Graphical abstract
graphic file with name fx1.jpg
[34]Open in a new tab
Highlights
* •
DETACH successfully decouples CTL and ETL activities in bulk
transcriptomic data
* •
Decoupling CTL and ETL enhances ICI response prediction
* •
DETACH is positively associated with tumor-reactive T cell
infiltration and activation
__________________________________________________________________
Biological sciences; Immunology; Biocomputational method; Computational
bioinformatics; Cancer
Introduction
Immune checkpoint inhibitors (ICIs) are one of the most important and
effective therapeutic strategies rooted in cancer immunology. However,
approximately half of melanoma patients remain unresponsive to ICI
treatment. This underscores the pressing need to identify accurate
biomarkers that can forecast which patients will benefit from ICIs.
Moreover, there is a broader objective of anticipating novel and
potentially more efficacious therapies. Cytotoxic T lymphocytes (CTL)
play a pivotal role in shaping a patient’s response to
ICIs.[35]^1^,[36]^2 CTL activity, assessed through the expression of
genes specific to CTLs,[37]^3^,[38]^4 has been frequently employed to
predict ICIs response via bulk transcriptomics.[39]^5^,[40]^6^,[41]^7
However, akin to other numerous transcriptomics-based biomarkers, its
predictive efficacy has remained
limited.[42]^8^,[43]^9^,[44]^10^,[45]^11^,[46]^12^,[47]^13 It is
imperative to gain a comprehensive understanding of the cytotoxic
states of cells to further optimize the utility of the CTL signature in
predicting ICI responses.
CTL can encounter exhaustion due to the constant stimulation of cancer
cells and immunosuppression within the tumor microenvironment
(TME),[48]^14^,[49]^15^,[50]^16 mediated via elevating inhibitory
receptors (IRs) expression.[51]^14 Such exhausted T cells are
characterized by the loss of effector functions and elevated and
sustained expression of inhibitory receptors.[52]^14 The emergence of
immune checkpoint blocking therapy as a strategy to treat cancer is
based on the ability of monoclonal antibodies to block the interaction
between specific IRs on exhausted T lymphocytes (ETL) and their
corresponding ligands on cancer and other antigen-presenting
cells.[53]^17 Blocking such inhibitory interactions promotes the
expansion and recovery of effector function in ETLs, leading to tumor
regression in cancer patients. It’s crucial to note, however, that the
dysfunctional state of terminally exhausted T cells remains resistant
to reprogramming by ICIs.[54]^18^,[55]^19 Recent studies have reported
that majority of tumor neo-antigen-specific CD8^+ T cells were in a
state of exhaustion.[56]^20^,[57]^21 Furthermore, the cytotoxic and
exhausted cell states, as defined by transcriptional signatures, are
closely intertwined, potentially regulating common gene
programs.[58]^22^,[59]^23^,[60]^24^,[61]^25 Collectively, these
previous findings suggest that the conventional CTL gene signature
alone may not be sufficient for assessing CTL activity in the TME
following ICI treatment, thereby limiting its predictive power for ICI
response.
In our analysis of both tumor TCGA bulk data and publicly available
single-cell gene expression datasets we observed a robust positive
correlation between CTL and ETL activities, estimated based on
conventional gene signatures, across various cancer types. We
hypothesized that this strong concordance between CTL and ETL
activities in bulk expression may underlie the limited predictive
capacity of CTL activity, potentially resulting in their activities
mutually nullifying their antagonistic effects on ICI response.
Decoupling these two activities could thus potentially enhance the
predictive power of CTLs in ICI forecasting. Consequently, we have set
out to develop a computational framework, DETACH (Decoupling ExhausTed
And Cytotoxic lympHocytes), to identify a set of genes whose expression
can pinpoint a subset of patients where the CTL and ETL correlation is
diminished as much as possible. Within this framework, our initial step
involves identifying a group of genes whose overexpression
significantly reduces the correlation between CTL and ETL, and we term
this set as a “DETACH Signature” (DS). Utilizing the DS score, we can
indeed pinpoint a subgroup of patients in whom CTL and ETL activities
are effectively decoupled. Subsequently, this successful decoupling
enhances the predictive capacity of CTL activity in forecasting ICI
response among patients with high DS scores, yielding superior
predictive accuracy when compared to existing state-of-the-art ICI
predictive transcriptomic signatures.
Results
Identifying a signature of genes whose upregulation identifies a subset of
melanoma patients with reduced correlation between cytotoxic and exhausted
T cell activities
To systemically characterize the association between CTL and ETL
activities in bulk RNAseq data, we used established gene signatures
of CTL[62]^26 and ETL[63]^19 activities. Given these signatures, we
analyzed the bulk RNA-seq data via ssGSEA algorithm[64]^27 to estimate
CTL and ETL activities in TCGA patients. By calculating the correlation
between CTL and ETL activities across all samples in each cancer type,
we found that the CTL and ETL activities were highly positively
correlated in all cancer types (mean Pearson correlation: 0.65 ±
0.11) ([65]Figures 1A and [66]S1). The scatterplot of the two
signatures activities across all melanoma samples is shown in
[67]Figure 1B. We hypothesized that this correlation may cancel out
their opposing associations with ICI response and blunt the predictive
power of CTL activity.
Figure 1.
[68]Figure 1
[69]Open in a new tab
Characterization and decoupling of CTL and ETL activities
(A) The Pearson correlation between CTL and ETL activities, shown for
many different cancer types in the TCGA cohort.
(B) A sample-wise scatterplot of CTL and ETL activities in TCGA skin
cutaneous melanoma (SKCM) patients.
(C) The interaction multivariate linear regression used to identify the
DETACH signature set of genes and is applied to all genes, one by one.
Variables
[MATH: E :MATH]
and
[MATH: G :MATH]
represent the relation between T cell exhausted activity and expression
of a given candidate gene, respectively. The coefficient of covariate (
[MATH:
E∗G)
:MATH]
represents the conjunct interaction effect between
[MATH: E :MATH]
and
[MATH: G :MATH]
variables activity and CTL activity. The slopes between T cell
cytotoxic and exhausted activity are shown by the arrow lines. Since
the gene expressions used in this model are positive values, the change
of the slope reflects the value of the coefficient
[MATH: β3 :MATH]
.
(D) The distribution of significance T values of all genes; those
included in the DETACH signatures are colored in blue and a few top
ones are specified; a negative T value of the covariate (
[MATH:
E∗G)
:MATH]
for all tested genes indicates that the increased expression of this
gene can mitigate the correlation between T cell cytotoxic and
exhausted activity; if this effect is significant ([70]STAR Methods) it
is included in the DETACH signature set.
(E) DETACH signature score adjusted correlations between CTL and ETL
activities versus those observed when using the random control
signatures or the unadjusted (non-decoupled) correlations.
(F) CTL and ETL activities correlations in the high-DS-score patients’
group (top 20 percent DS scores).
As melanoma has rich transcriptomic datasets of patients treated with
ICIs, we decided to focus our study on this cancer type. In addition,
melanoma has already quite a few published transcriptomics biomarker
ICI signatures that we can compare with. We set out to study and
validate the observed positive correlation between CTL and ETL
activity, as seen in the bulk RNA-seq data in a cell-type manner, to
ensure that this relationship was not influenced by variations in
cell-type composition. First, we employed CODEFACS[71]^28 to deconvolve
the bulk TCGA SKCM RNA-seq data and estimate the gene expression that
is specific to CD8^+ T cells within each tumor sample, disentangling
the effects of other cell types. Remarkably, we found that the strong
positive correlation between the CTL and ETL activity is also evident
in the expression of this specific cell-type ([72]Figure S2A). This
outcome substantially strengthens our initial observation, reinforcing
the robustness of the correlation and its independence from cell type
heterogeneity. Second, to further solidify the notion that this
positive correlation is indeed inherent to CD8^+ T cells, we collected
data from three distinct melanoma single-cell cohorts, with each cohort
comprising of a minimum of 20 patients.[73]^29^,[74]^30^,[75]^31
Employing these datasets, our analysis conclusively demonstrates that
the high correlation between CTL and ETL activity across patients in
single-cell transcriptomics. ([76]Figures S2B–S2D, [77]STAR Methods).
Furthermore, by examining the ETL and CTL activities of CD8^+ T cells
across three distinct cohorts, we identified a subset of CD8^+ T cells
with elevated levels of both ETL and CTL activities
([78]Figures S3A–S3C). These findings underscore the interdependence of
CTL and ETL activities as an inherent property of CD8^+ T cells across
diverse contexts, forming the basis of our research goal.
To decouple the CTL and ETL CD8^+ T cell states, we developed DETACH, a
computational method designed to identifying genes whose expression
status mitigates the positive correlation between CTL and ETL
activities. DETACH employs an interaction linear regression model in
which the association between CTL and ETL activities can be represented
by the coefficient denoting the slope of CTL and ETL activities
([79]Figure 1C). Since the gene expression activities used in this
model are positive values, the change in slope depends on the
coefficients of the covariates.[80]^32 A positive coefficient value
indicates that higher expression of this gene increases the association
of CTL and ETL activities, conversely, a negative value indicates that
the higher expression of this gene reduces the association. We applied
DETACH to TCGA melanoma datasets and identified a gene set of 66 genes
([81]Figure 1D) ([82]File S1), whose increased expression decouples CTL
and ETL activities. We termed this gene set the DETACH Signature (DS).
To assess DETACH’s robustness, we applied it to down sampled subsets of
the original training dataset. Notably, the original DETACH genes
consistently appeared enriched at the top of the gene list identified
within these sample subsets ([83]Figure S4A). This robustness was
further validated across independent melanoma
cohorts[84]^33^,[85]^34^,[86]^35 ([87]Figure S4B). Subsequently, in
each individual tumor sample, its DS score denotes the enrichment score
of the DETACH signature, calculated using single samples Gene Set
Enrichment Analysis algorithm (ssGSEA).[88]^27 A high DS score
indicates a weaker positive correlation between CTL and ETL activities.
Specifically, when the DS score is high, high CTL activity corresponds
to low ETL activity, and vice versa. Among the DETACH signature genes,
some effector and memory T cell-associated
genes[89]^29^,[90]^36^,[91]^37 ranked in the top, such as IFNG, TBX21,
and GZMH. The expression of these genes in the patients indicates that
the CD8^+ T cells in the TME were still functional and had not entered
a dysfunctional state.
To verify the capability of the DETACH signature to decouple of CTL and
ETL activities, we first calculated the DS scores for TCGA melanoma
patients (labeled SKCM) and for five independent cohorts ICIs-treated
melanoma patients (Gide,[92]^33 Liu,[93]^38 Riaz,[94]^34 Cui,[95]^39
and Van Allen[96]^35). We randomly selected the same number of genes as
in the DETACH signature to serve as random control, comparison
signatures ([97]STAR Methods). We then employed a partial correlation
analysis to compute the correlation between CTL and ETL activities
after adjusting for the effect of the DETACH signature and that of a
given control signature. Then, we compared both correlations to that of
the original correlation (unadjusted) between the cytotoxic and
exhausted signatures ([98]Figure 1E). As expected, the correlation of
CTL and ETL activities is indeed markedly decreased after adjusting for
the DS score compared to that observed after adjusting to the control
scores or to the original, unadjusted correlation ([99]Figure 1E).
Moreover, to leverage the DETACH signature to enhance the predictive
capability of CTL in ICB response, it is essential to establish an
optimal threshold for the DS score, allowing for the selection of a
high-DS-score patient group. This is driven by the premise
that a higher DS score signifies lower correlation (between CTL and
ETL) and greater prediction accuracy. The threshold for inclusion in
the high-DS-score group was set according to two criteria: (1) to
encompass as many patients as possible (at least 10%) in the
high-DS-score group; (2) to minimize the correlation between CTL and
ETL activity within the high-DS-score group. Initially, we established
cohort-specific thresholds for both the TCGA and ICIs-treated melanoma
cohorts (see [100]Figure S5), adhering to the two specified criteria.
It is crucial to highlight that this process maintains the integrity of
avoiding data leakage, as response label data were not involved in
determining the threshold. Leveraging the six cohort specific
thresholds found in this procedure, we adopted the top 20% as a
universal threshold to identify patients with high DS scores.
Subsequently, we isolated a subset of patients representing the top 20%
with high DS scores. In both the training dataset (TCGA melanoma) and
the testing datasets (melanoma treated with ICIs), the high DS score
groups exhibit a significant lower correlation between CTL and ETL
activities, as anticipated ([101]Figure 1F). It is worth noting that
the correlation between ETL and CTL was not diminished significantly in
Liu et al. and Cui et al. datasets compared to other datasets. This may
be attributed to the use of FFPE samples for RNAseq in Liu et al. and
Cui et al., while other datasets utilized frozen specimens for
extracting the gene expression signature, similar to the TCGA samples,
where the DETACH signature was derived from. These results affirm the
capacity of the TCGA-inferred DETACH signature to reduce the
correlation between CTLs and ETL activities.
CTL activity is highly predictive of ICI response in high DS-score melanoma
patients
We turned to assess the performance of CTL activity in predicting ICIs
response in the high-DS-score patients (top 20% patient group). We
measured the prediction performance of CTL activity in five independent
ICI cohorts using two complementary standard measures, the area under
the receiver operating curve (AUC) and the Odds ratio of responders to
non-responders (OR). The AUC is a standard measure in machine learning
for evaluating the overall predictive performance of a classifier
across all possible decision thresholds. The OR denotes the odds to
respond when the treatment is recommended divided by the odds when the
treatment is not recommended. It quantifies the performance at a chosen
decision threshold and is hence a more clinically oriented measure
([102]STAR Methods). In 7 of the 8 treatment groups we studied, CTL
activity achieved better performance in the prediction of ICIs response
in the high-DS-score ([103]Figures 2A and 2B). Subsequently, we
evaluated the predictive efficacy of CTL activity on this patients
group in comparison to various other contemporary transcriptomics-based
ICI response prediction methods and biomarkers, TIDE,[104]^7
IMPRES,[105]^40 CD274 (PDL1),[106]^13 stroma EMT,[107]^41 CD8 T cell
effector,[108]^42 and TGFB[109]^43 ([110]STAR Methods) in the high DS
score patient group. We find that CTL activity exhibits a substantially
superior performance compared to other methods and biomarkers. However,
it is important to acknowledge that not all of the observed differences
reached statistical significance, likely due to the limited sample size
([111]Figures 2C and 2D). Moreover, we have observed that CTL activity
exhibits superior performance in predicting ICI response among patients
with high DS scores, as determined using a cohort-specific threshold
([112]Figure S6, [113]STAR Methods). These findings propose a potential
two-stage approach for stratifying melanoma patients for ICI treatment
based on bulk tumor expression: (1) Initially, ascertain whether a
patient possesses a high DS score, which can be achieved through either
a global threshold (top 20th percentile) or a cohort-specific
threshold. (2) If the patient meets the criteria for a high DS score,
proceed to assess their response to ICI treatment based on the CTL
score computed from their tumor expression.
Figure 2.
[114]Figure 2
[115]Open in a new tab
CTL activity is strongly predictive of ICI response in high-DS-score
patients
Bar plots showing the accuracy of ICI response prediction for cytotoxic
activity in different melanoma cohorts. This is shown in terms of both
odds ratio (A) and area under the ROC curve (AUC, B) in the high DS
groups compared to the groups that include all patients. The Riaz Pre
CTLA4 treatment group of patients with high DS scores was excluded from
this analysis as it does not include any non-responders. The odds ratio
(C) and AUC (D) quantifying ICI prediction performance of CTL to the
previously established transcriptomics-based signatures in the
high-DS-score patients. One-sided p values were displayed at the top of
each box and were calculated using a paired Wilcoxon rank test to
compare the control (CTL) group with other signatures. In all panels
the four ICI-treated melanoma cohorts studied are sub-divided into
seven cohorts according to the treatments the patients have received.
The DETACH signature activity is strongly positively associated with T cell
activation and immune cell infiltration in melanoma
To further characterize the biological pathways contributing to the
DETACH signature, we identified the pathways enriched in these genes
([116]STAR Methods). Those point to T cell activation/differentiation
and lymphocyte activation and differentiation ([117]Figure 3A).
Furthermore, the DS scores are positively correlated with
computationally estimated T CD8, T CD4, macrophage, and B cell
abundances ([118]STAR Methods, [119]Figure 3B). Notably, they are
anti-correlated with abundance of tumor cells (a correlate of tumor
purity).
Figure 3.
[120]Figure 3
[121]Open in a new tab
DETACH signature activity is associated with T cell activation, immune
cell infiltration, and antigen-specific reactive T cell activity
(A) Pathway enrichment analysis of DETACH signature genes; the x axis
represents the number of DETACH signature genes in each enriched
pathway, the colors represent the corresponding enrichment
hypergeometric statistical significance, as indicated in the color
schema on the right side ([122]STAR Methods).
(B) The correlation between the DS score and cell abundances across
TCGA SKCM; the stars at the top denote significance values ([123]STAR
Methods). ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, ns p>=0.05.
(C) The distribution of DS scores among patients with different
pathology-determined lymphocyte infiltration status.
(D) The distribution of DS scores in hot versus cold tumors.
(E) Bar plot illustrating the correlation between the reactive T cell
score and DS score across six distinct melanoma cohorts.
(F) Scatterplots depicting the relationship between DS scores and tumor
antigen-specific reactive T cell activity scores across six distinct
melanoma patient cohorts. Each data point represents an individual
patient.
We next set out to study to what extent is the DETACH signature genes
activity associated with estimates of T cell abundance and infiltration
done by pathologists reviewing tumor H&E slides. To test that, we
retrieved lymphocyte status annotations of TCGA melanoma patients from
a study that assessed lymphocyte status based on pathological slides
data.[124]^44 In this study, patients were assigned to one of five
patterns based on tumor-infiltrating lymphocyte status: Brisk diffuse,
Brisk band-like, Non-brisk multi-focal, Non-brisk focal, and
None[125]^44 ([126]STAR Methods). Due to its limited sample size, we
filtered the “None” group from further analysis. “Brisk” denotes a
“hot” tumor displaying a moderate to strong immune response,
characterized by the presence of diffusely infiltrative scattered TILs
covering at least 30% of the tumor’s area or forming band-like
boundaries along the tumor’s periphery. Conversely, “Non-brisk”
indicates a “cold” tumor signifying a weak immune response, with
sparsely scattered TILs confined to a small region within the tumor. By
comparing the distribution of DS scores between samples in the
different patterns, we find that the DS scores are positively
associated with tumor-infiltrating lymphocytes abundance and
infiltration in melanoma ([127]Figure 3C). We then classified patients
dichotomously into “Hot” (Brisk diffuse, Brisk Band-like) and “Cold”
tumors (Non-brisk multi-focal, Non-brisk focus, None). Again, we find
that “Hot” tumors have significantly higher DS scores than “Cold”
tumors ([128]Figure 3D). Taking together, these strong associations
indicate that in tumors highly infiltrated by T cells, the CTL and ETL
activities are relatively lowly correlated, suggesting that the
activated CD8^+ T cells are indeed pre-exhausted.
Previous studies have shown that tumor-infiltrating CD8^+ T cells
consist of virus-specific bystander T cells and tumor antigen-specific
reactive T cells.[129]^45^,[130]^46 A recent study characterized
antigen-specific reactive T cells by combining single-cell sequencing
and TCR sequencing technologies.[131]^47 To study the association
between DS scores and tumor antigen-specific reactive T cell activity,
we estimated the reactive T cell activity in each patient’s tumor in
the melanoma cohorts we study, based on the reactive T cell activity
signature.[132]^47 As the tumor bulk expression is a mixture of all
cell types in the tumor microenvironment, we used the deconvoluted gene
expression profiles of CD8^+ T cells[133]^28 to estimate the reactive
T cell activity in melanoma patients in the different cohorts we have
studied. Notably, we find that the DS scores are highly positively
correlated with the estimated reactive antigen-specific T cell
activities ([134]Figures 3E and 3F). Of interest, among the
transcriptomic-based ICB prediction signatures, our DS score, CTL and
effector T cell signatures exhibit stronger positive correlation with
reactive antigen-specific T cell activities compared to other methods
([135]Figure S7). These results suggest that the DS score is not only
associated with the overall abundance of TILs but also of with the
abundance of tumor antigen-specific reactive T cells. It further
supports that the notion that in the subset of tumors where the CTL and
ETL activities are not correlated, the tumor environment is indeed
enriched with cytotoxic non-exhausted and reactive T cells.
Additionally, these results underscore the importance of reactive
T cell activity in CD8 T cell-based predictors (CTL, effector T cell)
for predicting ICB response.
Discussion
Motivated by the correlations observed between cytotoxic lymphocyte
(CTL) and exhausted T cell lymphocyte (ETL) activities inferred from
both bulk and single-cell RNAseq data across various cancer types, our
primary aim was to formulate a computational framework capable of
discerning their distinct contributions. In this study, we introduced
DETACH, a computational approach to effectively segregate CTL and ETL
activities in melanoma. Importantly, DETACH rescues the prognostic
value of CTL activity for predicting the response of melanoma patients
to immune checkpoint inhibitors within a subgroup of high-scoring
patients, surpassing the predictive capacity of other contemporary
transcriptomics-based models. The genes comprising the DETACH signature
are enriched in pathways associated with immune responses, particularly
T cell activation and differentiation. The DS score exhibits a positive
correlation with lymphocyte infiltration and a negative correlation
with estimates of tumor purity. These findings imply that the
predictive power of CTL in anticipating immune checkpoint blockade
response is contingent on the extent of T cell infiltration, as
indicated by the magnitude of the DETACH signature.
The results presented herein introduce a conceptually innovative
approach for stratifying patients for immune checkpoint inhibitor (ICI)
therapy by leveraging the sequential application of two
transcriptomic-based biomarkers. This approach does come with an
inherent tradeoff, as it achieves heightened predictive accuracy within
a restricted subset of high-scoring patients, rather than across the
entire population. Nevertheless, this tradeoff is justified as it
enables to attain a higher degree of accurate patients stratification,
albeit on a subset of the patients. Furthermore, it is imperative to
acknowledge that our current investigation is confined to melanoma, and
exploring the potential applicability of the presented approach in
predicting ICI response across various other cancer types warrants
further investigation in future prospective studies. While our
functional analysis has provided valuable insights into the identified
DETACH signature, our comprehension of how these signatures mediate the
efficacy of CTL remains somewhat limited. One hand, we hypothesize that
the DETACH signature genes are additional signature genes for CTLs,
higher DS levels indicate more likely CTLs. On the other hand, due to
the observed association with TILs and abundance of reactive T cells,
the DS score could imply that beyond the activity level, the abundance
of CTLs also plays a major role in mediating ICI response.
This study demonstrates the utility of our dual-pronged approach in
predicting patient responses, specifically focusing on those whose DS
scores exceed a predetermined threshold. Our emphasis lies in
leveraging bulk transcriptomics, a data type abundant with valuable
clinical information. While previous studies have widely employed CTL
signatures for ICI response prediction using bulk transcriptomics,
their predictive accuracy has been limited. Our study offers a
potential explanation and a solution addressing this limitation. As the
correlation between CTL and ETL remains intact in single-cell data, our
approach may hold promise in providing further insights into this type
of data in future research endeavors.
In summary, we have introduced a computational method, facilitating a
two-step approach for predicting ICI response specifically for patients
in the higher-score group, utilizing CTL signatures. This method
significantly bolsters the effectiveness of CTLs in ICI prediction and
enhances our understanding of the mediating role of CTLs within the
TME.
Limitations of the study
Our study primarily focuses on melanoma, limiting the generalizability
of our findings to other cancer types. Further investigation across
diverse cancer cohorts is necessary to ascertain the broader
applicability of the DETACH framework. It’s important to acknowledge
that only a subset of patients may benefit from the DETACH framework
due to its reliance on identifying high-scoring patients. This
selective applicability may limit its broader utility across all
melanoma patients.
STAR★Methods
Key resources table
REAGENT or RESOURCE SOURCE IDENTIFIER
Software and algorithms
__________________________________________________________________
R version 4.3.0 R software foundation [136]https://www.r-project.org
clusterProfiler 4.8.3 Yu et al.[137]^48
[138]https://bioconductor.org/packages/release/bioc/html/clusterProfile
r.html
ppcor 1.1 Kim et al.[139]^52
[140]https://cran.r-project.org/web/packages/ppcor/index.html
GSVA 1.48.3 Hänzelmann et al.[141]^53
[142]https://bioconductor.org/packages/release/bioc/html/GSVA.html
__________________________________________________________________
Other
__________________________________________________________________
Gide et al.[143]^33 EMBL-EBI ENA: PRJEB23709
Riaz et al.[144]^34 GEO [145]GSE91061
Liu et al.[146]^38^,[147]^16 dbGaP phs000452.v3.p1
Cui et al.[148]^39 China National Center for Bioinformation HRA000524
Van Allen et al.[149]^35 dbGaP phs000452.v2.p1
[150]Open in a new tab
Resource availability
Lead contact
Further information and requests for resources and reagents should be
directed to and will be fulfilled by the lead contact, Eytan Ruppin
(eytan.ruppin@nih.gov).
Materials availability
This study did not generate new unique reagents.
Data and code availability
* •
This paper analyzes existing, publicly available data. These
accession numbers for the datasets are listed in the [151]key
resources table.
* •
All original code has been deposited at Github
([152]https://github.com/wbb1813/DETACH.git) and is publicly
available as of the date of publication.
* •
Any additional information required to reanalyze the data reported
in this paper is available from the [153]lead contact upon request.
Method details
Transcriptomic data and gene signatures
Gene expression data of TCGA patients were downloaded from GDC:
[154]https://portal.gdc.cancer.gov. Transcriptomic data, cell type
abundances, and cell-type specific expression profile of ICI treated
melanoma cohorts[155]^33^,[156]^34^,[157]^38 were retrieved from
CODEFACS.[158]^28 The following published gene signatures were obtained
from original publications: cytotoxic T lymphocytes (CTL)
signature,[159]^4 exhausted T lymphocytes (ETL) signature,[160]^19
reactive T cell signature.[161]^47
Prediction of ICIs response, patient cohort and clinical end points
We collected five RNA-Seq
datasets[162]^33^,[163]^34^,[164]^35^,[165]^38^,[166]^39 from melanoma
patients undergoing immune checkpoint blockade therapies. These
datasets include gene expression profiles of pre-treatment tumors,
along with corresponding response information. For each dataset, we
normalized expression counts to obtain transcripts per kilobase million
(TPM) values. Subsequently, the datasets were divided into subgroups
based on the treatment received: PD-1/CTLA-4 monotherapy or a
combination therapy involving both PD-1 and CTLA-4 inhibitors.
Response status of ICIs treated melanoma patients based on the RECIST
criteria were retrieved from original publications. ‘‘CR/PR’’ patients
were classified as responders and ‘‘SD/PD’’ patients were classified as
non-responders. Previously published biomarkers, TIDE,[167]^7
IMPRES,[168]^24 CD274 (PDL1),[169]^13 stroma EMT,[170]^41 CD8 T cell
effector,[171]^42 and TGFB,[172]^43 were collected from the literature
and tested for association with response to ICIs therapy. Sample-wise
scores were calculated from bulk RNA-seq data using TPM values and
following the methodology described in corresponding studies. Genes
with unavailable expression data were excluded from calculations of
gene signature scores. The predictive utility of these immune
signatures was evaluated with AUC values derived from ROC curves of
gene signature scores and odd ratio calculated from confusion matrix.
Cutoffs for determining responders and non-responders were optimized by
maximizing the sum of specificity and sensitivity.
DETACH
To identify genes that can mitigate the correlation between CTL and ETL
activities, DETACH employed a variable interaction test in a
multivariate linear regression to TCGA SKCM cohort.
For each gene
[MATH: G :MATH]
, we performed the following regression:
[MATH:
Cytotoxicity=β0+β1∗Ex
haustion+β2∗G+β3∗
Exhaust<
mi>ion∗G :MATH]
Where
[MATH:
Cytotox
icity :MATH]
and
[MATH:
Exhaustion
:MATH]
denote the CTL and ETL activity levels which were estimated by
calculating the enrichment score for CTL and ETL signatures.
[MATH: G :MATH]
represents the expression level of gene
[MATH: G :MATH]
in a tumor.
To further understand the variable interaction test, we can rewrite the
model as follows:
[MATH:
Cytotox
icity=(β1+β3∗<
mi>G)∗Exhaustion+β2∗<
mi>G+β0
:MATH]
The association between CTL and ETL activities is
[MATH: (β1+β3∗<
mi>G) :MATH]
. The coefficient
[MATH: β1 :MATH]
is typically positive because the CTL and ETL activities are positively
correlated ([173]Figure 1A). The expression value used in this model
are positive values, which means a negative coefficient
[MATH: β3
:MATH]
will reduce the positive association between CTL and ETL activities,
whereas a positive coefficient will enhance the positive association.
The DETACH signature was identified according to the t value:
[MATH: β3
:MATH]
/ StdErr(
[MATH: β3
:MATH]
) and FDR. A total of 66 genes had significant negative coefficient
values and were identified as DETACH signature using cutoff T value < 0
and FDR <= 0.01.
Determination of thresholds for patients with high DS score
A series of thresholds, ranging from 5% to 100% in increments of 5%,
were applied to subset melanoma patients from TCGA SKCM, Gide et al.,
Riaz et al., Van Allen et al., Cui et al., and Liu et al. cohorts.
Pearson correlation was used to calculate the CTL and ETL correlation
for each subgroup. Individual thresholds for each cohort were
determined by identifying the percentage at which the CTL and ETL
exhibited the lowest correlation coefficient value. It is worth noting
that the thresholds were set to be greater than 10% to ensure an
adequate inclusion of samples. Specifically, the individual thresholds
for TCGA SKCM, Gide et al., Riaz et al., Van Allen et al., Cui et al.,
and Liu et al. cohorts were found to be 25%, 15%, 15%, 15%, 15%, and
15%, respectively.
Robustness evaluation of DETACH signature
The TCGA SKCM dataset was down sampled to 90%, 80%, and 70% a hundred
times at each percentage to generate pseudo-new datasets. Subsequently,
DETACH was applied to these downsampled datasets as well as three
independent ICB datasets[174]^33^,[175]^34^,[176]^35 to evaluate the
ability of each gene to decouple ETL and CTL activity. The enrichment
score of the DETACH signature and a control gene set, randomly selected
to match the number of genes in the DETACH signature, against the down
sampled and three independent ICB datasets, was assessed using GSEA. A
high positive Normalized Enrichment Score (NES) indicates that genes in
the gene set are ranked at the top of the gene list identified using
the down sampled and independent ICB datasets. Statistical significance
of the NES between the DETACH and control gene sets was calculated
using a one-tailed Wilcoxon test.
TCGA Tumor Infiltrated Lymphocyte (TIL) patterns
Tumor Infiltrated Lymphocyte (TIL) patterns for TCGA SKCM patients were
retrieved from a study.[177]^44 Briefly, pathology sides were used to
characterize the TILs patterns for TCGA patients. TILs patterns were
visually assigned by a pathologist into one of five categories:
* 1.
Brisk, diffuse: diffused TILs scattered throughout at least 30% of
the area of the tumor.
* 2.
Brisk, band-like: band-like boundaries formed by TILs bordering the
tumor at its periphery.
* 3.
Non- brisk, multi-focal: loosely scattered TILs present in less
than 30% but more than 5% of the area of the tumor.
* 4.
Non-brisk, focal: loosely scattered TILs present less than 5% but
greater than 1% of the area of the tumor.
* 5.
None: few TILs were present involving 1% or less of the area of the
tumor.
Gene function enrichment analysis
Gene set enrichment analysis of DETACH signature genes was conducted
against the GO BP database using clusterProfiler[178]^48 with the
follow settings: OrgDb = org.Hs.eg.db, ont = "MF", pAdjustMethod =
"BH", pvalueCutoff = 0.01.
Quantification and statistical analysis
Differences between two-continuous variable were assessed using
Wilcoxon rank sum test.[179]^49 ANOVA test[180]^50 was used to
determine if there is a statistically significant difference between
more than two categorical groups. Association between two-continuous
variable were measured by Pearson correlation.[181]^51 R package
“ppcor”[182]^52 was used to eliminate the effect of other variables
when assessing the correlation between CTL and ETL activities.[183]^52
Significance levels were denoted using asterisks (∗ P<0.05, ∗∗ P<0.01,
∗∗∗ P<0.001, ∗∗∗∗ P<0.0001), with 'ns' indicating non-significance
(P>=0.05).
Acknowledgments