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

使用抗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景观。在晚期膀胱癌患者中， CPI可以引发前所未有的持久反应。然而，只有少数患者对这种情况有反应 治疗需要对原发耐药机制有更好的了解，以促进 预测生物标志物的识别和合理组合方法。我们使用了一种综合的方法 在转移性膀胱癌患者的大型临床试验队列中识别生物标记物 独立地与对CPI的反应/耐药相关，而不仅仅是肿瘤突变负荷(TMB)。我们 从治疗前肿瘤转录本数据中识别出三个关键基因模块：“良好的免疫力” 与CPI反应相关的模块，与CPI抵抗相关的基质模块，以及“坏” 免疫“模块也与对CPI的抵抗力有关，但这似乎介导了 基于CPI结果的基质模块。使用新鲜生成的单细胞RNA测序数据 膀胱癌标本解剖细胞成分和分子相互作用 这3个基因模块的表达，我们确定“良好的免疫”模块很大程度上源于T- 细胞和NK细胞，间质模块来自癌症相关的成纤维细胞(CAF)，以及“BAD” 免疫“模块主要来自单核细胞-巨噬细胞(MΦ)。我们假设髓系细胞 在膀胱癌中，细胞驱动对CPI的主要耐药。我们进一步假设，综合细胞 膀胱癌的分子图谱将有助于识别精确的单核细胞-MΦ亚群，以及 CAF-髓系-T细胞相互作用，可用于定义新的生物标记物和治疗靶点。 获得一套独特的临床试验队列，将提供肿瘤组织、血液和尿液 样本和结果数据到CPI，我们的独特定位是：(目标1)剖析基因模块 使用细胞和分子景观的高分辨率地图与对CPI的响应/抗性相关联 肌肉浸润性膀胱癌；目标2：确定“免疫不良”模块基因在调控 单核细胞-M-Φ介导的免疫抑制；目标3：提纯和验证单核-M-Φ相关基因 签名作为临床试验队列中CPI耐药性的生物标记物。 该提案由多学科协作调查员(GU)组成的综合网络组成 肿瘤学家、泌尿科医生、免疫学家和生物信息学家)，以加快翻译研究和 最大限度地提高未来的临床效益。这些努力将导致生物标志物的发现和潜在的 治疗目标，将CPI的好处扩大到15%-20%的晚期膀胱癌患者 对治疗有反应的人。

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.