Biomarker Discovery Program, Center of Individualized Medicine, Mayo Clinic, Rochester, Minnesota.
Genome Analysis Core, Medical Genome Facility, Center of Individualized Medicine, Mayo Clinic, Rochester, Minnesota.
J Mol Diagn. 2021 Apr;23(4):375-388. doi: 10.1016/j.jmoldx.2020.12.006. Epub 2020 Dec 31.
DNA junctions (DNAJs) frequently impact clinically relevant genes in tumors and are important for diagnostic and therapeutic purposes. Although routinely screened through fluorescence in situ hybridization assays, such testing only allows the interrogation of single-gene regions or known fusion partners. Comprehensive assessment of DNAJs present across the entire genome can only be determined from whole-genome sequencing. Structural variance analysis from whole-genome paired-end sequencing data is, however, frequently restricted to copy number changes without DNAJ detection. Through optimized whole-genome sequencing and specialized bioinformatics algorithms, complete structural variance analysis is reported, including DNAJs, from formalin-fixed DNA. Selective library assembly from larger fragments (>500 bp) and economical sequencing depths (300 to 400 million reads) provide representative genomic coverage profiles and increased allelic coverage to levels compatible with DNAJ calling (40× to 60×). Although consistently fragmented, more recently formalin-fixed, specimens (<2 years' storage) revealed consistent populations of larger DNA fragments. Optimized bioinformatics efficiently detected >90% of DNAJs in two prostate tumors (approximately 60% tumor) previously analyzed by mate-pair sequencing on fresh frozen tissue, with evidence of at least one spanning-read in 99% of DNAJs. Rigorous masking with data from unrelated formalin-fixed tissue progressively eliminated many false-positive DNAJs, without loss of true positives, resulting in low numbers of false-positive passing current filters. This methodology enables more comprehensive clinical genomics testing on formalin-fixed clinical specimens.
DNA 连接点 (DNAJs) 经常影响肿瘤中具有临床意义的基因,对于诊断和治疗目的非常重要。虽然通过荧光原位杂交检测进行了常规筛查,但这种检测只能检查单个基因区域或已知的融合伙伴。只有通过全基因组测序才能全面评估整个基因组中存在的 DNAJs。然而,全基因组配对末端测序数据的结构变异分析通常仅限于拷贝数变化,而无法检测 DNAJ。通过优化的全基因组测序和专门的生物信息学算法,可以从福尔马林固定的 DNA 中报告完整的结构变异分析,包括 DNAJ。从较大的片段(>500bp)选择性构建文库,并采用经济的测序深度(3 亿至 4 亿个读数),可以提供具有代表性的基因组覆盖图谱,并增加等位基因覆盖水平,使其与 DNAJ 调用兼容(40×至 60×)。尽管最近福尔马林固定的样本(<2 年储存)一直存在碎片化问题,但较大的 DNA 片段的一致性群体仍然得以揭示。优化的生物信息学算法可以有效地检测到两个前列腺肿瘤中超过 90%的 DNAJs(大约 60%的肿瘤),这些肿瘤之前曾在新鲜冷冻组织上进行过基于配对测序的分析,在 99%的 DNAJs 中都有至少一个跨越读取的证据。使用来自无关福尔马林固定组织的数据进行严格的屏蔽,逐步消除了许多假阳性 DNAJ,而不会丢失真正的阳性结果,从而使通过当前过滤器的假阳性数量减少。这种方法使我们能够在福尔马林固定的临床标本上进行更全面的临床基因组学检测。