Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India.
Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore, India.
J Immunother Cancer. 2023 Sep;11(9). doi: 10.1136/jitc-2023-006766.
Phenotypic heterogeneity of melanoma cells contributes to drug tolerance, increased metastasis, and immune evasion in patients with progressive disease. Diverse mechanisms have been individually reported to shape extensive intra-tumor and inter-tumor phenotypic heterogeneity, such as IFNγ signaling and proliferative to invasive transition, but how their crosstalk impacts tumor progression remains largely elusive.
Here, we integrate dynamical systems modeling with transcriptomic data analysis at bulk and single-cell levels to investigate underlying mechanisms behind phenotypic heterogeneity in melanoma and its impact on adaptation to targeted therapy and immune checkpoint inhibitors. We construct a minimal core regulatory network involving transcription factors implicated in this process and identify the multiple 'attractors' in the phenotypic landscape enabled by this network. Our model predictions about synergistic control of PD-L1 by IFNγ signaling and proliferative to invasive transition were validated experimentally in three melanoma cell lines-MALME3, SK-MEL-5 and A375.
We demonstrate that the emergent dynamics of our regulatory network comprising MITF, SOX10, SOX9, JUN and ZEB1 can recapitulate experimental observations about the co-existence of diverse phenotypes (proliferative, neural crest-like, invasive) and reversible cell-state transitions among them, including in response to targeted therapy and immune checkpoint inhibitors. These phenotypes have varied levels of PD-L1, driving heterogeneity in immunosuppression. This heterogeneity in PD-L1 can be aggravated by combinatorial dynamics of these regulators with IFNγ signaling. Our model predictions about changes in proliferative to invasive transition and PD-L1 levels as melanoma cells evade targeted therapy and immune checkpoint inhibitors were validated in multiple RNA-seq data sets from in vitro and in vivo experiments.
Our calibrated dynamical model offers a platform to test combinatorial therapies and provide rational avenues for the treatment of metastatic melanoma. This improved understanding of crosstalk among PD-L1 expression, proliferative to invasive transition and IFNγ signaling can be leveraged to improve the clinical management of therapy-resistant and metastatic melanoma.
黑色素瘤细胞的表型异质性导致进展期患者的药物耐受性增加、转移增加和免疫逃逸。已有多种机制分别报道可塑造广泛的肿瘤内和肿瘤间表型异质性，如 IFNγ 信号和增殖到侵袭性转变，但它们的相互作用如何影响肿瘤进展仍很大程度上难以捉摸。
在这里，我们将动态系统建模与批量和单细胞水平的转录组数据分析相结合，以研究黑色素瘤中表型异质性的潜在机制及其对适应靶向治疗和免疫检查点抑制剂的影响。我们构建了一个涉及该过程中涉及的转录因子的最小核心调控网络，并确定了该网络启用的表型景观中的多个“吸引子”。我们的模型预测 IFNγ 信号和增殖到侵袭性转变的协同控制对三种黑色素瘤细胞系-MALME3、SK-MEL-5 和 A375 的实验验证。
我们证明，包含 MITF、SOX10、SOX9、JUN 和 ZEB1 的调控网络的涌现动力学可以再现关于不同表型（增殖、神经嵴样、侵袭性）共存的实验观察结果以及它们之间的可逆细胞状态转变，包括对靶向治疗和免疫检查点抑制剂的反应。这些表型具有不同水平的 PD-L1，导致免疫抑制的异质性。这些调节剂与 IFNγ 信号的组合动力学可加剧 PD-L1 的异质性。我们关于黑色素瘤细胞逃避靶向治疗和免疫检查点抑制剂时增殖到侵袭性转变和 PD-L1 水平变化的模型预测在来自体外和体内实验的多个 RNA-seq 数据集中得到验证。
我们校准的动力模型为测试组合疗法提供了一个平台，并为治疗转移性黑色素瘤提供了合理的途径。对 PD-L1 表达、增殖到侵袭性转变和 IFNγ 信号之间的相互作用的这种改进理解可以被利用来改善治疗抵抗和转移性黑色素瘤的临床管理。
