Advancing the translatability of mouse models for cancer immunotherapy

摘要： 肿瘤学中最大的挑战之一是一旦癌症转移就将其根除。癌症免疫治疗 (IMT)使用检查点阻断药物，如α-PD1，在 癌症患者的子集。但即使是在黑色素瘤等敏感肿瘤类型中，也只有25%左右 病人会有反应。要充分利用现有的和新药重新激活抗逆转录病毒药物，仍然存在许多挑战 肿瘤免疫。因此，人们迫切需要能够更好地反映人类癌症的小鼠模型，这种需求尚未得到满足。 我们的目标是利用特征良好的化学诱导皮肤鳞状细胞癌(CSCC)。 模型以i)确定建立IMT敏感性的肿瘤的遗传特征，ii)定义机制和 α-PD-1天然耐药和获得性耐药的生物标志物 α转化生长因子β和αPD1治疗失败的原因。我们将以两个新颖而重要的观察为基础 我们取得了以下研究成果：一、新型α-pANTGF-β抗体与αPD-1协同作用，具有与IMT相同的作用。 将IMT应答率提高一倍，在CSCC中引发强大的肿瘤排斥反应和持续的肿瘤免疫 模特。其次，就像在人类癌症中一样，我们发现对IMT的反应在化学诱导下更高。 具有高突变单核苷酸变异(SNV)负载的CSCC比具有低SNV负载的CSCC，包括 转基因小鼠模型(GEMM)。在目标1中，我们将讨论α转化生长因子β治疗如何增强αPD-1 反应，通过研究药物对肿瘤免疫细胞数量和功能的影响。我们将验证我们的 细胞免疫印迹法初步发现Treg参与αPD1和αβ药物的协同作用 分析。我们还将调查其他免疫细胞类型的参与，特别是髓系细胞，这些细胞可能 有助于α、转化生长因子、β/α、PD-1药物的协同作用。在目标2中，我们将确定肿瘤细胞的遗传特性 在一个独立的小组中确定IMT的反应，以及推动IMT耐药性发展的事件 DMBA/TPA诱导Kras或Kras不同化学诱导突变所致的同源原代CSCC系 HRA。我们将研究a)不同的RAS驱动突变b)SNV载量和新抗原质量的影响，以及c) α-PD-1和/或α转化生长因子β反应的miRNA和rna转录组图谱，我们将扩展我们的研究 ，并验证AIMS 1和2中发现的预测性生物标记物的使用。 这种更现实、更异质的人类癌症模型。最后，在目标3中，我们将验证我们的临床前 通过询问预测IMT反应的突变、转录和/或免疫特征的发现 αPD-1应答者和无应答者治疗前和治疗后临床肝细胞癌标本 在UCSF12-14接受治疗的HNSCC患者。通过项目完成，我们将验证一个翻译 IMT的模型，可被其他人用于开发新的IMT试剂，以及用于 将为癌症患者提供个性化的治疗选择。

ABSTRACT: One of greatest challenges in oncology is eradicating cancer once it has metastasized. Cancer immunotherapy (IMT) with checkpoint blockade drugs like α-PD1 has shown remarkable sustained complete responses in a subset of cancer patients. But even within sensitive tumor types such as melanoma, only around 25% of patients respond. Many challenges remain to fully capitalize on existing and novel drugs that reactivate anti- tumor immunity. There is, therefore, an urgent unmet need for mouse models that better reflect human cancer. Our goal is to utilize the well characterized chemically-induced cutaneous squamous cell carcinoma (cSCC) model to i) identify genetic features of tumors that establish IMT sensitivity, ii) define mechanisms and biomarkers of innate and acquired α-PD-1 drug resistance iii) define mechanisms of cooperation between αTGFβ and αPD1 therapy, and causes of failure. We will build on two novel and important observations that we have made: First, that a new α-panTGFβ antibody is as effective as IMT and cooperates with αPD-1 to double the IMT response rate, eliciting robust tumor rejection and sustained tumor immunity in the cSCC model. Secondly, just as in human cancers, we find that response to IMT is higher in chemically-induced cSCCs with high mutational single nucleotide variant (SNV) loads than those with low SNV load, including a genetically engineered mouse model (GEMM). In Aim 1 we will address how αTGFβ therapy enhances αPD-1 responses, by investigating drug effects on tumor immune cell numbers and function. We will validate our preliminary finding of Treg involvement in cooperativity between αPD1 and αTGFβ drugs using CyTO and MIBI analysis. We will also investigate involvement of other immune cell types, particularly myeloid, that may contribute to αTGFβ/αPD-1 drug cooperativity. In Aim 2, we will identify tumor cell genetic properties that determine IMT responses, and events driving development of IMT drug resistance, in a panel of independent DMBA/TPA induced syngeneic primary cSCC lines driven by distinct chemically-induced mutations of Kras or Hras. We will investigate effects of a) distinct Ras driver mutations b) SNV loads and neoantigen quality, and c) miRNA and RNA transcriptome profiles, on α-PD-1 and/or αTGFβ responses, and we will extend our studies into primary chemically induced sSCC, and validate the use of predictive biomarkers found in Aims 1 and 2 in this more realistic and heterogeneous model of human cancer. Finally, in Aim 3, we will validate our preclinical findings of mutational, transcriptomic and/or immune signatures predictive of IMT responses by interrogating pre-treatment and post-treatment clinical HNSCC samples from αPD-1 responding versus non-responding HNSCC patients under treatment at UCSF12-14. By project completion, we will have validated a translational model for IMT that may be utilized by others for novel IMT agent development, and for mechanistic studies that will provide personalized treatment options to cancer patients.