High ratio of resident to exhausted CD4+ T cells predicts favorable prognosis and potentially better immunotherapeutic efficacy in hepatocellular carcinoma

doi:10.21203/rs.3.rs-3524256/v1

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High ratio of resident to exhausted CD4+ T cells predicts favorable prognosis and potentially better immunotherapeutic efficacy in hepatocellular carcinoma

https://doi.org/10.21203/rs.3.rs-3524256/v1

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Background: Tumor-infiltrating lymphocytes (TILs) are significantly implicated in regulating the tumor immune microenvironment (TIME) and immunotherapeutic response. However, little is known about the impact of the resident and exhausted status of TILs in hepatocellular carcinoma (HCC).

Methods: Single-cell RNA sequencing data was applied to discover resident and exhausted signatures of TILs. Survival outcomes, biological function, immune infiltration, genomic variation, immunotherapeutic efficacy, and sorafenib response were further explored the clinical significance and molecular association of TILs in HCC. Moreover, a candidate gene with predictive capability for the dismal subtype was identified through univariate Cox regression analysis, survival analysis, and BEST website.

Results: Single-cell analysis revealed that CD8+ T, CD4+ T, and NK cells were strongly associated with resident and exhausted patterns. Specific resident and exhausted signatures for each subpopulation were extracted in HCC. Further multivariate Cox analysis revealed that the ratio of resident to exhausted CD4+ T cells in TIME was an independent prognosis factor. After incorporating tumor purity with the ratio of resident to exhausted CD4+ T cells, we stratified HCC patients into three subtypes and found that (i) CD4 residency_highexhaustion_low subtype was endowed with favorable prognosis, immune activation, and sensitivity to immunotherapy; (ii) CD4 exhaustion_highresidency_low subtype was characterized by genome instability and sensitivity to sorafenib; (iii) Immune-desert subtype was associated with malignant-related pathways and poor prognosis. Furthermore, spindle assembly abnormal protein 6 homolog (SASS6) was identified as a key gene, which accurately predicted the immune-desert subtype. Prognostic analysis and in vitro experiments further demonstrated that SASS6 was closely associated with tumor prognosis, proliferation, and migration.

Conclusions: The ratio of resident to exhausted CD4+ T cells could serve as a candidate biomarker for evaluating prognosis and potential response to immunotherapy in HCC and SASS6 was a novel biomarker and candidate therapeutic target for prognostic assessment of HCC.

tumor immune microenvironment

tumor-infiltrating lymphocytes

resident and exhausted patterns

immunotherapy

hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the leading cause of cancer-related deaths worldwide(1). Clinical routine therapies, such as surgical resection, radiotherapy, chemotherapy, and multi-kinase inhibitors, have failed to significantly improve clinical outcomes of HCC patients(2, 3). Recently, immunotherapy represented by immune checkpoint inhibitors (ICIs) has revolutionized the treatment of solid tumors, but only 25% of patients have achieved durable benefits(4). More recently, durvalumab plus tremelimumab yielded superior overall survival (OS) versus sorafenib and atezolizumab plus cabozantinib yielded superior progression-free survival(5). Unfortunately, no robust biomarker is available to help stratify patients and guide clinical decision-making. Therefore, novel biomarkers devoted to predicting prognosis and immunotherapeutic efficacy are urgent in HCC.

The tumor immune microenvironment (TIME) profoundly impacts the development and progression of tumors by balancing between suppressive and cytotoxic responses(6). Tumor-infiltrating lymphocytes (TILs) are the primary components of TIME and have diverse functions, such as killing tumor cells and secreting cytokines(7). Indeed, the characteristics and infiltration fraction of TILs are the key factors to determine immunotherapeutic response(8, 9). Recently, the composition, differentiation status, and exhausted pattern of TILs have been abundantly explored(10–13). Previous studies have revealed that resident T lymphocytes are tightly related to better immunotherapeutic response and favorable survival(10, 11). Conversely, TILs can predominantly undergo distinct differentiation patterns to acquire tissue exhausted characteristics, potentially impacting immunotherapeutic responses(12, 13).

In this work, we identify specific gene signatures for resident and exhausted patterns in CD8 + T, CD4 + T, and NK cells by single-cell RNA sequencing. Moreover, only the ratio of resident to exhausted CD4 + T cells was an independent prognostic factor. After incorporating tumor purity with the ratio of resident to exhausted CD4 + T cells, HCC patients were stratified into three subtypes endowed with distinct prognosis, biological function, immune response, immunotherapeutic efficacy, and sorafenib sensitivity. Moreover, receiver operating characteristic (ROC) curves, univariate Cox analyses and survival analysis were employed to identify candidate biomarkers which could accurately predict the worst prognosis subtype. Spindle assembly abnormal protein 6 homolog (SASS6) possessed robust and accurate ability in HCC prognosis and the high expression of SASS6 predicted worse OS. Overall, our study deepened the understanding of the characteristics of TILs in TIME and identified a promising biomarker for evaluating prognosis and potential response to immunotherapy in HCC.

Data acquisition

Single-cell RNA sequencing data: The 10X and Smart-seq2 single-cell transcriptome data from GSE140228 (including 73261 cells) were downloaded from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/)(14).

Cancer cell lines: Transcriptomic data for cancer cell lines were generated from the cancer cell line encyclopedia (CCLE, https://portals.broadinstitute.org/ccle/)(15).

Multi-omics data for TCGA- LIHC bulk cohort: The RNA-sequencing data, clinical information, and mutational data of TCGA-LIHC were retrieved from the UCSC Xena (http://xena.ucsc.edu/).

Treatment cohorts

319 patients from seven immunotherapeutic cohorts, including GSE100797, GSE111636, GSE135222, GSE136961, GSE35640, GSE91061, and GSE93157, were collected to assess the immunotherapeutic response. The information of 67 HCC patients accepting sorafenib was downloaded from GSE109211 to predict the sorafenib efficacy.

Quality control and cell type identification

The single cell quality control process was performed with the Seurat R package. After filtering out doublets and eligible cells, cells with the following criterion were retained: (i) 200 to 5000 genes were detected; (ii) the total unique molecular identifiers (UMIs) number ≤ 40000; (iii) UMIs arising from mitochondrial percentage ≤ 15%. The top 2000 variable genes were selected for principal component analysis. The top 30 principal components were utilized for Uniform Manifold Approximation and Projection (UMAP) analysis. The UMAP scatter plots were drawn to validate the accuracy of cell annotations by canonical genes: CD8A, MS4A1, LYZ, GNLY, CD3E, and FCER1A. Resident and exhausted markers, including CD69, ITGAE (CD103), ITGA1, PDCD1 (PD-1), CTLA4, and LAG3, were selected to identify TIL subpopulations influenced by resident and exhausted patterns(12).

Obtaining initial and final resident and exhausted signatures

Only cells from tumor tissue of 10X single-cell data were retained to reflect the TIME. Additionally, an extensive list of resident and exhausted genes was extracted based on previous reports(12, 16–18) (Additional file 2: Table S1).

Removing canonical resident and exhausted markers that showed a significant negative correlation with each other(12). Subsequently, we identified canonical resident markers, including CD69, ITGAE, RGS1, and CXCR6 for NK cells, the same genes added CXCR6 to CD8 + T cells, and resident marker ZNF683 (Hobit) was included in CD4 + T cells compared with CD8 + T cells. Meanwhile, canonical exhausted markers for CD8 + T, CD4 + T, and NK cells consisted of HAVCR2 (Tim-3), PDCD1, CTLA4, LAYN, CXCL13, and LAG3.

Calculated correlation between resident/exhausted genes in comprehensive gene lists described in step 1 and canonical resident/exhausted markers. Genes with the correlation coefficient > 0.25 were retained for CD8 + and CD4 + T cells. But for NK exhausted genes, the threshold was set to 0.2 due to the differences in gene expression distribution.

Deleted the duplicated or highly correlated genes between resident and exhausted gene lists. Subsequently, the sparse canonical correlation analysis was performed to further reduce the correlation between resident and exhausted gene sets by removing genes with a correlation coefficient of the first component ≤ -0.1. Finally, we obtained the initial resident and exhausted signatures for CD8 + T, CD4 + T, and NK cells (Additional file 2: Table S2).

Based on the initial resident and exhausted signatures, the singscore package was used to calculate gene-set enrichment scores at the single cell level(19). For CD8 + T, CD4 + T, and NK cells, exhausted cells owned the characteristics with exhausted score above the 90th percentile and resident score below the 60th percentile, and a similar threshold to resident cells. However, due to the peculiarities of the distribution of gene expression, the top threshold for NK cells was established at the 85th percentile.

Performed differential expression analysis between resident and exhausted cells by the Wilcoxon rank-sum test and the hurdle model. Genes with adjusted p-value < 0.05 and log₂FC > 0.3 were collected.

Refined the signatures by comparing transcript abundance percentile for resident vs. exhausted cells. Trajectory inference with Slingshot was performed based on the final gene markers(20).

Validation of single-cell data

The efficacy of resident and exhausted signatures was validated using Smart-seq2 data from GSE140228. Similar to the previous quality control procedure, the final resident and exhausted signatures were selected to score for CD8 + T, CD4 + T, and NK cells, respectively. Subsequently, resident and exhausted cells were identified according to the prior threshold for each cell type in order to validate the stability of resident and exhausted signatures.

Refining genes to pass “bulk threshold”

For HCC cell lines data, only genes with counts per million > 1 in more than five cell lines were filtered. Then, we calculated scaling factors to convert the raw library into an effective library and further performed reads per kilobase per million mapped reads transformation. Next, resident and exhausted signatures less than the 75th percentile of the value expressed across all genes in HCC cell lines were filtered. Subsequently, we correlated the scoring information of three cell types with the expression information of overlapping genes in TCGA and reassigned the overlapping genes according to the correlation. Ultimately, the selected genes were considered to pass the “bulk threshold” and were suitable to be analyzed in bulk data.

Subtyping and enrichment analysis

The ESTIMATE package was used to predict the tumor purity of each patient in TCGA(21), and the resident and exhausted scores were calculated by Single-Sample Gene Set Enrichment Analysis (ssGSEA). Subsequently, we stratified patients with tumor purity > 90% as the immune-desert subtype in TCGA-LIHC and the remaining patients as the high-scoring and low-scoring subtypes according to the mean value of resident, exhausted, and ratio scores among CD4 + T, CD8 + T, and NK cells. Gene Set Enrichment Analysis (GSEA) was used to perform Hallmark, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG), and further identified specific functional pathways for three subtypes, respectively.

Immune cell infiltration assessment

Using ssGSEA, the infiltration abundance of 28 immune cells was measured in HCC bulk tissues. CIBERSORT was conducted to further validate our findings. Co-stimulatory and co-inhibitory molecules were extracted from a previous report, and the relative differences were compared among different subtypes(22). Additionally, the immune subtypes, leukocyte fraction, lymphocyte infiltration signature scores, T cell receptor (TCR), and cancer immunity cycle (CIC) were compared among different subtypes(14, 23).

Genomic variation

The maftools package was used to calculate the top 20 hypervariable genes among three subtypes. The recurrent amplified and deleted genome regions information for TCGA-LIHC was downloaded from the Firebrowse database (http://firebrowse.org/). We focused on the regions of amplified copy number alternation (CNA) > 15% or deleted CNA > 20%. After correction for differences in the number of patients among three subtypes, the proportion of hypervariable genes and CNA in each subtype was calculated. We finally calculated the fraction genome alteration (FGA), fraction genome gain (FGG), fraction genome loss (FGL), arm gain, arm loss, focal gain, and focal loss of each sample.

Immunotherapeutic assessment

Patients with immunotherapeutic information were stratified into three subtypes based on resident and exhausted scores of CD4 + T cells. We assessed percentile and the area under the ROC curve (AUC) differences in immunotherapeutic responses among different subtypes. Subclass mapping (Submap) was used to compare the expression similarity between three subtypes and different immunotherapeutic responders/nonresponders(24). However, since GSE136961 and GSE93157 contained only a few genes for ssGSEA scoring, these two datasets were only used to perform Submap analysis, but not for the calculation of the differences in immunotherapy responses.

Identification of prognostic hub gene

To elucidate the fundamental prognostic mechanisms of the unfavorable subtype, screening strategies for essential biomarkers were examined from a genetic standpoint.

Firstly, AUC was calculated by the pROC package to identify genes with predictive capability for the dismal subtype. Then, genes with AUC > 0.7 were selected for univariate Cox regression analysis, employing a threshold of hazard ratio > 1 and P < 0.05. Thirdly, genes whose expression associated with tumor purity greater than 0.4 were preserved. Subsequently, Kaplan-Meier survival analysis and BEST website (https://rookieutopia.com/app_direct/BEST/) were further utilized to validate these candidate genes obtained by the above methods. Ultimately, SASS6 was selected to be the diagnostic gene for the worst prognosis, despite its extensive study in other cancers but lack of exploration in HCC.

Cell line and cell transfection

HCC cell line (MHCC-97H) was used in our research, which was cultured in Dulbecco’s Modified Eagle’s medium (DMEM)/High-Glucose (Solarbio, Beijing, China) containing 10% fetal bovine serum (Cyagen, Guangzhou, China) at 37°C with 5% CO2. Negative control (NC) and SASS6 small interfering RNA (siRNA) (RiboBio, Guangzhou, China) were transfected into MHCC-97H cells according to the manufacturer’s instruction. Quantitative real-time polymerase chain reaction (qRT-PCR) was employed to confirm the transfection efficiency. The expression value of SASS6 was normalized to GAPDH and then log2 transformation for subsequent analysis. The 2^-ΔΔCT approach was applied to compute the relative RNA expression.

Cell counting kit-8 (CCK-8) assay

Transfected MHCC-97H cells were seeded in 96-well plates and cultured under suitable conditions for 24 hours. Once the cells were adherent, the CCK-8 assay solution (US Everbright, Suzhou, China) was added to each well. After 2 hours of additional incubation, the optical density (OD) value at 450 nm was measured with a microplate reader (Infinite F50, Tecan, Switzerland).

5-Ethynyl-2'-deoxyuridine (EdU) incorporation assay

The assessment of cell proliferation capacity was conducted using the EdU incorporation assay. Prior to the assay, MHCC-97H cells were pretreated and subsequently exposed to 50 µm EdU for 2 hours at 37°C. Next, the cells were fixed using a 4% formaldehyde solution for 30 minutes, and then permeabilized using 0.5% Triton X-100 for 10 minutes. Subsequently, each well was treated with 1 × Apollo solution for 30 minutes, followed by staining of the nuclei with 1 × Hoechst 33342 for an additional 30 minutes. Finally, the EdU-positive cells (red) and Hoechst-positive cells (blue) were observed and quantified using a fluorescence microscope.

Wound healing assay

The NC and SASS6 siRNA cells were cultured in 6-well plates and incubated at 37°C overnight in a 5% CO2 incubator. The medium was then removed, and the surface of the cells was gently scratched and marked using a 200 uL pipette tip. Subsequently, the cells were washed. Photographs of the scratches were taken at 0 hour and 48 hours, respectively. This experiment was repeated three times. The distance of cell migration towards the injured area was measured during the specified time period.

Transwell migration assay

Transwell chambers were employed for the assessment of cellular migration capability. Approximately, 2.5 × 10⁴ cells transiently transfected cells were cultured in the upper chamber using serum-free medium, while a complete medium was added to the lower chamber. The cells were incubated at 37°C for 48 hours, subsequently washed with physiological saline, and fixed with methanol. After that, the cells were stained and subsequently photographed under a microscope, with the stained cells being quantified.

Statistical analysis

R software (v-4.2.0) was used for all data processing and statistical analysis. Pearson’s correlation analysis was applied to calculate the correlations between two continuous variables. Categorical variables comparison was performed by the chi-squared or fisher exact test, and continuous variables by the T-test or Wilcoxon rank-sum test. Multiple subtypes were compared based on the ANOVA or Kruskal-Wallis test. Differential expression analysis between resident and exhausted cells by the Wilcoxon rank-sum test and the hurdle model. Based on the survival package, Kaplan-Meier analysis was performed. GSEA analysis was performed by the clusterProfiler package. The ROC curve was implemented through the pROC package. The Slingshot package conducted the trajectory analysis. The ESTIMATE package estimated the tumor purity. All statistical tests were two-sided P < 0.05 was regarded as statistically significant.

CD8 + T, CD4 + T, and NK cells strongly correlated with resident and exhausted patterns

Figure 1. CD8 + T, CD4 + T, and NK cells expressed highly resident and exhausted makers. A-B. Cell clustering and annotation based on the meta-data (A) and canonical cell markers (B). C. Expression level of six canonical genes. The color of the dots represented the expression level of canonical genes. D-E. Expression level of canonical resident and exhausted signatures based on UMAP.

The workflow of our study is shown in Additional file 1: Figure S1. According to the standard Seurat pipeline, cell clustering and annotation were performed. We found that TILs from different patients and tissues were mixed (Additional file 1: Figure S2A and S2B) and mainly divided into five major subpopulations: T cells, NK cells, Myeloid, B cells, and B plasma (Fig. 1A and 1B). To validate the accuracy of cell annotation, canonical cell markers including CD8A, MS4A1, LYZ, GNLY, CD3E, and FCER1A were utilized to delineate expression profiles, which were highly consistent with specific cell regions (Fig. 1C). Subsequently, canonical resident and exhausted markers were selected to identify which subpopulations were significantly featured by resident and exhausted patterns. Interestingly, these markers were dramatically concentrated in CD8 + T, CD4 + T, and NK cell regions (Fig. 1D and 1E).

Obtaining and verifying resident and exhausted signatures

To identify specific resident and exhausted signatures in HCC, we introduced a seven-step pipeline (see Methods). For each cell subpopulation, we first retained genes highly correlated with canonical resident and exhausted markers. Due to the crosstalk of resident and exhausted patterns, genes were further refined by removing overlapping and significantly related genes between resident and exhausted gene sets (Additional file 2: Table S2)(25). Subsequently, we calculated gene-set enrichment scores to designate resident and exhausted cells (Additional file 1: Figure S2C). Spatial analysis further demonstrated that resident and exhausted cells were well separated (Additional file 1: Figure S2D).

To accurately identify the resident and exhausted signatures of each cell subpopulation, genes were extracted based on differential expression analysis between resident and exhausted cells. Meanwhile, the comparison between resident and exhausted percentile thresholds further refined signatures. Using final gene signatures, single cells were rescored and recategorized. We then observed that resident and exhausted cells were robustly separated (Fig. 2A). Further analysis elaborated the evolutionary trajectory from residency to exhaustion to distinguish between resident and exhausted patterns (Additional file 1: Figure S2E).

As previously reported, resident and exhausted signatures are highly expressed in TILs but not tumor cells(12). Thus, resident and exhausted signatures passing the “bulk threshold” were selected based on the percentile abundance comparison between tumor cells and TILs (Fig. 2B). To further reduce the crosstalk among three cell types, overlapping genes were reassigned based on the correlation between signature scores and gene expression for three cell types (Fig. 2C and 2D). Ultimately, Smart-seq2 data was utilized to confirm the stability of results from 10X single-cell RNA sequencing data (Fig. 2E and Fig. 3A).

Prognostic significance of the ratio of resident to exhausted CD4 + T cells

We observed a significant association between resident and exhausted patterns in TCGA-LIHC (Fig. 3B). In bulk tissues, tumor purity is a crucial factor for quantifying the components of tumor microenvironment(26, 27). In this study, correlation analysis showed a significant decrease in tumor purity with increasing resident and exhausted scores in HCC patients (Fig. 3B), suggesting tumor purity might be a confounding factor for investigating TIME. Thus, tumor purity, resident scores, exhausted scores, and the ratio of resident to exhausted scores in three cell types were combined to form nine subtyping methods. For each method, patients were stratified into three subtypes, including residency_highexhaustion_low, exhaustion_highresidency_low, and immune-desert subtypes. Survival analyses revealed that the immune-desert subtype had the worst prognosis, whereas subtypes with high levels of resident CD8 + T cells and high ratio of resident to exhausted CD4 and NK cells displayed favorable prognosis (Fig. 3C). Furthermore, multivariate Cox analysis demonstrated that only the ratio of resident to exhausted CD4 + T cells had an independent prognostic impact on HCC patients after removing confounding factors (Fig. 3D). Herein, subsequent analysis will focus on the subtyping method of the ratio of resident to exhausted CD4 + T cells.

Biological characteristics and immune landscape of three subtypes

Due to the survival difference among three subtypes, we hypothesized that the subtypes may be endowed with different biological attributes. Enrichment analysis revealed that CD4 residency_highexhaustion_low subtype focused on immune activation (Fig. 4A), CD4 exhaustion_highresidency_low subtype was featured by negative regulation of immune responses and antigen presentation pathways (Fig. 4B), and immune-desert subtype was associated with proliferation-related pathways (Fig. 4C). The immune landscape among three subtypes was profiled to further delineate the difference in TIME among three subtypes. Rich immune cells were particularly evident in CD4 residency_highexhaustion_low subtype and rare in immune-desert subtype (Fig. 4D and 4E). Likewise, CIBERSORT results coincided with these findings (Additional file 1: Figure S3A and S3B). To reflect overall immune cell differences across three subtypes, leukocyte fraction and lymphocyte infiltration fraction were extracted from a previous

Figure 4. Biological characteristics and immune landscape of three subtypes. A-C. GSEA showed specific pathways in the CD4 residency_highexhaustion_low (A), CD4 exhaustion_highresidency_low (B), and immune-desert subtype (C). D. The heatmap of 28 immune cells among three subtypes. E. The infiltration difference of 28 cells among three subtypes. The residency_highexhaustion_low subtype was represented by red; the exhaustion_highresidency_low subtype was represented by blue, and the immune-desert subtype was represented by green. The asterisks represented the statistical p value (**P < 0.01; ***P < 0.001; ***P < 0.0001). F-G. The immune landscape difference of leukocyte faction (F) and lymphocyte infiltration fraction (G) among three subtypes. GSEA = Gene Set Enrichment Analysis.

report(28), which deteriorated from CD4 residency_highexhaustion_low subtype to immune-desert subtype (Fig. 4F and 4G). In parallel, multiple co-stimulatory and co-inhibitory molecules were highly expressed in CD4 residency_highexhaustion_low subtype (Fig. 5A and Additional file 1: S3C). Additionally, based on the classical immune subtypes described by V. Thorsson et al.(28), the association of CD4 ratio subtyping with reported immune subtypes was further explored. We found that C3 (inflammatory) was significantly involved in CD4 residency_highexhaustion_low subtype, while C4 (lymphocyte depleted) mainly concentrated in immune-desert subtype, consistent with the previous findings (Fig. 5B). Furthermore, to broadly evaluate the potential response to immunotherapy among different subtypes, TCR richness and CIC were assessed and showed that CD4 residency_highexhaustion_low subtype was endowed with higher score, implying that this subtype more likely benefited from immunotherapy (Fig. 5C, Additional file 1: Figure S3D).

Genomic variation landscape among different subtypes

To systematically reveal the genomic traits of each subtype, we further characterized the landscape of genomic variation among three subtypes. As illustrated in Fig. 5D, immune-desert subtype significantly harbored more mutations of TP53 (40%, P = 0.016), CTNNB1 (50%, P < 0.001), and OBSCN (60%, P = 0.03); CD4 exhaustion_highresidency_low subtype concentrated in more mutation of ABCA13 (56%, P = 0.024); whereas CD4 residency_highexhaustion_low subtype displayed overall rare mutations across all hypervariable genes (Additional file 2: Table S3). Additionally, we further investigated chromosomal instability, chromosomal alteration regions (17q25.3 and Xq28 amplifications) were statistically gathered in immune-desert subtype, and chromosomal loss regions significantly enriched in CD4 exhaustion_highresidency_low subtype (Fig. 5D, Additional file 2: Table S3). Similarly, FGA, FGG, and FGL in immune-desert and CD4 exhaustion_highresidency_low subtypes had higher level, suggesting robust ability of tumor proliferation and immune escape (Fig. 5E, Additional file 2: Table S3). The copy number load was consistent with the above-mentioned findings (Fig. 5F).

Different responses to immunotherapy and sorafenib among three subtypes

To explore the difference in immunotherapeutic responses among three subtypes, patients from seven immunotherapeutic cohorts were assigned to three subtypes based on the same pipeline. Specifically, CD4 residency_highexhaustion_low subtype favored immunotherapy benefits (Fig. 6A-D, Additional file 1: Figure S3E). The ROC analysis was further performed to validate the efficacy of the ratio of resident to exhausted CD4 + T cells in predicting immunotherapeutic response. The results turned out that the ratio of resident to exhausted CD4 + T cells demonstrated a stable and robust performance with AUC = 0.692 in GSE100797 (Fig. 6E), 0.967 in GSE111636 (Fig. 6F), 0.664 in GSE135222 (Fig. 6G), 0.764 in GSE356401 (Fig. 6H), and 0.655 in GSE91061 (Additional file 1: Figure S3F). Submap analysis further indicated a significantly similar expression pattern between CD4 residency_highexhaustion_low subtype and immunotherapeutic responders after Bonferroni correction (Fig. 6I-N, Additional file 1: Figure S3G). As previously reported, sorafenib efficacy is also associated with the characteristics and infiltration fraction of TILs(29). Therefore, a sorafenib cohort (GSE109211) was employed to test the possibility in our subtypes. We found that CD4 exhaustion_highresidency_low subtype was more sensitive to sorafenib and the AUC reached 0.819 (Fig. 6O and 6P). Overall, the ratio of resident to exhausted CD4 + T cells might serve as a promising biomarker for assessing the efficacy of immunotherapy and sorafenib.

SASS6, a hub gene tightly associated with prognosis

In light of the darkest survival outcome among patients in the desert group, an optimal biomarker with practical clinical applications was necessary. Ultimately, we successfully identified a candidate gene, SASS6, capable of accurately distinguishing patients within the desert group in the TCGA-LIHC cohort (AUC = 0.712, Additional file 1: Figure S4A). Based on Kaplan-Meier curve analyses, elevated SASS6 expression was significantly associated with poorer OS for TCGA-LIHC cohort (Additional file 1: Figure S4B). Using BEST website, we further validated the above results. Cox regression analysis demonstrated that SASS6 consistently act as a risk factor in more than half of the cohorts with OS (Additional file 1: Figure S4C). To verify the prognostic impact of SASS6, after stratifying HCC patients into high and low SASS6 expression groups according to the median value, Kaplan-Meier survival analysis and log-rank test showed that the high group had a significantly shorter OS (Additional file 1: Figure S4D and S4E). We also found SASS6 expression in the tumor was markedly higher than that in adjacent normal tissues (Additional file 1: Figure S4F). Furthermore, the level of SASS6 expression was significantly associated with cancer progression (Additional file 1: Figure S4G). Moreover, the GSEA enrichment analysis also revealed that SASS6 was involved in cancer proliferation, invasion and metastasis signaling pathways (Additional file 1: Figure S4H-J).

Cell culture and functional assay

To validate the biological function of SASS6 in HCC cells, a special siRNA was designed to suppress the expression of SASS6 in MHCC-97H cell line. As exhibited in Fig. 7A, the siRNA-2 efficiently reduced the expression of SASS6 in MHCC-97H cells. The CCK8 assay demonstrated that the proliferation ability of MHCC-97H cells was significantly inhibited after SASS6 knockdown (P < 0.05) (Fig. 7B). Further validation by EdU assay revealed that the suppression of SASS6 in MHCC-97H cell line markedly restrained cell proliferation compared to the control group (Fig. 7C). Wound healing assay showed that depletion of SASS6 significantly inhibited the healing of scratched wound (Fig. 7D and 7E). Moreover, we examined the effect of SASS6 knockdown on HCC cell migration through Transwell assay, and the significantly reduced number of cells in the siRNA group suggested that SASS6 knockdown significantly inhibited migration ability (Fig. 7F and 7G). Taken together, the inhibition of proliferation and migration of MHCC-97H cells after SASS6 knockdown suggests that SASS6 may promote the proliferation and metastasis of HCC.

Currently, immunotherapy has made considerable progress in solid tumors, but unfortunately, only a subset of patients generates benefits. TILs are the main components in TIME, and their resident and exhausted characteristics have a significant effect on the immunotherapeutic response(7, 12, 13). Previous studies demonstrate the high percentage of tissue-resident memory T cells is associated with improved outcomes following checkpoint treatment(13). However, studies focusing on the effect of T cell exhausted status on immunotherapeutic response have yielded conflicting results. Hus and colleagues discovered that high CD8 + T cell exhausted status might indicate better efficacy of ICIs therapy(30), whereas M. Barsch et al. presented evidence that high ratio of exhausted T cells suggests poor prognosis and scarce response to immunotherapy(13). Our study aims to investigate the impact of resident and exhausted patterns on prognosis and immunotherapeutic response and identified a stable and powerful biomarker that predict the HCC prognosis.

Recent studies have highlighted the importance of CD8 resident and exhausted patterns, whereas the sight of these patterns also gradually increased in CD4 + T cells and NK cells(11–13, 31, 32). Similarly, mounting evidence showed that the resident and exhausted patterns in CD8 + T, CD4 + T, and NK cells could significantly affect the survival and immunotherapeutic response(10, 13, 25, 32). In this study, we found that the resident and exhausted patterns of TILs mainly exhibited effects on CD8 + T, CD4 + T, and NK cells. Thus, we included these cells in our scope to comprehensively elucidate the impact of resident and exhausted patterns on immunotherapy. Although resident and exhausted signatures have been described, significant signature differences and different outcomes pose a challenge to accurately distinguish resident and exhausted patterns based on previous signatures(9, 12, 13). Thus, we extracted resident and exhausted signatures according to the seven-step pipeline, which had great efficacy in distinguishing resident and exhausted patterns. The ratio subtype of resident to exhausted TILs was reported with the ability to reflect the resident and exhausted patterns together(12, 13). Thus, we further increased the ratio subtypes of resident to exhausted CD8 + T, CD4 + T, and NK cells to better reflect the two patterns.

Patients with predominantly exhausted T cells are associated with a poor prognosis, whereas the high ratio of resident to exhausted TILs favored patient outcomes(13). Here, no significant survival difference was observed between the exhaustion_high and exhaustion_low subtypes of CD8 T cells. Together, although there were statistically significant survival differences in some grouping methods, which were consistent with previous studies(11, 13). Moreover, crosstalk among different factors have proven to lead to the survival result difference(12). We then conducted multivariate Cox analysis, and found that only the ratio of resident to exhausted CD4 + T cells significantly influenced survival. Oja, A. E. et al. found that resident CD4 + T cells are associated with patient survival and immunotherapeutic response(31). Likewise, the ratio of resident to exhausted T cells has been reported to be a robust biomarker in predicting survival and immunotherapeutic response(12, 13). Thus, we hypothesized that the ratio of resident and exhausted CD4 + T cells could potentially enhance the precise management of HCC. Due to the essential roles of tumor purity on survival and immunotherapeutic response(27), we stratified HCC patients into three subtypes based on both ratio of resident and exhausted CD4 + T cells and tumor purity, in which the survival deteriorated gradually from CD4 residency_highexhaustion_low subtype to immune-desert subtype.

Functional enrichment analysis revealed that immune-desert subtype was endowed with malignant-related functions, such as E2F targets and DNA repair. As previously reported, high expression of E2F targets indicates that tumor cells have strong proliferation ability and may evade immune system recognition and attack by down-regulating immunogenicity(33), suggesting immune-desert subtype is with higher proliferative capacity. Also, we found that immune activation was assigned to the CD4 residency_highexhaustion_low subtype, while the CD4 exhaustion_highresidency_low subtype was primarily endowed with negative immune-related pathways. Specifically, immune-related characteristics were significantly enriched in CD4 residency_highexhaustion_low subtype: Rich immune cells (such as B, CD8, and CD4 cells) were observed to concentrated in CD4 residency_highexhaustion_low subtype. The abundance of TILs has been reported to be significantly correlated with survival and immunotherapeutic response(34, 35). Several immune relevant factors have been shown to affect the clinical outcome. For example, co-stimulatory and co-inhibitory molecules, such as CD27, ICOS, PDCD1, and HAVCR2, are associated with immunotherapeutic response(36). Moreover, the increasing TCR richness and CIC also predict better immunotherapeutic outcome(23, 37–40). Our data demonstrated that CD4 residency_highexhaustion_low subtype highly expressed these co-stimulatory/inhibitory molecules and was associated with the high immunity cycle score and TCR richness, indicating potentially better immunotherapeutic outcome.

To further comprehensively reveal the differences in survival among three subtypes, the different landscapes of molecular variation were analyzed. From a global perspective, the overall variation was predominantly assigned to immune-desert and CD4 exhaustion_highresidency_low subtypes, which belonged to mutation-driven and copy number loss-driven, separately. Specifically, the immune-desert subtype was endowed with more alterations of TP53, CTNNB1 and OBSCN. TP53 mutation has been well-known to promote cancer cell survival and evade tumor immune surveillance(41). CTNNB1 mutation is related with immune-excluded response in HCC, which has been used to predict innate insensitivity to immunotherapy(42). Of note, the copy number loss robustly occurred in CD4 exhaustion_highresidency_low subtype. The copy number loss is reported to be mainly associated with cell proliferation, worse prognosis, and evading immune surveillance, which could predict the response to immunotherapy(43, 44). Overall, these genomic variations suggested that distinct mutation not only led to different immune status and survival outcome, but also provided the potential mechanisms for the response to immunotherapy among three subtypes, which might throw light on guiding the precise treatment for HCC.

PD-L1 expression and immune exhausted status are clinically popular indicators for predicting the efficacy of HCC immunotherapy, but many studies have found them unsatisfactory in prediction accuracy(12, 13, 45). Thus, to explore a potential better biomarker to predict the efficacy of immunotherapy, the immunotherapeutic response fraction among different subtypes and the AUC of immunotherapeutic response were assessed. Consistent with the immune landscape results, TCR richness, and CIC analyses, the result revealed that the CD4 residency_highexhaustion_low subtype showed a better immunotherapeutic response, which was supported by Submap analysis. Our study also confirmed that the ratio of resident to exhausted CD4 + T cells in TIME accurately predicted the immunotherapeutic response in HCC, with an AUC exceeding 0.65 in all tested cohorts. A previous study in murine liver cancer model has revealed that Sorafenib could modulate immunosuppressive cell populations to enhance anti-tumor immunity(46). Therefore, to verify the relationship between sorafenib efficacy and exhausted procedures, the response to sorafenib was assessed and the results showed that CD4 exhaustion_highresidency_low subtype had a significantly higher sorafenib response, with the prediction accuracy reaching an AUC value of 0.819.

SASS6 is an essential protein required for centrosome replication, involved in initiating centriole formation and stabilizing centriole intermediates. Previous studies have revealed that SASS6 expression was significantly upregulated in colon cancer, breast cancer, esophageal squamous carcinoma and other tumor tissues and associated with unsatisfactory prognosis of lung adenocarcinoma, colon cancer, kidney renal clear cell carcinoma, and esophageal squamous carcinoma(47–49). In this study, we demonstrated that SASS6, which could accurately predict the worst prognosis subtype, was highly expressed in HCC and associated with poorer prognosis in a multicenter cohort through the BEST web tool. Further, through multiple functional assays, we confirmed that SASS6 indeed inhibited the proliferation and migration of HCC cells in vitro. These results are consistent with previous research in other tumors, suggesting that SASS6 may be an independent predictor of poor prognosis in HCC and a potential therapeutic target in the future.

Our study demonstrated that the CD4 residency_highexhaustion_low subtype predicted better immunotherapeutic responses, which can potentially facilitate accurate clinical guidance of immunotherapy in HCC. Nevertheless, our study also had several limitations. Firstly, lacking public data on immunotherapy for HCC, we predicted the immunotherapeutic results for HCC based on immunotherapeutic cohorts of other cancer types. Further validation in immunotherapeutic cohorts of HCC is needed. Secondly, our study focused mainly on the impact of resident and exhausted patterns on HCC, and more investigations should be conducted on pan-cancer.

We proposed a new stratification strategy based on the ratio of resident to exhausted CD4 + T cells, which showed great performance in the prediction of the prognosis and immunotherapeutic response. Subsequently, we highlighted the potential of SASS6 as a therapeutic target for treatment of HCC, warranting further exploration in animal models as well as preclinical and clinical studies. Overall, these findings might improve prognostic stratification management and individualized immunotherapy in HCC patients.

TILs

tumor-infiltrating lymphocytes

TIME

tumor immune microenvironment

HCC

hepatocellular carcinoma

ICIs

immune checkpoint inhibitors

overall survival

ROC

receiver operating characteristic

GEO

Gene Expression Omnibus

CCLE

cancer cell line encyclopedia

UMIs

unique molecular identifiers

UMAP

Uniform Manifold Approximation and Projection

ssGSEA

Single-Sample Gene Set Enrichment Analysis

GSEA

Gene Set Enrichment Analysis

Gene Ontology

KEGG

Kyoto Encyclopedia of Genes and Genomes

TCR

T cell receptor

CIC

cancer immunity cycle

CNA

copy number alternation

FGA

fraction genome alteration

FGG

fraction genome gain

FGL

fraction genome loss

AUC

the area under the receiver operating characteristic curve

Submap

Subclass mapping

SASS6

spindle assembly abnormal protein 6 homolog

negative control

siRNA

small interfering RNA

qRT-PCR

quantitative real-time polymerase chain reaction

CCK-8

cell counting kit-8

optical density

EdU

5-Ethynyl-2'-deoxyuridine.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used in this study can be found on the respective websites listed in the article. Other necessary data and supporting codes will be made available by contacting the corresponding author.

Competing interests

The authors declare that they have no competing interests.

Funding

Not applicable.

Authors' contributions

XWH, ZQL, and LBW provided direction and guidance throughout the preparation of this manuscript. ANZ, JXL, WLJ, ZQL, and LBW wrote and edited the manuscript. ZQL reviewed and made significant revisions to the manuscript. YHB, STL, YYZ, SYW, HX, LL, WLJ, LBW, and ZQL collected and prepared the related papers. All authors read and approved the final manuscript.

Acknowledgments

Not applicable.

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No competing interests reported.

Additionalfile1.docx
Additional file 1: Figure S1. Workflow of the analyses performed in this study. Figure S2. Cell clustering and trajectory analysis. Figure S3. Immune infiltration assessment and predicting response to immunotherapy. Figure S4. The identification and prognostic value assessment of SASS6.
Additionalfile2.xlsx
Additional file 2: Table S1: Resident and exhausted genes from previous reports. Table S2: Initial resident and exhausted signatures. Table S3: Mutations across all hypervariable genes.

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High ratio of resident to exhausted CD4+ T cells predicts favorable prognosis and potentially better immunotherapeutic efficacy in hepatocellular carcinoma

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Abstract

Figures

Introduction

Materials and methods

Data acquisition

Quality control and cell type identification

Validation of single-cell data

Refining genes to pass “bulk threshold”

Subtyping and enrichment analysis

Immune cell infiltration assessment

Genomic variation

Immunotherapeutic assessment

Identification of prognostic hub gene

Cell line and cell transfection

Cell counting kit-8 (CCK-8) assay

5-Ethynyl-2'-deoxyuridine (EdU) incorporation assay

Wound healing assay

Transwell migration assay

Statistical analysis

Results

CD8 + T, CD4 + T, and NK cells strongly correlated with resident and exhausted patterns

Obtaining and verifying resident and exhausted signatures

Prognostic significance of the ratio of resident to exhausted CD4 + T cells

Biological characteristics and immune landscape of three subtypes

Genomic variation landscape among different subtypes

Different responses to immunotherapy and sorafenib among three subtypes

Cell culture and functional assay

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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