T cells exhibit heterogeneous functional states, which correlate with responsiveness to immune checkpoint blockade and prognosis of tumor patients. However, the molecular regulatory mechanisms underlying the dynamic process of T cell state transition remain largely unknown. Based on single-cell transcriptome data of T cells in non-small cell lung cancer, we combined cell states and pseudo-times to propose a pipeline to construct dynamic regulatory networks for dissecting the process of T cell dysfunction. Candidate regulators at different stages were revealed in the process of tumor-infiltrating T cell dysfunction. Through comparing dynamic networks across the T cell state transition, we revealed frequent regulatory interaction rewiring and further refined critical regulators mediating each state transition. Several known regulators were identified, including TCF7, EOMES, ID2, and TOX. Notably, one of the critical regulators, TSC22D3, was frequently identified in the state transitions from the intermediate state to the pre-dysfunction and dysfunction state, exerting diverse roles in each state transition by regulatory interaction rewiring. Moreover, higher expression of TSC22D3 was associated with the clinical outcome of tumor patients. Our study embedded transcription factors (TFs) within the temporal dynamic networks, providing a comprehensive view of dynamic regulatory mechanisms controlling the process of T cell state transition.

Background: Immune checkpoint blockade (ICB) therapy has yielded successful clinical responses in treatment of a minority of patients in certain cancer types. Substantial efforts were made to establish biomarkers for predicting responsiveness to ICB. However, the systematic assessment of these ICB response biomarkers remains insufficient. Methods: We collected 22 transcriptome-based biomarkers for ICB response and constructed multiple benchmark datasets to evaluate the associations with clinical response, predictive performance, and clinical efficacy of them in pre-treatment patients with distinct ICB agents in diverse cancers. Results: Overall, “Immune-checkpoint molecule” biomarkers PD-L1, PD-L2, CTLA-4 and IMPRES and the “Effector molecule” biomarker CYT showed significant associations with ICB response and clinical outcomes. These immune-checkpoint biomarkers and another immune effector IFN-gamma presented predictive ability in melanoma, urothelial cancer (UC) and clear cell renal-cell cancer (ccRCC). In non-small cell lung cancer (NSCLC), only PD-L2 and CTLA-4 showed preferable correlation with clinical response. Under different ICB therapies, the top-performing biomarkers were usually mutually exclusive in patients with anti-PD-1 and anti-CTLA-4 therapy, and most of biomarkers presented outstanding predictive power in patients with combined anti-PD-1 and anti-CTLA-4 therapy. Conclusions: Our results show these biomarkers had different performance in predicting ICB response across distinct ICB agents in diverse cancers.

High heterogeneity and complex interactions of malignant cells in breast cancer has been recognized as a driver of cancer progression and therapeutic failure. However, complete understanding of common cancer cell states and their underlying driver factors remain scarce and challenging. Here, we revealed seven consensus cancer cell states recurring cross patients by integrative analysis of single-cell RNA sequencing data of breast cancer. The distinct biological functions, the subtype-specific distribution, the potential cells of origin and the interrelation of consensus cancer cell states were systematically elucidated and validated in multiple independent datasets. We further uncovered the internal regulons and external cell components in tumor microenvironments, which contribute to the consensus cancer cell states. Using the state-specific signature, we also inferred the abundance of cells with each consensus cancer cell state by deconvolution of large breast cancer RNA-seq cohorts, revealing the association of immune-related state with better survival. Our study provides new insights for the cancer cell state composition and potential therapeutic strategies of breast cancer.

Single-cell RNA sequencing presents the sophisticated delineation of cell transcriptomes in many cancer types and highlights the tumor heterogeneity at higher resolution, which provides a new chance to explore the molecular mechanism in a minority of cells. In this study, we utilized publicly available single-cell RNA-seq data to discover and comprehensively dissect rare genes existing in few glioblastoma (GBM) cells. Moreover, we designed a framework to systematically identify 51 rare protein-coding genes (PCGs) and 47 rare long non-coding RNAs (lncRNAs) in GBM. Patients with high expression levels of rare genes like CYB5R2 and TPPP3 had worse overall survival and disease-free survival, implying their potential implication in GBM progression and prognosis. We found that these rare genes tended to be specifically expressed in GBM cancer stem cells, which emphasized their ability to characterize stem-like cancer cells and implied their contribution to GBM growth. Furthermore, rare genes were enriched in a 17-cell subset, which was located in an individual branch of the pseudotime trajectory of cancer progression and exhibited high cell cycle activity and invasive potential. Our study captures the rare genes highly expressed in few cells, deepens our understanding of special states during GBM tumorigenesis and progression such as cancer stemness and invasion, and proposes potential targets for cancer therapy.

Glioblastoma stem cells (GSCs) contributed to the progression, treatment resistance, and relapse of glioblastoma (GBM). However, current researches on GSCs were performed usually outside the human tumor microenvironment, ignoring the importance of the cellular states of primary GSCs. In this study, we leveraged single-cell transcriptome sequencing data of 6 independent GBM cohorts from public databases, and combined lineage and stemness features to identify primary GSCs. We dissected the cell states of GSCs and correlated them with the clinical outcomes of patients. As a result, we constructed a cellular hierarchy where GSCs resided at the center. In addition, we identified and characterized 2 different and recurrent GSCs subpopulations: proliferative GSCs (pGSCs) and quiescent GSCs (qGSCs). The pGSCs showed high cell cycle activity, indicating rapid cell division, while qGSCs showed a quiescent state. Then we traced the processes of tumor development by pseudo-time analysis and tumor phylogeny, and found that GSCs accumulated throughout the whole tumor development period. During the process, pGSCs mainly contributed to the early stage and qGSCs were enriched in the later stage. Finally, we constructed an 8-gene prognostic signature reflecting pGSCs activity and found that patients whose tumors were enriched for the pGSC signature had poor clinical outcomes. Our study highlights the primary GSCs heterogeneity and its correlation to tumor development and clinical outcomes, providing the potential targets for GBM treatment.

The remarkable success of cancer immunotherapy has revolutionized cancer treatment, emphasizing the importance of tumor–immune interactions in cancer evolution and treatment. Cancer immunoediting describes the dual effect of tumor–immune interactions: inhibiting tumor growth by destroying tumor cells and facilitating tumor escape by shaping tumor immunogenicity. To better understand tumor–immune interactions, it is critical to develop computational methods to measure the extent of cancer immunoediting. In this review, we provide a comprehensive overview of the computational methods for quantifying cancer immunoediting. We focus on describing the basic ideas, computational processes, advantages, limitations, and influential factors. We also summarize recent advances in quantifying cancer immunoediting studies and highlight future research directions. As the methods for quantifying cancer immunoediting are continuously improved, future research will further help define the role of immunity in tumorigenesis and hopefully provide a basis for the design of new personalized cancer immunotherapy strategies.