Single-cell multi-omics in cancer immunotherapy: from tumor heterogeneity to personalized precision treatment

Abstract

Cancer immunotherapy has revolutionized clinical oncology; however, the inherent complexity and heterogeneity of cancer present substantial challenges to achieving broad therapeutic efficacy. Tumor heterogeneity manifests not only among different patients but also within individual tumors, further complicating personalized treatment approaches. Single-cell sequencing technologies encompassing genomics, transcriptomics, epigenomics, proteomics, and spatial omics have significantly enhanced our ability to dissect tumor heterogeneity at single-cell resolution with multi-layered depth. These approaches have illuminated tumor biology, immune escape mechanisms, treatment resistance, and patient-specific immune response mechanisms, thereby substantially advancing precision oncology strategies. This review systematically examines recent advances in single-cell multi-omics technologies across various cancer research areas, emphasizing their transformative impacts on understanding tumor heterogeneity, immunotherapy, minimal residual disease monitoring, and neoantigen discovery. Additionally, we discuss current technical and analytical limitations and unresolved questions associated with single-cell technologies. We anticipate single-cell multi-omics technologies will become central to precision oncology, facilitating truly personalized therapeutic interventions.

Keywords: Immunotherapy; Personalized therapy; Single-cell sequencing; Tumor heterogeneity.

Fig. 1

Representative approaches for single-cell isolation from tissue samples. Several techniques are used to isolate single cells for downstream omics analyses, each suited to different sample types and experimental needs. a Laser capture microdissection (LCM): tissue samples are fixed, embedded, sectioned, and stained. Selected cells are visualized and precisely excised using a laser, followed by nucleic acid or protein extraction. b Fluorescence-activated cell sorting (FACS): tissues are first enzymatically and mechanically dissociated into single-cell suspensions. Cells are then stained for surface or intracellular markers and sorted by fluorescence intensity to isolate specific populations. c Magnetic-activated cell sorting (MACS): dissociated cells are labeled with antibody-conjugated magnetic beads, and target cells are separated using a magnetic field. d Microfluidic technologies: single cells are suspended in solution and encapsulated into nanoliter droplets along with barcoded beads and lysis reagents. This high-throughput system enables parallel capture and molecular barcoding of individual cells for downstream analysis

Fig. 2

Single-cell technologies for dissecting tumor heterogeneity across the tumor microenvironment. Single-cell technologies enable high-resolution characterization of the complex cellular composition and dynamic states within the tumor microenvironment (TME), including malignant cells, immune subsets (T cells, B cells, NK cells, dendritic cells, MDSCs), and stromal components (e.g., CAFs). A range of single-cell modalities (genomics, transcriptomics, epigenomics, proteomics, TCR/BCR sequencing, spatial technologies, and multi-omics integration) have been applied to investigate key processes underlying tumor progression, including cancer initiation — identifying early molecular alterations during malignant transformation, metastasis — tracking cell states and routes of dissemination, intercellular interactions — mapping ligand–receptor networks and signaling cross-talk between cells, and clonal evolution — reconstructing lineage trajectories and mutational dynamics

Fig. 3

Applications of high-dimensional single-cell multi-omics analysis in personalized immunotherapy. a Samples from responders and non-responders, or collected before and after immunotherapy, are profiled using single-cell multi-omics approaches, including genomics, epigenomics, transcriptomics, proteomics and spatial omics. Integrated single-cell and bioinformatics analyses reveal cellular and molecular features associated with therapy response. Findings are experimentally validated in vitro and in vivo to identify therapeutic targets and predictive biomarkers, and to enable immunotherapy toxicity monitoring. b Following tumor resection or chemoradiation, liquid biopsy is performed to obtain blood samples from the patient. These samples undergo single-cell analysis to detect residual tumor-specific molecular signatures and inform downstream treatment strategies. Circulating tumor DNA (ctDNA) analysis is used for ongoing minimal residual disease (MRD) surveillance. If ctDNA is undetectable, patients continue with ctDNA monitoring. In cases where ctDNA is detected, patients may undergo immunotherapy guided by single-cell data. Based on the molecular profile, patients can either continue immunotherapy or receive newly tailored targeted therapies. This dynamic monitoring system allows adaptive treatment modification to improve outcomes. c Tumor and matched normal tissues are subjected to single-cell analysis to resolve cellular heterogeneity and detect somatic mutations. Identified mutations are computationally screened to predict candidate neoantigens and prioritize targets. Selected neoantigens inform the design and production of personalized vaccines, which are administered to elicit a tumor-specific immune response