Biology subjects

Kim, J. Y.

Publications and source records attributed to Kim, J. Y..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Noninvasive time-lapse 3D subcellular analysis of embryo development for machine learning-enabled prediction of blastocyst formation

Accurate embryo quality assessment is central to improving outcomes in in vitro fertilization (IVF), yet current practice relies mainly on subjective two-dimensional (2D) morphology. Here we present a label-free framework for quantitative three-dimensional (3D) embryo phenotyping using low-coherence holotomography (HT). Time-lapse HT enabled volumetric imaging of mouse embryos from the 2-cell stage to the blastocyst without affecting developmental competence, capturing subcellular features at high resolution. Quantitative analysis revealed that matured embryos exhibited higher blastomere counts, greater spatial variability, and tighter nuclear packing, whereas arrested embryos showed enlarged blastomeres, elevated cytoplasmic heterogeneity, and fewer, larger nuclei. Machine learning models trained on these features achieved robust prediction of blastocyst formation (AUC up to 0.958). Together, these findings demonstrate that HT provides objective and interpretable 3D biomarkers that could augment and transform embryo selection in IVF.

developmental biology↗

bioRxiv2024

Unveiling the influence of tumor and immune signatures on immune checkpoint therapy in advanced lung cancer

This study investigates the variability among patients with non-small cell lung cancer (NSCLC) in their responses to immune checkpoint inhibitors (ICI). Recognizing that patients with advanced-stage NSCLC rarely qualify for surgical interventions, it becomes crucial to identify biomarkers that influence responses to ICI therapy. We conducted an analysis of single-cell transcriptomes from 33 lung cancer biopsy samples, with a particular focus on 14 core samples taken before the initiation of palliative ICI treatment. Our objective was to link tumor and immune cell profiles with patient responses to ICI. We discovered that ICI non-responders exhibited a higher presence of CD4+ regulatory T cells, resident memory T cells, and TH17 cells. This contrasts with the diverse activated CD8+ T cells found in responders. Furthermore, tumor cells in non-responders frequently showed heightened transcriptional activity in the NF-kB and STAT3 pathways, suggesting a potential inherent resistance to ICI therapy. Through the integration of immune cell profiles and tumor molecular signatures, we achieved an discriminative power (AUC) exceeding 95% in identifying patient responses to ICI treatment. These results underscore the crucial importance of the interplay between tumor and immune microenvironment, including within metastatic sites, in affecting the effectiveness of ICIs in NSCLC.

bioinformatics↗

bioRxiv2023

Genomic hypomethylation in cell-free DNA predicts responses to checkpoint blockade in lung and breast cancer

Genomic hypomethylation has recently been identified as a determinant of therapeutic responses to immune checkpoint blockade (ICB). However, tumor tissue is often unattainable, and tissue-based methylation profiling suffers from low tumor purity. In this study, we developed an assay named iMethyl to estimate the genomic hypomethylation status from cell-free DNA (cfDNA) as well as tissue by deep targeted sequencing of young LINE-1 elements with > 400,000 reads per sample. iMethyl was applied to a total of 653 ICB samples encompassing lung cancer (cfDNA n=167; tissue n=137; cfDNA early during treatment n=40), breast cancer (cfDNA n=91; tissue n=50; PBMC n=50; cfDNA at progression n=44), and ovarian cancer (tissue n=74). iMethyl-tissue had better predictive power than tumor mutation burden and PD-L1 expression. Furthermore, iMethyl-liquid predicted ICB responses accurately regardless of the tumor purity of tissue samples. iMethyl-liquid was also able to monitor therapeutic responses early during treatment (3 or 6 weeks after initiation of ICB) and detect progressive hypomethylation accompanying tumor progression. In conclusion, our method allows for reliable noninvasive prediction, early evaluation, and monitoring of clinical responses to ICB therapy.

cancer biology↗

bioRxiv2023

Harnessing the pangenome for genomic surveillance: Salmonella enterica serotype Typhi as a paradigm

Public health genomic surveillance systems typically measure genome relatedness and infer molecular epidemiological relationships using chromosomal loci alone - an approximation of vertical evolution, or homology-by-descent. The accessory genome, composed of plasmids and other mobile genetic elements, reflects horizontal gene transfer and serves as an important mechanism of bacterial evolution, enabling rapid adaptation. Measuring homology in the accessory genome - homology-by-admixture - could offer important molecular epidemiological information for public health application. We applied Jaccard Index and a novel genome length distance metric to compute pangenome relatedness for the globally-important pathogen Salmonella enterica serotype Typhi (Typhi), and graphically express both homology-by-descent and homology-by-admixture in a reticulate network. Jaccard Index Network Analysis revealed structure in the Typhi pangenome that can be harnessed to enhance discriminatory power for surveillance, track antimicrobial resistance, and refine our understanding of homology for outbreak management and prevention. This offers a more intricate, multidimensional framework for understanding pathogen evolution. Significance StatementBacterial relatedness is often measured and visualized using chromosomal comparison and phylogenetic trees. While valuable, this approach captures only the vertical evolutionary dimension and excludes genetic material acquired or lost through horizontal gene transfer. We present an approach for measuring and visualizing bacterial relatedness using all core and accessory genetic material and discuss the interpretation of resulting reticulate networks of bacterial genomes. In application to Salmonella Typhi, Jaccard Index Network Analysis revealed structure in populations of this pathogen that may be harnessed for public health applications. This approach captures both vertical and horizontal evolutionary dimensions, offering an intricate genetic framework for exploring pathogen evolution.

genomics↗

bioRxiv2023

Handling difficult cryo-ET samples: A study with primary neurons from Drosophila melanogaster

Cellular neurobiology has benefited from recent advances in the field of cryo-electron tomography (cryo-ET). Numerous structural and ultrastructural insights have been obtained from plunge-frozen primary neurons cultured on electron microscopy grids. With most primary neurons been derived from rodent sources, we sought to expand the breadth of sample availability by using primary neurons derived from 3rd instar Drosophila melanogaster larval brains. Ultrastructural abnormalities were encountered while establishing this model system for cryo-ET, which were exemplified by excessive membrane blebbing and cellular fragmentation. To optimize neuronal samples, we integrated substrate selection, micropatterning, montage data collection, and chemical fixation. Efforts to address difficulties in establishing Drosophila neurons for future cryo-ET studies in cellular neurobiology also provided insights that future practitioners can use when attempting to establish other cell-based model systems.

biophysics↗

bioRxiv2023

Data-driven discovery of innate immunomodulators via machine learning-guided high throughput screening

The innate immune response is vital for the success of prophylactic vaccines and immunotherapies. Control of signaling in innate immune pathways can improve prophylactic vaccines by inhibiting unfavorable systemic inflammation and immunotherapies by enhancing immune stimulation. In this work, we developed a machine learning-enabled active learning pipeline to guide in vitro experimental screening and discovery of small molecule immunomodulators that improve immune responses by altering the signaling activity of innate immune responses stimulated by traditional pattern recognition receptor agonists. Molecules were tested by in vitro high throughput screening (HTS) where we measured modulation of the nuclear factor{kappa} -light-chain-enhancer of activated B-cells (NF-{kappa}B) and the interferon regulatory factors (IRF) pathways. These data were used to train data-driven predictive models linking molecular structure to modulation of the NF-{kappa}B and IRF responses using deep representational learning, Gaussian process regression, and Bayesian optimization. By interleaving successive rounds of model training and in vitro HTS, we performed an active learning-guided traversal of a 139,998 molecule library. After sampling only[~] 2% of the library, we discovered viable molecules with unprecedented immunomodulatory capacity, including those capable of suppressing NF-{kappa}B activity by up to 15-fold, elevating NF-{kappa}B activity by up to 5-fold, and elevating IRF activity by up to 6-fold. We extracted chemical design rules identifying particular chemical fragments as principal drivers of specific immunomodulation behaviors. We validated the immunomodulatory effect of a subset of our top candidates by measuring cytokine release profiles. Of these, one molecule induced a 3-fold enhancement in IFN-{beta} production when delivered with a cyclic di-nucleotide stimulator of interferon genes (STING) agonist. In sum, our machine learning-enabled screening approach presents an efficient immunomodulator discovery pipeline that has furnished a library of novel small molecules with a strong capacity to enhance or suppress innate immune signaling pathways to shape and improve prophylactic vaccination and immunotherapies.

immunology↗

bioRxiv2023

Estimating Insulin Sensitivity and Beta-Cell Function from the Oral Glucose Tolerance Test: Validation of a new Insulin Sensitivity and Secretion (ISS) Model

Efficient and accurate methods to estimate insulin sensitivity (SI) and beta-cell function (BCF) are of great importance for studying the pathogenesis and treatment effectiveness of type 2 diabetes. Many methods exist, ranging in input data and technical requirements. Oral glucose tolerance tests (OGTTs) are preferred because they are simpler and more physiological. However, current analytical methods for OGTT-derived SI and BCF also range in complexity; the oral minimal models require mathematical expertise for deconvolution and fitting differential equations, and simple algebraic models (e.g., Matsuda index, insulinogenic index) may produce unphysiological values. We developed a new ISS (Insulin Secretion and Sensitivity) model for clinical research that provides precise and accurate estimates of SI and BCF from a standard OGTT, focusing on effectiveness, ease of implementation, and pragmatism. The model was developed by fitting a pair of differential equations to glucose and insulin without need of deconvolution or C-peptide data. The model is derived from a published model for longitudinal simulation of T2D progression that represents glucose-insulin homeostasis, including post-challenge suppression of hepatic glucose production and first- and second-phase insulin secretion. The ISS model was evaluated in three diverse cohorts including individuals at high risk of prediabetes (adult women with a wide range of BMI and adolescents with obesity). The new model had strong correlation with gold-standard estimates from intravenous glucose tolerance tests and hyperinsulinemic-euglycemic clamp. The ISS model has broad clinical applicability among diverse populations because it balances performance, fidelity, and complexity to provide a reliable phenotype of T2D risk.

physiology↗

bioRxiv2023

ER-to-lysosome Ca2+ refilling followed by K+ efflux-coupled store-operated Ca2+ entry in inflammasome activation and metabolic inflammation

We studied lysosomal Ca2+ in inflammasome. LPS+palmitic acid (PA) decreased lysosomal Ca2+ ([Ca2+]Lys) and increased [Ca2+]i through mitochondrial ROS, which was suppressed in Trpm2-KO macrophages. Inflammasome activation and metabolic inflammation in adipose tissue of high-fat diet (HFD)-fed mice were ameliorated by Trpm2 KO. ER[->]lysosome Ca2+ refilling occurred after lysosomal Ca2+ release whose blockade attenuated LPS+PA-induced inflammasome. Subsequently, store-operated Ca2+entry (SOCE) was activated whose inhibition suppressed inflammasome. SOCE was coupled with K+ efflux whose inhibition reduced ER Ca2+ content ([Ca2+]ER) and impaired [Ca2+]Lys recovery. LPS+PA activated KCa3.1 channel, a Ca2+-activated K+ channel. Inhibitors of KCa3.1 channel or Kcnn4 KO reduced [Ca2+]ER, attenuated increase of [Ca2+]i or inflammasome activation by LPS+PA, and ameliorated HFD-induced inflammasome or metabolic inflammation. Lysosomal Ca2+ release induced delayed JNK and ASC phosphorylation through CAMKII-ASK1. These results suggest a novel role of lysosomal Ca2+ release sustained by ER[->]lysosome Ca2+ refilling and K+ efflux through KCa3.1 channel in inflammasome activation and metabolic inflammation.

immunology↗

bioRxiv2023

Tubular Mitochondrial Pyruvate Carrier Disruption Elicits Redox Adaptations that Protect from Acute Kidney Injury

Energy-intensive kidney reabsorption processes essential for normal whole-body function are maintained by tubular epithelial cell metabolism. Tubular metabolism changes markedly following acute kidney injury (AKI), but which changes are adaptive versus maladaptive remain poorly understood. In publicly available data sets, we noticed a consistent downregulation of the mitochondrial pyruvate carrier (MPC) after AKI, which we experimentally confirmed. To test the functional consequences of MPC downregulation, we generated novel tubular epithelial cell-specific Mpc1 knockout (MPC TubKO) mice. 13C-glucose tracing, steady-state metabolomic profiling, and enzymatic activity assays revealed that MPC TubKO coordinately increased activities of the pentose phosphate pathway and the glutathione and thioredoxin oxidant defense systems. Following rhabdomyolysis-induced AKI, MPC TubKO decreased markers of kidney injury and oxidative damage and strikingly increased survival. Our findings suggest that decreased mitochondrial pyruvate uptake is a central adaptive response following AKI and raise the possibility of therapeutically modulating the MPC to attenuate AKI severity.

biochemistry↗

bioRxiv2022

Metabolic rewiring of mitochondria in senescence revealed by time-resolved analysis of the mitochondrial proteome

Mitochondrial dysfunction and cellular senescence are hallmarks of aging. However, the relationship between these two phenomena remains incompletely understood. In this study, we investigated the rewiring of mitochondria upon development of the senescent state in human IMR90 fibroblasts. Determining the bioenergetic activities and abundance of mitochondria, we demonstrate that senescent cells accumulate mitochondria with reduced OXPHOS activity, resulting in an overall increase of mitochondrial activities in senescent cells. Time-resolved proteomic analyses revealed extensive reprogramming of the mitochondrial proteome upon senescence development and allowed the identification of metabolic pathways that are rewired with different kinetics upon establishment of the senescent state. Among the early-responding pathways, the degradation of branched-chain amino acid (BCAA) was increased, while the one carbon-folate metabolism was decreased. Late-responding pathways include lipid metabolism and mitochondrial translation. These signatures were confirmed by metabolic tracing experiments, highlighting metabolic rewiring as a central feature of mitochondria in cellular senescence. Together, our data provide an unprecedentedly comprehensive view on the metabolic status of mitochondria in senescent cells and reveal how the mitochondrial proteome adapts to the induction of senescence.

cell biology↗

bioRxiv2022

Extensive mosaicism by somatic L1 retrotransposition in normal human cells

Over the course of an individuals lifetime, genomic alterations accumulate in somatic cells. However, the mutational landscape by retrotranspositions of long interspersed nuclear element-1 (L1), a widespread mobile element in the human genome, is poorly understood in normal cells. Here, we explored the whole-genome sequences of 892 single-cell clones established from various tissues collected from 28 individuals. Remarkably, 88% of colorectal epithelial cells acquired somatic L1 retrotranspositions (soL1Rs), carrying [~]3 events per cell on average with substantial intra- and inter-individual variances, which was accelerated at least 10-fold during tumourigenesis. Breakpoints of soL1Rs suggested that a few variant mechanisms can be involved in the L1 retrotransposition processes. Fingerprinting of donor L1s using source-specific unique sequences revealed 34 hot L1s, 44% of which were newly discovered in this study, and many ultra-rare hot L1s in the human population showed higher retrotransposition potential in somatic lineages than common sources. Multi-dimensional analysis of soL1Rs with early embryonic developmental relationships, genome-wide methylation, and gene expression profiles of the clones demonstrated that (1) soL1Rs occur from early embryogenesis at a substantial rate, (2) epigenetic activation of hot L1s is stochastically acquired during the wave of early global epigenomic reprogramming, rather than by the sporadic loss-of-methylation at the late stage, and (3) most L1 transcripts in the cytoplasm do not generate soL1Rs in somatic lineages. In summary, this study provides insights into the retrotransposition dynamics of L1s in the human genome and the resultant somatic mosaicism in normal human cells.

genomics↗

bioRxiv2022

Relative infectivity of the SARS-CoV-2 Omicron variant in human alveolar cells

With the emergence of multiple highly transmissible SARS-CoV-2 variants during the recent pandemic, the comparison of their infectivity has become a substantially critical issue for public health. However, a direct assessment of these viral characteristics has been challenging due to the lack of appropriate experimental models and efficient methods. Here, we integrated human alveolar organoids and single-cell transcriptome sequencing techniques to facilitate the evaluation. In a proof-of-concept study using the assay with four highly transmissible SARS-CoV-2 variants, including GR (B.1.1.119), Alpha (B.1.1.7), Delta (B.1.617.2), and Omicron (BA.1), a rapid evaluation of the relative infectivity was possible. Our results demonstrate that the Omicron (BA.1) variant is 3-5-fold more infectious to human alveolar cells than the other SARS-CoV-2 variants at the early phase of infection. To our knowledge, this study provides the first direct measurement of the infectivity of the Omicron variant and new experimental procedures that can be applied for monitoring newly emerging viral variants.

microbiology↗

bioRxiv2022

Mutational Changes and Enhanced Cell Migration during Transformation of Breast Non-tumorigenic Cells to Tumorigenic Cells

Although cancer stem cells (CSCs) play a major role in tumorigenesis and metastasis, the role of genetic alterations in invasiveness of CSCs is still unclear. Tumor microenvironment signals, such as extracellular matrix (ECM) composition, significantly influence cell behaviors. Unfortunately, these signals are often lost in in vitro cell culture. This study determines putative CSC populations, examines genetic changes during tumorigenesis of human breast epithelial stem cells, and investigates single-cell migration properties on ECM-mimetic platforms. Whole exome sequencing data indicate that tumorigenic cells have a higher somatic mutation burden than non-tumorigenic cells, and that mutations exclusive to tumorigenic cells exhibit higher predictive deleterious scores. Tumorigenic cells exhibit distinct somatic copy number variations (CNVs) including gain of duplications in chromosomes 5 and 8. ECM-mimetic topography selectively enhances migration speed of tumorigenic cells, but not of non-tumorigenic cells, and results in a wide distribution of tumorigenic single-cell migration speeds, suggesting heterogeneity in cellular sensing of contact guidance cues. This study identifies mutations and CNVs acquired during breast tumorigenesis, which can be associated with enhanced migration of breast tumorigenic cells, and demonstrates that a nanotopographically-defined platform can be applied to recapitulate an ECM structure for investigating cellular migration in the simulated tumor microenvironment.

cancer biology↗

bioRxiv2022

The OncoLoop network-based precision oncology framework

At present, prioritizing cancer treatments at the individual patient level remains challenging, and performing co-clinical studies using patient-derived models in real-time is often not feasible. To circumvent these challenges, we introduce OncoLoop, a precision medicine framework to predict and validate drug sensitivity in human tumors and their pre-existing high-fidelity (cognate) model(s) by leveraging perturbational profiles of clinically-relevant oncology drugs. As proof-of-concept, we applied OncoLoop to prostate cancer (PCa) using a series of genetically-engineered mouse models (GEMMs) that recapitulate a broad spectrum of disease states, including castration-resistant, metastatic, and neuroendocrine prostate cancer. Interrogation of published cohorts using Master Regulator (MR) conservation analysis revealed that most patients were represented by at least one cognate GEMM-derived tumor (GEMM-DT). Drugs predicted to invert MR activity in patients and their cognate GEMM-DTs were successfully validated, including in two cognate allografts and one cognate patient-derived xenograft (PDX). OncoLoop is a highly generalizable framework that can be extended to other cancers and potentially other diseases. Significance StatementOncoLoop is a transcriptomic-based experimental and computational framework that can support rapid-turnaround co-clinical studies to identify and validate drugs for individual patients, which can then be readily adapted to clinical practice. This framework should be applicable in many cancer contexts for which appropriate models and drug perturbation data are available.

cancer biology↗

bioRxiv2022

Neural mechanism of acute stress management by trace aminergic signalling in the lateral habenula

Stress management is a prerequisite for the survival of vertebrates because chronic stress may cause depression and is known to negatively modulate the dopaminergic reward system1. Enhanced excitability of neurons in the lateral habenula (LHb) induced by chronic stress is essential for silencing dopaminergic neurons in the ventral tegmental area (VTA) via GABAergic neurons in the rostromedial tegmental nucleus (RMTg)2-10. However, the effect of acute stress on the LHb-RMTg-VTA pathway is unknown11-14. Here, we show that both aromatic L-amino acid decarboxylase (AADC)-expressing neurons (D-neurons)15 in the LHb and dopaminergic neurons in the VTA are activated by acute stress, whereas GABAergic neurons in the RMTg are not. Selective stimulation of LHb D-neurons and AADC knockdown of these neurons reverse-regulate the RMTg-VTA pathway. Circuit tracing and electrophysiology data demonstrate that trace aminergic signalling by LHb D-neurons directly suppresses RMTg GABAergic neurons. Furthermore, local activation of trace amine-associated receptor 1 (TAAR1; a trace amine receptor) in the RMTg is sufficient to rescue the despair-like behaviour produced by the loss of AADC expression. Our results identify a novel efferent pathway from the LHb to the RMTg whereby trace aminergic signalling allows the brain to manage acute stress by preventing VTA dopaminergic neuron hypoactivity. The TAAR1-mediated trace aminergic signalling in the LHb-RMTg pathway may hold promise as a therapeutic target for stress-mediated psychological diseases.

neuroscience↗

bioRxiv2022

Correlative cryogenic montage electron tomography for comprehensive in-situ whole-cell structural studies

Imaging large fields of view while preserving high-resolution structural information remains a challenge in low-dose cryo-electron tomography. Here, we present robust tools for montage electron tomography tailored for vitrified specimens. The integration of correlative cryo-fluorescence microscopy, focused-ion beam milling, and micropatterning produces contextual three-dimensional architecture of cells. Montage tilt series may be processed in their entirety or as individual tiles suitable for sub-tomogram averaging, enabling efficient data processing and analysis.

biophysics↗

bioRxiv2021

Genuine Selective Caspase-2 Inhibition with new Irreversible Small Peptidomimetics

Caspase-2 (Casp2) is a promising therapeutic target in several human diseases including nonalcoholic steatohepatitis (NASH) and Alzheimers disease (AD). However, the design of active-site-directed inhibitor selective to individual caspase family members is challenging because caspases have extremely similar active sites. Here we present new peptidomimetics derived from the VDVAD pentapeptide structure, harboring non-natural modifications at the P2 position and an irreversible warhead. Enzyme kinetics shows that these new compounds, such as LJ2 or its specific isomer LJ2a, and LJ3a, strongly and irreversibly inhibit Casp2 with genuine selectivity. According to Casp2 role in cellular stress responses, LJ2 inhibits cell death induced by microtubule destabilization or hydroxamic acid-based deacetylase inhibition. The most potent peptidomimetic, LJ2a, inhibits human Casp2 with a remarkably high inactivation rate (k3/Ki ~ 5 500 000 M-1s-1) and the most selective inhibitor, LJ3a, has a near to 1000 times higher inactivation rate on Casp2 as compared to Casp3. Structural analysis of LJ3a shows that spatial configuration of C at the P2 position determines inhibitor efficacy. In transfected human cell lines overexpressing site-1 protease (S1P), sterol regulatory element-binding protein 2 (SREBP2) and Casp2, LJ2a and LJ3a fully inhibit SREBP2 activation, suggesting a potential to prevent NASH development. Furthermore, in primary hippocampal neurons treated with {beta}-amyloid oligomers, submicromolar concentrations of LJ2a and of LJ3a prevent synapse loss, indicating a potential for further investigations in AD treatment.

pharmacology and toxicology↗

bioRxiv2021

In vitro validation of neoantigen prediction algorithm for developing personalized cancer vaccine therapy

BackgroundThe development of personalized neoantigen-based therapeutic cancer vaccines relies on computational algorithm-based pipelines. One of the critical issues in the pipeline is obtaining higher positive predictive value (PPV) performance, i.e., how many are immunogenic when selecting the top 5 to 20 candidate neoepitopes for the vaccination. We attempted to test the PPV of a neoepitope prediction algorithm Neopepsee. MethodsSix breast cancer patients and patient-derived xenografts from three lung cancer patients and their paired peripheral blood samples were subjected to whole-exome and RNA sequencing. Neoantigen was predicted using two different algorithms (Neopepsee and pVACseq). Response of induced memory T cells to neopeptide candidates was evaluated by IFN-{gamma} Enzyme-linked immune absorbent spot (ELISpot) assays of peripheral blood mononuclear cell (PBMC) from three HLA-matched donors. Positive ELISpot response to a candidate peptide in at least 2 of 3 donor PBMC was regarded as an immunogenic response. ResultsNeopepsee predicted 159 HLA-A matched neoepitope candidates out of 898 somatic mutations in nine patients (six breast and three lung cancer patients), whereas pVACseq predicted 84 HLA-A matched candidates. A total of 26 neopeptide candidates overlapped between the two predicted candidate pools. Among the candidates, 28 (20%, 28/ 137) and 15 (20%, 15/ 75) were positive by ELISpot assay, respectively. Among 26 overlapped candidates, 20 could be tested, and 7 of them (35%) were validated by ELISpot. Neopepsee identified at least one neoepitope in 7 of 9 patients (range 0-6), compared to 6 by pVACseq (range 0-5). ConclusionAs suggested by Tumor Neoantigen Selection Alliance (TESLA), our results demonstrate low PPV of individual prediction models as well as the complementary nature of the Neopepsee and pVACseq and may help design neoepitope targeted cancer vaccines. Our data contribute a significant addition to the database of tested neoepitope candidates that can be utilized to further train and improve the prediction algorithms.

immunology↗