Serrano-Carbajal Erandi A, Espinal-Enríquez Jesús, Hernández-Lemus Enrique
Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico.
Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de Mexico, Mexico City, Mexico.
Front Oncol. 2020 Feb 11;10:97. doi: 10.3389/fonc.2020.00097. eCollection 2020.
Metabolic deregulation is an emergent hallmark of cancer. Altered patterns of metabolic pathways result in exacerbated synthesis of macromolecules, increased proliferation, and resistance to treatment via alteration of drug processing. In addition, molecular heterogeneity creates a barrier to therapeutic options. In breast cancer, this broad variation in molecular metabolism constitutes, simultaneously, a source of prognostic and therapeutic challenges and a doorway to novel interventions. In this work, we investigated the metabolic deregulation landscapes in breast cancer molecular subtypes. Such landscapes are the regulatory signatures behind subtype-specific metabolic features. = 735 breast cancer samples of the Luminal A, Luminal B, Her2+, and Basal subtypes, as well as = 113 healthy breast tissue samples were analyzed. By means of a single-sample-based algorithm, deregulation for all metabolic pathways in every sample was determined. Deregulation levels match almost perfectly with the molecular classification, indicating that metabolic anomalies are closely associated with gene-expression signatures. Luminal B tumors are the most deregulated but are also the ones with higher within-subtype variance. We argued that this variation may underlie the fact that Luminal B tumors usually present the worst prognosis, a high rate of recurrence, and the lowest response to treatment in the long term. Finally, we designed a therapeutic scheme to regulate purine metabolism in breast cancer, independently of the molecular subtype. This scheme is founded on a computational tool that provides a set of FDA-approved drugs to target pathway-specific differentially expressed genes. By providing metabolic deregulation patterns at the single-sample level in breast cancer subtypes, we have been able to further characterize tumor behavior. This approach, together with targeted therapy, may open novel avenues for the design of personalized diagnostic, prognostic, and therapeutic strategies.
代谢失调是癌症的一个新特征。代谢途径模式的改变导致大分子合成加剧、增殖增加,并通过改变药物处理方式产生耐药性。此外,分子异质性给治疗选择带来了障碍。在乳腺癌中,分子代谢的这种广泛差异同时构成了预后和治疗挑战的来源以及新干预措施的切入点。在这项工作中,我们研究了乳腺癌分子亚型中的代谢失调情况。这些情况是亚型特异性代谢特征背后的调控特征。分析了735例Luminal A、Luminal B、Her2+和基底亚型的乳腺癌样本以及113例健康乳腺组织样本。通过基于单样本的算法,确定了每个样本中所有代谢途径的失调情况。失调水平与分子分类几乎完美匹配,表明代谢异常与基因表达特征密切相关。Luminal B肿瘤的失调最为严重,但也是亚型内差异最大的肿瘤。我们认为这种差异可能是Luminal B肿瘤通常预后最差、复发率高且长期治疗反应最低的原因。最后,我们设计了一种治疗方案来调节乳腺癌中的嘌呤代谢,而不考虑分子亚型。该方案基于一种计算工具,该工具提供一组FDA批准的药物来靶向特定途径的差异表达基因。通过提供乳腺癌亚型中单样本水平的代谢失调模式,我们能够进一步表征肿瘤行为。这种方法与靶向治疗一起,可能为个性化诊断、预后和治疗策略的设计开辟新途径。