Utility of ctDNA in predicting response to neoadjuvant chemoradiotherapy and prognosis assessment in locally advanced rectal cancer: A prospective cohort study

Abstract

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.

Fig 1. Study design, sample collection, study objectives, and work scheme of the present study.

ctDNA, circulating tumor DNA; LARC, locally advanced rectal cancer; mrTRG, magnetic resonance tumor regression grade; nCRT, neoadjuvant chemoradiotherapy; pTRG, pathological tumor regression grade.

Fig 2. Baseline ctDNA features, ctDNA dynamic clearance, and acquisition were associated with pTRG and pCR.

(A) Distribution of mutation rates of TP53, APC, KRAS, POLD1, and 2 pathways (HRR and HMT) in pCR and non-pCR groups; Fisher exact test was used to compare the difference between pCR and non-pCR groups. (B) Distribution of mutation rates of the 4 genes and 2 pathways in different TRG groups. Y axis represents the proportion of patients carrying corresponding gene mutations accounting for total patients in the corresponding TRG group. Cochran–Armitage test was used to test the increasing trend (TP53, APC, and KRAS) and decreasing trend (POLD1, HRR, and HMT) in TRG groups. (C) Proportions of patients with T234_clearance or acquired mutations in pCR and non-pCR groups. Fisher exact test was used for intergroup comparison (two-sided). (D) Proportions of patients with T234_clearance or acquired mutations in various pTRG groups. Cochran–Armitage trend test was used to test increasing trend for acquired mutation status and decreasing trend for T234_clearance from pTRG0 to pTRG3 (one-sided). ctDNA, circulating tumor DNA; HMT, histone methyltransferase; HRR, homologous recombination; pCR, pathological complete response; pTRG, pathological tumor regression grade.

Fig 3. Predicting pCR/non-pCR by combining ctDNA and mrTRG information.

(A) Distribution of risk scores obtained from the 3 predictive models in pCR and non-pCR groups. Wilcoxon rank sum test was used for intergroup comparison (two-sided). (B) AUC analysis of the 3 models. 95% CI of AUC was calculated by “DeLong” method. (C) Predictive performance of the 3 models was evaluated by internal 5-fold cross-validation and 100 times repeats. The numbers on the top of the bars were average AUC ± standard deviation. Construction of the 3 models refers to Materials and methods section. AUC, area under the curve; ctDNA, circulating tumor DNA; mrTRG, magnetic resonance imaging tumor regression grade; pCR, pathological complete response; 95% CI, 95% confidence interval.

Fig 4. Recurrence risk assessment of LARC patients undergoing nCRT by ctDNA monitoring.

(A) Kaplan–Meier analysis of RFS stratified by pCR status. All 119 patients with pCR/non-pCR information were included in the analysis. (B) Kaplan–Meier analysis of RFS stratified by pCR status plus HR_feature. HR_ features included baseline-detectable TP53 or KRAS mutation, tumor deposits, PNI, vascular invasion, and lymph node metastasis. HR_feature (+) was defined as positive in at least 1 feature, otherwise, HR_feature (−). All 119 patients with pCR/non-pCR information were included in the analysis. (C) Kaplan–Meier analysis of RFS stratified by T4_driver_gene mutation detection status (Time4, after nCRT). T4_detectable was defined as at least 1 mutation of the 15 CRC driver genes could be detected at Time4 point, otherwise, T4_undetectable. A total of 103 patients who completed the whole study were included in the analysis. (D) Kaplan–Meier analysis of RFS stratified by T5_driver_gene mutation detection status (Time5, after surgery) plus HR_feature. A total of 103 patients who completed the whole study were included in the analysis. CRC, colorectal cancer; ctDNA, circulating tumor DNA; HR, hazard ratio; HR_feature, high-risk feature; HR_feature (+), HR_feature positive; LARC, locally advanced rectal cancer; nCRT, neoadjuvant chemoradiotherapy; pCR, pathological complete response; PNI, perineural invasion; RFS, recurrence-free survival; 95% CI, 95% confidence interval.