化疗是急性骨髓性白血病（acute myelogenous leukemia, AML）的主要治疗方式，所以一旦确诊，患者都首先利用对大多数人最有效的化疗药物发动第一波治疗攻势。不过，并不是所有的患者都能就此获得满意的疗效。对于哪些疗效不明显的患者，医生会转用其他的化疗药物，如果都没有效，甚至会尝试使用还在临床实验阶段的药物。药物效果的不确定性可能会浪费病人大量的时间而使病人错过最有效的治疗时机。病人也不得不承受药物的副作用而可能治疗效果却平平。
有什么办法能够迅速有效地知道哪些病人需要更大剂量的治疗，哪些病人应该采用非常用药物来治疗呢？
在最新的一期《Cell》（23 July 2004）上，the Stanford University School of Medicine由Garry Nolan领导的研究小组试图建立这样一种新的技术平台。研究者的思路可以这样来打一个比方：比如我们想知道在一个房间内的许多人，那些更具有攻击性，也许只凭看几眼很难发现；但是，如果我们对每个人都敲击一下，从他们的反应当中，我们当然可以容易地做出判断。同样的道理，研究者设想，如果首先对病人的细胞给予各种刺激，从细胞的变化中，我们则可以得到有用的信息。好了，现在的问题是我们应该给予什么样的刺激，又应该观察细胞什么方面的变化呢？
高度协调的信号传递网络保证了细胞表面接收到的信号能够有效地传递到细胞核内，而很多癌变细胞的这个信号传递网络出现故障——将信号传递给错误的下游分子，或者没有接到上游信号就自动产生信号传递给下游。而我们也知道，分子的磷酸化是信号传递的重要标记。因此，研究者利用流式细胞分析（flow cytometry），来比较健康者、对化疗敏感的AML患者和对化疗有抗性的AML患者的磷酸化蛋白网络的差异。
实验中，研究者检测了两个和AML相关的信号途径——STAT途径和Ras/MAPK途径的重要蛋白的基本磷酸化程度和在细胞因子刺激后的磷酸化程度。发现，不同的病人的磷酸化差异明显。而且，这种差异和化疗的效果有直接的相关性。
流式细胞分析虽然已经是十分成熟的实验技术，但利用它来检测癌信号网络，还是全新的应用。和以往的Microarray等技术显示多细胞的平均值相比，流式细胞分析能够采集所有单个细胞的数值，从而获得了分析样品中多个不同亚型的可能性。举例说，流式细胞分析可以显示样品中存在某一个参数的高和低两个亚型，但是，如果用Microarray等其他技术，显示的结果是参数值为中等（高和低的平均）。
后继的实验中，研究者将进一步对网络信号和药物治疗的效果作精细分析。也许不久，这些信息就会给医生选择对患者的最有效的治疗方式提供帮助。
Single Cell Profiling of Potentiated Phospho-Protein Networks in Cancer Cells
Altered growth factor responses in phospho-protein-driven signaling networks are crucial to cancer cell survival and pathology. Profiles of cancer cell signaling networks might therefore identify mechanisms by which such cells interpret environmental cues for continued growth. Using multiparameter flow cytometry, we monitored phospho-protein responses to environmental cues in acute myeloid leukemia at the single cell level. By exposing cancer cell signaling networks to potentiating inputs, rather than relying upon the basal levels of protein phosphorylation alone, we could discern unique cancer network profiles that correlated with genetics and disease outcome. Strikingly, individual cancers manifested multiple cell subsets with unique network profiles, reflecting cancer heterogeneity at the level of signaling response. The results revealed a dramatic remodeling of signaling networks in cancer cells. Thus, single cell measurements of phospho-protein responses reveal shifts in signaling potential of a phospho-protein network, allowing for categorizing of cell network phenotypes by multidimensional molecular profiles of signaling.
Figure 1. A Cytokine Response Panel Reveals Potentiated Signal Transduction Nodes in Primary Acute Myeloid Leukemias
(A) Stimulation states, shown in rows, included unstimulated or 20 ng/ml of FL, GM-CSF, G-CSF, IL-3, or IFNγ. Target phosphorylations were detected using phospho-specific antibodies for Stat1, Stat3, Stat5, Stat6, p38, and Erk1/2, shown in columns. Each square in the grid represents the response of one phosphorylation site to one condition. The relationship between the grid and the flow cytometry data on which it is based is diagrammed for U937 cells.
(B) Representative cytokine response panels of the HL-60 AML cell line, normal CD33+ leukocytes, and six AML patient samples. Repeat experiments using these AML blasts yielded similar results (n = 3), and variation among normal, healthy donors was minimal (n = 6). The response to stimulation at each signaling node is calculated as log2 (MFIstimulated/MFIunstimulated).
Figure 2. Basal and Potentiated Signaling Nodes that Varied among Cancer Samples Were Used to Define an AML Biosignature
(A) The cytokine response panel of 30 total AML patient samples. The first row of each has been colored to show the variation in the basal phosphorylation (relative to the minimum among the AML blasts). Of 900 cytokine responses assayed, 93 (10.3%) displayed a detectable phosphorylation increase following stimulation (greater than 0.55-fold on a log2 scale).
(B) Significant cytokine responses were restricted primarily to the 7/30 cytokine response nodes with a variance across cancers greater than 0.1 (yellow circles).
(C) A graph of the absolute median plotted against the variance for each node state indicates the signal-to-noise threshold.
Figure 3. AML Patients Grouped by Signal Transduction Biosignature Form Four Groups that Exhibit Significant Correlations to Clinical Prognostic Markers
(A) The 13-parameter biosignatures of differentiated CD33+ myeloid cells from six normal blood donors (D01 – D06), U937 and HL-60 cancer cell lines, and 30 AML patient samples were grouped according to similarity using hierarchical clustering. The heat map for AML and cancer cell line cytokine responses was scaled by subtracting donor sample medians to provide a dynamic color range. As shown previously, basal responses are relative to the minimum among AML samples.
(B) Four main groups of AML patients were identified based on the similarity of their signal transduction biosignatures. We designated these groups with signaling cluster (SC) nomenclature based on the signaling that defined them and mapped several clinical markers within the identified patient groups.
Figure 4. Flt3 Mutation in Primary AMLs Is Associated with Potentiated Myeloid Signal Transduction Nodes
Basal and cytokine response node states of patient samples with wild-type or mutant Flt3 are shown for the 30 AML samples assayed. Each circle represents the level of STAT phosphorylation detected in an individual AML patient sample, grouped according to wild-type Flt3 (black) or detected mutant Flt3 (yellow). (A) To assess basal phosphorylation, samples were compared to the minimum observed among cancers. (B–E) The phosphorylation of Stat5 detected following GM-CSF, G-CSF, and IL-3 and of Stat3 following G-CSF is shown as a fold increase above basal. (F) Cumulative myeloid cytokine responses, calculated by summing individual responses (B–E), were compared in patients with and without Flt3 mutations.
Figure 5. Representative 2D Flow Cytometry Plots of Stat5 and Stat3 Phosphorylation following G-CSF Stimulation in AML Patient Samples with Wild-Type Flt3 and Mutant Flt3 (ITD)
Two-dimensional contour plot representations of Stat5 and Stat3 phosphorylation (y and x axis, respectively) in patient samples from SC-NP and SC-P2. Both the level of basal phosphorylation and the response to G-CSF are shown.
Figure 6. Cancer Biosignatures and a Potentiated Model of Cancer Cell Signaling
(A) A general method for identifying a cancer biosignature is shown using an example of STAT and Ras/MAPK signaling node states in AML.
(B) Composite maps of cancer networks from profiles SC-NP and SC-P2 were built out of common signaling events observed in each cluster. Highlighted nodes were detected to be high basal or potentiated in most of the samples from a profile group.
Figure 7. Three AML Patients Profiled as SC-P2 Showed Similarities and Differences in Potentiated Signaling Mechanisms
Pathway maps summarizing the signaling phenotype of individual patient samples are shown for three profiled as SC-P2, as per Figure 6. Differences and similarities are highlighted in the text.
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