Single-cell RNA-seq reveals clonal diversity and prognostic genes of relapsed multiple myeloma.

BACKGROUND: Multiple myeloma (MM) is a clinically and biologically heterogeneous plasma-cell malignancy. Despite extensive research, disease heterogeneity and relapse remain a big challenge in MM therapeutics. We tried to dissect this disease and identify novel biomarkers for patient stratification and treatment outcome prediction by applying single-cell technology. METHODS: We performed single-cell RNA sequencing (scRNA-seq) and variable-diversity-joining regions-targeted sequencing (scVDJ-seq) concurrently on bone marrow samples from a cohort of 18 patients with newly diagnosed MM (NDMM; n = 12) or refractory/relapsed MM (RRMM; n = 6). We analysed the malignant clonotypes using scVDJ-seq data and conducted data integration and cell-type annotation through the CCA algorithm based on gene expression profiling. Furthermore, we identified disease status-specific genes and modules by comparison of NDMM and RRMM datasets and explored the findings in a larger MM cohort from the MMRF CoMMpass study. RESULTS: We found that all the myeloma cells in either diagnosed or relapsed samples were dominated by a major clone, with a few subclones in several samples (n = 5). Next, we investigated the universal transcriptional features of myeloma cells and identified eight meta-programs correlated with this disease, especially meta-programs 1 and 8 (M1 and M8), which were the most significant and related to cell cycle and stress response, respectively. Furthermore, we classified the malignant plasma cells into eight clusters and found that the cell numbers in clusters 2/6/7 were exclusively higher in relapsed samples. Besides, we identified several attractive candidates for biomarkers (e.g. SMAD1 and STMN1) associated with disease progression and relapse in our dataset and related to overall survival in the CoMMpass dataset. CONCLUSIONS: Our data provide insights into the heterogeneity of MM as well as highlight the relevance of intra-tumour heterogeneity and discover novel biomarkers that might be a potent therapy.

Single-Cell RNA-seq Reveals Characteristics of Malignant Cells and Immune Microenvironment in Subcutaneous Panniculitis-Like T-Cell Lymphoma.

BACKGROUND: Subcutaneous panniculitis-like T-cell lymphoma (SPTCL) is a malignant primary T-cell lymphoma that is challenging to distinguish from autoimmune disorders and reactive panniculitides. Delay in diagnosis and a high misdiagnosis rate affect the prognosis and survival of patients. The difficulty of diagnosis is mainly due to an incomplete understanding of disease pathogenesis. METHODS: We performed single-cell RNA sequencing of matched subcutaneous lesion tissue, peripheral blood, and bone marrow from a patient with SPTCL, as well as peripheral blood, bone marrow, lymph node, and lung tissue samples from healthy donors as normal controls. We conducted cell clustering, gene expression program identification, gene differential expression analysis, and cell-cell interaction analysis to investigate the ecosystem of SPTCL. RESULTS: Based on gene expression profiles in a single-cell resolution, we identified and characterized the malignant cells and immune subsets from a patient with SPTCL. Our analysis showed that SPTCL malignant cells expressed a distinct gene signature, including chemokines families, cytotoxic proteins, T cell immune checkpoint molecules, and the immunoglobulin family. By comparing with normal T cells, we identified potential novel markers for SPTCL (e.g., CYTOR, CXCL13, VCAM1, and TIMD4) specifically differentially expressed in the malignant cells. We also found that macrophages and fibroblasts dominated the cell-cell communication landscape with the SPTCL malignant cells. CONCLUSIONS: This work offers insight into the heterogeneity of subcutaneous panniculitis-like T-cell lymphoma, providing a better understanding of the transcription characteristics and immune microenvironment of this rare tumor.

Identification of leukemia stem cell expression signatures through Monte Carlo feature selection strategy and support vector machine.

Acute myeloid leukemia (AML) is a type of blood cancer characterized by the rapid growth of immature white blood cells from the bone marrow. Therapy resistance resulting from the persistence of leukemia stem cells (LSCs) are found in numerous patients. Comparative transcriptome studies have been previously conducted to analyze differentially expressed genes between LSC+ and LSC- cells. However, these studies mainly focused on a limited number of genes with the most obvious expression differences between the two cell types. We developed a computational approach incorporating several machine learning algorithms, including Monte Carlo feature selection (MCFS), incremental feature selection (IFS), support vector machine (SVM), Repeated Incremental Pruning to Produce Error Reduction (RIPPER), to identify gene expression features specific to LSCs. One thousand 0ne hudred fifty-nine features (genes) were first identified, which can be used to build the optimal SVM classifier for distinguishing LSC+ and LSC- cells. Among these 1159 genes, the top 17 genes were identified as LSC-specific biomarkers. In addition, six classification rules were produced by RIPPER algorithm. The subsequent literature review on these features/genes and the classification rules and functional enrichment analyses of the 1159 features/genes confirmed the relevance of extracted genes and rules to the characteristics of LSCs.

Network-based method for mining novel HPV infection related genes using random walk with restart algorithm.

The human papillomavirus (HPV), a common virus that infects the reproductive tract, may lead to malignant changes within the infection area in certain cases and is directly associated with such cancers as cervical cancer, anal cancer, and vaginal cancer. Identification of novel HPV infection related genes can lead to a better understanding of the specific signal pathways and cellular processes related to HPV infection, providing information for the development of more efficient therapies. In this study, several novel HPV infection related genes were predicted by a computation method based on the known genes involved in HPV infection from HPVbase. This method applied the algorithm of random walk with restart (RWR) to a protein-protein interaction (PPI) network. The candidate genes were further filtered by the permutation and association tests. These steps eliminated genes occupying special positions in the PPI network and selected key genes with strong associations to known HPV infection related genes based on the interaction confidence and functional similarity obtained from published databases, such as STRING, gene ontology (GO) terms and KEGG pathways. Our study identified 104 novel HPV infection related genes, a number of which were confirmed to relate to the infection processes and complications of HPV infection, as reported in the literature. These results demonstrate the reliability of our method in identifying HPV infection related genes. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.

Identifying and analyzing different cancer subtypes using RNA-seq data of blood platelets.

Detection and diagnosis of cancer are especially important for early prevention and effective treatments. Traditional methods of cancer detection are usually time-consuming and expensive. Liquid biopsy, a newly proposed noninvasive detection approach, can promote the accuracy and decrease the cost of detection according to a personalized expression profile. However, few studies have been performed to analyze this type of data, which can promote more effective methods for detection of different cancer subtypes. In this study, we applied some reliable machine learning algorithms to analyze data retrieved from patients who had one of six cancer subtypes (breast cancer, colorectal cancer, glioblastoma, hepatobiliary cancer, lung cancer and pancreatic cancer) as well as healthy persons. Quantitative gene expression profiles were used to encode each sample. Then, they were analyzed by the maximum relevance minimum redundancy method. Two feature lists were obtained in which genes were ranked rigorously. The incremental feature selection method was applied to the mRMR feature list to extract the optimal feature subset, which can be used in the support vector machine algorithm to determine the best performance for the detection of cancer subtypes and healthy controls. The ten-fold cross-validation for the constructed optimal classification model yielded an overall accuracy of 0.751. On the other hand, we extracted the top eighteen features (genes), including TTN, RHOH, RPS20, TRBC2, in another feature list, the MaxRel feature list, and performed a detailed analysis of them. The results indicated that these genes could be important biomarkers for discriminating different cancer subtypes and healthy controls.