Precision Neoantigen Discovery Using Large-Scale Immunopeptidomes and Composite Modeling of MHC Peptide Presentation.

Major histocompatibility complex (MHC)-bound peptides that originate from tumor-specific genetic alterations, known as neoantigens, are an important class of anticancer therapeutic targets. Accurately predicting peptide presentation by MHC complexes is a key aspect of discovering therapeutically relevant neoantigens. Technological improvements in mass spectrometry-based immunopeptidomics and advanced modeling techniques have vastly improved MHC presentation prediction over the past 2 decades. However, improvement in the accuracy of prediction algorithms is needed for clinical applications like the development of personalized cancer vaccines, the discovery of biomarkers for response to immunotherapies, and the quantification of autoimmune risk in gene therapies. Toward this end, we generated allele-specific immunopeptidomics data using 25 monoallelic cell lines and created Systematic Human Leukocyte Antigen (HLA) Epitope Ranking Pan Algorithm (SHERPA), a pan-allelic MHC-peptide algorithm for predicting MHC-peptide binding and presentation. In contrast to previously published large-scale monoallelic data, we used an HLA-null K562 parental cell line and a stable transfection of HLA allele to better emulate native presentation. Our dataset includes five previously unprofiled alleles that expand MHC diversity in the training data and extend allelic coverage in underprofiled populations. To improve generalizability, SHERPA systematically integrates 128 monoallelic and 384 multiallelic samples with publicly available immunoproteomics data and binding assay data. Using this dataset, we developed two features that empirically estimate the propensities of genes and specific regions within gene bodies to engender immunopeptides to represent antigen processing. Using a composite model constructed with gradient boosting decision trees, multiallelic deconvolution, and 2.15 million peptides encompassing 167 alleles, we achieved a 1.44-fold improvement of positive predictive value compared with existing tools when evaluated on independent monoallelic datasets and a 1.17-fold improvement when evaluating on tumor samples. With a high degree of accuracy, SHERPA has the potential to enable precision neoantigen discovery for future clinical applications.

A machine learning algorithm with subclonal sensitivity reveals widespread pan-cancer human leukocyte antigen loss of heterozygosity.

Human leukocyte antigen loss of heterozygosity (HLA LOH) allows cancer cells to escape immune recognition by deleting HLA alleles, causing the suppressed presentation of tumor neoantigens. Despite its importance in immunotherapy response, few methods exist to detect HLA LOH, and their accuracy is not well understood. Here, we develop DASH (Deletion of Allele-Specific HLAs), a machine learning-based algorithm to detect HLA LOH from paired tumor-normal sequencing data. With cell line mixtures, we demonstrate increased sensitivity compared to previously published tools. Moreover, our patient-specific digital PCR validation approach provides a sensitive, robust orthogonal approach that could be used for clinical validation. Using DASH on 610 patients across 15 tumor types, we find that 18% of patients have HLA LOH. Moreover, we show inflated HLA LOH rates compared to genome-wide LOH and correlations between CD274 (encodes PD-L1) expression and microsatellite instability status, suggesting the HLA LOH is a key immune resistance strategy.

Precision Neoantigen Discovery Using Large-scale Immunopeptidomes and Composite Modeling of MHC Peptide Presentation.

Major histocompatibility complex (MHC)-bound peptides that originate from tumor-specific genetic alterations, known as neoantigens, are an important class of anticancer therapeutic targets. Accurately predicting peptide presentation by MHC complexes is a key aspect of discovering therapeutically relevant neoantigens. Technological improvements in mass-spectrometry-based immunopeptidomics and advanced modeling techniques have vastly improved MHC presentation prediction over the past two decades. However, improvement in the sensitivity and specificity of prediction algorithms is needed for clinical applications such as the development of personalized cancer vaccines, the discovery of biomarkers for response to checkpoint blockade, and the quantification of autoimmune risk in gene therapies. Toward this end, we generated allele-specific immunopeptidomics data using 25 monoallelic cell lines and created Systematic HLA Epitope Ranking Pan Algorithm (SHERPA), a pan-allelic MHC-peptide algorithm for predicting MHC-peptide binding and presentation. In contrast to previously published large-scale monoallelic data, we used an HLA-null K562 parental cell line and a stable transfection of HLA alleles to better emulate native presentation. Our dataset includes five previously unprofiled alleles that expand MHC-binding pocket diversity in the training data and extend allelic coverage in under profiled populations. To improve generalizability, SHERPA systematically integrates 128 monoallelic and 384 multiallelic samples with publicly available immunoproteomics data and binding assay data. Using this dataset, we developed two features that empirically estimate the propensities of genes and specific regions within gene bodies to engender immunopeptides to represent antigen processing. Using a composite model constructed with gradient boosting decision trees, multiallelic deconvolution, and 2.15 million peptides encompassing 167 alleles, we achieved a 1.44-fold improvement of positive predictive value compared with existing tools when evaluated on independent monoallelic datasets and a 1.15-fold improvement when evaluating on tumor samples. With a high degree of accuracy, SHERPA has the potential to enable precision neoantigen discovery for future clinical applications.

Short-term memory trace mediated by termination kinetics of olfactory receptor.

Odorants activate receptors in the peripheral olfactory neurons, which sends information to higher brain centers where behavioral valence is determined. Movement and airflow continuously change what odor plumes an animal encounters and little is known about the effect one plume has on the detection of another. Using the simple Drosophila melanogaster larval model to study this relationship we identify an unexpected phenomenon: response to an attractant can be selectively blocked by previous exposure to some odorants that activates the same receptor. At a mechanistic level, we find that exposure to this type of odorant causes prolonged tonic responses from a receptor (Or42b), which can block subsequent detection of a strong activator of that same receptor. We identify naturally occurring odorants with prolonged tonic responses for other odorant receptors (Ors) as well, suggesting that termination-kinetics is a factor for olfactory coding mechanisms. This mechanism has implications for odor-coding in any system and for designing applications to modify odor-driven behaviors.

Targeting a dual detector of skin and CO2 to modify mosquito host seeking.

Female mosquitoes that transmit deadly diseases locate human hosts by detecting exhaled CO2 and skin odor. The identities of olfactory neurons and receptors required for attraction to skin odor remain a mystery. Here, we show that the CO2-sensitive olfactory neuron is also a sensitive detector of human skin odorants in both Aedes aegypti and Anopheles gambiae. We demonstrate that activity of this neuron is important for attraction to skin odor, establishing it as a key target for intervention. We screen ~0.5 million compounds in silico and identify several CO2 receptor ligands, including an antagonist that reduces attraction to skin and an agonist that lures mosquitoes to traps as effectively as CO2. Analysis of the CO2 receptor ligand space provides a foundation for understanding mosquito host-seeking behavior and identifies odors that are potentially safe, pleasant, and affordable for use in a new generation of mosquito control strategies worldwide.

Odour receptors and neurons for DEET and new insect repellents.

There are major impediments to finding improved DEET alternatives because the receptors causing olfactory repellency are unknown, and new chemicals require exorbitant costs to determine safety for human use. Here we identify DEET-sensitive neurons in a pit-like structure in the Drosophila melanogaster antenna called the sacculus. They express a highly conserved receptor, Ir40a, and flies in which these neurons are silenced or Ir40a is knocked down lose avoidance to DEET. We used a computational structure-activity screen of >400,000 compounds that identified >100 natural compounds as candidate repellents. We tested several and found that most activate Ir40a(+) neurons and are repellents for Drosophila. These compounds are also strong repellents for mosquitoes. The candidates contain chemicals that do not dissolve plastic, are affordable and smell mildly like grapes, with three considered safe in human foods. Our findings pave the way to discover new generations of repellents that will help fight deadly insect-borne diseases worldwide.

Expanding the olfactory code by in silico decoding of odor-receptor chemical space.

Coding of information in the peripheral olfactory system depends on two fundamental : interaction of individual odors with subsets of the odorant receptor repertoire and mode of signaling that an individual receptor-odor interaction elicits, activation or inhibition. We develop a cheminformatics pipeline that predicts receptor-odorant interactions from a large collection of chemical structures (>240,000) for receptors that have been tested to a smaller panel of odorants (∼100). Using a computational approach, we first identify shared structural features from known ligands of individual receptors. We then use these features to screen in silico new candidate ligands from >240,000 potential volatiles for several Odorant receptors (Ors) in the Drosophila antenna. Functional experiments from 9 Ors support a high success rate (∼71%) for the screen, resulting in identification of numerous new activators and inhibitors. Such computational prediction of receptor-odor interactions has the potential to enable systems level analysis of olfactory receptor repertoires in organisms. DOI:http://dx.doi.org/10.7554/eLife.01120.001.