Integrative systems approach to identify local oncogenic modulation of the IL12 axis

 描述（由申请人提供）：免疫疗法，如免疫检查点调节剂，有望改变癌症的治疗前景。虽然这些疗法在一部分患者中引起了显著的反应，但扩大临床获益的一个关键障碍是确定能够缓解组织微环境中肿瘤诱导的变化（阻断内源性抗肿瘤信号）的附带靶点。为了鉴定这些间接靶点，我们开发了一种表型筛选来鉴定恶性细胞用于阻断免疫细胞对促进抗肿瘤免疫的细胞因子（如白细胞介素-12）的应答的机制。这项生物工程研究资助提案的目的是通过筛选一组可移植的乳腺癌和肺癌模型来确定这些癌症的间接靶点，并使用临床前小鼠模型和人类数据来验证这些靶点。鉴于肿瘤发生是一个涉及重复突变和选择的进化过程，我们的中心假设是恶性细胞进化为分泌蛋白质，这些蛋白质共同交叉调节CD 4+和CD 8 + T细胞对白细胞介素-12（IL-12）的反应，IL-12是一种有效的抗肿瘤免疫佐剂。为了检验我们的中心假设，我们将首先建立可以使用机械数学模型预测T细胞对IL 12的应答的条件。接下来，我们将在体外重建肿瘤诱导的免疫抑制，其中数学模型将用于定义T细胞对IL 12的反应在与肺癌和乳腺癌的不同可移植模型共培养时如何改变。将使用基于LC-MS/MS的蛋白质组学鉴定肿瘤细胞分泌的蛋白质，这些蛋白质重建了观察到的表型。体外确定的免疫抑制机制将使用临床前小鼠模型和回顾性队列研究进行验证，该研究使用从大型癌症研究中获得的数据，这些研究将“组学”与临床结果联系起来。这种多学科方法预计将产生以下预期结果：1）能够基于机制病理学对患者进行分层，2）指导开发针对这些间接靶点的药物，以及3）确定可能从特定联合免疫疗法中获益的患者队列。

 DESCRIPTION (provided by applicant): Immunotherapies, like immune checkpoint modulators, are poised to transform the therapeutic landscape for cancer. While these therapies elicit remarkable responses in a subset of patients, a key hurdle for broadening the clinical benefit is identifying collateral targets that relieve tumor-induced changes in the tissue microenvironment that block endogenous anti-tumor signals. To identify these collateral targets, we have developed a phenotypic screen to identify mechanisms that malignant cells use to block immune cell response to cytokines that promote anti-tumor immunity, such as Interleukin-12. The objective of this Bioengineering Research Grant proposal is to identify collateral targets in breast and lung carcinomas by screening a set of transplantable models for these cancers and to validate these targets using pre-clinical mouse models and human data. Given that oncogenesis is an evolutionary process involving repeated mutation and selection, our central hypothesis is that malignant cells evolve to secrete proteins that collectively cross-regulate the response of CD4+ and CD8+ T cells to Interleukin-12 (IL12), a potent adjuvant of anti-tumor immunity. To test our central hypothesis, we will first establish conditions where a T cell response to IL12 can be predicted using a mechanistic math model. Next, we will recreate tumor-induced immunosuppression in vitro, where the mathematical model will be used to define how the T cell response to IL12 is altered upon co-culture with the different transplantable models for lung and breast cancer. Proteins secreted by tumor cells that recreate the observed phenotype will be identified using LC-MS/MS-based proteomics. Immunosuppressive mechanisms identified in vitro will be validated using pre-clinical mouse models and a retrospective cohort study using data obtained from large cancer studies that relate `omics with clinical outcomes. This multidisciplinary approach is projected to yield the following expected outcomes: 1) enable stratifying patients based on mechanistic pathology, 2) guide developing drugs against these collateral targets, and 3) identify patient cohorts that are likely to benefit from specific combination immunotherapies.