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Predictive gene lists for breast cancer prognosis: A topographic visualisation study

机译：预测乳腺癌预后的基因清单：地形可视化研究

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Background The controversy surrounding the non-uniqueness of predictive gene lists (PGL) of small selected subsets of genes from very large potential candidates as available in DNA microarray experiments is now widely acknowledged [ 1 ]. Many of these studies have focused on constructing discriminative semi-parametric models and as such are also subject to the issue of random correlations of sparse model selection in high dimensional spaces. In this work we outline a different approach based around an unsupervised patient-specific nonlinear topographic projection in predictive gene lists. Methods We construct nonlinear topographic projection maps based on inter-patient gene-list relative dissimilarities. The Neuroscale, the Stochastic Neighbor Embedding(SNE) and the Locally Linear Embedding(LLE) techniques have been used to construct two-dimensional projective visualisation plots of 70 dimensional PGLs per patient, classifiers are also constructed to identify the prognosis indicator of each patient using the resulting projections from those visualisation techniques and investigate whether a-posteriori two prognosis groups are separable on the evidence of the gene lists. A literature-proposed predictive gene list for breast cancer is benchmarked against a separate gene list using the above methods. Generalisation ability is investigated by using the mapping capability of Neuroscale to visualise the follow-up study, but based on the projections derived from the original dataset. Results The results indicate that small subsets of patient-specific PGLs have insufficient prognostic dissimilarity to permit a distinction between two prognosis patients. Uncertainty and diversity across multiple gene expressions prevents unambiguous or even confident patient grouping. Comparative projections across different PGLs provide similar results. Conclusion The random correlation effect to an arbitrary outcome induced by small subset selection from very high dimensional interrelated gene expression profiles leads to an outcome with associated uncertainty. This continuum and uncertainty precludes any attempts at constructing discriminative classifiers. However a patient's gene expression profile could possibly be used in treatment planning, based on knowledge of other patients' responses. We conclude that many of the patients involved in such medical studies are intrinsically unclassifiable on the basis of provided PGL evidence. This additional category of 'unclassifiable' should be accommodated within medical decision support systems if serious errors and unnecessary adjuvant therapy are to be avoided.
机译：背景技术围绕DNA微阵列实验中可用的非常大的潜在候选基因的小部分选定基因子集的预测基因列表（PGL）的非唯一性争议已广为人知[1]。这些研究中的许多研究都集中在构造判别性半参数模型上，因此也受到高维空间中稀疏模型选择的随机相关性问题的困扰。在这项工作中，我们概述了基于预测基因列表中无监督的患者特定的非线性地形投影的不同方法。方法我们基于患者之间的基因表相对差异构建非线性地形投影图。已使用Neuroscale，随机邻居嵌入（SNE）和局部线性嵌入（LLE）技术构建每位患者70维PGL的二维投影可视化图，还构建了分类器以使用以下方法识别每位患者的预后指标通过这些可视化技术得出的预测结果，并研究基因列表的证据是否可以区分前后两个预后组。使用上述方法，将文献中提出的乳腺癌预测基因列表与单独的基因列表进行比较。通过使用Neuroscale的映射功能来可视化后续研究来研究泛化能力，但要基于从原始数据集得出的预测。结果结果表明，一小部分患者特异性PGL的预后相似性不足，无法区分两名预后患者。跨多个基因表达的不确定性和多样性会阻止明确甚至自信的患者分组。跨不同PGL的比较预测提供了相似的结果。结论从非常高维的相关基因表达谱中选择小子集会导致随机结果与任意结果的随机相关，从而导致结果具有不确定性。这种连续性和不确定性排除了构造判别式分类器的任何尝试。但是，根据其他患者的反应知识，可以将患者的基因表达谱用于治疗计划。我们得出结论，根据提供的PGL证据，许多参与此类医学研究的患者本质上无法分类。如果要避免严重错误和不必要的辅助治疗，则应在医疗决策支持系统中纳入“无法分类”这一额外类别。

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