Drug-Induced Resistance and Phenotypic Switch in Triple-Negative Breast Cancer Can Be Controlled via Resolution and Targeting of Individualized Signaling Signatures.
Drug-Induced Resistance and Phenotypic Switch in Triple-Negative Breast Cancer Can Be Controlled via Resolution and Targeting of Individualized Signaling Signatures.
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三阴性乳腺癌中药物诱导的耐药性和表型转换可以通过个体化信号转导信号的解析和靶向来控制。
DOI：
10.3390/cancers13195009
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发表时间：
2021-10-06
期刊：
影响因子：
5.2
通讯作者：
Kravchenko-Balasha N
中科院分区：
文献类型：
--
作者：
Vasudevan S;Adejumobi IA;Alkhatib H;Roy Chowdhury S;Stefansky S;Rubinstein AM;Kravchenko-Balasha N
三阴性乳腺癌（TNBC）患者由于肿瘤间高度异质性和缺乏有效的靶向治疗而预后不良。通过量化每个TNBC患者正在进行的过程，我们提出了一个解释，为什么某些以前建议的单一疗法，如抗EGFR，是无效的。我们通过实验证明，没有根据患者特定的正在进行的过程进行准确调整的单一疗法或药物组合可能会对肿瘤产生进化压力，导致以前未检测到或未靶向的细胞亚群的出现。例如，我们表明某些TNBC肿瘤可能受益于靶向雌激素受体（ER）的疗法，类似于ER阳性癌症。当非靶向时，这些肿瘤可能发展为大的ER阳性亚群。我们认为，抗TNBC治疗应准确地针对个性化的分子过程，不完整或“错误”的治疗可能会产生不同的TNBC肿瘤进化途径，导致耐药性。三阴性乳腺癌（TNBC）是乳腺癌的侵袭性亚组，其主要用化疗和放疗治疗。表皮生长因子受体（EGFR）被认为是TNBC中频繁表达的，因此被建议作为治疗靶点。然而，EGFR抑制剂的临床试验失败了。在这项研究中，我们研究了患者特异性TNBC网络结构与抗EGFR治疗耐药的可能机制之间的关系。使用来自TCGA数据集的747个乳腺肿瘤的信息理论分析，我们解析了个体化的蛋白质网络结构，即每个肿瘤的患者特异性信号特征（PaSSS）。每个PaSSS的特征在于一组1-4个改变的蛋白质-蛋白质子网络。发现31%的TNBC PaSSS携带EGFR作为网络的一部分，并且预测只要与抗雌激素受体（ER）治疗联合使用，即可从抗EGFR治疗中获益。使用一系列的单细胞实验，然后在体内的支持，我们表明，药物组合，这是不准确地定制每个PaSSS可能会产生恶性肿瘤的进化压力，导致以前未检测到的或非靶向的亚群，如ER+人口的扩张。这对应于基于PaSSS的预测，其表明在某些抗TNBC治疗中掺入抗ER药物。这些发现强调了针对每个PaSSS定制抗TNBC靶向治疗的必要性，以防止TNBC肿瘤的不同演变和耐药性发展。
Patients with Triple Negative Breast Cancer (TNBC) have a poor prognosis due to high inter-tumor heterogeneity and absence of effective targeted treatments. Through quantification of ongoing processes in each individual with TNBC, we propose an explanation on why certain previously suggested monotherapies, such as anti-EGFR, are not effective. We experimentally demonstrate that monotherapies or drug combinations that are not adjusted accurately to the patient-specific ongoing processes may create an evolutionary pressure on a tumor leading to the emergence of previously undetected or untargeted cellular subpopulations. We show for example that certain TNBC tumors may benefit from therapies targeting estrogen receptors (ER), similarly to ER positive cancers. When untargeted, those tumors may develop large ER positive subpopulations. We propose that anti-TNBC therapy should be accurately tailored to the personalized molecular processes and that incomplete or “wrong” treatments may generate diverse evolutionary routes of TNBC tumors leading to drug resistance. Triple-negative breast cancer (TNBC) is an aggressive subgroup of breast cancers which is treated mainly with chemotherapy and radiotherapy. Epidermal growth factor receptor (EGFR) was considered to be frequently expressed in TNBC, and therefore was suggested as a therapeutic target. However, clinical trials of EGFR inhibitors have failed. In this study, we examine the relationship between the patient-specific TNBC network structures and possible mechanisms of resistance to anti-EGFR therapy. Using an information-theoretical analysis of 747 breast tumors from the TCGA dataset, we resolved individualized protein network structures, namely patient-specific signaling signatures (PaSSS) for each tumor. Each PaSSS was characterized by a set of 1–4 altered protein–protein subnetworks. Thirty-one percent of TNBC PaSSSs were found to harbor EGFR as a part of the network and were predicted to benefit from anti-EGFR therapy as long as it is combined with anti-estrogen receptor (ER) therapy. Using a series of single-cell experiments, followed by in vivo support, we show that drug combinations which are not tailored accurately to each PaSSS may generate evolutionary pressure in malignancies leading to an expansion of the previously undetected or untargeted subpopulations, such as ER+ populations. This corresponds to the PaSSS-based predictions suggesting to incorporate anti-ER drugs in certain anti-TNBC treatments. These findings highlight the need to tailor anti-TNBC targeted therapy to each PaSSS to prevent diverse evolutions of TNBC tumors and drug resistance development.
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影响因子：
82.9
作者：
Hrustanovic G;Olivas V;Pazarentzos E;Tulpule A;Asthana S;Blakely CM;Okimoto RA;Lin L;Neel DS;Sabnis A;Flanagan J;Chan E;Varella-Garcia M;Aisner DL;Vaishnavi A;Ou SH;Collisson EA;Ichihara E;Mack PC;Lovly CM;Karachaliou N;Rosell R;Riess JW;Doebele RC;Bivona TG
通讯作者：
Bivona TG
影响因子：
8
作者：
Pazarentzos E;Bivona TG
通讯作者：
Bivona TG
影响因子：
50.5
作者：
Corkery, B.;Crown, J.;O'Donovan, N.
通讯作者：
O'Donovan, N.
影响因子：
50.5
作者：
Nabholtz, J. M.;Abrial, C.;Penault-Llorca, F.
通讯作者：
Penault-Llorca, F.
影响因子：
6.2
作者：
Jubran, Maria R.;Rubinstein, Ariel M.;Kravchenko-Balasha, Nataly
通讯作者：
Kravchenko-Balasha, Nataly