Laboratory of Mammary and Leukaemic Oncogenesis, Inserm U1218 ACTION, Bergonié Cancer Institute, University of Bordeaux, 146 rue Léo Saignat, bâtiment TP 4ème étage, case 50, 33076, Bordeaux, France.
Team EPICENE, Inserm U1219 BPH, Bergonié Cancer Institute, University of Bordeaux, Bordeaux, France.
Hum Genomics. 2019 Aug 30;13(1):41. doi: 10.1186/s40246-019-0235-1.
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.
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.
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.
靶向治疗极大地改善了癌症患者的预后。例如，慢性髓性白血病现在可以用伊马替尼（一种酪氨酸激酶抑制剂）很好地治疗，约 80%的患者达到完全缓解。然而，尽管疗效显著，一些患者对该药物仍有耐药性。这种反应的异质性可能与药代动力学参数有关，由于遗传变异，个体之间的参数存在差异。为了评估这个问题，可以对患者样本进行大panel 基因的下一代测序。然而，在药物遗传学研究中常见的问题是样本的可用性，通常是有限的。最终，从小样本量中获得了大量的测序数据；因此，经典的统计分析不能应用于识别有趣的目标。为了解决这个问题，我们在这里描述了原始的、未被充分利用的统计方法，用于分析来自少数样本的大量测序数据。
为了评估我们方法的相关性，我们对 24 例慢性髓性白血病患者进行了下一代测序，对 48 个涉及药代动力学的基因进行了测序，这些患者对伊马替尼治疗敏感或耐药。通过图形表示，从 708 个鉴定的多态性中，获得了一个减少到 115 个候选者的列表。然后，通过分析每个基因和变异等位基因的分布，突出了几个候选者，如 UGT1A9、PTPN22 和 ERCC5。这些基因在以前的研究中已经与药物的转运、代谢甚至对伊马替尼的敏感性有关。
这些相关测试是推断统计学的很好替代方法，不适用于在小样本量上进行的下一代测序实验。这些方法可以减少目标数量，并为进一步的治疗敏感性研究找到好的候选者。
