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Supplementary Data from Comprehensive Characterizations of Immune Receptor Repertoire in Tumors and Cancer Immunotherapy Studies

We applied our computational algorithm TRUST4 to assemble immune receptor (T-cell receptor/B-cell receptor) repertoires from approximately 12,000 RNA sequencing samples from The Cancer Genome Atlas and seven immunotherapy studies. From over 35 million assembled complete complementary-determining region 3 sequences, we observed that the expression of CCL5 and MZB1 is the most positively correlated genes with T-cell clonal expansion and B-cell clonal expansion, respectively. We analyzed amino acid evolution during B-cell receptor somatic hypermutation and identified tyrosine as the preferred residue. We found that IgG1+IgG3 antibodies together with FcRn were associated with complement-dependent cytotoxicity and antibody-dependent cellular cytotoxicity or phagocytosis. In addition to B-cell infiltration, we discovered that B-cell clonal expansion and IgG1+IgG3 antibodies are also correlated with better patient outcomes. Finally, we created a website, VisualizIRR, for users to interactively explore and visualize the immune repertoires in this study.See related Spotlight by Liu and Han, p. 786

Supplementary Figure from Comprehensive Characterizations of Immune Receptor Repertoire in Tumors and Cancer Immunotherapy Studies

The widespread use of nuclear energy and nuclear facilities has dramatically increased the risks associated with nuclear radiation exposure. Effective radioprotective agents that are easy to administer and have low toxicity are urgently needed to minimize the significant harm caused by exposure to nuclear radiation. Appropriate animal models are also required for investigation of the effectiveness of radioprotective agents. However, the animal models presently used to study the intestinal form of acute radiation sickness (ARS) are limited mainly to rodents, and large animal models (for example, Beagle dogs and non-human primates) are still under exploration. We have developed a Beagle model for investigation of GI-ARS in which the head, chest, and extremities are shielded to reduce the possibility of bleeding and/or damage to other more sensitive organs and rehydration support is provided after irradiation of 12.5 Gy with 60Co γ-rays. Significant vomiting and diarrhea were seen in this model, with typical signs of radiation sickness that progressively worsened with time since irradiation. The 6-day mortality rate was 100%, and significant intestinal damage was observed on gross autopsy and histopathology. By reflecting GI-ARS based on relatively objective evaluation of clinical manifestations and histopathological examination, this model provides a new concept and methodology for further experimental evaluation of the effectiveness of radioprotective agents in animals.

We applied our computational algorithm TRUST4 to assemble immune receptor (T-cell receptor/B-cell receptor) repertoires from approximately 12,000 RNA sequencing samples from The Cancer Genome Atlas and seven immunotherapy studies. From over 35 million assembled complete complementary-determining region 3 sequences, we observed that the expression of CCL5 and MZB1 is the most positively correlated genes with T-cell clonal expansion and B-cell clonal expansion, respectively. We analyzed amino acid evolution during B-cell receptor somatic hypermutation and identified tyrosine as the preferred residue. We found that IgG1+IgG3 antibodies together with FcRn were associated with complement-dependent cytotoxicity and antibody-dependent cellular cytotoxicity or phagocytosis. In addition to B-cell infiltration, we discovered that B-cell clonal expansion and IgG1+IgG3 antibodies are also correlated with better patient outcomes. Finally, we created a website, VisualizIRR, for users to interactively explore and visualize the immune repertoires in this study. See related Spotlight by Liu and Han, p. 786

We introduce the TRUST4 open-source algorithm for reconstruction of immune receptor repertoires in αβ/γδ T cells and B cells from RNA-sequencing (RNA-seq) data. Compared with competing methods, TRUST4 supports both FASTQ and BAM format and is faster and more sensitive in assembling longer—even full-length—receptor repertoires. TRUST4 can also call repertoire sequences from single-cell RNA-seq (scRNA-seq) data without V(D)J enrichment, and is compatible with both SMART-seq and 5′ 10x Genomics platforms. TRUST4 is a computational tool for reconstructing T-cell and B-cell receptor repertoires using bulk and single-cell RNA-seq data.

KIAA1429 (also known as vir-like m(6)A methyltransferase-associated protein (VIRMA)), a newly identified component of the RNA m(6)A methyltransferase complex, plays critical roles in guiding region-selective m(6)A deposition. However, in mammals, whether KIAA1429 mediates RNA m(6)A regulatory pathway functions in vivo remains unknown. Here, we show that the Kiaa1429-specific deficiency in oocytes resulted in female infertility with defective follicular development and fully grown germinal vesicle (GV) oocytes failing to undergo germinal vesicle breakdown (GVBD) and consequently losing the ability to resume meiosis. The oocyte growth is accompanied by the accumulation of abundant RNAs and posttranscriptional regulation. We found that the loss of Kiaa1429 could also lead to abnormal RNA metabolism in GV oocytes. RNA-seq profiling revealed that Kiaa1429 deletion altered the expression pattern of the oocyte-derived factors essential for follicular development. In addition, our data show that the conditional depletion of Kiaa1429 decreased the m(6)A levels in oocytes and mainly affected the alternative splicing of genes associated with oogenesis. In summary, the m(6)A methyltransferase KIAA1429-mediated RNA metabolism plays critical roles in folliculogenesis and the maintenance of oocyte competence.

Background Acute myeloid leukemia (AML), caused by the abnormal proliferation of immature myeloid cells in the blood or bone marrow, is one of the most common hematologic malignancies. Currently, the interactions between malignant myeloid cells and the immune microenvironment, especially T cells and B cells, remain poorly characterized. Methods In this study, we systematically analyzed the T cell receptor and B cell receptor (TCR and BCR) repertoires from the RNA-seq data of 145 pediatric and 151 adult AML samples as well as 73 non-tumor peripheral blood samples. Results We inferred over 225,000 complementarity-determining region 3 (CDR3) sequences in TCR α, β, γ, and δ chains and 1,210,000 CDR3 sequences in B cell immunoglobulin (Ig) heavy and light chains. We found higher clonal expansion of both T cells and B cells in the AML microenvironment and observed many differences between pediatric and adult AML. Most notably, adult AML samples have significantly higher level of B cell activation and more secondary Ig class switch events than pediatric AML or non-tumor samples. Furthermore, adult AML with highly expanded IgA2 B cells, which might represent an immunosuppressive microenvironment, are associated with regulatory T cells and worse overall survival. Conclusions Our comprehensive characterization of the AML immune receptor repertoires improved our understanding of T cell and B cell immunity in AML, which may provide insights into immunotherapies in hematological malignancies.

BackgroundImmune checkpoint blockade (ICB) therapy has improved patient survival in a variety of cancers, but only a minority of cancer patients respond. Multiple studies have sought to identify general biomarkers of ICB response, but elucidating the molecular and cellular drivers of resistance for individual tumors remains challenging. We sought to determine whether a tumor with defined genetic background exhibits a stereotypic or heterogeneous response to ICB treatment.ResultsWe establish a unique mouse system that utilizes clonal tracing and mathematical modeling to monitor the growth of each cancer clone, as well as the bulk tumor, in response to ICB. We find that tumors derived from the same clonal populations showed heterogeneous ICB response and diverse response patterns. Primary response is associated with higher immune infiltration and leads to enrichment of pre-existing ICB-resistant cancer clones. We further identify several cancer cell-intrinsic gene expression signatures associated with ICB resistance, including increased interferon response genes and glucocorticoid response genes. These findings are supported by clinical data from ICB treatment cohorts.ConclusionsOur study demonstrates diverse response patterns from the same ancestor cancer cells in response to ICB. This suggests the value of monitoring clonal constitution and tumor microenvironment over time to optimize ICB response and to design new combination therapies. Furthermore, as ICB response may enrich for cancer cell-intrinsic resistance signatures, this can affect interpretations of tumor RNA-seq data for response-signature association studies.

Additional file 1. Supplementary Figures.

Lnc2Catlas (http://lnc2catlas.bioinfotech.org/) is an atlas of long noncoding RNAs (lncRNAs) associated with cancer risk. LncRNAs are a class of functional noncoding RNAs with lengths over 200 nt and play a vital role in diverse biological processes. Increasing evidence shows that lncRNA dysfunction is associated with many human cancers/diseases. It is therefore important to understand the underlying relationship between lncRNAs and cancers. To this end, we developed Lnc2Catlas to compile quantitative associations between lncRNAs and cancers using three computational methods, assessing secondary structure disruption, lncRNA-protein interactions, and co-expression networks. Lnc2Catlas was constructed based on 27,670 well-annotated lncRNAs, 31,749,216 SNPs, 1,473 cancer-associated proteins, and 10,539 expression profiles of 33 cancers from The Cancer Genome Atlas (TCGA). Lnc2Catlas contains 247,124 lncRNA-SNP pairs, over two millions lncRNA-protein interactions, and 6,902 co-expression clusters. We deposited Lnc2Catlas on Alibaba Cloud and developed interactive, mobile device-compatible, user-friendly interfaces to help users search and browse Lnc2Catlas with ultra-low latency. Lnc2Catlas can aid in the investigation of associations between lncRNAs and cancers and can provide candidate lncRNAs for further experimental validation. Lnc2Catlas will facilitate an understanding of the associations between lncRNAs and cancer and will help reveal the critical role of lncRNAs in cancer.

Motivation Enhancer elements are noncoding stretches of DNA that play key roles in controlling gene expression programmes. Despite major efforts to develop accurate enhancer prediction methods, identifying enhancer sequences continues to be a challenge in the annotation of mammalian genomes. One of the major issues is the lack of large, sufficiently comprehensive and experimentally validated enhancers for humans or other species. Thus, the development of computational methods based on limited experimentally validated enhancers and deciphering the transcriptional regulatory code encoded in the enhancer sequences is urgent. Results We present a deep-learning-based hybrid architecture, BiRen, which predicts enhancers using the DNA sequence alone. Our results demonstrate that BiRen can learn common enhancer patterns directly from the DNA sequence and exhibits superior accuracy, robustness and generalizability in enhancer prediction relative to other state-of-the-art enhancer predictors based on sequence characteristics. Our BiRen will enable researchers to acquire a deeper understanding of the regulatory code of enhancer sequences. Availability and Implementation Our BiRen method can be freely accessed at https://github.com/wenjiegroup/BiRen. Supplementary information Supplementary data are available at Bioinformatics online.

Advances in studies of long noncoding RNAs (lncRNAs) have provided data regarding the regulatory roles of lncRNAs, which perform functional roles through interactions with other functional elements. To track the underlying relationships among lncRNAs, various databases have been developed as repositories for lncRNA data. However, the ability to comprehensively explore the diverse interactions between lncRNAs and other functional elements is limited. To this end, we developed LIVE (LncRNA Interaction Validated Encyclopaedia), an interactive resource to integrate the diverse interactions of functional elements with lncRNAs. LIVE is a manually curated database of experimentally validated interactions of lncRNAs with genes, proteins and other various functional elements. By mining publications, we constructed LIVE with the following three interaction networks: a binding interaction network, a regulation network and a disease network; then, we combined them to form a comprehensive lncRNA interaction network. The current release of LIVE contains the validated interactions of 572 lncRNAs in humans and mice with 103 proteins, 209 genes, 56 transcription factors and 194 diseases. LIVE provides an interactive interface with charts and figures to aid users in searching and browsing interactions with lncRNAs. LIVE will greatly facilitate further investigation into the regulatory roles of lncRNAs and is freely available.

A-to-I editing, as a post-transcriptional modification process mediated by ADAR, plays a crucial role in many biological processes in metazoans. However, how and to what extent A-to-I editing diversifies and shapes population diversity at the RNA level are largely unknown. Here, we used 462 mRNA-sequencing samples from five populations of the Geuvadis Project and identified 16,518 A-to-I editing sites, with false detection rate of 1.03%. These sites form the landscape of the RNA editome of the human genome. By exploring RNA editing within and between populations, we revealed the geographic restriction of rare editing sites and population-specific patterns of edQTL editing sites. Moreover, we showed that RNA editing can be used to characterize the subtle but substantial diversity between different populations, especially those from different continents. Taken together, our results demonstrated that the nature and structure of populations at the RNA level are illustrated well by RNA editing, which provides insights into the process of how A-to-I editing shapes population diversity at the transcriptomic level. Our work will facilitate the understanding of the landscape of the RNA editome at the population scale and will be helpful for interpreting differences in the distribution and prevalence of disease among individuals and across populations.

RNA editing is a post-transcriptional RNA sequence alteration. Current methods have identified editing sites and facilitated research but require sufficient genomic annotations and prior-knowledge-based filtering steps, resulting in a cumbersome, time-consuming identification process. Moreover, these methods have limited generalizability and applicability in species with insufficient genomic annotations or in conditions of limited prior knowledge. We developed DeepRed, a deep learning-based method that identifies RNA editing from primitive RNA sequences without prior-knowledge-based filtering steps or genomic annotations. DeepRed achieved 98.1% and 97.9% area under the curve (AUC) in training and test sets, respectively. We further validated DeepRed using experimentally verified U87 cell RNA-seq data, achieving 97.9% positive predictive value (PPV). We demonstrated that DeepRed offers better prediction accuracy and computational efficiency than current methods with large-scale, mass RNA-seq data. We used DeepRed to assess the impact of multiple factors on editing identification with RNA-seq data from the Association of Biomolecular Resource Facilities and Sequencing Quality Control projects. We explored developmental RNA editing pattern changes during human early embryogenesis and evolutionary patterns in Drosophila species and the primate lineage using DeepRed. Our work illustrates DeepRed’s state-of-the-art performance; it may decipher the hidden principles behind RNA editing, making editing detection convenient and effective.

Accurately identifying the binding sites of transcription factors (TFs) is crucial to understanding the mechanisms of transcriptional regulation and human disease. We present incorporating Find Occurrence of Regulatory Motifs (iFORM), an easy-to-use and efficient tool for scanning DNA sequences with TF motifs described as position weight matrices (PWMs). Both performance assessment with a receiver operating characteristic (ROC) curve and a correlation-based approach demonstrated that iFORM achieves higher accuracy and sensitivity by integrating five classical motif discovery programs using Fisher's combined probability test. We have used iFORM to provide accurate results on a variety of data in the ENCODE Project and the NIH Roadmap Epigenomics Project, and the tool has demonstrated its utility in further elucidating individual roles of functional elements. Both the source and binary codes for iFORM can be freely accessed at https://github.com/wenjiegroup/iFORM. The identified TF binding sites across human cell and tissue types using iFORM have been deposited in the Gene Expression Omnibus under the accession ID GSE53962.

Additional file 1: Table S1. Clinical characteristics of AML samples. Table S2. Clinical characteristics of non-tumor samples.

Additional file 2: Figure S1. In silico validation on the accuracy of TRUST for assembling TCR and BCR from RNA-seq data by using single cell RNA-seq data of CD45 positive white blood cells from pre-treatment melanoma patients. Figure S2. Overview of TCR CDR3s in AML and non-tumor samples. Figure S3. Comparison of TCR CPK and γδ T cell fraction between PB and BM samples in pediatric AML. Figure S4. Overview of BCR CDR3s in AML and non-tumor samples. Figure S5. Comparison of BCR CDR3s between PB and BM samples in pediatric AML. Figure S6. Violin plot showing the expression level of AICDA is significantly higher in adult AML than pediatric AML. Figure S7. Boxplots showing the normalized number of IgA/IgG/IgM + IgD CDR3s in AML and non-tumor groups. Figure S8. Kaplan-Meier curves showing the survival difference relative to IgA fraction in pediatric and adult AML. Figure S9. Distribution of 9 Ig isotypes across adult AML IgA2 low (0–5%, n = 64), medium (5%–10%, n = 64) and high (> 10%, n = 23) group. Figure S10. The enriched GO terms with IgA2 fraction in adult AML. Figure S11. Boxplot showing the expression level of PD-L1 in pediatric AML IgA1 high/low group and adult AML IgA2 high/low group. Figure S12. t-SNE plots showing the expression level of different gene makers in normal and AML (M6, one patient) peripheral blood single cell RNA-seq data.

Additional file 2: Figure S1. In silico validation on the accuracy of TRUST for assembling TCR and BCR from RNA-seq data by using single cell RNA-seq data of CD45 positive white blood cells from pre-treatment melanoma patients. Figure S2. Overview of TCR CDR3s in AML and non-tumor samples. Figure S3. Comparison of TCR CPK and γδ T cell fraction between PB and BM samples in pediatric AML. Figure S4. Overview of BCR CDR3s in AML and non-tumor samples. Figure S5. Comparison of BCR CDR3s between PB and BM samples in pediatric AML. Figure S6. Violin plot showing the expression level of AICDA is significantly higher in adult AML than pediatric AML. Figure S7. Boxplots showing the normalized number of IgA/IgG/IgM + IgD CDR3s in AML and non-tumor groups. Figure S8. Kaplan-Meier curves showing the survival difference relative to IgA fraction in pediatric and adult AML. Figure S9. Distribution of 9 Ig isotypes across adult AML IgA2 low (0–5%, n = 64), medium (5%–10%, n = 64) and high (> 10%, n = 23) group. Figure S10. The enriched GO terms with IgA2 fraction in adult AML. Figure S11. Boxplot showing the expression level of PD-L1 in pediatric AML IgA1 high/low group and adult AML IgA2 high/low group. Figure S12. t-SNE plots showing the expression level of different gene makers in normal and AML (M6, one patient) peripheral blood single cell RNA-seq data.

Additional file 3. Supplementary methods.

Additional file 3. Supplementary methods.

Additional file 1: Table S1. Clinical characteristics of AML samples. Table S2. Clinical characteristics of non-tumor samples.