Utility of ctDNA in predicting response to neoadjuvant chemoradiotherapy and prognosis assessment in locally advanced rectal cancer: A prospective cohort study.
Utility of ctDNA in predicting response to neoadjuvant chemoradiotherapy and prognosis assessment in locally advanced rectal cancer: A prospective cohort study.
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ctDNA 在预测局部晚期直肠癌新辅助放化疗反应和预后评估中的应用：一项前瞻性队列研究
DOI：
10.1371/journal.pmed.1003741
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发表时间：
2021-08
期刊：
影响因子：
15.8
通讯作者：
Zhang Z
中科院分区：
文献类型：
--
作者：
Wang Y;Yang L;Bao H;Fan X;Xia F;Wan J;Shen L;Guan Y;Bao H;Wu X;Xu Y;Shao Y;Sun Y;Tong T;Li X;Xu Y;Cai S;Zhu J;Zhang Z
背景 对于接受新辅助放化疗（nCRT）的局部晚期直肠癌（LARC）患者，术前尚无可靠的指标能够准确预测病理完全缓解（pCR）。对于临床完全缓解（cCR）的患者，可以采用“观察和等待”（W&W）方法来改善生活质量。然而，W&W 方法可能会增加被判断为 cCR 但有微小残留病 (MRD) 的患者的复发风险。磁共振成像 (MRI) 是评估 nCRT 反应的主要工具；然而，其预测 PCR 的能力有待提高。在这项前瞻性队列研究中，我们探讨了循环肿瘤 DNA (ctDNA) 与 MRI 相结合在术前预测 pCR 中的价值，并研究了 ctDNA 在接受 nCRT 和全直肠系膜切除 (TME) 患者的风险分层和预后预测中的效用。方法和结果我们招募了 119 名中国 LARC 患者（cT3-4/N0-2/M0；中位年龄 57；85 名男性），他们于 2016 年 2 月 7 日至 2017 年 10 月 31 日在复旦大学上海肿瘤中心（中国）接受 nCRT 加 TME 治疗。收集了基线、nCRT 期间和手术后的血浆样本。总共收集了 531 份血浆样本，并对 422 个癌症相关基因进行了深度靶向测序。分析了 ctDNA 状态、治疗反应和预后之间的关联。评估了单独 ctDNA、单独 MRI 以及 ctDNA 与 MRI 相结合预测 pCR/非 pCR 的能力。从肿瘤完全消退（病理肿瘤消退0级；pTRG0）到较差消退（pTRG3），nCRT期间ctDNA清除率呈显着下降趋势（pTRG 0、1、2和3组分别为95.7%、77.8%、71.1%和66.7%，P = 0.008），而ctDNA获得性突变的检出率则呈上升趋势趋势（pTRG 0、1、2 和 3 组分别为 3.8%、8.3%、19.2% 和 23.1%，P = 0.02）。单变量逻辑回归显示，ctDNA 清除率与非 pCR 的低概率相关（比值比 = 0.11，95% 置信区间 [95% CI] = 0.01 至 0.6，P = 0.04）。开发了一种风险评分预测模型，该模型结合了 ctDNA（即基线 ctDNA、ctDNA 清除率和获得性突变状态的特征）和 MRI 肿瘤消退等级 (mrTRG)，并证明与仅源自 ctDNA 的模型 (AUC = 0.818，95% CI = 0.725 至 0.912）或仅 mrTRG（AUC = 0.729，95% CI = 0.641 至 0.816）。 nCRT 后 ctDNA 中检测到潜在结直肠癌 (CRC) 驱动基因表明无复发生存 (RFS) 显着较差（风险比 [HR] = 9.29，95% CI = 3.74 至 23.10，P < 0.001）。手术后具有可检测驱动突变和阳性高危特征（HR_feature）的患者复发风险最高（HR = 90.29，95% CI = 17.01至479.26，P < 0.001）。局限性包括样本量相对较小、缺乏独立的外部验证、术后没有连续 ctDNA 检测以及随访期相对较短。结论 ctDNA与MRI相结合的模型与基于个体信息的模型相比提高了预测性能，并且ctDNA与HR_feature相结合可以对高复发风险患者进行分层。因此，ctDNA可以补充MRI以更好地预测nCRT反应，并且有可能帮助患者选择非手术治疗并指导具有不同复发风险的患者的治疗策略。
Background For locally advanced rectal cancer (LARC) patients who receive neoadjuvant chemoradiotherapy (nCRT), there are no reliable indicators to accurately predict pathological complete response (pCR) before surgery. For patients with clinical complete response (cCR), a “Watch and Wait” (W&W) approach can be adopted to improve quality of life. However, W&W approach may increase the recurrence risk in patients who are judged to be cCR but have minimal residual disease (MRD). Magnetic resonance imaging (MRI) is a major tool to evaluate response to nCRT; however, its ability to predict pCR needs to be improved. In this prospective cohort study, we explored the value of circulating tumor DNA (ctDNA) in combination with MRI in the prediction of pCR before surgery and investigated the utility of ctDNA in risk stratification and prognostic prediction for patients undergoing nCRT and total mesorectal excision (TME). Methods and findings We recruited 119 Chinese LARC patients (cT3-4/N0-2/M0; median age of 57; 85 males) who were treated with nCRT plus TME at Fudan University Shanghai Cancer Center (China) from February 7, 2016 to October 31, 2017. Plasma samples at baseline, during nCRT, and after surgery were collected. A total of 531 plasma samples were collected and subjected to deep targeted panel sequencing of 422 cancer-related genes. The association among ctDNA status, treatment response, and prognosis was analyzed. The performance of ctDNA alone, MRI alone, and combining ctDNA with MRI was evaluated for their ability to predict pCR/non-pCR. Ranging from complete tumor regression (pathological tumor regression grade 0; pTRG0) to poor regression (pTRG3), the ctDNA clearance rate during nCRT showed a significant decreasing trend (95.7%, 77.8%, 71.1%, and 66.7% in pTRG 0, 1, 2, and 3 groups, respectively, P = 0.008), while the detection rate of acquired mutations in ctDNA showed an increasing trend (3.8%, 8.3%, 19.2%, and 23.1% in pTRG 0, 1, 2, and 3 groups, respectively, P = 0.02). Univariable logistic regression showed that ctDNA clearance was associated with a low probability of non-pCR (odds ratio = 0.11, 95% confidence interval [95% CI] = 0.01 to 0.6, P = 0.04). A risk score predictive model, which incorporated both ctDNA (i.e., features of baseline ctDNA, ctDNA clearance, and acquired mutation status) and MRI tumor regression grade (mrTRG), was developed and demonstrated improved performance in predicting pCR/non-pCR (area under the curve [AUC] = 0.886, 95% CI = 0.810 to 0.962) compared with models derived from only ctDNA (AUC = 0.818, 95% CI = 0.725 to 0.912) or only mrTRG (AUC = 0.729, 95% CI = 0.641 to 0.816). The detection of potential colorectal cancer (CRC) driver genes in ctDNA after nCRT indicated a significantly worse recurrence-free survival (RFS) (hazard ratio [HR] = 9.29, 95% CI = 3.74 to 23.10, P < 0.001). Patients with detectable driver mutations and positive high-risk feature (HR_feature) after surgery had the highest recurrence risk (HR = 90.29, 95% CI = 17.01 to 479.26, P < 0.001). Limitations include relatively small sample size, lack of independent external validation, no serial ctDNA testing after surgery, and a relatively short follow-up period. Conclusions The model combining ctDNA and MRI improved the predictive performance compared with the models derived from individual information, and combining ctDNA with HR_feature can stratify patients with a high risk of recurrence. Therefore, ctDNA can supplement MRI to better predict nCRT response, and it could potentially help patient selection for nonoperative management and guide the treatment strategy for those with different recurrence risks.
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影响因子：
16.9
作者：
Bailey CE;Hu CY;You YN;Bednarski BK;Rodriguez-Bigas MA;Skibber JM;Cantor SB;Chang GJ
通讯作者：
Chang GJ
影响因子：
11.5
作者：
Garlan, Fanny;Laurent-Puig, Pierre;Zaanan, Aziz
通讯作者：
Zaanan, Aziz
影响因子：
11.5
作者：
Khakoo, Shelize;Carter, Paul David;Cunningham, David
通讯作者：
Cunningham, David
影响因子：
3.4
作者：
Battersby, Nick;Glasbey, James C.;Frasson, Matteo
通讯作者：
Frasson, Matteo
影响因子：
3.7
作者：
Maas M;Lambregts DM;Nelemans PJ;Heijnen LA;Martens MH;Leijtens JW;Sosef M;Hulsewé KW;Hoff C;Breukink SO;Stassen L;Beets-Tan RG;Beets GL
通讯作者：
Beets GL