用签名系数分析少量血样，精准识别恶性肿瘤动态变化。
Detecting malignant dynamics on very few blood sample using signature coefficients
- 结合连续时间马尔可夫模型与签名理论，建模ctDNA动态变化。
- 在每例患者仅有限血样条件下仍能准确检测恶性肿瘤。
- 适合小样本癌症早期监测，尤其对低侵入性检测有重要意义。
近年研究发现，通过血液中循环肿瘤DNA（ctDNA）水平进行癌症监测具有较高准确性，且对患者负担极低。ctDNA的释放可能源于细胞凋亡、坏死或主动分泌等多种机制。近期研究提出，监测ctDNA水平的动态变化即可实现早期多癌种检测，已有公司如GRAIL将其转化为商业化产品。本文提出利用签名理论分析血液样本，以识别侵袭性肿瘤。方法结合连续时间马尔可夫模型刻画ctDNA动态，并采用签名理论提取不规则采样信号的有效特征。所提方法有效应对了单个患者血样极少带来的数据稀缺难题。大量数值实验验证了该流程的高效性与可行性。
原文摘要 · Abstract (English)
Recent discoveries have suggested that the promising avenue of using circulating tumor DNA (ctDNA) levels in blood samples provides reasonable accuracy for cancer monitoring, with extremely low burden on the patient's side. It is known that the presence of ctDNA can result from various mechanisms leading to DNA release from cells, such as apoptosis, necrosis or active secretion. One key idea in recent cancer monitoring studies is that monitoring the dynamics of ctDNA levels might be sufficient for early multi-cancer detection. This interesting idea has been turned into commercial products, e.g. in the company named GRAIL. In the present work, we propose to explore the use of Signature theory for detecting aggressive cancer tumors based on the analysis of blood samples. Our approach combines tools from continuous time Markov modelling for the dynamics of ctDNA levels in the blood, with Signature theory for building efficient testing procedures. Signature theory is a topic of growing interest in the Machine Learning community (see Chevyrev2016 and Fermanian2021), which is now recognised as a powerful feature extraction tool for irregularly sampled signals. The method proposed in the present paper is shown to correctly address the challenging problem of overcoming the inherent data scarsity due to the extremely small number of blood samples per patient. The relevance of our approach is illustrated with extensive numerical experiments that confirm the efficiency of the proposed pipeline.
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