specifically refers to T cells harvested from a patient suffering from cancer, which is genetically
manipulated to express chimeric antigen receptor on their surface, expanded in vitro, and
then reinjected into the patient’s body. It is important to acknowledge that the efficiency
of CAR T-therapy stems from the ability of the T cells to find tumor sites (primary and
malignant), infiltrate to reach cancer cells, bind and kill them.
CD3 bispecific antibody approach involves the infusion of bispecific antibodies that
bind to CD3 receptor on the surface of T-cells at one end and a tumor cell-specific
475Clinical translation and challenges in cancer immunotherapies
476 Amit Singh
antigen on the other end to bind the two cells together and mediate tumor destruction.
The bispecific antibody could be of different types ranging simply from small proteins
that can bind to two antigens on two cell surfaces to large immunoglobulins (IgG) molecules and therefore bring tremendous novelty in molecular design. These molecules also
rely on immune cells that infiltrate the tumors and so may not be as efficient in the treatment of immune excluded or deserted tumors though some reports suggest that synthetic
immunity may promote proliferation of tumor-infiltrating T cells as well as recruitment
of nontumor residing T cells [21]. Synthetic immunity mediated cytotoxic T cell therapy
has also shown to increase IFNγ levels, which in turn increases the expression of PD-L1
and thus antagonizes the mounting immune response. Use of the endogenous immunity
approach via CPIs therapy by blocking PD-L1/PD-1 pathway would benefit the outcome of the synthetic immunity approach as has been seen clinically as well [22].
One of the main reasons why cancer manifests itself with great success is that majority
of cancers do not have strong endogenous immunity to overcome the disease, nor do
they have a strong response to synthetic immunity alone to prevent it. In this regard,
combining the endogenous and synthetic immunity therapy shows synergy that may
be extremely beneficial in targeting immune-compromised tumors. It is noteworthy that
synthetic immunity is highly dependent on the expression of surface receptors, which
may vary from patient to patient and therefore opens up avenues for personalized therapy.
However, the success of such combination therapies will involve a strong understanding
of molecular and cellular diversity, immune makeup and receptor expression levels that
require well-planned studies with stringent data analysis.
2.6 Endpoint assessment and data integration
One of the major challenges with all breakthrough therapies is the pace of data collationand
assessment for the number of ongoing studies and clinical trials. A deeper understanding of
cancer biology at the cellular and molecular levels and the availability of multiple “omics”
tools has led to rapid identification and screening of potential immunotherapy targets with
more than 1000 ongoing clinical trials. While the fast pace of discovery of potential therapeutic molecules is welcoming, it also adds to the problem that a massive set of data generated has to be collated, analyzed, assessed, and integrated to identify the rational
combination of therapies to modulate different biological pathways for optimized efficacy.
However, the complexity of the biological pathways and changes induced by monotherapy
or combination of therapies presents a unique challenge of identifying the endpoints of a
study and mapping the changes to a tangible and conclusive efficacy. Appropriate preclinical models that mirror the clinical signaturesof cancer become very critical in screening the
combinatorial possibilities of different immunotherapies with some degree of correlation to
potential clinical outcomes. It also becomes important to understand if the mechanism of
action of two (or more) drugs tested in combination are supplementary or complementary
to each other with respect to clinical outcome. This adds another layer of complexity where
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a comprehensive biomarker studying strategy has to be mapped out including those that
have pharmacodynamic and efficacy predictability. More specifically in immunotherapy
drug combination studies, biomarkers that can predict synergistic, additive, or antagonistic
response are highly desired.
The majority of immunotherapies that have been approved for clinical use have been
accepted based on their monotherapy clinical outcome for safety and efficacy, which is
relatively easy to assess when compared with the combination treatment. The assessment
is further complicated by the heterogeneity of the disease, patient population, and subtle
variabilities in the disease as well as immunological profile. More thorough and diversified profiling of the patient’s disease and immune biology using specialized diagnostics
and specific imaging approaches may provide vital insights into the disease staging,
microenvironment, and organ-specific variations that can be decisive in designing combination immunotherapies. Historically, most clinically successful combination therapies
have been one where combination drugs have an independent anticancer activity that
works in synergy. That is not a prerequisite for immunotherapy where on drug in the
combination therapy may be used only to sensitize and prime the tumor for immunotherapy which the other agent provides the therapeutic benefit, such as vaccine and
immunotherapy combination. However, a deeper understanding of the pathways and
their interdependencies may allow the design of immunotherapies with significantly
higher potency.
477Clinical translation and challenges in cancer immunotherapies
2.7 Characterization of autoimmunity and anticancer immunity
The clinical success from all the cancer immunotherapies targeting PD1/PD-L1 axis or
cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) has been unprecedented but has
also raised some toxicity-related issues. One of the hallmarks of immunotherapy is that it
reinstates the anticancer activity of the host’s own immune system that was earlier rendered useless through evasive mechanisms of the disease. However, this therapeutic
approach has also led to the significant increase in immune-related adverse events including colitis, hypophysitis, diabetes mellitus, hypothyroidism, and pneumonitis [23]. Cor-
ticosteroids are commonly used for the treatment of autoimmune diseases including the
adverse events of cancer immunotherapy but are known to affect the immune system and
most prominently the population of circulating T cells [24]. The sensitivity to steroids is
more pronounced in the autoimmunity induced by checkpoint inhibitors to the extent
that it can cause a reversal of the therapeutic effect. Clinical observations suggest that
despite steroid treatment of patients suffering from immunotherapy-induced autoimmunity, patients do show therapeutic benefit, though the source of the response is not very
clear [25]. There is a poor understanding about the driving factor of the response despite
steroid treatment, whether it is related to the residual immunotherapy response or is it
478 Amit Singh
driven by the immune make-up of certain patients that are stronger than others. Such a
patient population may create a bias in the result interpretation if it is not characterized
appropriately. Besides, the impact of initiation of the systemic administration of corticosteroid with respect to the immunotherapy administration and the temporal dependence
of the two therapies is no clear.
One of the critical questions that need to be addressed is the impact of steroids on
anticancer immunity and the best way to solve the issue would be to incorporate it as
a study arm in the clinical trial design. However, designing a study to deliberately include
steroid therapy without a clinical need for it would be unethical and would not be permitted. Therefore, based on the current clinical data interpretation, the only understanding that is obvious is that the use of steroid therapy to curb the autoimmunity caused by
immunotherapy weakens the anticancer response but does not completely obliterate it.
The impact of the steroid therapy may depend on the time of administration with respect
to anticancer response, dose administered, dose regimen, type of steroid, and strength of
the immune response. Since the clinical validation of the effect of these factors may not be
possible, these effects should be studied in preclinical models that mirror the disease properties closely and such studies may help in extrapolating and predicting the clinical effects.
Another approach to address this challenge could be to develop immunotherapies that
demonstrate strong anticancer activity without eliciting any autoimmunity effect. Alternative approaches to mitigate the autoimmune effects of cancer immunotherapy could be
another key area of exploration to circumvent this challenge. Whatever be the approach,
but it is really important to understand the connection between steroid therapy and its
impact on the anticancer effects of immunotherapy to rationally develop immunotherapies in the future.
2.8 Maximize personalized therapeutic approach
It is well established that no two cancers are the same and there are tremendous
intratumoral and intertumoral heterogeneity at the cellular and molecular level. Besides,
with several thousands of ongoing clinical trials to develop immunotherapies as
standalone or in combination with other drugs against different types of cancer, the cancer immunotherapy landscape is rapidly evolving. It, therefore, becomes intuitive to
design therapies customized to the properties and behavior of the disease on a caseto-case basis. Biomarkers research in cancer provide clinical diagnostic, predictive, prognostic, and pharmacogenomic capabilities [26]. However, current clinical practices do
not always integrate diagnostic testing and detailed characterization for every type of
cancer, partially due to associated costs, lack of platform technologies/facilities, and its
usefulness toward informed decision making for a course of therapy. Besides, the identification of suitable biomarkers for different cancers, their correlation with the disease
progression, and their variability from patient to patient is poorly understood and
developed. The regular course of “personalized targeted” therapy whenever possible for a
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patient, is directed toward a target that is either highly overexpressed or a mutated form
specific to a cancer type or the patient. However, with cancer immunotherapy, the biomarkers investigation are not yes/no type assays but rather a characterization of graded or
continuous expression levels and thus their association in clinical benefits is difficult to
predict. Scenarios like this are addressed by scoring the biomarkers expression level (such
as PD-1) from patient to patient for a type of indication and cut-offs are set for population
selection. It is also important to appreciate that majority of the biomarker levels are sensitive to TME, tumor staging, sampling size, and tissue age (fresh vs preserved) that can
result in poor prediction of the biomarker levels.
The lack of suitable biomarkers that can differentiate the cancer cells from normal cells,
often appropriately called tumor-specific antigens (TSAs) is one of the biggest challenges in
designing immunotherapy. Antigens that are overexpressed in the case of cancer cells but at
normal levels in regular cells (tumor-associated antigens; TAAs) serve as poor targets for
immunotherapy because of the off-target effects leading to unintended toxicities. TSAs
on the other hand are specifically expressed on the surface of the cancer cells and so do
not pose similar off-target effects. Similarly, predictive biomarkers will be invaluable in
the selection of patients who will maximize the therapeutic benefit from a course of therapy
and exclude those who may show poor response. Correlating the biomarker levels with the
clinical outcome becomes very critical in that respect and properly designed studies would
allow rigorous analysis, even if performed retrospectively, to develop algorithms that can
help in the development of the course of treatment. More and more clinical trials with a
built in predefined plan for diagnostic, predictive, and prognostic biomarker analysis are
necessary to develop the capability to predict clinical outcomes with accuracy. Simultaneously, our capability to assess clinically relevant biomarkers rapidly yet accurately has
to be developed and disseminated across the hospitals to maximize learningfrom the clinical
trials. Such algorithms, once developed, will have the potential to become a powerful tool
in the hands of physicians to make educated and informed decisions on a case-to-case basis
not only at the initiation of therapy but also during the course of the therapy to dose optimization or even change in drug regimen as suited.
479Clinical translation and challenges in cancer immunotherapies
2.9 Improved regulatory endpoints
Traditionally clinical success of anticancer therapy is defined by parameters such as objective response rate (ORR), progression-free survival (PFS), or overall survival (OR), which
has served well with the development of immunotherapy thus far. PD-1/PD-L1 axis as the
most clinically studied target for cancer immunotherapy with multiple approved drugs
including pembrolizumab, nivolumab, atezolizumab, durvalumab, and avelumab is a testament to that success [27]. However, unlike the conventional anticancer therapy involving
chemotherapeutic drugs, immunotherapeutic modalities utilize a more complex pathway
480 Amit Singh
involving the host immune system to exert their effects. The complexity of the mechanism
of action includes mitigating the immune subverting pathways to build up a cellular
immune response against the cancer cells, thereby reducing the tumor burden and improving the patient’s survival. These biological processes follow very different kinetics compared with the standard line of therapy where each step presents itself as a clinically
measurable effect and thus requires a revised yardstick for establishing endpoints. The need
for this revision has been acknowledged by regulatory agencies across the globe through
different initiatives to create a program centered around the drug development needs
for immunotherapy including amendments in clinical endpoints [28].
Three novel recommendations were made based on rigorous analysis of preclinical
and clinical data from various immunotherapy studies. First, the complexity of the
T-cell immune response was acknowledged with the assertion that the assays available
for measuring these immune responses have high variability and poor reproducibility.
This assay characterizes the first and the most important biological event in the course
of immunotherapy; restoration of the host’s immune capability to fight the cancer cells
and thus reproducibility and accuracy of the assay would be of great value in predicting
therapeutic outcome. Data-driven optimization and assessment led to guidelines for the
harmonization of the enzyme-linked immunosorbent spot (ELISPOT) immune response
assay [28]. The second change was incorporated in measuring the antitumor response to
diversify from the criteria laid down by the World Health Organization (WHO) and
Response Evaluation Criteria in Solid Tumors (RECIST). It has been observed that
unlike standard chemotherapy where tumor shrinkage is measured as the criteria for successful response; immunotherapy may initially follow a different course of response
where the tumor may not shrink and stays stable, or even increase in size or show
new lesions [29]. It was therefore important to understand the clinical patterns in immunotherapy and define novel immune-related response criteria (irRC) based on the clinical
observations. The delay in response in the case of immunotherapy could be attributed to
the versatile nature of the immune system, which involves the expansion of immune
cells, infiltration into the tumor, and anticancer activity leading to tumor shrinkage. This
delay in the response leads to a delayed separation of the treatment group from the placebo in the Kaplan–Meier survival curve, which compromises the statistical predictive
capability of the data set. The recommendation was made to develop new statistical
models that factor in hazard ratios as a function of response time. These models however
require large datasets and iterative adjustments to validate the predictive accuracy and its
application is often limited to the immunotherapy on which it was validated.
2.10 Optimize survival through combination therapy
The complexity of cancer as a disease cannot be emphasized enough and its hallmark
properties include high adaptability to any change in its microenvironment at the cellular,
Fig. 1 A model illustrating the potential impact of single-agent and combination cancer immunother-
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apies on survival. Cancer treatment regimens that include immunotherapy can raise the tail of the
curve, increasing overall survival relative to standard cancer therapies. Targeted therapies enhance
response rates relative to standard chemotherapies, but overall survival is minimally impacted.
Single-agent immunotherapy is associated with long-term survival rates ranging from 10%
(ipilimumab) to 20%–30% (PD-1 blockade). Combination immunotherapy targeting the CTLA-4
(ipilimumab) and PD-1 pathways (nivolumab/pembrolizumab) are associated with long-term survival
rates of 50%–60%. Third-generation combination immunotherapy regimens (illustrated as
ipilimumab + nivolumab + X) have the potential to further maximize overall survival, moving closer
to cure.
481Clinical translation and challenges in cancer immunotherapies
molecular or physiological level. The majority of new cancer patients respond well to the
first line of therapy until the cancer cells adapt novel pathways to circumvent the effects of
the drugs and develop resistance [30]. A multipronged approach targeting different pathways simultaneously may involve the use of different therapeutic modalities in combination to obtain synergy and consequently improved benefits [31] (Fig. 1). The
combination therapy may involve drugs to kill cancer cells, sensitize them to immunotherapy, block immune evasion pathways, activate host immune response, target their
ability to induce drug resistance or any such pathway that provides survival benefits to
the cancer cells. A better understanding of patient’s tumor characterization can lead to
a personalized therapeutic regimen focusing on maximum antitumor response with minimum associated toxicities or the possibility of drug resistance. The ability to develop
such focused therapy will require carefully designed clinical trials such as umbrella trials
that emphasize tumor histology and their treatment is designed using specific biomarkers
or the basket trial, which are designed around a genomic alternation or other tumor characteristics. The most versatile aspect of such clinical trials is its modular nature where the
combinations can be reshuffled, doses can be adjusted, and the dosing schedule can be
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