Cancer treatment is constantly evolving from a one-size-fits-all towards bespoke approaches for each patient. In certain solid cancers, including breast and lung, tumor genome profiling has been incorporated into therapeutic decision-making. For chronic phase (CP) chronic myeloid leukemia (CML), while tyrosine kinase inhibitor (TKI) therapy is the standard treatment, current clinical scoring systems cannot accurately predict the heterogeneous treatment outcomes observed in patients. Biomarkers capable of segregating patients according to outcome at diagnosis are needed to improve management, and facilitate enrolment in clinical trials seeking to prevent blast crisis transformation and improve the depth of molecular responses. To this end, gene expression (GE) profiling studies have evaluated whether GE signatures at diagnosis are clinically informative. Patient material from a variety of sources have been profiled using microarrays, RNA sequencing, and, more recently, single-cell RNA sequencing. However, differences in the cell types profiled, the technologies used, and the inherent complexities associated with the interpretation of genomic data, pose challenges in distilling GE datasets into biomarkers with clinical utility. The goal of this paper is to review previous studies evaluating GE profiling in CML, and explore their potential as risk assessment tools for individualized CML treatment. We also review the contribution that acquired mutations, including those seen in clonal hematopoiesis, make to GE profiles, and how a model integrating contributions of genetic and epigenetic factors in TKI resistance and BC transformation can define a route to GE-based biomarkers. Finally, we outline a four-stage approach for the development of GE-based biomarkers in CML.

中文翻译：

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在慢性粒细胞白血病风险评估中整合遗传和表观遗传因素：基于基因表达的生物标志物。

癌症治疗不断从千篇一律的方法演变为针对每位患者的定制方法。在某些实体癌中，包括乳腺癌和肺癌，肿瘤基因组分析已被纳入治疗决策。对于慢性期 (CP) 慢性粒细胞白血病 (CML)，虽然酪氨酸激酶抑制剂 (TKI) 治疗是标准治疗，但目前的临床评分系统无法准确预测在患者中观察到的异质治疗结果。需要能够根据诊断结果分离患者的生物标志物来改善管理，并促进临床试验的注册，以防止爆炸危机转化并提高分子反应的深度。为此，基因表达 (GE) 分析研究评估了诊断时的 GE 特征是否具有临床信息。来自各种来源的患者材料已使用微阵列、RNA 测序以及最近的单细胞 RNA 测序进行了分析。然而，所分析的细胞类型、使用的技术以及与基因组数据解释相关的固有复杂性的差异，对将 GE 数据集提炼成具有临床实用性的生物标志物提出了挑战。本文的目的是回顾以往评估 CML 中 GE 分析的研究，并探索它们作为个体化 CML 治疗风险评估工具的潜力。我们还回顾了获得性突变（包括克隆造血中所见的突变）对 GE 谱的贡献，以及整合遗传和表观遗传因素在 TKI 抗性和 BC 转化中的贡献的模型如何定义基于 GE 的生物标志物的途径。最后，我们概述了在 CML 中开发基于 GE 的生物标志物的四阶段方法。