Computational quantification and characterization of independently evolving cellular subpopulations within tumors is critical to inhibit anti-cancer therapy resistance.
Computational quantification and characterization of independently evolving cellular subpopulations within tumors is critical to inhibit anti-cancer therapy resistance.
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DOI：
10.1186/s13073-022-01121-y
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发表时间：
2022-10-20
期刊：
影响因子：
12.3
通讯作者：
中科院分区：
文献类型：
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作者：
耐药性仍然是各种抗癌疗法的主要限制因素。癌症的可塑性增加了这一挑战的复杂性，其中一种癌症亚型会响应治疗而转变为另一种癌症亚型，例如，三阴性乳腺癌（TNBC）转变为Her 2阳性乳腺癌。为了获得最佳治疗效果，准确的肿瘤诊断和后续治疗决策至关重要。本研究评估了一种新的方法来表征治疗诱导的不同肿瘤细胞亚群的进化变化，以识别和治疗利用抗癌药物耐药性。在这项研究中，开发了一种信息理论单细胞定量策略，为定制的治疗方法提供高分辨率和个性化的肿瘤成分评估。简而言之，这种单细胞定量策略基于来自每个实验的至少100，000个肿瘤细胞计算细胞条形码，并揭示由每个细胞中的一组正在进行的过程组成的细胞特异性信号传导特征（CSSS）。使用这些基于CSS的条形码，揭示并绘制了肿瘤内响应外部影响（如抗癌治疗）而进化的不同亚群。条形码进一步应用于将靶向药物组合分配给每个个体肿瘤，以优化肿瘤对治疗的反应。该策略使用TNBC模型和已知响应于放疗（RT）而改变表型的患者来源的肿瘤进行了验证。我们发现，条形码引导的靶向药物鸡尾酒显着增强了肿瘤对RT的反应，并防止了曾经耐药的肿瘤的再生长。本文提出的策略在预防癌症治疗抗性方面显示出希望，在临床应用中具有显著的适用性。在线版本包含补充材料，可通过10.1186/s13073-022-01121-y获得。
Drug resistance continues to be a major limiting factor across diverse anti-cancer therapies. Contributing to the complexity of this challenge is cancer plasticity, in which one cancer subtype switches to another in response to treatment, for example, triple-negative breast cancer (TNBC) to Her2-positive breast cancer. For optimal treatment outcomes, accurate tumor diagnosis and subsequent therapeutic decisions are vital. This study assessed a novel approach to characterize treatment-induced evolutionary changes of distinct tumor cell subpopulations to identify and therapeutically exploit anticancer drug resistance. In this research, an information-theoretic single-cell quantification strategy was developed to provide a high-resolution and individualized assessment of tumor composition for a customized treatment approach. Briefly, this single-cell quantification strategy computes cell barcodes based on at least 100,000 tumor cells from each experiment and reveals a cell-specific signaling signature (CSSS) composed of a set of ongoing processes in each cell. Using these CSSS-based barcodes, distinct subpopulations evolving within the tumor in response to an outside influence, like anticancer treatments, were revealed and mapped. Barcodes were further applied to assign targeted drug combinations to each individual tumor to optimize tumor response to therapy. The strategy was validated using TNBC models and patient-derived tumors known to switch phenotypes in response to radiotherapy (RT). We show that a barcode-guided targeted drug cocktail significantly enhances tumor response to RT and prevents regrowth of once-resistant tumors. The strategy presented herein shows promise in preventing cancer treatment resistance, with significant applicability in clinical use. The online version contains supplementary material available at 10.1186/s13073-022-01121-y.
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影响因子：
4.2
作者：
Jing X;Liang H;Hao C;Yang X;Cui X
通讯作者：
Cui X
影响因子：
2.6
作者：
Arnold KM;Opdenaker LM;Flynn NJ;Appeah DK;Sims-Mourtada J
通讯作者：
Sims-Mourtada J
影响因子：
45.3
作者：
Kyndi, Marianne;Sorensen, Flemming B.;Overgaard, Jens
通讯作者：
Overgaard, Jens
DOI：
10.1016/j.neo.2014.10.006
发表时间：
2014-12
期刊：
Neoplasia (New York, N.Y.)
影响因子：
--
作者：
Ferrari-Amorotti G;Chiodoni C;Shen F;Cattelani S;Soliera AR;Manzotti G;Grisendi G;Dominici M;Rivasi F;Colombo MP;Fatatis A;Calabretta B
通讯作者：
Calabretta B
DOI：
10.2147/bctt.s114659
发表时间：
2016
期刊：
Breast cancer (Dove Medical Press)
影响因子：
--
作者：
Fleisher B;Clarke C;Ait-Oudhia S
通讯作者：
Ait-Oudhia S