The pathogenetic role, including its target genes, of the recurrent 3p12-p14 loss in cervical can... more The pathogenetic role, including its target genes, of the recurrent 3p12-p14 loss in cervical cancer has remained unclear. To determine the onset of the event during carcinogenesis, we used microarray techniques and found that the loss was the most frequent 3p event, occurring in 61% of 92 invasive carcinomas, in only 2% of 43 high-grade intraepithelial lesions (CIN2/3), and in 33% of 6 CIN3 lesions adjacent to invasive carcinomas, suggesting a role in acquisition of invasiveness or early during the invasive phase. We performed an integrative DNA copy number and expression analysis of 77 invasive carcinomas, where all genes within the recurrent region were included. We selected eight genes, THOC7, PSMD6, SLC25A26, TMF1, RYBP , SHQ1, EBLN2, and GBE1, which were highly down-regulated in cases with loss, as confirmed at the protein level for RYBP and TMF1 by immunohistochemistry. The eight genes were subjected to network analysis based on the expression profiles, revealing interaction partners of proteins encoded by the genes that were coordinately regulated in tumours with loss. Several partners were shared among the eight genes, indicating crosstalk in their signalling. Gene ontology analysis showed enrichment of biological processes such as apoptosis, proliferation, and stress response in the network and suggested a relationship between down-regulation of the eight genes and activation of tumourigenic pathways. Survival analysis showed prognostic impact of the eight-gene signature that was confirmed in a validation cohort of 74 patients and was independent of clinical parameters. These results support the role of the eight candidate genes as targets of the 3p12-p14 loss in cervical cancer and suggest that the strong selection advantage of the loss during carcinogenesis might be caused by a synergetic effect of several tumourigenic processes controlled by these targets.
Poor overall survival of hematopoietic stem cell transplantation (HSCT) recipients who developed ... more Poor overall survival of hematopoietic stem cell transplantation (HSCT) recipients who developed COVID-19 underlies the importance of SARS-CoV-2 vaccination. Previous studies of vaccine efficacy have reported weak humoral responses but conflicting results on T cell immunity. Here, we have examined the relationship between humoral and T cell response in 48 HSCT recipients who received two doses of Moderna's mRNA-1273 or Pfizer/BioNTech's BNT162b2 vaccines. Nearly all HSCT patients had robust T cell immunity regardless of protective humoral responses, with 18/48 (37%, IQR 8.679-5601 BAU/mL) displaying protective IgG anti-receptor binding domain (RBD) levels (>2000 BAU/mL). Flow cytometry analysis of activation induced markers (AIMs) revealed that 90% and 74% of HSCT patients showed reactivity towards immunodominant spike peptides in CD8 + and CD4 + T cells, respectively. The response rate increased to 90% for CD4 + T cells as well when we challenged the cells with a complete set of overlapping peptides spanning the entire spike protein. T cell response was detectable as early as 3 months after transplant, but only CD4 + T cell reactivity correlated with IgG anti-RBD level and time after Frontiers in Immunology frontiersin.org 01
bioRxiv (Cold Spring Harbor Laboratory), May 7, 2019
Natural killer cell repertoires are functionally diversified as a result of differentiation, home... more Natural killer cell repertoires are functionally diversified as a result of differentiation, homeostatic receptor-ligand interactions and adaptive responses to viral infections. However, the regulatory gene-circuits that define the manifold cell states and drive NK cell differentiation have not been clearly resolved. Here, we performed single-cell RNA sequencing of 26,506 cells derived from sorted phenotypically-defined human NK cell subsets to delineate a tightly coordinated differentiation process from a small population of CD56 bright precursors to adaptive NKG2C + CD56 dim NK cells. RNA velocity analysis identified a clear directionality in the transition from CD56 bright to CD56 dim NK cells, which was dominated by genes involved in transcription and translation as well as acquisition of NK cell effector function. Gene expression trends mapped to pseudotime, defined by increasing entropy, identified three distinct transcriptional checkpoints, reflecting important changes in regulatory gene-circuits. The CD56 bright NK cell population dominated pseudotime with two distinct checkpoints separating precursors from intermediate states that gradually took on transcriptional signatures similar to CD56 dim NK cells. The final checkpoint occurred during late terminal differentiation of CD56 dim NK cells and was associated with unique divergent gene-expression trends. Furthermore, we utilized this single-cell RNA sequencing resource to decipher the regulation of genes involved in lysosomal biogenesis and found a coordinated gradual increase in the RAB4 and BLOC1S gene families with differentiation into CD56 dim NK cells. These results identify important gene programs driving functional diversification and specialization during NK cell differentiation and hold potential to guide new strategies for NK cell-based cancer immunotherapy. .
Purpose: To evaluate the safety, efficacy, and immunobiological correlates of allogeneic NK-cell-... more Purpose: To evaluate the safety, efficacy, and immunobiological correlates of allogeneic NK-cell-based therapy in primary chemotherapy-refractory or relapsed high-risk myelodysplastic syndrome (MDS), secondary AML (MDS/AML), and de novo AML patients. Experimental Design: Sixteen patients received fludarabine/ cyclophosphamide conditioning combined with total lymphoid irradiation followed by adoptive immunotherapy with IL2activated haploidentical NK cells. Results: NK-cell infusions were well-tolerated, with only transient adverse events observed in the 16 patients. Six patients achieved objective responses with complete remission (CR), marrow CR, or partial remission (PR). Five patients proceeded to allogeneic hematopoietic stem cell transplantation (HSCT). Three patients are still free from disease >3 years after treatment. All evaluable patients with objective responses (5/5 evaluable) had detectable donor NK cells at days 7/14 following infusion and displayed reduction of tumor cell clones, some of which carried poor prognosis mutations. Residual lin À CD34 þ CD123 þ CD45RA þ blast cells in responders had increased total HLA class I and HLA-E expression. Responding patients displayed less pronounced activation of CD8 þ T cells and lower levels of inflammatory cytokines following NK-cell infusion. Intriguingly, despite omission of systemic IL2, all patients displayed increased frequencies of activated Ki-67 þ CD127 À FoxP3 þ CD25 hi CD4 þ Treg cells of recipient origin following NK-cell therapy. Conclusions: Overall, this study suggests that high-risk MDS is responsive to NK-cell therapy and supports the use of haploidentical NK-cell infusions as a bridge to HSCT in refractory patients. Objective clinical responses and reduction of high-risk clones were associated with detectable donor-derived NK cells, immunoediting of residual blast cells, and less pronounced host immune activation. Clin Cancer Res; 24(8); 1834-44. Ó2018 AACR.
T cells monitor the health status of cells by identifying foreign peptides displayed on their sur... more T cells monitor the health status of cells by identifying foreign peptides displayed on their surface. T-cell receptors (TCRs), which are protein complexes found on the surface of T cells, are able to bind to these peptides. This process is known as TCR recognition and constitutes a key step for immune response. Optimizing TCR sequences for TCR recognition represents a fundamental step towards the development of personalized treatments to trigger immune responses killing cancerous or virus-infected cells. In this paper, we formulated the search for these optimized TCRs as a reinforcement learning (RL) problem, and presented a framework TCRPPO with a mutation policy using proximal policy optimization. TCRPPO mutates TCRs into effective ones that can recognize given peptides. TCRPPO leverages a reward function that combines the likelihoods of mutated sequences being valid TCRs measured by a new scoring function based on deep autoencoders, with the probabilities of mutated sequences recognizing peptides from a peptide-TCR interaction predictor. We compared TCRPPO with multiple baseline methods and demonstrated that TCRPPO significantly outperforms all the baseline methods to generate positive binding and valid TCRs. These results demonstrate the potential of TCRPPO for both precision immunotherapy and peptide-recognizing TCR motif discovery.
bioRxiv (Cold Spring Harbor Laboratory), Apr 24, 2018
Inhibitory signaling during natural killer (NK) cell education translates into increased responsi... more Inhibitory signaling during natural killer (NK) cell education translates into increased responsiveness to activation; however, the intracellular mechanism for functional tuning by inhibitory receptors remains unclear. Secretory lysosomes are part of the acidic lysosomal compartment that mediates intracellular signalling in several cell types. Here we show that educated NK cells expressing self-MHC specific inhibitory killer cell immunoglobulin-like receptors (KIR) accumulate granzyme B in dense-core secretory lysosomes that converge close to the centrosome. This discrete morphological phenotype is independent of transcriptional programs that regulate effector function, metabolism and lysosomal biogenesis. Meanwhile, interference of signaling from acidic Ca 2+ stores in primary NK cells reduces target-specific Ca 2+-flux, degranulation and cytokine production. Furthermore, inhibition of PI (3,5)P 2 synthesis, or genetic silencing of the PI(3,5)P 2-regulated lysosomal Ca 2+-channel TRPML1, leads to increased granzyme B and enhanced functional potential, thereby mimicking the educated state. These results indicate an intrinsic role for lysosomal remodeling in NK cell education.
Purpose: To evaluate the safety, efficacy, and immunobiological correlates of allogeneic NK-cell-... more Purpose: To evaluate the safety, efficacy, and immunobiological correlates of allogeneic NK-cell-based therapy in primary chemotherapy-refractory or relapsed high-risk myelodysplastic syndrome (MDS), secondary AML (MDS/AML), and de novo AML patients. Experimental Design: Sixteen patients received fludarabine/ cyclophosphamide conditioning combined with total lymphoid irradiation followed by adoptive immunotherapy with IL2activated haploidentical NK cells. Results: NK-cell infusions were well-tolerated, with only transient adverse events observed in the 16 patients. Six patients achieved objective responses with complete remission (CR), marrow CR, or partial remission (PR). Five patients proceeded to allogeneic hematopoietic stem cell transplantation (HSCT). Three patients are still free from disease >3 years after treatment. All evaluable patients with objective responses (5/5 evaluable) had detectable donor NK cells at days 7/14 following infusion and displayed reduction of tumor cell clones, some of which carried poor prognosis mutations. Residual lin À CD34 þ CD123 þ CD45RA þ blast cells in responders had increased total HLA class I and HLA-E expression. Responding patients displayed less pronounced activation of CD8 þ T cells and lower levels of inflammatory cytokines following NK-cell infusion. Intriguingly, despite omission of systemic IL2, all patients displayed increased frequencies of activated Ki-67 þ CD127 À FoxP3 þ CD25 hi CD4 þ Treg cells of recipient origin following NK-cell therapy. Conclusions: Overall, this study suggests that high-risk MDS is responsive to NK-cell therapy and supports the use of haploidentical NK-cell infusions as a bridge to HSCT in refractory patients. Objective clinical responses and reduction of high-risk clones were associated with detectable donor-derived NK cells, immunoediting of residual blast cells, and less pronounced host immune activation. Clin Cancer Res; 24(8); 1834-44. Ó2018 AACR.
Two T helper (Th) cell subsets, namely Th1 and Th2 cells, play an important role in inflammatory ... more Two T helper (Th) cell subsets, namely Th1 and Th2 cells, play an important role in inflammatory diseases. The two subsets are thought to counter-regulate each other, and alterations in their balance result in different diseases. This paradigm has been challenged by recent clinical and experimental data. Because of the large number of genes involved in regulating Th1 and Th2 cells, assessment of this paradigm by modeling or experiments is difficult. Novel algorithms based on formal methods now permit the analysis of large gene regulatory networks. By combining these algorithms with in silico knockouts and gene expression microarray data from human T cells, we examined if the results were compatible with a counterregulatory role of Th1 and Th2 cells. We constructed a directed network model of genes regulating Th1 and Th2 cells through text mining and manual curation. We identified four attractors in the network, three of which included genes that corresponded to Th0, Th1 and Th2 cells. The fourth attractor contained a mixture of Th1 and Th2 genes. We found that neither in silico knockouts of the Th1 and Th2 attractor genes nor gene expression microarray data from patients with immunological disorders and healthy subjects supported a counter-regulatory role of Th1 and Th2 cells. By combining network modeling with transcriptomic data analysis and in silico knockouts, we have devised a practical way to help unravel complex regulatory network topology and to increase our understanding of how network actions may differ in health and disease.
Binding peptide generation for MHC Class I proteins with deep reinforcement learning
Bioinformatics, Jan 24, 2023
Motivation MHC Class I protein plays an important role in immunotherapy by presenting immunogenic... more Motivation MHC Class I protein plays an important role in immunotherapy by presenting immunogenic peptides to anti-tumor immune cells. The repertoires of peptides for various MHC Class I proteins are distinct, which can be reflected by their diverse binding motifs. To characterize binding motifs for MHC Class I proteins, in vitro experiments have been conducted to screen peptides with high binding affinities to hundreds of given MHC Class I proteins. However, considering tens of thousands of known MHC Class I proteins, conducting in vitro experiments for extensive MHC proteins is infeasible, and thus a more efficient and scalable way to characterize binding motifs is needed. Results We presented a de novo generation framework, coined PepPPO, to characterize binding motif for any given MHC Class I proteins via generating repertoires of peptides presented by them. PepPPO leverages a reinforcement learning agent with a mutation policy to mutate random input peptides into positive presented ones. Using PepPPO, we characterized binding motifs for around 10 000 known human MHC Class I proteins with and without experimental data. These computed motifs demonstrated high similarities with those derived from experimental data. In addition, we found that the motifs could be used for the rapid screening of neoantigens at a much lower time cost than previous deep-learning methods. Availability and implementation The software can be found in https://github.com/minrq/pMHC. Supplementary information Supplementary data are available at Bioinformatics online.
bioRxiv (Cold Spring Harbor Laboratory), Jun 6, 2022
During the COVID-19 pandemic, several SARS-CoV-2 variants of concern (VOC) emerged, bringing with... more During the COVID-19 pandemic, several SARS-CoV-2 variants of concern (VOC) emerged, bringing with them varying degrees of health and socioeconomic burdens. In particular, the Omicron VOC displayed distinct features of increased transmissibility accompanied by antigenic drift in the spike protein that partially circumvented the ability of pre-existing antibody responses in the global population to neutralize the virus. However, T cell immunity has remained robust throughout all the different VOC transmission waves and has emerged as a critically important correlate of protection against SARS-CoV-2 and it's VOCs, in both vaccinated and infected individuals. Therefore, as SARS-CoV-2 VOCs continue to evolve, it is crucial that we characterize the correlates of protection and the potential for immune escape for both B cell and T cell human immunity in the population. Generating the insights necessary to understand T cell immunity, experimentally, for the global human population is at present critical but a time consuming, expensive, and laborious process. Further, it is not feasible to generate global or universal insights into T cell immunity in an actionable time frame for potential future emerging VOCs. However, using computational means we can expedite and provide early insights into the correlates of T cell protection. In this study, we generated and reveal insights on the T cell epitope landscape for the five main SARS-CoV-2 VOCs observed to date. We demonstrated here using a unique AI prediction platform, a strong concordance in global T cell protection across all mutated peptides for each VOC. This was modeled using the most frequent HLA alleles in the human population and covers the most common HLA haplotypes in the human population. The AI resource generated through this computational study and associated insights may guide the development of T cell vaccines and diagnostics that are even more robust against current and future VOCs, and their emerging subvariants.
CHAPTER 13. Protein Quantification by MRM for Biomarker Validation
Quantitative Proteomics, 2014
In this chapter we describe how mass spectrometry-based quantitative protein measurements by mult... more In this chapter we describe how mass spectrometry-based quantitative protein measurements by multiple reaction monitoring (MRM) have opened up the opportunity for the assembly of large panels of candidate protein biomarkers that can be simultaneously validated in large clinical cohorts to identify diagnostic protein biomarker signatures. We outline a workflow in which candidate protein biomarker panels are initially assembled from multiple diverse sources of discovery data, including proteomics and transcriptomics experiments, as well as from candidates found in the literature. Subsequently, the individual candidates in these large panels may be prioritised by application of a range of bioinformatics tools to generate a refined panel for which MRM assays may be developed. We describe a process for MRM assay design and implementation, and illustrate how the data generated from these multiplexed MRM measurements of prioritised candidates may be subjected to a range of statistical tools to create robust biomarker signatures for further clinical validation in large patient sample cohorts. Through this overall approach MRM has the potential to not only support individual biomarker validation but also facilitate the development of clinically useful protein biomarker signatures.
Introduction 3. Materials and Methods 3.1. Cell cultures 3.2. Real Time-PCR 3.2.1. miRNA RT-PCR 3... more Introduction 3. Materials and Methods 3.1. Cell cultures 3.2. Real Time-PCR 3.2.1. miRNA RT-PCR 3.2.2. mRNA RT-PCR 3.3. Gene expression re-analysis during human monocyte differentiation to DCs 3.4. Gene expression analysis after miR-21 over-expression 3.5. Computational target prediction, GO and protein interaction analysis 4. Results 4.1. miRNA expression during human monocyte differentiation to DCs 4.2. Computationally-predicted mRNA targets of miR-21 4.3. Multiple computationally-predicted miRNA-21 targets were repressed during monocyte differentiation to DCs 4.4. Gene expression profile in response to miR-21 upregulation 4.5. Cross validation of the selected genes 4.6. Protein interaction maps of the identified targets 5. Discussion 6. Acknowledgment 7. References
Background: The accurate screening of tumor genomic landscapes for somatic mutations using high-t... more Background: The accurate screening of tumor genomic landscapes for somatic mutations using high-throughput sequencing involves a crucial step in precise clinical diagnosis and targeted therapy. However, the complex inherent features of cancer tissue, especially, tumor genetic intra-heterogeneity coupled with the problem of sequencing and alignment artifacts, makes somatic variant calling a challenging task. Current variant filtering strategies, such as rule-based filtering and consensus voting of different algorithms, have previously helped to increase specificity, although comes at the cost of sensitivity. Methods: In light of this, we have developed the NeoMutate framework which incorporates 7 supervised machine learning (ML) algorithms to exploit the strengths of multiple variant callers, using a non-redundant set of biological and sequence features. We benchmarked NeoMutate by simulating more than 10,000 bona fide cancer-related mutations into three well-characterized Genome in a Bottle (GIAB) reference samples. Results: A robust and exhaustive evaluation of NeoMutate's performance based on 5-fold cross validation experiments, in addition to 3 independent tests, demonstrated a substantially improved variant detection accuracy compared to any of its individual composite variant callers and consensus calling of multiple tools. Conclusions: We show here that integrating multiple tools in an ensemble ML layer optimizes somatic variant detection rates, leading to a potentially improved variant selection framework for the diagnosis and treatment of cancer.
Recently, the Immunological Genome Project (ImmGen) completed the first phase of the goal to unde... more Recently, the Immunological Genome Project (ImmGen) completed the first phase of the goal to understand the molecular circuitry underlying the immune cell lineage in mice. That milestone resulted in the creation of the most comprehensive collection of gene expression profiles in the immune cell lineage in any model organism of human disease. There is now a requisite to examine this resource using bioinformatics integration with other molecular information, with the aim of gaining deeper insights into the underlying processes that characterize this immune cell lineage. We present here a bioinformatics approach to study differential protein interaction mechanisms across the entire immune cell lineage, achieved using affinity propagation applied to a protein interaction network similarity matrix. We demonstrate that the integration of protein interaction networks with the most comprehensive database of gene expression profiles of the immune cells can be used to generate hypotheses into the underlying mechanisms governing the differentiation and the differential functional activity across the immune cell lineage. This approach may not only serve as a hypothesis engine to derive understanding of differentiation and mechanisms across the immune cell lineage, but also help identify possible immune lineage specific and common lineage mechanism in the cells protein networks.
Data from Membranous Expression of Ectodomain Isoforms of the Epidermal Growth Factor Receptor Predicts Outcome after Chemoradiotherapy of Lymph Node–Negative Cervical Cancer
Precise HLA genotyping is of great clinical importance, albeit a challenging bioinformatics endea... more Precise HLA genotyping is of great clinical importance, albeit a challenging bioinformatics endeavor because of the hyper polymorphism of the HLA region. The ever-increasing availability of next-generation sequencing (NGS) solutions has spurred the development of several computational methods for predicting HLA genotypes from NGS data. Although some of these tools genotype HLA Class I alleles reasonably well, there is a need to incorporate integrative parameters related to ethnicity frequency information, in order to improve performance for both Class I and Class II alleles. Here, we present a bioinformatics method that addresses some of the current shortfalls in HLA genotyping from NGS. First, reads that map to the HLA region is aligned against a comprehensive library of reference HLA alleles. The allele type was then subsequently determined on the basis of the distribution of aligned reads, and the prior probabilities of the ethnic frequencies of alleles. Three public NGS datasets were used to benchmark the approach against six similar tools. The method outlined in this manuscript displayed an overall accuracy of 98.73% for Class I and 96.37% for Class II alleles. We illustrate an improved integrative approach that outperforms existing tools and is able to predict HLA alleles with improved fidelity for both Class I and Class II alleles.
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Papers by Trevor Clancy