Integrative approach to identify genomic features that shape the immune landscape and predict immunotherapy response in diffuse large B cell lymphoma

项目总结 复发/难治性弥漫性大B细胞淋巴瘤(DLBCL)患者的预后仍然很差 最近的治疗进展，特别是在免疫治疗领域。实体瘤的分类 通过批量转录特征分析，免疫“发炎”或“非发炎”的微环境丰富了 对检查点封锁治疗(CBT)有反应的子集。相反，DLBCL免疫格局具有 没有得到很好的表征，以及免疫环境特征可以预测的程度 DLBCL患者的免疫治疗反应尚不清楚。鉴于越来越多的免疫疗法是 在复发/难治性DLBCL领域进行探索，对DLBCL免疫有更深的理解 景观可能会发现有助于识别可能受益于检查站封锁和/或 汽车T细胞疗法。此外，越来越多的证据表明，癌细胞固有的改变可以 深刻影响肿瘤免疫环境，直接影响免疫治疗的敏感性。多么 恶性B细胞中反复发生的基因改变和相关途径有助于DLBCL的形成 免疫环境尚不清楚。显然需要更多的研究来填补这些空白。 知识。为此，我们将精选的免疫和起源细胞(COO)相关基因集整合到 874例DLBCL转录本的GSVA分析在四个集群中 出现的(生发中心B细胞(GCB)热、GCB冷、激活B细胞(ABC)热和ABC冷)，分析 全部外显子组测序数据显示，每个外显子组都有显著丰富的遗传变化。例如，损失- JAK/STAT信号的负调控因子SOCS1的功能缺失(LOF)突变在GCB HOT中得到丰富 DLBCL，提示这些淋巴瘤可能对干扰素γ信号特别敏感，而对抗体易感。 PD-1治疗。相反，调节磷脂酰丝氨酸(PS)取向的TMEM30A的LOF改变 在质膜中，在ABC冷DLBCL中很常见，这可能导致这些淋巴瘤 对增强巨噬细胞吞噬功能的免疫疗法敏感。这些观察结果支持中央 淋巴瘤细胞内在机制对形成独特的DLBCL免疫有重要作用的假说 环境，其特征将确定哪些患者将受益于CBT或CAR T 细胞疗法。该提案的主要目标是：1)确定选择基因的机制 淋巴瘤细胞的改变塑造了DLBCL的免疫环境，2)形成了DLBCL的免疫 并确定其在识别PD-1阻断或CAR T细胞治疗的患者方面的有效性 有效。首席研究员，一位在淋巴瘤方面具有临床专长的内科科学家和一项研究 癌症免疫学背景，非常适合监督本申请中提出的实验。在……里面 为了执行提案的计算方面，首席调查员招募了一名后起之秀 田野。他们的互补技能将确保成功完成计划中的研究。

PROJECT SUMMARY Outcomes for patients with relapsed/refractory diffuse large B cell lymphoma (DLBCL) remain poor despite recent therapeutic advances, particularly in the area of immunotherapy. Classification of solid tumor microenvironments as immune “inflamed” or “non-inflamed” through bulk transcriptional profiling enriches for a subset that is checkpoint blockade therapy (CBT) responsive. Conversely, the DLBCL immune landscape has not been as well-characterized, and the extent to which immune environmental features can predict for immunotherapy response in DLBCL patients is unknown. Given that a growing number of immunotherapies are being explored in the relapsed/refractory DLBCL space, a deeper understanding of the DLBCL immune landscape might uncover clues that aid in identifying patients likely to benefit from checkpoint blockade and/or CAR T cell therapies. Additionally, growing evidence indicates that cancer cell-intrinsic alterations can profoundly affect the tumor immune environment, which directly impacts immunotherapy sensitivity. How recurring genetic alterations and related pathways in malignant B cells contribute to shaping the DLBCL immune environment is unclear. Additional research is clearly needed in order to address these gaps in knowledge. Toward that end, we incorporated curated immune- and cell-of-origin (COO)-related gene sets into a gene set variation analysis (GSVA) on transcriptomes of 874 DLBCL specimens. Among the four clusters that emerged (germinal center B cell (GCB) hot, GCB cold, activated B cell (ABC) hot, and ABC cold), analysis of whole exome sequencing data revealed significantly enriched genetic alterations in each. For instance, loss- of-function (LOF) mutations in SOCS1, a negative regulator of JAK/STAT signaling, were enriched in GCB hot DLBCLs, suggesting these lymphomas may be particularly sensitive to IFNγ signaling and vulnerable to anti- PD-1 therapy. Conversely, LOF alterations in TMEM30A, which regulates phosphatidylserine (PS) orientation in the plasma membrane, were common among ABC cold DLBCLs, which may render these lymphomas sensitive to immunotherapies that enhance macrophage phagocytosis. These observations support the central hypothesis that lymphoma cell-intrinsic mechanisms contribute significantly to shaping unique DLBCL immune environments, the characterization of which will identify patients who will or will not benefit from CBT or CAR T cell therapy. The main objectives of the proposal are: 1) to determine mechanisms by which select genetic alterations in lymphoma cells shape the DLBCL immune environment, and 2) to develop a DLBCL “immune score” and determine its utility in identifying patients for whom PD-1 blockade or CAR T cell therapy will be effective. The principal investigator, a physician-scientist with clinical expertise in lymphoma and a research background in cancer immunology, is well-suited to oversee the experiments proposed in this application. In order to execute computational aspects of the proposal, the principal investigator has recruited a rising star in the field. Together, their complementary skills will ensure successful completion of the planned research.