- Email: [email protected]
Improving Cancer Immunotherapies through Empirical Neoantigen Selection
TRECAN 227 No. of Pages 3
Forum
Improving Cancer Immunotherapies through Empirical Neoantigen Selection Catarina Nogueira,1 Johanna K. Kaufmann,1 Hubert Lam,1,* and Jessica B. Flechtner1,* Targeting neoantigens has become an attractive strategy for cancer immunotherapy. Epitope prediction algorithms facilitate rapid selection of potential neoantigens, but are plagued with high false-positive and false-negative rates. Here we review ex vivo technologies for biological identification of neoantigens to improve empirical prioritization for immunotherapy. Neoantigens are emerging as attractive vaccine targets for personalized cancer immunotherapy. Unlike aberrantly expressed tumor-associated antigens (TAAs), neoantigens contain nonsynonymous mutations that enable their identification as foreign targets not subject to central tolerance in the thymus. Recent work demonstrated that neoantigen vaccination generates more robust T cell responses than TAA vaccination in melanoma patients [1]. While recognized as promising targets, neoantigen identification is complex. First, the mutational landscape of a patient’s tumor is analyzed by whole-exome sequencing. Yet, not all mutations result in immunogenic neoantigens. Mutant sequences must be expressed, processed appropriately by the antigen processing machinery within antigen [71_TD$IF]presenting cells (APCs), and the resulting peptides must contain sufficient binding affinity for class I (MHC-I) or class II (MHC-II) major histocompatibility complexes. Subsequently, T cell receptor
(TCR) recognition of the MHC–peptide complex must occur to induce activation of CD8+[68_TD$IF] or CD4+ T cells. It is critical that these variables are considered during selection of bona fide neoantigen targets to maximize therapeutic efficacy. Many in silico approaches have been developed for neoantigen prediction, but these primarily focus on epitope binding to MHC molecules. This is complex due to each individual’s unique set of MHC-I and MHC-II alleles. Because there are over 12 000 MHC alleles in the human population, each with different affinities for distinct peptides, the possible neoepitope presentome is highly diverse. Although computational approaches continue to improve as MHC binding is studied by mass spectrometry, population-based predictions still have only 30% positive predictive value, and the primary focus has been on peptide binding to the most abundant MHC-I haplotypes [2,3]. While the importance of antitumor CD4+ T cell responses is increasingly appreciated, MHC-II-binding prediction is more complex and less accurate due to increased length of MHC-II epitopes and increased degree of freedom for peptide contacts [4]. Predictive algorithms are fast and of low cost, but typically ignore proteosomal antigen processing, peptide transport, and T [68_TD$IF]cell binding. Because of these limitations, unbiased ex vivo technologies are currently being developed for biological identification of relevant neoantigens with greater accuracy than prediction algorithms.
Biological Identification of Neoantigens To empirically identify patient-specific neoantigens, Robbins and colleagues [5] developed a screening approach using tandem minigenes (TMGs). Each TMG construct encodes up to 24 mutated sequences identified by whole-exome sequencing. TMG library pools are electroporated into autologous APCs or into
cell lines expressing patient-specific MHC molecules, mimicking patient-unique antigen expression, processing, and presentation. Although the TMG technique is inherently suited to target antigens for MHC-I presentation and consequent CD8+[68_TD$IF] T [68_TD$IF]cell recognition, the TMG construct can additionally express the sorting signal of the lysosomal-associated membrane protein LAMP-1 to direct the antigens to the endosomal and lysosomal compartments of the APCs, enabling MHC-II presentation and CD4+ T cell activation [6]. Immunogenic TMGs can then be identified using patient-specific CD4+[68_TD$IF] and CD8+ T cells isolated either from blood or directly from the tumor. This novel approach facilitates the discovery of target neoantigens for use in personalized cancer vaccines, as well as the isolation of neoantigen-specific T cells that can be engineered ex vivo for adoptive T cell therapies [5,7]. Recent clinical studies demonstrated that this approach successfully discovered immunogenic neoantigens in cancer patients [7]. However, the need for secondary deconvolution to identify the relevant neoantigen from the pool of epitopes within an antigenic TMG may pose technical and biological limitations. The process can be time-consuming and depends on the availability of patientderived cells. In addition, antigenic competition between putative neoantigens within a TMG may impact the ability to detect antigen-specific responses [8]. An alternative discovery platform developed by Heath and Baltimore and employed by PACT Pharmai[68_TD$IF] is using a microfluidics system combined with barcoded neoantigen–MHC complexes and patient-specific T cells. Individual neoantigen-reactive TCRs can then be identified and used for development of personalized adoptive T [68_TD$IF]cell therapies. This technology could be adapted for identification of immunogenic neoantigens, however, Trends in Cancer, Month Year, Vol. xx, No. yy
1
TRECAN 227 No. of Pages 3
(A)
+ Autologous CD4+ or CD8+ T cell
Autologous monocytederived dendriƟc cell (MDDC) Blood
ATLAS readout + neoanƟgen ID
Tumor
NeoanƟgen vaccine manufacturing
130 120 GA T AAAT CT GGTCTTATTCC
NGS
Mutant anƟgen plasmid library
(B)
Mutant anƟgen expression in E. coli
(C) E. coli expressing target protein
E. coli expressing target protein
Phagocytosis
Phagocytosis cLLO
Phagosome
PutaƟve neoanƟgen
z (pH drop)
Autologous MDDC
Cytokine release
cLLO pore
Proteasome
Phagosome
PutaƟve neoanƟgen
Cytokine release
Phagolysosome (pH drop)
Autologous MDDC Clip
TAP
Autologous CD8+ T cell
Immunological synapse
Mature MHC I
Loading of MHC I
Autologous CD4+ T cell
Loading of MHC II Immunological synapse
Mature MHC II
Figure 1. Experimental Neoantigen Identification Using Genocea’s ATLAS Platform. (A) The ATLAS neoantigen identification pipeline. Patient-specific mutations are identified via next-generation sequencing (NGS) and/or RNA-seq including normal control tissue as a reference sequence. Fragments containing all individual putative neoantigens are cloned into a plasmid library and expressed in two Escherichia coli strains facilitating processing and presentation on MHC-I or MHCII. In addition, whole blood is collected to isolate CD4+ and CD8+ T cell populations as well as CD14+[68_TD$IF] monocytes, which are derived into dendritic cells [monocytederived dendritic cells (MDDCs)] for use as antigen-presenting cells. MDDCs are incubated with E. coli libraries for activation and autologous processing and presentation, before co-incubation with respective T [68_TD$IF]cell subsets. After an overnight incubation, antigen-specific memory recall responses are detected by comparing cytokine production in response to each putative neoantigen with the background secretion in response to a nonimmunogenic control protein. The exact cytokine readouts are customizable, although classic Th1 cytokines like interferon-g (IFN-g) or tumor necrosis factor-a (TNF-a) appear informative in the context of antitumor immunity. ATLAS-identified immunogenic neoantigens are then prioritized for inclusion in manufacturing of a personalized vaccine. (B) Mechanism for antigen presentation on MHC-I using bacterial neoantigen expression libraries. E. coli co-expressing any putative neoantigen and a cytoplasmic variant of listeriolysin O (cLLO) are phagocytosed by MDDCs. Upon acidification of the phagolysosome, cLLO proteins oligomerize and form pores, enabling the release of the contents of the phagolysosome including the putative neoantigen into the cytoplasm. After autologous proteasomal processing, the transporter associated with antigen processing (TAP) facilitates loading of antigen fragments onto MHC-I molecules, mirroring the patient’s antigen processing and presentation ex vivo. Upon recognition of an effector CD8+ T cell, cytokines are released and quantified to inform neoantigen prioritization. (C) Mechanism for conventional antigen presentation on MHC-II using bacterial neoantigen expression libraries. The second E. coli strain does not express cLLO and therefore the contents of the phagolysosome remain in the vesicles and are proteolytically processed. The fragments are then loaded onto autologous MHC-II molecules, replacing the class II-associated invariant chain peptide (CLIP) that stabilizes the MHC-II complex before loading. CD4+[69_TD$IF] effector T [68_TD$IF]cell recognition of neoantigens is measured by cytokine release and also utilized to prioritize neoantigens.
2
Trends in Cancer, Month Year, Vol. xx, No. yy
TRECAN 227 No. of Pages 3
screening throughput and MHC haplo- timepoint-exclusive neoantigen-specific type specificities are potential limitations responsesiii. In this patient, where multiple to be addressed. neoantigen-specific responses were measurable pretreatment, there was an Genocea Biosciences’ ATLASTM platform increase in the breadth of neoantigens that addresses some of the shortcomings of induced CD4+ T cell responses after treatthe previously described platforms, ment, and there was very little overlap enabling a comprehensive and unbiased between the neoantigens that induced approach to antigen identification by pro- CD4+[68_TD$IF] and CD8+ T cell responses. In addifiling T [68_TD$IF]cell recall responses to many indi- tion, epitope prediction algorithms had an vidual neoantigens (Figure 1A). Libraries 11% positive predictive value and nearly of all putative neoantigens are expressed 50% false-negative rate. Since autologous in an ordered array as short, mutation- APCs are used, the platform is not containing protein fragments in two restricted to common MHC haplotypes. distinct Escherichia coli strains: (i) coex- Currently, a next-generation ATLAS platpressing a modified variant of the Listeria form is being developed to enable profiling monocytogenes pore-forming protein mutational burden with lower blood volume listeriolysin O (cLLO) to facilitate MHC-I and allow more comprehensive profiling of presentation (Figure 1B), and (ii) without neoantigen specificities in tumor-infiltrating cLLO for conventional MHC-II presenta- lymphocytes. tion (Figure 1C) [9]. This allows for selective screening of CD8+ and CD4+ T cell To date, ATLAS has been utilized priresponses, respectively, measured by marily to inform the selection of antigens antigen-specific cytokine secretion after for subunit vaccines. However, it also an overnight incubation. This enables provides the opportunity to characterize identification of biologically relevant recall the responding T [68_TD$IF]cell populations in responses to neoantigens from periph- more detail, either directly or after addieral blood samples without artificially tional antigen-specific expansion. This may include characterization by flow inducing new specificities. cytometry and molecular analyses such ATLAS has been successfully employed as TCR sequencing, enabling functional in the context of various infectious patho- studies or novel immunotherapeutic gens, including herpes simplex virus 2, for modalities. which screening against the entire proteome identified antigens that are being Summary clinically evaluated as part of an immuno- While the success of immunotherapies therapeutic vaccine for genital herpes such as checkpoint inhibitor and T [68_TD$IF]7cell [10]. This technology is applicable to therapies established the capacity of any disease for which T [68_TD$IF]cell responses the immune system to identify and are important. In cancer, ATLAS can be destroy cancer, only a subset of patients used for profiling responses to TAAs and benefits from treatment and significant neoantigens for both therapeutic and toxicities have been observed. Neoantigen vaccines are an additional modality to diagnostic purposesii[68_TD$IF]. leverage the immune system, providing Using markers of T [68_TD$IF]cell activation, proof of focused tumor killing while minimizing concept for neoantigen identification using off-target toxicity. Of note, the potential ATLAS was established in a nonsmall cell of personalized cancer vaccines as a clinlung cancer patient before and after clini- ical treatment has been recently demoncally effective checkpoint blockade treat- strated [1,11]. However, current in silico ment, identifying both common and tools for predicting targets are inefficient,
leading to only a fraction of vaccine antigens eliciting a biological response. The technologies described here can enhance the accuracy of neoantigen identification and therefore result in more effective immunotherapies, which on their own, or in combination with checkpoint inhibitor therapies, could lead to durable tumor regression in cancer patients. Resources [68_TD$IF]http://pactpharma.com/personalized-medicine
i
ii
https://www.genocea.com/assets/
Flechtner_Keystone2017.pdf iii
https://www.genocea.com/assets/
Kaufmann_AACR2017.pdf 1
Genocea Biosciences, Inc., Cambridge, MA, USA
*Correspondence: [email protected] (H. Lam) and jessica.fl[email protected] (J.B. Flechtner). https://doi.org/10.1016/j.trecan.2017.12.003 References 1. Sahin, U. et al. (2017) Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer. Nature 547, 222–226 2. Yadav, M. et al. (2014) Predicting immunogenic tumour mutations by combining mass spectrometry and exome sequencing. Nature 515, 572–576 3. Abelin, J.G. et al. (2017) Mass spectrometry profiling of HLA-associated peptidomes in mono-allelic cells enables more accurate epitope prediction. Immunity 46, 315–326 4. Bordner, A.J. (2010) Towards universal structure-based prediction of class II MHC epitopes for diverse allotypes. PLoS One 5, e14383 5. Lu, Y.C. et al. (2014) Efficient identification of mutated cancer antigens recognized by T cells associated with durable tumor regressions. Clin. Cancer Res. 20, 3401–3410 6. Wu, T.C. et al. (1995) Engineering an intracellular pathway for major histocompatibility complex class II presentation of antigens. Proc. Natl. Acad. Sci. U. S. A. 92, 11671–11675 7. Lu, Y.C. and Robbins, P.F. (2016) Cancer immunotherapy targeting neoantigens. Semin. Immunol. 28, 22–27 8. Aurisicchio, L. et al. (2014) A novel minigene scaffold for therapeutic cancer vaccines. Oncoimmunology 3, e27529 9. Hu, P.Q. et al. (2004) Escherichia coli expressing recombinant antigen and listeriolysin O stimulate class Irestricted CD8+ T cells following uptake by human APC. J. Immunol. 172, 1595–1601 10. Bernstein, D.I. et al. (2017) Therapeutic vaccine for genital herpes simplex virus-2 infection: findings from a randomized trial. J. Infect. Dis. 215, 856–864 11. Ott, P.A. et al. (2017) An immunogenic personal neoantigen vaccine for patients with melanoma. Nature 547, 217–221
Trends in Cancer, Month Year, Vol. xx, No. yy
3
Forum
Improving Cancer Immunotherapies through Empirical Neoantigen Selection Catarina Nogueira,1 Johanna K. Kaufmann,1 Hubert Lam,1,* and Jessica B. Flechtner1,* Targeting neoantigens has become an attractive strategy for cancer immunotherapy. Epitope prediction algorithms facilitate rapid selection of potential neoantigens, but are plagued with high false-positive and false-negative rates. Here we review ex vivo technologies for biological identification of neoantigens to improve empirical prioritization for immunotherapy. Neoantigens are emerging as attractive vaccine targets for personalized cancer immunotherapy. Unlike aberrantly expressed tumor-associated antigens (TAAs), neoantigens contain nonsynonymous mutations that enable their identification as foreign targets not subject to central tolerance in the thymus. Recent work demonstrated that neoantigen vaccination generates more robust T cell responses than TAA vaccination in melanoma patients [1]. While recognized as promising targets, neoantigen identification is complex. First, the mutational landscape of a patient’s tumor is analyzed by whole-exome sequencing. Yet, not all mutations result in immunogenic neoantigens. Mutant sequences must be expressed, processed appropriately by the antigen processing machinery within antigen [71_TD$IF]presenting cells (APCs), and the resulting peptides must contain sufficient binding affinity for class I (MHC-I) or class II (MHC-II) major histocompatibility complexes. Subsequently, T cell receptor
(TCR) recognition of the MHC–peptide complex must occur to induce activation of CD8+[68_TD$IF] or CD4+ T cells. It is critical that these variables are considered during selection of bona fide neoantigen targets to maximize therapeutic efficacy. Many in silico approaches have been developed for neoantigen prediction, but these primarily focus on epitope binding to MHC molecules. This is complex due to each individual’s unique set of MHC-I and MHC-II alleles. Because there are over 12 000 MHC alleles in the human population, each with different affinities for distinct peptides, the possible neoepitope presentome is highly diverse. Although computational approaches continue to improve as MHC binding is studied by mass spectrometry, population-based predictions still have only 30% positive predictive value, and the primary focus has been on peptide binding to the most abundant MHC-I haplotypes [2,3]. While the importance of antitumor CD4+ T cell responses is increasingly appreciated, MHC-II-binding prediction is more complex and less accurate due to increased length of MHC-II epitopes and increased degree of freedom for peptide contacts [4]. Predictive algorithms are fast and of low cost, but typically ignore proteosomal antigen processing, peptide transport, and T [68_TD$IF]cell binding. Because of these limitations, unbiased ex vivo technologies are currently being developed for biological identification of relevant neoantigens with greater accuracy than prediction algorithms.
Biological Identification of Neoantigens To empirically identify patient-specific neoantigens, Robbins and colleagues [5] developed a screening approach using tandem minigenes (TMGs). Each TMG construct encodes up to 24 mutated sequences identified by whole-exome sequencing. TMG library pools are electroporated into autologous APCs or into
cell lines expressing patient-specific MHC molecules, mimicking patient-unique antigen expression, processing, and presentation. Although the TMG technique is inherently suited to target antigens for MHC-I presentation and consequent CD8+[68_TD$IF] T [68_TD$IF]cell recognition, the TMG construct can additionally express the sorting signal of the lysosomal-associated membrane protein LAMP-1 to direct the antigens to the endosomal and lysosomal compartments of the APCs, enabling MHC-II presentation and CD4+ T cell activation [6]. Immunogenic TMGs can then be identified using patient-specific CD4+[68_TD$IF] and CD8+ T cells isolated either from blood or directly from the tumor. This novel approach facilitates the discovery of target neoantigens for use in personalized cancer vaccines, as well as the isolation of neoantigen-specific T cells that can be engineered ex vivo for adoptive T cell therapies [5,7]. Recent clinical studies demonstrated that this approach successfully discovered immunogenic neoantigens in cancer patients [7]. However, the need for secondary deconvolution to identify the relevant neoantigen from the pool of epitopes within an antigenic TMG may pose technical and biological limitations. The process can be time-consuming and depends on the availability of patientderived cells. In addition, antigenic competition between putative neoantigens within a TMG may impact the ability to detect antigen-specific responses [8]. An alternative discovery platform developed by Heath and Baltimore and employed by PACT Pharmai[68_TD$IF] is using a microfluidics system combined with barcoded neoantigen–MHC complexes and patient-specific T cells. Individual neoantigen-reactive TCRs can then be identified and used for development of personalized adoptive T [68_TD$IF]cell therapies. This technology could be adapted for identification of immunogenic neoantigens, however, Trends in Cancer, Month Year, Vol. xx, No. yy
1
TRECAN 227 No. of Pages 3
(A)
+ Autologous CD4+ or CD8+ T cell
Autologous monocytederived dendriƟc cell (MDDC) Blood
ATLAS readout + neoanƟgen ID
Tumor
NeoanƟgen vaccine manufacturing
130 120 GA T AAAT CT GGTCTTATTCC
NGS
Mutant anƟgen plasmid library
(B)
Mutant anƟgen expression in E. coli
(C) E. coli expressing target protein
E. coli expressing target protein
Phagocytosis
Phagocytosis cLLO
Phagosome
PutaƟve neoanƟgen
z (pH drop)
Autologous MDDC
Cytokine release
cLLO pore
Proteasome
Phagosome
PutaƟve neoanƟgen
Cytokine release
Phagolysosome (pH drop)
Autologous MDDC Clip
TAP
Autologous CD8+ T cell
Immunological synapse
Mature MHC I
Loading of MHC I
Autologous CD4+ T cell
Loading of MHC II Immunological synapse
Mature MHC II
Figure 1. Experimental Neoantigen Identification Using Genocea’s ATLAS Platform. (A) The ATLAS neoantigen identification pipeline. Patient-specific mutations are identified via next-generation sequencing (NGS) and/or RNA-seq including normal control tissue as a reference sequence. Fragments containing all individual putative neoantigens are cloned into a plasmid library and expressed in two Escherichia coli strains facilitating processing and presentation on MHC-I or MHCII. In addition, whole blood is collected to isolate CD4+ and CD8+ T cell populations as well as CD14+[68_TD$IF] monocytes, which are derived into dendritic cells [monocytederived dendritic cells (MDDCs)] for use as antigen-presenting cells. MDDCs are incubated with E. coli libraries for activation and autologous processing and presentation, before co-incubation with respective T [68_TD$IF]cell subsets. After an overnight incubation, antigen-specific memory recall responses are detected by comparing cytokine production in response to each putative neoantigen with the background secretion in response to a nonimmunogenic control protein. The exact cytokine readouts are customizable, although classic Th1 cytokines like interferon-g (IFN-g) or tumor necrosis factor-a (TNF-a) appear informative in the context of antitumor immunity. ATLAS-identified immunogenic neoantigens are then prioritized for inclusion in manufacturing of a personalized vaccine. (B) Mechanism for antigen presentation on MHC-I using bacterial neoantigen expression libraries. E. coli co-expressing any putative neoantigen and a cytoplasmic variant of listeriolysin O (cLLO) are phagocytosed by MDDCs. Upon acidification of the phagolysosome, cLLO proteins oligomerize and form pores, enabling the release of the contents of the phagolysosome including the putative neoantigen into the cytoplasm. After autologous proteasomal processing, the transporter associated with antigen processing (TAP) facilitates loading of antigen fragments onto MHC-I molecules, mirroring the patient’s antigen processing and presentation ex vivo. Upon recognition of an effector CD8+ T cell, cytokines are released and quantified to inform neoantigen prioritization. (C) Mechanism for conventional antigen presentation on MHC-II using bacterial neoantigen expression libraries. The second E. coli strain does not express cLLO and therefore the contents of the phagolysosome remain in the vesicles and are proteolytically processed. The fragments are then loaded onto autologous MHC-II molecules, replacing the class II-associated invariant chain peptide (CLIP) that stabilizes the MHC-II complex before loading. CD4+[69_TD$IF] effector T [68_TD$IF]cell recognition of neoantigens is measured by cytokine release and also utilized to prioritize neoantigens.
2
Trends in Cancer, Month Year, Vol. xx, No. yy
TRECAN 227 No. of Pages 3
screening throughput and MHC haplo- timepoint-exclusive neoantigen-specific type specificities are potential limitations responsesiii. In this patient, where multiple to be addressed. neoantigen-specific responses were measurable pretreatment, there was an Genocea Biosciences’ ATLASTM platform increase in the breadth of neoantigens that addresses some of the shortcomings of induced CD4+ T cell responses after treatthe previously described platforms, ment, and there was very little overlap enabling a comprehensive and unbiased between the neoantigens that induced approach to antigen identification by pro- CD4+[68_TD$IF] and CD8+ T cell responses. In addifiling T [68_TD$IF]cell recall responses to many indi- tion, epitope prediction algorithms had an vidual neoantigens (Figure 1A). Libraries 11% positive predictive value and nearly of all putative neoantigens are expressed 50% false-negative rate. Since autologous in an ordered array as short, mutation- APCs are used, the platform is not containing protein fragments in two restricted to common MHC haplotypes. distinct Escherichia coli strains: (i) coex- Currently, a next-generation ATLAS platpressing a modified variant of the Listeria form is being developed to enable profiling monocytogenes pore-forming protein mutational burden with lower blood volume listeriolysin O (cLLO) to facilitate MHC-I and allow more comprehensive profiling of presentation (Figure 1B), and (ii) without neoantigen specificities in tumor-infiltrating cLLO for conventional MHC-II presenta- lymphocytes. tion (Figure 1C) [9]. This allows for selective screening of CD8+ and CD4+ T cell To date, ATLAS has been utilized priresponses, respectively, measured by marily to inform the selection of antigens antigen-specific cytokine secretion after for subunit vaccines. However, it also an overnight incubation. This enables provides the opportunity to characterize identification of biologically relevant recall the responding T [68_TD$IF]cell populations in responses to neoantigens from periph- more detail, either directly or after addieral blood samples without artificially tional antigen-specific expansion. This may include characterization by flow inducing new specificities. cytometry and molecular analyses such ATLAS has been successfully employed as TCR sequencing, enabling functional in the context of various infectious patho- studies or novel immunotherapeutic gens, including herpes simplex virus 2, for modalities. which screening against the entire proteome identified antigens that are being Summary clinically evaluated as part of an immuno- While the success of immunotherapies therapeutic vaccine for genital herpes such as checkpoint inhibitor and T [68_TD$IF]7cell [10]. This technology is applicable to therapies established the capacity of any disease for which T [68_TD$IF]cell responses the immune system to identify and are important. In cancer, ATLAS can be destroy cancer, only a subset of patients used for profiling responses to TAAs and benefits from treatment and significant neoantigens for both therapeutic and toxicities have been observed. Neoantigen vaccines are an additional modality to diagnostic purposesii[68_TD$IF]. leverage the immune system, providing Using markers of T [68_TD$IF]cell activation, proof of focused tumor killing while minimizing concept for neoantigen identification using off-target toxicity. Of note, the potential ATLAS was established in a nonsmall cell of personalized cancer vaccines as a clinlung cancer patient before and after clini- ical treatment has been recently demoncally effective checkpoint blockade treat- strated [1,11]. However, current in silico ment, identifying both common and tools for predicting targets are inefficient,
leading to only a fraction of vaccine antigens eliciting a biological response. The technologies described here can enhance the accuracy of neoantigen identification and therefore result in more effective immunotherapies, which on their own, or in combination with checkpoint inhibitor therapies, could lead to durable tumor regression in cancer patients. Resources [68_TD$IF]http://pactpharma.com/personalized-medicine
i
ii
https://www.genocea.com/assets/
Flechtner_Keystone2017.pdf iii
https://www.genocea.com/assets/
Kaufmann_AACR2017.pdf 1
Genocea Biosciences, Inc., Cambridge, MA, USA
*Correspondence: [email protected] (H. Lam) and jessica.fl[email protected] (J.B. Flechtner). https://doi.org/10.1016/j.trecan.2017.12.003 References 1. Sahin, U. et al. (2017) Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer. Nature 547, 222–226 2. Yadav, M. et al. (2014) Predicting immunogenic tumour mutations by combining mass spectrometry and exome sequencing. Nature 515, 572–576 3. Abelin, J.G. et al. (2017) Mass spectrometry profiling of HLA-associated peptidomes in mono-allelic cells enables more accurate epitope prediction. Immunity 46, 315–326 4. Bordner, A.J. (2010) Towards universal structure-based prediction of class II MHC epitopes for diverse allotypes. PLoS One 5, e14383 5. Lu, Y.C. et al. (2014) Efficient identification of mutated cancer antigens recognized by T cells associated with durable tumor regressions. Clin. Cancer Res. 20, 3401–3410 6. Wu, T.C. et al. (1995) Engineering an intracellular pathway for major histocompatibility complex class II presentation of antigens. Proc. Natl. Acad. Sci. U. S. A. 92, 11671–11675 7. Lu, Y.C. and Robbins, P.F. (2016) Cancer immunotherapy targeting neoantigens. Semin. Immunol. 28, 22–27 8. Aurisicchio, L. et al. (2014) A novel minigene scaffold for therapeutic cancer vaccines. Oncoimmunology 3, e27529 9. Hu, P.Q. et al. (2004) Escherichia coli expressing recombinant antigen and listeriolysin O stimulate class Irestricted CD8+ T cells following uptake by human APC. J. Immunol. 172, 1595–1601 10. Bernstein, D.I. et al. (2017) Therapeutic vaccine for genital herpes simplex virus-2 infection: findings from a randomized trial. J. Infect. Dis. 215, 856–864 11. Ott, P.A. et al. (2017) An immunogenic personal neoantigen vaccine for patients with melanoma. Nature 547, 217–221
Trends in Cancer, Month Year, Vol. xx, No. yy
3