Dissecting myeloid cell-mediated resistance to immune checkpoint blockade in bladder cancer

抗PD-1/PD-L1抗体的免疫检查点抑制（CPI）改变了治疗方法 包括膀胱癌在内的几种癌症的前景。在晚期膀胱癌患者中， 消费物价指数可以引发前所未有的持久反应。然而，只有一部分患者对这种药物有反应。 治疗需要更好地了解原发性耐药的机制，以促进 预测性生物标志物的鉴定和合理的组合方法。我们采用了综合方法 在转移性膀胱癌患者的大型临床试验队列中鉴定生物标志物， 在单独的肿瘤突变负荷（TMB）之外，与对CPI的应答/抗性独立相关。我们 从治疗前的肿瘤转录组学数据中确定了三个关键基因模块： 与对CPI的反应相关的基质模块、与对CPI的抗性相关的基质模块和与对CPI的反应相关的“坏”基质模块。 免疫”模块也与对CPI的抗性相关，但似乎介导了 基质模块对CPI结果的影响。使用从新鲜细胞中产生的单细胞RNA测序数据， 膀胱癌标本解剖细胞成分和分子相互作用负责 这3个基因模块的表达，我们确定“良好的免疫”模块主要来自T- 细胞和NK细胞，基质模块来自癌症相关的成纤维细胞（CAF），而“坏”细胞则来自肿瘤细胞。 “免疫”模块主要来自单核细胞-巨噬细胞（MΦ）。我们假设骨髓 细胞驱动膀胱癌中对CPI的主要抗性。我们进一步假设， 膀胱癌的分子图谱将有助于精确识别单核细胞-M Φ亚群， CAF-骨髓-T细胞相互作用，可用于定义新的生物标志物和治疗靶点。 通过使用一组独特的临床试验队列， 标本以及CPI的结果数据，我们具有独特的定位：（目标1）解剖基因模块 使用细胞和分子景观的高分辨率图， 目的2：定义“不良免疫”模块基因在控制膀胱癌中的作用。 单核细胞-M Φ介导的免疫抑制;目的3：筛选和验证单核细胞-M Φ相关基因 作为临床试验队列中CPI耐药生物标志物的特征。 该提案包括一个多学科合作研究者（GU）的综合网络 肿瘤学家，泌尿科医生，免疫学家和生物信息学家），以加速转化研究， 最大化未来的临床效益。这些努力将导致生物标志物的发现， 治疗靶点，将CPI的受益范围扩大到15-20%的晚期膀胱癌患者 对治疗有反应

Immune checkpoint inhibition (CPI) with anti-PD-1/PD-L1 antibodies has changed the treatment landscape for several types of cancers including bladder cancer. In patients with advanced bladder cancer, CPI can induce unprecedented durable responses. However, only a subset of patients responds to such treatment necessitating a better understanding of mechanisms of primary resistance to facilitate the identification of predictive biomarkers and rational combination approaches. We used an integrative approach to identify biomarkers in a large clinical trial cohort of patients with metastatic bladder cancer that are independently associated with response/resistance to CPI beyond tumor mutational burden (TMB) alone. We identified three key gene modules derived from pre-treatment tumor transcriptomic data: a “good immune” module associated with response to CPI, a stromal module associated with resistance to CPI, and a “bad immune” module also associated with resistance to CPI, but that appears to mediate the negative impact of the stromal module upon outcomes with CPI. Using single cell RNA sequencing data generated from fresh bladder cancer specimens to dissect the cellular components and molecular interactions responsible for expression of these 3 gene modules, we determined that the “good immune” module emanates largely from T- cells and NK cells, the stromal module emanates from cancer-associated fibroblasts (CAFs), and the “bad immune” module emanates predominantly from monocytes-macrophages (MΦ). We hypothesize that myeloid cells drive primary resistance to CPI in bladder cancer. We further hypothesize that comprehensive cellular and molecular maps of bladder cancer will facilitate identification of precise monocyte-MΦ subpopulations, and CAF-myeloid-T-cell interactions, that can be leveraged to define novel biomarkers and therapeutic targets. With access to a unique set of clinical trial cohorts that will provide tumor tissue, blood and urine specimens as well as outcome data to CPI, we are uniquely positioned to: (Aim 1) Dissect gene modules associated with response/resistance to CPI using high resolution maps of the cellular and molecular landscape of muscle-invasive bladder cancer; Aim 2: Define the role of “bad immune” module genes in governing monocyte-MΦ-mediated immune suppression; Aim 3: Refine and validate a monocyte-MΦ-related gene signature as a biomarker of CPI resistance in clinical trial cohorts. This proposal comprises an integrated network of multi-disciplinary collaborative investigators (GU oncologists, urologists, immunologists and bioinformaticians) to accelerate translational research and maximize future clinical benefits. These efforts will lead to the discovery of biomarkers, and potential therapeutic targets, to extend the benefits of CPI beyond the 15-20% of patients with advanced bladder cancer that respond to treatment.