PROJECT 1: TIME-Based Spatiotemporal Cancer Immunograms Predictive for Immunotherapy-Targeted Therapy Sequential Combinations
项目 1:基于时间的时空癌症免疫图预测免疫治疗靶向治疗顺序组合
基本信息
- 批准号:10526103
- 负责人:
- 金额:$ 82.5万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-09-22 至 2027-08-31
- 项目状态:未结题
- 来源:
- 关键词:Adoptive Cell TransfersAftercareAntigensAreaBiopsyCancer HistologyCellsChildClinicalClinical DataClinical TrialsClonal ExpansionCombination immunotherapyCombined Modality TherapyCutaneous MelanomaDataData AnalysesData SetDoseEngineeringEnvironmentFormalinFoundationsFreezingGoalsImmuneImmune systemImmunocompetentImmunologic FactorsImmunotherapyInflammatoryLeadMAP Kinase GeneMEKsMalignant NeoplasmsMetastatic malignant neoplasm to brainMitogen-Activated Protein Kinase InhibitorModelingMolecularMultiomic DataMusMutationOncogenesOutcomeParaffin EmbeddingPatientsPhysiologicalProgression-Free SurvivalsRegimenResistanceResolutionResourcesRetrospective StudiesRoleSamplingT cell therapyT-Cell ReceptorT-LymphocyteTestingTherapeuticTherapy trialTimeTissuesTreatment EfficacyTriplet Multiple BirthTumor AntigensTumor-associated macrophagesValidationWorkanti-CTLA4anti-PD-1anti-PD-L1basebiobankclinical developmentclinically relevantcombinatorialcomputational pipelinesdata resourcedesigndriver mutationengineered T cellsimmune checkpoint blockadeimmunogenicimmunotherapy trialsimprovedin vivoinhibitorinhibitor therapyinsightmelanomamoviemultiple omicsmutantneoplastic celloverexpressionposterspreventrational designresistance mechanismresponsespatiotemporalsubcutaneoustargeted treatmenttherapy outcometooltreatment responsetumortumor-immune system interactions
项目摘要
Project 1 Summary/Abstract
Combining immunotherapy with other therapy regimens, particularly targeted therapy, is a highly active area of
exploration with the goal of improving anti-tumor efficacy and extending therapeutic benefits to more patients or
tumor types. As the first mutation-immune co-targeted therapy, the simultaneous combination of anti-PD-L1 with
BRAFV600MUT and MEK inhibitors (so-called “triplet” therapy) has been approved for patients with BRAFV600MUT
melanoma. However, the data on this triplet appear mixed, with other trials not meeting key endpoints,
suggesting that simultaneous combination is not optimal. Our recent work in syngeneic murine melanoma
models showed uniformly, across tumor models of distinct driver mutations and cancer histologies, that a
regimen of 1-week anti-PD-1/L1 (± anti-CTLA-4) pretreatment augments the efficacy of triplet therapy by
enhancing MAPKi durability and dramatically suppressing melanoma brain metastasis. The improved therapy
efficacy resulted from the promotion of pro-inflammatory polarization of tumor-associated macrophages and the
elicitation of robust T cell clonal expansion and clonotypic convergence within the tumor-immune
microenvironment (TIME) induced by the anti-PD-1/L1 lead-in. This is consistent with observations in the clinical
trial data that prior immunotherapy before MAPKi is associated with improved progression-free survival. These
results highlight the vital role of the sequence/timing of each therapy component in the rational design of
combination therapies and also point to the need for a mechanistic understanding of the early-stage impact of
each combinatorial therapy component on the TIME.
However, the design of such sequential combination therapy trials is challenging because of the sheer number
of variables (sequence order, dosing, and timing) to be tested. The level of complexity calls for a predictive
framework to significantly reduce the parameter space and inform the identification of effective sequential
immunotherapy-targeted inhibitor combinations. Herein, we hypothesize that a spatiotemporal, multi-omics
analysis of early-stage (few days) monotherapy-induced changes in the TIME can provide deep insights
for greatly simplifying the design of immunotherapy-targeted inhibitor sequential combination trials. The
goal of Project 1 is to provide a data set that can be mined to inform the design of effective sequential combination
regimens. We will leverage state-of-the-art, spatial multi-omics tissue profiling tools to build a spatiotemporal
“movie” of the evolving TIME in established syngeneic melanoma tumor models, and their associated brain
metastases, after treatment with each of the combinatorial therapy components. The resultant spatiotemporal
multi-omic data will be analyzed to extract a number of highly informative TIME features from which agent-based
models (Project 2) for predicting effective sequential combination regimens can be constructed. Retrospective
studies of clinical tumor biopsies are proposed to validate the model findings.
项目1概要/摘要
将免疫治疗与其他治疗方案,特别是靶向治疗相结合,是免疫治疗的一个高度活跃的领域。
旨在提高抗肿瘤疗效并将治疗获益扩展至更多患者的探索,或
肿瘤类型作为第一种突变-免疫共靶向治疗,抗PD-L1与
BRAFV 600 MUT和MEK抑制剂(所谓的“三联”疗法)已被批准用于BRAFV 600 MUT患者
黑素瘤然而,这三个方面的数据似乎是混合的,其他试验没有达到关键终点,
这表明同时组合不是最佳的。我们最近在同基因小鼠黑色素瘤方面的工作
模型一致显示,在不同驱动突变和癌症组织学的肿瘤模型中,
1周抗PD-1/L1(±抗CTLA-4)预处理方案通过以下方式增强三联疗法的疗效:
增强MAPKi的持久性并显著抑制黑色素瘤脑转移。改良疗法
疗效来自于促进肿瘤相关巨噬细胞的促炎性极化,
引发肿瘤免疫中稳健的T细胞克隆扩增和克隆型收敛
抗PD-1/L1导入诱导的微环境(TIME)。这与临床观察结果一致。
试验数据表明,MAPKi之前的免疫治疗与改善的无进展生存期相关。这些
结果强调了每个治疗组分的顺序/定时在合理设计中的重要作用,
联合治疗,也指出需要一个机制的理解的早期阶段的影响,
每一个组合治疗成分的时间。
然而,这种序贯联合治疗试验的设计是具有挑战性的,因为数量庞大,
待测变量(序列顺序、给药和时间)。复杂程度要求预测
框架,以显着减少参数空间,并告知有效的顺序识别
免疫治疗靶向抑制剂组合。在此,我们假设时空,多组学
对早期(几天)单药治疗诱导的TIME变化的分析可以提供深刻的见解
大大简化了免疫治疗靶向抑制剂序贯组合试验的设计。的
项目1的目标是提供一个可以挖掘的数据集,为有效的顺序组合设计提供信息
养生法我们将利用最先进的空间多组学组织分析工具,
在已建立的同基因黑色素瘤肿瘤模型及其相关脑中不断演变的时间的“电影”
在用每种组合疗法组分治疗后,治疗转移的患者。由此产生的时空
多组学数据将被分析,以提取一些高度信息化的时间特征,
可以构建用于预测有效的序贯组合方案的模型(项目2)。回顾性
临床肿瘤活检的研究被提出来验证模型发现。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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James R. Heath其他文献
Correction: Rare predicted loss-of-function variants of type I IFN immunity genes are associated with life-threatening COVID-19
- DOI:
10.1186/s13073-023-01278-0 - 发表时间:
2024-01-06 - 期刊:
- 影响因子:11.200
- 作者:
Daniela Matuozzo;Estelle Talouarn;Astrid Marchal;Peng Zhang;Jeremy Manry;Yoann Seeleuthner;Yu Zhang;Alexandre Bolze;Matthieu Chaldebas;Baptiste Milisavljevic;Adrian Gervais;Paul Bastard;Takaki Asano;Lucy Bizien;Federica Barzaghi;Hassan Abolhassani;Ahmad Abou Tayoun;Alessandro Aiuti;Ilad Alavi Darazam;Luis M. Allende;Rebeca Alonso-Arias;Andrés Augusto Arias;Gokhan Aytekin;Peter Bergman;Simone Bondesan;Yenan T. Bryceson;Ingrid G. Bustos;Oscar Cabrera-Marante;Sheila Carcel;Paola Carrera;Giorgio Casari;Khalil Chaïbi;Roger Colobran;Antonio Condino-Neto;Laura E. Covill;Ottavia M. Delmonte;Loubna El Zein;Carlos Flores;Peter K. Gregersen;Marta Gut;Filomeen Haerynck;Rabih Halwani;Selda Hancerli;Lennart Hammarström;Nevin Hatipoğlu;Adem Karbuz;Sevgi Keles;Christèle Kyheng;Rafael Leon-Lopez;Jose Luis Franco;Davood Mansouri;Javier Martinez-Picado;Ozge Metin Akcan;Isabelle Migeotte;Pierre-Emmanuel Morange;Guillaume Morelle;Andrea Martin-Nalda;Giuseppe Novelli;Antonio Novelli;Tayfun Ozcelik;Figen Palabiyik;Qiang Pan-Hammarström;Rebeca Pérez de Diego;Laura Planas-Serra;Daniel E. Pleguezuelo;Carolina Prando;Aurora Pujol;Luis Felipe Reyes;Jacques G. Rivière;Carlos Rodriguez-Gallego;Julian Rojas;Patrizia Rovere-Querini;Agatha Schlüter;Mohammad Shahrooei;Ali Sobh;Pere Soler-Palacin;Yacine Tandjaoui-Lambiotte;Imran Tipu;Cristina Tresoldi;Jesus Troya;Diederik van de Beek;Mayana Zatz;Pawel Zawadzki;Saleh Zaid Al-Muhsen;Mohammed Faraj Alosaimi;Fahad M. Alsohime;Hagit Baris-Feldman;Manish J. Butte;Stefan N. Constantinescu;Megan A. Cooper;Clifton L. Dalgard;Jacques Fellay;James R. Heath;Yu-Lung Lau;Richard P. Lifton;Tom Maniatis;Trine H. Mogensen;Horst von Bernuth;Alban Lermine;Michel Vidaud;Anne Boland;Jean-François Deleuze;Robert Nussbaum;Amanda Kahn-Kirby;France Mentre;Sarah Tubiana;Guy Gorochov;Florence Tubach;Pierre Hausfater;Isabelle Meyts;Shen-Ying Zhang;Anne Puel;Luigi D. Notarangelo;Stephanie Boisson-Dupuis;Helen C. Su;Bertrand Boisson;Emmanuelle Jouanguy;Jean-Laurent Casanova;Qian Zhang;Laurent Abel;Aurélie Cobat - 通讯作者:
Aurélie Cobat
C60's smallest cousin
C60 的最小“亲戚”
- DOI:
10.1038/31579 - 发表时间:
1998-06-25 - 期刊:
- 影响因子:48.500
- 作者:
James R. Heath - 通讯作者:
James R. Heath
Protein Catalyzed Capture (PCC) Agents for Antigen Targeting.
用于抗原靶向的蛋白质催化捕获 (PCC) 试剂。
- DOI:
- 发表时间:
2022 - 期刊:
- 影响因子:0
- 作者:
M. Idso;B. Lai;Heather D Agnew;James R. Heath - 通讯作者:
James R. Heath
Planar Patch-Clamp Electrodes for Single Cell and Neural Network Studies
- DOI:
10.1016/j.bpj.2009.12.3287 - 发表时间:
2010-01-01 - 期刊:
- 影响因子:
- 作者:
John M. Nagarah;Daniel A. Wagenaar;James R. Heath - 通讯作者:
James R. Heath
Stereochemical engineering of a peptide macrocycle allosteric inhibitor of phospho-Akt2 controls cell penetration by fine-tuning macrocycle-cell membrane interactions
磷酸 Akt2 肽大环变构抑制剂的立体化学工程通过微调大环 - 细胞膜相互作用来控制细胞渗透
- DOI:
10.26434/chemrxiv-2021-kldh7 - 发表时间:
2021 - 期刊:
- 影响因子:5.9
- 作者:
Arundhati Nag;A. Mafi;Samir R Das;Mary Beth Yu;Belen Alvarez;W. Goddard;James R. Heath - 通讯作者:
James R. Heath
James R. Heath的其他文献
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{{ truncateString('James R. Heath', 18)}}的其他基金
Spatiotemporal Tumor Analytics for Guiding Sequential Targeted-Inhibitor: Immunotherapy Combinations (ST-Analytics)
用于指导序贯靶向抑制剂的时空肿瘤分析:免疫治疗组合(ST-Analytics)
- 批准号:
10708901 - 财政年份:2022
- 资助金额:
$ 82.5万 - 项目类别:
PROJECT 1: TIME-Based Spatiotemporal Cancer Immunograms Predictive for Immunotherapy-Targeted Therapy Sequential Combinations
项目 1:基于时间的时空癌症免疫图预测免疫治疗靶向治疗顺序组合
- 批准号:
10907268 - 财政年份:2022
- 资助金额:
$ 82.5万 - 项目类别:
Spatiotemporal Tumor Analytics for Guiding Sequential Targeted-Inhibitor: Immunotherapy Combinations (ST-Analytics)
用于指导序贯靶向抑制剂的时空肿瘤分析:免疫治疗组合(ST-Analytics)
- 批准号:
10526101 - 财政年份:2022
- 资助金额:
$ 82.5万 - 项目类别:
PROJECT 1: TIME-Based Spatiotemporal Cancer Immunograms Predictive for Immunotherapy-Targeted Therapy Sequential Combinations
项目 1:基于时间的时空癌症免疫图预测免疫治疗靶向治疗顺序组合
- 批准号:
10708924 - 财政年份:2022
- 资助金额:
$ 82.5万 - 项目类别:
Data-driven Patient-Specific Agent Based Models of Metastatic Melanoma for Immunotherapy Response Prediction
用于免疫治疗反应预测的数据驱动的基于患者特异性药物的转移性黑色素瘤模型
- 批准号:
10831325 - 财政年份:2022
- 资助金额:
$ 82.5万 - 项目类别:
Nano and biomolecular engineered technologies for neoantigen-specific T cell capture and characterization
用于新抗原特异性 T 细胞捕获和表征的纳米和生物分子工程技术
- 批准号:
10297588 - 财政年份:2021
- 资助金额:
$ 82.5万 - 项目类别:
Nano and biomolecular engineered technologies for neoantigen-specific T cell capture and characterization
用于新抗原特异性 T 细胞捕获和表征的纳米和生物分子工程技术
- 批准号:
10489832 - 财政年份:2021
- 资助金额:
$ 82.5万 - 项目类别:
Nano and biomolecular engineered technologies for neoantigen-specific T cell capture and characterization
用于新抗原特异性 T 细胞捕获和表征的纳米和生物分子工程技术
- 批准号:
10673935 - 财政年份:2021
- 资助金额:
$ 82.5万 - 项目类别:
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