Abstract
Background: Immunotherapies targeting immune checkpoint proteins
CTLA-4, PD-1, and PD-L1 have saved lives, but these therapies have only
been effective in some patients. Patients positive for expression of
immune checkpoint proteins in the tumor microenvironment show better
response to immune checkpoint inhibitors. Consequently, knowledge of
which genes are consistently expressed in lymphocytes within the tumor
microenvironment can convey potentially effective and complementary new
immunotherapy targets.
Results: We identified 54 genes that have higher co-expression with the
pan T-cell marker CD3E than CTLA4 or PDCD1. In a dataset of 26 patients
who received anti-PD-1 therapy, we observed that co-expression between
CD3E and PDCD1 was higher among responders than non-responders,
supporting our correlation-based approach.
Conclusions: The genes highlighted in these analyses, which include
CD6, TIGIT, CD96, and SLAMF6, warrant further investigation of their
therapeutic potential.
Methods: We analyzed and ranked genes that were co-expressed with the
pan T-cell marker CD3E in 9,601 human tumors, spanning 31 cancer types.
To further identify targets that may be complementary to existing PD-1
therapy, we examined and ranked genes with high CD3E co-expression and
relatively low PDCD1 co-expression.
Keywords: immunotherapy, tumor microenvironment, T lymphocyte, cancer
genomics, RNA-seq
INTRODUCTION
The long-term effectiveness of broad-spectrum chemotherapies [[34]1]
and molecularly-targeted therapies [[35]2] is mitigated by the
evolutionary dynamics of tumor cells, wherein natural selection favors
increasingly aggressive and drug-resistant clones. Therapeutic
resistance presents one of the universal challenges in cancer treatment
and can be attributed to the extensive genetic [[36]3] and phenotypic
[[37]4] diversity present within tumor cell populations. Recent
achievements in the field of immunotherapy [[38]5–[39]7] to treat
advanced cancers have renewed optimism about harnessing the power of
the adaptive immune system, and its ability to produce a
nearly-unlimited diversity of antigen-recognizing receptors, to achieve
lasting therapeutic results [[40]8–[41]10]. The promise of
immunomodulatory approaches lies in the potential to confront one
dynamic, diversity-rich system (the evolving tumor) with another (the
adaptive immune system) [[42]11–[43]14]. Nevertheless, there are
challenges to using the immune system to fight cancer: tumors appear to
have a diversity of immune-evasion mechanisms that modulate the immune
response [[44]15–[45]18]. As we learn more about the biomarkers that
identify which immunotherapy approaches have promise for each patient
[[46]19–[47]21], these challenges might be successfully addressed by
developing a variety of approaches for immunomodulation in diverse
patients and circumstances.
The goal of immunotherapy is typically to reverse tumor immune-evasion
mechanisms and restore local immune response against cancer cells
[[48]22–[49]25]. Cytotoxic T-lymphocyte associated protein 4 (CTLA-4),
which represses early T-cell activation, was the first immune
checkpoint receptor to be targeted therapeutically [[50]26, [51]27].
Most recent immunotherapy studies focus on antibodies to block the
programmed cell death protein 1 (PD-1) or its ligands (e.g. PD-L1),
which have enabled breakthroughs in the treatment of melanoma,
non-small cell lung cancer, and renal cell carcinoma [[52]28–[53]31].
These treatments can markedly improve patient survival, but only a
minority of patients respond to therapy [[54]32, [55]33]. Analysis of
PD-1 blockade response data reveals increased response in patients with
higher tumor PD-L1 expression, and higher PD-1 expression on the tumor
infiltrating lymphocytes (TILs) [[56]34]. These facts reaffirm the
biological basis for immunotherapy: by reprogramming the suppressed
TILs, it is possible to make tumors newly vulnerable to the immune
system.
The development of an arsenal of approaches to modulate immune response
is dependent on the identification and prioritization of targets that
are likely to modulate immune activity in tumors in complementary ways
to extant therapies [[57]11–[58]13]. To predict the best
immunomodulatory treatment targets that would reverse the suppression
of tumor-infiltrating T cells, an approach is needed that evaluates
potential immunomodulatory interactions with T cells in the tumor
microenvironment [[59]35–[60]37], analogous to approaches outside the
realm of immunotherapy that identify targets based on functional
associations of disease [[61]38]. We reasoned that immunomodulatory
targets would have to be relatively abundantly expressed in
tumor-infiltrating T cells. Accordingly, we used mRNA expression of the
pan T-cell marker CD3E as a metric for T-cell abundance within a tumor
[[62]12], and examined correlations between expression of CD3E and
putative targets across 9,601 human tumors spanning 31 cancer types. We
deemed genes whose expression is highly correlated with CD3E—and
therefore are likely to be expressed in T cells within the tumor
microenvironment—to be promising targets for therapy. We further
reasoned that the most complementary targets would be those that are
not co-expressed in common with PDCD1 (the gene coding for PD-1), as
they might function in individuals for whom anti-PD-1 therapy is
insufficient or inviable. Therefore, we also examined the correlation
of putative targets with PDCD1 expression. We identified targets with
high CD3E correlation and relatively low PDCD1 correlation, suggestive
of the presence of T cells with low PD-1 expression. These
immune-related genes could potentially be new targets for therapy
complementary to PD-1 blockade therapy. Lastly, we extended our
analysis to consider complementarity to therapies directed at both PD-1
and CTLA-4.
RESULTS
T-cell associated genes exhibit consistent expression in tumor
microenvironments across cancer types
We generated a heat map to indicate the strength of correlation in
expression between each gene in a 40-gene candidate panel and the pan
T-cell marker CD3E, across 31 cancer types. Candidate genes were
selected based on expert knowledge for their known functional relevance
in T-cell function and their known effects on CD8 T-cell function. CD3E
was selected as a T-cell marker because of its relatively reliable and
broad expression in T cells. Analyses using genes encoding other
components of CD3 gave similar results ([63]Supplementary Figure 1).
Differentiating between CD4^+ and CD8^+ T-cell markers did result in
different rankings, consistent with evidence that these T-cell types
play divergent roles in the tumor microenvironment [[64]39–[65]42]. We
chose not to use a combination of genes in a signature-based analysis
because we did not want our search for new targets to be biased by a
predefined gene signature. Four of the candidate genes we examined
showed more highly correlated expression with CD3E than was exhibited
by the sometimes highly effective immunotherapy target and checkpoint
inhibitor PDCD1. Another successful immunotherapy target, CTLA4, ranks
eighth on the list of candidate genes in terms of how strongly
correlated its co-expression is with the CD3E T-cell marker ([66]Figure
1A). Genes tend to show highly consistent CD3E co-expression patterns
across the 31 cancer types studied ([67]Figure 1A). Additional
potential gene targets are identified that have more highly correlated
expression with CD3E than PDCD1 (ranked #55) and CTLA4 (ranked #103)
when we expanded the list of studied genes from our 40-candidate list
to all genes known to be expressed in cancer ([68]Figure 1B,
[69]Supplementary Figure 2). Promisingly, these genes tend to have
known immune regulatory functions ([70]Table 1). The gene for the
co-stimulatory cell-adhesion molecule CD2 ranks at the top of both the
candidate and exome-wide lists.
Figure 1. CD3E co-expression across cancer types (TCGA abbreviations;
[71]Supplementary Table 2).
[72]Figure 1
[73]Open in a new tab
Among the (A) 40 candidate genes of the gene panel, several candidate
genes show more correlated co-expression with the pan T-cell marker
CD3E than successfully-targeted immune checkpoints like PDCD1 and CTLA4
(shown in bold). Candidate genes show remarkable consistency in their
co-expression patterns across a variety of cancer types. Correlation
coefficients in (B) a cancer exome-wide analysis reveal many genes that
are highly-correlated with gene expression of pan T-cell marker CD3E in
the tumor microenvironment. Higher correlation in expression than
exhibited by PDCD1 and CTLA4 is observed in 54 and 102 genes,
respectively. The gene CD2 tops both lists. Only two other candidate
genes (TIGIT and CD27, shown in bold) fall in the top-50 ranking in the
exome-wide analysis (#13 and #20; [74]Supplementary Figure 2). The
color of each tile indicates correlation with CD3E expression, (blue,
low to red, high; green: not assayed). The genes are ranked according
to the median of correlation coefficients across cancer types.
Candidate genes from the gene panel are shown in bold.
Table 1. Genes with highest CD3E co-expression in exome-wide analysis.
Rank^a Symbol Pearson’s r Gene description^b Panel?^c
1 CD2 0.955 T-cell surface antigen CD2 ✓
2 CD247 0.944 T-cell receptor T3 zeta chain
3 SIRPG 0.942 signal-regulatory protein gamma; CD172g antigen
4 CD3D 0.933 T-cell surface glycoprotein CD3 delta chain
5 LCK 0.918 lymphocyte-specific protein tyrosine kinase
6 SIT1 0.917 signaling threshold regulating transmembrane adaptor 1
7 CD6 0.912 T-cell differentiation antigen CD6
8 CXCR3 0.910 chemokine (C-X-C motif) receptor 3
9 SH2D1A 0.907 T-cell signal transduction molecule SAP
10 SLA2 0.904 Src-like-adaptor 2; modulator of antigen receptor
signaling
11 SLAMF6 0.902 SLAM family member 6; activating NK receptor
12 ITGAL 0.897 antigen CD11A (p180), lymphocyte function-associated
antigen 1
13 TIGIT 0.896 T cell immunoreceptor with Ig and ITIM domains ✓
14 CD96 0.896 T cell activation, increased late expression
15 TRAF3IP3 0.892 TRAF3-interacting JNK-activating modulator
16 ACAP1 0.882 ArfGAP with coiled-coil, ankyrin repeat and PH domains 1
17 RASAL3 0.882 RAS protein activator like 3
18 ITK 0.879 interleukin-2-inducible T cell kinase
19 TBC1D10C 0.879 carabin; TBC1 domain family, member 10C; RAS
signaling inhibitor
20 CD27 0.877 T-cell activation antigen CD27 ✓
21 CD48 0.872 CD48 antigen (B-cell membrane protein)
22 GZMK 0.870 granzyme K (granzyme 3; tryptase II)
23 SASH3 0.865 SAM and SH3 domain containing 3
24 LY9 0.865 lymphocyte antigen 9
25 SEPT1 0.865 septin 1; serologically defined breast cancer antigen
NY-BR-24
26 PTPRCAP 0.862 protein tyrosine phosphatase, receptor type,
C-associated protein
27 ARHGAP9 0.858 Rho GTPase activating protein 9
28 CD3G 0.857 T-cell receptor T3 gamma chain
29 TESPA1 0.856 thymocyte expressed, positive selection associated 1
30 MAP4K1 0.854 MAPK/ERK kinase kinase kinase 1
31 CORO1A 0.842 coronin, actin binding protein, 1A
32 SLAMF1 0.841 signaling lymphocytic activation molecule family member
1
33 NLRC3 0.839 NLR family, CARD domain containing 3
34 CST7 0.838 cystatin F; leukocystatin
35 IL2RG 0.838 interleukin 2 receptor, gamma
36 WAS 0.837 Wiskott-Aldrich syndrome
37 CD8A 0.834 T-cell surface glycoprotein CD8 alpha chain
38 CD5 0.829 CD5 antigen (p56-62)
39 GIMAP5 0.828 immunity-associated nucleotide 5 protein
40 IKZF1 0.828 IKAROS family zinc finger 1 (Ikaros)
41 ZAP70 0.828 zeta chain of T-cell receptor associated protein kinase
70
42 KLRK1 0.827 killer cell lectin like receptor K1
43 CCL5 0.827 C-C motif chemokine ligand 5
44 GPR171 0.826 G protein-coupled receptor 171
45 PVRIG 0.825 transmembrane protein PVRIG; CD112 receptor
46 PTPN7 0.824 protein tyrosine phosphatase, non-receptor type 7
47 CXCR6 0.823 C-X-C motif chemokine receptor 6
48 EVI2B 0.823 ecotropic viral integration site 2B
49 CCR5 0.819 C-C motif chemokine receptor 5
50 GZMA 0.815 granzyme A; Cytotoxic T-lymphocyte-associated serine
esterase-3
51 ICOS 0.815 inducible T-cell co-stimulator ✓
52 GRAP2 0.813 GRB2-related adaptor protein 2
53 PTPRC 0.808 CD45; protein tyrosine phosphatase, receptor type, C
54 GIMAP4 0.806 GTPase, immunity-associated protein 4
55 PDCD1 0.806 programmed cell death 1 ✓
103 CTLA4 0.727 cytotoxic T-lymphocyte associated protein 4 ✓
[75]Open in a new tab
^aGenes are ranked according to median CD3E co-expression across cancer
types.
^bGene descriptions are taken from the NCBI Gene database.
^cPresent in the 40-gene panel if indicated by a check mark.
Candidate gene targets to complement anti-PD1 therapies
We re-ranked the 40-candidate gene panel and the exome-wide gene lists,
using a “PDCD1-complementarity score” that simultaneously considers the
strength of correlation between the gene target and CD3E (as above),
and the strength of correlation in gene expression between the target
and PDCD1. This re-ranking provided a list of genes that could
potentially serve not only as effective therapeutic targets in general,
but as complementary therapies to anti-PD-1 therapy, in that they might
be especially effective for patients whose tumors do not respond to
anti-PD-1 therapy. Genes with the highest PDCD1-complementarity score
were highly correlated in expression with CD3E and less correlated with
PDCD1 expression ([76]Figure 2A, [77]Supplementary Figure 3). When
taking complementarity to PDCD1 into account, the C-C chemokine
receptor type 7 gene CCR7 tops the list, and CD2 ranks #24. Among genes
in our 40-candidate panel, CD40LG, CD2, and BTLA remain highly
complementary in our joint analysis of complementarity to therapies
directed at PDCD1 and to CTLA4 ([78]Figure 2B–[79]2D, [80]Supplementary
Figure 4).
Figure 2. PDCD1-complementarity and joint CTLA4-PDCD1 complementarity scores
of exome-wide gene list.
[81]Figure 2
[82]Open in a new tab
Scatter plots show correlations for each target gene, with results from
each cancer type superimposed and colored (blue, low to yellow, high)
according to (A) PDCD1-complementarity score, which is high when
expression of CD3E and PDCD1 are respectively correlated and
uncorrelated with the target gene—genes with the highest
PDCD1-complementarity scores are highly correlated with CD3E
(approaching one on the x-axis) and less correlated with PDCD1
(approaching zero on the y-axis); and (B) joint CTLA4-PDCD1
complementarity score (including only scores above zero), which is high
when expression of CD3E is correlated with the target gene and
expression of PDCD1 and CTLA4 are uncorrelated with the target gene.
Genes with the highest joint complementarity scores have close to zero
correlation in expression with both CTLA4 and PDCD1. Bar plots show the
top 50 genes ranked according to (C) their median PDCD1-complementarity
score and (D) their joint CTLA4-PDCD1 complementarity score across
cancer types. Top candidate genes (red bars) and top genes from the
cancer exome (blue bars) are shown. To the right of each bar is its
ranking based on complementarity score (CS) and CD3E co-expression
(CD3E). Ranks up to 50 are in boldface.
GSEA pathway enrichment analysis of the exome-wide gene list ranked by
CD3E co-expression revealed significant enrichment of immune-related
pathways ([83]Table 2; [84]Supplementary Table 1). The top 50 pathways
all have FDR Q value below 1 × 10^−5, ranked by normalized enrichment
scores in the range 2.06–2.38. The top-ranked pathway is TCR signaling
in naïve CD4^+ T cells (curated by the Pathway Interaction Database,
[85]Supplementary Figure 5). Other pathways of interest include the CD8
TCR pathway, numerous immunoregulatory interactions, natural killer
cell mediated cytotoxicity, and costimulation by the CD28 family; PD-1
has recently been demonstrated to exert its primary effect via
regulation of CD28 [[86]43–[87]45].
Table 2. The top ten enriched pathways from GSEA.
Rank Pathway name NES^a
1 TCR pathway (PID) 2.383
2 CD8 TCR pathway (PID) 2.366
3 Immunoregulatory interactions between lymphoid & non-lymphoid cells
(REACTOME) 2.360
4 Hematopoietic cell lineage (KEGG) 2.315
5 Class A1 rhodopsin like receptors (REACTOME) 2.302
6 Interferon gamma signaling (REACTOME) 2.302
7 Natural killer cell mediated cytotoxicity (KEGG) 2.300
8 Cell adhesion molecules cams (KEGG) 2.280
9 Cytokine cytokine receptor interaction (KEGG) 2.259
10 Costimulation by the CD28 family (REACTOME) 2.251
[88]Open in a new tab
^aNormalized Enrichment Score. All pathways listed have a false
discovery rate Q < 1 × 10^−5.
Patients with response to anti-PD1 therapy have higher correlation between
PDCD1 and CD3E expression than non-responders
To examine how the relationship between CD3E expression and PDCD1
expression influences response to anti-PD-1 therapy, we analyzed
RNA-Seq transcriptome data from pretreatment samples of 26 metastatic
melanoma patients who received anti-PD-1 therapy [[89]82]. Patients had
been categorized as ‘responders’ (n = 10 who experienced partial
response, n = 4 who experienced complete response) and ‘non-responders’
(n = 12 who experienced progressive disease). Comparing CD3E and PDCD1
expression among responders and non-responders, we find higher
correlation among responders than non-responders ([90]Figure 3A). Among
the ‘responder’ patients, the Pearson’s correlation coefficient for
transcript per million (TPM) expression values for CD3E and PDCD1 is
0.998, while ‘non-responders’ have a lower correlation coefficient of
0.688. A linear regression of these expression values for responders
yields an R^2 statistic of 0.997, compared to a value of 0.473 for
non-responders, indicating that a linear model has over twice the
explanatory power for responders than non-responders. Squared residuals
from the linear regressions are significantly lower for responders than
non-responders ([91]Figure 3B; P = 0.003, Mann–Whitney U test). One
patient in the cohort, a responder, was an outlier with very high PDCD1
and CD3E expression values ([92]Figure 3A). When this patient is
excluded from the analysis, the pattern holds. Analysis of the
responder cohort excluding the outlier yields a CD3E-PDCD1 correlation
coefficient of 0.931, R^2 of 0.868, and squared residuals from the
linear regression remain significantly lower than non-responders; P =
0.005. These findings support our hypothesis that the immune regulatory
genes we have discovered with highly correlated expression with CD3E
may be promising candidates for new targeted-therapies.
Figure 3. PDCD1 and CD3E expression in responders and non-responders to
anti-PD-1 therapy.
[93]Figure 3
[94]Open in a new tab
Pretreatment samples from metastatic melanoma patients who ultimately
responded to anti-PD-1 therapy show more highly correlated
co-expression between PDCD1 and the pan T-cell marker CD3E, as
indicated by (A) a linear regression of the expression values for
responders with a coefficient of determination R^2 = 0.997, compared to
R^2 = 0.473 for non-responders; and (B) squared residuals from the
linear regression of CD3E and PDCD1 that are significantly lower for
responders than non-responders (P = 0.003).
DISCUSSION
We examined a 40-gene panel of candidate immune regulators and an
unbiased list of 12,082 expressed genes in cancer to find genes
consistently co-expressed with the T-cell marker CD3E in the tumor
microenvironment. We found that expression patterns were remarkably
consistent across the 31 cancer types analyzed, and that the top genes
were highly enriched within immune-related pathways, indicating that
tumor-infiltrating T cells may have universal characteristics that
could be targeted effectively in multiple cancers. We also identified
T-cell associated genes whose expression patterns do not correlate
highly with PCDC1 that might be targeted for development of
complementary therapies to anti-PD-1, or for patients who do not
respond to anti-PD-1 therapy. When we examined anti-PD-1 responders and
non-responders in metastatic melanoma, we found that responders had a
significantly higher correlation between CD3E and PDCD1 than did
non-responders, which supports the hypothesis that correlation in
expression with CD3E in the tumor microenvironment can be a useful
criterion for identifying new therapeutic targets with potential for
therapeutic response. It has been shown that although PD-L1 expression
in tumor biopsies does appear to predict response to anti-PD-1
therapies, many tumors predicted as PD-L1 positive do not respond,
while some responses occur in PD-L1-negative tumors [[95]46–[96]51].
Our results are similar in that we find that some patients with high
CD3E-PDCD1 correlation are not responders, whereas others with lower
correlation do respond to anti-PD1 therapy. Also, when we performed a
similar analysis on response to immune checkpoint therapies among
patients with clear cell renal carcinoma, we did not find significant
differences in CD3E-PDCD1 correlation in responders and non-responders
[[97]52]. More detailed expression analyses of responders and
non-responders in additional cancer types will help to shed light on
additional genetic and tumor microenvironmental factors that influence
response to new and existing and therapies [[98]52, [99]53].
As a purely correlation-based approach, our exome-wide expression
analysis is very coarse-grained, and does not incorporate multi-omic
pharmacogenomic data [[100]54] or the specific molecular biology of the
genes identified. Extensive molecular biological research on their
functional and structural properties is required to assess their
viability as immunomodulatory targets [[101]55, [102]56]. However, the
guidance provided by our approach complements extant molecular
biological investigation, highlighting well-studied genes that deserve
continued attention as well as pointing out genes whose molecular
biology is less well known, but which could be important for future
research. While a subset of these genes will lack exploitable
properties or would produce undesirable outcomes if they were targeted,
it is also likely that novel and complementary targets that do have
high potential are highlighted by our analysis. Furthermore, the
consistency of the results of our analysis across cancer types inspires
confidence that successful targeting of the genes could yield a high
breadth of therapeutic applicability either alone or in combination
with other therapies.
Among the more well-studied genes with potential for targeting, some
have been previously identified by other approaches. CD6, a known
T-cell co-stimulatory molecule, was identified in the exome-wide
analysis as being co-expressed with CD3E. The binding of CD6 to CD166
(also called ALCAM: activated leukocyte-cell adhesion molecule) enables
formation of a functional immune synapse. Indeed, an antibody that
particularly antagonizes the function of CD6—Itolizumab—is under
investigation for use as an anti-inflammatory in psoriasis patients
[[103]57, [104]58]. This antibody presents a possibility of stabilizing
(i.e. agonizing) the CD6:CD166 interaction in the immune synapse so as
to stimulate effector T-cell function. Of course, the systemic
consequences of such an agent might outweigh the local benefits of a
productive anti-tumor immune response.
Other well-studied targets are encoded by TIGIT and CD96 [[105]59,
[106]60], which were similarly highly co-expressed with CD3E, and are
known to inhibit effector T-cell activation. TIGIT and CD96 compete
against the stimulatory receptor CD226 for shared ligands (i.e. CD155)
and thus suppress CD226 activation [[107]61]. TIGIT can suppress
effector T-cell function by directly suppressing CD226 in cis or
suppress APC function by signaling through CD155 in trans [[108]62].
TIGIT and CD96 both suppress natural killer (NK) cell function as well
[[109]63, [110]64], and animal models of CD96 knockout mice revealed
hyperinflammatory status with increased IFN-γ production in NK cells
[[111]64]. These mice are also resistant to a experimental lung
metastasis model, suggesting a potential therapeutic role of CD96
blockade in cancer treatment [[112]65]. TIGIT is known to additionally
result in immunosuppression mediated by T regulatory cells (via
secretion of IL-10 and TGF-β) [[113]66]. Immunoregulatory function of
TIGIT was shown to occur as a consequence of engagement with CD155 on
dendritic cells, which results in increase IL-10 production,
suppressing the effector T cells while promoting regulatory T cells
[[114]62]. Given the known functions of these receptors, the CD3E
co-expression data reported here provides additional rationale for the
development of selective antagonists and context for potential
therapeutic application.
Expression of SLAMF6 (also called Ly108)—a CD2 family member that plays
a critical role in NK-cell development and activation—is also
correlated with CD3E expression. SLAMF6 is known to be expressed on T
cells, and its co-stimulation was shown to drive naïve CD4^+ T cells
toward a Th1 phenotype, inducing IFN-γ production [[115]67]. SLAMF6
educates NK cells by forming homodimers at a synapse between cells.
This homodimerization reduces NK-cell activity toward hematopoietic
(i.e. SLAMF6^+) cells while enhancing activity toward non-hematopoietic
(SLAMF6^−) tumor cells [[116]68]. During development, SLAMF6 also
reduces NK-cell differentiation and proliferation [[117]69, [118]70].
Antibodies targeting SLAMF6 have demonstrated efficacy in mouse
oncology models [[119]71], underscoring the therapeutic potential of
targeting this receptor and highlighting the potential of NK cells to
play a critical role in anti-tumor immunity.
Additional immunotherapy-relevant patterns emerge when the genes that
are highly ranked in our analyses are considered in aggregate. For
instance, the high rank of the NK-cell mediated cytotoxicity pathway in
the GSEA analysis, as well as pathways that include NK-cell associated
proteins (e.g. SLAMF6), could reflect the prevalence of MHC loss or
reduction in T-cell rich tumors. Loss or reduction of MHC is an
emerging mechanism of immune evasion by tumor cells [[120]72–[121]74].
MHC loss or reduction would simultaneously be expected to reduce
presentation of tumor-associated antigens to the T cells and,
conversely, to make the tumor cells more susceptible to NK-cell
targeting. This implication of susceptibility indicates potential value
in stimulating NK-cell activation toward tumor cells that have lost MHC
expression. Our analysis does not partition gene expression
associations among the various T-cell populations (e.g. conventional ab
T cells, gd T cells or NK T cells), but future work could examine
correlations within partitions.
Our analysis informs immunomodulatory target selection for tumors with
infiltrating T cells but low PDCD1 expression. Other
target-identification efforts could be aimed at recruiting T cells to
the tumor or eliminating physical barriers (e.g. extracellular matrix)
that limit the ability of effector T cells to exert cytotoxic effects
on the tumor cell directly. It is possible that both approaches will be
required in concert to ultimately drive the desired response in
patients. For example, the remarkable success of chimeric-antigen
receptor T-cell therapy (CAR-T therapy) in leukemias has been
contrasted against more limited effects in solid tumors and some
lymphomas. Targets identified in this report could also be relevant to
the engineering of T cells to target solid tumors. Limited efficacy of
CAR-T therapy in solid tumors might arise due to local signals that
suppress cytotoxic effects upon arrival of the engineered T cells to
the tumor microenvironment. The targets and pathways identified in this
report might provide guidance regarding engineering approaches (i.e.
Cas9-mediated knockouts or expression of decoy receptors) that could be
applied where traditional antibody and small-molecule inhibitors are
not feasible.
Immunotherapy approaches have produced some remarkable therapeutic
successes, yet there is much uncharted territory left to explore. By
identifying genes expressed in common with T-cell markers within the
tumor microenvironments of 9,601 patients and 31 types of cancer, our
work helps to map the boundaries of this landscape, narrowing the list
of gene candidates for new therapeutic targets. Continued research will
map out the immune composition and dynamic nature of the tumor
microenvironment—providing opportunities to identify novel and
complementary targets, and expanding the efficacy of therapeutics and
the breadth of patients who benefit.
MATERIALS AND METHODS
Detecting T-cell associated gene expression in the tumor microenvironment
RNA-Seq V2 data from 31 tumor types (including 9601 tumor samples
total) were obtained from The Cancer Genome Atlas (TCGA) using the open
platform cBio Cancer Genomics Portal [[122]75, [123]76] in the form of
RSEM z-scores. Though available from this source, we excluded thymoma
from our analysis because tumors of the thymus (where T cells mature)
are known to directly alter the T-cell composition [[124]77]. To find
candidate genes expressed by T cells in the tumor microenvironment, we
generated lists of genes for each cancer type, ranked by the strength
of their correlation with CD3E expression, a pan T-cell marker
[[125]78]. Pearson product-moment correlation coefficients were
calculated using the corrcoef method of Python’s numpy library, applied
to all tumors with expression values for both CD3E and a given gene of
interest. A weak correlation between a candidate gene and the T-cell
marker could indicate that not all T cells in the tumor express the
gene, that there is variability in the amount of expression among T
cells in the tumor, and/or that cells other than T cells in the tumor
are expressing the gene. Each of these possibilities could have
implications for the effectiveness of T-cell-based targeted therapy,
and all support the conclusion that targets with weaker correlations
may be less successful than targets with stronger correlations.
We first performed this analysis on a candidate gene panel of 40 known
immune-associated genes, and then we extended the analysis to an
unbiased ranking of the cancer exome (12082 genes found to be expressed
in cancer) [[126]79]. The candidate gene panel included costimulatory
genes (CD2, CD27, CD28, TNFRSF8, CD40, CD40LG, CD70, ICOS, ICOSLG,
TNFRSF4, TNFSF4, TNFRSF9), putative T-cell inhibitory genes (CTLA4,
PDCD1, CD274, PDCD1LG2, VSIR, CD160, TNFRSF14, CD200, CD200R1, TIGIT,
CD276, VTCN1, BTLA, LAG3, HAVCR2), regulatory markers (FOXP3,
TNFRSF18), and metabolic checkpoints (ADORA1, ADORA2A, ADORA2B, HK2,
GLS, IDO1, TPI1). We also included several myeloid cell-related immune
checkpoints (CD14, CSF1R, KIR2DL1, PVR), based on their involvement in
the complex cell-cell interaction within the tumor microenvironment.
Identifying candidate gene targets to complement anti-PD1 therapies
We developed a scoring metric to rank candidate genes according to
their potential usefulness as alternative or complementary targets to
PDCD1, an immune checkpoint gene that has already been successfully
targeted in several cancer types. This metric, which we term ‘PDCD1
complementarity score’, takes into account each gene’s strength of
correlation with CD3E expression (to indicate consistent expression by
T cells in the tumor microenvironment), and also minimizes the overlap
in expression pattern with PDCD1. The goal of this analysis is to
identify genes that will potentially be useful as targets for combined
immunotherapy and/or for the subset of patients who do not respond to
anti-PD-1 therapy. The promise of a target gene can be quantified with
a function of the co-expressions of two pairs of genes: the target gene
and CD3E; and the target gene and PDCD1, by the score
[MATH:
|ρTC|(|ρTC<
/mi>|−|ρ<
mi>TP|), :MATH]
where co-expression is captured by the absolute value of their Pearson
correlation coefficients, denoted |ρ[TC]| and |ρ[TP]|, respectively.
While negative co-expression with CD3E could in principle give a high
complementarity score, no high-scoring genes (e.g. in the top 600 of
our ranked list) exhibited negative co-expression with CD3E.
Identifying candidate gene targets to complement anti-PD1 and anti-CTLA-4
therapies
To identify genes that could be complementary to CTLA4 as well as
PDCD1, we extended the complementarity score to include co-expression
with CTLA4. The joint complementarity score for a candidate gene was
quantified as
[MATH:
|ρTC| min{|ρ<
/mi>TC|−|ρTP|,|ρTC|−|ρTA<
/mi>|}, :MATH]
where |ρ[TP]| is the absolute value of the Pearson correlation
coefficient for the target expression and CTLA4 expression. Genes with
a high joint complementarity score have a high co-expression with CD3E
and relatively low co-expression with both PDCD1 and CTLA4.
Gene set enrichment analysis
To identify overrepresented pathways among genes with high CD3E
co-expression, we used the gene- set enrichment software GSEA (v3.0)
from the Broad Institute [[127]80, [128]81], using the ‘GSEAPreranked’
algorithm. Our input rank file consisted of the exome-wide gene set and
each gene’s corresponding median co-expression value across the 31
cancer types. We tested against the ‘Canonical Pathways’ gene set
(v6.1), selecting the weighted algorithm with default parameters.
Results were compared using Normalized Enrichment Score (NES) and false
discovery rate (FDR).
Linking anti-PD1 therapy response to strength of correlation between CD3E and
PD-1 expression in the tumor microenvironment
We accessed RNA-Seq transcriptome data from pretreatment samples of 26
metastatic melanoma patients who received anti-PD1 therapy, and that
had been categorized as ”responders” (n = 10 who experienced partial
response, plus n = 4 who experienced complete response) and
”non-responders” (n = 12 who experienced progressive disease) according
to irRECIST criteria [[129]82]. We looked for differences between
responders and non-responders in terms of the strength of correlation
between CD3E expression and PDCD1 expression. Because patients whose
tumor-infiltrating T cells consistently express PDCD1 are more likely
to respond to anti-PD1 therapy [[130]5, [131]83, [132]84], we
hypothesized a stronger correlation for responders than non-responders.
Following the same logic, we also hypothesized that genes that are
candidates for future tumor-infiltrating T-cell therapies will show
high correlation in expression with CD3E in the tumor microenvironment.
To assess differences in the CD3E-PDCD1 correlation between responders
and non-responders, we calculated Pearson’s correlation coefficients as
described above, and performed linear regressions using the ols
function of Python’s statsmodels library. We quantified linear model
fits by comparing their coefficients of determination (R^2), and
evaluated the statistical significance of the best fits using the
Mann–Whitney U test.
SUPPLEMENTARY MATERIALS
[133]oncotarget-10-4532-s001.pdf^ (3.2MB, pdf)
ACKNOWLEDGMENTS