Worley Jeremy, Noh Heeju, You Daoqi, Turunen Mikko M, Ding Hongxu, Paull Evan, Griffin Aaron T, Grunn Adina, Zhang Mingxuan, Guillan Kristina, Bush Erin C, Brosius Samantha J, Hibshoosh Hanina, Mundi Prabhjot S, Sims Peter, Dalerba Piero, Dela Cruz Filemon S, Kung Andrew L, Califano Andrea
Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA 10032.
J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY USA 10032.
bioRxiv. 2025 Feb 12:2023.11.08.562798. doi: 10.1101/2023.11.08.562798.
Tumors frequently harbor isogenic yet epigenetically distinct subpopulations of multi-potent cells with high tumor-initiating potential-often called Cancer Stem-Like Cells (CSLCs). These can display preferential resistance to standard-of-care chemotherapy. Single-cell analyses can help elucidate Master Regulator (MR) proteins responsible for governing the transcriptional state of these cells, thus revealing complementary dependencies that may be leveraged via combination therapy. Interrogation of single-cell RNA sequencing profiles from seven metastatic breast cancer patients, using perturbational profiles of clinically relevant drugs, identified drugs predicted to invert the activity of MR proteins governing the transcriptional state of chemoresistant CSLCs, which were then validated by CROP-seq assays. The top drug, the anthelmintic albendazole, depleted this subpopulation without noticeable cytotoxicity Moreover, sequential cycles of albendazole and paclitaxel-a commonly used chemotherapeutic -displayed significant synergy in a patient-derived xenograft (PDX) from a TNBC patient, suggesting that network-based approaches can help develop mechanism-based combinatorial therapies targeting complementary subpopulations.
Network-based approaches, as shown in a study on metastatic breast cancer, can develop effective combinatorial therapies targeting complementary subpopulations. By analyzing scRNA-seq data and using clinically relevant drugs, researchers identified and depleted chemoresistant Cancer Stem-Like Cells, enhancing the efficacy of standard chemotherapies.
肿瘤中常常含有具有高肿瘤起始潜能的同基因但表观遗传上不同的多能细胞亚群——通常称为癌症干细胞样细胞（CSLCs）。这些细胞可能对标准护理化疗表现出优先抗性。单细胞分析有助于阐明负责调控这些细胞转录状态的主调控（MR）蛋白，从而揭示可能通过联合疗法加以利用的互补依赖性。利用临床相关药物的扰动图谱对7名转移性乳腺癌患者的单细胞RNA测序图谱进行分析，鉴定出预测可逆转调控化疗抗性CSLCs转录状态的MR蛋白活性的药物，随后通过CROP-seq分析进行了验证。排名第一的药物，驱虫药阿苯达唑，可耗尽该亚群细胞，且无明显细胞毒性。此外，在一名三阴性乳腺癌（TNBC）患者来源的异种移植瘤（PDX）中，阿苯达唑和常用化疗药物紫杉醇的序贯周期显示出显著的协同作用，这表明基于网络的方法有助于开发针对互补亚群的基于机制的联合疗法。
如一项关于转移性乳腺癌的研究所显示，基于网络的方法可开发针对互补亚群的有效联合疗法。通过分析单细胞RNA测序数据并使用临床相关药物，研究人员鉴定并耗尽了化疗抗性癌症干细胞样细胞，提高了标准化疗的疗效。
