Clonal tumor mutations in homologous recombination genes predict favorable clinical outcome in ovarian cancer treated with platinum-based chemotherapy.

OBJECTIVE: Platinum-based chemotherapy remains the first-line treatment for ovarian carcinoma by inducing DNA damage. The therapeutic impact of clonal and subclonal somatic mutations in DNA damage repair (DDR) pathways remains unexplored. METHODS: We performed an integrated analysis to infer the clonality of somatic deleterious mutations in 385 ovarian carcinomas treated with platinum-based chemotherapy. The Kaplan-Meier method was performed for visualization and the differences between survival curves were calculated by log-rank test. Proportional hazards models were used to estimate relative hazards for platinum-free interval (PFI), progression-free survival (PFS) and overall survival (OS). RESULTS: We found that somatic deleterious mutations in DDR pathways exhibited widespread clonal heterogeneity, and that patients with DDR clonal mutations exhibited a "hypermutator phenotype". Clonal somatic mutations in homologous recombination repair (HRR) pathway were significantly associated with better OS (HR = 0.19 (95% CI, 0.06-0.59), P = 0.0044) and PFS (HR = 0.20 (95% CI, 0.08-0.49), P = 0.0005) than HRR wild-type, while HRR subclonal mutations were not associated with prognosis. Moreover, HRR clonal mutations were associated with significantly higher chemotherapy sensitive rate (P = 0.0027) and longer PFI (HR = 0.20 (95% CI, 0.08-0.49), P = 0.0005) than HRR wild-type, while HRR subclonal mutations were not. We validated our findings using an independent cohort of 93 ovarian cancer patients that received platinum-based chemotherapy. CONCLUSIONS: HRR clonal mutations, but not subclonal mutations, were associated with improved survival, chemotherapy response, and genome instability compared with HRR wild-type.

Computational Quantification of Cancer Immunoediting.

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.

Annotation of cell types (ACT): a convenient web server for cell type annotation.

BACKGROUND: The advancement of single-cell sequencing has progressed our ability to solve biological questions. Cell type annotation is of vital importance to this process, allowing for the analysis and interpretation of enormous single-cell datasets. At present, however, manual cell annotation which is the predominant approach remains limited by both speed and the requirement of expert knowledge. METHODS: To address these challenges, we constructed a hierarchically organized marker map through manually curating over 26,000 cell marker entries from about 7000 publications. We then developed WISE, a weighted and integrated gene set enrichment method, to integrate the prevalence of canonical markers and ordered differentially expressed genes of specific cell types in the marker map. Benchmarking analysis suggested that our method outperformed state-of-the-art methods. RESULTS: By integrating the marker map and WISE, we developed a user-friendly and convenient web server, ACT ( http://xteam.xbio.top/ACT/ or http://biocc.hrbmu.edu.cn/ACT/ ), which only takes a simple list of upregulated genes as input and provides interactive hierarchy maps, together with well-designed charts and statistical information, to accelerate the assignment of cell identities and made the results comparable to expert manual annotation. Besides, a pan-tissue marker map was constructed to assist in cell assignments in less-studied tissues. Applying ACT to three case studies showed that all cell clusters were quickly and accurately annotated, and multi-level and more refined cell types were identified. CONCLUSIONS: We developed a knowledge-based resource and a corresponding method, together with an intuitive graphical web interface, for cell type annotation. We believe that ACT, emerging as a powerful tool for cell type annotation, would be widely used in single-cell research and considerably accelerate the process of cell type identification.

Decoding the associations between cell functional states in head and neck cancer based on single-cell transcriptome.

OBJECTIVES: Systematically identifying cancer cell functional states, especially their associations, is key to understanding the pathogenesis of cancers. MATERIALS AND METHODS: Here, we systematically identified six cancer-related states, including epithelial-mesenchymal transition (EMT), immune response, epithelial differentiation, stress, G1/S and G2/M phases, in head and neck squamous cell carcinoma (HNSCC) based on single-cell RNA-sequencing (scRNA-seq). RESULTS AND CONCLUSION: We defined the association patterns between these functional states and found the patterns were correlated with the state activity. Particularly, immune response and EMT were negatively, positively, or non-significantly correlated in samples with the highest immune response activity, the lowest activity of the two states, or with the highest EMT activity, respectively. Combining scRNA-seq data of immune cells and four independent HNSCC cohorts, we found the negative relationship between EMT and immune response was correlated with an activated immune microenvironment and a longer survival, while the non-significant relationship was correlated with an immunosuppressed microenvironment and a poor prognosis. Collectively, our results provide insight into the association patterns between functional states in HNSCC, and may facilitate the elucidation of the interactions between cancer cells and immune system during cancer progression.

Single-Cell Transcriptomic Analysis Reveals a Tumor-Reactive T Cell Signature Associated With Clinical Outcome and Immunotherapy Response In Melanoma.

The infiltration of tumor-reactive T cells in the tumor site is associated with better survival and immunotherapy response. However, tumor-reactive T cells were often represented by the infiltration of total CD8+ T cells, which was confounded by the presence of bystander T cells. To identify tumor-reactive T cells at the cancer lesion, we performed integration analyses of three scRNA-seq data sets of T cells in melanoma. Extensive heterogeneous functional states of T cells were revealed in the tumor microenvironment. Among these states, we identified a subset of tumor-reactive T cells which specifically expressed tumor-reactive markers and T cell activation signature, and were strongly enriched for larger T cell receptor (TCR) clones. We further identified and validated a tumor-reactive T cell signature (TRS) to evaluate the tumor reactivity of T cells in tumor patients. Patients with high TRS scores have strong immune activity and high mutation burden in the TCGA-SKCM cohort. We also demonstrated a significant association of the TRS with the clinical outcomes of melanoma patients, with higher TRS scores representing better survival, which was validated in four external independent cohorts. Furthermore, the TRS scores exhibited greater performance on prognosis prediction than infiltration levels of CD8+ T cells and previously published prognosis-related signatures. Finally, we observed the capability of TRS to predict immunotherapy response in melanoma. Together, based on integrated analysis of single-cell RNA-sequencing, we developed and validated a tumor-reactive-related signature that demonstrated significant association with clinical outcomes and response to immunotherapy.

Systematic Assessment of Transcriptomic Biomarkers for Immune Checkpoint Blockade Response in Cancer Immunotherapy.

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.

Identification of Dysregulated Competitive Endogenous RNA Networks Driven by Copy Number Variations in Malignant Gliomas.

Gliomas represent 80% of malignant brain tumors. Because of the high heterogeneity, the oncogenic mechanisms in gliomas are still unclear. In this study, we developed a new approach to identify dysregulated competitive endogenous RNA (ceRNA) interactions driven by copy number variation (CNV) in both lower-grade glioma (LGG) and glioblastoma multiforme (GBM). By analyzing genome and transcriptome data from The Cancer Genome Atlas (TCGA), we first found out the protein coding genes and long non-coding RNAs (lncRNAs) significantly affected by CNVs and further determined CNV-driven dysregulated ceRNA interactions by a customized pipeline. We obtained 13,776 CNV-driven dysregulated ceRNA pairs (including 3,954 mRNAs and 306 lncRNAs) in LGG and 262 pairs (including 221 mRNAs and 11 lncRNAs) in GBM, respectively. Our results showed that most of the ceRNA interactions were weakened by CNVs in both LGG and GBM, and many CNV-driven genes shared the same ceRNAs in the dysregulated ceRNA networks. Functional analysis indicated that the CNV-driven ceRNA network involved in some important mechanisms of tumorigenesis, such as cell cycle, p53 signaling pathway and TGF-beta signaling pathway. Further investigation of the ceRNA pairs in the communities from the dysregulated ceRNA network revealed more detailed biological functions related to the oncogenesis of malignant gliomas. Moreover, by exploring the association of CNV-driven ceRNAs with prognosis and histological subtype, we found that the copy number status of MTAP, KLHL9, and ELAVL2 related to the overall survival in LGG and showed high correlation with histological subtype. In conclusion, this study provided new insight into the molecular mechanisms and clinical biomarkers in gliomas.