Anti-PD1 therapy achieved better outcomes in non-liver cancer patients with HBV infection

doi:10.21203/rs.3.rs-1888416/v1

Download PDF

Article

Anti-PD1 therapy achieved better outcomes in non-liver cancer patients with HBV infection

https://doi.org/10.21203/rs.3.rs-1888416/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 20 Apr, 2023

Read the published version in Oncogenesis →

You are reading this latest preprint version

Anti-PD1 therapy has shown promising outcomes in different types of cancer. It is valuable to analyze whether the efficacy of anti-PD1 therapy has been impacted in cancer patients infected with hepatitis B virus (HBV) since the comorbidity of HBV and cancer cannot be neglected. We designed a multicenter retrospective study to evaluate the efficacy of anti-PD1 therapy on non-liver cancer patients infected with HBV. In addition, we performed single-cell RNA-seq (scRNA-seq) on peripheral blood mononuclear cells (PBMCs) from esophageal squamous cell carcinoma (ESCC) patients to compare the responses of HBV + ESCC patients and HBV- ESCC patients to anti-PD1 therapy. We found anti-PD1 therapy achieved better outcomes in HBV + non-liver cancer patients than their HBV- counterparts. Further analysis showed cytotoxicity of T cells and antigen presentation capacity of B cells significantly increased after anti-PD1 therapy in the 2 HBV + ESCC patients. We also identified CX3CR1^high T_EFF, a subset of CD8⁺ T_EFF, associated with better clinical outcome of HBV + patients. Lastly, we found CD8⁺ T_EFF from HBV + patients have higher fraction of Exhaustion^hi T and lower cytotoxicity score than their HBV- counterpart. In summary, Anti-PD1 therapy on HBV + non-liver patients is safe and achieves better outcomes than that on HBV- non-liver patients, potentially because HBV + patients had stronger immunosuppression which made them more efficiently respond to anti-PD1 therapy.

Immune checkpoint blockade (ICB) therapy is a promising immunotherapy approach. Among them, anti-programmed cell death 1 (anti-PD1) therapy yielded promising outcomes in treatment of dozens of types of cancer, including melanoma, hepatocellular carcinoma (HCC), gastric cancer and esophageal cancer^1–6. However, only a fraction of cancer patients responded to anti-PD1 therapy. The efficacy of anti-PD1 therapy is affected by many factors including tumor microenvironment, commensal microbiota, antibiotics, steroids and virus^3,7−9. In particular, about 54% of HCC cases are related to hepatitis B virus (HBV)¹⁰, early anti-PD1 therapy trials exclude HCC patients with HBV infection due to the concerns about the activation of HBV and uncertain about immune microenvironment. Enrollment of HCC patients with HBV infection showed low and intermediate HBV-DNA level (< 500 IU/ml) did not impact the efficacy and safety of anti-PD1 therapy^1,11, even HCC patients with high HBV-DNA level did not show increased incidence of HBV-associated hepatitis^12,13.

Simultaneously, the success of anti-PD1 in cancer therapy potentially indicates that anti-PD1 may be effective for treating infectious diseases since chronic infection also showed high expression of PD1 and T cell exhaustion¹⁴. Since over 350 million people in the world and 70 million people in China infect HBV, which was the one of the most common comorbidity with non-liver cancer¹⁵. However, only a few studies investigated anti-PD1 therapy on patients with comorbidity of non-liver cancer and active HBV infection^16–18. Furthermore, these studies only enrolled patients with low HBV-DNA level and only studied the safety of anit-PD1 in these patients. It is still unknown whether patients with comorbidity of non-liver cancer and HBV infection should be treated with anti-PD1 therapy.

Here, we performed a multi-center retrospective study on 84 non-liver cancer patients including 35 HBV + patients and 49 HBV- patients. We further performed single-cell RNA-seq (scRNA-seq) on peripheral blood mononuclear cells (PBMCs) from three esophageal squamous cell carcinoma (ESCC) patients pre-and post anti-PD1 therapy to analyze their response to anti-PD1 therapy. We further analyzed the immune microenvironment difference between HBV + patients and HBV- patients to explore the mechanism underlying their response differences to anti-PD1 therapy.

HBV- patients and HBV + patients are well matched

In order to analyses the effect of HBV infection on anti-PD1 therapy in non-liver cancer patients, total 35 HBV + non-liver cancer patients were enrolled from 2018 to 2022 (Fig. 1A, Supplementary Table 1). We selected 49 HBV- non-liver cancer patients that matched HBV + non-liver cancer patients for cancer types, age, and gender (Fig. 1A, Supplementary Table 2). The median age of HBV + patients and HBV- patients was 55-year-old (range: 33–74 years, with 7 patients ≥ 65) and 55-year-old (range: 37–79 years, with 9 patients ≥ 65), respectively. Both HBV + patients and HBV- patients were mainly males, with 27 males (77.1%) and 34 males (69.3%), respectively (Supplementary Table 3). Besides, 42.9% HBV + patients and 34.7% HBV- patients received anti-PD1 therapy as first line therapy, which is not significant difference (p-value = 0.499) (Supplementary Table 3).

Anti-PD1 therapy achieved better outcomes in HBV + non-liver cancer patients

Surprisingly, anti-PD1 therapy achieved significantly better outcomes in HBV + non-liver cancer patients than that in HBV- patients (p-value = 0.0052), with 15 HBV+ (42.86%) non-liver cancer patients achieved partial response (PR) comparing with 7 HBV- (14.29%) patients achieved PR (Fig. 1B, Supplementary Table 1–3). In addition, 22 (44.9%) HBV- patients developed progressive disease (PD), while only 6 (17.1%) HBV + patients developed PD. Based on object response rate (ORR), the odds ratio (OR) of HBV + patients comparing with HBV- patients is 4.41 (95%CI; 1.43–14.99), further indicating that HBV + patients have much better outcome than their HBV- counterparts (Fig. 1B).

These patients received anti-PD1 therapy with different PD1 antibodies, but there is no significantly different outcome between different PD1 antibodies (Fig. 1B). Although the outcomes of combination anti-PD1 therapy showed higher ORR events (30.19%) than that of monotherapy (19.35%), the difference is not significant (p-value = 0.32) (Fig. 1B), which might be caused by the small sample size in this study. Furthermore, OR of first line treatment comparing with second and beyond line is 2.49 (95%CI; 0.83–7.68), which is not significant difference and essentially consists with recent study by Hughes¹⁹.

Change of population abundances in PBMCs after anti-PD1 therapy

To figure out why HBV + patients have better response to anti-PD1 treatment, three ESCC patients, with one HBV negative patient (HBV-#1) and two HBV positive patients (HBV+#1 and HBV+#2), were enrolled from the Southwest Hospital for further analyses. HBV+#1 and HBV+#2, both with hepatitis B surface antigen (HBsAg)-positive and hepatitis B core antibody (HBcAb)-positive, had 1910 IU/mL HBV-DNA and 48600 IU/mL HBV-DNA in blood, respectively (Supplementary Table 4). We conducted 10X genomic scRNA-seq on the PBMCs of the three patients before anti-PD1 therapy. All the three patients received sintilimab (anti-PD1, IBI 308) and paclitaxel liposome as first line therapy. HBV-#1 did not respond to anti-PD1 therapy after 4-weeks of treatment. HBV+#1 and HBV+#2 achieved stable disease (SD) and PR, respectively. We conducted 10X genomic scRNA-seq on the PBMCs of the three patients after 4-weeks anti-PD1 combination therapy. In total, 44,253 PBMCs from the 3 ESCC patients passed quality control, and were clustered into 17 subsets that were annotated according to their specific expression of classic markers (Fig. 2A, B, Supplementary Fig. S1). The fractions of CD4⁺ T_N, CD4⁺ T_M, CD8⁺ T_N, B_N and B_M consistently decreased across patients after anti-PD1 therapy, while the fraction of cMo increased in both HBV-#1 and HBV+#2 (Fig. 2B). We developed population change index (PopIndex) to describe change of immune subsets pre-and post anti-PD1 therapy. We found the PopIndex of CD4⁺ T_EFF, CD8⁺ T_EFF and plasma cells had pronounced increase in HBV+#2, which may contribute to partial response of HBV+#2 (Fig. 2C). These results were consistent with previous reports that increase of T_EFF or increase of plasma cells is correlated with tumor regression^20–22.

Change of T cell features and a T cell subset after anti-PD1 therapy

To analyze the effect of anti-PD1 therapy on cell states, we identified the significantly differentially expressed genes and performed GO enrichment analysis using Metascape²³ between pre-and post anti-PD1 therapy in the 6 T cell subsets, namely CD4⁺ T_N, CD4⁺ T_M, CD4⁺ T_EFF, CD8⁺ T_N, CD8⁺ T_M and CD8⁺ T_EFF. Interestingly, we found cytokine signaling and T cell activation signaling was significantly enriched in HBV + patients, particularly in CD8⁺ T_EFF (Supplementary Fig. S2, Supplementary Table 5). We further found cytotoxicity scores of CD8⁺ T_EFF significantly increased after anti-PD1 therapy in HBV + patients with HBV+#2 showing the most pronounced increase, while cytotoxicity scores of CD8⁺ T_EFF significantly decreased in HBV-#1 (Fig. 3A).

We further analyzed CD8⁺ T_EFF since it has the highest increase of population size and the highest increase of cytotoxicity score in HBV+#2. We clustered CD8⁺ T_EFF and identified two clusters, namely CX3CR1^hi T_EFF and CX3CR1^low T_EFF (Fig. 3B). Particularly, CX3CR1 was a biomarker response of ICB therapy^24,25. CX3CR1^hi T_EFF highly expressed cytotoxicity-associated genes such as GZMA and GZMB (Fig. 3C, Supplementary Table 6), consistent with the reports that CX3CR1⁺ T_EFF lowly expressed co-inhibitor and higher expressed cytotoxicity-associated genes^24,25. On the other hand, CX3CR1^low T_EFF highly expressed activator protein-1 (AP-1) family genes (FOS, FOSB, JUNB, JUN and JUND) which were cancer-related transcription factors ²⁶. It highly expressed regulator of G protein signaling (RGS1 and RGS2) that was related to lower infiltration to cancer²⁷. It also highly expressed CXCR4 that was correlated with T cells migration and promoted tumorigenesis (Fig. 3C)²⁸. These results indicated that CX3CR1^low T_EFF and CX3CR1^hi T_EFF have opposite functions. Interestingly, CX3CR1^low T_EFF were mainly enriched in HBV-#1 after anit-PD1 therapy, while CX3CR1^hi T_EFF were mainly in HBV + patient after anit-PD1 therapy (Fig. 3D), potentially indicating CX3CR1^hi T_EFF are associated with better response of anti-PD1 therapy. In addition, we conducted survival analysis of esophagus carcinoma (ESCA) patients in The Cancer Genome Atlas (TCGA) data using the top 50 CX3CR1^low T_EFF specific genes²⁹. The ESCA patients highly expressed CX3CR1^low T_EFF specific genes, called CX3CR1^low T_EFF signature high patients, have lower survival than other ESCA patients, further supporting that CX3CR1^low and CX3CR1^hi T_EFF have different functional effect (Fig. 3E).

Change of B/plasma cell features and subsets after anti-PD1 therapy

Recent studies demonstrated B cells played an important role in antitumor responses, which presented tumor-derived antigen to T cells and provoke T cells to regress tumor ²⁰. Therefore, we identified differential expression genes (DEGs) of each B cell subset pre-and post treatment in each patient. We found major histocompatibility complex (MHC)-associated genes in B_N and B_M significantly increased in HBV + patients after anti-PD1 therapy, while it significantly decreased in HBV-#1 (Fig. 3F and Supplementary Table 7). Furthermore, we found MHC I score and MHC II score of B_N and B_M in HBV + patients significantly increased after anti-PD1 therapy, while it is no change or decrease in HBV-#1 (Fig. 3G). In addition, many immunoglobulin genes such as IGHG3, IGHG4, IGHG1, IGHM, IGLC2, IGLC1 and IGKC were up regulated in HBV + patients after anti-PD1 therapy, while down-regulated in HBV-#1 (Supplementary Fig. S3, Supplementary Table 8). Therefore, B cells in HBV + patients showed higher antigen presentation capacity and higher expression of immunoglobulin genes after anti-PD1 therapy.

The immune cells in HBV + patients showed stronger immunosuppression

Our analyses showed that immune cells from HBV + patients significantly increased immune related function after anti-PD1 therapy while HBV- patient did not show much change, which might be caused by immune microenvironment differences between HBV + patients and HBV- patients. We conducted a systemic comparison of immune microenvironment between HBV + patients and HBV- patients by integrating scRNA-seq data from Dinh et al³⁰. Total 40,434 cells from 7 untreated ESCC patients were projected into two-dimensional space with UMAP (Supplementary Fig. S4A, B). We calculated the cytotoxicity score and exhaustion score of each CD8⁺ T_EFF for better understand its features. Interestingly, the distribution of exhaustion scores of CD8⁺ T_EFF showed there are two CD8⁺ T_EFF subsets (Fig. 4C), namely Exhaustion^hi T and Exhaustion^low T based on whether the normalized exhaustion score is > 0 or not. HBV + patients had much higher fraction of Exhaustion^hi T than HBV- patients (Fig. 4D). In addition, we found significant lower cytotoxicity score in CD8⁺ T_EFF in HBV + patients than that in HBV- patients (Fig. 4E). Therefore, our analyses showed HBV + ESCC patients have higher fraction of Exhaustion^hi T and stronger immunosuppression than HBV- patients, which maybe is a reason why HBV + patients had better efficacy to anti-PD1 therapy.

Different from other studies focus on HBV infection and HCC comorbidity^{1,11,13,31,32}, this study focuses on HBV infection and non-liver cancer comorbidity. Our analyses showed patients with HBV + non-liver cancer did not have increased irAEs than their HBV- counterparts during therapy. Intriguingly, HBV + non-liver patients have better response to anti-PD1 therapy than their HBV- counterparts. Similar phenomena have been reported in other virus infections and cancer comorbidities. E.g. either patients with Epstein-Barr virus and gastric cancer or non-Hodgkin lymphoma comorbidity have better response to anti-PD1 therapy than their Epstein-Barr virus negative counterparts^3,33. Finally, our results indicated that anti-PD1 therapy should be encouraged to treat HBV + cancer patients since they could achieve better outcome.

To explore the mechanism underlying HBV + non-liver cancer patients respond better to anti-PD1 therapy, we performed scRNA-seq on PBMCs and compared the features of each cell subset pre-and-post therapy in 3 ESCC patients. We found some T cell subsets and B cell subsets showing increased functions in HBV + ESCC patients. In particular, the expression of cytotoxicity-associated genes and expression of MHC-associated genes were significantly increased in CD8⁺ T_EFF and B cells in HBV + patients after anti-PD1 therapy, respectively. Furthermore, we identified two CD8⁺ T_EFF subsets showed distinguished functions, in which CX3CR1^high T_EFF were associated with better outcome and dominated in HBV + patients, while CX3CR1^low T_EFF were associated with poor prognosis and dominated in HBV- patients before anti-PD1 therapy. The increased functions of PBMCs in HBV + patients after anti-PD1 therapy leads us to speculate HBV + cancer might have stronger immunosuppression microenvironment before anti-PD1 therapy. We indeed found that HBV + non-liver cancer patients have much high fraction of Exhaustion^hi T and lower cytotoxicity score than that of HBV- counterparts by integrating public data. Our observations that HBV + non-liver cancer patients are subject to stronger immunosuppression are reasonable since it is well known that both HBV infection^14,34 and cancer³⁵ leads immunosuppression and T cell exhaustion. The stronger immunosuppression in HBV + non-liver cancer patients may lead they respond the anti-PD1 therapy more efficiently.

Study design and patients

We designed a multicenter retrospective study to evaluate the efficacy of anti-PD1 therapy on HBV + non-liver cancer patients by comparing with their HBV- counterparts. This study was approved by IRB at the Southwest Hospital, AMU (ID: KY2021112). Total 84 eligible non-liver cancer patients (35 HBV + patients and 49 matched HBV- patients) were enrolled from database of the Southwest Hospital, Xinqiao Hospital and the Third Affiliated Hospital, CQMU from 2018 to 2021.

The 35 HBV + patients are HBsAg-positive (35; 100%) and HBcAb-positive (33; 94.3%). Furthermore, HBV-DNA was detected in all the 35 HBV + patients, indicating HBV was active in these patients (Supplementary Table 1). Clinical data of each patient, including age, gender, cancer types, metastasis, medical history and therapy history, were collected. HBV + patients and HBV- patients had matched for tumor types, with lung carcinoma, digestive system carcinoma, nasopharyngeal carcinoma, urogenital neoplasms, melanoma and esophageal carcinoma. The 49 HBV- non-liver cancer patients were selected to match the 35 HBV + non-liver cancer patients for cancer types, age, and gender, namely the features of HBV- patients were similar to that of HBV + patients (Supplementary Table 1–3).

Anti-PD1 therapy

Overall, 28 out of 35 HBV + non-liver cancer patients received anti-PD1 combination therapy (anti-PD1 plus chemotherapy), while the other 9 patients received anti-PD1 monotherapy. The anti-PD1 antibodies in this study include Toripalimab, Sintilimab, Pembrolizumab, Camrelizumab, Nivolumab, and Tislelizumab. Similarly, 39 out of the 49 HBV- cancer patients received anti-PD1 combination therapy, while the other 10 patients received anti-PD1 monotherapy (Supplementary Table 1–3).

The enrolled patients had no immune-related adverse event (irAE) or low grade (1 grade or 2 grade) irAEs during and after anti-PD1 therapy, except three 3-grade irAEs, namely maculopapular rash in a HBV + patient, cardiotoxicity in a HBV- patient and interstitial pneumonia in a HBV- patient. None of patient died of irAEs or developed HBV-associated disease (Supplementary Table 1–3). Overall, irAEs in HBV + non-liver cancer patients are not significantly different from that in their HBV- counterparts, consistent with recent reports that anti-PD1 therapy is safe for treatment of HBV + non-liver cancer patients^31,32,36.

Single cell RNA-seq (scRNA-seq) library and sequencing

We performed scRNA-seq to blood samples of three ESCC patients. This study was approved by IRB at the Southwest Hospital, AMU (ID: KY2021112). All individuals signed an informed consent form approved by the IRBs when the patient was enrolled. Patients receive treatment according to clinical routine. The scRNA-seq library preparing and data were analyzed similar to our previous studies^37–39.

Approximately 5ml blood sample was phlebotomized from each of the three ESCC patients pre-and post-treatment, and was transported to the lab in EDTA anticoagulant tube (WEGO, Blood Collection Tube with Vacuum for Single Use, EDTA-K2). Centrifuging the blood sample in condition of 1750rpm around 8min to collect the white blood cells. Lymphocyte Separation Medium (Human) (TBD, LTS1077) was added to substratum cell precipitation to obtain PBMCs after centrifugating at 1950rpm for 20 min and washed in 0.04% BSA of PBS (Gibco PBS pH7.4 basic 1x, 8121456) for twice with centrifuging at 1000rpm for 5min. Trypan blue (Beyotime Trypan blue 10ml, C360I-2) staining was used to check the cell viable. The single cell RNA sequencing libraries of PBMCs were generated using Chromium Next GEM Single Cell 3’ Reagent Kit v3.1 from 10X genomics. In brief, 20000 cells were loaded into each channel of the 10x Chromium controller to generate single cell GEMs. Single cell RNA-seq libraries were prepared using the Chromium Single Cell 3’ Gel Bead and Library Kit (10x Genomics), according to manufacturer instructions. Sequencing libraries were loaded at 2.4 pM on an Illumina NovaSeq 6000 with 2 × 75 paired-end kits.

Single cell RNA-seq data process

In brief, we mapped the reads to human reference genome GRCh38 and aggregated the gene expression matrixes using Cell Ranger (V.5.0.0). We constituted a Seurat object from the raw expression matrixes for Seurat analysis. Seurat package (V.3.2.3)⁴⁰ and Harmony package (V.1.0)⁴¹ were used for the following analysis. Genes expressed in < 20 cells were filtered out. Low quality cells were filtered out if they met the criteria: (1) < 200 features number; (2) < 800 UMI number; or (3) > 15% mitochondrial genome UMIs. We further normalized the gene expression matrix using NormalizeData function. We identified the top 3,000 high variable genes using FindVariableFeatures function and further scaled the gene expression matrix using ScaleData function. We conducted principal component analysis (PCA) for linear dimension reduction using RunPCA function and the top 30 PCs were used for further analysis. The RunHarmony function in Harmony package was used to remove batch effect in our data. Finally, Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP) were conducted using RunUMAP function.

Clustering and identification of cell subsets

The clusters were inferred using FindNeighbors and FindClusters functions based on the normalized gene expression matrix. We identified 17 clusters that were annotated according to their specific expression of classic markers, including 9 T cell subsets (CD3D), 2 natural killer cell (NK) subsets (KLRF1), 2 monocyte subsets (FCN1), 2 B cell subsets (CD19), mDC (CLEC10A) and plasma cell (MZB1) (Supplementary Fig. S1B). The 2 NK cell subsets are NCAM1^bright NK and NCAM1^dim NK. The 2 B cell subsets are naïve B cells (B_N) (TCL1A⁺) and memory B cells (B_M) (CD27⁺). The 2 monocyte subsets are classic monocyte (cMo) (CD14⁺) and non-classical Monocyte (ncMo) (FCGR3A⁺). The 9 T cell subsets are CD4⁺ Naïve T cells (CD4⁺ T_N) (CD4⁺ CCR7⁺), CD8⁺ Naïve T cells (CD8⁺ T_N) (CD8⁺ CCR7⁺), CD4⁺ memory T cells (CD4⁺ T_M) (CD4⁺ SELL^lo CCR7^lo), CD8⁺ memory T cells (CD8⁺ T_M) (CD8⁺ SELL^lo CCR7^lo), CD4⁺ effector T cells (CD4⁺ T_EFF) (CD4⁺ GZMA⁺ GZMB⁺), CD8⁺ effector T cells (CD8⁺ T_EFF) (CD8⁺ GZMA⁺ GZMB⁺), CD4⁺ regulatory T cells (CD4⁺ T_reg) (CD4⁺ FOXP3⁺), γδT (TRDC⁺ TRGC1⁺) and mucosal-associated invariant T cell (MAIT) (ZBTB16⁺ SLC4A10⁺) (Supplementary Fig. S2B).

Comparison of population size and differential expression genes (DEGs)

We calculated the population change index (PopIndex) of each cell subset to measure the relative change of population size after anti-PD1 therapy. The PopIndex was defined as PopIndex=(F_post-F_pre)/F_pre, where F_pre is the fraction of a cell subset in PBMCs before anti-PD1 treatment, and F_post is the fraction of the same cell subset in PBMCs after anti-PD1 treatment. A positive value of PopIndex indicates the population size increase after anti-PD1 treatment, and a negative value index population size decrease after anti-PD1 treatment.

FindMarkers function was used to find differential expression genes between cell subsets. The differential expression genes with bonferroni-adjusted p-values < 0.05 and average log (fold-change) (avg_logFC) > 0.2 were kept for further analyses.

Comparison of functional features for each cell subset pre-and post anti-PD1 therapy

In order to evaluate the changes of functional features of each cell subset pre-and post anti-PD1 therapy, we calculate cytotoxicity score, exhaustion score, major histocompatibility complex (MHC)-I score and MHC-II score of each cell using AddModuleScore function. The cytotoxicity score was calculated based on 12 cytotoxicity-associated genes: PRF1, IFNG, GNLY, NKG7, GZMB, GZMA, GZMH, KLRK1, KLRB1, KLRD1, CTSW and CST7. The exhaustion score was calculated based on 6 exhaustion-associated genes: LAG3, TIGIT, PDCD1, CTLA4, HAVCR2 and TOX. MHC-I score was calculated based on 9 MHC-I-associated genes: HLA-A, HLA-B, HLA-C PSMB8, PSMB9, TAP1, TAP2, TAPBP and B2M. MHC-II score was calculated based on 11 MHC-II-associated genes: HLA-DRA, HLA-DRB1, HLA-DRB5, HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQA2, HLA-DQB1, HLA-DQB2, HLA-DMA and HLA-DMB.

DATA AND CODE AVAILABILITY

The sequencing data has been deposited in Genome Sequence Archive in BIG Data Center under accession number HRA002492. Code used for analysis is available upon request.

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

This study was approved by IRB at the Southwest Hospital, AMU (ID: KY2021112). All individuals signed an informed consent form approved by the IRBs when the patient was enrolled.

COMPETING INTERESTS

The authors declare that they have no conflict of interest.

FUNDING

This study was sponsored by Chongqing International Institute for Immunology (2020YJC08), National Key R&D Program of China (2021YFF1200900), National Natural Science Foundation of China (81802783, 81872330, 32170646), Shenzhen Science and Technology Program (KQTD20180411143432337), Shenzhen Innovation Committee of Science and Technology (ZDSYS20200811144002008).

ACKNOWLEDGMENTS

We thank all members from the Jin lab for the helpful discussion of the project. We acknowledge the assistance of Core Facilities of SUSTech. The computational work was supported by Center for Computational Science and Engineering at SUSTech.

AUTHORS’ CONTRIBUTIONS

Y.Z and W.J conceived the project. J.Z, S.T, X.X, X.H, Y.Z and Y.P collected the clinical samples. J.W, X.S, J.W and W.S performed experiments with help from T.Z and Y.W. G.C carried out the computational analysis with help from B.Z. Z.R, Y.Z and N.H supervised the project. G.C. and W.J. prepared the manuscript, with all authors’ contribution.

El-Khoueiry, A. B. et al. Nivolumab in patients with advanced hepatocellular carcinoma (CheckMate 040): an open-label, non-comparative, phase 1/2 dose escalation and expansion trial. Lancet 389, 2492–2502, doi:10.1016/S0140-6736(17)31046-2 (2017).
Schachter, J. et al. Pembrolizumab versus ipilimumab for advanced melanoma: final overall survival results of a multicentre, randomised, open-label phase 3 study (KEYNOTE-006). Lancet 390, 1853–1862, doi:10.1016/S0140-6736(17)31601-X (2017).
Kim, S. T. et al. Comprehensive molecular characterization of clinical responses to PD-1 inhibition in metastatic gastric cancer. Nat Med 24, 1449–1458, doi:10.1038/s41591-018-0101-z (2018).
Weber, J. S. et al. Nivolumab versus chemotherapy in patients with advanced melanoma who progressed after anti-CTLA-4 treatment (CheckMate 037): a randomised, controlled, open-label, phase 3 trial. Lancet Oncol 16, 375–384, doi:10.1016/S1470-2045(15)70076-8 (2015).
Reck, M. et al. Pembrolizumab versus Chemotherapy for PD-L1-Positive Non-Small-Cell Lung Cancer. N Engl J Med 375, 1823–1833, doi:10.1056/NEJMoa1606774 (2016).
Kudo, T. et al. Nivolumab treatment for oesophageal squamous-cell carcinoma: an open-label, multicentre, phase 2 trial. Lancet Oncol 18, 631–639, doi:10.1016/S1470-2045(17)30181-X (2017).
Matson, V., Chervin, C. S. & Gajewski, T. F. Cancer and the Microbiome-Influence of the Commensal Microbiota on Cancer, Immune Responses, and Immunotherapy. Gastroenterology 160, 600–613, doi:10.1053/j.gastro.2020.11.041 (2021).
Elkrief, A., Derosa, L., Kroemer, G., Zitvogel, L. & Routy, B. The negative impact of antibiotics on outcomes in cancer patients treated with immunotherapy: a new independent prognostic factor? Annals of Oncology 30, 1572–1579 (2019).
Arbour, K. C. et al. Impact of Baseline Steroids on Efficacy of Programmed Cell Death-1 and Programmed Death-Ligand 1 Blockade in Patients With Non-Small-Cell Lung Cancer. J Clin Oncol 36, 2872–2878, doi:10.1200/jco.2018.79.0006 (2018).
Park, J. W. et al. Global patterns of hepatocellular carcinoma management from diagnosis to death: the BRIDGE Study. Liver Int 35, 2155–2166, doi:10.1111/liv.12818 (2015).
Zhu, A. X. et al. Pembrolizumab in patients with advanced hepatocellular carcinoma previously treated with sorafenib (KEYNOTE-224): a non-randomised, open-label phase 2 trial. Lancet Oncol 19, 940–952, doi:10.1016/S1470-2045(18)30351-6 (2018).
He, M. K. et al. Comparison of HBV reactivation between patients with high HBV-DNA and low HBV-DNA loads undergoing PD-1 inhibitor and concurrent antiviral prophylaxis. Cancer Immunol Immunother 70, 3207–3216, doi:10.1007/s00262-021-02911-w (2021).
Finn, R. S. et al. Pembrolizumab As Second-Line Therapy in Patients With Advanced Hepatocellular Carcinoma in KEYNOTE-240: A Randomized, Double-Blind, Phase III Trial. J Clin Oncol 38, 193–202, doi:10.1200/JCO.19.01307 (2020).
Wykes, M. N. & Lewin, S. R. Immune checkpoint blockade in infectious diseases. Nat Rev Immunol 18, 91–104, doi:10.1038/nri.2017.112 (2018).
Ramsey, S. D. et al. Prevalence of Hepatitis B Virus, Hepatitis C Virus, and HIV Infection Among Patients With Newly Diagnosed Cancer From Academic and Community Oncology Practices. JAMA Oncol 5, 497–505, doi:10.1001/jamaoncol.2018.6437 (2019).
Wen, X., Yao, W., Ding, Y., Li, D. & Zhang, X. S. Safety of immune checkpoint inhibitors in Chinese patients with melanoma. Melanoma Research 26, 284 (2016).
Tio, M. et al. Anti-PD-1/PD-L1 immunotherapy in patients with solid organ transplant, HIV or hepatitis B/C infection. European Journal of Cancer 104, 137–144 (2018).
Kothapalli, Anita, Khattak & Muhammad, A. Safety and efficacy of anti-PD-1 therapy for metastatic melanoma and non-small-cell lung cancer in patients with viral hepatitis: a case series. Melanoma Research 28, 155–158 (2018).
Hughes, B. G. M. et al. Pembrolizumab for locally advanced and recurrent/metastatic cutaneous squamous cell carcinoma (KEYNOTE-629 study): an open-label, nonrandomized, multicenter, phase II trial. Ann Oncol 32, 1276–1285, doi:10.1016/j.annonc.2021.07.008 (2021).
Sharonov, G. V., Serebrovskaya, E. O., Yuzhakova, D. V., Britanova, O. V. & Chudakov, D. M. B cells, plasma cells and antibody repertoires in the tumour microenvironment. Nat Rev Immunol 20, 294–307, doi:10.1038/s41577-019-0257-x (2020).
Lei, X. et al. Immune cells within the tumor microenvironment: Biological functions and roles in cancer immunotherapy. Cancer Lett 470, 126–133, doi:10.1016/j.canlet.2019.11.009 (2020).
Gajewski, T. F., Schreiber, H. & Fu, Y. X. Innate and adaptive immune cells in the tumor microenvironment. Nat Immunol 14, 1014–1022, doi:10.1038/ni.2703 (2013).
Zhou, Y., Zhou, B., Pache, L., Chang, M. & Chanda, S. K. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nature Communications 10 (2019).
Yamauchi, T. et al. T-cell CX3CR1 expression as a dynamic blood-based biomarker of response to immune checkpoint inhibitors. Nat Commun 12, 1402, doi:10.1038/s41467-021-21619-0 (2021).
Yamauchi, T. et al. CX3CR1-CD8 + T cells are critical in antitumor efficacy but functionally suppressed in the tumor microenvironment. JCI Insight 5, doi:10.1172/jci.insight.133920 (2020).
Eferl, R. & Wagner, E. F. AP-1: a double-edged sword in tumorigenesis. Nat Rev Cancer 3, 859–868, doi:10.1038/nrc1209 (2003).
Huang, D. et al. Targeting regulator of G protein signaling 1 in tumor-specific T cells enhances their trafficking to breast cancer. Nat Immunol 22, 865–879, doi:10.1038/s41590-021-00939-9 (2021).
Seo, Y. D. et al. Mobilization of CD8(+) T Cells via CXCR4 Blockade Facilitates PD-1 Checkpoint Therapy in Human Pancreatic Cancer. Clin Cancer Res 25, 3934–3945, doi:10.1158/1078-0432.CCR-19-0081 (2019).
Tang, Z., Kang, B., Li, C., Chen, T. & Zhang, Z. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res 47, W556-w560, doi:10.1093/nar/gkz430 (2019).
Dinh, H. Q. et al. Integrated single-cell transcriptome analysis reveals heterogeneity of esophageal squamous cell carcinoma microenvironment. Nat Commun 12, 7335, doi:10.1038/s41467-021-27599-5 (2021).
Kothapalli, A. & Khattak, M. A. Safety and efficacy of anti-PD-1 therapy for metastatic melanoma and non-small-cell lung cancer in patients with viral hepatitis: a case series. Melanoma Res 28, 155–158, doi:10.1097/CMR.0000000000000434 (2018).
Wen, X. et al. Safety of immune checkpoint inhibitors in Chinese patients with melanoma. Melanoma Res 26, 284–289, doi:10.1097/CMR.0000000000000256 (2016).
Kim, S. J., Hyeon, J., Cho, I., Ko, Y. H. & Kim, W. S. Comparison of Efficacy of Pembrolizumab between Epstein-Barr Virus–Positive and –Negative Relapsed or Refractory Non-Hodgkin Lymphomas. Cancer Res Treat 51, 611–622, doi:10.4143/crt.2018.191 (2019).
Tang, L. S. Y., Covert, E., Wilson, E. & Kottilil, S. Chronic Hepatitis B Infection: A Review. Jama 319, 1802–1813, doi:10.1001/jama.2018.3795 (2018).
Zheng, Y. et al. Immune suppressive landscape in the human esophageal squamous cell carcinoma microenvironment. Nat Commun 11, 6268, doi:10.1038/s41467-020-20019-0 (2020).
Tio, M. et al. Anti-PD-1/PD-L1 immunotherapy in patients with solid organ transplant, HIV or hepatitis B/C infection. Eur J Cancer 104, 137–144, doi:10.1016/j.ejca.2018.09.017 (2018).
Zhou, B. & Jin, W. Visualization of Single Cell RNA-Seq Data Using t-SNE in R. Methods Mol Biol 2117, 159–167, doi:10.1007/978-1-0716-0301-7_8 (2020).
Qin, P. et al. Integrated decoding hematopoiesis and leukemogenesis using single-cell sequencing and its medical implication. Cell Discov 7, 2, doi:10.1038/s41421-020-00223-4 (2021).
Wang, X. et al. Reinvestigation of Classic T Cell Subsets and Identification of Novel Cell Subpopulations by Single-Cell RNA Sequencing. J Immunol 208, 396–406, doi:10.4049/jimmunol.2100581 (2022).
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C. & Satija, R. Comprehensive Integration of Single-Cell Data. Cell 177, 1888–1902.e1821 (2019).
Korsunsky, I., Millard, N., Fan, J., Slowikowski, K. & Raychaudhuri, S. Fast, sensitive and accurate integration of single-cell data with Harmony. Nature Methods 16, 1–8 (2019).

There is NO conflict of interest to disclose.

Download PDF

Journal Publication

published 20 Apr, 2023

Read the published version in Oncogenesis →

Editorial decision: revise
14 Sep, 2022
Review #2 received at journal
05 Sep, 2022
Review #1 received at journal
31 Aug, 2022
Reviewer #2 agreed at journal
19 Aug, 2022
Reviewer #1 agreed at journal
11 Aug, 2022
Reviewers invited by journal
05 Aug, 2022
Submission checks completed at journal
02 Aug, 2022
Unknown event
01 Aug, 2022
First submitted to journal
29 Jul, 2022
Editor assigned by journal
29 Jul, 2022

You are reading this latest preprint version

Anti-PD1 therapy achieved better outcomes in non-liver cancer patients with HBV infection

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Results

HBV- patients and HBV + patients are well matched

Anti-PD1 therapy achieved better outcomes in HBV + non-liver cancer patients

Change of population abundances in PBMCs after anti-PD1 therapy

Change of T cell features and a T cell subset after anti-PD1 therapy

Change of B/plasma cell features and subsets after anti-PD1 therapy

The immune cells in HBV + patients showed stronger immunosuppression

Discussion

Methods

Study design and patients

Anti-PD1 therapy

Single cell RNA-seq (scRNA-seq) library and sequencing

Single cell RNA-seq data process

Clustering and identification of cell subsets

Comparison of population size and differential expression genes (DEGs)

Comparison of functional features for each cell subset pre-and post anti-PD1 therapy

DATA AND CODE AVAILABILITY

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1