Can perinatal outcomes of fetal omphalocele be improved at a tertiary center in South China?

OBJECTIVE: To assess the efficacy of positive feedback closed-loop management system (PFCMS) protocol in influencing parents' decision about pregnancy continuation in pregnancies diagnosed with omphalocele. METHODS: This was a retrospective cohort study of patients who were diagnosed with fetal omphalocele prior to 20 weeks' gestation by ultrasound and were referred to Fetal Care Center at a mainland Chinese medical center during an 11-year period. Two management strategies were offered during the two stages of the study period: a single consultant with a routine protocol and a multidisciplinary support team with PFCMS, respectively. We analyzed the two protocols influencing parents' decision about pregnancy continuation. RESULTS: Forty-nine patients diagnosed with fetal omphalocele were included in this study. In Group A including 16 patients with routine protocol during the first stage of the study period, the majority opted for termination, and only five continued the pregnancy. In Group B including 33 patients with PFCMS during the second stage of the study period, less than one third chose TOP, and 23 ended in live births. There was a significantly lower TOP rate in patients treated with the PFCMS protocol. CONCLUSION: The PFCMS protocol may be an efficient approach in managing pregnancies complicated by omphalocele, which may help in preventing unnecessary pregnancy terminations.

Prenatal diagnosis and postnatal management of congenital mesoblastic nephroma: Experience at a single center in China.

OBJECTIVE: To review the prenatal and postnatal clinical characteristics and pathological subtypes, as well as the surgical outcome for congenital mesoblastic nephroma (CMN) cases. METHOD: A retrospective review was performed in 11 cases with CMN prenatally diagnosed at a single center between 2015 and 2019. The clinical characteristics, surgical outcome, histopathology, and follow-up were retrospectively obtained and reviewed. RESULTS: The median gestational age at which the sonographic diagnosis was made was 35 weeks. Polyhydramnios was found in four (36.4%) cases, and all resulted in a preterm birth. Nine infants had hypertension. Ten cases underwent radical nephrectomy, and one underwent radical nephrectomy and partial adrenalectomy. The pathological results showed that six tumors were classical variants, four mixed variants, and one was a cellular variant. Three cases presented as a stage I, eight as stage II, and no stage III or IV cases were diagnosed. All patients are alive so far. At a median follow-up of 14 months, no local recurrence, or remote metastases were found. CONCLUSION: The prognosis of prenatal CMN cases is excellent after early surgery.

TANTIGEN: a comprehensive database of tumor T cell antigens.

Tumor T cell antigens are both diagnostically and therapeutically valuable molecules. A large number of new peptides are examined as potential tumor epitopes each year, yet there is no infrastructure for storing and accessing the results of these experiments. We have retroactively cataloged more than 1000 tumor peptides from 368 different proteins, and implemented a web-accessible infrastructure for storing and accessing these experimental results. All peptides in TANTIGEN are labeled as one of the four categories: (1) peptides measured in vitro to bind the HLA, but not reported to elicit either in vivo or in vitro T cell response, (2) peptides found to bind the HLA and to elicit an in vitro T cell response, (3) peptides shown to elicit in vivo tumor rejection, and (4) peptides processed and naturally presented as defined by physical detection. In addition to T cell response, we also annotate peptides that are naturally processed HLA binders, e.g., peptides eluted from HLA in mass spectrometry studies. TANTIGEN provides a rich data resource for tumor-associated epitope and neoepitope discovery studies and is freely available at http://cvc.dfci.harvard.edu/tantigen/ or http://projects.met-hilab.org/tadb (mirror).

Mass Spectrometry Profiling of HLA-Associated Peptidomes in Mono-allelic Cells Enables More Accurate Epitope Prediction.

Identification of human leukocyte antigen (HLA)-bound peptides by liquid chromatography-tandem mass spectrometry (LC-MS/MS) is poised to provide a deep understanding of rules underlying antigen presentation. However, a key obstacle is the ambiguity that arises from the co-expression of multiple HLA alleles. Here, we have implemented a scalable mono-allelic strategy for profiling the HLA peptidome. By using cell lines expressing a single HLA allele, optimizing immunopurifications, and developing an application-specific spectral search algorithm, we identified thousands of peptides bound to 16 different HLA class I alleles. These data enabled the discovery of subdominant binding motifs and an integrative analysis quantifying the contribution of factors critical to epitope presentation, such as protein cleavage and gene expression. We trained neural-network prediction algorithms with our large dataset (>24,000 peptides) and outperformed algorithms trained on datasets of peptides with measured affinities. We thus demonstrate a strategy for systematically learning the rules of endogenous antigen presentation.

Prenatal diagnosis and early postnatal management of pyriform sinus cyst: experience at a single medical center in mainland China.

OBJECTIVE: The aim of this study was to determine whether prenatal diagnosis of pyriform sinus cyst can improve the prognosis of this disorder. METHODS: A retrospective review was performed in 15 neonates with a pyriform sinus cyst seen at a single center between 2010 and 2014. Among the 15 cases, the diagnosis was made prenatally in eight cases (PreD), while the diagnosis was made postnatally in seven cases (PostD). Neonatal outcome was compared in the two subgroups. RESULTS: The mean gestational age at diagnosis of PreD was 27 ± 6.8 weeks, while the mean age at admission of PostD was 10.1 ± 8.8 days. Cervical mass, fever, respiratory distress, and hoarseness were common symptoms. The mean duration of postoperative mechanical ventilation was 11.5 ± 13.9 and 100.71 ± 80.0 h, respectively, in PreD and PostD (p < 0.01). The average postoperative length of stay and the length of hospital stay were 11.3 ± 3.34 and 19.6 ± 4.41 days in PreD, and 15.14 ± 8.28 and 24.14 ± 8.51 days in PostD, respectively. CONCLUSION: Prenatal diagnosis and timely postnatal sequential intervention were associated with less complications and shortened duration of postoperative mechanical ventilation. © 2016 John Wiley & Sons, Ltd.

FluKB: A Knowledge-Based System for Influenza Vaccine Target Discovery and Analysis of the Immunological Properties of Influenza Viruses.

FluKB is a knowledge-based system focusing on data and analytical tools for influenza vaccine discovery. The main goal of FluKB is to provide access to curated influenza sequence and epitope data and enhance the analysis of influenza sequence diversity and the analysis of targets of immune responses. FluKB consists of more than 400,000 influenza protein sequences, known epitope data (357 verified T-cell epitopes, 685 HLA binders, and 16 naturally processed MHC ligands), and a collection of 28 influenza antibodies and their structurally defined B-cell epitopes. FluKB was built using a modular framework allowing the implementation of analytical workflows and includes standard search tools, such as keyword search and sequence similarity queries, as well as advanced tools for the analysis of sequence variability. The advanced analytical tools for vaccine discovery include visual mapping of T- and B-cell vaccine targets and assessment of neutralizing antibody coverage. FluKB supports the discovery of vaccine targets and the analysis of viral diversity and its implications for vaccine discovery as well as potential T-cell breadth and antibody cross neutralization involving multiple strains. FluKB is representation of a new generation of databases that integrates data, analytical tools, and analytical workflows that enable comprehensive analysis and automatic generation of analysis reports.

Physical detection of influenza A epitopes identifies a stealth subset on human lung epithelium evading natural CD8 immunity.

Vaccines eliciting immunity against influenza A viruses (IAVs) are currently antibody-based with hemagglutinin-directed antibody titer the only universally accepted immune correlate of protection. To investigate the disconnection between observed CD8 T-cell responses and immunity to IAV, we used a Poisson liquid chromatography data-independent acquisition MS method to physically detect PR8/34 (H1N1), X31 (H3N2), and Victoria/75 (H3N2) epitopes bound to HLA-A*02:01 on human epithelial cells following in vitro infection. Among 32 PR8 peptides (8-10mers) with predicted IC50 < 60 nM, 9 were present, whereas 23 were absent. At 18 h postinfection, epitope copies per cell varied from a low of 0.5 for M13-11 to a high of >500 for M1(58-66) with PA, HA, PB1, PB2, and NA epitopes also detected. However, aside from M1(58-66), natural CD8 memory responses against conserved presented epitopes were either absent or only weakly observed by blood Elispot. Moreover, the functional avidities of the immunodominant M1(58-66)/HLA-A*02:01-specific T cells were so poor as to be unable to effectively recognize infected human epithelium. Analysis of T-cell responses to primary PR8 infection in HLA-A*02:01 transgenic B6 mice underscores the poor avidity of T cells recognizing M1(58-66). By maintaining high levels of surface expression of this epitope on epithelial and dendritic cells, the virus exploits the combination of immunodominance and functional inadequacy to evade HLA-A*02:01-restricted T-cell immunity. A rational approach to CD8 vaccines must characterize processing and presentation of pathogen-derived epitopes as well as resultant immune responses. Correspondingly, vaccines may be directed against "stealth" epitopes, overriding viral chicanery.

Human leukocyte antigen typing using a knowledge base coupled with a high-throughput oligonucleotide probe array analysis.

Human leukocyte antigens (HLA) are important biomarkers because multiple diseases, drug toxicity, and vaccine responses reveal strong HLA associations. Current clinical HLA typing is an elimination process requiring serial testing. We present an alternative in situ synthesized DNA-based microarray method that contains hundreds of thousands of probes representing a complete overlapping set covering 1,610 clinically relevant HLA class I alleles accompanied by computational tools for assigning HLA type to 4-digit resolution. Our proof-of-concept experiment included 21 blood samples, 18 cell lines, and multiple controls. The method is accurate, robust, and amenable to automation. Typing errors were restricted to homozygous samples or those with very closely related alleles from the same locus, but readily resolved by targeted DNA sequencing validation of flagged samples. High-throughput HLA typing technologies that are effective, yet inexpensive, can be used to analyze the world's populations, benefiting both global public health and personalized health care.

Pathway analysis and transcriptomics improve protein identification by shotgun proteomics from samples comprising small number of cells--a benchmarking study.

BACKGROUND: Proteomics research is enabled with the high-throughput technologies, but our ability to identify expressed proteome is limited in small samples. The coverage and consistency of proteome expression are critical problems in proteomics. Here, we propose pathway analysis and combination of microproteomics and transcriptomics analyses to improve mass-spectrometry protein identification from small size samples. RESULTS: Multiple proteomics runs using MCF-7 cell line detected 4,957 expressed proteins. About 80% of expressed proteins were present in MCF-7 transcripts data; highly expressed transcripts are more likely to have expressed proteins. Approximately 1,000 proteins were detected in each run of the small sample proteomics. These proteins were mapped to gene symbols and compared with gene sets representing canonical pathways, more than 4,000 genes were extracted from the enriched gene sets. The identified canonical pathways were largely overlapping between individual runs. Of identified pathways 182 were shared between three individual small sample runs. CONCLUSIONS: Current technologies enable us to directly detect 10% of expressed proteomes from small sample comprising as few as 50 cells. We used knowledge-based approaches to elucidate the missing proteome that can be verified by targeted proteomics. This knowledge-based approach includes pathway analysis and combination of gene expression and protein expression data for target prioritization. Genes present in both the enriched gene sets (canonical pathways collection) and in small sample proteomics data correspond to approximately 50% of expressed proteomes in larger sample proteomics data. In addition, 90% of targets from canonical pathways were estimated to be expressed. The comparison of proteomics and transcriptomics data, suggests that highly expressed transcripts have high probability of protein expression. However, approximately 10% of expressed proteins could not be matched with the expressed transcripts.

Big data analytics in immunology: a knowledge-based approach.

With the vast amount of immunological data available, immunology research is entering the big data era. These data vary in granularity, quality, and complexity and are stored in various formats, including publications, technical reports, and databases. The challenge is to make the transition from data to actionable knowledge and wisdom and bridge the knowledge gap and application gap. We report a knowledge-based approach based on a framework called KB-builder that facilitates data mining by enabling fast development and deployment of web-accessible immunological data knowledge warehouses. Immunological knowledge discovery relies heavily on both the availability of accurate, up-to-date, and well-organized data and the proper analytics tools. We propose the use of knowledge-based approaches by developing knowledgebases combining well-annotated data with specialized analytical tools and integrating them into analytical workflow. A set of well-defined workflow types with rich summarization and visualization capacity facilitates the transformation from data to critical information and knowledge. By using KB-builder, we enabled streamlining of normally time-consuming processes of database development. The knowledgebases built using KB-builder will speed up rational vaccine design by providing accurate and well-annotated data coupled with tailored computational analysis tools and workflow.

HPVdb: a data mining system for knowledge discovery in human papillomavirus with applications in T cell immunology and vaccinology.

High-risk human papillomaviruses (HPVs) are the causes of many cancers, including cervical, anal, vulvar, vaginal, penile and oropharyngeal. To facilitate diagnosis, prognosis and characterization of these cancers, it is necessary to make full use of the immunological data on HPV available through publications, technical reports and databases. These data vary in granularity, quality and complexity. The extraction of knowledge from the vast amount of immunological data using data mining techniques remains a challenging task. To support integration of data and knowledge in virology and vaccinology, we developed a framework called KB-builder to streamline the development and deployment of web-accessible immunological knowledge systems. The framework consists of seven major functional modules, each facilitating a specific aspect of the knowledgebase construction process. Using KB-builder, we constructed the Human Papillomavirus T cell Antigen Database (HPVdb). It contains 2781 curated antigen entries of antigenic proteins derived from 18 genotypes of high-risk HPV and 18 genotypes of low-risk HPV. The HPVdb also catalogs 191 verified T cell epitopes and 45 verified human leukocyte antigen (HLA) ligands. Primary amino acid sequences of HPV antigens were collected and annotated from the UniProtKB. T cell epitopes and HLA ligands were collected from data mining of scientific literature and databases. The data were subject to extensive quality control (redundancy elimination, error detection and vocabulary consolidation). A set of computational tools for an in-depth analysis, such as sequence comparison using BLAST search, multiple alignments of antigens, classification of HPV types based on cancer risk, T cell epitope/HLA ligand visualization, T cell epitope/HLA ligand conservation analysis and sequence variability analysis, has been integrated within the HPVdb. Predicted Class I and Class II HLA binding peptides for 15 common HLA alleles are included in this database as putative targets. HPVdb is a knowledge-based system that integrates curated data and information with tailored analysis tools to facilitate data mining for HPV vaccinology and immunology. To our best knowledge, HPVdb is a unique data source providing a comprehensive list of HPV antigens and peptides. Database URL: http://cvc.dfci.harvard.edu/hpv/.

EGFR T790M mutation as a possible target for immunotherapy; identification of HLA-A*0201-restricted T cell epitopes derived from the EGFR T790M mutation.

Treatment with epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs), such as gefitinib and erlotinib, has achieved high clinical response rates in patients with non-small cell lung cancers (NSCLCs). However, over time, most tumors develop acquired resistance to EGFR-TKIs, which is associated with the secondary EGFR T790M resistance mutation in about half the cases. Currently there are no effective treatment options for patients with this resistance mutation. Here we identified two novel HLA-A*0201 (A2)-restricted T cell epitopes containing the mutated methionine residue of the EGFR T790M mutation, T790M-5 (MQLMPFGCLL) and T790M-7 (LIMQLMPFGCL), as potential targets for EGFR-TKI-resistant patients. When peripheral blood cells were repeatedly stimulated in vitro with these two peptides and assessed by antigen-specific IFN-γ secretion, T cell lines responsive to T790M-5 and T790M-7 were established in 5 of 6 (83%) and 3 of 6 (50%) healthy donors, respectively. Additionally, the T790M-5- and T790M-7-specific T cell lines displayed an MHC class I-restricted reactivity against NSCLC cell lines expressing both HLA-A2 and the T790M mutation. Interestingly, the NSCLC patients with antigen-specific T cell responses to these epitopes showed a significantly less frequency of EGFR-T790M mutation than those without them [1 of 7 (14%) vs 9 of 15 (60%); chi-squared test, p  =  0.0449], indicating the negative correlation between the immune responses to the EGFR-T790M-derived epitopes and the presence of EGFR-T790M mutation in NSCLC patients. This finding could possibly be explained by the hypothesis that immune responses to the mutated neo-antigens derived from T790M might prevent the emergence of tumor cell variants with the T790M resistance mutation in NSCLC patients during EGFR-TKI treatment. Together, our results suggest that the identified T cell epitopes might provide a novel immunotherapeutic approach for prevention and/or treatment of EGFR-TKI resistance with the secondary EGFR T790M resistance mutation in NSCLC patients.

BlockLogo: visualization of peptide and sequence motif conservation.

BlockLogo is a web-server application for the visualization of protein and nucleotide fragments, continuous protein sequence motifs, and discontinuous sequence motifs using calculation of block entropy from multiple sequence alignments. The user input consists of a multiple sequence alignment, selection of motif positions, type of sequence, and output format definition. The output has BlockLogo along with the sequence logo, and a table of motif frequencies. We deployed BlockLogo as an online application and have demonstrated its utility through examples that show visualization of T-cell epitopes and B-cell epitopes (both continuous and discontinuous). Our additional example shows a visualization and analysis of structural motifs that determine the specificity of peptide binding to HLA-DR molecules. The BlockLogo server also employs selected experimentally validated prediction algorithms to enable on-the-fly prediction of MHC binding affinity to 15 common HLA class I and class II alleles as well as visual analysis of discontinuous epitopes from multiple sequence alignments. It enables the visualization and analysis of structural and functional motifs that are usually described as regular expressions. It provides a compact view of discontinuous motifs composed of distant positions within biological sequences. BlockLogo is available at: http://research4.dfci.harvard.edu/cvc/blocklogo/ and http://met-hilab.bu.edu/blocklogo/.

Mutated BCR-ABL generates immunogenic T-cell epitopes in CML patients.

PURPOSE: Characterization of an approach to identify leukemia neoantigens arising in the context of drug resistance. EXPERIMENTAL DESIGN: We assessed whether leukemia neoantigens could be generated from drug-resistant mutations in BCR-ABL after imatinib relapse in patients with chronic myelogenous leukemia (CML). RESULTS: We computationally predicted that approximately 70 peptides derived from 26 BCR-ABL mutations would bind eight common alleles of MHC class I (IC(50) < 1,000 nmol/L). Seven of nine imatinib-resistant CML patients were predicted to generate at least 1 peptide that binds autologous HLA alleles. We predicted and confirmed that an E255K mutation-derived peptide would bind HLA-A3 with high affinity (IC(50) = 28 nmol/L), and showed that this peptide is endogenously processed and presented. Polyfunctional E255K-specific CD8+ T cells were detected in two imatinib-resistant HLA-A3+ CML patients concurrent with an effective anti-CML response to further therapy. CONCLUSIONS: Our in vitro studies support the hypothesis that leukemia-driven genetic alterations are targeted by the immune system in association with a clinical response, and suggest the possibility of immunizing relapsed patients with CML against newly acquired tumor neoantigens.

Novel myeloma-associated antigens revealed in the context of syngeneic hematopoietic stem cell transplantation.

Targets of curative donor-derived graft-versus-myeloma (GVM) responses after allogeneic hematopoietic stem cell transplantation (HSCT) remain poorly defined, partly because immunity against minor histocompatibility Ags (mHAgs) complicates the elucidation of multiple myeloma (MM)-specific targets. We hypothesized that syngeneic HSCT would facilitate the identification of GVM-associated Ags because donor immune responses in this setting should exclusively target unique tumor Ags in the absence of donor-host genetic disparities. Therefore, in the present study, we investigated the development of tumor immunity in an HLA-A0201(+) MM patient who achieved durable remission after myeloablative syngeneic HSCT. Using high-density protein microarrays to screen post-HSCT plasma, we identified 6 Ags that elicited high-titer (1:5000-1:10 000) Abs that correlated with clinical tumor regression. Two Ags (DAPK2 and PIM1) had enriched expression in primary MM tissues. Both elicited Ab responses in other MM patients after chemotherapy or HSCT (11 and 6 of 32 patients for DAPK2 and PIM1, respectively). The index patient also developed specific CD8(+) T-cell responses to HLA-A2-restricted peptides derived from DAPK2 and PIM1. Peptide-specific T cells recognized HLA-A2(+) MM-derived cell lines and primary MM tumor cells. Coordinated T- and B-cell immunity develops against MM-associated Ags after syngeneic HSCT. DAPK1 and PIM1 are promising target Ags for MM-directed immunotherapy.

Dana-Farber repository for machine learning in immunology.

The immune system is characterized by high combinatorial complexity that necessitates the use of specialized computational tools for analysis of immunological data. Machine learning (ML) algorithms are used in combination with classical experimentation for the selection of vaccine targets and in computational simulations that reduce the number of necessary experiments. The development of ML algorithms requires standardized data sets, consistent measurement methods, and uniform scales. To bridge the gap between the immunology community and the ML community, we designed a repository for machine learning in immunology named Dana-Farber Repository for Machine Learning in Immunology (DFRMLI). This repository provides standardized data sets of HLA-binding peptides with all binding affinities mapped onto a common scale. It also provides a list of experimentally validated naturally processed T cell epitopes derived from tumor or virus antigens. The DFRMLI data were preprocessed and ensure consistency, comparability, detailed descriptions, and statistically meaningful sample sizes for peptides that bind to various HLA molecules. The repository is accessible at http://bio.dfci.harvard.edu/DFRMLI/.

Data processing and analysis for protein microarrays.

Protein microarrays are a high-throughput technology capable of generating large quantities of proteomics data. They can be used for general research or for clinical diagnostics. Bioinformatics and statistical analysis techniques are required for interpretation and reaching biologically relevant conclusions from raw data. We describe essential algorithms for processing protein microarray data, including spot-finding on slide images, Z score, and significance analysis of microarrays (SAM) calculations, as well as the concentration dependent analysis (CDA). We also describe available tools for protein microarray analysis, and provide a template for a step-by-step approach to performing an analysis centered on the CDA method. We conclude with a discussion of fundamental and practical issues and considerations.