BACKGROUND Targeted therapies have greatly improved cancer patient prognosis. For instance, chronic myeloid leukemia is now well treated with imatinib, a tyrosine kinase inhibitor. Around 80% of the patients reach complete remission. However, despite its great efficiency, some patients are resistant to the drug. This heterogeneity in the response might be associated with pharmacokinetic parameters, varying between individuals because of genetic variants. To assess this issue, next-generation sequencing of large panels of genes can be performed from patient samples. However, the common problem in pharmacogenetic studies is the availability of samples, often limited. In the end, large sequencing data are obtained from small sample sizes; therefore, classical statistical analyses cannot be applied to identify interesting targets. To overcome this concern, here, we described original and underused statistical methods to analyze large sequencing data from a restricted number of samples. RESULTS To evaluate the relevance of our method, 48 genes involved in pharmacokinetics were sequenced by next-generation sequencing from 24 chronic myeloid leukemia patients, either sensitive or resistant to imatinib treatment. Using a graphical representation, from 708 identified polymorphisms, a reduced list of 115 candidates was obtained. Then, by analyzing each gene and the distribution of variant alleles, several candidates were highlighted such as UGT1A9, PTPN22, and ERCC5. These genes were already associated with the transport, the metabolism, and even the sensitivity to imatinib in previous studies. CONCLUSIONS These relevant tests are great alternatives to inferential statistics not applicable to next-generation sequencing experiments performed on small sample sizes. These approaches permit to reduce the number of targets and find good candidates for further treatment sensitivity studies.

中文翻译：

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新颖的分析方法可从小样本中解释大的测序数据。

背景技术靶向疗法已大大改善了癌症患者的预后。例如，现在用酪氨酸激酶抑制剂伊马替尼可以很好地治疗慢性粒细胞白血病。大约80％的患者可以完全缓解。然而，尽管其效率很高，但是一些患者仍对该药有抵抗力。反应中的这种异质性可能与药代动力学参数有关，由于遗传变异，个体之间会有所不同。为了评估此问题，可以从患者样品中进行大基因组的下一代测序。但是，药物遗传学研究中的常见问题是样品的可用性，通常是有限的。最后，从小样本量获得大量测序数据。因此，经典的统计分析无法应用于识别有趣的目标。为了克服这种担忧，在这里，我们描述了原始的和未充分利用的统计方法，用于分析来自数量有限的样本的大量测序数据。结果为了评估我们方法的相关性，通过下一代测序对24位对伊马替尼治疗敏感或耐药的慢性髓细胞白血病患者的药代动力学中的48个基因进行了测序。使用图形表示法，从708个已识别的多态性中，减少了115个候选对象的列表。然后，通过分析每个基因和变异等位基因的分布，突出显示了几个候选对象，例如UGT1A9，PTPN22和ERCC5。在先前的研究中，这些基因已经与转运，代谢甚至对伊马替尼的敏感性有关。结论这些相关测试是推论统计的理想选择，不适用于对小样本量进行的下一代测序实验。这些方法可以减少靶标的数量，并为进一步的治疗敏感性研究找到良好的候选者。