Gosline Sara J C, Tognon Cristina, Nestor Michael, Joshi Sunil, Modak Rucha, Damnernsawad Alisa, Posso Camilo, Moon Jamie, Hansen Joshua R, Hutchinson-Bunch Chelsea, Pino James C, Gritsenko Marina A, Weitz Karl K, Traer Elie, Tyner Jeffrey, Druker Brian, Agarwal Anupriya, Piehowski Paul, McDermott Jason E, Rodland Karin
Pacific Northwest National Laboratory, Seattle, WA, USA.
Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA.
Clin Proteomics. 2022 Jul 27;19(1):30. doi: 10.1186/s12014-022-09367-9.
Acute Myeloid Leukemia (AML) affects 20,000 patients in the US annually with a five-year survival rate of approximately 25%. One reason for the low survival rate is the high prevalence of clonal evolution that gives rise to heterogeneous sub-populations of leukemic cells with diverse mutation spectra, which eventually leads to disease relapse. This genetic heterogeneity drives the activation of complex signaling pathways that is reflected at the protein level. This diversity makes it difficult to treat AML with targeted therapy, requiring custom patient treatment protocols tailored to each individual's leukemia. Toward this end, the Beat AML research program prospectively collected genomic and transcriptomic data from over 1000 AML patients and carried out ex vivo drug sensitivity assays to identify genomic signatures that could predict patient-specific drug responses. However, there are inherent weaknesses in using only genetic and transcriptomic measurements as surrogates of drug response, particularly the absence of direct information about phosphorylation-mediated signal transduction. As a member of the Clinical Proteomic Tumor Analysis Consortium, we have extended the molecular characterization of this cohort by collecting proteomic and phosphoproteomic measurements from a subset of these patient samples (38 in total) to evaluate the hypothesis that proteomic signatures can improve the ability to predict response to 26 drugs in AML ex vivo samples. In this work we describe our systematic, multi-omic approach to evaluate proteomic signatures of drug response and compare protein levels to other markers of drug response such as mutational patterns. We explore the nuances of this approach using two drugs that target key pathways activated in AML: quizartinib (FLT3) and trametinib (Ras/MEK), and show how patient-derived signatures can be interpreted biologically and validated in cell lines. In conclusion, this pilot study demonstrates strong promise for proteomics-based patient stratification to assess drug sensitivity in AML.
急性髓系白血病（AML）在美国每年影响20000名患者，其五年生存率约为25%。生存率低的一个原因是克隆进化的高发生率，这会产生具有不同突变谱的白血病细胞异质亚群，最终导致疾病复发。这种基因异质性驱动了复杂信号通路的激活，这在蛋白质水平上有所体现。这种多样性使得用靶向疗法治疗AML变得困难，需要针对每个患者的白血病定制个性化的治疗方案。为此，“击败AML”研究项目前瞻性地收集了1000多名AML患者的基因组和转录组数据，并进行了体外药物敏感性测定，以识别可预测患者特异性药物反应的基因组特征。然而，仅使用基因和转录组测量作为药物反应的替代指标存在固有缺陷，尤其是缺乏关于磷酸化介导的信号转导的直接信息。作为临床蛋白质组肿瘤分析联盟的成员，我们通过从这些患者样本的一个子集中（总共38个）收集蛋白质组和磷酸蛋白质组测量数据，扩展了该队列的分子特征，以评估蛋白质组特征能否提高预测AML体外样本中26种药物反应能力的假设。在这项工作中，我们描述了我们系统的多组学方法来评估药物反应的蛋白质组特征，并将蛋白质水平与其他药物反应标记物（如突变模式）进行比较。我们使用两种靶向AML中激活的关键通路的药物：quizartinib（FLT3）和trametinib（Ras/MEK）来探索这种方法的细微差别，并展示如何在生物学上解释患者来源的特征并在细胞系中进行验证。总之，这项初步研究表明基于蛋白质组学的患者分层在评估AML药物敏感性方面具有巨大潜力。
