决定异体 CAR T 细胞排斥与扩增的细胞和分子机制
Cellular and molecular mechanisms determining allogeneic CAR T cell rejection and expansion.
我们评估了11例接受单一批次cemacabtagene ansegedleucel(cema-cel)治疗的大B细胞淋巴瘤患者,cemacabtagene ansegedleucel是一种异体抗CD19 CAR T产品。
英文原题:A conceptual blueprint for "turning cold to hot" in Osteosarcoma: from TME stratification hypotheses to adaptive therapeutic prospects.
这些要素共同为骨肉瘤的迭代状态评估和适应性治疗顺序定义了一个理论蓝图。
骨肉瘤(OS)是一种典型的冷肿瘤,转移性或复发性疾病患者的结局数十年来一直不佳。免疫检查点抑制剂(ICIs)在 OS 中的失败反映的是多层免疫抑制架构,而非单一主导性病变。本综述解构了该架构内的三个主要屏障:(1)由致密纤维化基质和异常血管驱动的物理性 T 细胞排斥;(2)富含肿瘤相关巨噬细胞(TAMs)和髓源性抑制细胞(MDSCs)的髓系主导抑制性网络;以及(3)抗原提呈机制(APM)缺陷,最常见的是 MHC-I/B2M 缺失。与此同时,原发肿瘤通过外泌体和中性粒细胞胞外诱捕网(NETs)主动改造肺部环境,建立促进肺转移的转移前生态位(PMN)。 整合单细胞和空间组学、多模态成像(放射组学、数字病理)以及液体活检(ctDNA-微小残留病 [MRD])的证据,本综述将 OS 微环境的生物学解码转化为一个产生假设的操作框架。我们提出一个 OS-TME 亚型分型 V1.0 模型,其中亚型被视为动态的、主导屏障的系统状态,而不是固定的生物学类别。在当前的概念形式中,状态分配被设想为一个半结构化、基于规则的过程,利用来自病理/空间读数、影像替代指标和 ctDNA/免疫背景的一致性信号;混合或不一致病例被有意保留为不确定状态以供重新评估,而非强行分类。在此基础上,我们概述了一种针对不同屏障主导状态的序贯去抑制—启动—检查点逻辑。对于髓系优势状态,我们优先进行髓系重编程(例如靶向 CSF1R/CCR2 轴)联合免疫原性启动。对于致密纤维化间质/血管异常状态,我们强调在检查点治疗前先进行血管/间质重塑。对于 APM 缺陷状态,我们讨论靶向 B7-H3 或 GD2 的 MHC 非依赖性方法(例如 CAR-T/NK 细胞、抗体-药物偶联物),同时明确承认靶点异质性、迁移屏障以及靶向/肿瘤外风险。为缩小转化差距,我们进一步概述一种围手术期机会窗口(Window of Opportunity,WoO)试验原型,以及一个整合动态影像、病理学和 MRD 监测的概念性冷转热准备度指数(Cold-to-Hot Readiness Index,RI)。RI 仅作为一种说明性、产生假设的汇总变量呈现,旨在用于回顾性分层、模拟建模或生物标志物指导的早期试验设计,而非近期常规临床决策。这些要素共同定义了骨肉瘤中迭代状态评估和适应性治疗排序的理论蓝图。
Osteosarcoma (OS) is a quintessential cold tumor, and outcomes for patients with metastatic or recurrent disease have remained poor for decades. The failure of immune checkpoint inhibitors (ICIs) in OS reflects a multilayered immunosuppressive architecture rather than a single dominant lesion. This review deconstructs three principal barriers within that architecture: (1) physical T-cell exclusion driven by a dense fibrotic stroma and aberrant vasculature; (2) a myeloid-dominant suppressive network enriched for Tumor-Associated Macrophages (TAMs) and Myeloid-Derived Suppressor Cells (MDSCs); and (3) Antigen Presentation Machinery (APM) defects, most commonly loss of MHC-I/B2M. In parallel, the primary tumor actively engineers the pulmonary environment through exosomes and Neutrophil Extracellular Traps (NETs), establishing a Pre-metastatic Niche (PMN) that facilitates lung metastasis.Integrating evidence from single-cell and spatial omics, multi-modal imaging (radiomics, digital pathology), and liquid biopsy (ctDNA-minimal residual disease [MRD]), this review translates biological decoding of the OS microenvironment into a hypothesis-generating operational framework. We propose an OS-TME Subtyping V1.0 model in which subtypes are treated as dynamic, dominant-barrier system states rather than fixed biological classes. In its current conceptual form, state assignment is envisioned as a semi-structured, rule-based process using concordant signals from pathology/spatial readouts, imaging surrogates, and ctDNA/immune context; mixed or discordant cases are intentionally retained as indeterminate states for reassessment rather than forcibly classified. On this basis, we outline a sequential De-suppression Priming Checkpoint logic tailored to different barrier-dominant states. For myeloid-dominant states, we prioritize myeloid reprogramming (e.g., CSF1R/CCR2-axis targeting) combined with immunogenic priming. For dense fibrotic stroma/angio-abnormal states, we emphasize up-front vessel/stroma remodeling before checkpoint therapy. For APM-defective states, we discuss MHC-independent approaches targeting B7-H3 or GD2 (e.g., CAR-T/NK cells, antibody-drug conjugates), while explicitly acknowledging target heterogeneity, trafficking barriers, and on-target/off-tumor risk.To narrow the translational gap, we further outline a perioperative Window of Opportunity (WoO) trial prototype and a conceptual Cold-to-Hot Readiness Index (RI) that integrates dynamic imaging, pathology, and MRD monitoring. The RI is presented only as an illustrative, hypothesis-generating summary variable intended for retrospective stratification, simulation modeling, or biomarker-guided early-phase trial design, rather than near-term routine clinical decision-making. Together, these elements define a theoretical blueprint for iterative state assessment and adaptive therapeutic sequencing in osteosarcoma.
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