Personal tumor neoantigens for immunity against chronic lymphocytic leukemia

用于抵抗慢性淋巴细胞白血病的个体肿瘤新抗原

基本信息

批准号：
10365041
负责人：
Catherine Ju-Ying Wu
金额：
$ 77.11万
依托单位：
DANA-FARBER CANCER INST
依托单位国家：
美国
项目类别：
财政年份：
2011
资助国家：
美国
起止时间：
2011-06-01 至 2027-01-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10365041
关键词：
Allogenic Antigens Autologous Binding Biological Assay Biotechnology Blood Cancer Burden Chronic Lymphocytic Leukemia Clinical Data Data Set Detection Disease Elements Epitopes Evaluation Event Feedback Generations Genomics Glioblastoma Hematopoietic Neoplasms Human Immune response Immune system Immunity Immunotherapy Ligands MHC Interaction Malignant Neoplasms Mass Spectrum Analysis Measurement Memory Methods Molecular Mutate Mutation Myeloid Cells Natural History Output Patients Peptide Library Peptide/MHC Complex Peptides Principal Investigator Progress Reports Protein Isoforms RNA Splicing Recording of previous events Reporting Ribosomes Role Sampling Shapes Single Nucleotide Polymorphism Site Source Surface T cell response T-Lymphocyte Technology The Cancer Genome Atlas Time Training Transcript Translating Tumor Antigens Vaccination Vaccinee Vaccines Validation Variant Viral Antigens analysis pipeline antigen-specific T cells base cancer genome cancer immunotherapy chimeric antigen receptor T cells chronic lymphocytic leukemia cell cohort computer infrastructure experience graft vs leukemia effect hematopoietic cell transplantation immune checkpoint blockade immunogenic immunogenicity improved inhibitor innovation leukemia melanoma methylome neoantigen vaccination neoantigens neoplastic cell next generation sequencing nonsynonymous mutation novel novel sequencing technology novel therapeutics predictive modeling predictive tools programs prospective response tool transcriptome transcriptome sequencing treatment response tumor

Allogenic Antigens Autologous Binding Biological Assay Biotechnology Blood Cancer Burden Chronic Lymphocytic Leukemia Clinical Data Data Set Detection Disease Elements Epitopes Evaluation Event Feedback Generations Genomics Glioblastoma Hematopoietic Neoplasms Human Immune response Immune system Immunity Immunotherapy Ligands MHC Interaction Malignant Neoplasms Mass Spectrum Analysis Measurement Memory Methods Molecular Mutate Mutation Myeloid Cells Natural History Output Patients Peptide Library Peptide/MHC Complex Peptides Principal Investigator Progress Reports Protein Isoforms RNA Splicing Recording of previous events Reporting Ribosomes Role Sampling Shapes Single Nucleotide Polymorphism Site Source Surface T cell response T-Lymphocyte Technology The Cancer Genome Atlas Time Training Transcript Translating Tumor Antigens Vaccination Vaccinee Vaccines Validation Variant Viral Antigens analysis pipeline antigen-specific T cells base cancer genome cancer immunotherapy chimeric antigen receptor T cells chronic lymphocytic leukemia cell cohort computer infrastructure experience graft vs leukemia effect hematopoietic cell transplantation immune checkpoint blockade immunogenic immunogenicity improved inhibitor innovation leukemia melanoma methylome neoantigen vaccination neoantigens neoplastic cell next generation sequencing nonsynonymous mutation novel novel sequencing technology novel therapeutics predictive modeling predictive tools programs prospective response tool transcriptome transcriptome sequencing treatment response tumor

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项目摘要

Project Summary Personal neoantigens have been conceptualized as optimal tumor antigens but their broad applicability across cancers has been limited by the variability in mutational burden across tumors. Recent clinical experiences in targeting neoantigens, such as by vaccination, in high and moderate mutation-rate tumors (e.g. melanoma, glioblastoma) have been promising, but such promise for low mutation burden cancers remains under evaluation. Chronic lymphocytic leukemia (CLL) is an example of such a low mutation burden malignancy where improved tumor antigen identification could open new therapeutic opportunities. Indeed, although a growing armamentarium of clinically active therapies is now available for CLL (i.e. targeted inhibitors, CAR-T cells, CPB), CLL remains largely incurable. While inherently immunogenic, as highlighted by evidence of spontaneous regression and its responsiveness to the graft-versus-leukemia (GvL) activity of allogeneic hematopoietic cell transplantation (allo-HCT), CLL patients demonstrate inconsistent ability to mount antigen-specific immunity. Notably, we have achieved several exciting technologic advances over the last 5 years that impact antigen discovery, including: (i) new understanding of the cancer genome and transcriptome, advanced by new sequencing technologies, yielding potential alternative sources of cancer neoantigens; (ii) establishment of a scalable approach to rapidly validate peptide-MHC interactions, providing essential experimental feedback to our prediction efforts; (iii) improvements in the sensitivity, throughput and analysis of immunopeptidome data; (iv) expanded computational infrastructure to rapidly incorporate findings into prediction models for HLA class I. In this application, we hypothesize that novel classes of neoantigens are discoverable in CLL and that antigen-specific interactions arising from recognition of these alternative neoantigens could contribute to leukemia control. We propose an integrative strategy for the discovery and validation of novel neoantigen species. This commences with detection analyses of alterations that could encode these alternative CLL neoantigens through long-read and ribosomal sequencing, that can be used to inform improvements on the analysis pipelines. It then proceeds with validation using new high throughput binding assays, immunopeptidome detection and T cell response assessments (Aim 1). Our strategy further involves systematic improvement of our class I prediction tools through generation of experimental data from which we can iteratively correct and refine the epitope predictions. We furthermore incorporate the feature of peptide-MHC stability, which has been increasingly identified as relevant in peptide-MHC immunogenicity (Aim 2). Finally, we propose to confirm the functional relevance of alternative tumor neoantigens through their evaluation in the context of variation in natural history of CLL in a newly-assembled cohort of >1000 molecularly characterized samples, and within the setting of therapeutic response following whole CLL cell vaccination after alloHCT (Aim 3). We thus propose to expand the ‘universe’ of CLL neoantigens and to define their impact within informative clinically annotated CLL datasets.

项目摘要个人新抗原已被概念化为最佳肿瘤抗原，但它们的广泛适用性癌症受到肿瘤突变烧伤的变化的限制。最近的临床经验靶向新抗原，例如通过疫苗接种，在高和中等突变率肿瘤中（例如黑色素瘤，已承诺胶质母细胞瘤），但是低突变伯嫩癌的这种希望仍在评估中。慢性淋巴细胞性白血病（CLL）是如此低突变的伯宁恶性肿瘤的一个例子肿瘤抗原识别可以打开新的治疗机会。确实，虽然增长临床活性疗法的Armammentarium现在可用于CLL（即有针对性抑制剂，CAR-T细胞，CPB），CPB CLL在很大程度上无法治愈。固有的免疫原性，这是由赞助的证据所强调的回归及其对同种异体造血细胞的移植物 - 抗链球菌（GVL）活性的反应移植（Allo-HCT），CLL患者表现出不一致的抗原特异性免疫力的能力。值得注意的是，在过去的5年中，我们取得了一些令人兴奋的技术进步，影响了抗原发现，包括：（i）对癌症基因组和转录组的新理解，由新的测序技术，产生癌症新抗原的潜在替代来源；（ii）建立可扩展的方法来快速验证肽-MHC相互作用，从而提供必要的实验反馈我们的预测努力；（iii）改善免疫肽组数据的敏感性，吞吐量和分析；（iv）将计算基础架构扩展到将发现迅速纳入HLA I类的预测模型中。在此应用中，我们假设新型的新抗原在CLL中是可以发现的，并且识别这些替代新抗原的抗原特异性相互作用可能有助于控制白血病。我们提出了一种综合策略，以发现和验证新型新抗原物种。这是从可能编码这些替代CLL的变化的检测分析开始的通过长阅读和核糖体测序的新抗原，可以用来告知改进分析管道。然后，它使用新的高吞吐量结合测定法进行验证，免疫肽组检测和T细胞反应评估（AIM 1）。我们的策略进一步涉及系统的改进我们的类I通过生成的实验数据预测工具，我们可以从中迭代正确，并且完善情节预测。我们进一步纳入了胡椒-MHC稳定性的特征越来越多地识别为与肽-MHC免疫原性相关（AIM 2）。最后，我们建议确认替代性肿瘤新抗原通过自然差异的评估的功能相关性在新组装的队列中，Cll的历史> 1000个分子表征，并在设置中在全hyhct后全CLL细胞疫苗后的治疗反应（AIM 3）。因此，我们建议扩展 CLL Neoantigens的“宇宙”，并定义其在信息丰富的临床注释CLL数据集中的影响。