Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine, The University of Newcastle, Callaghan, New South Wales, Australia.
PLoS One. 2010 Aug 18;5(8):e12262. doi: 10.1371/journal.pone.0012262.
It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods.
METHODOLOGY/PRINCIPAL FINDINGS: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer.
CONCLUSIONS/SIGNIFICANCE: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases.
人们普遍认为，癌细胞在疾病进展过程中会改变其转录状态。我们提出，使用高通量技术可以有效地跟踪癌细胞向恶性表型的进展，这些技术通过采用香农的通讯数学理论来跟踪在基因表达谱中观察到的逐渐变化。基于信息论的方法可以量化癌细胞转录谱与起源组织中正常细胞转录谱的差异。该方法的相关性可以使用公共领域中可用的微阵列数据集进行评估，但该方法原则上适用于其他高通量方法。
方法/主要发现：使用黑色素瘤和前列腺癌数据集，我们说明了如何使用香农熵和 Jensen-Shannon 散度来追踪疾病的转录变化进展。我们确定了这两个度量的变化如何与癌症进展的既定生物标志物相关。信息论度量允许我们识别出导致恶性表型的渐进和相对突然的转录变化的新型生物标志物。同时，该方法能够验证大量似乎与黑色素瘤和前列腺癌进展有关的基因和过程。
结论/意义：因此，我们提出了一个定量指导原则，即癌症的一个新的统一特征：癌细胞的转录组变化导致可测量的归一化香农熵值的观察性转变（通过高通量技术测量）。同时，肿瘤细胞通过创建我们可能无法直接测量的状态，增加了它们与正常组织谱的差异，从而增加了它们的无序性。这个统一的特征允许通过 Jensen-Shannon 散度来识别来自基因表达谱的过程的时间箭头，并有助于绘制特定癌症亚型的表型和分子特征。该方法的深刻数学基础使我们能够提出这样一个假设，即该原理有望普遍适用于其他疾病。
