Genetic changes in repetitive sequences are a hallmark of cancer and other diseases, but characterizing these has been challenging using standard sequencing approaches. We developed a de novo kmer finding approach, called ARTEMIS (Analysis of RepeaT EleMents in dISease), to identify repeat elements from whole-genome sequencing. Using this method, we analyzed 1.2 billion kmers in 2837 tissue and plasma samples from 1975 patients, including those with lung, breast, colorectal, ovarian, liver, gastric, head and neck, bladder, cervical, thyroid, or prostate cancer. We identified tumor-specific changes in these patients in 1280 repeat element types from the LINE, SINE, LTR, transposable element, and human satellite families. These included changes to known repeats and 820 elements that were not previously known to be altered in human cancer. Repeat elements were enriched in regions of driver genes, and their representation was altered by structural changes and epigenetic states. Machine learning analyses of genome-wide repeat landscapes and fragmentation profiles in cfDNA detected patients with early-stage lung or liver cancer in cross-validated and externally validated cohorts. In addition, these repeat landscapes could be used to noninvasively identify the tissue of origin of tumors. These analyses reveal widespread changes in repeat landscapes of human cancers and provide an approach for their detection and characterization that could benefit early detection and disease monitoring of patients with cancer.

中文翻译：

---

癌症和游离 DNA 中的全基因组重复景观

重复序列的遗传变化是癌症和其他疾病的标志，但使用标准测序方法来表征这些变化一直具有挑战性。我们开发了一种名为 ARTEMIS（疾病重复元件分析）的从头寻找方法，用于从全基因组测序中识别重复元件。使用这种方法，我们分析了 1975 名患者的 2837 份组织和血浆样本中的 12 亿 kmers，其中包括肺癌、乳腺癌、结直肠癌、卵巢癌、肝癌、胃癌、头颈癌、膀胱癌、宫颈癌、甲状腺癌或前列腺癌患者。我们在来自 LINE、SINE、LTR、转座元件和人类卫星家族的 1280 个重复元件类型中鉴定了这些患者的肿瘤特异性变化。其中包括对已知重复序列和 820 个元素的改变，这些元素此前在人类癌症中并未发生改变。重复元件在驱动基因区域中富集，并且它们的表达因结构变化和表观遗传状态而改变。对 cfDNA 中的全基因组重复序列和碎片谱进行机器学习分析，在交叉验证和外部验证的队列中检测到早期肺癌或肝癌患者。此外，这些重复景观可用于非侵入性地识别肿瘤起源的组织。这些分析揭示了人类癌症重复情况的广泛变化，并提供了一种检测和表征的方法，有利于癌症患者的早期检测和疾病监测。