LBPO.CL04 · 临床研究 · Late-Breaking

人源化PDX模型揭示调控DLBCL中T细胞治疗反应的药物可逆表观遗传免疫排斥，并通过EPICORE分层器在患者中捕获

Humanized PDX models uncover drug-reversible epigenetic immune exclusion governing T-cell therapy response in DLBCL, captured in patients by EPICORE stratifier

编号 LB427 展板 17 时间 4/22 09:00–12:00 区域 Section 51 主讲 Giovanni Medico, MD

分会场 Late-Breaking Research: Clinical Research 4

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作者与单位 Authors & Affiliations

Giovanni Medico¹, Giorgia Zanetti², Giorgio Bertolazzi³, Francesco Vallania⁴, Paul Zumbo¹, Maider Amiama¹, Doron Betel¹, Maria Cacciapuoti¹, Abigail Taylor¹, Clarisse Kayembe¹, Sanjay Patel¹, Arvin Ruiz¹, Jennifer Ziello⁵, Jason Weirather⁶, Gulpreet Kaur⁶, Luca Cappelli⁷, Luca Paruzzo⁸, Marco Ruella⁸, Zhenyuan Dong⁹, Larry Kwak⁹, Nicolás Di Siervi¹, Maria Revuelta¹, Claudio Tripodo¹⁰, Leandro Cerchietti¹, Giorgio Inghirami¹

¹Weill Cornell Medicine, New York, NY,²Columbia University, New York, NY,³Kore University of Enna, Enna, Italy,⁴immunoLogic inc., San Francisco, CA,⁵Cell Signaling Technology, Beverly, MA,⁶Elucidate Bio, Medford, MA,⁷Sapienza University of Rome, Rome, Italy,⁸Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA,⁹City of Hope Comprehensive Cancer Center, Duarte, CA,¹⁰IFOM ETS-The AIRC Institute of Molecular Oncology, Milan, Italy

摘要 Abstract

中文摘要

背景：弥漫大B细胞淋巴瘤（DLBCL）是一种异质性疾病，对T细胞治疗的反应各异，凸显出对临床可用的患者分层器的需求。为剖析DLBCL复杂的肿瘤-宿主免疫相互作用，我们建立了完全人源化的DLBCL PDX模型（HuMice PDX），并对患者样本进行了整合的多组学分析。方法：我们使用bulk RNA-seq、单细胞RNA/TCR-seq、WES/CAPP-seq和CosMx SMI分析（6000种RNA、64种蛋白），分析了132例DLBCL（112例R-CHOP，20例CAR-T患者）、48例配对PDX、9例HuMice PDX以及经CAR-19/CAR-BAFFR治疗的PDX。使用大型DLBCL数据集构建预测分类器。将异源/同种异体CD34+人干/祖细胞（HSPC）植入NSG-SGM3xNBSGW小鼠以生成HuMice。结果：我们鉴定出EPICORE——一个12基因的表观遗传基因表达特征，能够预测DLBCL呈现免疫耗竭（ID）还是免疫富集（IR）的肿瘤微环境。与IR/EPICORE低的患者相比，ID/EPICORE高的患者在R-CHOP（p<0.0001）和CAR-19（p=0.002）治疗后的结局均显著更差。为研究宿主-淋巴瘤相互作用，我们生成HuMice并植入ID/EPICORE高和IR/EPICORE低的PDX模型。与患者的结局相仿，只有IR模型在HuMice中表现出肿瘤生长减少，而ID-DLBCL则不受阻碍地生长。流式细胞术、mIHC和RNAseq/scRNAseq分析显示，ID-DLBCL-HuPDX缺乏肿瘤浸润淋巴细胞（TIL），而IR-DLBCL-HuPDX则表现出丰富的TIL（p<2e-04）和克隆扩增。随后我们在九个ID-和IR-PDX-HuMice模型中体内测试了两种不同的CART（CD19、BAFFR）。IR/EPICORE低的模型被清除，而ID/EPICORE高的模型对两种CAR均难治。为增强ID-DLBCL的反应，我们对已被证明能改善免疫反应的临床期表观遗传靶向药物进行了药物筛选。Valemetostat和来那度胺治疗在体外和体内均与改善的CAR-T细胞杀伤相关。药物暴露导致EPICORE评分降低，以及炎症信号通路（NFkb、IFNg/a、IFN依赖性趋化因子）的富集，从而将ID-DLBCL转化为IR-DLBCL（p<0.05-0.0005）。结论：EPICORE作为一种新型分类器，可预测DLBCL的T细胞浸润和治疗结局，从而实现患者分层。人源化PDX准确反映DLBCL的病理生理学和免疫-宿主相互作用，为验证下一代免疫治疗提供了平台。表观遗传治疗将EPICORE高/ID-DLBCL重编程为EPICORE低/IR-DLBCL，克服免疫难治性并增强T细胞浸润和肿瘤免疫识别。

查看英文原文 English abstract

Background: Diffuse large B-cell lymphoma (DLBCL) is a heterogenous disease, with variable response to T-cell therapies, highlighting the need for a clinically applicable patient stratifier. To dissect DLBCL complex tumor-host immune interactions, we established fully humanized DLBCL PDX models (HuMice PDX) and performed integrated multi-omics profiling of patient samples. Methods: We analyzed 132 DLBCL (112 R-CHOP, 20 CAR-T patients), 48 matched PDX, 9 HuMice PDX and CAR-19/CAR-BAFFR treated PDX, using bulk RNA-seq, single-cell RNA/TCR-seq, WES/CAPP-seq and CosMx SMI profiling (6000 RNA, 64 proteins). Large DLBCL datasets were used to build a prediction classifier. heterologous/allogeneic CD34+ Human Stem Progenitor Cell (HSPC) were implanted in NSG-SGM3xNBSGW mice, to generate HuMice. Results: We identified EPICORE, a 12-gene epigenetic gene-expression signature, able to predict whether DLBCL displays an immune-depleted (ID) versus an immune-rich (IR) tumor microenvironment. ID/EPICORE-high patients displayed significantly worse outcomes following both R-CHOP-(p<0.0001) and CAR-19 (p=0.002) treatment, compared to IR/EPICORE-low patients. To investigate host-lymphoma interactions, we generated HuMice and implanted them with ID/EPICORE-high and IR/EPICORE-low PDX models. Mimicking patients' outcomes, only IR models displayed reduced tumor growth in HuMice, while ID-DLBCL showed unimpeded growth. Flow cytometry, mIHC, and RNAseq/scRNAseq analyses revealed that ID-DLBCL-HuPDX were devoid of Tumor-Infiltrating Lymphocytes (TILs), whereas IR-DLBCL-HuPDX displayed abundant (TILs) (p<2e-04) and clonal expansion. We then tested two different CART (CD19, BAFFR) in-vivo in nine ID- and IR-PDX-HuMice models. IR/EPICORE-low models were eradicated, while ID/EPICORE-high were refractory to both CAR. To enhance ID-DLBCL responses, we performed drug screening of clinical phase epigenetic targeting drugs shown to improve immune responses. Valemetostat and Lenalidomide treatments were associated with improved CAR-T cell killing, both in-vitro and in-vivo. Exposure to drug led to a reduction of EPICORE score, and enrichment of inflammatory signaling pathways (NFkb, IFNg/a, IFN-dependent chemokines), thereby converting ID-DLBCL into IR-DLBCL (p<0.05-0.0005) Conclusions: EPICORE, a novel classifier, predicts DLBCL T-cell infiltration and therapy outcomes allowing patients stratification. Humanized PDX accurately reflect DLBCL physiopathology and immune-host interactions, providing a platform for validating next-generation immunotherapies. Epigenetic therapies reprogram EPICORE-high/ID- into EPICORE-low/IR-DLBCL, overcoming immune refractoriness and enhancing T-cell infiltration and tumor immunorecognition.

利益披露 Disclosure

G. Medico, None.. G. Zanetti, None.. G. Bertolazzi, None. F. Vallania, immunoLogic inc. Stock, Patent. P. Zumbo, None.. M. Amiama, None.. D. Betel, None.. M. Cacciapuoti, None.. A. Taylor, None.. C. Kayembe, None.. S. Patel, None.. A. Ruiz, None. J. Ziello, Cell signaling Technology Employment, Stock, Patent. J. Weirather, Elucidate Bio Employment, Stock, Patent. G. Kaur, Elucidate Bio Employment, Stock, Patent. L. Cappelli, None.. L. Paruzzo, None. M. Ruella, Acera Bio Other, consultancy. AbClon ), Other, consultancy. viTToria biotherapeutics Stock, ), Patent. Guidepoint Other, consultancy. ONI Other, consultancy. GLG Other, consultancy. Biocartis Other, consultancy. CART technologies Stock, Patent. Tmunity Stock, Patent. ModeX Other, consultancy. Lumicks ). Beckman-Coulter ). Z. Dong, None. L. Kwak, Pepromene Bio Stock, ), Other, consulting. Innolifes Stock. N. Di Siervi, None.. M. Revuelta, None.. C. Tripodo, None.. L. Cerchietti, None. G. Inghirami, Daiichi Sankyo Other, consultancy.

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