Application of single-cell and spatial omics in deciphering cellular hallmarks of cancer drug response and resistance.

Introduction

The phenomenon whereby cancer cells develop insensitivity to antitumor drugs is termed cancer drug resistance [ 1 ]. Drug resistance is a major challenge in cancer treatment, severely impacting patient prognosis and quality of life. Tumor heterogeneity and the tumor microenvironment (TME) are two critical factors influencing the onset and progression of drug resistance [ 2 ]. The emergence of drug resistance not only escalates treatment costs but may also necessitate that patients experiment with additional drugs and treatment regimens, thereby further diminishing survival rates [ 3 ]. Therefore, an in-depth investigation of the mechanisms underlying drug resistance, alongside the identification of hallmarks and therapeutic strategies, is essential for enhancing the effectiveness of cancer treatment and improving patient survival.

Tumor heterogeneity refers to the molecular or genetic alterations observed in the daughter cells of tumors following multiple divisions and proliferation during growth, resulting in variations in tumor growth rates, invasive capabilities, drug sensitivity, and prognosis [ 4 ]. Broadly, tumor heterogeneity can be classified into two categories: intra-tumoral heterogeneity (ITH) and inter-tumoral heterogeneity [ 5 ]. Due to tumor heterogeneity, the same anticancer drug can yield markedly different therapeutic outcomes among patients with the same cancer type. Heterogeneity is a prevalent phenomenon in tumors, and the consequent drug resistance complicates treatment strategies [ 6 ]. Consequently, a significant barrier to precision medicine is the absence of reliable biomarkers for predicting drug response and resistance [ 7 ]. With advancements in science and technology, single-cell and spatial omics have emerged as pivotal tools for identifying hallmarks, predicting treatment responses, and monitoring tumor evolution, thereby offering breakthroughs in precision medicine [ 8 – 10 ].

Tumor is not merely a collection of cancer cells; rather, it represents a complex ecosystem characterized by the interactions among multiple cell types and the extracellular matrix (ECM) surrounding the cancer cells. The TME encompasses various immune cell types, cancer-associated fibroblasts (CAFs), endothelial cells, and diverse extracellular components. Among these, immune cells serve as pivotal constituents of the TME, including T and B cells, tumor-associated macrophages (TAMs), myeloid-derived suppressor cells (MDSCs), tumor-associated neutrophils (TANs), as well as natural killer cells (NKs) and dendritic cells (DCs) [ 11 ]. Cells within the TME can exert both tumor-suppressive effects and provide support to the tumor [ 12 ]. Tumor-suppressive cells possess the capability to recognize and attack cancer cells, thus enhancing the efficacy of therapeutic agents. Conversely, tumor-supportive cells facilitate an immunosuppressive microenvironment, enabling cancer cells to evade immune surveillance, promoting tumor proliferation and invasion, diminishing drug penetration, and ultimately contributing to drug resistance. A comprehensive understanding of the dynamics between cancer cells and the TME is essential for the development of novel therapeutic strategies to combat drug resistance [ 13 – 15 ].

In this review, we introduce the development of single-cell and spatial omics technologies and discuss their roles in predicting the hallmarks of drug response or resistance. Based on tumor hallmarks, including tumor heterogeneity, the TME reprogramming, cell–cell interactions, signaling pathway regulation, and metabolic regulation, we integrate cellular biomarkers for drug response or failure in 25 cancer types, showing light on personalized treatment strategies for cancer patients.

Single-cell omics in identifying cellular hallmarks for cancer drug response or resistance

Traditional sequencing technologies offer several advantages, including high throughput, low cost, and short turnaround times. However, they detect DNA and RNA from a large number of cells and therefore produce data that reflect averages across a population of cells [ 16 ]. Cellular heterogeneity can lead to significant differences in genetic information among individual cells, and the averaging of results from a multitude of cells may obscure critical gene expression information from rare cell populations. Understanding the heterogeneity of cancer cells is essential for accurate tumor diagnosis, elucidating tumor characteristics, and developing personalized therapies [ 17 ]. To address this limitation, single-cell omics emerged as a groundbreaking approach. The evolution of single-cell sequencing technology has progressed from initial trials to its current mature applications [ 18 ]. In 2009, Tang et al. [ 19 ] successfully performed single-cell RNA sequencing (scRNA-seq) for the first time using high-throughput sequencing technology. This development constituted a major breakthrough, as it enabled the measurement of gene expression at the single-cell level, thereby revealing cellular heterogeneity. Since its inception, single-cell sequencing has found extensive applications in life sciences, and in 2013, single-cell omics was recognized as the “Method of the Year” by Nature Methods [ 20 ].

Single-cell omics sequencing represents a technology that facilitates high-throughput analysis of individual cells at the transcriptomic, genomic, epigenomic, and proteomic levels, effectively detecting differential information in individual cells. The workflow for single-cell omics sequencing in cancer drug response or resistance involves four critical steps: (1) isolating single cells from a population, (2) extracting, processing, and amplifying the genetic material from each isolated cell, (3) preparing a sequencing library that encompasses the genetic material of the isolated cells, and (4) sequencing the library and analysis in cellular information for cancer drug response or resistance [ 21 , 22 ] (Fig. 1 A). Through the integration of spatial omics, hallmarks that predict drug response or resistance can be identified, together with spatial distribution information about these cells and the effects of cell location on function [ 23 , 24 ] (Fig. 1 B).

The technology of single-cell transcriptomics in identifying hallmarks of cancer drug response or r esistance

Single-cell RNA-seq (scRNA-seq) is a powerful method for identifying differences in gene expression across various cell types or states. For instance, it can pinpoint characteristic genes associated with different cancer cell subtypes, offering crucial insights into tumor heterogeneity, clonal development, and the evolutionary dynamics of cancer cells. This capability is essential for constructing cellular pedigree maps that detail these processes [ 25 , 26 ]. Additionally, scRNA-seq facilitates the identification and characterization of cellular interactions in the TME, which is critical for understanding how cancer cells engage with their surroundings [ 25 ]. Furthermore, this uncovers novel cell populations within complex tissues, particularly in studies of the immune system. By analyzing the transcriptomes of immune cells, researchers can discover previously unrecognized subpopulations, shedding light on their specific roles in the immune response [ 27 , 28 ]. The technique also elucidates cellular interactions within the TME, enhancing our understanding of the dynamic relationships between cancer cells and both immune and stromal cells [ 29 ]. However, the limitations of single-cell transcriptomics include limited sequencing depth, insufficient sensitivity for gene expression detection, which leads to the omission of important gene expression information, inadequate transcript coverage, technical noise, RNA quality issues, amplification bias, batch effects (Table 1 ). By examining the transcriptomic profiles of drug-resistant cells, scRNA-seq can reveal hallmarks associated with drug response, thereby illuminating the evolution of drug resistance during cancer treatment and informing personalized therapeutic strategies [ 23 , 30 – 34 ]. Overall, transcriptomics serves as a robust tool for dissecting cellular functions and disease mechanisms, ultimately guiding personalized immunotherapy approaches [ 35 , 36 ].

Single-cell epigenomics in studying hallmarks of cancer drug response or resistance

Single-cell epigenomics is a technique for studying epigenetic regulatory mechanisms, such as DNA modification, chromatin structure, and non-coding RNAs, at the single-cell level. Commonly used techniques include DNase-seq, ATAC-seq, WGBS, and cChIP-seq [ 37 ]. Among these, scATAC-seq can reveal differences in chromatin status and function among various cells by combining single-cell sequencing with transposases. In this method, cellular chromatin is fragmented and ligated with sequencing aptamers, and each fragment is labeled and sequenced individually [ 38 ]. By analyzing these sequencing data, researchers can determine the accessibility of each chromatin region, thereby identifying different cell types and subtypes and constructing classifications and genealogies [ 39 ]. Additionally, scATAC-seq aids in identifying regulatory elements such as promoters, enhancers, and transcription factor binding sites, thereby revealing their functions and activities [ 37 , 40 ]. By comparing chromatin accessibility among different cells, researchers can explore the heterogeneity of cell states and the dynamics of developmental trajectories [ 41 ]. The integration of epigenomics with transcriptomics enables the construction of gene regulatory networks, helping to reveal the epigenetic status of tumor and immune cells, and thus assessing their resistance mechanisms to therapy [ 42 ].

The overview of single-cell proteomics in cancer drug response or resistance

Single-cell proteomics is a technique used to resolve protein expression and modification status at the single-cell level. This method relies on technologies such as mass spectrometry, antibody labeling, or microfluidics to identify and quantify protein expression profiles in individual cells using highly sensitive assays [ 43 ]. Since each cell possesses a unique protein expression profile under varying conditions, this approach reveals heterogeneity among cells and their functions in different physiological and pathological states. Isoplexis measures the total immune function of each immune cell through functional immunological profiling, applying a unique metric known as polyfunctional strength index (PSI) [ 44 ]. These methods can identify the protein expression profiles of different cell types and assist researchers in distinguishing between various cell subpopulations through the analysis of specific markers, thereby elucidating the interactions between immune cells, cancer cells, and the TME[ 45 , 46 ]. By analyzing changes in protein expression within the TME, researchers can reveal the dynamic changes of cancer cells and immune cells before and after treatment, which is crucial for predicting responses to immunotherapy [ 39 ]. Single-cell proteomics also have several limitations, including the challenges of analyzing limited samples and the inability to amplify proteins, which often leads to the omission of low-abundance proteins[ 47 ]. Additionally, issues related to the standardization of data quality, reproducibility, and the complexity of analysis pose significant hurdles. Common problems in mass spectrometry detection, such as incomplete enzymatic digestion, affinity biases, and a narrow dynamic range, further complicate the reliability of the results. Despite these constraints, the technique remains pivotal for revealing cellular dynamics.

Single-cell immune repertoire analysis in cancer drug response or resistance

Single-cell T-cell receptor sequencing (scTCR-seq) and single-cell B-cell receptor sequencing (scBCR-seq) are techniques employed to analyze the sequence diversity of TCRs and BCRs, thereby elucidating the roles of T and B cells in the immune response [ 48 ]. The TCR recognizes antigenic peptides that bind to the major histocompatibility complex (MHC), thereby activating T cells to perform their immune functions, while the BCR directly recognizes free antigens and activates B cells to produce specific antibodies that neutralize or eliminate the antigen [ 49 ]. ScTCR-seq and scBCR-seq enable the acquisition of sequence information pertaining to TCR or BCR genes in individual immune cells, capturing their variation and diversity [ 50 ]. This technology aids scientists in studying immune cell diversity, clonal evolution, immune memory, and immune responses to various diseases and infections.

ScTCR-seq and scBCR-seq serve several purposes: (1) Analyzing the sequence information of TCRs and BCRs enables the understanding of the characteristics of various immune cell subpopulations, the identification of potential therapeutic targets, the assessment of drug effects on distinct cellular subpopulations, and the optimization of drug design and efficacy evaluation [ 51 , 52 ]. (2) ScTCR-seq and scBCR-seq aids in identifying specific clonal cells within a patient’s immune system and assessing their response to therapy. This facilitates the development of personalized immunotherapies, such as CAR-T cell therapy or monoclonal antibody therapy. (3) Monitoring the TCRs and BCRs allows for the tracking of immune cell evolution and expansion, assessment of immune response effectiveness, and observation of changes in immune dynamics during treatment [ 53 , 54 ]. This is crucial for evaluating drug efficacy, predicting disease progression, and monitoring efficacy.

Spatial omics in studying hallmarks of cancer drug response or resistance

The methodologies of spatial transcriptomics

Spatial transcriptomics (ST), such as Xenium, CosMx Spatial Molecular Imager (SMI), is an umbrella term that encompasses all methods for assigning transcriptomic data to its original location within tissue samples [ 55 ]. Recognized as the Technology of the Year by Nature in 2020, ST enables the resolution of gene expression patterns at the level of individual cells, capturing both temporal and spatial dimensions, as well as the spatial positional, there are two main approaches to ST that allow transcriptome analysis while preserving spatial information [ 56 ]. However, the resolution and gene detection efficiency of ST at the single-cell level are still inferior to that of scRNA-seq, and there are significant issues such as the large number of spatial features and severe fragmentation of cross-platform data[ 57 ]. Presently, there are two main ST methods for transcriptome analysis while preserving spatial information. The first approach is an imaging-based technique that involves imaging mRNAs in situ using a microscope, relying on in situ hybridization (ISH) or in situ sequencing (ISS) techniques to distinguish between various mRNA types [ 58 ]. The second method is a sequencing-based technique that extracts mRNAs from tissues while preserving their spatial information, followed by analysis using high-throughput sequencing for mRNA species [ 59 ]. Common methods for preserving spatial information include microdissection, microfluidics, or the direct capture of mRNA using microarrays with spatially encoded probes [ 60 ].

ST is widely utilized for gene expression localization, providing transcript localization information at subcellular resolution and integrating it with cellular distribution data to achieve multi-level personalized analyses. First, ST permits the precise localization of mRNA molecules within tissue sections, allowing for the determination of gene expression distribution both within and between cells [ 61 ]. This subcellular-resolution information is vital for analyzing gene expression dynamics within organelles and localized regions. Second, personalized analyses enable researchers to identify the spatial locations of various cell types, thereby revealing potential interactions and communication pathways among cells [ 62 ]. For instance, in tumor tissues, the spatial distribution of different cell types reflects the complexity and heterogeneity of the TME [ 63 ]. Furthermore, differential expression analysis can elucidate patterns of changes in gene expression within specific regions or cell populations [ 64 ]. This information is especially crucial for understanding the mechanisms of gene regulation during disease progression. For example, ST can identify cell populations with specific molecular characteristics in tumors and delineate the spatial distribution of immune cells that contribute to immunotherapy responses, thereby providing data to support personalized medicine [ 30 , 65 ]. Such analyses offer unprecedented resolution and precision in revealing the spatial distribution of gene expression, cell–cell interactions, and their roles in disease mechanisms.

The workflow of spatial proteomics in studying hallmarks of cancer drug response or resistance

In order to determine the subcellular localization of proteins, the structure of organelles, and intracellular interactions, spatial proteomics (SP) is a technique that is used to study the spatial distribution and localization of proteins within cells or tissues [ 66 ]. SP allows for accurate mapping of protein distribution by combining high-resolution imaging technologies with mass spectrometry analysis [ 67 ]. Specifically, we categorize the techniques into three main types: imaging mass spectrometry (IMS) based, liquid chromatography with tandem mass spectrometry (LC–MS/MS) based, and antibody-based [ 68 ]. The co-detection by indexing (CODEX) technique combines the advantages of single-cell biology and histology to provide single-cell spatial resolution, imaging more than 40 biomarkers in a single tissue section and making it easier to analyze both spatial and high-dimensional data [ 69 ]. Digital Spatial Profiling (DSP) is the method of identifying and measuring the geographical distribution of multiple proteins to disclose the biological properties and the TME [ 70 ]. SP has significant applications in disease research, where the analysis of protein spatial distribution in disease samples can aid in the identification of potential disease hallmarks. By examining alterations in protein expression or localization in diseased tissues, SP provides critical insights for early diagnosis and treatment [ 30 , 71 ]. Furthermore, SP can be used to investigate the mechanisms of drug action on proteins, thus optimizing drug design, enhancing efficacy, and minimizing side effects [ 64 , 72 ].

The application of spatial metabolomics in studying hallmarks of cancer drug response or resistance

Spatial metabolomics is an emerging interdisciplinary field that integrates metabolomics with spatial data, facilitating the in situ identification and quantitative analysis of metabolites within biological tissues. This technique reveals the distribution and metabolic variations of compounds at distinct spatial sites [ 73 ]. A key advantage of spatial metabolomics is its ability to provide not only detailed structural and quantitative information about metabolites but also their spatial distribution within tissue samples through high-resolution methods. Spatial localization allows researchers to gain a more comprehensive understanding of complex biological processes, particularly in oncology, where it contributes to elucidating the metabolic characteristics, heterogeneity, and dynamic changes within the TME [ 74 ]. The principal techniques employed in spatial metabolomics include mass spectrometry imaging, magnetic resonance spectroscopic imaging (MRSI) and fluorescence imaging among others [ 75 ]. These technologies enable the acquisition of high-resolution spatial metabolic data directly from tissue sections or in situ samples, thereby offering critical insights into the metabolic reprogramming of tumors and their microenvironment.

In cancer research, spatial metabolomics plays a crucial role by revealing alterations in metabolic pathways, analyzing the distribution of metabolites within the TME and providing deeper insights into tumorigenesis, immune evasion and metastasis [ 76 ]. Furthermore, spatial metabolomics is invaluable for early cancer detection, as metabolite shifts may occur in the early stages of tumor formation. It also holds promise as hallmarks for cancer treatment, facilitating the monitoring of metabolic changes across different tumor regions, assessing therapeutic responses, predicting recurrence and refining personalized treatment strategies [ 77 ]. Spatial metabolomics represents a powerful tool in advancing early cancer diagnosis, therapeutic monitoring, and the personalization of treatment regimens.

Integrating transcriptomics and proteomics in molecular mechanisms of cancer response and resistance

Technologies that integrate single-cell transcriptomics with proteomics enable a more comprehensive analysis of cellular states, shedding light on the relationship between gene expression and protein function [ 78 ]. CITE-seq enables the simultaneous measurement of both mRNA and surface proteins using DNA barcode-labeled antibodies in combination with single-cell transcriptome sequencing. In contrast, REAP-seq is optimized for antibody screening, reduces background noise, and is well-suited for high-throughput analysis [ 79 ]. This integration facilitates the linking of cellular immunophenotypes with gene expression, enables more precise annotation of cell subpopulations or rare cell types, and provides a more accurate resolution of cellular heterogeneity and its functional implications [ 80 ]. Moreover, this approach is instrumental in identifying key hallmarks and elucidating complex intracellular regulatory mechanisms, contributing to the understanding of disease pathogenesis, and is widely applied in the study of tumors, immune responses, and other complex diseases [ 81 , 82 ]. Based on tumor hallmarks, we integrate cellular biomarkers for drug response or failure in 25 cancer types, showing light on personalized treatment strategies for cancer patients (Tables 2 – 3 ).

Hallmarks for predicting tumor drug response or resistance

Breast cancer

Breast cancer is one of the most prevalent malignant tumors among Chinese women, with its incidence rate ranking first among female malignant tumors, and this rate has been increasing annually, with a concerning trend towards younger patients [ 83 ]. Triple-negative breast cancer (TNBC), characterized by the highest recurrence and mortality rates among breast cancer subtypes, is a highly aggressive disease marked by significant ITH and treatment options remain limited [ 84 ]. Cancer-targeted immunotherapy first entered clinical practice in the late 1990 s [ 85 ]. Currently, three classes of T-cell immunotherapies have been approved for routine clinical use in cancer patients: (1) immune checkpoint inhibitors (ICIs), (2) bispecific antibodies, and (3) chimeric antigen receptor T (CAR-T) cells [ 86 ]. While ICIs have achieved durable responses in some cancer patients, their introduction has offered hope for TNBC treatment; however, clinical trials have reported only modest responses[ 87 ]. This has prompted investigations into the combination of ICIs and chemotherapy for TNBC treatment, although the resistance mechanisms associated with chemotherapy and ICIs remain unclear [ 88 ]. Single-cell and spatial omics can assess tumor heterogeneity and the TME at single-cell and spatial resolution, thereby elucidating the resistance mechanisms (Table 2 ).

Cancer cells of breast cancer

Single-cell and spatial omics can be employed to explore subsets of drug-resistant cells after anti-tumor therapy, providing valuable insights for subsequent therapeutic strategies. Ge et al. [ 23 ] found that ITH in TNBC was strongly associated with poor outcomes and resistance to immunotherapy. Deficiency of ZNF689 has been identified as a key driver of ITH in TNBC, both in vitro and in vivo. Mechanistic studies reveal that the ZNF689-TRIM28 complex mediates transcriptional silencing by binding to the promoter region of long interspersed nuclear element-1 (LINE-1). The loss of this complex leads to the reactivation of LINE-1 retrotransposition, which increases genomic instability and promotes ITH. Further analyses, including scRNA-seq, spatially resolved transcriptomics, and flow cytometry, have demonstrated that ZNF689 deficiency disrupts antigen presentation and impairs T-cell activation, thereby promoting immune evasion and contributing to immunotherapy resistance. However, pharmacological inhibition of LINE-1 sensitizes TNBC tumors exhibiting high ITH to immunotherapy. Additionally, higher expression levels of ZNF689 correlate with improved patient prognosis and enhanced responses to immunotherapy. Targeting LINE-1 in combination with immunotherapy represents a promising strategy for eradicating high ITH tumors in TNBC, highlighting the potential for precision treatment using LINE-1 inhibitors alongside immunotherapy (Fig. 2 A).

In addition, a study analyzed scRNA-seq data from breast cancer and stromal immune cells and found that the sensitivity of radiotherapy was correlated with tumor mutational burden (TMB) and PD-L1 positivity rate ( 31 ). TNBC expresses higher levels of immune checkpoint molecules, so the combination of radiotherapy and ICIs is an effective treatment for patients with TNBC who are sensitive to radiation and overexpressing PD-L1 and have high TMB (Fig. 2 B).

Cancer-associated fibroblasts (CAFs) of breast cancer

CAFs constitute a heterogeneous population within the TME and can be subdivided into distinct subpopulations based on specific surface markers [ 89 ]. However, their origins and roles in tumor occurrence, development, and treatment responses remain inadequately understood. Single-cell sequencing technology has identified different types of CAF in breast and adenocarcinoma tumors, which may originate from different parental cells, and are mainly involved in the recruitment of immune cells to tumors and the induction of epithelial-mesenchymal transition of cancer cells [ 90 ].

In investigating the mechanisms underlying tamoxifen resistance in breast cancer, Gao et al. [ 91 ] combined single-cell transcriptome sequencing and exosomal miRNA analysis, discovering that, CD63 + CAFs reduces the expression of estrogen receptor α (ERα) in cancer cells through miR-22 contained in exosomes secreted by itself, thereby reducing the sensitivity of cancer cells to tamoxifen and leading to the emergence of drug resistance. Based on these results, the authors successfully improved the drug sensitivity of breast cancer cells in mouse models using CD63 neutralizing antibody and cRGD-miR-22-sponge nanoparticles, thus providing an in-depth molecular mechanism and precise solution to address the clinical issue of tamoxifen resistance.

On the basis of the expression of six fibroblast markers, CAF in breast cancer is divided into four subsets: CAF-S1, CAF-S2, CAF-S3 and CAF-S4 [ 92 ]. To explore the function of CAF-S1 cell subsets, the study analyzed over 19,000 individual CAF-S1 cells in breast cancer using scRNA-seq technology and identified eight clusters of CAF-S1 cells, including 3 inflammatory CAFs (iCAFs) and 5 myofibroblastic CAFs (myCAFs) [ 32 ]. Detox-iCAF and IL-iCAF are associated with an immunoactive environment, while both ecm-myCAF and TGFβ-myCAF are associated with an immunosuppressive environment and validated using publicly available data from metastatic melanoma and metastatic NSCLC patients receiving anti-PD-1 therapy.

T cells of breast cancer

Although T cells constitute the predominant population of tumor-infiltrating lymphocytes, the relationship between quantitative and qualitative differences in T cell subsets and patient prognosis remains unclear [ 93 ]. The study collected 78 paired samples from 22 TNBC patients (11 receiving atezolizumab in combination with paclitaxel and 11 receiving paclitaxel alone) pre- and post-treatment using 10X Genomics [ 42 ]. Dynamic mapping of the high-resolution transcriptome and epigenome of immune cells in the TME and peripheral blood of TNBC patients was constructed by integrating scTCR-seq and scATAC-seq. It was found that after paclitaxel combined with atezolizumab treatment, lymphoid tissue inducer (LTi) cells, follicular B (Bfoc) cells, CXCL13 + T cells and conventional type 1 dendritic cells (cDC1), and B cells may promote anti-tumor immunity by activating CD4-CXCL13 through interaction with CD4-CXCL13, and CD4-CXCL13 helps to activate CD8-CXCL13 through cDC1. The study highlighted the importance of CXCL13 + T cells in the process of responding to anti-PD-L1 therapy, and in TNBC, paclitaxel selectively reduces the expansion of key anti-tumor immune cells while increasing immunosuppressive macrophages, which may be detrimental to the clinical outcome of atezolizumab.

The functional status of T cells within tumors serves as a potential marker for predicting patient prognosis and treatment response. Bassez et al. [ 81 ] examined 11 breast cancer patients and 29 newly diagnosed patients received anti-PD-1 antibody therapy after neoadjuvant chemotherapy (NAC), followed by a fine-needle biopsy after about 10 days of treatment, and then had the tumor surgically removed. The researchers analyzed scRNA-seq, scTCR-seq, and CITE-seq on preoperative and postoperative biopsies. T cells with activation and exhausti-like characteristics are known as T EX cells. The findings demonstrated that the degree of differentiation and clonal proliferation of CD8 + T EX cells and CD4 + T H1 and T FH cells could be used as indicators to predict response to anti-PD-1 treatment, and anti-PD-1 treatment may further promote the differentiation of these cells. In addition, high expression of PD-L1 in macrophages and dendritic cells (DCs) is also thought to be a useful marker for predicting response to immunotherapy. In order to characterize the immunological state of various breast cancer subtypes, Wagner et al. [ 94 ] used CyTOF on 144 breast tumor samples and 50 non-tumor samples. High-grade ER + and ER − tumors were discovered to have a higher frequency of PD-L1 + TAMs and exhausted T cells, which were linked to immunosuppression and poor prognosis. By using single-cell sequencing technology, tumor heterogeneity and the TME can be understood, leading to better prognoses and the selection of suitable treatment alternatives.

A recent study leveraged Xenium spatial single-cell transcriptomics technology to undertake a comprehensive investigation of the TME in various breast cancer subtypes [ 95 ]. The study meticulously annotated 15 distinct cell types at true single-cell resolution, thereby providing a detailed characterization of the complex architectural landscape of the TME. The findings revealed that, in breast cancer patients responsive to PD-1 inhibitor therapy, immune cells—such as effector T cells, proliferative T cells, and macrophages—were preferentially localized in proximity to tumor cells, where they facilitated immune-mediated cytotoxicity via CD274/CD80 ligand-receptor interactions. These spatially defined immune cell interactions hold considerable promise as biomarkers for patient stratification and prognostic assessment in precision immunotherapy. This study not only enhances our theoretical understanding of the efficacy of precision immunotherapy in breast cancer and other malignancies but also advances our comprehension of the TME, thus opening avenues for the identification of novel biomarkers with potential clinical utility in immunotherapy.

B cells of breast cancer

Lu et al. [ 96 ] conducted scRNA-seq on B cells infiltrated by tumor before and after treatment in order to better understand the anti-tumor immune response of B cells induced by chemotherapy in breast cancer. They discovered that ICOSL + B cell subsets appeared following NAC, and that the number of ICOSL + B cells in the partial or complete response group was significantly higher than that in patients in stable or advanced stage, indicating that this subset was associated with improved efficacy. Furthermore, in breast cancer, ICOSL + B cell enrichment is a independent positive predictive factor for both OS and disease-free survival (DFS). Following chemotherapy, the complement pathway inhibitory protein CD55 can influence ICOSL + B cell development and induce chemoresistance. Therefore, targeting CD55 can promote ICOSL + B cells after chemotherapy and promote anti-tumor immunity (Fig. 2 C).

Program integrating of single-cell phenotypes and characteristics of breast cancer

Gambardella et al. [ 7 ] used the transcriptional profiles of 35,276 individual cells from 32 breast cancer cell lines to generate a single-cell atlas that offers a comprehensive understanding of tumor heterogeneity. To enable cell line-based patient classification, the scientists discovered a significant degree of variability in biomarker expression and employed a deconvolution technique to ascertain cell line composition based on bulk gene expression patterns from tumor tissues. In order to compute and forecast drud response beginning from single-cell features, they lastly linked the outcomes of extensive in vitro drug screening in cell lines with single-cell data. The findings demonstrate the potential of single-cell analysis in customized cancer treatments and imply that Drug Estimation from Single-cell Expression Profiles (DREEP) can predict drug susceptibility from single-cell transcriptional profiling. They also show that dynamic heterogeneity in gene expression does play a significant role in how cell populations respond to drug treatments.

Melanoma

With an incidence of about 5% of malignancies, melanoma is a very aggressive cancer, and the 5-year relative survival rate for metastatic melanoma is only 25% [ 97 ]. Immunotherapy has revolutionized treatment, but there are no hallmarks to assess its effectiveness [ 98 , 99 ]. We summarized the biomarkers for predicting drug response or failure in melanoma (Table 3 ).

Cancer cells of melanoma

Melanoma cells in a mesenchymal-like (MES) state were shown to be considerably enriched in early-treatment biopsy samples from ICI-non-responders, a cell type known to be resistant to targeted therapy, according to analyses using bulk RNA-seq, scRNA-seq, Visium, RNA-scope, and CODEX [ 30 ]. As a repressor of melanocytes and antigen-presenting transcriptional programs, TCF4 is a crucial regulator of this process. Consequently, melanoma becomes resistant to ICIs and targeted treatments, exposing a TCF4-dependent regulatory network. TCF4 expression targeting makes patients more susceptible to ICIs and targeted treatments.

T cells of melanoma

Sade-Feldman et al. [ 100 ] used scRNA-seq to examine tumor samples from 48 melanoma patients receiving ICI treatment. Increased TCF7 + CD8 + T cell frequencies were observed to predict the response to anti-PD-1 treatment when combined with whole exome sequencing (WES), immunofluorescence, and flow cytometry (IF). Additionally, it was shown that non-responsive patients had considerably more CD8 + T cells expressing the exhaustion markers TIM3 and CD39. Targeting CD39 and TIM3 at the same time strengthened the anti-tumor impact of anti-PD-1, which successfully reduced tumor growth and increased patient survival. Prior to and during anti-PD-1 treatment, responding melanoma patients showed a substantial increase in CD8 + T cell proliferation, according to early immunohistochemistry (IHC) [ 101 ]. Within three weeks of starting immunotherapy, the University of Oxford study team examined worldwide changes in peripheral CD8 + T cell gene transcription and expression [ 100 ]. In conjunction with TCR analysis, it was discovered that the quantity of TCR clones 21 days following ICI was associated with the ability to react [ 102 ]. Patient prognosis is only linked to a limited group of memory effector CD8 + T cells (T EM cells) that have a significant increase in the copy number of the gene encoding TCR. Memory effector CD8 + T cells have clusters of CCL4, GNLY and NKG7, which also indicates a better prognosis for patients.

The results of another research were comparable. Valpione et al. [ 82 ] performed TCR-seq on plasma cell-free DNA (cfDNA) and peripheral blood mononuclear cells (PBMCs) from 50 patients with metastatic melanoma. The most critical change in peripheral T cells is the TCR hypervariable region CDR3, which is genetically rearranged to change in response to specific antigens. Rearrangement efficiency score (RES) was designed to assess the proportion of available T cells. PBMC and cfDNA RES were similar in healthy people and melanoma patients. The majority of patients with nearly unaltered RES for PBMC and a little rise in RES for cfDNA attained objective remission following three weeks of immunotherapy. Changes in RES values were used to identify immune effector T cells (T IE cells), which are T cells that were truly activated by ICIs. The effectiveness of immunotherapy can be predicted by dynamic changes in peripheral T IE cells at an early stage. In another study, Diab et al. [ 103 ] used IsoPlexis to find that the CD8 + T cell PSI can be utilized as a predictive marker for the effectiveness of bempegaldesleukin (BEMPEG) plus nivolumab (NIVO) immune combination therapy in patients with metastatic melanoma. Only the percentage of multifunctional CD8 + T cells was considerably greater in the group of patients in objective remission, according to the analysis of blood CD8 + and CD4 + T cells as well as NK cells. This percentage remained unchanged in non-responders. This is also consistent with the discovery that CD8 + T cells are the primary target of ICIs. The researchers found that patients with a significant PSI difference in CD8 + T cells had a greater objective remission rate in a follow-up 3-year analysis.

Additionally, it has been discovered that CD4 + T cells support immunotherapy response [ 104 ]. The objective of the study was to to forecast how advanced melanoma patients will react to anti-CTLA4 either by alone or in conjunction with anti-PD-1. The first is that CD4 + T cells in responding patients were shown to rise dramatically after treatment by CyTOF, these increases were mostly in naïve and central memory T cells rather than regulatory T cells. IsoPlexis then carried out a functional characterization analysis, and it was consistent with the findings of CyTOF that the PSI of CD4 + T cells increased significantly after treatment, primarily due to an increase in effector cytokines, while regulatory cytokines decreased in responding patients.

Program integrating of single-cell phenotypes and characteristics of melanoma

Using single-cell RNA sequencing data, Jerby-Arnon et al. [ 105 ] analyzed thousands of melanoma cells from more than 30 melanoma patients, half of whom were resistant to immunotherapy. A cancer resistance gene expression program was discovered that correlates with T cell clearance, immunotherapy resistance, and other features in tumors. The study found that CDK4/6 is the main"regulator". CDK4/6 inhibitors have been shown to inhibit cell proliferation, significantly improving individual response to immunotherapy in mouse models. This resistance expression program can help predict resistance to immunotherapy before treatment is initiated.

Ovarian cancer

Ovarian cancer is one of the most lethal gynecological malignancies, ranking as the eighth most common cancer among women worldwide and the fourth leading cause of cancer-related deaths [ 106 ]. The most prevalent histological subtype of ovarian cancer is epithelial ovarian cancer (EOC), which also includes serous, endometrial, mucinous, and clear cell carcinomas (OCCC) [ 107 ]. The most prevalent and severe kind of ovarian cancer, high-grade serous ovarian carcinoma (HGSOC), is frequently discovered at an advanced stage [ 108 ]. The majority of ovarian cancer patients will recur and eventually develop drug resistance, despite the effectiveness of first-line treatment [ 109 ]. Drug resistance and the complicated treatment history of ovarian cancer result in treatment failure and disease progression (Table 2 ).

Izar et al. [ 33 ] utilized scRNA-seq to analyze approximately 11,000 cells from 22 ascitic samples of 11 HGSOC patients, revealing significant differences in cellular biological functions and immune functions between malignant and non-malignant cells. Malignant cells exhibit differentiation and proliferation roles, while among the non-malignant cells, CAFs subgroup has immune regulatory functions. The cytokines they express, such as interleukin-6 and the chemokine CXCL12, can activate the JAK/STAT signaling pathway in tumors, thereby promoting tumor growth and resistance. The aforementioned research indicates that different cells perform distinct functions within the ascitic ecosystem, and the communication and interactions among these cells may balance disease progression and treatment responses. Application of JAK-STAT inhibitors showed significant anticancer activity both in vivo and in vitro (Fig. 2 D).

Therapeutically altering this balance may be a potential approach to reshape the resistant environment.To explore the mechanisms of EOC resistance and recurrence, the researchers performed scRNA-seq on cells from 4 primary tumors, 2 untreated peritoneal metastases, and 2 recurrent tumors [ 110 ]. “Stress” subpopulation were identified by analysis and found to be associated with relapse and drug resistance. The specific marker CYR61 was screened from “Stress” subpopulation and validated by immunohistochemistry. The percentage of CYR61 + cells was found to be positively correlated with tumor resistance and could predict the recurrence of EOC. It was also found that CYR61 activated Wnt/β-catenin signaling and increased survivin expression, leading to chemoresistance in TNBC patients [ 111 ].

The integration of single-nucleus RNA sequencing (snRNA-seq) with 10X Visium has elucidated the cellular interaction network mechanisms underlying chemotherapy resistance in OCCC [ 112 ]. This study found that chemotherapy resistance arises from the spatial cooperation between specific subpopulations of cancer cells and CAFs within the tumor microenvironment. Through snRNA-seq, 6 epithelial cancer cell subpopulations were identified, among which hypoxia-inducible factor (HIF) + cancer cells was significantly associated with chemotherapy resistance and poor prognosis in patients. Spatial transcriptomics further revealed that this subpopulation is specifically localized to the tumor-stroma interface enriched with CAFs, exhibiting characteristics of HIF-1α pathway activation. In resistant patients, HIF + cancer cells closely co-localize with myCAFs, forming a unique microenvironment conducive to resistance. Functional experiments confirm that the interaction between HIF + cancer cells and CAFs enhances chemotherapy resistance through a bidirectional feedback loop. These findings provide new avenues for targeting the tumor-stroma interaction network to overcome resistance.

Bladder cancer

Bladder cancer can be divided into non-muscle-invasive bladder cancer (NMIBC), muscle-invasive bladder cancer (MIBC), and distant metastatic tumors [ 113 ]. The standard of care for patients with MIBC is curative cyclophosphamide (RC) and cisplatin-based adjuvant chemotherapy (ACT), but the prognosis is poor [ 114 ]. It is important to note that although ICIs are significantly more effective than conventional platinum-based chemotherapy, it is estimated that only 1/5 of patients can benefit from the treatment of ICIs [ 115 ]. Therefore, it is of great clinical significance to evaluate the response of patients to immunotherapy in order to achieve optimal and personalized treatment and improve the prognosis of these patients.

TAMs are infiltrating macrophages in tumor tissues that differentiate mainly from monocytes [ 116 ]. Xu et al. [ 117 ] included two cohorts of MIBC and performed cell communication analysis using scRNA-seq data to explore the predictive value of IL-10 + TAMs invasion on clinical outcomes and drug response in MIBC. Studies have found that patients with high infiltration of IL-10 + TAMs are more likely to benefit from ACT and have a poor prognosis. Patients with higher levels of IL-10 + TAMs infiltration are more sensitive to EGFR-targeted therapy and immunotherapy. The potential of IL-10 + TAMs and their regulators as therapeutic targets for MIBC will be another promising direction for future research. Another study focused on specific T cells that mediate the mechanisms of resistance to immunotherapy [ 118 ]. A total of 30,604 T cells from seven bladder tumors and non-malignant tissues were evaluated by scRNA-seq and scTCR-seq. There was no statistically significant difference in the level of CD8 + T cells in tumor tissues and non-tumor tissues. Intratumoral CD4 + T cells mediate anti-tumor cytotoxicity in bladder cancer. The gene signature of cytotoxic CD4 + T cells affects the immune response and targeted therapy effect of bladder cancer, predicting the clinical response of anti-PD-L1 (Table 2 ).

Cisplatin-based chemotherapy remains the cornerstone of treatment for metastatic bladder cancer; however, the majority of patients ultimately experience relapse and acquire resistance [ 119 ]. Li et al. [ 120 ] elucidated a novel mechanism underlying cisplatin resistance in bladder cancer and highlighted the central role of lactylation in mediating this phenomenon. In brief, the study employed scRNA-seq transcriptomic data to identify and characterize a distinct epithelial cell cluster associated with cisplatin resistance. These resistant cells exhibited alterations in their metabolic pathways, accompanied by an upregulation of lactylation modifications. Subsequently, through ChIP-seq, downstream targets regulated by H3K18la were identified within the tumor, revealing that H3K18la is enriched at the promoter regions of transcription factors implicated in drug resistance. This enrichment was shown to impair the sensitivity of cancer cells to cisplatin. These findings advance our understanding of the molecular mechanisms driving cisplatin resistance and provide critical insights into potential therapeutic targets for overcoming drug resistance in bladder cancer.

Non-small cell lung cancer

Lung cancer is the most common oncological disease, with approximately 85% of lung cancer patients diagnosed with non-small cell lung cancer (NSCLC), with lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) being the most common subtypes of NSCLC [ 121 ]. Small cell lung cancer (SCLC) is a highly aggressive neuroendocrine malignancy, accounting for approximately 15% of all lung cancer cases [ 122 ]. The PD-1 blockade pembrolizumab has been recommended for the first-line treatment of patients with high PD-L1 expression in metastatic NSCLC, but some PD-L1-negative patients still benefit from it, and the underlying mechanism is unclear [ 87 , 123 , 124 ]. ScRNA-seq of peripheral blood mononuclear cells from a PD-L1-negative LUSC patient revealed the immune cell and molecular signatures of PD-L1-negative LUSC patients benefiting from pembrolizumab [ 125 ]. At the gene level, ID2, PIK3CD, MATK, MZB1, IL7R, and TRGC2 were significantly positively correlated with PD-1 expression, and UQCR10 was negatively correlated with PD-1 expression, which may serve as a biomarker in PD-L1-negative LUSC patients (Table 2 ).

To investigate the mechanisms of resistance to ICIs, Horton et al. [ 126 ] studied T cells in a mouse model of NSCLC. The researchers used Seq-Well technology to sequence the messenger RNA of reactive and non-reactive T cells in order to identify any differences between T cells. By analyzing the gene expression patterns of T cells, it was found that some T cells were not in a state of exhaustion, but rather were dysfunctional due to a failed up-regulation of effector and exhausted T cell characteristics prior to entering the tumor. Further studies revealed that these unresponsive T cells had lower cytokine receptor expression. The researchers then added the corresponding cytokines to the treatment of these mice, so that the T cells regained their role and effectively killed cancer cells.

Single-cell and spatial omics analyses have unveiled the core mechanisms underlying immunotherapy chemotherapy resistance [ 127 ]. Notably, COL11A1 + CAFs are significantly enriched in the tumor periphery of resistant patients. These CAFs create a physical barrier that encapsulates malignant cells, thereby restricting the infiltration of CD8 + T cells into the tumor core. Furthermore, these CAFs activate pathways associated with hypoxia, TGF-β signaling, and pro-angiogenesis, thereby driving an immunosuppressive microenvironment. Their abundance is positively correlated with SPP1 + macrophages, which collaboratively inhibit T cell functionality. Clinical data demonstrate that high infiltration of COL11A1 + CAFs is directly associated with immune chemotherapy resistance and poor prognosis, suggesting their potential as predictive biomarkers for therapeutic response. Targeting COL11A1 + CAFs and intercellular interaction networks may offer a promising strategy to overcome treatment resistance in NSCLC.

Glioblastoma

Glioblastoma (GBM) remains among the most aggressive malignancy of the brain [ 128 ]. The immune environment of GBM differs from that of other cancers, with its associated macrophages and depleted lymphocytes having an immunosuppressive phenotype [ 129 ]. Cancer cells recruit suppressor cells through cytokines, influencing transcriptional profile. While immune checkpoint inhibitors such as cytotoxic T lymphocyte-associated protein 4 (CTLA-4) and PD-1 have been successful in other cancers, they have not been shown to improve survival in patients with GBM, with the microenvironment and lack of reliable hallmarks being major challenges [ 130 , 131 ].

NK receptors, expressed by T cells and NK cells, play a key role in cancer immune surveillance [ 132 ]. GBM has elevated expression of natural killer (NK) cell receptors on NK and T cells [ 133 ]. However, the role of these receptors, particularly in regulating the intratumoral microenvironment, is not fully understood. Olaya et al. [ 34 ] integrated bulk and single­cell RNA sequencing GBM data sets and found that KLRC2, the gene encoding the activating receptor NKG2C, was significantly overexpressed in GBM cells, mainly at the edge of tumor invasion. Overexpression of NKG2C contributes to the formation of a microenvironment conducive to immune responses, reduces immunosuppressive properties, and enhances the anti-tumor effect of PD-1 monoclonal antibody therapy. The analysis showed that high levels of NKG2C and low levels of inhibitory receptors were associated with higher survival in response to immunotherapy, suggesting its potential as predictive biomarkers (Table 3 ).

Multiple myeloma

BCMAxCD3 bispecific T cell engagers (TCEs) has been approved for the treatment of multiple myeloma (MM) by both the Federal Drug Administration and European Medical Agencies [ 134 ]. TCEs achieve a better clinical response, however, drug resistance is also inevitable, and recent studies have shown that target antigen deletion is associated with acquired resistance to TCEs, but it is not yet clear whether there is a mechanism of primary and acquired resistance [ 135 ]. Furthermore, given that primary response failure occurs in some patients, it is critical to identify predictors of TCE response to guide therapeutic strategies. Using scRNA-seq and deep TCRB sequencing, Friedrich et al. [ 136 ] found that specific CD8 + T cells drove effective immune responses and induced TCE sensitization, whereas the abundance of exhausted-like CD8 + T cell clonal correlated with clinical response failure. Target epitopes and MHC class I loss are inherent adaptations of tumors to TCE, and thus individual differences in immunity can determine the clinical response to therapy.

BCMA CAR-T cell therapy has been approved for the treatment of MM with significant efficacy, and it is critical to identify early predictors of response to guide treatment strategies [ 137 , 138 ]. A study performed scRNA-seq on paired bone marrow samples from seven patients with multiple myeloma at baseline and after day 28 of CAR-T [ 139 ]. Analysis of paired pre- and post-treatment data revealed that patients in the complete remission group had higher CD8 + effector CAR-T cell abundance and cytotoxic activity, which correlated with long-term patient outcomes. The study also noted that treatment response did not correlate well with the baseline immune microenvironment and myeloma cell load. In a separate study utilizing scRNA-seq, 3 distinct molecular pathways of resistance were identified in MM: hypoxic tolerance, protein folding dysregulation, and mitochondrial respiratory abnormalities [ 140 ]. The key enzyme peptidyl-prolyl isomerase A (PPIA) was recognized as a pivotal regulator of protein folding stress, positioning it as a potential novel target for combating resistant MM.

Colorectal cancer

In colorectal cancer (CRC), PD-1 blockade are only effective in a subset of patients, and the cellular and molecular mechanisms of their action are not fully understood [ 141 ]. Therefore, a better understanding of the dynamic changes of cells and molecules in the TME is an important basis for further optimization of existing therapeutic strategies. Chen et al. [ 142 ] used scRNA-seq and scTCR-seq to present the spatiotemporal characteristics of cell dynamics in the TME of colorectal cancer patients during PD-1 blockade therapy. Higher baseline tumor-reactive-like CD8 + T (T tr -like) cells infiltration was found to be associated with a better response, suggesting that the abundance and functional status of blood-associated T tr -like cells are key hallmarks in predicting treatment response. The abundance of exhausted CD8 + T (T ex ) cell can also be used as a biomarker to predict response.

In a recent study employing scRNA-seq and ST, Zhou et al.[ 143 ] elucidated the pivotal role of THBS2 + CAFs in the mechanism of oxaliplatin resistance. The findings demonstrated that THBS2 is significantly associated with the activation of CAFs and the process of epithelial-mesenchymal transition (EMT). Moreover, THBS2 was shown to enhance resistance by secreting COL8A1, which interacts with the ITGB1 receptor on resistant cancer cells, thereby activating the PI3K-AKT signaling pathway and promoting EMT. This discovery underscores the potential of THBS2 + CAFs as promising biomarkers and therapeutic targets, offering novel insights into the challenges of overcoming chemoresistance in colorectal cancer. Notably, elevated levels of COL8A1 correlate with resistance and poor patient prognosis.

Hepatocellular carcinoma

For advanced hepatocellular carcinoma (HCC), atezolizumab with bevacizumab has been recognized as a first-line therapeutic option, nonetheless, the medication is ineffective for most patients [ 144 ]. Wang et al. [ 24 ] used CODEX technology to map the single-cell spatial protein patterns of 401 HCC samples in order to quantify the spatial heterogeneity of HCC. The spatial co-localization of vimentin high macrophages and Treg cells, which promotes the development of the immunosuppressive microenvironment, is crucial for the progression of HCC, and aids in immunotherapy, was discovered by this study by combining multi-omics and in vitro experiments.

A recent study employed ST in combination with scRNA-seq and bulk RNA-seq to systematically delineate CAFs and fetal liver fibroblast atlases in HCC [ 145 ]. This study identified, a pivotal cell population, POSTN + CAF, that participates in the onco-fetal reprogramming process in HCC for the first time. Furthermore, the study revealed the spatial co-localization of three distinct cell populations—POSTN + CAFs, FOLR2 + TAMs, and PLVAP + endothelial cells (ECs) —demonstrating their critical roles in this reprogramming. These findings underscore the significant impact of onco-fetal reprogramming on postoperative recurrence and immune therapy resistance in HCC patients, offering crucial molecular targets for precision diagnosis and the development of novel immunotherapeutic strategies.

In a study investigating the core mechanisms of resistance to anti-CTLA4 and anti-PD-L1 in HCC, Myojin et al. [ 146 ] discovered that the spatial interactions between Treg cells and CD8 + T cells directly drive the formation of an immunosuppressive microenvironment. In the tumor tissues of patients exhibiting resistance, Treg cells were found to express high levels of immunosuppressive molecules and established a close spatial co-localization with CD8 + T cells through physical proximity. This interaction led to functional suppression of CD8 + T cells, as evidenced by the upregulation of exhaustion markers PDCD1 and LAG3, thereby further reinforcing immune evasion. Spatial proteomics analysis revealed that the co-localization structures of Treg cells and CD8 + T cells were predominantly enriched in immune-dense regions. Moreover, experiments using animal models confirmed a significant reduction in the relative distance between these cell types during the progression of resistance. Additionally, clinical correlation studies demonstrated a direct association between the spatial aggregation of Treg cells and both lack of therapeutic response and poor prognosis. Therefore, targeting the spatial interaction network between Treg cells and CD8 + T cells presents a promising strategy for overcoming resistance in HCC.

Prostate cancer

Castration therapy (ADT) is a common treatment for prostate cancer, but the efficacy of androgen receptor (AR) inhibitors after cancer recurrence varies from person to person [ 147 ]. Researchers from Harvard Medical School obtained circulating cancer cells (CTCs) in blood samples of prostate cancer for scRNA-seq using microfluidic enrichment technology to look for patient-to-patient heterogeneity and differences from early-stage tumor samples [ 148 ]. Pathway Interaction Database (PID) was used to confirm 21 pathways up-regulated in prostate CTC, and the drug resistance mechanism was related to noncanonical Wnt signaling, which is expected to improve the efficacy of clinical treatment.

Head and neck squamous cell carcinoma

In recurrent or metastatic head and neck squamous cell carcinoma (HNSCC), anti-PD-1 combined with anti-CTLA4 is superior to PD-1 monotherapy [ 149 ]. In order to reveal the immune response mechanism of combination therapy, single-cell multiomics and spatial multiomics analysis of tumor biopsy samples before and after treatment were studied [ 65 ]. Combination therapy was revealed to improve anti-tumor immune response by enhancing the activation and expansion of CD4 + T cells. Recruitment and activation of CD4 + T cells in tumor-draining lymph nodes (tdLNs) is a hallmark of early response to combination anti-PD-L1 and anti-CTLA4 therapy in HNSCC.

Pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor with a high degree of malignancy and high mortality, and has a very poor prognosis [ 150 ]. Since only a small percentage of PDAC patients respond to cancer immunotherapy, a better understanding of the TME is needed [ 151 ]. Dominguez et al. [ 152 ] identified a novel cancer-associated fibroblast (CAF) marker, LCCR15, by single-cell sequencing of fibroblast transcriptome changes during PDAC development in animal models. The formation of LRRC15 + CAFs is dependent on the TGFβ receptor signaling pathway, and this cell population is involved in tumor progression. Myofibroblasts with high expression of LRRC15 exhibited highly activation of the TGFβ receptor signaling pathway and poor response to PD-L1 blockers. Elimination of LRRC15 + CAFs can impair tumor growth.

Acute myeloid leukemia

The researchers administered azacitidine in combination with nivolumab, a PD-1 checkpoint inhibitor, in patients with relapsed/refractory (R/R) acute myeloid leukemia (AML) [ 153 , 154 ]. Patients treated with azacitidine and nivolumab usually have a short-term effect, but long-term treatment is rare [ 155 , 156 ]. Single-cell functional proteomics was used to analyze CD8 + and CD4 + T cells in bone marrow before and after anti-PD-1 treatment [ 157 ]. A significant increase in CD4 + T cell PSI was found in responding patients, and IFN-γ and TNF-α were the main drivers of increased CD4 + T cell versatility. The study used IsoPlexis to reveal that CD4 + T cells PSI could serve as a potential biomarker that could predict a patient's response to combination therapy.

Large B-cell lymphomas

CD19-directed CAR-T cell therapy is changing the treatment landscape for large B-cell lymphomas (LBCLs) [ 158 ]. However, long-term remission rates are not high, and therapies are limited to some extent by their risk of toxicity [ 159 ]. To unravel the differences in response between patients, the researchers performed single-cell RNA sequencing and targeted gene sequencing of autologous anti-CD19 CAR-T cell infusions in 24 LBCL patients and compared them with the results of the treatment response at follow-up [ 160 ]. The study found that long-term treatment outcomes can be predicted by analyzing cell-free DNA (cfDNA) sequencing in plasma after treatment to determine early molecular response (EMR). Significant enrichment of exhausted CD8 + and CD4 + T cells in infusion products in partial response (PR) patients was found, as well as significant enrichment of memory CD8 + T cells in patients achieving complete response (CR). There is an association between T cell exhaustion traits ( LAG3 and TIM3 ) and low EMR.

Small cell lung cancer

The limited clinical benefit of SCLC patients from ICIs is mainly attributed to the hypoinflammatory state of the tumor immune microenvironment (TIME) of SCLC and the extremely low level of PD-L1 expression, resulting in a limited anti-tumor immune response [ 161 ]. In addition, there is significant heterogeneity within the TIME of SCLC, which may further affect the patient's response to immunotherapy. The aim of this study was to systematically analyze the spatial heterogeneity of TIME in SCLC by scRNA-seq and DSP [ 162 ]. Tumor samples were collected from 44 untreated patients with limited-stage SCLC, and through scRNA-seq and DSP, it was found that SCLC patients with high RE1 silencing factor (REST) expression had stronger immune response potential. REST promises to be a potential target for SCLC immunotherapy to enhance immune response and prolong patient survival.

A recent study employed CODEX and multi-omics methodologies to conduct a comprehensive analysis of tumor tissues from 165 SCLC patients [ 163 ]. The analysis uncovered that approximately 10% of tumor cells exhibited multi-positive tumor cells (MPTCs), wherein the cells co-expressed two or more transcription factors, a characteristic strongly correlated with adverse prognosis. Additionally, the researchers developed the ColonyMap algorithm to assess cellular colonies within the tumor microenvironment, successfully identifying the MT 2 immune microenvironment. This microenvironment was notably enriched with anti-tumor macrophages, CD8 + T cells, and natural killer T cells. Patients with a more extensive MT 2 microenvironment demonstrated significantly improved long-term survival rates, and the proportion of M1-like macrophages within this microenvironment was found to correlate with clinical benefits from immune therapy.

Stomach adenocarcinoma

In order to investigate the complex metabolic reprogramming and metabolic interactions within the TME of highly heterogeneous stomach adenocarcinoma (STAD) tissues, Sun et al. [ 77 ] integrated spatial metabolomics, lipidomics, and transcriptomics. The study found that cancer cells have notable metabolic and transcriptional abnormalities in pathways related to phospholipid production and metabolism, fatty acid biosynthesis, and arginine and proline metabolism. It was shown that when cancer progresses, the metabolism of oxidative phosphorylation in cancer cells gradually increases, and downstream metabolites show constant alterations. Interestingly, a narrow"tumor boundary region"that is rich in immune cells was found; in this area, the immune cells go through metabolic reprogramming. In contrast to immune cells in normal tissues, those within the tumor boundary exhibited significant upregulation of glutamine metabolism and expression of polyunsaturated fatty acids. This finding suggests a potential correlation between tumor immune evasion and alterations in glutamine metabolism as well as polyunsaturated fatty acid metabolism. Cell-specific metabolic reprogramming plays crucial roles in the pathogenesis and progression of STAD, as well as in the mechanisms of immune escape, presenting promising avenues for the identification of precise therapeutic targets in oncology.

Another study utilized spatial metabolomics to reveal distinct tumor and stromal metabolic subtypes, elucidating the relationship between metabolic reprogramming, molecular characteristics, and clinical prognosis [ 164 ]. A novel tumor and stroma-specific classification model was developed, identifying three tumor-specific subtypes: T1 (HER2 + MIB1 + CD3 + ), T2 (HER2 − MIB1 − CD3 − ), and T3 (pEGFR + ), each exhibiting significant differences in metabolic pathways. T1 was enriched in carbohydrate, amino acid, and nucleotide metabolism, with nucleotide metabolism, as well as ascorbic acid and aldose metabolism, uniquely upregulated in this subtype. In contrast, T2 was characterized by carbohydrate and amino acid metabolism, while T3 was associated with biotin metabolism and cytosolic DNA sensing pathways. Notably, in an independent cohort, T1 demonstrated higher sensitivity to trastuzumab treatment, with patients showing significantly better prognoses compared to those with T2. These findings suggest that metabolic characteristics may serve as potential biomarkers for predicting responses to targeted therapies, providing new metabolic insights for the precise classification and individualized treatment strategies.

Melanoma, head and neck squamous cell carcinoma, and non-small cell lung cancer

The immune microenvironment of tumors has an important impact on the efficiency of ICIs treatment. Analysis of single-cell transcriptome sequencing datasets for melanoma, HNSCC, and NSCLC revealed that macrophages are the main cell types producing CXCL9, CXCL10, and CXCL11 [ 165 ]. CXCL9 and CXCL10 are produced by antigen-presenting cells as well as by cancer cells, and serve as important components of the TME for T cell infiltration [ 166 ]. These chemokines not only activate chemokine networks, but also promote the formation of a “hot” TME, which contributes to the recruitment of immune cells to the tumor. High expression of CXCL9 and CXCL10 is associated with an increase in the number of intratumoral CD8 + T cells and response to immunotherapy in patients treated with ICIs. In addition, CXCL9 and CXCL10 co-localize with CD8 + T cells expressing checkpoint molecules (LAG-3 and PD-1) in melanoma, suggesting that these T cells become an ideal target for ICIs therapies to intervene and enhance anti-tumor immune responses [ 167 ].

Basal cell carcinoma and squamous cell carcinoma

Yost et al. [ 168 ] performed scRNA-seq and scTCR-seq on 79,046 locus-matched cancer cells from patients with basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) before and after anti-PD-1 therapy. The single-cell profile and TME characteristics of basal cell carcinoma and squamous cell carcinoma were constructed, and the molecular characteristics and clonal replacement analysis of tumor-infiltrating T cells (TILs) after PD-1 treatment were constructed. Exhausted CD8 + T cells are enriched after anti-PD-1 therapy and express tumor-specific markers, including CD39 and CD103. T cell response to ICI relies on the recruitment of new T cells rather than the reactivation of pre-existing TILs.

Multiple cancer

PD-1/PD-L1 immunotherapy has been shown to be effective in all solid tumors, including lung cancer, gastrointestinal tumors, breast cancer, urinary tumors, skin cancer, lymphoma, and has substantially improved the survival of patients with advanced tumors, but the mechanism involved is still unknown [ 169 ]. The study [ 170 ] sequenced 141,263 cells from 14 patients with 4 different types of cancer using scRNA-seq and scTCR-seq and obtained transcriptional profiles and immune repertoire of various T cells and T cell receptors in tumors, normal adjacent tissues, and peripheral blood. Through the combined analysis, it was found that clonal expansion of effector-like T cells was observed in both tumors and normal adjacent tissues, and could be detected in peripheral blood. Patients with this clonotype-dilated gene profile respond best to anti-PD-L1 therapy.

Liu et al. [ 171 ] performed a meta-analysis of scRNA-seq datasets for five human solid tumor types. The results suggest that tumor-specific CXCL13 + CD8 + T cells play a key role in the course of treatment, and that the degree of infiltration prior to treatment predicts the efficacy of ICIs. In addition, simultaneous detection of the proportion of CXCL13 + CD4 + T and CXCL13 + CD8 + T cell populations in tumors was found to more accurately predict the efficacy of ICIs, with a prediction accuracy of > 90% in multiple cancer types. To investigate the specific mechanism of CXCR4 inhibitors in enhancing the effects of cancer immunotherapy, Cao et al. [ 172 ] analyzed the expression pattern of CXCR4 in normal and tumor tissues using a single-cell transcriptome and found that CXCR4 was highly expressed in CD8 + PD-1 + T ex cells. In the mouse model, inhibition of CXCR4 was found to attenuate the CD8 + T ex phenotype and enhance the anti-tumor immune effect by modulating the JAK-STAT signaling pathway. The mechanism by which CXCR4 coordinates CD8 + T cell exhaustion is also explained at the epigenetic level by scRNA-seq, scTCR-seq and scATAC-seq. By analyzing clinical samples, CXCR4 + CD8 + T cells were found to be enriched in patients with poor therapeutic response. The combination of CXCR4 antagonists and ICIs may hold great promise for cancer treatment.

Concluding remarks and future perspectives

Single-cell and spatial sequencing technology provides detailed insights into cells at the transcriptome, genome, epigenome, proteome, and metabolome levels, offering new perspectives on tumor heterogeneity, the TME, and drug responses [ 21 , 57 , 173 ] (Fig. 3 ). Compared to traditional omics, single-cell and spatial omics are associated with higher costs and more demanding requirements for sample collection and processing [ 174 ]. The necessity for specialized equipment and reagents limits the application of these techniques in various laboratories, thereby constraining their utilization in clinical research. Technical disparities among different methodologies can lead to inconsistencies in gene coverage and sensitivity, particularly for low-abundance genes, which may hinder a comprehensive understanding of biological processes [ 175 ]. Spatial multi-omics currently lacks the capability to simultaneously detect multiple omics modalities within the same tissue section, and instead relies on integrative analyses of adjacent Sects. [ 174 ]. Additionally, limitations in spatial resolution obstruct precise localization of cellular structures; despite ongoing technological advancements, subtle intercellular structures and interactions may remain inadequately resolved, especially in samples with thicker tissue sections or higher cell densities. This can result in data ambiguity and challenges in interpretation. The vast amount of multidimensional data presents a significant obstacle for researchers striving to process and analyze such data effectively to extract meaningful biological insights [ 176 , 177 ]. Addressing these challenges is therefore critical to unlock the full potential and ensure the reliable application of single-cell and spatial omics in clinical research.

Indeed, despite these hurdles, the application of single-cell and spatial omics in predicting tumor drug responses holds significant promise. First, the integration of multi-omics data is poised to revolutionize this field. By amalgamating transcriptomic data with genomic, epigenomic, and metabolomic information, researchers can achieve a more profound understanding of the complex biology underlying tumors and their responses to therapeutic agents. Spatial sequencing technology further enriches our understanding by incorporating the spatial context of single-cell omics, thereby facilitating the identification of novel hallmarks [ 178 ]. Second, advancements in machine learning and artificial intelligence provide robust tools for data analysis [ 179 ]. By leveraging sophisticated algorithms, researchers can distill valuable insights from intricate single-cell datasets, pinpoint potential hallmarks, and forecast patient responses to medications [ 180 ]. This data-driven approach is poised to propel advancements in precision medicine and pave the way for personalized treatment strategies.

As single-cell and spatial omics data become increasingly integral to advancing precision medicine and personalized treatment, the widespread sharing of these rich datasets for research necessitates careful consideration of privacy protection [ 181 ]. The public availability of these datasets has raised growing concerns regarding privacy protection, especially the potential disclosure of sensitive phenotypic information related to participants. To address this issue, it is essential to enhance our understanding of the privacy risks associated with molecular data to guide responsible data-sharing practices. Furthermore, raising awareness among researchers, institutions, and funding agencies about the potential risks of privacy breaches is crucial. Advocacy for relevant policies and regulations is needed to safeguard individual privacy in genomic data sharing, particularly as technological advancements continue to expand the scope of genomic data collection. Future research should focus on developing effective strategies to obscure genotype information in single-cell datasets to ensure the protection of individual privacy, thereby fostering a balance between scientific inquiry and ethical responsibility.

We have discovered therapy-related hallmarks using single-cell and spatial omics, identifying pertinent genes and altering activities and cell–cell interactions to tilt the scales in favor of therapy. However, a number of crucial steps must be taken in order to translate fundamental research into clinical use. First, in-depth investigations into the mechanisms by which these biomarkers operate within the TME are required to develop therapies. Second, the effectiveness and usefulness of the biomarkers must be confirmed by extensive prospective clinical trials and retrospective research. Third, in order to promote repeatability in various contexts, it is crucial to guarantee the standardization of these methods and to provide clinical application guidelines [ 182 ]. At the same time, cooperation with regulatory bodies is required to comprehend and follow pertinent rules in order to hasten biomarker acceptance. Lastly, personalized treatment strategies should be considered, alongside dynamic monitoring of biomarker changes in patients to optimize therapeutic outcomes, and enhancing patient engagement in clinical trials through education is vital for the successful implementation of these personalized and dynamically monitored approaches. By following these steps, we can effectively advance the development of precision medicine in oncology.

In conclusion, by addressing the challenges and harnessing the potential through the translational steps, single-cell and spatial omics are paving new avenues for cancer therapy. It provides a novel tool for discovering cellular hallmarks for cancer therapeutics, designing individualized treatment regimens, and optimizing therapy strategies. With the development of single-cell multi-omics combined with spatial analysis, researchers are gaining deeper insights into spatial organization, which is critical for unraveling complex interaction networks in precise cancer treatment.

Acknowledgements

This work was supported by the National Key R&D Program of China (2023YFC2705802 to J.W. and 2022YFC2704205 to Q.G.), the National Natural Science Foundation of China (82203453 to C.C.), the Natural Science Foundation of Hubei Province (2025AFB580 to C.C.), and the Foundation of Tongji Hospital, China (2023CXZH014 and 24-2KYC13057-08 to C.C.).

Authors’ contributions

J.W., C.C., Q. G., and X.C. conceived the whole article and completed the original draft preparation. J.W. and C.C. performed major reviews and edited the structure of the article. The primary drawing effort was done by T.P., Y.Y., and J.L. During subsequent revisions of the article, T. C. were primarily responsible for refining the manuscript's structure and enhancing the clarity of the content. All authors reviewed the manuscript.

Funding

This work was supported by the National Key R&D Program of China (2023YFC2705802 to J.W.), the National Natural Science Foundation of China (82203453 to C.C.), the Natural Science Foundation of Hubei Province (2025AFB580 to C. C.), and the Foundation of Tongji Hospital, China (2023CXZH014 and 24-2KYC13057-08 to C.C.).

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

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Contributor Information

Qinglei Gao, Email: qingleigao@hotmail.com.

Canhui Cao, Email: canhuicao@foxmail.com.

Juncheng Wei, Email: wjcwjc999@126.com.

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Associated Data

Data Availability Statement

No datasets were generated or analysed during the current study.