Publisher Correction: Actively personalized vaccination trial for newly diagnosed glioblastoma.

The additional author support information was erroneously omitted from the Supplementary Information. This has been corrected online.

Actively personalized vaccination trial for newly diagnosed glioblastoma.

Patients with glioblastoma currently do not sufficiently benefit from recent breakthroughs in cancer treatment that use checkpoint inhibitors1,2. For treatments using checkpoint inhibitors to be successful, a high mutational load and responses to neoepitopes are thought to be essential3. There is limited intratumoural infiltration of immune cells4 in glioblastoma and these tumours contain only 30-50 non-synonymous mutations5. Exploitation of the full repertoire of tumour antigens-that is, both unmutated antigens and neoepitopes-may offer more effective immunotherapies, especially for tumours with a low mutational load. Here, in the phase I trial GAPVAC-101 of the Glioma Actively Personalized Vaccine Consortium (GAPVAC), we integrated highly individualized vaccinations with both types of tumour antigens into standard care to optimally exploit the limited target space for patients with newly diagnosed glioblastoma. Fifteen patients with glioblastomas positive for human leukocyte antigen (HLA)-A*02:01 or HLA-A*24:02 were treated with a vaccine (APVAC1) derived from a premanufactured library of unmutated antigens followed by treatment with APVAC2, which preferentially targeted neoepitopes. Personalization was based on mutations and analyses of the transcriptomes and immunopeptidomes of the individual tumours. The GAPVAC approach was feasible and vaccines that had poly-ICLC (polyriboinosinic-polyribocytidylic acid-poly-L-lysine carboxymethylcellulose) and granulocyte-macrophage colony-stimulating factor as adjuvants displayed favourable safety and strong immunogenicity. Unmutated APVAC1 antigens elicited sustained responses of central memory CD8+ T cells. APVAC2 induced predominantly CD4+ T cell responses of T helper 1 type against predicted neoepitopes.

Mutant MHC class II epitopes drive therapeutic immune responses to cancer.

Tumour-specific mutations are ideal targets for cancer immunotherapy as they lack expression in healthy tissues and can potentially be recognized as neo-antigens by the mature T-cell repertoire. Their systematic targeting by vaccine approaches, however, has been hampered by the fact that every patient's tumour possesses a unique set of mutations ('the mutanome') that must first be identified. Recently, we proposed a personalized immunotherapy approach to target the full spectrum of a patient's individual tumour-specific mutations. Here we show in three independent murine tumour models that a considerable fraction of non-synonymous cancer mutations is immunogenic and that, unexpectedly, the majority of the immunogenic mutanome is recognized by CD4(+) T cells. Vaccination with such CD4(+) immunogenic mutations confers strong antitumour activity. Encouraged by these findings, we established a process by which mutations identified by exome sequencing could be selected as vaccine targets solely through bioinformatic prioritization on the basis of their expression levels and major histocompatibility complex (MHC) class II-binding capacity for rapid production as synthetic poly-neo-epitope messenger RNA vaccines. We show that vaccination with such polytope mRNA vaccines induces potent tumour control and complete rejection of established aggressively growing tumours in mice. Moreover, we demonstrate that CD4(+) T cell neo-epitope vaccination reshapes the tumour microenvironment and induces cytotoxic T lymphocyte responses against an independent immunodominant antigen in mice, indicating orchestration of antigen spread. Finally, we demonstrate an abundance of mutations predicted to bind to MHC class II in human cancers as well by employing the same predictive algorithm on corresponding human cancer types. Thus, the tailored immunotherapy approach introduced here may be regarded as a universally applicable blueprint for comprehensive exploitation of the substantial neo-epitope target repertoire of cancers, enabling the effective targeting of every patient's tumour with vaccines produced 'just in time'.

The SysteMHC Atlas project.

Mass spectrometry (MS)-based immunopeptidomics investigates the repertoire of peptides presented at the cell surface by major histocompatibility complex (MHC) molecules. The broad clinical relevance of MHC-associated peptides, e.g. in precision medicine, provides a strong rationale for the large-scale generation of immunopeptidomic datasets and recent developments in MS-based peptide analysis technologies now support the generation of the required data. Importantly, the availability of diverse immunopeptidomic datasets has resulted in an increasing need to standardize, store and exchange this type of data to enable better collaborations among researchers, to advance the field more efficiently and to establish quality measures required for the meaningful comparison of datasets. Here we present the SysteMHC Atlas (https://systemhcatlas.org), a public database that aims at collecting, organizing, sharing, visualizing and exploring immunopeptidomic data generated by MS. The Atlas includes raw mass spectrometer output files collected from several laboratories around the globe, a catalog of context-specific datasets of MHC class I and class II peptides, standardized MHC allele-specific peptide spectral libraries consisting of consensus spectra calculated from repeat measurements of the same peptide sequence, and links to other proteomics and immunology databases. The SysteMHC Atlas project was created and will be further expanded using a uniform and open computational pipeline that controls the quality of peptide identifications and peptide annotations. Thus, the SysteMHC Atlas disseminates quality controlled immunopeptidomic information to the public domain and serves as a community resource toward the generation of a high-quality comprehensive map of the human immunopeptidome and the support of consistent measurement of immunopeptidomic sample cohorts.

Immunomic, genomic and transcriptomic characterization of CT26 colorectal carcinoma.

BACKGROUND: Tumor models are critical for our understanding of cancer and the development of cancer therapeutics. Here, we present an integrated map of the genome, transcriptome and immunome of an epithelial mouse tumor, the CT26 colon carcinoma cell line. RESULTS: We found that Kras is homozygously mutated at p.G12D, Apc and Tp53 are not mutated, and Cdkn2a is homozygously deleted. Proliferation and stem-cell markers, including Top2a, Birc5 (Survivin), Cldn6 and Mki67, are highly expressed while differentiation and top-crypt markers Muc2, Ms4a8a (MS4A8B) and Epcam are not. Myc, Trp53 (tp53), Mdm2, Hif1a, and Nras are highly expressed while Egfr and Flt1 are not. MHC class I but not MHC class II is expressed. Several known cancer-testis antigens are expressed, including Atad2, Cep55, and Pbk. The highest expressed gene is a mutated form of the mouse tumor antigen gp70. Of the 1,688 non-synonymous point variations, 154 are both in expressed genes and in peptides predicted to bind MHC and thus potential targets for immunotherapy development. Based on its molecular signature, we predicted that CT26 is refractory to anti-EGFR mAbs and sensitive to MEK and MET inhibitors, as have been previously reported. CONCLUSIONS: CT26 cells share molecular features with aggressive, undifferentiated, refractory human colorectal carcinoma cells. As CT26 is one of the most extensively used syngeneic mouse tumor models, our data provide a map for the rationale design of mode-of-action studies for pre-clinical evaluation of targeted- and immunotherapies.

A boy with homozygous microdeletion of NEUROG1 presents with a congenital cranial dysinnervation disorder [Moebius syndrome variant].

BACKGROUND: We report on a 6-year-old Turkish boy with profound sensorineural deafness, balance disorder, severe disorder of oral motor function, and mild developmental delay. Further findings included scaphocephaly, plagiocephaly, long palpebral fissures, high narrow palate, low-set posteriorly rotated ears, torticollis, hypoplastic genitalia and faulty foot posture. Parents were consanguineous. METHODS AND RESULTS: Computed tomography and magnetic resonance imaging showed bilateral single widened cochlear turn, narrowing of the internal auditory canal, and bilateral truncation of the vestibulo-cochlear nerve. Microarray analysis and next generation sequencing showed a homozygous deletion of chromosome 5q31.1 spanning 115.3 kb and including three genes: NEUROG1 (encoding neurogenin 1), DCNP1 (dendritic cell nuclear protein 1, C5ORF20) and TIFAB (TIFA-related protein). The inability to chew and swallow, deafness and balance disorder represented congenital palsies of cranial nerves V (trigeminal nerve) and VIII (vestibulo-cochlear nerve) and thus a congenital cranial dysinnervation disorder. CONCLUSIONS: Based on reported phenotypes of neurog1 null mutant mice and other vertebrates, we strongly propose NEUROG1 as the causative gene in this boy. The human NEUROG1 resides within the DFNB60 locus for non-syndromic autosomal recessive deafness on chromosome 5q22-q31, but linkage data have excluded it from being causative in the DFNB60 patients. Given its large size (35 Mb, >100 genes), the 5q22-q31 area could harbor more than one deafness gene. We propose NEUROG1 as a new gene for syndromic autosomal recessive hearing loss and congenital cranial dysinnervation disorder including cranial nerves V and VIII.

Confidence-based somatic mutation evaluation and prioritization.

Next generation sequencing (NGS) has enabled high throughput discovery of somatic mutations. Detection depends on experimental design, lab platforms, parameters and analysis algorithms. However, NGS-based somatic mutation detection is prone to erroneous calls, with reported validation rates near 54% and congruence between algorithms less than 50%. Here, we developed an algorithm to assign a single statistic, a false discovery rate (FDR), to each somatic mutation identified by NGS. This FDR confidence value accurately discriminates true mutations from erroneous calls. Using sequencing data generated from triplicate exome profiling of C57BL/6 mice and B16-F10 melanoma cells, we used the existing algorithms GATK, SAMtools and SomaticSNiPer to identify somatic mutations. For each identified mutation, our algorithm assigned an FDR. We selected 139 mutations for validation, including 50 somatic mutations assigned a low FDR (high confidence) and 44 mutations assigned a high FDR (low confidence). All of the high confidence somatic mutations validated (50 of 50), none of the 44 low confidence somatic mutations validated, and 15 of 45 mutations with an intermediate FDR validated. Furthermore, the assignment of a single FDR to individual mutations enables statistical comparisons of lab and computation methodologies, including ROC curves and AUC metrics. Using the HiSeq 2000, single end 50 nt reads from replicates generate the highest confidence somatic mutation call set.

Digital transcriptome profiling using selective hexamer priming for cDNA synthesis.

We developed a procedure for the preparation of whole transcriptome cDNA libraries depleted of ribosomal RNA from only 1 microg of total RNA. The method relies on a collection of short, computationally selected oligonucleotides, called 'not-so-random' (NSR) primers, to obtain full-length, strand-specific representation of nonribosomal RNA transcripts. In this study we validated the technique by profiling human whole brain and universal human reference RNA using ultra-high-throughput sequencing.

Global regulation of alternative splicing during myogenic differentiation.

Recent genome-wide analyses have elucidated the extent of alternative splicing (AS) in mammals, often focusing on comparisons of splice isoforms between differentiated tissues. However, regulated splicing changes are likely to be important in biological transitions such as cellular differentiation, or response to environmental stimuli. To assess the extent and significance of AS in myogenesis, we used splicing-sensitive microarray analysis of differentiating C2C12 myoblasts. We identified 95 AS events that undergo robust splicing transitions during C2C12 differentiation. More than half of the splicing transitions are conserved during differentiation of avian myoblasts, suggesting the products and timing of transitions are functionally significant. The majority of splicing transitions during C2C12 differentiation fall into four temporal patterns and were dependent on the myogenic program, suggesting that they are integral components of myogenic differentiation. Computational analyses revealed enrichment of many sequence motifs within the upstream and downstream intronic regions near the alternatively spliced regions corresponding to binding sites of splicing regulators. Western analyses demonstrated that several splicing regulators undergo dynamic changes in nuclear abundance during differentiation. These findings show that within a developmental context, AS is a highly regulated and conserved process, suggesting a major role for AS regulation in myogenic differentiation.

Activity of the Rhodopseudomonas palustris p-coumaroyl-homoserine lactone-responsive transcription factor RpaR.

The Rhodopseudomonas palustris transcriptional regulator RpaR responds to the RpaI-synthesized quorum-sensing signal p-coumaroyl-homoserine lactone (pC-HSL). Other characterized RpaR homologs respond to fatty acyl-HSLs. We show here that RpaR functions as a transcriptional activator, which binds directly to the rpaI promoter. We developed an RNAseq method that does not require a ribosome depletion step to define a set of transcripts regulated by pC-HSL and RpaR. The transcripts include several noncoding RNAs. A footprint analysis showed that purified His-tagged RpaR (His(6)-RpaR) binds to an inverted repeat element centered 48.5 bp upstream of the rpaI transcript start site, which we mapped by S1 nuclease protection and primer extension analyses. Although pC-HSL-RpaR bound to rpaI promoter DNA, it did not bind to the promoter regions of a number of RpaR-regulated genes not in the rpaI operon. This indicates that RpaR control of these other genes is indirect. Because the RNAseq analysis allowed us to track transcript strand specificity, we discovered that there is pC-HSL-RpaR-activated antisense transcription of rpaR. These data raise the possibility that this antisense RNA or other RpaR-activated noncoding RNAs mediate the indirect activation of genes in the RpaR-controlled regulon.

rapmad: Robust analysis of peptide microarray data.

BACKGROUND: Peptide microarrays offer an enormous potential as a screening tool for peptidomics experiments and have recently seen an increased field of application ranging from immunological studies to systems biology. By allowing the parallel analysis of thousands of peptides in a single run they are suitable for high-throughput settings. Since data characteristics of peptide microarrays differ from DNA oligonucleotide microarrays, computational methods need to be tailored to these specifications to allow a robust and automated data analysis. While follow-up experiments can ensure the specificity of results, sensitivity cannot be recovered in later steps. Providing sensitivity is thus a primary goal of data analysis procedures. To this end we created rapmad (Robust Alignment of Peptide MicroArray Data), a novel computational tool implemented in R. RESULTS: We evaluated rapmad in antibody reactivity experiments for several thousand peptide spots and compared it to two existing algorithms for the analysis of peptide microarrays. rapmad displays competitive and superior behavior to existing software solutions. Particularly, it shows substantially improved sensitivity for low intensity settings without sacrificing specificity. It thereby contributes to increasing the effectiveness of high throughput screening experiments. CONCLUSIONS: rapmad allows the robust and sensitive, automated analysis of high-throughput peptide array data. The rapmad R-package as well as the data sets are available from http://www.tron-mz.de/compmed.

A postnatal switch of CELF and MBNL proteins reprograms alternative splicing in the developing heart.

From a large-scale screen using splicing microarrays and RT-PCR, we identified 63 alternative splicing (AS) events that are coordinated in 3 distinct temporal patterns during mouse heart development. More than half of these splicing transitions are evolutionarily conserved between mouse and chicken. Computational analysis of the introns flanking these splicing events identified enriched and conserved motifs including binding sites for CUGBP and ETR-3-like factors (CELF), muscleblind-like (MBNL) and Fox proteins. We show that CELF proteins are down-regulated >10-fold during heart development, and MBNL1 protein is concomitantly up-regulated nearly 4-fold. Using transgenic and knockout mice, we show that reproducing the embryonic expression patterns for CUGBP1 and MBNL1 in adult heart induces the embryonic splicing patterns for more than half of the developmentally regulated AS transitions. These findings indicate that CELF and MBNL proteins are determinative for a large subset of splicing transitions that occur during postnatal heart development.

Current status of use of high throughput nucleotide sequencing in rheumatology.

OBJECTIVE: Here, we assess the usage of high throughput sequencing (HTS) in rheumatic research and the availability of public HTS data of rheumatic samples. METHODS: We performed a semiautomated literature review on PubMed, consisting of an R-script and manual curation as well as a manual search on the Sequence Read Archive for public available HTS data. RESULTS: Of the 699 identified articles, rheumatoid arthritis (n=182 publications, 26%), systemic lupus erythematous (n=161, 23%) and osteoarthritis (n=152, 22%) are among the rheumatic diseases with the most reported use of HTS assays. The most represented assay is RNA-Seq (n=457, 65%) for the identification of biomarkers in blood or synovial tissue. We also find, that the quality of accompanying clinical characterisation of the sequenced patients differs dramatically and we propose a minimal set of clinical data necessary to accompany rheumatological-relevant HTS data. CONCLUSION: HTS allows the analysis of a broad spectrum of molecular features in many samples at the same time. It offers enormous potential in novel personalised diagnosis and treatment strategies for patients with rheumatic diseases. Being established in cancer research and in the field of Mendelian diseases, rheumatic diseases are about to become the third disease domain for HTS, especially the RNA-Seq assay. However, we need to start a discussion about reporting of clinical characterisation accompany rheumatological-relevant HTS data to make clinical meaningful use of this data.

DNA copy number, including telomeres and mitochondria, assayed using next-generation sequencing.

BACKGROUND: DNA copy number variations occur within populations and aberrations can cause disease. We sought to develop an improved lab-automatable, cost-efficient, accurate platform to profile DNA copy number. RESULTS: We developed a sequencing-based assay of nuclear, mitochondrial, and telomeric DNA copy number that draws on the unbiased nature of next-generation sequencing and incorporates techniques developed for RNA expression profiling. To demonstrate this platform, we assayed UMC-11 cells using 5 million 33 nt reads and found tremendous copy number variation, including regions of single and homogeneous deletions and amplifications to 29 copies; 5 times more mitochondria and 4 times less telomeric sequence than a pool of non-diseased, blood-derived DNA; and that UMC-11 was derived from a male individual. CONCLUSION: The described assay outputs absolute copy number, outputs an error estimate (p-value), and is more accurate than array-based platforms at high copy number. The platform enables profiling of mitochondrial levels and telomeric length. The assay is lab-automatable and has a genomic resolution and cost that are tunable based on the number of sequence reads.

Definition, conservation and epigenetics of housekeeping and tissue-enriched genes.

BACKGROUND: Housekeeping genes (HKG) are constitutively expressed in all tissues while tissue-enriched genes (TEG) are expressed at a much higher level in a single tissue type than in others. HKGs serve as valuable experimental controls in gene and protein expression experiments, while TEGs tend to represent distinct physiological processes and are frequently candidates for biomarkers or drug targets. The genomic features of these two groups of genes expressed in opposing patterns may shed light on the mechanisms by which cells maintain basic and tissue-specific functions. RESULTS: Here, we generate gene expression profiles of 42 normal human tissues on custom high-density microarrays to systematically identify 1,522 HKGs and 975 TEGs and compile a small subset of 20 housekeeping genes which are highly expressed in all tissues with lower variance than many commonly used HKGs. Cross-species comparison shows that both the functions and expression patterns of HKGs are conserved. TEGs are enriched with respect to both segmental duplication and copy number variation, while no such enrichment is observed for HKGs, suggesting the high expression of HKGs are not due to high copy numbers. Analysis of genomic and epigenetic features of HKGs and TEGs reveals that the high expression of HKGs across different tissues is associated with decreased nucleosome occupancy at the transcription start site as indicated by enhanced DNase hypersensitivity. Additionally, we systematically and quantitatively demonstrated that the CpG islands' enrichment in HKGs transcription start sites (TSS) and their depletion in TEGs TSS. Histone methylation patterns differ significantly between HKGs and TEGs, suggesting that methylation contributes to the differential expression patterns as well. CONCLUSION: We have compiled a set of high quality HKGs that should provide higher and more consistent expression when used as references in laboratory experiments than currently used HKGs. The comparison of genomic features between HKGs and TEGs shows that HKGs are more conserved than TEGs in terms of functions, expression pattern and polymorphisms. In addition, our results identify chromatin structure and epigenetic features of HKGs and TEGs that are likely to play an important role in regulating their strikingly different expression patterns.

Mutation-Derived Neoantigens for Cancer Immunotherapy.

Mutation-derived neoantigens distinguish tumor from normal cells. T cells can sense the HLA-presented mutations, recognize tumor cells as non-self and destroy them. Therapeutically, immunotherapy antibodies can increase the virulence of the immune system by increasing T-cell cytotoxicity targeted toward neoantigens. Neoantigen vaccines act through antigen-presenting cells, such as dendritic cells, to activate patient-endogenous T cells that recognize vaccine-encoded mutations. Infusion of mutation-targeting T cells by adoptive cell therapy (ACT) directly increases the number and frequency of cytotoxic T cells recognizing and killing tumor cells. At the same time, publicly-funded consortia have profiled tumor genomes across many indications, identifying mutations in each tumor. For example, we find basal and HER2 positive tumors contain more mutated proteins and more TP53 mutations than luminal A/B breast tumors. HPV negative tumors have more mutated proteins than HPV positive head and neck tumors and in agreement with the hypothesis that HPV activity interferes with p53 activity, only 14% of the HPV positive mutations have TP53 mutations vs. 86% of the HPV negative tumors. Lung adenocarcinomas in smokers have over four times more mutated proteins relative to those in never smokers (median 248 vs. 61, respectively). With an eye toward immunotherapy applications, we review the spectrum of mutations in multiple indications, show variations in indication sub-types, and examine intra- and inter-indication prevalence of re-occurring mutation neoantigens that could be used for warehouse vaccines and ACT.

Bioinformatic methods for cancer neoantigen prediction.

Tumor cells accumulate aberrations not present in normal cells, leading to presentation of neoantigens on MHC molecules on their surface. These non-self neoantigens distinguish tumor cells from normal cells to the immune system and are thus targets for cancer immunotherapy. The rapid development of molecular profiling platforms, such as next-generation sequencing, has enabled the generation of large datasets characterizing tumor cells. The simultaneous development of algorithms has enabled rapid and accurate processing of these data. Bioinformatic software tools encoding the algorithms can be strung together in a workflow to identify neoantigens. Here, with a focus on high-throughput sequencing, we review state-of-the art bioinformatic tools along with the steps and challenges involved in neoantigen identification and recognition.

Correction to: Angiosarcoma patients treated with immune checkpoint inhibitors: a case series of seven patients from a single institution.

Following publication of the original article [1], the authors have reported that the following sentence "While of the same IgG1 class as ipilimumab, preclinical data suggests this molecule may have enhanced activity against T regulatory cells".

TCR Fingerprinting and Off-Target Peptide Identification.

Adoptive T cell therapy using patient T cells redirected to recognize tumor-specific antigens by expressing genetically engineered high-affinity T-cell receptors (TCRs) has therapeutic potential for melanoma and other solid tumors. Clinical trials implementing genetically modified TCRs in melanoma patients have raised concerns regarding off-target toxicities resulting in lethal destruction of healthy tissue, highlighting the urgency of assessing which off-target peptides can be recognized by a TCR. As a model system we used the clinically efficacious NY-ESO-1-specific TCR C259, which recognizes the peptide epitope SLLMWITQC presented by HLA-A*02:01. We investigated which amino acids at each position enable a TCR interaction by sequentially replacing every amino acid position outside of anchor positions 2 and 9 with all 19 possible alternative amino acids, resulting in 134 peptides (133 altered peptides plus epitope peptide). Each peptide was individually evaluated using three different in vitro assays: binding of the NY-ESOc259 TCR to the peptide, peptide-dependent activation of TCR-expressing cells, and killing of peptide-presenting target cells. To represent the TCR recognition kernel, we defined Position Weight Matrices (PWMs) for each assay by assigning normalized measurements to each of the 20 amino acids in each position. To predict potential off-target peptides, we applied a novel algorithm projecting the PWM-defined kernel into the human proteome, scoring NY-ESOc259 TCR recognition of 336,921 predicted human HLA-A*02:01 binding 9-mer peptides. Of the 12 peptides with high predicted score, we confirmed 7 (including NY-ESO-1 antigen SLLMWITQC) strongly activate human primary NY-ESOc259-expressing T cells. These off-target peptides include peptides with up to 7 amino acid changes (of 9 possible), which could not be predicted using the recognition motif as determined by alanine scans. Thus, this replacement scan assay determines the "TCR fingerprint" and, when coupled with the algorithm applied to the database of human 9-mer peptides binding to HLA-A*02:01, enables the identification of potential off-target antigens and the tissues where they are expressed. This platform enables both screening of multiple TCRs to identify the best candidate for clinical development and identification of TCR-specific cross-reactive peptide recognition and constitutes an improved methodology for the identification of potential off-target peptides presented on MHC class I molecules.