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A rapid, simple, and sensitive fluorescent detection method for brown spot of tobacco is established by lambda exonuclease-induced Mg2+-dependent DNAzyme amplification.

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

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.

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

In this issue of Immunity, Meylan et al. (2022) uses spatial transcriptomics to examine B cell immunity within intratumoral tertiary lymphoid structures (TLSs). They find that B cells expand and mature into plasma cells (PCs) within the TLS, migrate along fibroblastic tracks to tumor beds, and produce IgG antibodies that target cancer cells.

PD-1 blockade is highly effective in classical Hodgkin lymphomas (cHLs), which exhibit frequent copy-number gains of CD274 (PD-L1) and PDC1LG2 (PD-L2) on chromosome 9p24.1. However, in this largely MHC-class-I-negative tumor, the mechanism of action of anti-PD-1 therapy remains undefined. We utilized the complementary approaches of T cell receptor (TCR) sequencing and cytometry by time-of-flight analysis to obtain a peripheral immune signature of responsiveness to PD-1 blockade in 56 patients treated in the CheckMate 205 phase II clinical trial (NCT02181738). Anti-PD-1 therapy was most effective in patients with a diverse baseline TCR repertoire and an associated expansion of singleton clones during treatment. CD4+, but not CD8+, TCR diversity significantly increased during therapy, most strikingly in patients who had achieved complete responses. Additionally, patients who responded to therapy had an increased abundance of activated natural killer cells and a newly identified CD3−CD68+CD4+GrB+ subset. These studies highlight the roles of recently expanded, clonally diverse CD4+ T cells and innate effectors in the efficacy of PD-1 blockade in cHL. In a phase II clinical trial of patients with classical Hodgkin lymphoma, peripheral CD4+ T cell receptor diversity and the abundance of mature natural killer cells and CD3−CD68+CD4+GrB+ innate cells were associated with favorable responses to anti-PD-1 monotherapy.

The purpose of this study was to investigate the response of physicochemical properties of tobacco-growing soils and N/P/K accumulation of tobacco plant to tobacco straw biochar. Two contrasting tobacco-growing soils of paddy soil (Ultisol in USDA taxonomy) and brown soil (Alfisol in USDA taxonomy) were selected to conduct the pot experiments. Five addition rates (w/w) of tobacco straw biochar of 0%, 1%, 2%, 4%, and 10% without or with tobacco (Nicotiana tabacum) were applied in a greenhouse. The 7.41–35.90% increase of soil field capacity, 0.71–15.11% increase of total porosity, 3.40–17.55% increase of capillary porosity, 15.98–293.13% increase of organic matter, 6.52–58.75% increase of available phosphorus, 109.07–1789.70% increase of potassium, 3.23–147.71% increase of exchangeable cation, and 12.13–48.48% increase of soil pH were observed after biochar addition. And this improvement increased with the increasing application rate. The same trend was also found in the tobacco N/P/K uptake in the biochar application range of 0–4% and subsequently decreased at 10% rate. Meanwhile, biochar decreased the soil bulk density of 0.68–14.06%. The step regression equation showed that the exchangeable potassium and aluminum for Ultisol as well as the exchangeable potassium and hydrogen for Alfisol were the predominant factors that determined the soil pH. According to the PCA results, exchangeable calcium > exchangeable potassium > exchangeable cation > pH > organic matter > exchangeable magnesium > available potassium > available phosphorus and organic matter > exchangeable potassium > available phosphorus > exchangeable cation > available potassium > pH > exchangeable calcium had a significant impact on soil fertility of Ultisol and Alfisol, respectively. Finally, a comprehensive evaluation of soil fertility by PCA revealed that ST10 (10% rate, w/w) performed best, followed by the ST4 (4% rate, w/w) in both soils. The application of tobacco straw biochar, with appropriate rate, is an effect method to improve physicochemical properties of tobacco-growing soil and N/P/K uptake of tobacco plant.

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.

Tumor-infiltrating B cells are an important component in the microenvironment with unclear anti-tumor impacts. We enhanced our previous computational algorithm TRUST to extract the B cell immunoglobulin (Ig) hypervariable regions from bulk tumor RNA-seq data. TRUST assembled over 30 million complementarity-determining region 3 (CDR3s) of the B cell heavy chain (IgH) from The Cancer Genome Atlas (TCGA). Widespread B cell clonal expansions and Ig subclass switch events were observed in diverse human cancers. Prevalent somatic copy number alterations in MICA and MICB genes related to antibody-dependent cell mediated cytotoxicity (ADCC) were identified in tumors with elevated B cell activity. IgG3-1 subclass switch interacts with the B cell receptor affinity maturation and defects in the ADCC pathway. Comprehensive pan-cancer analyses of tumor-infiltrating B cell receptor repertoires identified novel tumor immune evasion mechanisms through genetic alterations. The IgH sequences identified here are potentially useful resources for future development of immunotherapies., Editorial summary: This comprehensive pan-cancer analysis of RNA sequencing data from bulk tumors defines the landscape of tumor-infiltrating B cell receptor repertoires and highlights new mechanisms of tumor immune evasion through genetic alterations.

Phage display technology in combination with next-generation sequencing (NGS) currently is a state-of-the-art method for the enrichment and isolation of monoclonal antibodies from diverse libraries. However, the current NGS methods employed for sequencing phage display libraries are limited by the short contiguous read lengths associated with second-generation sequencing platforms. Consequently, the identification of antibody sequences has conventionally been restricted to individual antibody domains or to the analysis of single domain binding moieties such as camelid VHH or cartilaginous fish IgNAR antibodies. In this study, we report the application of third-generation sequencing to address this limitation. We used single molecule real time (SMRT) sequencing coupled with hairpin adaptor loop ligation to facilitate the accurate interrogation of full-length single-chain Fv (scFv) libraries. Our method facilitated the rapid isolation and testing of scFv antibodies enriched from phage display libraries within days following panning. Two libraries against CD160 and CD123 were panned and monitored by NGS. Analysis of NGS antibody data sets led to the isolation of several functional scFv antibodies that were not identified by conventional panning and screening strategies. Our approach, which combines phage display selection of immune libraries with the full-length interrogation of scFv fragments, is an easy method to discover functional antibodies, with a range of affinities and biophysical characteristics.

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.

The molecular mechanism in pancreatic β cells underlying hyperlipidemia and insulin insufficiency remains unclear. Here, we find that the fatty acid-induced decrease in insulin levels occurs due to a decrease in insulin translation. Since regulation at the translational level is generally mediated through RNA-binding proteins, using RNA antisense purification coupled with mass spectrometry, we identify a novel insulin mRNA-binding protein, namely, DDX1, that is sensitive to palmitate treatment. Notably, the knockdown or overexpression of DDX1 affects insulin translation, and the knockdown of DDX1 eliminates the palmitate-induced repression of insulin translation. Molecular mechanism studies show that palmitate treatment causes DDX1 phosphorylation at S295 and dissociates DDX1 from insulin mRNA, thereby leading to the suppression of insulin translation. In addition, DDX1 may interact with the translation initiation factors eIF3A and eIF4B to regulate translation. In high-fat diet mice, the inhibition of insulin translation happens at an early prediabetic stage before the elevation of glucose levels. We speculate that the DDX1-mediated repression of insulin translation worsens the situation of insulin resistance and contributes to the elevation of blood glucose levels in obese animals.

Activation-induced cytidine deaminase (AID) mediates class switching by binding to a small fraction of single-stranded DNA (ssDNA) to diversify the antibody repertoire. The precise mechanism for highly selective AID targeting in the genome has remained elusive. Here, we report an RNA-binding protein, ROD1 (also known as PTBP3), that is both required and sufficient to define AID-binding sites genome-wide in activated B cells. ROD1 interacts with AID via an ultraconserved loop, which proves to be critical for the recruitment of AID to ssDNA using bi-directionally transcribed nascent RNAs as stepping stones. Strikingly, AID-specific mutations identified in human patients with hyper-IgM syndrome type 2 (HIGM2) completely disrupt the AID interacting surface with ROD1, thereby abolishing the recruitment of AID to immunoglobulin (Ig) loci. Together, our results suggest that bi-directionally transcribed RNA traps the RNA-binding protein ROD1, which serves as a guiding system for AID to load onto specific genomic loci to induce DNA rearrangement during immune responses.

Cancer treatment by immune checkpoint blockade (ICB) can bring long-lasting clinical benefits, but only a fraction of patients respond to treatment. To predict ICB response, we developed TIDE, a computational method to model two primary mechanisms of tumor immune evasion: the induction of T cell dysfunction in tumors with high infiltration of cytotoxic T lymphocytes (CTL) and the prevention of T cell infiltration in tumors with low CTL level. We identified signatures of T cell dysfunction from large tumor cohorts by testing how the expression of each gene in tumors interacts with the CTL infiltration level to influence patient survival. We also modeled factors that exclude T cell infiltration into tumors using expression signatures from immunosuppressive cells. Using this framework and pre-treatment RNA-Seq or NanoString tumor expression profiles, TIDE predicted the outcome of melanoma patients treated with first-line anti-PD1 or anti-CTLA4 more accurately than other biomarkers such as PD-L1 level and mutation load. TIDE also revealed new candidate ICB resistance regulators, such as SERPINB9, demonstrating utility for immunotherapy research.

We propose a new method for determining the target genes of transcriptional enhancers in specific cells and tissues. It combines global trends across many samples and sample-specific information, and considers the joint effect of multiple enhancers. Our method outperforms existing methods when predicting the target genes of enhancers in unseen samples, as evaluated by independent experimental data. Requiring few types of input data, we are able to apply our method to reconstruct the enhancer-target networks in 935 samples of human primary cells, tissues and cell lines, which constitute by far the largest set of enhancer-target networks. The similarity of these networks from different samples closely follows their cell and tissue lineages. We discover three major co-regulation modes of enhancers and find defense-related genes often simultaneously regulated by multiple enhancers bound by different transcription factors. We also identify differentially methylated enhancers in hepatocellular carcinoma (HCC) and experimentally confirm their altered regulation of HCC-related genes.

Direct conversion of somatic cells into neurons holds great promise for regenerative medicine. However, neuronal conversion is relatively inefficient in human cells compared to mouse cells. It has been unclear what might be the key barriers to reprogramming in human cells. We recently elucidated an RNA program mediated by the polypyrimidine tract binding protein PTB to convert mouse embryonic fibroblasts (MEFs) into functional neurons. In human adult fibroblasts (HAFs), however, we unexpectedly found that invoking the documented PTB-REST-miR-124 loop generates only immature neurons. We now report that the functionality requires sequential inactivation of PTB and the PTB paralog nPTB in HAFs. Inactivation of nPTB triggers another self-enforcing loop essential for neuronal maturation, which comprises nPTB, the transcription factor BRN2, and miR-9. These findings suggest that two separate gatekeepers control neuronal conversion and maturation and consecutively overcoming these gatekeepers enables deterministic reprogramming of HAFs into functional neurons.

In classical Hodgkin lymphoma (cHL), the host antitumor immune response is ineffective. Hodgkin Reed-Sternberg (HRS) cells have multifaceted mechanisms to evade the immune system, including 9p24.1/CD274(PD-L1)/PDCD1LG2(PD-L2) genetic alterations, overexpression of PD-1 ligands, and associated T-cell exhaustion and additional structural bases of aberrant antigen presentation. The clinical success of PD-1 blockade in cHL suggests that the tumor microenvironment (TME) contains reversibly exhausted T effector cells (Teffs). However, durable responses are observed in patients with β2-microglobulin/major histocompatibility complex (MHC) class I loss on HRS cells, raising the possibility of non-CD8(+) T cell–mediated mechanisms of efficacy of PD-1 blockade. These observations highlight the need for a detailed analysis of the cHL TME. Using a customized time-of-flight mass cytometry panel, we simultaneously assessed cell suspensions from diagnostic cHL biopsies and control reactive lymph node/tonsil (RLNT) samples. Precise phenotyping of immune cell subsets revealed salient differences between cHLs and RLNTs. The TME in cHL is CD4(+) T-cell rich, with frequent loss of MHC class I expression on HRS cells. In cHLs, we found concomitant expansion of T helper 1 (Th1)-polarized Teffs and regulatory T cells (Tregs). The cHL Th1 Tregs expressed little or no PD-1, whereas the Th1 Teffs were PD-1(+). The differential PD-1 expression and likely functional Th1-polarized CD4(+) Tregs and exhausted Teffs may represent complementary mechanisms of immunosuppression in cHL.

Signaling between programmed cell death protein 1 (PD-1) and the PD-1 ligands (PD-L1, PD-L2) is essential for malignant Hodgkin Reed-Sternberg (HRS) cells to evade antitumor immunity in classical Hodgkin lymphoma (cHL). Copy number alterations of 9p24.1/ CD274(PD-L1)/PDCD1LG2 (PD-L2) contribute to robust PD-L1 and PD-L2 expression by HRS cells. PD-L1 is also expressed by nonmalignant tumor-associated macrophages (TAMs), but the relationships among PD-L11 HRS cells, PD-L11 TAMs, and PD-11 T cells remain undefined. We used multiplex immunofluorescence and digital image analysis to examine the topography of PD-L11 and PD-11 cells in the tumor microenvironment (TME) of cHL. We find that the majority of PD-L1 in the TME is expressed by the abundant PD-L11 TAMs, which physically colocalize with PD-L11 HRS cells in a microenvironmental niche. PD-L11 TAMs are enriched for contacts with T cells, and PD-L11 HRS cells are enriched for contacts with CD41 T cells, a subset of which are PD-11. Our data define a unique topology of cHL in which PD-L11 TAMs surround HRS cells and implicate CD41 T cells as a target of PD-1 blockade.

Recently, several experimental techniques have emerged for probing RNA structures based on high-throughput sequencing. However, most secondary structure prediction tools that incorporate probing data are designed and optimized for particular types of experiments. For example, RNAstructure-Fold is optimized for SHAPE data, while SeqFold is optimized for PARS data. Here, we report a new RNA secondary structure prediction method, restrained MaxExpect (RME), which can incorporate multiple types of experimental probing data and is based on a free energy model and an MEA (maximizing expected accuracy) algorithm. We first demonstrated that RME substantially improved secondary structure prediction with perfect restraints (base pair information of known structures). Next, we collected structure-probing data from diverse experiments (e.g. SHAPE, PARS and DMS-seq) and transformed them into a unified set of pairing probabilities with a posterior probabilistic model. By using the probability scores as restraints in RME, we compared its secondary structure prediction performance with two other well-known tools, RNAstructure-Fold (based on a free energy minimization algorithm) and SeqFold (based on a sampling algorithm). For SHAPE data, RME and RNAstructure-Fold performed better than SeqFold, because they markedly altered the energy model with the experimental restraints. For high-throughput data (e.g. PARS and DMS-seq) with lower probing efficiency, the secondary structure prediction performances of the tested tools were comparable, with performance improvements for only a portion of the tested RNAs. However, when the effects of tertiary structure and protein interactions were removed, RME showed the highest prediction accuracy in the DMS-accessible regions by incorporating in vivo DMS-seq data.

To find signature features shared by various ncRNA sub-types and characterize novel ncRNAs, we have developed a method, RNAfeature, to investigate >600 sets of genomic and epigenomic data with various evolutionary and biophysical scores. RNAfeature utilizes a fine-tuned intra-species wrapper algorithm that is followed by a novel feature selection strategy across species. It considers long distance effect of certain features (e.g. histone modification at the promoter region). We finally narrow down on 10 informative features (including sequences, structures, expression profiles and epigenetic signals). These features are complementary to each other and as a whole can accurately distinguish canonical ncRNAs from CDSs and UTRs (accuracies: >92% in human, mouse, worm and fly). Moreover, the feature pattern is conserved across multiple species. For instance, the supervised 10-feature model derived from animal species can predict ncRNAs in Arabidopsis (accuracy: 82%). Subsequently, we integrate the 10 features to define a set of noncoding potential scores, which can identify, evaluate and characterize novel noncoding RNAs. The score covers all transcribed regions (including unconserved ncRNAs), without requiring assembly of the full-length transcripts. Importantly, the noncoding potential allows us to identify and characterize potential functional domains with feature patterns similar to canonical ncRNAs (e.g. tRNA, snRNA, miRNA, etc) on ∼70% of human long ncRNAs (lncRNAs).

Lung cancer is the leading cause of cancer death in the world, and early lung cancer detection is vital for improving the overall survival. Besides x-ray and CT scan, few genetic tests have been widely used for detecting early lung tumors. We hypothesize that early lung cancer patients have some unique features in the immune repertoires which can be utilized for early cancer detection and profiling disease development. In this study, we performed RNA-seq for paired tumor and tumor-adjacent normal samples from 59 early lung adenocarcinoma patients. We found that early lung tumors have more cytokine activities, higher B cell and CD4+ T cell infiltration, and lower CD8+ T cell infiltration compared to normal lung tissues. We assembled B cell receptors from RNA-seq reads and found more B cell clonal expansion and somatic hypermutations in the tumor than in tumor-adjacent normal tissues, similar to the B cell repertoires in late-stage lung cancer patients. In both early- and late-stage lung tumors, we identified a few cytokines that positively correlated with more IgA isotypes and associated with patient survival. To gain more insights on the B cell binding specificity, we performed motif enrichment analysis for paired tumor and normal samples and validated some tumor-enriched receptor motifs in matched patient blood samples, which implies a new way of early cancer detection. More interesting, we assembled microbial transcripts from RNA-seq data and identified some known lung disease-related species in both tumor and normal samples. The complex interactions between cancer cells, B cells, and microbiota in the early lung tumor development are waiting to be elucidated. Citation Format: Jin Wang, Xihao Hu, Peng Zhang, Xiaole Shirley Liu. Characteristics of immune repertoires in early lung cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4966.

Immune checkpoint blockade (ICB) therapy has improved patient survival in a variety of cancers, but its resistance presents a major clinical challenge. It remains unclear whether therapeutic resistance is driven predominantly by refractory clones of cancer cells or by immunosuppressive microenvironment. To address this, we utilized syngeneic transplantation model and performed clonal tracing of cancer cells to monitor the response to ICB. Different cancer clones displayed variable sensitivity to ICB treatment, reflecting intratumoral heterogeneity in therapy response. Furthermore, different recipients manifested distinct resistance mechanisms. ICB-responders developed resistance through selection and expansion of pre-existing ICB-resistant cancer clones, whereas non-responders presented with irresponsive microenvironment rather than dominance of resistant cancer clones. Extrapolation of the tumor-infiltrating immune repertoire revealed that higher IgG and lower IgA strongly correlated with better ICB response. This biomarker was further validated in multiple clinical cohorts. Our approach discriminates different sources of resistance to ICB, and identifies humoral immunity as biomarker of ICB response. Citation Format: Shengqing Gu, Xihao Hu, Xiaoqing Wang, Peng Jiang, Ziyi Li, Nicole Traugh, Xia Bu, Xiaofang Xing, Gordon J. Freeman, Myles Brown, Xiaole S. Liu. Clonal tracing reveals different mechanisms of resistance to immune checkpoint blockade [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3779.

Cancer treatment by immune checkpoint blockade (ICB) can bring long-lasting clinical benefits, but only a fraction of patients responds to treatment. To predict ICB response we developed TIDE, a computational method to model two primary mechanisms of tumor immune evasion: inducing T-cell dysfunction in tumors with high infiltration of cytotoxic T lymphocytes (CTL) and preventing T-cell infiltration in tumors with low CTL level. We identified signatures of T-cell dysfunction from large tumor cohorts by testing how the expression of each gene in tumors interacts with the CTL infiltration level to influence patient survival. We also modeled factors that exclude T-cell infiltration into tumors using expression signatures from immunosuppressive cells. Using this framework and pre-treatment RNA-Seq or NanoString tumor expression profiles, TIDE predicted the outcome of melanoma patients treated with first-line anti-PD1 or anti-CTLA4 more accurately than other biomarkers such as PD-L1 level and mutation load. TIDE also revealed new candidate ICB resistance regulators, such as SERPINB9, demonstrating utility for immunotherapy research. TIDE source code and a web application are available at http://tide.dfci.harvard.edu. Citation Format: Peng Jiang, Shengqing Gu, Deng Pan, Jingxin Fu, Avinash Sahu, Xihao Hu, Ziyi Li, Nicole Traugh, Xia Bu, Bo Li, Jun Liu, Gordon J Freeman, Myles A Brown, Kai W. Wucherpfennig, Xiaole Shirley Liu. Signatures of T-cell dysfunction and exclusion predict cancer immunotherapy response [abstract]. In: Proceedings of the Fourth CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; Sept 30-Oct 3, 2018; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2019;7(2 Suppl):Abstract nr B077.

Motivation: High-throughput sequencing has been used to probe RNA structures, by treating RNAs with reagents that preferentially cleave or mark certain nucleotides according to their local structures, followed by sequencing of the resulting fragments. The data produced contain valuable information for studying various RNA properties. Results: We developed methods for statistically modeling these structure-probing data and extracting structural features from them. We show that the extracted features can be used to predict RNA ‘zipcodes’ in yeast, regions bound by the She complex in asymmetric localization. The prediction accuracy was better than using raw RNA probing data or sequence features. We further demonstrate the use of the extracted features in identifying binding sites of RNA binding proteins from whole-transcriptome global photoactivatable-ribonucleoside-enhanced cross-linking and immunopurification (gPAR-CLIP) data. Availability: The source code of our implemented methods is available at http://yiplab.cse.cuhk.edu.hk/probrna/. Contact: kevinyip@cse.cuhk.edu.hk Supplementary information: Supplementary data are available at Bioinformatics online.

Accurately predicting peptides binding to major histocompatibility complex (MHC) I molecules is of great importance to immunologists for elucidating the underlying mechanism of immune recognition and facilitating the design of peptide-based vaccine. Various computational methods have been developed for MHC I-peptide binding prediction, and several of them are reported to achieve high accuracy in recent evaluation on benchmark datasets. For attending the machine learning in immunology competition (MLIC) in prediction of human leukocyte antigen (HLA)-binding peptides, we (FudanCS) have made use of ensemble approaches to further improve the prediction performance by integrating the outputs of several leading predictors. Two ensemble approaches, PM and AvgTanh, have been implemented for attending MLIC. AvgTanh and PM achieved the fourth and the seventh out of all 20 submissions in MLIC in terms of the average AUC. In addition, AvgTanh was awarded the winner in the category of HLA-A*0101 of 9-mer. Overall, the competition results validate the effectiveness of ensemble approaches.

Background: In classical Hodgkin lymphoma (cHL), the rare malignant Hodgkin Reed-Sternberg (HRS) cells are surrounded by an inflammatory infiltrate. Yet, the host anti-tumor immune response is ineffective. HRS cells have multifaceted mechanisms to evade the immune system including 9p24.1/PD-L1/PD-L2 genetic alterations leading to overexpression of PD-1 ligands and subsequent T cell exhaustion, aberrant antigen presentation and modulation of the tumor microenvironment (TME). The clinical success of PD-1 blockade in cHL suggests the TME contains reversibly exhausted T-effectors (Teff). Paradoxically, durable responses are observed in patients with β2M/MHC class I loss on HRS cells, raising the possibility of non-CD8+ mediated mechanisms of efficacy of PD-1 blockade. For this reason, we sought to characterize HRS cells and the surrounding TME. Methods: Using CyTOF technology, we evaluated 7 primary cHL suspensions and 10 reactive lymphoid tissue (RLT) samples at the single-cell protein level. We designed a custom panel of 39 isotope-conjugated antibodies. A combination of surface and intracellular markers distinguish T cell subsets according to lineage, differentiation, polarization, activation and exhaustion. Additional markers were incorporated to identify B cells, NK cells and macrophages. HRS cells were defined by CD15/CD30/Pax5 positivity. Inclusion of β2M and MHC class I allowed assessment of antigen presentation on HRS cells. The data was acquired on a Helios CyTOF and analyzed using a fast k-weighted nearest neighbor algorithm, X-shift. X-shift clustered cells with phenotypic similarities together. Then, samples were separated into cHL and RLT and the contribution of a sample to a given cluster was quantified. Results: Comparison of viable cell suspensions from RLT and cHL revealed loss of naïve T-cells and skewing towards differentiation of Teff in both CD4+ and CD8+ subsets in cHLs. This prompted a second X-shift analysis focused on CD3+ cells, which highlighted salient differences between cHL and RLT within the CD4+ subset. In cHL, we found expansion of Teff and regulatory T cells (Treg) with a reduction of follicular helper T cells. Furthermore, both Treg and Teff populations were largely Th1 (T-bet+/CCR5+) polarized. Evaluation of PD-1 expression showed Tregs had little/no PD-1 while Teff had intermediate/high expression. Hence, Tregs retain functionality in contrast to Teff, which are exhausted, providing two mechanisms of immunosuppression. Manual gating identified HRS cells with a characteristic phenotype: CD15, CD30, Pax5, rosetted by CD4+ T cells. Importantly, we found loss or decrease of β2M and MHC class I in 5/7 cases. Conclusions: The TME in cHL is CD4+ T cell rich with frequent loss of MHC class I on HRS cells. Differential PD-1 expression results in functional CD4+ Tregs and exhausted Teff, a synergistic bases for the observed immunosuppression in cHL. Citation Format: Fathima Z. Cader, Ron C. Schackmann, Xihao Hu, Kirsty Wienand, Robert A. Redd, Bjoern Chapuy, Jing Ouyang, Nicole E. Paul, Evisa Gjini, Mikel Lipschitz, Laura M. Selfors, Philippe Armand, David Wu, Jonathan R. Fromm, Donna Neuberg, Xiaole S. Liu, Scott J. Rodig, Margaret A. Shipp. Single-cell mass cytometry of classical Hodgkin lymphoma defines an exhausted and immunosuppressive microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5675.

Identifying essential proteins is very important for understanding the minimal requirements of cellular life and finding human disease genes as well as potential drug targets. Experimental methods for identifying essential proteins are often costly, time-consuming, and laborious. Many computational methods for such task have been proposed based on the topological properties of protein-protein interaction networks (PINs). However, most of these methods have limited prediction accuracy due to the noisy and incomplete natures of PINs and the fact that protein essentiality may relate to multiple biological factors. In this work, we proposed a new centrality measure, OGN, by integrating orthologous information, gene expressions, and PINs together. OGN determines a protein's essentiality by capturing its co-clustering and co-expression properties, as well as its conservation in the evolution process. The performance of OGN was tested on the species of Saccharomyces cerevisiae. Compared with several published centrality measures, OGN achieves higher prediction accuracy in both working alone and ensemble.

High-throughput sequencing has been used to study posttranscriptional regulations, where the identification of protein-RNA binding is a major and fast-developing sub-area, which is in turn benefited by the sequencing methods for whole-transcriptome probing of RNA secondary structures. In the study of RNA secondary structures using high-throughput sequencing, bases are modified or cleaved according to their structural features, which alter the resulting composition of sequencing reads. In the study of protein-RNA binding, methods have been proposed to immuno-precipitate (IP) protein-bound RNA transcripts in vitro or in vivo By sequencing these transcripts, the protein-RNA interactions and the binding locations can be identified. For both types of data, read counts are affected by a combination of confounding factors, including expression levels of transcripts, sequence biases, mapping errors and the probing or IP efficiency of the experimental protocols. Careful processing of the sequencing data and proper extraction of important features are fundamentally important to a successful analysis. Here we review and compare different experimental methods for probing RNA secondary structures and binding sites of RNA-binding proteins (RBPs), and the computational methods proposed for analyzing the corresponding sequencing data. We suggest how these two types of data should be integrated to study the structural properties of RBP binding sites as a systematic way to better understand posttranscriptional regulations.

Peptide-major histocompatibility complex (MHC) binding is an important prerequisite event and has immediate consequences to immune response. Those peptides binding to MHC molecules can activate the T-cell immunity, and they are useful for understanding the immune mechanism and developing vaccines for diseases. Recently, researchers are interested in making prediction about binding affinity instead of differentiating the peptides as binder or non-binder. In this paper, we use sparse Bayesian regression algorithm proposed by Tipping [M.E. Tipping, Sparse Bayesian learning and the relevance vector machine. J. Mach. Learn. Res. (2001)] to derive position-specific scoring matrices from allele-related peptides, and develop the models allowing for the prediction of MHC-II binding affinity. We explore the peptide length and peptide flanking residue length's impact on binding affinity, and incorporate these factors into our models to enhance prediction performance. When applied to the datasets from AntiJen database and IEDB database, our method produces better performances than several popular quantitative methods.

Background Peptide Recognition Domains (PRDs) are commonly found in signaling proteins. They mediate protein-protein interactions by recognizing and binding short motifs in their ligands. Although a great deal is known about PRDs and their interactions, prediction of PRD specificities remains largely an unsolved problem. Results We present a novel approach to identifying these Specificity Determining Residues (SDRs). Our algorithm generalizes earlier information theoretic approaches to coevolution analysis, to become applicable to this problem. It leverages the growing wealth of binding data between PRDs and large numbers of random peptides, and searches for PRD residues that exhibit strong evolutionary covariation with some positions of the statistical profiles of bound peptides. The calculations involve only information from sequences, and thus can be applied to PRDs without crystal structures. We applied the approach to PDZ, SH3 and kinase domains, and evaluated the results using both residue proximity in co-crystal structures and verified binding specificity maps from mutagenesis studies. Discussion Our predictions were found to be strongly correlated with the physical proximity of residues, demonstrating the ability of our approach to detect physical interactions of the binding partners. Some high-scoring pairs were further confirmed to affect binding specificity using previous experimental results. Combining the covariation results also allowed us to predict binding profiles with higher reliability than two other methods that do not explicitly take residue covariation into account. Conclusions The general applicability of our approach to the three different domain families demonstrated in this paper suggests its potential in predicting binding targets and assisting the exploration of binding mechanisms.

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