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.

[Posterior reversible encephalopathy syndrome (PRES) as a presentation of AL amyloidosis]. / Síndrome de encefalopatía posterior reversible (PRES) como forma de presentación de una amiloidosis AL.

Posterior reversible encephalopathy syndrome (PRES) is a clinical and radiological entity linked to multiple aetiologies with similar neuroimaging findings. Its incidence is unknown, and its pathogenesis is multifactorial, encompassing phenomena of endothelial dysfunction and cerebral flow autoregulation, inter alia. There is a wide variety of associated conditions, the most frequent being hypertension, eclampsia, and immunosuppressive therapy, along with other drugs, autoimmune diseases, and even uraemia. We present the case of a reversible posterior encephalopathy syndrome secondary to renal involvement as a debut form of AL amyloidosis.

Prediction of Immunotherapy Response in Melanoma through Combined Modeling of Neoantigen Burden and Immune-Related Resistance Mechanisms.

PURPOSE: While immune checkpoint blockade (ICB) has become a pillar of cancer treatment, biomarkers that consistently predict patient response remain elusive due to the complex mechanisms driving immune response to tumors. We hypothesized that a multi-dimensional approach modeling both tumor and immune-related molecular mechanisms would better predict ICB response than simpler mutation-focused biomarkers, such as tumor mutational burden (TMB). EXPERIMENTAL DESIGN: Tumors from a cohort of patients with late-stage melanoma (n = 51) were profiled using an immune-enhanced exome and transcriptome platform. We demonstrate increasing predictive power with deeper modeling of neoantigens and immune-related resistance mechanisms to ICB. RESULTS: Our neoantigen burden score, which integrates both exome and transcriptome features, more significantly stratified responders and nonresponders (P = 0.016) than TMB alone (P = 0.049). Extension of this model to include immune-related resistance mechanisms affecting the antigen presentation machinery, such as HLA allele-specific LOH, resulted in a composite neoantigen presentation score (NEOPS) that demonstrated further increased association with therapy response (P = 0.002). CONCLUSIONS: NEOPS proved the statistically strongest biomarker compared with all single-gene biomarkers, expression signatures, and TMB biomarkers evaluated in this cohort. Subsequent confirmation of these findings in an independent cohort of patients (n = 110) suggests that NEOPS is a robust, novel biomarker of ICB response in melanoma.

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.

Neoadjuvant chemotherapy in locally advanced colon cancer: a systematic review.

BACKGROUND: Preoperative or neoadjuvant chemotherapy (NAC) has emerged as a novel alternative to treat locally advanced colon cancer (LACC), as in other gastrointestinal malignancies. However, evidence of its efficacy and safety has not yet been gathered in the literature. The aim of the present study was to perform an extensive review of the scientific evidence for NAC in patients with LACC. METHODS: PubMed, EMBASE, MEDLINE and Cochrane Library were searched for a systematic review of the literature from 2010 to 2019. Six eligible studies were included, with a total of 27,937 patients, 1232 of them (4.4%) treated with NAC. There were only one randomized controlled trial, three phase II non-randomized single arm studies and two retrospective studies. RESULTS: The baseline computed tomography scan showed that most of patients had a T3 tumor. The completion rate of the planned neoadjuvant treatment ranged from 52.5 to 93.8%. Between 97.2 and 100% of patients had the scheduled surgery. The median tumor volume reduction after NAC ranged from 62.5 to 63.7%. The anastomotic leak rate in the NAC group ranged from 0 to 7%, with no cases of postoperative mortality. There was major pathological tumor regression in 4-34.7% of cases. Between 84 and 100% of NAC patients had R0-surgery. Survival after NAC seems to be encouraging although significant improvement has only been proven in T4b tumours. CONCLUSIONS: According to our systematic review, the NAC may be a safe and effective emerging therapeutic alternative for treating LACC. This approach, which is still being tested, increases the reliance on accurate radiological staging.

Intraspecific Genetic Variation Underlying Postmating Reproductive Barriers between Species in the Wild Tomato Clade (Solanum sect. Lycopersicon).

A goal of speciation genetics is to understand how the genetic components underlying interspecific reproductive barriers originate within species. Unilateral incompatibility (UI) is a postmating prezygotic barrier in which pollen rejection in the female reproductive tract (style) occurs in only one direction of an interspecific cross. Natural variation in the strength of UI has been observed among populations within species in the wild tomato clade. In some cases, molecular loci underlying self-incompatibility (SI) are associated with this variation in UI, but the mechanistic connection between these intra- and inter-specific pollen rejection behaviors is poorly understood in most instances. We generated an F2 population between SI and SC genotypes of a single species, Solanum pennellii, to examine the genetic basis of intraspecific variation in UI against other species, and to determine whether loci underlying SI are genetically associated with this variation. We found that F2 individuals vary in the rate at which UI rejection occurs. One large effect QTL detected for this trait co-localized with the SI-determining S-locus. Moreover, individuals that expressed S-RNase-the S-locus protein involved in SI pollen rejection-in their styles had much more rapid UI responses compared with those without S-RNase protein. Our analysis shows that intraspecific variation at mate choice loci-in this case at loci that prevent self-fertilization-can contribute to variation in the expression of interspecific isolation, including postmating prezygotic barriers. Understanding the nature of such intraspecific variation can provide insight into the accumulation of these barriers between diverging lineages.

Evolutionary superscaffolding and chromosome anchoring to improve Anopheles genome assemblies.

BACKGROUND: New sequencing technologies have lowered financial barriers to whole genome sequencing, but resulting assemblies are often fragmented and far from 'finished'. Updating multi-scaffold drafts to chromosome-level status can be achieved through experimental mapping or re-sequencing efforts. Avoiding the costs associated with such approaches, comparative genomic analysis of gene order conservation (synteny) to predict scaffold neighbours (adjacencies) offers a potentially useful complementary method for improving draft assemblies. RESULTS: We evaluated and employed 3 gene synteny-based methods applied to 21 Anopheles mosquito assemblies to produce consensus sets of scaffold adjacencies. For subsets of the assemblies, we integrated these with additional supporting data to confirm and complement the synteny-based adjacencies: 6 with physical mapping data that anchor scaffolds to chromosome locations, 13 with paired-end RNA sequencing (RNAseq) data, and 3 with new assemblies based on re-scaffolding or long-read data. Our combined analyses produced 20 new superscaffolded assemblies with improved contiguities: 7 for which assignments of non-anchored scaffolds to chromosome arms span more than 75% of the assemblies, and a further 7 with chromosome anchoring including an 88% anchored Anopheles arabiensis assembly and, respectively, 73% and 84% anchored assemblies with comprehensively updated cytogenetic photomaps for Anopheles funestus and Anopheles stephensi. CONCLUSIONS: Experimental data from probe mapping, RNAseq, or long-read technologies, where available, all contribute to successful upgrading of draft assemblies. Our evaluations show that gene synteny-based computational methods represent a valuable alternative or complementary approach. Our improved Anopheles reference assemblies highlight the utility of applying comparative genomics approaches to improve community genomic resources.

The sequencing and interpretation of the genome obtained from a Serbian individual.

Recent genetic studies and whole-genome sequencing projects have greatly improved our understanding of human variation and clinically actionable genetic information. Smaller ethnic populations, however, remain underrepresented in both individual and large-scale sequencing efforts and hence present an opportunity to discover new variants of biomedical and demographic significance. This report describes the sequencing and analysis of a genome obtained from an individual of Serbian origin, introducing tens of thousands of previously unknown variants to the currently available pool. Ancestry analysis places this individual in close proximity to Central and Eastern European populations; i.e., closest to Croatian, Bulgarian and Hungarian individuals and, in terms of other Europeans, furthest from Ashkenazi Jewish, Spanish, Sicilian and Baltic individuals. Our analysis confirmed gene flow between Neanderthal and ancestral pan-European populations, with similar contributions to the Serbian genome as those observed in other European groups. Finally, to assess the burden of potentially disease-causing/clinically relevant variation in the sequenced genome, we utilized manually curated genotype-phenotype association databases and variant-effect predictors. We identified several variants that have previously been associated with severe early-onset disease that is not evident in the proband, as well as putatively impactful variants that could yet prove to be clinically relevant to the proband over the next decades. The presence of numerous private and low-frequency variants, along with the observed and predicted disease-causing mutations in this genome, exemplify some of the global challenges of genome interpretation, especially in the context of under-studied ethnic groups.

AGOUTI: improving genome assembly and annotation using transcriptome data.

BACKGROUND: Genomes sequenced using short-read, next-generation sequencing technologies can have many errors and may be fragmented into thousands of small contigs. These incomplete and fragmented assemblies lead to errors in gene identification, such that single genes spread across multiple contigs are annotated as separate gene models. Such biases can confound inferences about the number and identity of genes within species, as well as gene gain and loss between species. RESULTS: We present AGOUTI (Annotated Genome Optimization Using Transcriptome Information), a tool that uses RNA sequencing data to simultaneously combine contigs into scaffolds and fragmented gene models into single models. We show that AGOUTI improves both the contiguity of genome assemblies and the accuracy of gene annotation, providing updated versions of each as output. Running AGOUTI on both simulated and real datasets, we show that it is highly accurate and that it achieves greater accuracy and contiguity when compared with other existing methods. CONCLUSION: AGOUTI is a powerful and effective scaffolder and, unlike most scaffolders, is expected to be more effective in larger genomes because of the commensurate increase in intron length. AGOUTI is able to scaffold thousands of contigs while simultaneously reducing the number of gene models by hundreds or thousands. The software is available free of charge under the MIT license.

Sequencing, assembling, and correcting draft genomes using recombinant populations.

Current de novo whole-genome sequencing approaches often are inadequate for organisms lacking substantial preexisting genetic data. Problems with these methods are manifest as: large numbers of scaffolds that are not ordered within chromosomes or assigned to individual chromosomes, misassembly of allelic sequences as separate loci when the individual(s) being sequenced are heterozygous, and the collapse of recently duplicated sequences into a single locus, regardless of levels of heterozygosity. Here we propose a new approach for producing de novo whole-genome sequences-which we call recombinant population genome construction-that solves many of the problems encountered in standard genome assembly and that can be applied in model and nonmodel organisms. Our approach takes advantage of next-generation sequencing technologies to simultaneously barcode and sequence a large number of individuals from a recombinant population. The sequences of all recombinants can be combined to create an initial de novo assembly, followed by the use of individual recombinant genotypes to correct assembly splitting/collapsing and to order and orient scaffolds within linkage groups. Recombinant population genome construction can rapidly accelerate the transformation of nonmodel species into genome-enabled systems by simultaneously producing a high-quality genome assembly and providing genomic tools (e.g., high-confidence single-nucleotide polymorphisms) for immediate applications. In populations segregating for important functional traits, this approach also enables simultaneous mapping of quantitative trait loci. We demonstrate our method using simulated Illumina data from a recombinant population of Caenorhabditis elegans and show that the method can produce a high-fidelity, high-quality genome assembly for both parents of the cross.

The impact of education and occupation on the employment status of cancer survivors.

We explored the effect of a diagnosis of cancer on employment according to cancer type, education, occupation, age, gender, mother tongue (Swedish or Finnish), calendar time and hospital district. All 12,542 new cancer cases diagnosed in 1987-1988 and 1992-1993, aged 15-60 years at the time of the diagnosis were identified from the Finnish Cancer Registry. The employment rate of the cancer survivors 2-3 years after the diagnosis was only 9% lower than their gender- and age-matched referents. However, we found that education and occupation modified the effect of cancer on the employment; the difference between cancer survivors and their referents in the probability of being employed was greater in the lower than in the higher educational groups. A modifying effect of education on the probability of employment was found among people with cancer of the lung, stomach, rectum and cervix uteri and those with cancers of the nervous system.

Work injuries among Finnish farmers: a national register linkage study 1996-1997.

BACKGROUND: Farming is one of the most injury-prone occupations in Finland as it is in other countries. Our goals were to describe work injuries of Finnish farmers and to compare occupational injury rates between various subgroups. METHODS: A national cohort of 69,629 full-time farmers and their 11,657 compensated injuries were identified from an insurance company database. Cohort data were merged with a population census and farm register. Relative incidence rates were calculated using Poisson regression. RESULTS: Men had higher injury rates than women, except with regard to injuries caused by animals. Dairy and hog farming were the riskiest activities, and injury rates increased with the number of dairy cows. CONCLUSIONS: One-half of insured farmers in Finland are full-time farmers, which may have lead to underestimation of risk in Finnish injury statistics. Dairy farming is of particular concern because it is both common and has a high injury rate.

Mortality and cancer incidence of workers in Finnish road paving companies.

BACKGROUND: This study, a component of the International Agency for Research on Cancer (IARC) Multicentric Study on Cancer Risk Among European Asphalt Workers, aimed at identifying major mortality risks among workers in Finnish road paving companies. METHODS: The Finnish cohort was comprised of 9,643 men and women from six road paving companies. The mortality of men employed during at least one season (5,676) was followed up from 1964 until end of 1994; an average of 17 years. Standardized mortality ratios (SMR) and relative risks (RR), the latter based on multivariate Poisson regression models were estimated by occupational group and by various metrics of occupational exposures. RESULTS: All-cause mortality was elevated (SMR 1.11, 95% confidence interval, CI 1.03-1.20), mainly due to excesses in accidents, poisonings, and violence (1.29; CI 1.12-1.49), and lung cancer (1.38; 1.03-1.81). Workers exposed to bitumen fumes had a slightly elevated mortality from lung cancer (1.16; 0.69-1.83). Multivariate Poisson regression models with 15-year lag period suggested trends by cumulative exposure to coal tar, organic vapors, silica dust, diesel exhaust, and bitumen fume. CONCLUSIONS: The elevated mortality from external causes among Finnish building/ground construction workers was probably due to living conditions and related lifestyles. Some evidence was found for a risk of lung cancer due to occupational exposure, but the confirmation of these findings would require a longer follow-up and improved control for confounding.

Socioeconomic inequalities in cardiovascular mortality and the role of work: a register study of Finnish men.

BACKGROUND: In Finland, socioeconomic inequalities in mortality have been well documented. However, the role of working conditions in the emergence of those inequalities has not been thoroughly examined. METHODS: Data came from the Longitudinal Census file, which included censuses since 1970 (every 5 years). The cohort consisted of men who were in the same occupation in 1975 and 1980, and who were between 25 and 64 years old in 1980. Farm work, mining and military occupations were excluded. Cardiovascular mortality of this cohort was followed up 1981-1994 (5.4 million person-years). Information on marital status, education and income was updated in 1985 and 1990. Working conditions were evaluated at occupational level (job exposure matrix). Poisson regression analyses were conducted to estimate the impact of independent variables on mortality. Inequalities were assessed in relation to occupational class and occupational category. RESULTS: According to the models, elimination of unfavourable working conditions would have reduced the number of all cardiovascular deaths by 8%, myocardial infarctions by 10%, and cerebrovascular deaths by 18%. The most influential job exposures appeared to be high workload, low control, noise, and shift work. Income had a strong effect on mortality. CONCLUSIONS: Working conditions explained a relatively small portion of socioeconomic inequalities in mortality. Inequalities associated with occupational category and class were more attributable to varying levels of education and income.

Exposure to environmental tobacco smoke in Finland in 2000.

OBJECTIVES: Exposure to environmental tobacco smoke at workplaces, homes and other places was assessed. METHODS: Exposure to environmental tobacco smoke was defined as occurring when a person reported inhaling, at least occasionally, tobacco smoke from other people's smoke. Some of the exposed were also smokers themselves. Questionnaire-based survey data and industrial hygiene measurements on environmental tobacco smoke were used to estimate the numbers of exposed persons by exposure level in Finland in January 2000. RESULTS: About 340,000 workers (16% of the employed population) were exposed to environmental tobacco smoke at work, of them 30,000 were exposed almost continuously (1.4% of the employed population). The mean level of exposure was 1 microg/m2 as measured by nicotine in workroom air. The nicotine concentration ranged from < 1 to > 100 microg/m3. Nearly 600,000 Finns (1% of the population) were exposed to environmental tobacco smoke at home. According measurements abroad, their mean nicotine exposure corresponded to about 4 microg/m3 at work. In addition, over 1 million Finns were exposed during leisure time to an unknown mean level of environmental tobacco smoke. Annual exposure of the Finnish population in January 2000 was estimated to originate mainly from smoking at home (48%) and leisure time in smoky restaurants (45%). Smoking was restricted at workplaces in 1995 and occupational exposure constituted 7% of the total population exposure in January 2000. New restrictions on smoking in restaurants should decrease the exposure of restaurant workers and customers even further. CONCLUSIONS: In spite of regulations, environmental tobacco smoke still remains the most common occupational exposure to chemical carcinogens in Finland.