A multimodal approach for precision immuno-oncoloy in lymphoma treated with CAR-T cells
CAR-T 细胞治疗淋巴瘤的精准免疫肿瘤多模式方法
基本信息
- 批准号:10722590
- 负责人:
- 金额:$ 27.77万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-08-07 至 2028-07-31
- 项目状态:未结题
- 来源:
- 关键词:AddressAntigensAutologousAutomobile DrivingB-Cell LymphomasBCL2 geneBioinformaticsBiological MarkersBiologyBiopsyCAR T cell therapyCD19 geneCell Death InductionCell TherapyCellsCessation of lifeClinicalCodeComplementDNA Sequence AlterationDataDecision Support SystemsDiseaseFlow CytometryGenesGeneticGenomicsGenotypeGoalsHeterogeneityImageImmuneImmune checkpoint inhibitorImmunologyImmunotherapyIndividualInflammatoryInterferon Type IIKnowledgeLaboratoriesLymphomaMachine LearningMalignant NeoplasmsMeasuresMediatingMedical ImagingMentorshipMethodologyModalityModelingMolecularMutationOutcomePathologyPathway interactionsPatient-Focused OutcomesPatientsPhenotypePrognostic FactorPrognostic MarkerPublishingRadiology SpecialtyReceptor SignalingRecurrent diseaseRefractoryRelapseResistanceRiskRoleSamplingShapesSignal TransductionStimulusTP53 geneTreatment EfficacyTreatment FailureTumor BurdenWorkacquired treatment resistancebiobankbiomarker discoverybiomarker identificationcancer cellchimeric antigen receptor T cellsclinical decision-makingcohortcombinatorialcomputer infrastructurecomputerized toolscytotoxiccytotoxicityexperiencegenomic biomarkerhigh riskimprovedindividual patientinterpatient variabilitymachine learning algorithmmultidisciplinarymultimodal datamultimodalitymultiple data sourcesmultiple omicsnew therapeutic targetnovelnovel markerpersonalized approachpersonalized carepersonalized medicinepredicting responsepredictive markerpredictive modelingpressureradiological imagingradiomicsresistance mechanismresponsesuccesstherapy resistanttranscriptomic profilingtransmission processtumor
项目摘要
PROJECT SUMMARY/ABSTRACT
Autologous CD19-directed chimeric antigen receptor T-cells (CAR-T) have resulted in extraordinary response
rates in relapsing and refractory large B-cell lymphoma (LBCL). However, over 60% of CD19-CAR-T recipients
will experience disease recurrence or progression. Most of these patients will die from their disease.
Mechanisms of CAR-T treatment failure are partially understood and biomarkers informing patient outcomes
and management have limited clinical utility. Our central hypothesis is that orthogonal modalities (e.g., clinical,
molecular, genomic, and radiomic [quantitative measures from medical images]) complement one another,
together providing information on resistance mechanisms and patient outcomes beyond that accessible
through any individual modality. We present results suggesting that machine learning is an effective
methodology for synthesizing and modeling multiple sources of data together. Cancer cells harness genomic
heterogeneity to evade pressure applied by immunotherapies, such as immune checkpoint inhibitors. Our
preliminary data also demonstrate that TP53 genomic alterations strongly determine response to CAR-T.
Furthermore, using transcriptomic profiling, we found that cancer cellular pathways required for effective
transmission of CAR-T cytotoxic signals are distorted in TP53-altered lymphoma. These early findings provide
a proof-of-concept for the utility of genomics to inform disease biology and risk after CAR-T.
We hypothesize that tumor genetic aberrations in cellular pathways used by CAR-T cells to exert cytotoxicity
drive treatment resistance by rendering cancer cells insensitive to CAR-T stimuli and supporting immune
escape. In Aim 1, we will use comprehensive genotypic and phenotypic tumor profiling before and after CAR-T
to study the role of a priori determined genes and pathways in mediating inherent and acquired treatment
resistance. We also hypothesize that orthogonal modalities for patient and tumor profiling are complementary,
and their integration into a unified, multimodal model could accurately predict CAR-T outcomes. In Aim 2, we
will synthesize data from multiple modalities and use machine learning algorithms to predict CAR-T response
and identify novel biomarkers.
To meet our goals, we have compiled one of the largest CAR-T patient and sample biobanks. A group of
leading experts in immunology, genetics, pathology, radiology, machine learning, and bioinformatics will guide
the candidate in this multidisciplinary work. If successful, we expect our combinatorial approach to uncover
genetic features underlying inherent and acquired CAR-T resistance and identify new druggable targets.
Furthermore, our machine learning approach will support treatment personalization by establishing decision
support systems and identifying biomarkers of high-risk patients. Finally, we will introduce novel methodologies
for modeling CAR-T outcomes, which are extendable to other forms of treatment.
项目摘要/摘要
自体CD19导向的嵌合抗原受体T细胞(CAR-T)引起的异常应答
复发率和难治性大B细胞淋巴瘤(LBCL)。然而,超过60%的CD19-CAR-T接受者
将经历疾病复发或进展。这些患者中的大多数将死于他们的疾病。
CAR-T治疗失败的机制部分被了解,生物标记物影响患者的结果
和管理方面的临床应用有限。我们的中心假设是正交模式(例如,临床,
分子、基因组和放射[来自医学图像的定量测量])相辅相成,
共同提供有关耐药机制和患者预后的信息
通过任何单独的方式。我们提出的结果表明,机器学习是一种有效的
将多个数据源合成和建模的方法。癌细胞利用基因组
异质性,以逃避免疫疗法施加的压力,如免疫检查点抑制剂。我们的
初步数据还表明,TP53基因改变强烈地决定了对CAR-T的反应。
此外,利用转录图谱,我们发现癌细胞途径需要有效的
在TP53基因改变的淋巴瘤中,CAR-T细胞毒信号的传递被扭曲。这些早期的发现提供了
在CAR-T之后,基因组学在告知疾病生物学和风险方面的效用的概念验证。
我们假设CAR-T细胞利用细胞通路中的肿瘤遗传异常来发挥细胞毒作用
通过使癌细胞对CAR-T刺激不敏感并支持免疫来提高治疗耐药性
逃走。在目标1中,我们将在CAR-T前后使用综合的基因和表型肿瘤分析
研究先验决定的基因和通路在调节固有和获得性治疗中的作用
抵抗。我们还假设,患者和肿瘤简档的正交模式是互补的,
将它们整合到一个统一的多模式模型中可以准确地预测CAR-T的结果。在目标2中,我们
将综合来自多个医疗设备的数据并使用机器学习算法来预测CAR-T响应
并确定新的生物标记物。
为了实现我们的目标,我们编制了最大的CAR-T患者和样本生物库之一。一群
免疫学、遗传学、病理学、放射学、机器学习和生物信息学方面的顶尖专家将指导
这项多学科工作的候选人。如果成功,我们预计我们的组合方法将发现
研究CAR-T固有和获得性耐药性的遗传特征,并确定新的可用药靶点。
此外,我们的机器学习方法将通过建立决策来支持个性化治疗
支持系统和识别高危患者的生物标记物。最后,我们将介绍新的方法
用于模拟CAR-T结果,可扩展到其他形式的治疗。
项目成果
期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Fecal microbiota transplantation in capsules for the treatment of steroid refractory and steroid dependent acute graft vs. host disease: a pilot study.
胶囊中粪便微生物移植用于治疗类固醇难治性和类固醇依赖性急性移植物抗宿主病:一项试点研究。
- DOI:10.1038/s41409-024-02198-2
- 发表时间:2024
- 期刊:
- 影响因子:4.8
- 作者:Youngster,Ilan;Eshel,Adi;Geva,Mika;Danylesko,Ivetta;Henig,Israel;Zuckerman,Tsila;Fried,Shalev;Yerushalmi,Ronit;Shem-Tov,Noga;Fein,JoshuaA;Bomze,David;Shimoni,Avichai;Koren,Omry;Shouval,Roni;Nagler,Arnon
- 通讯作者:Nagler,Arnon
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