Phenotype Transitions in Small Cell Lung Cancer

小细胞肺癌的表型转变

基本信息

批准号：
10411428
负责人：
Carlos Federico Lopez
金额：
$ 0.84万
依托单位：
VANDERBILT UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-06-09 至 2023-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10411428
关键词：
Alleles Basic Science Bioinformatics Biopsy Cancer Etiology Cell Line Cells Cessation of life Clinical Computer Models Consensus Cytometry DNA Sequence Alteration Data Diagnostic Drug Tolerance Drug resistance Epigenetic Process Excision Exhibits Flow Cytometry Genes Genetic Transcription Heterogeneity Histone Deacetylase Inhibitor Human Hybrid Cells Hybrids In Vitro Link Logic Malignant Neoplasms Malignant neoplasm of lung Mediating Mesenchymal Modeling Neurosecretory Systems Operative Surgical Procedures Outcome Pathway Analysis Pathway interactions Patient-Focused Outcomes Pharmaceutical Preparations Pharmacotherapy Phenotype Phosphotransferases Prognostic Marker Regulator Genes Relapse Resistance Role Route Series Signal Pathway Signal Transduction Source System TP53 gene Testing Time Tumor Cell Line Validation base cancer heterogeneity cancer subtypes cancer therapy cancer type cell killing chemotherapy driver mutation drug sensitivity drug standard drug-sensitive empowered experimental study improved in vivo induced pluripotent stem cell lung cancer cell lung small cell carcinoma network models notch protein novel predictive modeling programs rapid growth response simulation success targeted treatment transcription factor translational impact treatment response treatment strategy tumor

项目摘要

Abstract Lung cancer is the leading cause of cancer related deaths. In its most lethal form, small-cell lung cancer (SCLC), heterogeneity correlates with aggressiveness, however no driver mutations distinguishing SCLC subtypes have been identified. Another singularity of SCLC is that it responds well to initial treatment but quickly relapses into resistance, suggesting phenotypic plasticity. In this basic project, we will investigate the role of transcriptional and signaling mechanisms in promoting SCLC phenotypic heterogeneity and plastic state transitions, leading to aggressiveness and rapid relapse. Our preliminary results indicate that SCLC heterogeneity is more extensive than the canonical neuroendocrine (NE) and mesenchymal-like (ML) subtypes, and includes multiple hybrid states. Most significantly, we found that drug treatment results in phenotypic transitions toward the hybrid states, implicating them in resistance. Based on these data, our central hypothesis is that SCLC is a heterogeneous mix of NE, ML and hybrid phenotypic states and that, due to phenotypic plasticity, transitions between these states is a key mechanism of treatment evasion in SCLC. To test this hypothesis, we will combine computation and experiments to characterize the global landscape of phenotypes in SCLC, and define the impact of phenotypic transitions on resistance. In Aim1, we will identify a regulatory transcription factor (TF) network that controls the differentiation of SCLC cells into NE, ML, and hybrid phenotypic states; validate model predicted phenotypes and quantify their drug sensitivity; and, define reprogramming pathways to drug- sensitive states. Our approach pipeline is comprised of phenotypic clustering and gene co-expression network analysis on SCLC tumor and cell line data, simulations of logic-based TF network models to prioritize TF targets for reprogramming, and experimental validation of model predictions in vitro and in vivo. In Aim2, we will quantify phenotype sensitivity to chemotherapy and plasticity in response to signaling perturbations; identify perturbations that promote phenotype switching; and, test optimal drug/perturbagen combinations that maximize SCLC cell killing under treatment. Phenotypes and signaling pathways will be defined by flow and mass cytometry. SCLC clonal dynamics in response to perturbations will be quantified using a stochastic phenotype transition to prioritize drug/perturbagen combinations for experimental validation. Drug sensitivity and plasticity of SCLC phenotypes will be assessed with the drug-induced proliferation rate metric, which we recently described, and time series single-cell flow or mass cytometry. Success of this project will have translational impact by empowering searches for targeted therapies that reprogram drug-resistant cells toward drug-sensitive cells, which we anticipate will lead to significantly improved patient outcomes in SCLC. We further anticipate that this approach will be useful in other cancer types, opening the doors to a new paradigm of cancer treatment based on epigenetic tumor reprogramming.

抽象的肺癌是癌症相关死亡的主要原因。小细胞肺癌（SCLC）是最致命的形式，异质性与侵袭性相关，但没有区分 SCLC 亚型的驱动突变已被识别。 SCLC 的另一个奇点是它对初始治疗反应良好，但很快就会复发抗性，表明表型可塑性。在这个基础项目中，我们将研究转录的作用以及促进 SCLC 表型异质性和可塑性状态转变的信号机制，导致攻击性和快速复发。我们的初步结果表明 SCLC 异质性是比典型的神经内分泌 (NE) 和间充质样 (ML) 亚型更广泛，包括多个混合状态。最重要的是，我们发现药物治疗会导致表型转变混合状态，意味着它们参与抵抗。根据这些数据，我们的中心假设是 SCLC 是 NE、ML 和混合表型状态的异质混合，并且由于表型可塑性，这些状态之间的转变是 SCLC 逃避治疗的关键机制。为了检验这个假设，我们将结合计算和实验来描述 SCLC 中表型的全局景观，以及定义表型转变对耐药性的影响。在 Aim1 中，我们将鉴定一个调节转录因子 (TF) 控制 SCLC 细胞分化为 NE、ML 和混合表型状态的网络；证实模型预测表型并量化其药物敏感性；并且，定义药物重编程途径敏感国家。我们的方法管道由表型聚类和基因共表达网络组成分析 SCLC 肿瘤和细胞系数据，模拟基于逻辑的 TF 网络模型以优先考虑 TF 目标用于重新编程以及体外和体内模型预测的实验验证。在Aim2中，我们将量化对化疗的表型敏感性和对信号干扰的可塑性；识别扰动促进表型转换；并测试可最大化 SCLC 细胞的最佳药物/扰动剂组合正在治疗中被杀。表型和信号通路将通过流式和质量细胞术来定义。小细胞肺癌将使用随机表型转变来量化响应扰动的克隆动力学优先考虑药物/扰动剂组合以进行实验验证。 SCLC 的药物敏感性和可塑性表型将使用我们最近描述的药物诱导的增殖率指标进行评估，并且时间序列单细胞流式或质谱流式细胞术。该项目的成功将产生以下转化影响：促进寻找将耐药细胞重新编程为药物敏感细胞的靶向疗法，我们预计这将显着改善 SCLC 患者的治疗效果。我们进一步预计，这该方法将适用于其他癌症类型，为基于癌症治疗的新范式打开大门关于表观遗传肿瘤重编程。

项目成果

期刊论文数量（20）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Quantifying cancer cell plasticity with gene regulatory networks and single-cell dynamics.

DOI：
10.3389/fnetp.2023.1225736
发表时间：
2023
期刊：
Frontiers in network physiology
影响因子：
0
作者：
Groves, Sarah M.;Quaranta, Vito
通讯作者：
Quaranta, Vito

Microbench: automated metadata management for systems biology benchmarking and reproducibility in Python.

DOI：
10.1093/bioinformatics/btac580
发表时间：
2022-10-14
期刊：
Bioinformatics (Oxford, England)
影响因子：
0
作者：
通讯作者：

Real-time luminescence enables continuous drug-response analysis in adherent and suspension cell lines.

DOI：
10.1080/15384047.2022.2065182
发表时间：
2022-12-31
期刊：
CANCER BIOLOGY & THERAPY
影响因子：
3.6
作者：
Wandishin, Clayton M.;Robbins, Charles John;Tyson, Darren R.;Harris, Leonard A.;Quaranta, Vito
通讯作者：
Quaranta, Vito

Unified tumor growth mechanisms from multimodel inference and dataset integration.