化療是急性骨髓性白血?。╝cute myelogenous leukemia, AML）的主要治療方式，所以一旦確診,，患者都首先利用對大多數(shù)人最有效的化療藥物發(fā)動第一波治療攻勢,。不過，并不是所有的患者都能就此獲得滿意的療效,。對于哪些療效不明顯的患者,，醫(yī)生會轉(zhuǎn)用其他的化療藥物，如果都沒有效,，甚至會嘗試使用還在臨床實驗階段的藥物,。藥物效果的不確定性可能會浪費病人大量的時間而使病人錯過最有效的治療時機。病人也不得不承受藥物的副作用而可能治療效果卻平平,。
有什么辦法能夠迅速有效地知道哪些病人需要更大劑量的治療,，哪些病人應(yīng)該采用非常用藥物來治療呢？
在最新的一期《Cell》（23 July 2004）上,，the Stanford University School of Medicine由Garry Nolan領(lǐng)導(dǎo)的研究小組試圖建立這樣一種新的技術(shù)平臺,。研究者的思路可以這樣來打一個比方：比如我們想知道在一個房間內(nèi)的許多人，那些更具有攻擊性,，也許只憑看幾眼很難發(fā)現(xiàn),；但是，如果我們對每個人都敲擊一下,，從他們的反應(yīng)當中,，我們當然可以容易地做出判斷。同樣的道理,，研究者設(shè)想,，如果首先對病人的細胞給予各種刺激,，從細胞的變化中，我們則可以得到有用的信息,。好了,，現(xiàn)在的問題是我們應(yīng)該給予什么樣的刺激，又應(yīng)該觀察細胞什么方面的變化呢,？
高度協(xié)調(diào)的信號傳遞網(wǎng)絡(luò)保證了細胞表面接收到的信號能夠有效地傳遞到細胞核內(nèi),，而很多癌變細胞的這個信號傳遞網(wǎng)絡(luò)出現(xiàn)故障——將信號傳遞給錯誤的下游分子，或者沒有接到上游信號就自動產(chǎn)生信號傳遞給下游,。而我們也知道,，分子的磷酸化是信號傳遞的重要標記。因此,，研究者利用流式細胞分析（flow cytometry）,，來比較健康者、對化療敏感的AML患者和對化療有抗性的AML患者的磷酸化蛋白網(wǎng)絡(luò)的差異,。
實驗中,，研究者檢測了兩個和AML相關(guān)的信號途徑——STAT途徑和Ras/MAPK途徑的重要蛋白的基本磷酸化程度和在細胞因子刺激后的磷酸化程度。發(fā)現(xiàn),，不同的病人的磷酸化差異明顯,。而且，這種差異和化療的效果有直接的相關(guān)性,。
流式細胞分析雖然已經(jīng)是十分成熟的實驗技術(shù),，但利用它來檢測癌信號網(wǎng)絡(luò)，還是全新的應(yīng)用,。和以往的Microarray等技術(shù)顯示多細胞的平均值相比,，流式細胞分析能夠采集所有單個細胞的數(shù)值，從而獲得了分析樣品中多個不同亞型的可能性,。舉例說,，流式細胞分析可以顯示樣品中存在某一個參數(shù)的高和低兩個亞型，但是,，如果用Microarray等其他技術(shù),，顯示的結(jié)果是參數(shù)值為中等（高和低的平均）。
后繼的實驗中,，研究者將進一步對網(wǎng)絡(luò)信號和藥物治療的效果作精細分析,。也許不久，這些信息就會給醫(yī)生選擇對患者的最有效的治療方式提供幫助,。
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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