IBM Research, IBM, 1101 Kitchawan Road, 10598, NY, USA.
Brief Bioinform. 2023 Mar 19;24(2). doi: 10.1093/bib/bbad059.
Sampling circulating tumor DNA (ctDNA) using liquid biopsies offers clinically important benefits for monitoring cancer progression. A single ctDNA sample represents a mixture of shed tumor DNA from all known and unknown lesions within a patient. Although shedding levels have been suggested to hold the key to identifying targetable lesions and uncovering treatment resistance mechanisms, the amount of DNA shed by any one specific lesion is still not well characterized. We designed the Lesion Shedding Model (LSM) to order lesions from the strongest to the poorest shedding for a given patient. By characterizing the lesion-specific ctDNA shedding levels, we can better understand the mechanisms of shedding and more accurately interpret ctDNA assays to improve their clinical impact. We verified the accuracy of the LSM under controlled conditions using a simulation approach as well as testing the model on three cancer patients. The LSM obtained an accurate partial order of the lesions according to their assigned shedding levels in simulations and its accuracy in identifying the top shedding lesion was not significantly impacted by number of lesions. Applying LSM to three cancer patients, we found that indeed there were lesions that consistently shed more than others into the patients' blood. In two of the patients, the top shedding lesion was one of the only clinically progressing lesions at the time of biopsy suggesting a connection between high ctDNA shedding and clinical progression. The LSM provides a much needed framework with which to understand ctDNA shedding and to accelerate discovery of ctDNA biomarkers. The LSM source code has been available in the IBM BioMedSciAI Github (https://github.com/BiomedSciAI/Geno4SD).
利用液体活检对循环肿瘤 DNA (ctDNA) 进行采样，为监测癌症进展提供了具有重要临床意义的益处。单个 ctDNA 样本代表了来自患者体内所有已知和未知病变的脱落肿瘤 DNA 的混合物。虽然脱落水平被认为是识别可靶向病变和揭示治疗抵抗机制的关键，但任何一个特定病变脱落的 DNA 量仍未得到很好的描述。我们设计了病变脱落模型 (LSM)，以便为特定患者的给定病变进行从最强到最差的脱落排序。通过对病变特异性 ctDNA 脱落水平进行特征描述，我们可以更好地理解脱落机制，并更准确地解释 ctDNA 检测结果，以提高其临床影响。我们通过模拟方法在受控条件下验证了 LSM 的准确性，并用三种癌症患者的测试对模型进行了测试。在模拟中，LSM 根据其分配的脱落水平获得了病变的准确部分排序，并且其识别顶级脱落病变的准确性不受病变数量的显著影响。将 LSM 应用于三名癌症患者，我们发现确实存在比其他病变更频繁地进入患者血液的病变。在两名患者中，顶级脱落病变是活检时唯一的临床进展病变之一，这表明高 ctDNA 脱落与临床进展之间存在联系。LSM 提供了一个急需的框架，用于理解 ctDNA 脱落并加速 ctDNA 生物标志物的发现。LSM 的源代码已在 IBM BioMedSciAI Github（https://github.com/BiomedSciAI/Geno4SD）上提供。
