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Numerous clinical studies reported higher ICB response rates and longer progressionfree survival in patients whose tumors expressed PD-L1 as determined by the immunohistochemical (IHC) staining. The association of tumor PD-L1 IHC positivity and
PD-1/PD-L1 checkpoint therapy response is particularly evident in melanoma and nonsmall cell lung cancer (NSCLC) (Table 1, see also [13] for review). These clinical findings
resulted in the approval of PD-L1 IHC as a companion diagnostic for anti-PD-1 therapy
[14]. In other words, IHC positivity is required for an FDA-approved use of this therapy.
For example, at least 50% of tumor cells must be positive for PD-L1 by IHC to receive
anti-PD-1 antibody pembrolizumab as a first-line therapy for NSCLC, and at least 1% of
tumor cells must be positive to receive it in a second line therapy settings [14].
While prognostic in some tumor types, the IHC test for PD-L1 positivity does not
always predict anti-PD-1 response in other types of tumors. For example, anti-PD1
response did not vary significantly based on tumor PD-L1 levels in patients with squamous NSCLC [15]. This phenomenon may be explained by the heterogeneity of PD-L1
expression on both temporal and spatial levels. Spatial heterogeneity is beautifully illustrated by the study of matched primary and metastatic lesions of patients with renal cell
carcinoma that reported striking 20% discordance of PD-L1 expression in distinct lesions
obtained from the same patient [16]. Similarly, temporal variation of PD-L1 levels can
also complicate its biomarker utility. First, tumors evolve over time and this dynamic
process is associated with changes in the gene expression profiles [17], including that
of PD-L1. Furthermore, PD-L1 expression is often induced as an adaptive mechanism
in response to immune mediators secreted by the immune cells within the tumor microenvironment. Thus, tumors with low immune infiltrate may have low PD-L1 levels prior
to therapy, while treatment may enhance immune cell presence in the tumor [18] leading
to a compensatory increase of PD-L1 expression. Therefore, the determination of a true
PD-L1 status can be skewed by the factors such as how long prior to immunotherapy
commencement the biopsy was obtained, what lesion it was obtained from, and whether
immune cells were present in the area the biopsy was taken from.
Another set of limitations of PD-L1 IHC relate to the technical aspects of the assay.
Several distinct PD-L1 antibody clones are used in the clinic, some display membranous
expression patterns while others show cytoplasmic staining [19, 20]. The set cut-off
values for PD-L1 expression to be classified as high or low vary greatly between different
tests using different antibodies in distinct tumor types, ranging from 1% to 50% PD-L1
positive tumor cells. Furthermore, there may be a difference in the prognostic value of
PD-L1 positivity based on whether it is detected on malignant or on immune cells within
the tumor microenvironment [14]. These issues make it challenging to standardize the
interpretation of IHC staining results.
Clearly, PD-L1 IHC status is not a perfect “go” or “no go” biomarker for immunotherapy assignment. Even though patients with PD-L1 positive tumors are more likely to
respondto PD-1/PD-L1 checkpoint blockade, there are cases of PD-L1-low and -negative
401Personalized cancer immunotherapy
Table 1 Select clinical studies reporting an association of anti-PD-1/PD-L1 therapy outcome and tumor PD-L1 expression evaluated by the IHC staining.
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Tumor type Drug (target)
Response rate,
low
PD-L1
Response rate,
high
PD-L1
Survival, months
low
PD-L1
Survival, months
high
PD-L1
Reference
Melanoma Nivolumab (PD-1) 3/18 (17%) 7/16 (44%) OS 12.5
PFS 2.0
Melanoma Pembrolizumab
6% 51% PFS 3 PFS 12 [6]
OS 21.1
PFS 9.1
[5]
(PD-1)
Melanoma Nivolumab (PD-1) 20.3% 43.6% NA NA [7]
Melanoma Nivolumab (PD-1) 33.1% 52.7% NR NR [8]
NSCLC
(nonsquamous)
Nivolumab (PD-1) 10% 36% (based on 5%
PD-L1 expression
cut-off )
OS and
PFS treatment
by PD-L1
[9]
expression
interaction
P-value <0.001
NSCLC Pembrolizumab
(PD-1)
NSCLC Atezolizumab (PD-
16.5%
(PD-L1 expression
<50%)
45.2% (PD-L1
expression >50%)
PFS 4.0–4.1
OS 10.4–10.6
(PD-L1 expression
<50%)
PFS 6.4
OS NR
(PD-L1 expression
>50%)
[10]
NA NA OS 12.6 OS 20.5 [11]
L1)
RCC 18% 31% PFS 2.9
OS 18.2
NSCLC—nonsmall cell lung cancer, RCC—renal cell carcinoma, NA—data not available, NR—not reached, OS—overall survival, PFS—progression-free survival.
PFS 4.9
OS NR
[12]
tumors that also respond (Table 1). If immune checkpoint blockade therapy is only given to
patients with PD-L1-positive tumors, a small but not insignificant proportion of patients
will be denied potentially highly effective treatment. Additional biomarkers that enable
more accurate prediction of response to immune checkpoint therapy are needed to address
this clinical dilemma.
2. Biomarkers based on tumor “foreignness”
2.1 Tumor mutational burden
The immune system discriminates between “self” and “nonself” antigens. Cancer cells
can often express aberrant proteins that can be recognized as “nonself” by the immune
system. One example of this phenomenon comes from highly mutated cancers. De-novo
somatic mutations play a key role in the etiology of cancer, however, the total count of
mutations per tumor (mutational burden) varies significantly across human malignancies.
Cancers associated with exogenous carcinogen exposure, such as melanoma associated
with UV mutagenesis, and lung cancer associated with smoking, tend to have a high load
of somatic mutations [21] (Fig. 1). Notably, these types of tumors are among the most
responsive to immune checkpoint therapy [22]. High levels of somatic mutations increase
the likelihood of tumor-expressing altered proteins. These mutated proteins are known
as neoantigens [23–25], which are “nonself” antigens unique to the tumor that can trigger
an adaptive immune response if neoantigen epitopes are presented to the immune system.
It was postulated that PD-1/PD-L1 checkpoint inhibition can reinvigorate preexisting adaptive immune responses towards neoantigens resulting in effective antitumor
immunity. Indeed, the total amount of nonsynonymous mutations in tumor cells, known
as tumor mutational burden (TMB), has been linked with responsiveness to PD-1 blockade in many retrospective studies [26, 27]. More recently, a prospective clinical study,
Keynote-158, demonstrated an association of an improved outcome from
pembrolizumab (anti-PD-1) therapy and high TMB in patients with advanced solid
tumors across 10 distinct tumor types [28]. As a result, pembrolizumab was approved
by the FDA for the treatment of tumors with high-mutational burden (as defined by having 10 mutations/megabase) independent of tumor type [FDA approves
pembrolizumab for adults and children with TMB-H solid tumors. News release.
FDA. June 17, 2020. Accessed June 17, 2020. https://bit.ly/30QEt40].
403Personalized cancer immunotherapy
2.2 Microsatellite instability
Another established immune checkpoint therapy response biomarker related to the
assessment of relative tumor “foreignness” is the status of the mismatch repair system.
Deficiencies in mismatch repair (MMR) can give rise to nonsynonymous genetic mutations and, consequently, a higher likelihood of neoantigens [29]. A phase 2 study of
Fig. 1 The prevalence of somatic mutations across human cancer types. Red lines are the median numbers of mutations per megabase in
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indicated cancers. ALL, acute lymphoblastic leukemia; AML, acute myeloid leukemia; CLL, chronic lymphocytic leukemia. Figure is obtained from
Alexandrov, LB, et al. Signatures of mutational processes in human cancer. Nature 2013;500(7463):415–421 https://doi.org/10.1038/nature12477 with
permission.
pembrolizumab in 41 patients with progressive metastatic carcinoma demonstrated a dramatic increase of the therapeutic benefit in patients with MMR deficiencies as compared
to patients without MMR deficiency. Specifically, a 40% rate of immune-related objective response and a 78% rate of immune-related progression-free survival was reported for
MMR-deficient cancers, in contrast to 0% therapeutic benefit and 11% progression-free
survival rates for MMR-proficient cases [30]. These findings were confirmed in a phase 3
study Keynote-177 [31] . As a result, MMR deficiency status is now an FDA-approved
indication for pembrolizumab treatment in patients with metastatic colorectal cancer.
2.3 Viral antigens
In addition to neoantigens derived from protein products of mutated genes, tumors may
contain exogenous antigens that can be recognized by the immune system. These include
viral antigens in human-papillomavirus (HPV) or Epstein–Barr virus-associated cancers
[32]. For instance, clinical trials demonstrated that patients with head and neck squamous
cell carcinoma who were positive for HPV had a higher response rate to anti-PD-1 agent
pembrolizumab and anti-PD-L1 agent durvalumab, as compared to HPV-negative cases
[33, 34]. Furthermore, high response rates to immune checkpoint inhibitors were
observed in patients with metastatic gastric cancer that was positive for the Epstein–Barr
virus and in HIV-positive cancer patients [35, 36]. In addition to actual viruses, tumors
can sometimes reexpress virus-like antigens encoded by human endogenous retroviruses
(HERV). HERVs are germline genomic remnants of retroviral infections in our ancestor’s implicated in regulation of the innate immune responses [37]. HERV are normally
epigenetically silenced in human cancers, however, some tumors may reexpress HERV
genes as a result of epigenetic dysregulation or treatment with certain drugs that affect
epigenetic modulators [38]. Notably, multiple studies indicated that cancers expressing
HERV are immunogenic. For example, an association of the HERV expression with
the clinical efficacy of PD-1/PD-L1 blockade was reported in metastatic clear cell renal
cell carcinoma [39–41].
405Personalized cancer immunotherapy
2.4 Cancer/testis antigens
Finally, tumors may aberrantly produce proteins that are normally expressed only in
immune-privileged sites, such as cancer/testis antigens (CTA). CTAs are encoded by
276 genes expressed in testicular germ cells and placenta trophoblasts with minimal to
no expression in normal adult somatic cells [42]. Certain tumor types can reexpress
specific CTAs, such as MAGEA-1-4 found in 70% of metastatic melanomas, ACRBP
present in 70% of ovarian tumors, and NY-ESO-1 detected in 46% of breast cancers
[43]. Importantly, these proteins can be recognized as “nonself” by the immune cells
surveilling tumors, and, therefore, are promising candidate targets in immunooncology
[44]. Among all CTAs, NY-ESO-1 is considered the most immunogenic [45]. Some
406 Amrendra Kumar et al.
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trials are completed and ongoing using a NY-ESO-1 vaccination approach together with
adoptive cellular therapy, or as a combination treatment with immune checkpoint
inhibitors [45].
3. Tumor immune microenvironment and immunotherapy
response
There is strong evidence that the abundance and phenotypes of T cells within the
tumor microenvironment (TME) are important factors in the immunotherapy response.
Based on the presence of T cells, TME can be classified as “hot,” or highly infiltrated with
T cells, and “cold” where T cells are rare within the tumor [46] (Fig. 2). A study of
tumors from melanoma patients treated with PD-1 blockade therapy reported that
responders had high levels of tumor-infiltrating T cells at the invasive tumor margins
prior to therapy initiation and these levels further increase after treatment. In contrast,
nonresponders exhibited the “cold” type of TME that was not dramatically improved
Fig. 2 “Hot” and “cold” tumor microenvironment. The prevalence of somatic mutations across human
cancer types. Top panels show schematic illustrations of distinct immune TME. Bottom baned shows
representative results of immunofluorescent staining in tumors. CD3+ T cells are green. The figure is
obtained from van der Woude, LL, et al. Migrating into the tumor: a roadmap for T cells. Trends Cancer
2017;3(11):797–808 with permission.
after treatment [18]. Another melanoma study reported an association of the levels of
PD-1- and CTLA-4-expressing CD8+ T cells with response to anti-PD-1 therapy
and improved progression-free survival [47]. More recently, Cristescu et al. reported
the results of a study looking into the association of T cell infiltrate (estimated based
on the T cell-inflamed gene expression profile) with clinical response to pembrolizumab
in > 300 patient samples across 22 tumor types from four KEYNOTE clinical trials. This
comprehensive study concluded that patients with high expression of a T cell-inflamed
gene signature were more likely to respond to pembrolizumab vs those who exhibited
low expression [48].
While it does enrich patients for immunotherapy responders, a T-cell inflamed profile
alone may not be selective enough to serve as a prognostic biomarker to guide treatment
decisions. The overall response predictive value of the tumor-infiltrating T cells may be
better if a biopsy is taken early on treatment, rather than prior to treatment [49]. One can
potentially envision a tumoral T cell infiltrate as an early pharmacodynamic biomarker
useful to guide the decision for treatment continuation after an initial cycle of therapy.
4. “Omics ” technologies in personalized immunooncology
The rapid advancement of next-generation sequencing, multiplex protein and cell
profiling, and sophisticated imaging platforms has enabled the use of “omics” (genomics,
transcriptomics, proteomics, etc.) biomarkers in clinical oncology [50]. Furthermore,
“omics”-powered identification of novel genetic, protein, and pathway correlates of
immunotherapy response enhanced our understanding of tumor immunobiology.
One of such “omics”-enabled biomarker—tumor mutational burden (TMB, described
above)—has already advanced into clinical practice and is used to identify patients who
are most likely to benefit from immune checkpoint blockade therapy. TMB, which is a
quantitative measure of nonsynonymous mutations in the coding tumor genome, is
derived from the next-generation DNA sequencing data. TMB assay is commercially
available from Foundation Medicine and other vendors.
Next-generation DNA sequencing coupled with bioinformatics and machine learning tools is also used for neoantigen prediction. Neoantigen assessment is a key step in
designing highly personalized immunotherapies, such as neoantigen-specific vaccines
(described in more details below) [51]. Another important aspect of neoantigen research
enabled through genomics is the assessment of tumor antigen immunogenicity by computational comparison of tumor neoantigen peptide sequences with the epitopes that are
known to induce adaptive immune responses. With the use of this approach, it has been
shown that melanoma patients whose tumors expressed neoantigens that were predicted
to be highly immunogenic had longer survival after anti-PD-1 and anti-CTLA-4 therapy
as compared to tumors that expressed less immunogenic neoantigens [52]. These data
407Personalized cancer immunotherapy
408 Amrendra Kumar et al.
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suggest that the quality of tumor neoantigens, rather than their quantity, is a strong driver
of immune checkpoint blockade response.
Another group of “omics”-based correlates of immunotherapy response is derived
from transcriptomic data. These include gene expression signatures where a set of
response-associated genes is derived via the comparison on transcriptome profiles of
tumors in responsive and nonresponsive patients. For example, Ayers et al. identified
IFN-γ-related mRNA profile that predicted response to PD-1 blockade therapy with
pembrolizumab using a set of baseline biopsies from patients with metastatic melanoma
enrolled in the KEYNOTE-001 study (NCT01295827, ClinicalTrials.gov). The expres-
sion signature included genes involved in antigen presentation, chemokines, cytotoxic
activity, and others. Notably, the authors validated the response-predictive value of their
signature in an independent cohort of 96 head and neck squamous cell carcinoma
cases [53].
5. Immunotherapy response biomarkers not directly measured in
tumor
There has been a significant interest in developing immunotherapy correlates that
do not require tumor biopsy. These markers are of particular value as they are noninvasive or minimally invasive in nature, and therefore pose a relatively low risk to the
patients. These include patient microbiome-associated biomarkers. The human microbiome is the aggregate of all microorganisms that reside on or within the human body.
Interestingly, the presence of certain commensal gut microbiota has been shown to correlate with the immune checkpoint inhibitor efficacy [54–56]. While the studies do not
always agree on the exact species associated with anti-PD-1 response, the overall takehome message seems to converge to a view that microbial diversity is an important factor
of treatment outcome.
Another group of nontumor biomarkers worth noting here is the markers assessed in
the patients’ peripheral blood. These could be cell-based, such as an abundance of certain
immune cell populations in the blood or distinct phenotypes/exhaustion states of T cells
tested by multiplex cytometry and other methods [57, 58]. Alternatively, soluble
markers, such as cytokine IL8, IL6, and others, can be monitored longitudinally in serum
of patients receiving checkpoint blockade therapy to gain an idea of the dynamic changes
and the overall state of the immune response [59].
6. Integrating biomarkers to reach “precision” and tailor therapy
to patient’s unique immunome
To date, no singular biomarker capable to precisely stratify immunotherapy
responders from nonresponders has been found. Nevertheless, better prognostic power
may be achieved using a panel of distinct biomarkers. For instance, in the study investigating correlates of response to anti-PD-1 agent pembrolizumab across multiple tumor
types, both tumor mutational burden and T cell-inflamed gene expression signature were
independently associated with improved response rate. However, neither markers were a
perfect predictor of immunotherapy response. The stronger association with response
was observed when authors integrated these markers. Specifically, the highest rates of
objective response were seen in patients with high mutational burden and T cellinflamed expression profile (37%–57%). Tumors with low levels of both markers
exhibited the lowest rates of response to anti-PD-1 (0%–9%), while tumors with only
one of the markers present had intermediate response [48].
Integrative biomarker assessment can be valuable for designing personalized immu-
notherapy regimens. This concept is well illustrated by the cancer immunogram idea
proposed by Blank et al. Cancer immunogram is a way of graphical visualization of
patient’s unique aspects of tumor-immune interactions that can provide cues regarding
patients state of antitumor immunity and guide treatment selection [60]. It builds on the
key assumption that the outcome of tumor-immune interactions depends not on one
but on some key para meter s, such as tumor mutational burden, inhibitory factors of the
TME (metabolic, cytokine, and i mmune checkpoint T cell inhibitors), T cel l infiltration into the tumor, and the overall state of the immune system in the patient. With
such a comprehensive assessment of the state of patient-specific immunome, it will
be possible to make an informed decision on whether or not immunotherapy should
be prescribed and/or what type of cotreatme nt may benefit this patient. For ex ample,
immune checkpoint therapy may no t be the ri ght treatment choice for tumors lacking
strong neoantigen epitopes that can trigger an adaptive immune responses. On the
other hand, tumors expressing strong neoantigens but lacking T cell inf iltrate may
benefit from therapies that combine PD-1 blockade with agents that facilitate T cell
homing into the tumor. P otent ial options include epigenetic modifier drugs and
senescence-inducing agents t hat promote the expression of T cell-recruiting
chemokines within the tumor [61–63].
One can expect the immunogram to evolve as we gain more understanding of tumorimmune interplay [60]. Indeed, several recently emerged biomarkers of immunotherapy
response and the mechanisms of tumor immune evasion can be added to the list of the
essential parameters. Further, some of the original parameters can be updated for greater
precision. For instance, the progress of computational analysis, machine learning algorithms, and the use of artificial intelligence enable more accurate prediction of tumor
“foreignness” by identifying tumor mutations that are likely to translate into strong
neoantigen epitopes. Further, improvement of “omics” technologies and single-cell
analysis can aid in a comprehensive assessment of tumor-infiltrating T cells and identify
tumor-specific vs bystander cells, or T cells that can be invigorated by immunotherapy vs
those that are terminally exhausted.
409Personalized cancer immunotherapy
410 Amrendra Kumar et al.
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7. Part II. Highly personalized immunotherapy
The field of cancer immunotherapies is replete with literature describing the failure
of cancer immunotherapies in a large proportion of patients. Also, the most successful cancer immunotherapy, the checkpoint inhibitors are not specific to tumors and associated
with substantial autoimmunity toxicity. Thus, currently, the development of personalized
medicine is at the heart of cancer immunotherapy. Personalized cancer immunotherapies
aim to exploit patient’s immune cells and tumor-derived information towards designing
efficient therapeutic strategies. The long-term goal is to broaden the benefits of cancer
immunotherapies to the vast majority of the patient population with diverse cancer types
by rewiring immune cell functions for effective tumor control with minimal or no autoimmune toxicity.
8. Adoptive T cell transfer: A highly personalized therapy for
human cancers
Novel insights into roles played by the immune system have invigorated research
and development endeavors to harness the immune system for the treatment of cancers
[64–70]. Several dendritic cell (DC)-, natural killer (NK)-, and T lymphocyte (T)-
cell-based immunotherapies are at various stages of preclinical and clinical development
[71–81]. Adoptive cell therapy (ACT) is one such strategy where immune cells are mod-
ified and/or engineered ex vivo and re-infused into patients to elicit protective immune
responses against tumors. DC-, NK-, and T cell-based ACTs have been devised and
shown promising results in experimental models [71–81]. Recently, highly personalized
cancer immunotherapies such as ACT with patient’s own T cells, have gained wide and
significant attention. This however does not discount the fact that engagement of
multiple arms of the immune system is required to induce effective antitumor immunity.
Moreover, a personalized approach to DC-, NK cells based ACTs is also being investigated [71–80]. In addition, studies suggest that the use of other immune cells would
greatly enhance the efficacies of T cell-based ACTs. In this section, we keep our focus
on T cell-based ACTs and the exciting recent advances that hold promise for the development and fine-tuning of highly personalized cancer immunotherapies. The advent of
next-generation sequencing technologies, advancements in the field of gene therapy,
gene editing as well as enhanced understanding of T cell functions in the tumor microenvironments have made it possible to rewire T cells function to mount tumor-specific
response and achieve long term remission. We discuss how deep sequencing technologies
coupled with epitope prediction/identification have enabled the identification of individual and tumor-specific cancer rejection antigens known as neoantigens. The tumor
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