1 Introduction

Cell death, particularly apoptosis, is undoubtedly the cornerstone of numerous anti-cancer therapies, encompassing traditional chemotherapy and radiotherapy as well as advanced targeted therapy and immunotherapy [1]. Emerging evidence has provided insights into the intricate involvement of apoptosis in tumor biology and the tumor microenvironment, influencing cancer initiation and progression [2, 3]. Some researches emphasize the crucial role of apoptotic cells in adaptive immune responses, as they serve as a source of antigens [4]. When cellular apoptosis occurs, dendritic cells, situated in different skin layers, promptly phagocytize apoptotic cells, transporting antigens to activate T and B cells, thereby stimulating B cells to produce immunoglobulins and undergo clonal expansion [4, 5]. Other studies [6,7,8,9] have revealed that apoptotic cells possess a dual nature, influencing macrophage polarization towards M2-like reparatory and regenerative states that promote cancer development via diverse pathways. Additionally, apoptotic cells are preferentially engulfed by M1 macrophages, thus suppressing M1-mediated anti-tumor activity [10, 11]. Collectively, these findings unveil the dynamic plasticity of the tumor microenvironment orchestrated by apoptosis.

However, the precise characteristics of the tumor microenvironment associated with apoptosis in melanoma remain poorly understood. This is particularly important because apoptosis is without a doubt the spearhead of many anti-cancer therapies in melanoma. Gaining a comprehensive understanding of the impact of apoptosis on the immune microenvironment of melanoma and its therapeutic implications, particularly in immunotherapy, can provide novel strategies for the treatment and combination therapies for melanoma patients. This could also advance the identification of the patient population that stands to gain the most from such therapies.

Accumulating evidence substantiates that the influence of B cells on tumor prognosis and immunotherapy is multifaceted [12,13,14,15] and context-dependent, contingent upon their intricate interactions with other immune cells and factors within the tumor microenvironment (TME). Ultimately, the clinical outcomes are shaped by the composition and balance of these distinct B cell subsets, an equilibrium intricately governed by the TME milieu [16]. The prevailing notion is that B cell differentiation and the formation of long-lived plasma cells from “education” within the GC (germinal centers), a microenvironment characterized by elevated birth and apoptosis rates [17]. Apart from the impact of B cell and plasma cell apoptosis per se, the influence of overall apoptosis levels on these cells within the immune microenvironment remains uncertain.

This study therefore aimed to develop an apoptosis-related tumor microenvironment signature (ATM) in melanoma and reveal a comprehensive depiction of apoptosis-related tumor microenvironment characterations in multiple dimensions including bulk, single-cell transcriptomics and spatial transcriptomics. Specifically, we shed light on the central role played by plasma cells in orchestrating the dynamic alterations related to apoptosis status by interacting with other immune cells. Finally, our findings indicate that ATM is a prognostic factor and is associated with the immune response and drug sensitivity in melanoma, which establish a theoretical foundation for drug combinations and identify potential markers for immunotherapy response.

2 Methods

2.1 Data collection and processing

2.1.1 Clinical tissue samples collection

Paraffin sections of melanoma patients, classified as responders or non-responders to anti-PD1 treatment, were collected from ** with hisat2 and hisat-genotype. Nat. Biotechnol. 37(8), 907–915 (2019). https://doi.org/10.1038/s41587-019-0201-4 " href="/article/10.1007/s13402-024-00930-0#ref-CR18" id="ref-link-section-d144784451e653">18] software. The mapped reads were then assembled into transcripts or genes using the Stringtie [19] software along with the genome annotation file (http://hg38_ucsc.annotated.gtf). To address the biases arising from sequencing depths and gene lengths, the relative abundance of transcripts/genes was measured using normalized metrics, namely TPM (Transcripts per million mapped reads), and log2-transformed. The resulting normalized expression matrix can be found in Table S7.

2.1.3 Melanoma datasets collection

mRNA expression and clinical data from skin cutaneous melanoma (SKCM) samples from The Cancer Genome Atlas (TCGA) were downloaded from the TCGA data portal (https://portal.gdc.cancer.gov/) [3m).

3.4 ATM serves as a predictor of immunotherapy efficacy

The aforementioned analysis reveals a robust anti-tumor immune response orchestrated by plasma cells in collaboration with other immune cell populations. To investigate the association between ATM and immunotherapy efficacy and prognosis, multiple datasets with anti-PD-L1/PD1/CTLA4 cohorts were collected in the study. The in-house anti-PD1 treatment melanoma patients cohort, two public melanoma patients cohorts with immune checkpoint therapy (the Riaz N cohort [21]/GSE91061: Anti-PD1-treated advanced melanoma (Nivolumab)), and the Van Allen, E. M cohort [22]: Anti–CTLA4-treated metastatic melanoma) and other cohorts with immune checkpoint therapy (the Balar AV cohort [23]/IMvigor210: Anti-PD-L1-treated locally advanced and metastatic urothelial carcinoma and the Braun DA cohort [25]: anti-PD-1-treated advanced clear cell renal cell carcinoma) were taken for analysis.

In Braun DA cohort, Balar AV cohort, and in-house cohort (579 patients), immunotherapy responders were shown to have lower ATM (p-value = 0.016 in the Braun DA cohort; p-value = 0.0043 in the Balar AV cohort; p-value = 0.0081 in the in-house cohort) and the results of the chi-square analysis showed that the proportion of responder would be higher in the low ATM group (p-value = 0.033 in the Braun DA cohort; p-value = 0.058 in the Balar AV cohort; p-value = 0.012 in the in-house cohort) (Fig. 4a, b). Three immunotherapy datasets showed better OS in the group with low ATM, and the Braun DA cohort showed better OS and PFS in the group with low ATM (Fig. 4c, d). All these results exhibited that ATM has a robust independent prognostic ability and predictive power of immunotherapy efficacy.

Fig. 4
figure 4

ATM serve as a promising predictor of immunotherapy efficacy a Differentiation of ATM in response and non-response groups in two independent immunotherapy cohorts (Braun DA and Balar AV cohorts) and In-house cohort. b The proportion of response/non-response patients in high- and low-ATM groups in the Braun DA and the Balar AV cohorts and In-house cohort. c, d Kaplan–Meier curves for patients with high ATM and low ATM in four independent immunotherapy cohorts (the Van Allen, E. M; the Balar AV; Braun DA; and the Riaz N cohorts) and In-house cohort show patients with lower ATM (blue) exhibited better overall survival and/or progression-free survival

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3.5 Influence of apoptosis-related tumor microenvironment signature (ATM) on anti-cancer drug response

Massive studies have demonstrated that apoptosis can affect drug response mostly in chemotherapy. In order to explore the potential efficacy of influencing other drugs’ sensitivity by apoptosis and develop novel therapeutic hypotheses, we comprehensively depicted the associations between apoptosis-related tumor microenvironment and drug response. We calculated the correlation between the ATM and imputed drug data of drugs in CTRP [28, 29].

A total of 51 genes are targeted by 46 drugs including 6 Food and Drug Administration (FDA)-approved drugs, 10 clinically used drugs, and 30 probes that are associated with ATM, most of which are targeted therapy (Fig. 5a). Among these drugs, the area under the curve (AUC value) of 45 drugs is negatively correlated with ATM which means that these drugs are more sensitive in patients with low ATM. CHIR-99021 which is the GSK3B inhibitor is negatively correlated with ATM indicating that it is more sensitive in patients with low ATM with better prognosis (Figs. 4b and 5a). A previous study has also demonstrated that it may function as an antagonist of MYC degradation pathways to transiently elevate MYC levels and then confer chemosensing within a narrow window [52].

Fig. 5
figure 5

Influence of apoptosis-related tumor microenvironment signature (ATM) on anti-cancer drug response a The drug names, targeted gene symbol, and drug type of the three classes of drugs whose drug response correlates with the ATM. b Signaling paths targeted by drugs whose drug sensitivity correlates with the ATM. Orange (negative correlation) or blue (positive correlation). c Differentiation of AUC value in high- and low-ATM groups in Apoptosis inducer agents (SZ4TA2; gossypol) and agents targeting VEGF signaling (linifanib; tivozanib; quizartinib; vandetanib) was revealed by the Wilcox test. Asterisks denoted p-value. (“*”p < 0.05; “**”p < 0.01; “***”p < 0.001; “****”p < 0.0001; ns was the abbreviation of no significance)

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Notably, six of these drugs are majoring in the VEGF signally and the AUC of targeting VEGF signaling drugs such as linifanib, tivozanib, quizartinib, and vandetanib are significantly higher in the group with low ATM (Fig. 5b, c). In addition, two apoptosis inducer agents (SZ4TA2, gossypol) are also significantly higher in the group with low ATM (Fig. 5c).

3.6 Apoptosis-related tumor microenvironment signature (ATM) is significantly associated with tertiary lymphoid structures (TLS), exhibiting stronger patient stratification ability compared to classical “hot tumors”

To explore the association between ATM score and tumor categorization, we compiled genes related to cold and hot tumors from the study by Dong Wang et al., encompassing 12 hot tumor-related genes (CXCL9, CXCL10, CXCL11, CXCR3, CD3, CD4, CD8a, CD8b, CD274, PDCD1, CXCR4, and CCL5), and 3 cold tumor-related genes (CXCL1, CXCL2, and CCL20) [53]. In the TCGA-SKCM cohort, our analysis revealed that the ‘Hot’ group exhibited significantly improved overall survival, aligning with prior research findings. Notably, we further stratified patients based on ATM score and Hot tumor signature. Intriguingly, we observed that irrespective of tumor type (hot or cold), the low ATM group consistently displayed superior survival outcomes compared to the high ATM group. Most importantly, the high ATM score has the capability to identify high-risk patient groups even within ‘hot’ tumors, thus offering crucial insights for patient stratification compared to the conventional ‘hot’ and ‘cold’ tumor classifications. (Fig. 6b, c).

Fig. 6
figure 6

Apoptosis-related tumor microenvironment signature (ATM) is associated with tertiary lymphoid structures (TLS), exhibiting stronger patient stratification ability compared to classical “hot tumors” a Kaplan–Meier curves for group hot and cold patients based on the average expression of 12 hot tumor–related genes (CXCL9, CXCL10, CXCL11, CXCR3, CD3, CD4, CD8a, CD8b, CD274, PDCD1, CXCR4, and CCL5) in the TCGA-SKCM cohort show that group hot patients (red) exhibited better overall survival. b Kaplan–Meier curves for 4 groups of patients (Cold-High group, Cold-Low group, Hot-High group, and Hot-Low group) based on the average expression of 12 hot tumor and ATM score in the TCGA-SKCM cohort. c sankey diagram for four groups of patients stratified by hot tumor score and ATM score in the TCGA-SKCM cohort. d, e The top 10 enriched Gene Ontology (GO) signaling pathways associated with differential expression genes between the Hot-ATM low group and Hot-ATM high group (d) or between the Cold-ATM low group and Cold-ATM high group (e). f–h The correlation between ATM score and TLS score in TCGA-SKCM cohort and 4 validation cohorts (GSE65904, GSE19234, GSE54467, GSE22153). (ssGSEA was applied to calculated the TLS_9 score (CD79B, CD1D, CCR6, LAT, SKAP1, CETP, EIF1AY, RBP5 and PTGDS); TLS_12 score (CCL2, CCL3, CCL4, CCL5, CCL8, CCL18, CCL19, CCL21, CXCL9, CXCL10, CXCL11 and CXCL13); TLS_29 score (IGHA1, IGHG1, IGHG2, IGHG3, IGHG4, IGHGP, IGHM, IGKC, IGLC1, IGLC2, IGLC3, JCHAIN, CD79A, FCRL5, MZB1, SSR4, XBP1, TRBC2, IL7R, CXCL12, LUM, C1QA, C7, CD52, APOE, PTLP, PTGDS, PIM2, and DERL3 genes)

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Furthermore, we observed that among patients with either hot or cold tumors, the enriched signaling pathways of the differential gene functional analysis between patients with low ATM scores and those with high ATM scores contained a greater representation of B cell, cell-cell adhesion, tissue remodeling and humoral immune-related pathways (e.g., B cell receptor signaling pathway, humoral immune response mediated circulating immunoglobulin, adaptive immune response based on somatic recombination of immune receptors, regulation of B cell differentiation, lymphocyte costimulation, regulation of tissue remodeling, positive regulation of leukocyte cell-cell adhesion, humoral immune response, etc.) (Fig. 6d, e). This is consistent with the coordinated anti-tumor immune response underscored in our results, focusing on the role of plasma cells. And the observation of plasma cells in a highly adhesive state suggests their readiness for further differentiation or homing to specific effector tissues, enabling them to effectively carry out their anti-tumor immune effects. We further conducted an analysis of the correlation between ATM score and the Tertiary Lymphoid Structure (TLS) signature [54,55,56], and in all five datasets, we consistently observed that a low ATM score is associated with a higher TLS signature (Fig. 6f–h).

4 Discussion

In this study, we elucidate the unclear role of apoptosis in the melanoma microenvironment by establishing an apoptosis-related tumor microenvironment signature (ATM) and investigating its multidimensional alteration features. Our investigation reveals a correlation between ATM and an increased abundance of plasma cells, thereby enhancing the prognostic outlook for patients with melanoma. The chemotactic capability of plasma cells enables the attraction of B cells, Tfh cells, and myeloid cells, consequently fostering their coalescence and facilitating intercellular interactions. As a result, the emergence of long-lasting plasma cells is promoted, establishing an adhesive milieu within the plasma that, in turn, facilitates supplementary plasma cell chemotaxis and the subsequent secretion of antibodies. Notably, plasma cells also exhibit chemotactic tendencies towards CD8+Tem cells, effectively guiding them towards effector sites, where they can exert anti-tumor immune functions. Our study elucidates a comprehensive portrait of apoptosis-associated tumor microenvironment features, highlighting the central role played by plasma cells in orchestrating these dynamic alterations related to apoptosis. Additionally, we discern their potential clinical applications, particularly in the prognostic assessment and predicting immunotherapy responses, accompanied by their insightful implications in evaluating drug sensitivity.

The majority of ATM model genes (13/19) were also observed that they could invole in various biological functions or provided potential prognostic value by previous studies (Table S8). For example, TENT5C was identified as a tumor suppressor, exerting its function by inhibiting Plk4 activity in melanoma [57]. Shilpak et al. discovered that the anti-tumor efficacy of T cell therapy could be significantly enhanced by inhibiting PIM kinase in melanoma mice undergoing adoptive T cell therapy (ACT) [58]. In Hong et al.’s study, a set of 10 genes, including IGKJ5, was constructed to assess the expression levels of TLS, which is important for anti-tumor immune [59]. Previous studies have employed genes such as IGKV1D-42, IGLV5-37, IGKV2D-29, IGHV3-7, IGKV3D-11, and LINC00582 to construct prognostic models for tumors such as melanoma [

Data availability

The bulk/single-cell RNA sequencing, spatial transcriptome data, and clinical information of melanoma patients, or patients treated with immune checkpoint blockade were described in the method section “Data collection and processing”. The resources, and tools used in our analyses were described in each method section in the methods.

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Acknowledgements

This work was supported by Key Program of National Natural Science Foundation of China (U22A20329, 82130090, 81830096), Science Found for Creative Research Groups of the National Natural Science Foundation of China (82221002), the National Natural Science Foundation of China (62102455), China Postdoctoral Science Foundation (2020M682587), the science and technology innovation Program of Hunan Province (2023RC3078), National Key Research and Development Program of China (2022YFC2504700, 2022YFC2504702, 2019YFE0120800, 2019YFA0111600), the Natural Science Foundation of China for outstanding Young Scholars (82022060), and the Project of Intelligent Management Software for Multimodal Medical Big Data for New Generation Information Technology, Ministry of Industry and Information Technology of People’s Republic of China (TC210804V). The science and technology innovation Program of Hunan Province (2022RC3004), Central South University Research Programme of Advanced Interdisciplinary Studies (2023QYJC004), and the Scientific Research Program of FuRong Laboratory(No. 2023SK2095). We are very grateful to Gene-Expression Omnibus (GEO), the Cancer Genome Atlas (TCGA), and the 10x Genomics database for providing the transcriptome and clinical information. We would like to extend our sincere gratitude to Dr. Jun Zhang from Zhou Laboratory at the School of Life Science and Technology, China Pharmaceutical University, and to https://github.com/zhanghao-njmu/SCP, for develo** the R package. In addition, we want to show our appreciation to Biorender for providing the materials for making Fig. 1a.

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  1. **g Ye and Benliang Wei have contributed equally to this work.

Authors and Affiliations

  1. Department of Dermatology, **angya Hospital, Central South University, Changsha, Hunan, 410008, China

    **g Ye, Guowei Zhou, Yantao Xu, Yi He, **heng Hu, **ang Chen, Guanxiong Zhang & Hong Liu

  2. National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Changsha, Hunan, 410008, China

    **g Ye, Guowei Zhou, Yantao Xu, Yi He, **heng Hu, **ang Chen, Guanxiong Zhang & Hong Liu

  3. Hunan Key Laboratory of Skin Cancer and Psoriasis, Changsha, Hunan, 410008, China

    **g Ye, Guowei Zhou, Yantao Xu, Yi He, **ang Chen, Guanxiong Zhang & Hong Liu

  4. Hunan Engineering Research Center of Skin Health and Disease, Changsha, Hunan, 410008, China

    **g Ye, Guowei Zhou, Yantao Xu, Yi He, **ang Chen, Guanxiong Zhang & Hong Liu

  5. **angya Clinical Research Center for Cancer Immunotherapy, Changsha, Hunan, 410008, China

    **g Ye, Guowei Zhou, Yantao Xu, Yi He, **ang Chen, Guanxiong Zhang & Hong Liu

  6. Big Data Institute, Central South University, Changsha, Hunan, 410008, China

    Benliang Wei & Hong Liu

  7. Furong Laboratory, Changsha, Hunan, China

    **heng Hu & **ang Chen

  8. Department of Urology, **angya Hospital, Central South University, Changsha, China

    **heng Hu

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  1. **g Ye
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  9. Hong Liu
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Contributions

Guanxiong Zhang, Hong Liu, **heng Hu and **ang Chen conceived and supervised the project. **g Ye, Benliang Wei, Guanxiong Zhang, Hong Liu, and **ang Chen designed and performed the research. Hong Liu, Yi He, obtained patient data with immunotherapy. **g Ye, performed data analysis. **g Ye, Benliang Wei, Guowei Zhou and Yantao Xu, interpreted the results. **g Ye wrote the paper with input from all the other authors. All authors have read and approved the article.

Corresponding authors

Correspondence to **heng Hu, **ang Chen, Guanxiong Zhang or Hong Liu.

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The experimental protocol was established, according to the ethical guidelines of the Helsinki Declaration and was approved by the Medical Ethics Committee of **angya Hospital, Central South University. Written informed consent was obtained from individual or guardian participants.

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The authors declare no competing interests.

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Ye, J., Wei, B., Zhou, G. et al. Multi-dimensional characterization of apoptosis in the tumor microenvironment and therapeutic relevance in melanoma. Cell Oncol. (2024). https://doi.org/10.1007/s13402-024-00930-0

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