Abstract:Srivastava et al. define a new and improved way to predict immunoprotective cancer neoepitopes based in part on the difference in MHC-binding scores between the mutant epitope and its wild-type counterpart. Remarkably, all neoepitopes that elicited tumor regression bound to class I MHC molecules with very low affinity.
“…19 We evaluated the performance of the proposed “differential agretopic index” (DAI) and found that tested on our dataset, it is performing worse than NetMHCpan predictions alone with an AUC of 0.86 when using percentile rank (as suggested by the authors) and 0.902 when using rescaled ranks.…”
Section: Resultsmentioning
confidence: 99%
“…This is an important observation, as several studies used this as a criterion and deprioritized neoepitopes if the mutation did not improve binding affinity above the non-mutated counterpart. 19,27 Applying such a filter to our dataset would remove 39% of the neoepitopes, highlighting the need to use a threshold when filtering out neoepitopes with weaker binding affinity than the corresponding non-mutated peptide.10.1080/2162402X.2018.1492508-F0003Figure 3. Peptide pairs can be categorized into three distinct affinity patterns . Analysis of the distribution of length-rescaled ranks of mutated/non-mutated peptide pairs revealed three distinct affinity patterns (APs): AP1 corresponds to peptides for which a substantial increase in affinity was associated with the mutational event, AP2 corresponds to peptides for which no appreciable change in binding affinity was introduced by the mutation, and AP3 corresponds to peptides for which a substantial decrease in affinity was associated with the mutational event.…”
Section: Resultsmentioning
confidence: 99%
“…11 This threshold has been repeatedly validated for viral and other non-self origin epitopes but it is being controversially discussed whether the same threshold is also applicable for cancer epitopes. 15,18,19,47 Neoepitopes are generally very similar to their non-mutated counterparts which are self-proteins that are subject to central tolerance. Thus, studies have suggested that the binding threshold for neoepitopes should be substantially higher than 500nM.…”
Section: Discussionmentioning
confidence: 99%
“…However, this tenet was challenged by several studies showing that cancer and autoantigen associated epitopes also bound with affinities similar to those of their microbial counterparts. 16,17 The controversy was renewed in the setting of mutated cancer epitopes, with some studies suggesting that HLA binding is a crucial factor in selecting them 18 with others indicating that binding is of marginal importance 19 and that the MHC binding affinity of the non-mutated sequence from which the mutation is derived is also of importance.…”
Section: Introductionmentioning
confidence: 99%
“…TCR recognition is dependent on availability of a suitable TCR repertoire, which is in turn influenced by both positive and negative selection, and peripheral tolerance. 19 This complex process is hard to predict, but it seems to favor the recognition of amino acid residues with large and aromatic side chains types in peptide residue positions facing the TCR, which can be used to assign peptides an immunogenicity score independent of MHC binding and processing. 21 …”
Epitopes that arise from a somatic mutation, also called neoepitopes, are now known to play a key role in cancer immunology and immunotherapy. Recent advances in high-throughput sequencing have made it possible to identify all mutations and thereby all potential neoepitope candidates in an individual cancer. However, most of these neoepitope candidates are not recognized by T cells of cancer patients when tested in vivo or in vitro, meaning they are not immunogenic. Especially in patients with a high mutational load, usually hundreds of potential neoepitopes are detected, highlighting the need to further narrow down this candidate list. In our study, we assembled a dataset of known, naturally processed, immunogenic neoepitopes to dissect the properties that make these neoepitopes immunogenic. The tools to use and thresholds to apply for prioritizing neoepitopes have so far been largely based on experience with epitope identification in other settings such as infectious disease and allergy. Here, we performed a detailed analysis on our dataset of curated immunogenic neoepitopes to establish the appropriate tools and thresholds in the cancer setting. To this end, we evaluated different predictors for parameters that play a role in a neoepitope’s immunogenicity and suggest that using binding predictions and length-rescaling yields the best performance in discriminating immunogenic neoepitopes from a background set of mutated peptides. We furthermore show that almost all neoepitopes had strong predicted binding affinities (as expected), but more surprisingly, the corresponding non-mutated peptides had nearly as high affinities. Our results provide a rational basis for parameters in neoepitope filtering approaches that are being commonly used.Abbreviations: SNV: single nucleotide variant; nsSNV: nonsynonymous single nucleotide variant; ROC: receiver operating characteristic; AUC: area under ROC curve; HLA: human leukocyte antigen; MHC: major histocompatibility complex; PD-1: Programmed cell death protein 1; PD-L1 or CTLA-4: cytotoxic T-lymphocyte associated protein 4
“…19 We evaluated the performance of the proposed “differential agretopic index” (DAI) and found that tested on our dataset, it is performing worse than NetMHCpan predictions alone with an AUC of 0.86 when using percentile rank (as suggested by the authors) and 0.902 when using rescaled ranks.…”
Section: Resultsmentioning
confidence: 99%
“…This is an important observation, as several studies used this as a criterion and deprioritized neoepitopes if the mutation did not improve binding affinity above the non-mutated counterpart. 19,27 Applying such a filter to our dataset would remove 39% of the neoepitopes, highlighting the need to use a threshold when filtering out neoepitopes with weaker binding affinity than the corresponding non-mutated peptide.10.1080/2162402X.2018.1492508-F0003Figure 3. Peptide pairs can be categorized into three distinct affinity patterns . Analysis of the distribution of length-rescaled ranks of mutated/non-mutated peptide pairs revealed three distinct affinity patterns (APs): AP1 corresponds to peptides for which a substantial increase in affinity was associated with the mutational event, AP2 corresponds to peptides for which no appreciable change in binding affinity was introduced by the mutation, and AP3 corresponds to peptides for which a substantial decrease in affinity was associated with the mutational event.…”
Section: Resultsmentioning
confidence: 99%
“…11 This threshold has been repeatedly validated for viral and other non-self origin epitopes but it is being controversially discussed whether the same threshold is also applicable for cancer epitopes. 15,18,19,47 Neoepitopes are generally very similar to their non-mutated counterparts which are self-proteins that are subject to central tolerance. Thus, studies have suggested that the binding threshold for neoepitopes should be substantially higher than 500nM.…”
Section: Discussionmentioning
confidence: 99%
“…However, this tenet was challenged by several studies showing that cancer and autoantigen associated epitopes also bound with affinities similar to those of their microbial counterparts. 16,17 The controversy was renewed in the setting of mutated cancer epitopes, with some studies suggesting that HLA binding is a crucial factor in selecting them 18 with others indicating that binding is of marginal importance 19 and that the MHC binding affinity of the non-mutated sequence from which the mutation is derived is also of importance.…”
Section: Introductionmentioning
confidence: 99%
“…TCR recognition is dependent on availability of a suitable TCR repertoire, which is in turn influenced by both positive and negative selection, and peripheral tolerance. 19 This complex process is hard to predict, but it seems to favor the recognition of amino acid residues with large and aromatic side chains types in peptide residue positions facing the TCR, which can be used to assign peptides an immunogenicity score independent of MHC binding and processing. 21 …”
Epitopes that arise from a somatic mutation, also called neoepitopes, are now known to play a key role in cancer immunology and immunotherapy. Recent advances in high-throughput sequencing have made it possible to identify all mutations and thereby all potential neoepitope candidates in an individual cancer. However, most of these neoepitope candidates are not recognized by T cells of cancer patients when tested in vivo or in vitro, meaning they are not immunogenic. Especially in patients with a high mutational load, usually hundreds of potential neoepitopes are detected, highlighting the need to further narrow down this candidate list. In our study, we assembled a dataset of known, naturally processed, immunogenic neoepitopes to dissect the properties that make these neoepitopes immunogenic. The tools to use and thresholds to apply for prioritizing neoepitopes have so far been largely based on experience with epitope identification in other settings such as infectious disease and allergy. Here, we performed a detailed analysis on our dataset of curated immunogenic neoepitopes to establish the appropriate tools and thresholds in the cancer setting. To this end, we evaluated different predictors for parameters that play a role in a neoepitope’s immunogenicity and suggest that using binding predictions and length-rescaling yields the best performance in discriminating immunogenic neoepitopes from a background set of mutated peptides. We furthermore show that almost all neoepitopes had strong predicted binding affinities (as expected), but more surprisingly, the corresponding non-mutated peptides had nearly as high affinities. Our results provide a rational basis for parameters in neoepitope filtering approaches that are being commonly used.Abbreviations: SNV: single nucleotide variant; nsSNV: nonsynonymous single nucleotide variant; ROC: receiver operating characteristic; AUC: area under ROC curve; HLA: human leukocyte antigen; MHC: major histocompatibility complex; PD-1: Programmed cell death protein 1; PD-L1 or CTLA-4: cytotoxic T-lymphocyte associated protein 4
Circulating tumor cells (CTCs) are cancer cells that detach from the original site and reach the bloodstream. The most aggressive CTCs survive various immune system attacks and initiate metastasis formation. Importantly, CTCs are not specifically targeted by the current immunotherapies due to the limited knowledge on specific targets. Proteomic profiling can be a powerful tool for understanding some of the immune evasion mechanisms used by cancer cells and particularly CTCs. These mechanisms are generally linked to the expression of specific surface proteins/peptides (i.e. the surfaceome). The study of the peptides that bind to class I molecules of the major histocompatibility complex (MHC‐I) and of the various glycoproteins expressed on CTC surface may open a completely new avenue for the discovery of novel mechanisms of immune evasion. In this review, we discuss how immunopeptidomic and glycoproteomic studies of CTCs that interact with immune cells could help to better understand how metastasis‐initiator CTCs escape the host immune response. We also describe how immunopeptidomic and glycoproteomic studies are carried out.
Overview
The concept of personalized medicine in oncology drug development is no longer an aspiration; it is now, in large measure, the accepted paradigm of how oncology drugs should be developed. In modern oncology drug development, determining which molecularly characterized tumors will benefit the most from a drug is a critical part of the development plan. In addition, another important personalized medicine approach is emerging whereby a therapeutic is specific to an individual patient. Examples of this individualized therapeutic approach include engineering of autologous immune cells with reinfusion into the patient or cancer vaccines that are specific to an individual tumor.
A growing number of successful examples have demonstrated that the personalized medicine approach via biomarker selection can provide more benefit to patients and more rapid approval of drugs. The benefits of a personalized medicine strategy in oncology drug development are multiple and include an increased probability of overall success, smaller Phase 3 registration trials, and the potential for enhanced clinical benefits for patients. In a recent review, Falconi and colleagues examined the outcome of 676 Phase 1, 2, and 3 clinical trials of novel therapeutics for non‐small cell lung cancer conducted from 1998 to 2012.
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Overall, the cumulative success rate, as defined by the advancement to the next stage of clinical testing or to approval, was only 11%. However, when a biomarker was utilized for patient selection, the cumulative success rate increased by nearly sixfold to 62%. Thus, there is strong objective evidence for predictive biomarkers to enhance the probability of success in drug development. Using specific genetic and/or protein markers, a number of important drugs have been developed and approved in specific populations. This approach represents a paradigm shift that has proved so powerful that it is now a standard.
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