Biology subjects

Thompson, R. F.

Publications and source records attributed to Thompson, R. F..

12 recordsLinked to original sources

bioRxiv2025

Splicing neoepitope prediction is sensitive to methodological differences

MotivationCancer-specific neoepitopes may arise from abnormal splicing in the transcriptomic landscape (alternative splicing neoepitopes, ASNs), leading to divergent proteins with high potential immunogenicity. Predicting candidate ASNs requires a series of algorithmic and parameter choices, but no previous study has investigated the consistency and interpretability of these choices. ResultsWe apply two ASN prediction methods with 35 matched parameter sets to generate candidate ASNs for five breast and ovarian cancer samples from The Cancer Genome Atlas (TCGA). We find that: 1) junction 9-mer peptides generated by two similarly designed pipelines differed by, on average, 68.9% and 76.6% in the BRCA and OV cohorts, respectively, and putatively cancer-specific junction 9-mers from the two pipelines diverged further, by an average of 81.5 % in OV and 84.6 % in BRCA; 2) the most lenient filters in the BRCA cohort show the highest divergence, at 97 %; 3) the rate of mass spectrometry validation of ASNs protein presence in cells is dominated by the size of the input space; and 4) putatively cancer-specific ASNs found by the intersection of both pipelines can be validated when accounting for false discovery with an average of 1.74 and 284.4 candidates in the BRCA and OV cohorts, respectively. Taken together, these results highlight that ASN identification is fragile and difficult to reproduce across analysis platforms, with limited cross-pipeline overlap and strong dependence on parameter choices. Availability and ImplementationPython research code and scripts are available for download at https://github.com/ratschlab/projects2020_ohsu. Contactthompsre@ohsu.edu, raetsch@inf.ethz.ch

bioinformatics↗

bioRxiv2022

Discordant results among MHC binding affinity prediction tools

A large number of machine learning-based Major Histocompatibility Complex (MHC) binding affinity (BA) prediction tools have been developed and are widely used for both investigational and therapeutic applications, so it is important to explore differences in tool outputs. We examined predictions of four popular tools (netMHCpan, HLAthena, MHCflurry, and MHCnuggets) across a range of possible peptide sources (human, viral, and randomly generated) and MHC class I alleles. We uncovered inconsistencies in predictions of BA, allele promiscuity and the relationship between physical properties of peptides by source and BA predictions, as well as quality of training data. Our work raises fundamental questions about the fidelity of peptide-MHC binding prediction tools and their real-world implications.

bioinformatics↗

bioRxiv2022

Retained introns in long RNA-seq reads are not reliably detected in sample-matched short reads

There is growing interest in retained introns in a variety of disease contexts including cancer and aging. Many software tools have been developed to detect retained introns from short RNA-seq reads, but reliable detection is complicated by overlapping genes and transcripts as well as the presence of unprocessed or partially processed RNAs. We compared introns detected by 5 tools using short RNA-seq reads with introns observed in long RNA-seq reads from the same biological specimens and found: (1) significant disagreement among tools (Fleiss{kappa} = 0.231) such that 52.4% of all detected intron retentions were not called by more than one tool; (2) that no tool achieved greater than 20% precision or 35% recall under generous conditions; and (3) that retained intron detectability was adversely affected by greater intron length and overlap with annotated exons.

genomics↗

bioRxiv2021

Pepsickle rapidly and accurately predicts proteasomal cleavage sites for improved neoantigen identification

Proteasomal cleavage is a key component in protein turnover, as well as antigen presentation and subsequent immune response. Herein we present pepsickle, an open-source tool for proteasomal cleavage prediction with better in vivo prediction performance (AUC) and computational speed than current models available in the field, and with the ability to predict sites based on both constitutive and immunoproteasome profiles. Post-hoc filtering of predicted patient neoepitopes using pepsickle significantly enriches for immune-responsive epitopes and may represent a significant opportunity to improve current epitope prediction and vaccine development pipelines.

bioinformatics↗

bioRxiv2020

Higher-order structures of the foot-and-mouth disease virus RNA-dependent RNA polymerase required for dynamic inter-molecular interactions involved in viral genome replication

Replication of many positive-sense RNA viruses occurs within intracellular membrane-associated compartments. These are believed to provide a favourable environment for replication to occur, concentrating essential viral structural and non-structural components, as well as protecting these components from host-cell pathogen recognition and innate immune responses. However, the details of the molecular interactions and dynamics within these structures is very limited. One of the key components of the replication machinery is the RNA-dependent RNA polymerase, RdRp. This enzyme has been shown to form higher-order fibrils in vitro. Here, using the RdRp from foot-and-mouth disease virus (termed 3Dpol), we report fibril structures, solved at ~7-9 [A] resolution by cryo-EM, revealing multiple conformations of a flexible assembly. Fitting high-resolution coordinates led to the definition of potential intermolecular interactions. We employed mutagenesis using a sub-genomic replicon system to probe the importance of these interactions for replication. We use these data to propose models for the role of higher order 3Dpol complexes as a dynamic scaffold within which RNA replication can occur.

microbiology↗

bioRxiv2020

Human methylome variation across Infinium 450K data on the Gene Expression Omnibus

While DNA methylation (DNAm) is the most-studied epigenetic mark, few recent studies probe the breadth of publicly available DNAm array samples. We collectively analyzed 35,360 Illumina Infinium HumanMethylation450K DNAm array samples published on the Gene Expression Omnibus (GEO). We learned a controlled vocabulary of sample labels by applying regular expressions to metadata and used existing models to predict various sample properties including epigenetic age. We found approximately two-thirds of samples were from blood, one-quarter were from brain, and one-third were from cancer patients. 19% of samples failed at least one of Illuminas 17 prescribed quality assessments; signal distributions across samples suggest modifying manufacturer-recommended thresholds for failure would make these assessments more informative. We further analyzed DNAm variances in seven tissues (adipose, nasal, blood, brain, buccal, sperm, and liver) and characterized specific probes distinguishing them. Finally, we compiled DNAm array data and metadata, including our learned and predicted sample labels, into database files accessible via the recountmethylation R/Bioconductor companion package. Its vignettes walk the user through some analyses contained in this paper.

bioinformatics↗

bioRxiv2020

Structure of the TELO2-TTI1-TTI2 complex and its function in TOR recruitment to the R2TP chaperone

The R2TP (RUVBL1-RUVBL2-RPAP3-PIH1D1) complex, in collaboration with HSP90, functions as a chaperone for the assembly and stability of protein complexes, including RNA polymerases, snRNPs and PI3 kinase-like kinases (PIKK) such as TOR and SMG1. PIKK stabilisation depends on an additional complex of TELO2, TTI1 and TTI2 (TTT), whose structure and function are poorly understood. We have now determined the cryo-EM structure of the human R2TP-TTT complex that together with biochemical experiments reveals the mechanism of TOR recruitment to the R2TP-TTT chaperone. The HEAT-repeat TTT complex binds the kinase domain of TOR, without blocking its activity, and delivers TOR to the R2TP chaperone. In addition, TTT regulates the R2TP chaperone by inhibiting RUVBL1-RUVBL2 ATPase activity and by modulating the conformation and interactions of the PIH1D1 and RPAP3 components of R2TP. Together, our results show how TTT couples the recruitment of TOR to R2TP with the regulation of this chaperone system.

biochemistry↗

bioRxiv2020

Need for speed: Examining protein behaviour during cryoEM grid preparation at different timescales

A host of new technologies are under development to improve the quality and reproducibility of cryoEM grid preparation. Here we have systematically investigated the preparation of three macromolecular complexes using three different vitrification devices (Vitrobot, chameleon and a time-resolved cryoEM device) on various timescales, including grids made within 6 ms, (the fastest reported to date), to interrogate particle behaviour at the air-water interface for different timepoints. Results demonstrate that different macromolecular complexes can respond to the thin film environment formed during cryoEM sample preparation in highly variable ways, shedding light on why cryoEM sample preparation can be difficult to optimise. We demonstrate that reducing time between sample application and vitrification is just one tool to improve cryoEM grid quality, but that it is unlikely to be a generic silver bullet for improving the quality of every cryoEM sample preparation.

biochemistry↗

bioRxiv2019

Putatively cancer-specific alternative splicing is shared across patients and present in developmental and other non-cancer cells

We compared cancer and non-cancer RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression (GTEx) Project, and the Sequence Read Archive (SRA). We found that: 1) averaging across cancer types, 80.6% of exon-exon junctions thought to be cancer-specific based on comparison with tissue-matched samples are in fact present in other adult non-cancer tissues throughout the body; 2) 30.8% of junctions not present in any GTEx or TCGA normal tissues are shared by multiple samples within at least one cancer type cohort, and 87.4% of these distinguish between different cancer types; and 3) many of these junctions not found in GTEx or TCGA normal tissues (15.4% on average) are also found in embryological and other developmentally associated cells. This study probes the distribution of putatively cancer-specific junctions across a broad set of publicly available non-cancer human RNA-seq datasets. Overall, we identify a subset of shared cancer-specific junctions that could represent novel sources of cancer neoantigens. We further describe a framework for characterizing possible origins of these junctions, including potential developmental and embryological sources, as well as cell type-specific markers particularly related to cell types of cancer origin. These findings refine the meaning of RNA splicing event novelty, particularly with respect to the human neoepitope repertoire. Ultimately, cancer-specific exon-exon junctions may affect the anti-cancer immune response and may have a substantial causal relationship with the biology of disease.

cancer biology↗

bioRxiv2019

Burden of tumor mutations, neoepitopes, and other variants are cautionary predictors of cancer immunotherapy response and overall survival

BackgroundTumor mutational burden (TMB, the quantity of aberrant nucleotide sequences a given tumor may harbor) has been associated with response to immune checkpoint inhibitor therapy and is gaining broad acceptance as a result. However, TMB harbors intrinsic variability across cancer types, and its assessment and interpretation are poorly standardized. MethodsUsing a standardized approach, we quantify the robustness of TMB as a metric and its potential as a predictor of immunotherapy response and survival among a diverse cohort of cancer patients. We also explore the additive predictive potential of RNA-derived variants and neoepitope burden, incorporating several novel metrics of immunogenic potential. ResultsWe find that TMB is a partial predictor of immunotherapy response in melanoma and non-small cell lung cancer, but not renal cell carcinoma. We find that TMB is predictive of overall survival in melanoma patients receiving immunotherapy, but not in an immunotherapy-naive population. We also find that it is an unstable metric with potentially problematic repercussions for clinical cohort classification. We finally note minimal additional predictive benefit to assessing neoepitope burden or its bulk derivatives, including RNA-derived sources of neoepitopes. ConclusionsWe find sufficient cause to suggest that the predictive clinical value of TMB should not be overstated or oversimplified. While it is readily quantified, TMB is at best a limited surrogate biomarker of immunotherapy response. The data do not support isolated use of TMB in renal cell carcinoma.

cancer biology↗

bioRxiv2018

neoepiscope Improves Neoepitope Prediction with Multi-variant Phasing

The vast majority of tools for neoepitope prediction from DNA sequencing of complementary tumor and normal patient samples do not consider germline context or the potential for co-occurrence of two or more somatic variants on the same mRNA transcript. Without consideration of these phenomena, existing approaches are likely to produce both false positive and false negative results, resulting in an inaccurate and incomplete picture of the cancer neoepitope landscape. We developed neoepiscope chiefly to address this issue for single nucleotide variants (SNVs) and insertions/deletions (indels), and herein illustrate how germline and somatic variant phasing affects neoepitope prediction across multiple datasets. We estimate that up to [~]5% of neoepitopes arising from SNVs and indels may require variant phasing for their accurate assessment. neoepiscope is performant, flexible, and supports several major histocompatibility complex binding affinity prediction tools. We have released neoepiscope as open-source software (MIT license, https://github.com/pdxgx/neoepiscope) for broad use.\n\nKEY POINTSO_LIGermline context and somatic variant phasing are important for neoepitope prediction\nC_LIO_LIMany popular neoepitope prediction tools have issues of performance and reproducibility\nC_LIO_LIWe describe and provide performant software for accurate neoepitope prediction from DNA-seq data\nC_LI

bioinformatics↗

bioRxiv2017

Population-level distribution and putative immunogenicity of cancer neoepitopes

BackgroundTumor neoantigens are a driver of cancer immunotherapy response; however, current neoantigen prediction tools produce many candidates that require further prioritization for research/clinical applications. Additional filtration criteria and population-level understanding may help to produce refined lists of putative neoantigens. Herein, we show neoepitope immunogenicity is likely related to measures of peptide novelty and report population-level behavior of these and other metrics.\n\nMethodsWe propose four peptide novelty metrics to refine predicted neoantigenicity: tumor vs. paired normal peptide binding affinity difference, tumor vs. paired normal peptide sequence similarity, tumor vs. closest human peptide sequence similarity, and tumor vs. closest microbial peptide sequence similarity. We apply these metrics to tumor neoepitopes predicted from somatic missense mutations in The Cancer Genome Atlas (TCGA) and a cohort of melanoma patients, as well as to a group of peptides with neoepitope-specific immune response data using an extension of pVAC-Seq [1].\n\nResultsWe show neoepitope burden varies across TCGA disease sites and HLA alleles, with surprisingly low repetition of neoepitope sequences across patients or neoepitope preferences among sets of HLA alleles. Only 20.3% of predicted neoepitopes across TCGA patients displayed novel binding change based on our binding affinity difference criteria. Similarity of amino acid sequence was typically high between paired tumor-normal epitopes, but in 24.6% of cases, neoepitopes were more similar to other human peptides, or even to bacterial (56.8% of cases) or viral peptides (15.5% of cases), than their paired normal counterparts. Applied to peptides with neoepitope-specific immune response, a linear model incorporating neoepitope binding affinity, protein sequence similarity between neoepitopes and their closest viral peptides, and paired binding affinity difference was able to predict immunogenicity with an AUROC of 0.66.\n\nConclusionsOur proposed neoepitope prioritization criteria emphasize neoepitope novelty and refine patient neoepitope predictions for focus on biologically meaningful candidate neoantigens. We have demonstrated that neoepitopes should be considered not only with respect to their paired normal epitope, but with respect to the entire human proteome, as well as bacterial and viral peptides, with potential implications for neoepitope immunogenicity and personalized vaccines for cancer treatment. We conclude that putative neoantigens are highly variable across individuals as a function of both cancer genetics and personalized HLA repertoire, while the overall behavior of filtration criteria reflects predictable patterns.

cancer biology↗