The Fritz Haber Research Center, Hebrew University, Jerusalem, Israel.
PLoS One. 2013 Apr 23;8(4):e61554. doi: 10.1371/journal.pone.0061554. Print 2013.
Towards a reliable identification of the onset in time of a cancer phenotype, changes in transcription levels in cell models were tested. Surprisal analysis, an information-theoretic approach grounded in thermodynamics, was used to characterize the expression level of mRNAs as time changed. Surprisal Analysis provides a very compact representation for the measured expression levels of many thousands of mRNAs in terms of very few - three, four - transcription patterns. The patterns, that are a collection of transcripts that respond together, can be assigned definite biological phenotypic role. We identify a transcription pattern that is a clear marker of eventual malignancy. The weight of each transcription pattern is determined by surprisal analysis. The weight of this pattern changes with time; it is never strictly zero but it is very low at early times and then rises rather suddenly. We suggest that the low weights at early time points are primarily due to experimental noise. We develop the necessary formalism to determine at what point in time the value of that pattern becomes reliable. Beyond the point in time when a pattern is deemed reliable the data shows that the pattern remain reliable. We suggest that this allows a determination of the presence of a cancer forewarning. We apply the same formalism to the weight of the transcription patterns that account for healthy cell pathways, such as apoptosis, that need to be switched off in cancer cells. We show that their weight eventually falls below the threshold. Lastly we discuss patient heterogeneity as an additional source of fluctuation and show how to incorporate it within the developed formalism.
为了可靠地识别癌症表型的发生时间，测试了细胞模型中转录水平的变化。惊讶度分析是一种基于热力学的信息论方法，用于描述随时间变化的 mRNA 表达水平。惊讶度分析用非常少的 - 三个，四个 - 转录模式，为数千个 mRNA 的测量表达水平提供了非常紧凑的表示。这些模式是一起响应的转录本的集合，可以被赋予明确的生物学表型作用。我们确定了一个转录模式，它是恶性肿瘤的明确标志物。每个转录模式的权重由惊讶度分析确定。该模式的权重随时间变化；它在早期从未严格为零，但在早期非常低，然后突然上升。我们认为早期时间点低权重主要是由于实验噪声。我们开发了必要的形式主义来确定该模式的值何时变得可靠。超过该模式被认为可靠的时间点，数据显示该模式仍然可靠。我们建议这可以确定是否存在癌症预警。我们将相同的形式主义应用于转录模式的权重，这些模式解释了健康细胞途径，如细胞凋亡，在癌细胞中需要关闭。我们表明它们的权重最终会低于阈值。最后，我们讨论了患者异质性作为波动的另一个来源，并展示了如何在开发的形式主义中纳入它。
