A mathematical model of ctDNA shedding predicts tumor detection size.
A mathematical model of ctDNA shedding predicts tumor detection size.
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DOI：
10.1126/sciadv.abc4308
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发表时间：
2020-12
期刊：
影响因子：
13.6
通讯作者：
Reiter JG
中科院分区：
文献类型：
--
作者：
Avanzini S;Kurtz DM;Chabon JJ;Moding EJ;Hori SS;Gambhir SS;Alizadeh AA;Diehn M;Reiter JG
肿瘤演变和ctDNA脱落的数学模型为新的癌症早期检测方法提供了信息。早期癌症检测的目的是在肿瘤发展到不可治愈的阶段之前发现肿瘤。为了确定循环肿瘤DNA（ctDNA）用于癌症检测的潜力，我们开发了肿瘤演变和ctDNA脱落的数学模型，以预测肿瘤变得可检测的大小。从176例I期至III期肺癌患者中，我们推断，平均而言，每个细胞死亡有0.014%的肿瘤细胞DNA流入血液。对于年度筛查，该模型预测中位检测尺寸为2.0至2.3 cm，比当前中位检测尺寸3.5 cm减少约40%。对于知情的每月癌症复发测试，该模型预测的中位检测尺寸为0.83 cm，并表明治疗失败可以比基于成像的方法提前140天检测到。这种机制框架可以通过预先计算最有希望的癌症早期检测策略来帮助加速临床试验。
A mathematical model of tumor evolution and ctDNA shedding informs new cancer early detection approaches. Early cancer detection aims to find tumors before they progress to an incurable stage. To determine the potential of circulating tumor DNA (ctDNA) for cancer detection, we developed a mathematical model of tumor evolution and ctDNA shedding to predict the size at which tumors become detectable. From 176 patients with stage I to III lung cancer, we inferred that, on average, 0.014% of a tumor cell’s DNA is shed into the bloodstream per cell death. For annual screening, the model predicts median detection sizes of 2.0 to 2.3 cm representing a ~40% decrease from the current median detection size of 3.5 cm. For informed monthly cancer relapse testing, the model predicts a median detection size of 0.83 cm and suggests that treatment failure can be detected 140 days earlier than with imaging-based approaches. This mechanistic framework can help accelerate clinical trials by precomputing the most promising cancer early detection strategies.
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DOI：
10.1088/1742-5468/2011/08/p08018
发表时间：
2011-08-01
影响因子：
2.4
作者：
Antal, Tibor;Krapivsky, P. L.
通讯作者：
Krapivsky, P. L.
DOI：
10.1088/1742-5468/2010/07/p07028
发表时间：
2010-07-01
影响因子：
2.4
作者：
Antal, Tibor;Krapivsky, P. L.
通讯作者：
Krapivsky, P. L.
影响因子：
64.8
作者：
Abbosh C;Birkbak NJ;Wilson GA;Jamal-Hanjani M;Constantin T;Salari R;Le Quesne J;Moore DA;Veeriah S;Rosenthal R;Marafioti T;Kirkizlar E;Watkins TBK;McGranahan N;Ward S;Martinson L;Riley J;Fraioli F;Al Bakir M;Grönroos E;Zambrana F;Endozo R;Bi WL;Fennessy FM;Sponer N;Johnson D;Laycock J;Shafi S;Czyzewska-Khan J;Rowan A;Chambers T;Matthews N;Turajlic S;Hiley C;Lee SM;Forster MD;Ahmad T;Falzon M;Borg E;Lawrence D;Hayward M;Kolvekar S;Panagiotopoulos N;Janes SM;Thakrar R;Ahmed A;Blackhall F;Summers Y;Hafez D;Naik A;Ganguly A;Kareht S;Shah R;Joseph L;Marie Quinn A;Crosbie PA;Naidu B;Middleton G;Langman G;Trotter S;Nicolson M;Remmen H;Kerr K;Chetty M;Gomersall L;Fennell DA;Nakas A;Rathinam S;Anand G;Khan S;Russell P;Ezhil V;Ismail B;Irvin-Sellers M;Prakash V;Lester JF;Kornaszewska M;Attanoos R;Adams H;Davies H;Oukrif D;Akarca AU;Hartley JA;Lowe HL;Lock S;Iles N;Bell H;Ngai Y;Elgar G;Szallasi Z;Schwarz RF;Herrero J;Stewart A;Quezada SA;Peggs KS;Van Loo P;Dive C;Lin CJ;Rabinowitz M;Aerts HJWL;Hackshaw A;Shaw JA;Zimmermann BG;TRACERx consortium;PEACE consortium;Swanton C
通讯作者：
Swanton C
影响因子：
11.2
作者：
Hori, Sharon Seiko;Lutz, Amelie M.;Gambhir, Sanjiv Sam
通讯作者：
Gambhir, Sanjiv Sam
影响因子：
158.5
作者：
de Koning, H. J.;van der Aalst, C. M.;Oudkerk, M.
通讯作者：
Oudkerk, M.