Immune-based mutation classification enables neoantigen prioritization and immune feature discovery in cancer immunotherapy.
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ABSTRACT:
SUBMITTER: Bai P
PROVIDER: S-EPMC7833777 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Emerging clinical evidence on the role of the antitumor activity of the immune system has generated great interest in immunotherapy in all cancer types. Recent clinical data clearly demonstrated that human tumor cells express antigenic peptides (epitopes) that can be recognized by autologous tumor-specific T cells and that enhancement of such immune reactivity can potentially lead to cancer control and cancer regression in patients with advanced disease. However, in most cases, it is unclear which tumor antigens (Ags) mediated cancer regression. Mounting evidence indicates that numerous endogenous mutated cancer proteins, a hallmark of tumor cells, can be processed into peptides and presented on the surface of tumor cells, leading to their immune recognition in vivo as "non-self" or foreign. Massively parallel sequencing has now overcome the challenge of rapidly identifying the comprehensive mutational spectrum of individual tumors (i.e., the "mutanome") and current technologies, as well as computational tools, have emerged that allow the identification of private epitopes derived from their mutanome and called neoantigens (neoAgs). On this basis, both CD4(+) and CD8(+) neoantigen-specific T cells have been identified in multiple human cancers and shown to be associated with a favorable clinical outcome. Notably, emerging data also indicate that neoantigen recognition represents a major factor in the activity of clinical immunotherapies. In the post-genome era, the mutanome holds promise as a long-awaited 'gold mine' for the discovery of unique cancer cell targets, which are exclusively tumor-specific and unlikely to drive immune tolerance, hence offering the chance for highly promising clinical programs of cancer immunotherapy.
| S-EPMC4971382 | biostudies-literature
Project description:DNA repair is critical for genome stability and is maintained through conserved pathways. Traditional genome-wide mammalian screens are both expensive and laborious. However, computational approaches circumvent these limitations and are a powerful tool to identify new DNA repair factors. By analyzing the evolutionary relationships between genes in the major DNA repair pathways, we uncovered functional relationships between individual genes and identified partners. Here we ranked 17,487 mammalian genes for coevolution with 6 distinct DNA repair pathways. Direct comparison to genetic screens for homologous recombination or Fanconi anemia factors indicates that our evolution-based screen is comparable, if not superior, to traditional screening approaches. Demonstrating the utility of our strategy, we identify a role for the DNA damage-induced apoptosis suppressor (DDIAS) gene in double-strand break repair based on its coevolution with homologous recombination. DDIAS knockdown results in DNA double-strand breaks, indicated by ATM kinase activation and 53BP1 foci induction. Additionally, DDIAS-depleted cells are deficient for homologous recombination. Our results reveal that evolutionary analysis is a powerful tool to uncover novel factors and functional relationships in DNA repair.
| S-EPMC6765268 | biostudies-literature
Project description:BackgroundReliable prediction of stability changes in protein variants is an important aspect of computational protein design. A number of machine learning methods that allow a classification of stability changes knowing only the sequence of the protein emerged. However, their performance on amino acid substitutions of previously unseen non-homologous proteins is rather limited. Moreover, the performance varies for different types of mutations based on the secondary structure or accessible surface area of the mutation site.ResultsWe proposed feature-based multiple models with each model designed for a specific type of mutations. The new method is composed of five models trained for mutations in exposed, buried, helical, sheet, and coil residues. The classification of a mutation as stabilising or destabilising is made as a consensus of two models, one selected based on the predicted accessible surface area and the other based on the predicted secondary structure of the mutation site. We refer to our new method as Evolutionary, Amino acid, and Structural Encodings with Multiple Models (EASE-MM). Cross-validation results show that EASE-MM provides a notable improvement to our previous work reaching a Matthews correlation coefficient of 0.44. EASE-MM was able to correctly classify 73% and 75% of stabilising and destabilising protein variants, respectively. Using an independent test set of 238 mutations, we confirmed our results in a comparison with related work.ConclusionsEASE-MM not only outperformed other related methods but achieved more balanced results for different types of mutations based on the accessible surface area, secondary structure, or magnitude of stability changes. This can be attributed to using multiple models with the most relevant features selected for the given type of mutations. Therefore, our results support the presumption that different interactions govern stability changes in the exposed and buried residues or in residues with a different secondary structure.
| S-EPMC4083411 | biostudies-literature
Project description: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.
| S-EPMC3459886 | biostudies-other
Project description:Clonal mutations represent the initiating molecular defects related to cellular transition of a normal phenotype to a malignant phenotype. Molecular genomic assessment utilizing next generation and whole exome sequencing is now being increasingly applied to biomarker determination to refine the use of targeted immune therapies. Case examples followed by retrospective study assessment have convincingly demonstrated clonal neoantigens provide a relevant predictor of response to checkpoint inhibition. A meta-analysis, by Litchfield et al., of over 1000 cancer patients from 12 landmark trials demonstrated no clinical benefit to checkpoint inhibitor (CPI) therapy in correlation to high subclonal tumor mutational burden (TMB), whereas high clonal TMB was found to be significantly correlated with better overall survival (p = 0.000000029). We discuss the mechanism of clonal vs. subclonal neoantigen targeting relationship to homologous recombination proficient (HRP) profile, evidence of preclinical and clinical benefit related to clonal neoantigens, and review a novel developing therapy called Vigil®, designed to expand the clonal neoantigen targeting effector cell populations. Vigil® is an autologous cellular immunotherapy which is designed to carry the full set of personal clonal neoantigens. Phase 2b results demonstrate a durable recurrence-free survival (RFS) and overall survival (OS) advantage for Vigil® in a subset ovarian cancer population with an HRP cancer profile.
| S-EPMC10705148 | biostudies-literature
Project description:Network representations of systems from various scientific and societal domains are neither completely random nor fully regular, but instead appear to contain recurring structural features. These features tend to be shared by networks belonging to the same broad class, such as the class of social networks or the class of biological networks. Within each such class, networks describing similar systems tend to have similar features. This occurs presumably because networks representing similar systems would be expected to be generated by a shared set of domain specific mechanisms, and it should therefore be possible to classify networks based on their features at various structural levels. Here we describe and demonstrate a new hybrid approach that combines manual selection of network features of potential interest with existing automated classification methods. In particular, selecting well-known network features that have been studied extensively in social network analysis and network science literature, and then classifying networks on the basis of these features using methods such as random forest, which is known to handle the type of feature collinearity that arises in this setting, we find that our approach is able to achieve both higher accuracy and greater interpretability in shorter computation time than other methods.
| S-EPMC6980283 | biostudies-literature
Project description:Genomics-based neoantigen discovery can be enhanced by proteomic evidence, but there remains a lack of consensus on the performance of different quality control methods for variant peptide identification in proteogenomics. We propose to use the difference between accurately predicted and observed retention times for each peptide as a metric to evaluate different quality control methods. To this end, we develop AutoRT, a deep learning algorithm with high accuracy in retention time prediction. Analysis of three cancer data sets with a total of 287 tumor samples using different quality control strategies results in substantially different numbers of identified variant peptides and putative neoantigens. Our systematic evaluation, using the proposed retention time metric, provides insights and practical guidance on the selection of quality control strategies. We implement the recommended strategy in a computational workflow named NeoFlow to support proteogenomics-based neoantigen prioritization, enabling more sensitive discovery of putative neoantigens.
| S-EPMC7145864 | biostudies-literature
Project description:Neoantigens derived from non-synonymous somatic mutations are restricted to malignant cells and are thus considered ideal targets for T cell receptor (TCR)-based immunotherapy. Adoptive transfer of T cells bearing neoantigen-specific TCRs exhibits the ability to preferentially target tumor cells while remaining harmless to normal cells. High-avidity TCRs specific for neoantigens expressed on AML cells have been identified in vitro and verified using xenograft mouse models. Preclinical studies of these neoantigen-specific TCR-T cells are underway and offer great promise as safe and effective therapies. Additionally, TCR-based immunotherapies targeting tumor-associated antigens are used in early-phase clinical trials for the treatment of AML and show encouraging anti-leukemic effects. These clinical experiences support the application of TCR-T cells that are specifically designed to recognize neoantigens. In this review, we will provide a detailed profile of verified neoantigens in AML, describe the strategies to identify neoantigen-specific TCRs, and discuss the potential of neoantigen-specific T-cell-based immunotherapy in AML.
| S-EPMC9667632 | biostudies-literature
Project description:Although counteracting innate defenses allows oncolytic viruses (OVs) to better replicate and spread within tumors, CD8(+) T-cells restrict their capacity to trigger systemic anti-tumor immune responses. Herpes simplex virus-1 (HSV-1) evades CD8(+) T-cells by producing ICP47, which limits immune recognition of infected cells by inhibiting the transporter associated with antigen processing (TAP). Surprisingly, removing ICP47 was assumed to benefit OV immuno-therapy, but the impact of inhibiting TAP remains unknown because human HSV-1 ICP47 is not effective in rodents. Here, we engineer an HSV-1 OV to produce bovine herpesvirus UL49.5, which unlike ICP47, antagonizes rodent and human TAP. Significantly, UL49.5-expressing OVs showed superior efficacy treating bladder and breast cancer in murine models that was dependent upon CD8(+) T-cells. Besides injected subcutaneous tumors, UL49.5-OV reduced untreated, contralateral tumor size and metastases. These findings establish TAP inhibitor-armed OVs that evade CD8(+) T-cells as an immunotherapy strategy to elicit potent local and systemic anti-tumor responses.
| S-EPMC4816761 | biostudies-literature
Project description:BackgroundRegions of interest identified through genetic linkage studies regularly exceed 30 centimorgans in size and can contain hundreds of genes. Traditionally this number is reduced by matching functional annotation to knowledge of the disease or phenotype in question. However, here we show that disease genes share patterns of sequence-based features that can provide a good basis for automatic prioritization of candidates by machine learning.ResultsWe examined a variety of sequence-based features and found that for many of them there are significant differences between the sets of genes known to be involved in human hereditary disease and those not known to be involved in disease. We have created an automatic classifier called PROSPECTR based on those features using the alternating decision tree algorithm which ranks genes in the order of likelihood of involvement in disease. On average, PROSPECTR enriches lists for disease genes two-fold 77% of the time, five-fold 37% of the time and twenty-fold 11% of the time.ConclusionPROSPECTR is a simple and effective way to identify genes involved in Mendelian and oligogenic disorders. It performs markedly better than the single existing sequence-based classifier on novel data. PROSPECTR could save investigators looking at large regions of interest time and effort by prioritizing positional candidate genes for mutation detection and case-control association studies.
| S-EPMC1274252 | biostudies-literature