4.6
2.2
| Citation: |
Song Jun, Zhang Jifeng, Xu Jie, Garcia-Barrio Minerva, Chen Y. Eugene, Yang Dongshan. Genome engineering technologies in rabbits[J]. The Journal of Biomedical Research, 2021, 35(2): 135-147. DOI: 10.7555/JBR.34.20190133
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Nasopharyngeal carcinoma (NPC) is one of the most common malignancies in head and neck cancers. In 2018 alone, there were approximately 129 000 recorded NPC cases with 73 000 NPC-related deaths worldwide[1]. NPC generally responds to radiotherapy and chemotherapy, with radiotherapy being to frontline therapy for NPC. In recent years, the development of intensity-modulated radiotherapy (IMRT) has substantially improved the locoregional control rate for NPC patients. However, distant metastasis constitutes the primary cause of treatment failure[2]. Indeed, even after definitive chemoradiotherapy, 15%–30% of non-metastatic NPC cases develop distal metastasis[3–5].
Conventionally, distant metastases are considered an incurable systemic element of disease progression which is generally treated using palliative approaches. The National Comprehensive Cancer Network (NCCN) guidelines recommend platinum-based palliative chemotherapy (PCT) for metastatic NPC. First-line chemotherapeutics studies have highlighted a median survival for metastatic NPC patients ranging from 12.1 to 22.1 months[6–8]. However, metastatic disease comprises a broad spectrum of characteristics and prognosis. Hellman and Weichselbaum proposed the concept of "oligometastases", whereby oligometastatic diseases spread to specific organs despite initially having a limited capacity to do so[9]. Therefore, localized treatments may be effective within the primary foci or metastatic lesions. In the past few years, local treatments for metastatic NPC have become the focus of interest for many researchers. For example, a randomized phase Ⅲ study found that locoregional radiotherapy targeting primary tumors and metastatic lymph nodes following chemotherapy, significantly improves survival among chemo-sensitive patients with synchronous metastatic NPC[10]. Additionally, several retrospective studies have shown that local treatment of metastases can improve survival in patients with metastatic NPC[11–13]. Although, existing studies have not identified suitable candidates for local treatment in metastatic NPC.
Lacking an established standard for treating metastatic NPC may be due to the fact that there is no consensus around risk. Metastatic NPC patients are not an homogeneous group of people and there are within and between individual differences in terms of responses to treatment. This creates a whole host of unknown and there are, very few prospective studies which have assessed localized treatments of metastatic lesions. Therefore, we cannot confer survival benefits associated with each treatment modality for patients with metastatic NPC. It is reasonable to suggest that an individualized predictive model is needed to disentangle factors related to survival and it may then be possible to develop more individualized treatment strategies. Here, we developed a prognostic nomogram to identify NPC patients with metachronous metastasis, who would benefit most from local treatments.
Data from 347 NPC patients were collated and retrospectively reviewed. All patients had been diagnosed with metastatic NPC at the Affiliated Cancer Hospital of Nanjing Medical University (China) between January 2006 and December 2020. Patients were considered eligible according to the following criteria: (1) patients with histopathologically confirmed primary NPC; (2) those who had received locoregional radiotherapy with IMRT; and (3) those who had metachronous metastatic disease diagnosed more than 6 months after initial diagnosis[14–15].
Patients were excluded if they had: (1) synchronous metastatic disease; (2) or a Karnofsky Performance Score of <70 when diagnosed with distant metastasis; (3) other malignancies; (4) incomplete clinical or survival data; or (5) those who refused antitumor treatment(s). The 8th edition of the Union for International Cancer Control/American Joint Committee on Cancer (UICC/AJCC) system was implemented to restage patients. The study was conducted in accordance with the Declaration of Helsinki. The study was approved by the Research Ethics Committee of Jiangsu Cancer Hospital (Approval No. 2021-020), and individual consent for this retrospective study was not deemed necessary.
During the initial treatment period, all patients received radical IMRT to the nasopharynx and neck using simultaneous integrated boost with 6 MV X-ray radiation in our center. Pretreatment assessments included physical examination, electrocardiogram, chest X-ray, as well as hematologic and biochemical profiling.
An abnormal elevation in Epstein-Barr virus (EBV) DNA was considered a factor signifying potential disease relapse. One hundred and eighteen patients underwent EBV DNA detection pretreatment. Pathological analysis of metastatic lesions was established as the gold standard for diagnosing metastases. When pathological findings were unavailable, diagnosis was based on a multimodality imaging system, which included contrast-enhanced computed tomography (CT) of the chest and abdomen, whole-body bone scans, and positron emission tomography (PET-CT).
The total of metastatic lesions was calculated according to image findings, of which 1–5 lesions were defined as oligometastic. More than five lesions was defined as polymetastatic. Locoregional recurrence was confirmed using fiberoptic endoscopy of the nasopharynx and magnetic resonance imaging (MRI) scans of the head and neck.
All patients received PCT according to previous chemotherapy regimens and toxicity tolerance after first distant failure. The median PCT cycles was four, ranging from 1 to 12. PCT regimens included platinum plus 5-fluorouracil, taxane plus platinum, taxane plus platinum with 5-fluorouracil, and gemcitabine plus platinum (GP).
After receiving chemotherapy, 146 patients received local treatment for metastatic lesions, while the remaining patients did not. One or more localized treatments were administered to these patients of whom 16 underwent surgery (1, 11, and 4 cases with liver, lung, and distant lymphatic metastasis, respectively). One hundred and thirty-one patients received radiotherapy for one or more metastatic lesions of whom five received radiofrequency ablation for liver metastases.
Among the patients who received radiotherapy, 19 received stereotactic body radiosurgery (SBRT), including seven with liver metastases and 12 with lung metastases. Radiation doses ranged from 30 to 66 Gy (2–10 Gy/fraction), with a median biologically effective dose of 60 Gy (range: 35–100).
Follow-ups were arranged every three months for the first 2 years and then every 6 to 12 months thereafter, until death or the last follow-up date (June 30, 2021). During the follow-up period, nasopharyngoscopy, contrast-enhanced MRI of nasopharynx and neck, and contrast-enhanced CT of chest and abdomen were performed. PET-CT was considered if necessary. The disease-free interval (DFI) was calculated using the date of initial NPC diagnosis until the diagnosis of distant metastasis. OS was considered, the primary endpoint of this study and was calculated from the date of diagnosis of metastasis to the date of last follow-up or death.
SPSS version 22.0 (IBM, USA) was used to perform statistical analysis. All variables are categorical, and intergroup comparisons were performed using Pearson's chi-squared test or Fisher's test. Cox's proportional hazard model was implemented to investigate the independence of prognostic factors related to OS. Hazard ratios (HR) and corresponding 95% confidence intervals (CI) are reported as effect estimates. Variables that met the predetermined significance threshold (P<0.1) under univariable analysis were entered into multivariable analysis. Two-tailed P<0.05 were considered statistically significant.
R software (version 4.0.3) was used for model building. The prognostic nomogram was constructed using the "rms" package with independent prognostic factors[16]. The "nomogramFormula" package calculated scores for each variable as well as a total score for each patient[17]. The "nomogramEX" package was used to extract formulas from the nomogram[18]. The concordance index (C-index) and calibration curves were used to assess model performance. Kaplan-Meier curves were used to estimate the 1-, 3-, and 5-year OS rates and differences between groups of patients.
A total of 240 NPC patients with metachronous metastasis were considered eligible (Fig. 1). All patients had histologically confirmed non-keratinizing NPC at initial diagnosis. The median age was 49 years (range: 11–78), and 78.8% of this sample were male. 32.5% (n=78) of this sample developed distant metastasis within 12 months following the primary diagnosis (DFI≤12 months). The remaining patients developed distant metastasis after 12 months (DFI>12 months).
The incidence of lung, bone, liver, and distant nodal metastasis was 42.5%, 47.5%, 28.3%, and 28.3%, respectively. Oligometastatic disease was detected in 60.4% (n=145) patients (63 with single metastatic lesions and 82 with 2 to 5 lesions). 39.6% (n=95) patients had polymetastatic disease (>5 lesions). 13.8% (n=33) had simultaneous locoregional recurrence. One hundred and twenty (50%) patients received ≥4 cycles of PCT after the first distant failure, and the remainder received <4 cycles. Demographics and the clinical characteristics of patients with metastatic NPC have been summarized and are provided in Table 1.
| (n [%]) | ||||
| Characteristics | Total patients (n=240) | LT+PCT (n=146) | PCT (n=94) | P- value |
| Age (years) a | 0.202 | |||
| <49 | 117 (48.8) | 76 (52.1) | 41 (43.6) | |
| ≥49 | 123 (51.2) | 70 (47.9) | 53 (56.4) | |
| Sex | 0.104 | |||
| Female | 51 (21.2) | 26 (17.8) | 25 (26.6) | |
| Male | 189 (78.8) | 120 (82.2) | 69 (73.4) | |
| BMI | 0.653 | |||
| <23 | 109 (45.4) | 68 (46.6) | 41 (43.6) | |
| ≥23 | 131 (54.6) | 78 (53.4) | 53 (56.4) | |
| KPS | 0.644 | |||
| ≤80 | 104 (43.3) | 65 (44.5) | 39 (41.5) | |
| >80 | 136 (56.7) | 81 (55.5) | 55 (58.5) | |
| T stageb | 0.126 | |||
| T1 | 27 (11.3) | 11 (7.5) | 16 (17.0) | |
| T2 | 43 (17.9) | 27 (18.5) | 16 (17.0) | |
| T3 | 75 (31.2) | 50 (34.2) | 25 (26.6) | |
| T4 | 95 (39.6) | 58 (39.8) | 37 (39.4) | |
| N stageb | 0.940 | |||
| N0 | 3 (1.2) | 2 (1.4) | 1 (1.1) | |
| N1 | 77 (32.1) | 48 (32.9) | 29 (30.9) | |
| N2 | 109 (45.4) | 64 (43.8) | 45 (47.9) | |
| N3 | 51 (21.3) | 32 (21.9) | 19 (20.1) | |
| DFI (months) | 0.682 | |||
| ≤12 | 78 (32.5) | 46 (31.5) | 32 (34.0) | |
| >12 | 162 (67.5) | 100 (68.5) | 62 (66.0) | |
| Lung metastasis | <0.001 | |||
| No | 138 (57.5) | 98 (67.1) | 40 (42.6) | |
| Yes | 102 (42.5) | 48 (32.9) | 54 (57.4) | |
| Bone metastasis | <0.001 | |||
| No | 126 (52.5) | 61 (41.8) | 65 (69.1) | |
| Yes | 114 (47.5) | 85 (58.2) | 29 (30.9) | |
| Liver metastasis | 0.001 | |||
| No | 172 (71.7) | 116 (79.5) | 56 (59.6) | |
| Yes | 68 (28.3) | 30 (20.5) | 38 (40.4) | |
| Distant nodal metastasis | <0.001 | |||
| No | 165 (68.8) | 115 (78.8) | 50 (53.2) | |
| Yes | 75 (31.2) | 31 (21.2) | 44 (46.8) | |
| No. of metastatic lesions | <0.001 | |||
| 1 | 63 (26.2) | 50 (34.3) | 13 (13.8) | |
| 2–5 | 82 (34.2) | 50 (34.2) | 32 (34.0) | |
| >5 | 95 (39.6) | 46 (31.5) | 49 (52.2) | |
| Pretreatment EBV DNA | 0.148 | |||
| Undetectable | 49 (20.4) | 35 (24.0) | 14 (14.9) | |
| Detectable | 69 (28.8) | 37 (25.3) | 32 (34.0) | |
| Not available | 122 (50.8) | 74 (50.7) | 48 (51.1) | |
| Locoregional recurrence | 0.051 | |||
| No | 207 (86.2) | 131 (89.7) | 76 (80.9) | |
| Yes | 33 (13.8) | 15 (10.3) | 18 (19.1) | |
| No. of PCT cycles | 0.002 | |||
| <4 | 120 (50.0) | 85 (58.2) | 35 (37.2) | |
| ≥4 | 120 (50.0) | 61 (41.8) | 59 (62.8) | |
| aAge at diagnosis of distant metastasis. bAccording to the 8th UICC/AJCC staging system. LT: local treatment of metastases; PCT: palliative chemotherapy; BMI: body mass index; KPS: Karnofsky performance score; DFI: disease-free interval; EBV: Epstein-Barr virus; No.: number. | ||||
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The median follow-up duration was 23 months (ranging from 1 to 176 months). 169 patients died during this study period. The 1-, 3-, and 5-year OS rates were 76.1%, 39.8%, and 24.8%, respectively.
The results of univariable and multivariable regression analysis are provided in Table 2. Six variables were considered statistically significant intergroup differences, including sex, DFI, liver metastasis, number of metastatic lesions, locoregional recurrence, and number of PCT cycles. These variables were identified as independent prognostic factors for OS in metastatic NPC patients. Specifically, being female was associated with a better prognosis (HR=1.690; 95% CI: 1.095–2.542; P=0.012).
| Variables | Univariable | Multivariable | |||
| HR (95% CI) | P-value | HR (95% CI) | P-value | ||
| Age (years)a | |||||
| <49 | Reference | ||||
| ≥49 | 1.137 (0.839–1.542) | 0.407 | |||
| Sex | |||||
| Female | Reference | Reference | |||
| Male | 1.484 (1.009–2.182) | 0.045 | 1.690 (1.124–2.542) | 0.012 | |
| BMI | |||||
| <23 | Reference | ||||
| ≥23 | 0.991 (0.732–1.342) | 0.952 | |||
| KPS | |||||
| ≤80 | Reference | ||||
| >80 | 0.918 (0.677–1.244) | 0.580 | |||
| T stageb | |||||
| T1 | Reference | ||||
| T2 | 1.057 (0.602–1.857) | 0.847 | |||
| T3 | 0.847 (0.507–1.415) | 0.526 | |||
| T4 | 0.722 (0.436–1.196) | 0.206 | |||
| N stageb | |||||
| N0 | Reference | ||||
| N1 | 3.265 (0.451–23.624) | 0.241 | |||
| N2 | 3.572 (0.496–25.717) | 0.206 | |||
| N3 | 4.840 (0.663–35.327) | 0.120 | |||
| DFI (months) | |||||
| ≤12 | Reference | Reference | |||
| >12 | 0.604 (0.442–0.825) | 0.001 | 0.650 (0.466–0.908) | 0.012 | |
| Lung metastasis | |||||
| No | Reference | Reference | |||
| Yes | 0.754 (0.554–1.025) | 0.072 | 0.848 (0.580–1.239) | 0.394 | |
| Bone metastasis | |||||
| No | Reference | Reference | |||
| Yes | 1.397 (1.033–1.890) | 0.030 | 0.895 (0.612–1.310) | 0.569 | |
| Liver metastasis | |||||
| No | Reference | Reference | |||
| Yes | 1.737 (1.261–2.393) | 0.001 | 1.580 (1.113–2.243) | 0.011 | |
| Distant nodal metastasis | |||||
| No | Reference | Reference | |||
| Yes | 1.659 (1.210–2.276) | 0.002 | 0.866 (0.579–1.295) | 0.483 | |
| No. of metastatic lesions | |||||
| 1 | Reference | Reference | |||
| 2–5 | 1.746 (1.115–2.736) | 0.015 | 2.875 (1.749–4.725) | <0.001 | |
| >5 | 4.713 (3.052–7.279) | <0.001 | 7.955 (4.597–13.766) | <0.001 | |
| Locoregional recurrence | |||||
| No | Reference | Reference | |||
| Yes | 1.473 (0.974–2.229) | 0.067 | 2.079 (1.338–3.230) | 0.001 | |
| No. of PCT cycles | |||||
| <4 | Reference | Reference | |||
| ≥4 | 0.630 (0.465–0.853) | 0.003 | 0.403 (0.281–0.576) | <0.001 | |
| aAge at diagnosis of distant metastasis. bAccording to the 8th UICC/AJCC staging system. BMI: body mass index; KPS: Karnofsky performance score; DFI: disease-free interval; No.: number; PCT: palliative chemotherapy; HR: hazard ratio; CI: confidence interval. | |||||
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Patients who developed distant metastasis within 12 months after the primary diagnosis (DFI≤12) generally had poorer prognosis (HR=0.650; 95% CI: 0.466–0.908; P=0.012). Concerning the site of metastasis, liver metastasis (HR=1.580; 95% CI: 1.113–2.243; P=0.011) was an adverse prognostic factor for survival. The number of metastatic lesions had a profound impact on clinical outcomes compared to those with single lesions. Patients with 2 to 5 or more than 5 lesions had a significantly poorer prognosis (HR=2.875; 95% CI: 1.749–4.725 and HR=7.955; 95% CI: 4.597–13.766; both P<0.001).
Concurrent locoregional recurrence with distant metastasis conferred an increased mortality risk (HR=2.079; 95% CI: 1.338–3.230; P=0.001). Additionally, patients who received ≥4 cycles of PCT experienced significantly improved OS than compared to those with <4 cycles (HR=0.403; 95% CI: 0.281–0.576; P<0.001).
Based on the predictors derived through multivariable regression analysis, a prognostic nomogram was constructed to predict the 1-, 3-, and 5-year survival (Fig. 2). In order to use the nomogram, each variable subtype had to correspond with score on a specific point scale. The total score was then calculated using scores which corresponded to each variable, to estimate the 1-, 3-, and 5-year OS rates (Supplementary Table 1, available online). Nomogram formulas for generating OS probabilities were calculated, as follows:
1-year OS=1.54e−07×points^3−9.34e−05×points^2+0.012 174 368×points+0.443 082 846
3-year OS=1.54e−07×points^3−5.7954e−05× points^2+0.000 544 564×points+0.896 856 023
5-year OS=2.81e−07×points^3−7.6778e−05× points^2−1.627 6e−05×points+0.825 548 036
The C-index of the nomogram was 0.764 (95% CI: 0.649–0.806), which suggests good accuracy for predicting survival. Calibration curves obtained using the bootstrap method showed good fitness between predicted and observed survival (Fig. 3).
We used the median total score from the prognostic nomogram to stratify the cohort into the low- (risk score 0–122) and high-risk (risk score 123–260) groups (Table 3). Baseline characteristics of low- and high-risk patients have been provided in the Supplementary Tables 2 and 3 (available online), respectively. The 1-, 3-, and 5-year OS rates of patients in each group are also summarized in Supplementary Table 4 (available online).
| Variable | Low-risk (n=116) | High-risk (n=124) | P-value |
| Age (years)a | 0.240 | ||
| <49 | 52 (44.8) | 65 (52.4) | |
| ≥49 | 64 (55.2) | 59 (47.6) | |
| Sex | 0.020 | ||
| Female | 32 (27.6) | 19 (15.3) | |
| Male | 84 (72.4) | 105 (84.7) | |
| BMI | 0.390 | ||
| <23 | 56 (48.3) | 53 (42.7) | |
| ≥23 | 60 (51.7) | 71 (57.3) | |
| KPS | 0.330 | ||
| ≤80 | 54 (46.6) | 50 (40.3) | |
| >80 | 62 (53.4) | 74 (59.7) | |
| T stageb | 0.852 | ||
| T1 | 11 (9.5) | 16 (1.9) | |
| T2 | 21 (18.1) | 22 (17.7) | |
| T3 | 38 (32.8) | 37 (29.8) | |
| T4 | 46 (39.6) | 49 (39.6) | |
| N stageb | 0.530 | ||
| N0 | 2 (1.7) | 1 (0.8) | |
| N1 | 41 (35.3) | 36 (29.0) | |
| N2 | 52 (44.8) | 57 (46.0) | |
| N3 | 21 (18.2) | 30 (24.2) | |
| DFI (months) | 0.034 | ||
| ≤12 | 30 (25.9) | 48 (38.7) | |
| >12 | 86 (74.1) | 76 (61.3) | |
| Lung metastasis | 0.219 | ||
| No | 62 (53.4) | 76 (61.3) | |
| Yes | 54 (46.6) | 48 (38.7) | |
| Bone metastasis | 0.009 | ||
| No | 71 (61.2) | 55 (44.4) | |
| Yes | 45 (38.8) | 69 (55.6) | |
| Liver metastasis | 0.005 | ||
| No | 93 (80.2) | 79 (63.7) | |
| Yes | 23 (19.8) | 45 (36.3) | |
| Distant nodal metastasis | <0.001 | ||
| No | 96 (82.8) | 69 (55.6) | |
| Yes | 20 (17.2) | 55 (44.4) | |
| No. of metastatic lesions | <0.001 | ||
| 1 | 60 (51.7) | 3 (2.4) | |
| 2–5 | 53 (45.7) | 29 (23.4) | |
| >5 | 3 (2.6) | 92 (74.2) | |
| Locoregional recurrence | 0.138 | ||
| No | 104 (89.7) | 103 (83.1) | |
| Yes | 12 (10.3) | 21 (16.9) | |
| No. of PCT cycles | <0.001 | ||
| <4 | 44 (37.9) | 76 (61.3) | |
| ≥4 | 72 (62.1) | 48 (38.7) | |
| aAge at diagnosis of distant metastasis. bAccording to the 8th UICC/AJCC staging system. BMI: body mass index; KPS: Karnofsky performance score; DFI: disease-free interval; No.: number; PCT: palliative chemotherapy; LT: local treatment of metastases. | |||
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Kaplan-Meier curves for OS highlighted significant intergroup differences (P<0.001; Fig. 4), which also suggests this model is effective at stratifying metastatic NPC patients. With an estimated median OS of 45.0 months, the 1-, 3-, and 5-year OS rates of patients in the low-risk group were 93.6%, 60.0%, and 41.1% respectively, which were all significantly higher than the OS rates observed in high-risk patients (median OS: 15.0 months; 1-, 3-, and 5-year OS rates: 59.4%, 18.8%, and 6.4%). The nomogram remained a clinically and statistically significant prognostic model when this sample was stratified according to age (≥49 or <49 years), sex (female or male), T stage (T1–2 or T3–4), and N stage (N0–1 or N2–3) (Supplementary Fig. 1, available online).
To select patients who might benefit from local treatment of metastases, we further compared OS for patients with or without local treatment and within each risk group. For low-risk patients who received PCT plus local treatment or PCT alone, the 1-, 3-, and 5-year OS rates were 94.9%, 67.3%, 57.0%, and 91.9%, 53.1%, 21.7%, respectively (P=0.009, Fig. 5A). In the high-risk group, the 1-, 3-, and 5-year OS rates of patients with or without local treatment were 56.2%, 18.9%, 7.2%, and 63.2%, 18.8%, 5.6%, respectively (P=0.927, Fig. 5B).
According to multivariable analysis (Table 4), patients in the low-risk group, who received PCT plus local treatment, had a significantly better prognosis than those who received PCT alone (HR=0.570; 95% CI: 0.343–0.947; P=0.030). However, there was no significant intergroup difference in terms of survival between the two treatment groups in the high-risk group (HR=0.994; 95% CI: 0.678–1.458; P=0.975).
| Variable | Low-risk | High-risk | |||
| HR (95% CI) | P-value | HR (95% CI) | P-value | ||
| Treatment (LT+PCT vs. PCT) | 0.570 (0.343–0.947) | 0.030 | 0.994 (0.678–1.458) | 0.975 | |
| Risk score | 1.018 (1.007–1.028) | 0.001 | 1.022 (1.014–1.029) | <0.001 | |
| LT: local treatment of metastases; PCT: palliative chemotherapy; HR: hazard ratio; CI: confidence interval. | |||||
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Significant progress has been made in localized controls for NPC due to the development of IMRT and imaging systems which are now more precise at defining tumors and identifying organs at risk. However, distant metastases are still a challenge for patients with NPC, and there is no consensus regarding treatment strategies. According to the GEM20110714 study[7], the preferred first-line regimen of GP in metastatic NPC resulted in a median OS of 22.1 months and 1-, 3-, and 5-year OS rates of 79.9%, 31.8%, and 19.2%, respectively. However, prognosis for metastatic NPC patients has significant heterogeneity in the real world due to the broad spectrum of metastatic diseases and the use of various treatments. In this study, we established an easy-to-use nomogram for predicting OS for patients with metastatic NPC. This novel model appears to perform well at identifying candidates who would benefit from local treatments.
According to the NCCN guidelines, systemic chemotherapy remains the cornerstone of the therapeutic management of metastatic NPC patients. Consistent with previous reports[19–20], this study showed that patients who received more than 4 cycles of PCT had a significantly longer overall survival compared to those administered with fewer than four cycles. However, adequate chemotherapy does not mean that the course of chemotherapy should be extended indefinitely. Numerous reports have proposed that appropriate local treatment could improve the local control of metastatic lesions and confers survival benefits on some patients. For example Huang et al[11] reported that NPC patients with limited liver metastases treated by a partial hepatectomy achieved a median OS of 45.2 months, which was obviously elevated compared to 14.1 months in the control group. Likewise in patients with lung-only metastases from NPC, pulmonary metastasectomy provided reliable local control and survival benefits with significantly higher 5-year survival rates than in the nonsurgically treated group (75.5% vs. 47.8%, P=0.005)[21]. A retrospective studies also revealed that chemoradiotherapy conferred a better prognosis than chemotherapy alone in NPC patients with bone metastases[22–23].
In clinical practice, however, not all metastatic lesions of NPC will respond to local treatments. Based on the nomogram we established in this study, patients with metastatic NPC were stratified into low- and high-risk groups. We found that the survival benefit associated with local treatment which was solely observed in those considered at low-risk. There was no additional survival benefit in high-risk patients receiving local treatment compared with PCT alone. This may explain why local treatment did not demonstrate a statistically significant difference in OS across the entire cohort and further demonstrates the validity of our model. Therefore, clinicians can use our model to determine who responds best to local treatment and make individualized treatment recommendations. Our study fills the gap where previous studies failed to identify who would benefit from local treatment of metastases. Increasingly, evidence suggests that applying, potentially curative metastasis-directed radiotherapy for oligometastatic disease in various solid tumors, significantly improves survival and was associated with low treatment-related toxicity[24–27]. Due to the advancement of radiotherapeutic technologies, such as SBRT, ablative radiotherapy doses can be safely delivered to sites of metastasis. Regrettably, only 19 patients in our study underwent SBRT for metastatic lesions, and the sample size was too small for further analysis.
The definition of oligometastatic NPC has been widely discussed, and the generally accepted definition notes the presence of up to five metastatic lesions on imaging. In the present study, multivariable Cox regression indicated that the number of metastatic lesions was the most decisive prognostic parameter. Consistent with results reported in previous studies[20,28–29], the prognosis of patients with oligometastatic disease (1-5 lesions) is significantly better than that for those with polymetastatic disease (>5 lesions). The presence of a single lesion predicted superior survival outcomes than that of multiple metastatic lesions, possibly because a single lesion is more amenable to radical local treatment in the clinic. It is worth noting that we only included patients with metachronous metastatic disease or what is commonly referred to as post-treatment metastasis. Unlike those with synchronous metastatic NPC, patients with metachronous metastasis have already received aggressive radiotherapy to the primary foci. In the present study, concurrent locoregional recurrence was identified as a poor prognostic factor for metastatic NPC. Locoregional recurrent NPC after initial radiotherapy is likely to be radioresistant. Besides, an uncontrolled primary tumor may promote distant metastasis. It is currently unclear whether a locoregionally controlled primary tumor should become a precondition for diagnosing the oligometastatic disease, but it should be considered a crucial prognostic parameter for metastatic NPC.
Generally, patients with oligometastatic disease tend to have a low tumor burden and are more likely to receive aggressive metastasis-directed therapy, such as surgery or high-dose irradiation. In contrast, patients with polymetastatic disease are generally considered to have no curative potential and receive palliative systemic therapy. Nonetheless, in our study, not all cases with oligometastases were included in the low-risk group, and a proportion of patients with polymetastatic disease achieved better survival following comprehensive treatment. Metastatic status changes throughout the disease dynamically, and patients with polymetastatic disease may achieve oligometastatic status during successful systemic therapy[14–15]. Moreover, oligometastatic disease is diagnosed solely based on imaging because no biomarker is currently available for identifying patients with oligometastatic disease, in the clinic. However, imaging modalities fail to detect occult metastasis early, and biomarkers to assess tumor burden dynamics for surveillance during and after treatment are needed.
There are several limitations of this study that need to be noted. First, the retrospective study might result in a potential selection bias. Second, as the limitation of sample size and center number, we failed to conduct external validation. Third, only 118 patients in this study cohort underwent pretreatment plasma EBV DNA detection. Thus, it was hard to estimate the potential prognostic value of EBV DNA. Finally, innovations in chemotherapeutic regimens and advances in radiotherapeutic technology have inevitably changed treatment protocols, although the general treatment principles have remained unchanged.
In conclusion, we established an easy-to-use nomogram which demonstrated good performance in predicting survival and provided individualized stratification for patients with metastatic NPC. Using the prognostic model, patients who are sensitive to local treatment can be identified. In the future, multicenter external validation and prospective studies are needed to generalize the use of our model.
This work was supported by the National Natural Science Foundation of China (Grant No. 81872192) and China International Medical Foundation (Grant No. Z-2014-06-19392).
| [1] |
Esteves PJ, Abrantes J, Baldauf HM, et al. The wide utility of rabbits as models of human diseases[J]. Exp Mol Med, 2018, 50: 1–10. doi: 10.1038/s12276-018-0094-1
|
| [2] |
Fan JL, Watanabe T. Transgenic rabbits as therapeutic protein bioreactors and human disease models[J]. Pharmacol Ther, 2003, 99(3): 261–282. doi: 10.1016/S0163-7258(03)00069-X
|
| [3] |
Peng XW. Transgenic rabbit models for studying human cardiovascular diseases[J]. Comp Med, 2012, 62(6): 472–429. https://pubmed.ncbi.nlm.nih.gov/23561880/
|
| [4] |
Peng XW, Knouse JA, Hernon KM. Rabbit models for studying human infectious diseases[J]. Comp Med, 2015, 65(6): 499–507. https://pubmed.ncbi.nlm.nih.gov/26678367/
|
| [5] |
Fan JL, Kitajima S, Watanabe T, et al. Rabbit models for the study of human atherosclerosis: from pathophysiological mechanisms to translational medicine[J]. Pharmacol Ther, 2015, 146: 104–119. doi: 10.1016/j.pharmthera.2014.09.009
|
| [6] |
Li WH, Gouy M, Sharp PM, et al. Molecular phylogeny of Rodentia, Lagomorpha, Primates, Artiodactyla, and Carnivora and molecular clocks[J]. Proc Natl Acad Sci U S A, 1990, 87(17): 6703–6707. doi: 10.1073/pnas.87.17.6703
|
| [7] |
Graur D, Duret L, Gouy M. Phylogenetic position of the order Lagomorpha (rabbits, hares and allies)[J]. Nature, 1996, 379(6563): 333–335. doi: 10.1038/379333a0
|
| [8] |
Pearce J. Louis Pasteur and rabies: a brief note[J]. J Neurol Neurosurg Psychiatry, 2002, 73(1): 82. doi: 10.1136/jnnp.73.1.82
|
| [9] |
Cambau E, Drancourt M. Steps towards the discovery of Mycobacterium tuberculosis by Robert Koch, 1882[J]. Clin Microbiol Infect, 2014, 20(3): 196–201. doi: 10.1111/1469-0691.12555
|
| [10] |
Goldstein JL, Kita T, Brown MS. Defective lipoprotein receptors and atherosclerosis—Lessons from an animal counterpart of familial hypercholesterolemia[J]. N Engl J Med, 1983, 309(5): 288–296. doi: 10.1056/NEJM198308043090507
|
| [11] |
Endo A. Regulation of cholesterol synthesis, as focused on the regulation of HMG-CoA reductase (author's transl)[J]. Seikagaku (in Japanese), 1980, 52(10): 1033–1049.
|
| [12] |
Biggers JD. Walter Heape, FRS: a pioneer in reproductive biology. Centenary of his embryo transfer experiments[J]. J Reprod Fert, 1991, 93(1): 173–186. doi: 10.1530/jrf.0.0930173
|
| [13] |
Chang MC. Fertilization of rabbit ova in vitro[J]. Nature, 1959, 184(4684): 466–467. doi: 10.1038/184466a0
|
| [14] |
Hammer RE, Pursel VG, Rexroad CE Jr, et al. Production of transgenic rabbits, sheep and pigs by microinjection[J]. Nature, 1985, 315(6021): 680–683. doi: 10.1038/315680a0
|
| [15] |
Zernii EY, Baksheeva VE, Iomdina EN, et al. Rabbit models of ocular diseases: new relevance for classical approaches[J]. CNS Neurol Disord Drug Targets, 2016, 15(3): 267–291. doi: 10.2174/1871527315666151110124957
|
| [16] |
Kamaruzaman NA, Kardia E, Kamaldin N, et al. The rabbit as a model for studying lung disease and stem cell therapy[J]. Biomed Res Int, 2013, 2013: 691830.
|
| [17] |
Burkholder TH, Linton G, Hoyt RF Jr, et al. The rabbit as an experimental model[M]//Suckow MA, Stevens KA, Wilson RP. The Laboratory Rabbit, Guinea Pig, Hamster, and Other Rodents. Boston: Academic Press, 2012: 529–560.
|
| [18] |
Christensen ND, Peng XW. Rabbit genetics and transgenic models[M]//Suckow MA, Stevens KA, Wilson RP. The Laboratory Rabbit, Guinea Pig, Hamster, and Other Rodents. Boston: Academic Press, 2012: 165–193.
|
| [19] |
Isola LM, Gordon JW. Transgenic animals: a new era in developmental biology and medicine[J]. Biotechnology, 1991, 16: 3–20. https://pubmed.ncbi.nlm.nih.gov/2007198/
|
| [20] |
Gordon JW, Scangos GA, Plotkin DJ, et al. Genetic transformation of mouse embryos by microinjection of purified DNA[J]. Proc Natl Acad Sci U S A, 1980, 77(12): 7380–7384. doi: 10.1073/pnas.77.12.7380
|
| [21] |
Brinster RL, Chen HY, Trumbauer M, et al. Somatic expression of herpes thymidine kinase in mice following injection of a fusion gene into eggs[J]. Cell, 1981, 27(1): 223–231. doi: 10.1016/0092-8674(81)90376-7
|
| [22] |
Costantini F, Lacy E. Introduction of a rabbit β-globin gene into the mouse germ line[J]. Nature, 1981, 294(5836): 92–94. doi: 10.1038/294092a0
|
| [23] |
Gordon JW, Ruddle FH. Integration and stable germ line transmission of genes injected into mouse pronuclei[J]. Science, 1981, 214(4526): 1244–1246. doi: 10.1126/science.6272397
|
| [24] |
Wagner EF, Stewart TA, Mintz B. The human beta-globin gene and a functional viral thymidine kinase gene in developing mice[J]. Proc Natl Acad Sci U S A, 1981, 78(8): 5016–5020. doi: 10.1073/pnas.78.8.5016
|
| [25] |
Wagner TE, Hoppe PC, Jollick JD, et al. Microinjection of a rabbit beta-globin gene into zygotes and its subsequent expression in adult mice and their offspring[J]. Proc Natl Acad Sci U S A, 1981, 78(10): 6376–6380. doi: 10.1073/pnas.78.10.6376
|
| [26] |
Campbell KHS, McWhir J, Ritchie WA, et al. Sheep cloned by nuclear transfer from a cultured cell line[J]. Nature, 1996, 380(6569): 64–66. doi: 10.1038/380064a0
|
| [27] |
Bibikova M, Golic M, Golic KG, et al. Targeted chromosomal cleavage and mutagenesis in Drosophila using zinc-finger nucleases[J]. Genetics, 2002, 161(3): 1169–1175. https://pubmed.ncbi.nlm.nih.gov/12136019/
|
| [28] |
Chesne P, Adenot PG, Viglietta C, et al. Cloned rabbits produced by nuclear transfer from adult somatic cells[J]. Nat Biotechnol, 2002, 20(4): 366–369. doi: 10.1038/nbt0402-366
|
| [29] |
Li SG, Guo Y, Shi JJ, et al. Transgene expression of enhanced green fluorescent protein in cloned rabbits generated from in vitro-transfected adult fibroblasts[J]. Transgenic Res, 2009, 18(2): 227–235. doi: 10.1007/s11248-008-9227-y
|
| [30] |
Geurts AM, Cost GJ, Freyvert Y, et al. Knockout rats via embryo microinjection of zinc-finger nucleases[J]. Science, 2009, 325(5939): 433. doi: 10.1126/science.1172447
|
| [31] |
Flisikowska T, Thorey IS, Offner S, et al. Efficient immunoglobulin gene disruption and targeted replacement in rabbit using zinc finger nucleases[J]. PLoS One, 2011, 6(6): e21045. doi: 10.1371/journal.pone.0021045
|
| [32] |
Tesson L, Usal C, Menoret S, et al. Knockout rats generated by embryo microinjection of TALENs[J]. Nat Biotechnol, 2011, 29(8): 695–696. doi: 10.1038/nbt.1940
|
| [33] |
Gasiunas G, Barrangou R, Horvath P, et al. Cas9-crRNA ribonucleoprotein complex mediates specific DNA cleavage for adaptive immunity in bacteria[J]. Proc Natl Acad Sci U S A, 2012, 109(39): E2579–E2586. doi: 10.1073/pnas.1208507109
|
| [34] |
Jinek M, Chylinski K, Fonfara I, et al. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity[J]. Science, 2012, 337(6096): 816–821. doi: 10.1126/science.1225829
|
| [35] |
Wang H, Yang H, Shivalila CS, et al. One-step generation of mice carrying mutations in multiple genes by CRISPR/Cas-mediated genome engineering[J]. Cell, 2013, 153(4): 910–918. doi: 10.1016/j.cell.2013.04.025
|
| [36] |
Song J, Zhong J, Guo XG, et al. Generation of RAG 1- and 2-deficient rabbits by embryo microinjection of TALENs[J]. Cell Res, 2013, 23(8): 1059–1062. doi: 10.1038/cr.2013.85
|
| [37] |
Yang D, Xu J, Zhu T, et al. Effective gene targeting in rabbits using RNA-guided Cas9 nucleases[J]. J Mol Cell Biol, 2014, 6(1): 97–99. doi: 10.1093/jmcb/mjt047
|
| [38] |
Carneiro M, Rubin CJ, Di Palma F, et al. Rabbit genome analysis reveals a polygenic basis for phenotypic change during domestication[J]. Science, 2014, 345(6200): 1074–1079. doi: 10.1126/science.1253714
|
| [39] |
Komor AC, Kim YB, Packer MS, et al. Programmable editing of a target base in genomic DNA without double-stranded DNA cleavage[J]. Nature, 2016, 533(7603): 420–424. doi: 10.1038/nature17946
|
| [40] |
Wang Z, Zhang JF, Li H, et al. Hyperlipidemia-associated gene variations and expression patterns revealed by whole-genome and transcriptome sequencing of rabbit models[J]. Sci Rep, 2016, 6: 26942. doi: 10.1038/srep26942
|
| [41] |
Liu Z, Chen M, Chen S, et al. Highly efficient RNA-guided base editing in rabbit[J]. Nat Commun, 2018, 9(1): 2717. doi: 10.1038/s41467-018-05232-2
|
| [42] |
Shen W, Li L, Pan QJ, et al. Efficient and simple production of transgenic mice and rabbits using the new DMSO-sperm mediated exogenous DNA transfer method[J]. Mol Reprod Dev, 2006, 73(5): 589–594. doi: 10.1002/mrd.20401
|
| [43] |
Hiripi L, Negre D, Cosset FL, et al. Transgenic rabbit production with simian immunodeficiency virus-derived lentiviral vector[J]. Transgenic Res, 2010, 19(5): 799–808. doi: 10.1007/s11248-009-9356-y
|
| [44] |
Smith KR. Sperm-mediated gene transfer: concepts and controversies[M]. Sharjah, UAE: Bentham Science, 2012.
|
| [45] |
Kuznetsov AV, Kuznetsova IV, Schit IY. DNA interaction with rabbit sperm cells and its transfer into ova in vitro and in vivo[J]. Mol Reprod Dev, 2000, 56(S2): 292–297. doi: 10.1002/(SICI)1098-2795(200006)56:2+<292::AID-MRD18>3.0.CO;2-Z
|
| [46] |
Houdebine LM. The methods to generate transgenic animals and to control transgene expression[J]. J Biotechnol, 2002, 98(2-3): 145–160. doi: 10.1016/S0168-1656(02)00129-3
|
| [47] |
Katter K, Geurts AM, Hoffmann O, et al. Transposon-mediated transgenesis, transgenic rescue, and tissue-specific gene expression in rodents and rabbits[J]. FASEB J, 2013, 27(3): 930–941. doi: 10.1096/fj.12-205526
|
| [48] |
Ivics Z, Hiripi L, Hoffmann OI, et al. Germline transgenesis in rabbits by pronuclear microinjection of Sleeping beauty transposons[J]. Nat Protoc, 2014, 9(4): 794–809. doi: 10.1038/nprot.2014.009
|
| [49] |
Evans MJ, Kaufman MH. Establishment in culture of pluripotential cells from mouse embryos[J]. Nature, 1981, 292(5819): 154–156. doi: 10.1038/292154a0
|
| [50] |
Martin GR. Isolation of a pluripotent cell line from early mouse embryos cultured in medium conditioned by teratocarcinoma stem cells[J]. Proc Natl Acad Sci U S A, 1981, 78(12): 7634–7638. doi: 10.1073/pnas.78.12.7634
|
| [51] |
Gossler A, Doetschman T, Korn R, et al. Transgenesis by means of blastocyst-derived embryonic stem cell lines[J]. Proc Natl Acad Sci U S A, 1986, 83(23): 9065–9069. doi: 10.1073/pnas.83.23.9065
|
| [52] |
Robertson E, Bradley A, Kuehn M, et al. Germ-line transmission of genes introduced into cultured pluripotential cells by retroviral vector[J]. Nature, 1986, 323(6087): 445–448. doi: 10.1038/323445a0
|
| [53] |
Kuehn MR, Bradley A, Robertson EJ, et al. A potential animal model for Lesch-Nyhan syndrome through introduction of HPRT mutations into mice[J]. Nature, 1987, 326(6110): 295–298. doi: 10.1038/326295a0
|
| [54] |
Okita K, Ichisaka T, Yamanaka S. Generation of germline-competent induced pluripotent stem cells[J]. Nature, 2007, 448(7151): 313–317. doi: 10.1038/nature05934
|
| [55] |
Wernig M, Meissner A, Foreman R, et al. In vitro reprogramming of fibroblasts into a pluripotent ES-cell-like state[J]. Nature, 2007, 448(7151): 318–324. doi: 10.1038/nature05944
|
| [56] |
Boland MJ, Hazen JL, Nazor KL, et al. Adult mice generated from induced pluripotent stem cells[J]. Nature, 2009, 461(7260): 91–94. doi: 10.1038/nature08310
|
| [57] |
Kang L, Wang JL, Zhang Y, et al. iPS cells can support full-term development of tetraploid blastocyst-complemented embryos[J]. Cell Stem Cell, 2009, 5(2): 135–138. doi: 10.1016/j.stem.2009.07.001
|
| [58] |
Zhao XY, Li W, Lv Z, et al. iPS cells produce viable mice through tetraploid complementation[J]. Nature, 2009, 461(7260): 86–90. doi: 10.1038/nature08267
|
| [59] |
Capecchi MR. Gene targeting in mice: functional analysis of the mammalian genome for the twenty-first century[J]. Nat Rev Genet, 2005, 6(6): 507–512. doi: 10.1038/nrg1619
|
| [60] |
Fang ZF, Gai H, Huang YZ, et al. Rabbit embryonic stem cell lines derived from fertilized, parthenogenetic or somatic cell nuclear transfer embryos[J]. Exp Cell Res, 2006, 312(18): 3669–3682. doi: 10.1016/j.yexcr.2006.08.013
|
| [61] |
Wang SF, Tang XH, Niu YY, et al. Generation and characterization of rabbit embryonic stem cells[J]. Stem Cells, 2007, 25(2): 481–489. doi: 10.1634/stemcells.2006-0226
|
| [62] |
Honda A, Hirose M, Inoue K, et al. Stable embryonic stem cell lines in rabbits: potential small animal models for human research[J]. Reprod Biomed Online, 2008, 17(5): 706–715. doi: 10.1016/S1472-6483(10)60320-3
|
| [63] |
Osteil P, Tapponnier Y, Markossian S, et al. Induced pluripotent stem cells derived from rabbits exhibit some characteristics of naïve pluripotency[J]. Biol Open, 2013, 2(6): 613–628. doi: 10.1242/bio.20134242
|
| [64] |
Xue F, Ma YH, Chen YE, et al. Recombinant rabbit leukemia inhibitory factor and rabbit embryonic fibroblasts support the derivation and maintenance of rabbit embryonic stem cells[J]. Cell Reprogram, 2012, 14(4): 364–376. doi: 10.1089/cell.2012.0001
|
| [65] |
Du FL, Chen CH, Li Y, et al. Derivation of rabbit embryonic stem cells from vitrified-thawed embryos[J]. Cell Reprogram, 2015, 17(6): 453–462. doi: 10.1089/cell.2015.0044
|
| [66] |
Zakhartchenko V, Flisikowska T, Li S, et al. Cell-mediated transgenesis in rabbits: chimeric and nuclear transfer animals[J]. Biol Reprod, 2011, 84(2): 229–237. doi: 10.1095/biolreprod.110.087098
|
| [67] |
Tancos Z, Nemes C, Polgar Z, et al. Generation of rabbit pluripotent stem cell lines[J]. Theriogenology, 2012, 78(8): 1774–1786. doi: 10.1016/j.theriogenology.2012.06.017
|
| [68] |
McCreath KJ, Howcroft J, Campbell KHS, et al. Production of gene-targeted sheep by nuclear transfer from cultured somatic cells[J]. Nature, 2000, 405(6790): 1066–1069. doi: 10.1038/35016604
|
| [69] |
Lai LX, Kolber-Simonds D, Park KW, et al. Production of α-1,3-galactosyltransferase knockout pigs by nuclear transfer cloning[J]. Science, 2002, 295(5557): 1089–1092. doi: 10.1126/science.1068228
|
| [70] |
Richt JA, Kasinathan P, Hamir AN, et al. Production of cattle lacking prion protein[J]. Nat Biotechnol, 2007, 25(1): 132–138. doi: 10.1038/nbt1271
|
| [71] |
Stice SL, Robl JM. Nuclear reprogramming in nuclear transplant rabbit embryos[J]. Biol Reprod, 1988, 39(3): 657–664. doi: 10.1095/biolreprod39.3.657
|
| [72] |
Du FL, Xu J, Zhang JF, et al. Beneficial effect of young oocytes for rabbit somatic cell nuclear transfer[J]. Cloning Stem Cells, 2009, 11(1): 131–140. doi: 10.1089/clo.2008.0042
|
| [73] |
Li SG, Chen XJ, Fang ZF, et al. Rabbits generated from fibroblasts through nuclear transfer[J]. Reproduction, 2006, 131(6): 1085–1090. doi: 10.1530/rep.1.01065
|
| [74] |
Li SG, Flisikowska T, Kessler B, et al. Production of cloned transgenic rabbits from mesenchymal stem cells[J]. Reprod Fertil Dev, 2010, 22(1): 192–192. doi: 10.1071/RDv22n1Ab67
|
| [75] |
Yin MR, Jiang WH, Fang ZF, et al. Generation of hypoxanthine phosphoribosyltransferase gene knockout rabbits by homologous recombination and gene trapping through somatic cell nuclear transfer[J]. Sci Rep, 2015, 5: 16023. doi: 10.1038/srep16023
|
| [76] |
Gaj T, Gersbach CA, Barbas III CF. ZFN, TALEN, and CRISPR/Cas-based methods for genome engineering[J]. Trends Biotechnol, 2013, 31(7): 397–405. doi: 10.1016/j.tibtech.2013.04.004
|
| [77] |
Conklin BR. Sculpting genomes with a hammer and chisel[J]. Nat Methods, 2013, 10(9): 839–840. doi: 10.1038/nmeth.2608
|
| [78] |
Cui XX, Ji DA, Fisher DA, et al. Targeted integration in rat and mouse embryos with zinc-finger nucleases[J]. Nat Biotechnol, 2011, 29(1): 64–67. doi: 10.1038/nbt.1731
|
| [79] |
Paques F, Duchateau P. Meganucleases and DNA double-strand break-induced recombination: perspectives for gene therapy[J]. Curr Gene Ther, 2007, 7(1): 49–66. doi: 10.2174/156652307779940216
|
| [80] |
Kim YG, Cha J, Chandrasegaran S. Hybrid restriction enzymes: zinc finger fusions to Fok I cleavage domain[J]. Proc Natl Acad Sci U S A, 1996, 93(3): 1156–1160. doi: 10.1073/pnas.93.3.1156
|
| [81] |
Yang DS, Yang HQ, Li W, et al. Generation of PPARγ mono-allelic knockout pigs via zinc-finger nucleases and nuclear transfer cloning[J]. Cell Res, 2011, 21(6): 979–982. doi: 10.1038/cr.2011.70
|
| [82] |
Yu SL, Luo JJ, Song ZY, et al. Highly efficient modification of beta-lactoglobulin (BLG) gene via zinc-finger nucleases in cattle[J]. Cell Res, 2011, 21(11): 1638–1640. doi: 10.1038/cr.2011.153
|
| [83] |
Perez EE, Wang JB, Miller JC, et al. Establishment of HIV-1 resistance in CD4+ T cells by genome editing using zinc-finger nucleases[J]. Nat Biotechnol, 2008, 26(7): 808–816. doi: 10.1038/nbt1410
|
| [84] |
Meng XD, Noyes MB, Zhu LJ, et al. Targeted gene inactivation in zebrafish using engineered zinc-finger nucleases[J]. Nat Biotechnol, 2008, 26(6): 695–701. doi: 10.1038/nbt1398
|
| [85] |
Yang DS, Zhang JF, Xu J, et al. Production of apolipoprotein C-III knockout rabbits using zinc finger nucleases[J]. J Vis Exp, 2013, (81): e50957. doi: 10.3791/50957
|
| [86] |
Ji DA, Zhao GJ, Songstad A, et al. Efficient creation of an APOE knockout rabbit[J]. Transgenic Res, 2015, 24(2): 227–235. doi: 10.1007/s11248-014-9834-8
|
| [87] |
Niimi M, Yang DS, Kitajima S, et al. ApoE knockout rabbits: a novel model for the study of human hyperlipidemia[J]. Atherosclerosis, 2016, 245: 187–193. doi: 10.1016/j.atherosclerosis.2015.12.002
|
| [88] |
Zhang JF, Niimi M, Yang DS, et al. Deficiency of cholesteryl ester transfer protein protects against atherosclerosis in rabbits[J]. Arterioscler Thromb Vasc Biol, 2017, 37(6): 1068–1075. doi: 10.1161/ATVBAHA.117.309114
|
| [89] |
Miller JC, Tan SY, Qiao GJ, et al. A TALE nuclease architecture for efficient genome editing[J]. Nat Biotechnol, 2011, 29(2): 143–148. doi: 10.1038/nbt.1755
|
| [90] |
Christian M, Cermak T, Doyle EL, et al. Targeting DNA double-strand breaks with TAL effector nucleases[J]. Genetics, 2010, 186(2): 757–761. doi: 10.1534/genetics.110.120717
|
| [91] |
Boch J, Scholze H, Schornack S, et al. Breaking the code of DNA binding specificity of TAL-type III effectors[J]. Science, 2009, 326(5959): 1509–1512. doi: 10.1126/science.1178811
|
| [92] |
Moscou MJ, Bogdanove AJ. A simple cipher governs DNA recognition by TAL effectors[J]. Science, 2009, 326(5959): 1501. doi: 10.1126/science.1178817
|
| [93] |
Hsu PD, Lander ES, Zhang F. Development and applications of CRISPR-Cas9 for genome engineering[J]. Cell, 2014, 157(6): 1262–1278. doi: 10.1016/j.cell.2014.05.010
|
| [94] |
Yang DS, Xu J, Chen YE. Generation of rabbit models by gene editing nucleases[M]//Liu CY, Du YB. Microinjection. New York: Humana Press, 2019: 327–345.
|
| [95] |
Li L, Zhang QJ, Yang HQ, et al. Fumarylacetoacetate hydrolase knock-out rabbit model for hereditary tyrosinemia type 1[J]. J Biol Chem, 2017, 292(11): 4755–4763. doi: 10.1074/jbc.M116.764787
|
| [96] |
Chen M, Yao B, Yang QB, et al. Truncated C-terminus of fibrillin-1 induces Marfanoid-progeroid-lipodystrophy (MPL) syndrome in rabbit[J]. Dis Model Mech, 2018, 11(4): dmm031542. doi: 10.1242/dmm.031542
|
| [97] |
Sui TT, Lau YS, Liu D, et al. A novel rabbit model of Duchenne muscular dystrophy generated by CRISPR/Cas9[J]. Dis Model Mech, 2018, 11(6): dmm032201. doi: 10.1242/dmm.032201
|
| [98] |
Sui TT, Xu L, Lau YS, et al. Development of muscular dystrophy in a CRISPR-engineered mutant rabbit model with frame-disrupting ANO5 mutations[J]. Cell Death Dis, 2018, 9(6): 609. doi: 10.1038/s41419-018-0674-y
|
| [99] |
Yuan L, Yao HB, Xu YX, et al. CRISPR/cas9-mediated mutation of αA-crystallin gene induces congenital cataracts in rabbits[J]. Invest Ophthalmol Vis Sci, 2017, 58(6): BIO34–BIO41. doi: 10.1167/iovs.16-21287
|
| [100] |
Yuan L, Sui TT, Chen M, et al. CRISPR/Cas9-mediated GJA8 knockout in rabbits recapitulates human congenital cataracts[J]. Sci Rep, 2016, 6: 22024. doi: 10.1038/srep22024
|
| [101] |
Lu R, Yuan T, Wang Y, et al. Spontaneous severe hypercholesterolemia and atherosclerosis lesions in rabbits with deficiency of low-density lipoprotein receptor (LDLR) on exon 7[J]. EBioMedicine, 2018, 36: 29–38. doi: 10.1016/j.ebiom.2018.09.020
|
| [102] |
Guo R, Wan Y, Xu D, et al. Generation and evaluation of Myostatin knock-out rabbits and goats using CRISPR/Cas9 system[J]. Sci Rep, 2016, 6: 29855. doi: 10.1038/srep29855
|
| [103] |
Lv Q, Yuan L, Deng J, et al. Efficient Generation of Myostatin Gene Mutated Rabbit by CRISPR/Cas9[J]. Sci Rep, 2016, 6: 25029. doi: 10.1038/srep25029
|
| [104] |
Song Y, Liu T, Wang Y, et al. Mutation of the Sp1 binding site in the 5' flanking region of SRY causes sex reversal in rabbits[J]. Oncotarget, 2017, 8(24): 38176–38183. doi: 10.18632/oncotarget.16979
|
| [105] |
Song Y, Xu Y, Liang M, et al. CRISPR/Cas9-mediated mosaic mutation of SRY gene induces hermaphroditism in rabbits[J]. Biosci Rep, 2018, 38(2): BSR20171490. doi: 10.1042/BSR20171490
|
| [106] |
Sui T, Yuan L, Liu H, et al. CRISPR/Cas9-mediated mutation of PHEX in rabbit recapitulates human X-linked hypophosphatemia (XLH)[J]. Hum Mol Genet, 2016, 25(13): 2661–2671. doi: 10.1093/hmg/ddw125
|
| [107] |
Sui T, Liu D, Liu T, et al. LMNA-mutated rabbits: a model of premature aging syndrome with muscular dystrophy and dilated cardiomyopathy[J]. Aging Dis, 2019, 10(1): 102–115. doi: 10.14336/AD.2018.0209
|
| [108] |
Wu H, Liu Q, Shi H, et al. Engineering CRISPR/Cpf1 with tRNA promotes genome editing capability in mammalian systems[J]. Cell Mol Life Sci, 2018, 75(19): 3593–3607. doi: 10.1007/s00018-018-2810-3
|
| [109] |
Honda A, Hirose M, Sankai T, et al. Single-step generation of rabbits carrying a targeted allele of the tyrosinase gene using CRISPR/Cas9[J]. Exp Anim, 2015, 64(1): 31–37. doi: 10.1538/expanim.14-0034
|
| [110] |
Song Y, Xu Y, Deng J, et al. CRISPR/Cas9-mediated mutation of tyrosinase (Tyr) 3' UTR induce graying in rabbit[J]. Sci Rep, 2017, 7(1): 1569. doi: 10.1038/s41598-017-01727-y
|
| [111] |
Jiang WH, Liu LL, Chang QR, et al. Production of Wilson disease model rabbits with homology-directed precision point mutations in the ATP7B gene using the CRISPR/Cas9 system[J]. Sci Rep, 2018, 8: 1332. doi: 10.1038/s41598-018-19774-4
|
| [112] |
Song YN, Zhang YX, Chen M, et al. Functional validation of the albinism-associated tyrosinase T373K SNP by CRISPR/Cas9-mediated homology-directed repair (HDR) in rabbits[J]. EBioMedicine, 2018, 36: 517–525. doi: 10.1016/j.ebiom.2018.09.041
|
| [113] |
Song YN, Yuan L, Wang Y, et al. Efficient dual sgRNA-directed large gene deletion in rabbit with CRISPR/Cas9 system[J]. Cell Mol Life Sci, 2016, 73(15): 2959–2968. doi: 10.1007/s00018-016-2143-z
|
| [114] |
Song J, Wang GS, Hoenerhoff MJ, et al. Bacterial and Pneumocystis infections in the lungs of gene-knockout rabbits with severe combined immunodeficiency[J]. Front Immunol, 2018, 9: 429. doi: 10.3389/fimmu.2018.00429
|
| [115] |
Song J, Yang DS, Ruan JX, et al. Production of immunodeficient rabbits by multiplex embryo transfer and multiplex gene targeting[J]. Sci Rep, 2017, 7(1): 12202. doi: 10.1038/s41598-017-12201-0
|
| [116] |
Yan Q, Zhang Q, Yang H, et al. Generation of multi-gene knockout rabbits using the Cas9/gRNA system[J]. Cell Regen (Lond), 2014, 3(1): 12. doi: 10.1186/2045-9769-3-12
|
| [117] |
Liu H, Sui T, Liu D, et al. Multiple homologous genes knockout (KO) by CRISPR/Cas9 system in rabbit[J]. Gene, 2018, 647: 261–267. doi: 10.1016/j.gene.2018.01.044
|
| [118] |
Yang DS, Song J, Zhang JF, et al. Identification and characterization of rabbit ROSA26 for gene knock-in and stable reporter gene expression[J]. Sci Rep, 2016, 6: 25161. doi: 10.1038/srep25161
|
| [119] |
Song J, Yang DS, Xu J, et al. RS-1 enhances CRISPR/Cas9- and TALEN-mediated knock-in efficiency[J]. Nat Commun, 2016, 7: 10548. doi: 10.1038/ncomms10548
|
| [120] |
Gaudelli NM, Komor AC, Rees HA, et al. Programmable base editing of A·T to G·C in genomic DNA without DNA cleavage[J]. Nature, 2017, 551(7681): 464–471. doi: 10.1038/nature24644
|
| [121] |
Komor AC, Zhao KT, Packer MS, et al. Improved base excision repair inhibition and bacteriophage Mu Gam protein yields C: G-to-T: a base editors with higher efficiency and product purity[J]. Sci Adv, 2017, 3(8): eaao4774. doi: 10.1126/sciadv.aao4774
|
| [122] |
Koblan LW, Doman JL, Wilson C, et al. Improving cytidine and adenine base editors by expression optimization and ancestral reconstruction[J]. Nat Biotechnol, 2018, 36(9): 843–846. doi: 10.1038/nbt.4172
|
| [123] |
Hsu PD, Scott DA, Weinstein JA, et al. DNA targeting specificity of RNA-guided Cas9 nucleases[J]. Nat Biotechnol, 2013, 31(9): 827–832. doi: 10.1038/nbt.2647
|
| [124] |
Schmid-Burgk JL, Gao LY, Li D, et al. Highly parallel profiling of cas9 variant specificity[J]. Mol Cell, 2020, . doi: 10.1016/j.molcel.2020.02.023
|
| [125] |
Tycko J, Myer VE, Hsu PD. Methods for optimizing crispr-cas9 genome editing specificity[J]. Mol Cell, 2016, 63(3): 355–370. doi: 10.1016/j.molcel.2016.07.004
|
| [126] |
Chen SM, Yao YF, Zhang YC, et al. CRISPR system: discovery, development and off-target detection[J]. Cell Signal, 2020, 70: 109577. doi: 10.1016/j.cellsig.2020.109577
|
| [127] |
Li JJ, Hong SY, Chen WJ, et al. Advances in detecting and reducing off-target effects generated by CRISPR-mediated genome editing[J]. J Genet Genomics, 2019, 46(11): 513–521. doi: 10.1016/j.jgg.2019.11.002
|
| [128] |
Zischewski J, Fischer R, Bortesi L. Detection of on-target and off-target mutations generated by CRISPR/Cas9 and other sequence-specific nucleases[J]. Biotechnol Adv, 2017, 35(1): 95–104. doi: 10.1016/j.biotechadv.2016.12.003
|
| [129] |
Kosicki M, Tomberg K, Bradley A. Repair of double-strand breaks induced by CRISPR-Cas9 leads to large deletions and complex rearrangements[J]. Nat Biotechnol, 2018, 36(8): 765–771. doi: 10.1038/nbt.4192
|
| [130] |
Mou HW, Smith JL, Peng LT, et al. CRISPR/Cas9-mediated genome editing induces exon skipping by alternative splicing or exon deletion[J]. Genome Biol, 2017, 18(1): 108. doi: 10.1186/s13059-017-1237-8
|
| [131] |
Prykhozhij SV, Steele SL, Razaghi B, et al. A rapid and effective method for screening, sequencing and reporter verification of engineered frameshift mutations in zebrafish[J]. Dis Model Mech, 2017, 10(6): 811–822. doi: 10.1242/dmm.026765
|
| [132] |
Sharpe JJ, Cooper TA. Unexpected consequences: exon skipping caused by CRISPR-generated mutations[J]. Genome Biol, 2017, 18(1): 109. doi: 10.1186/s13059-017-1240-0
|
| [133] |
Sui TT, Song YN, Liu ZQ, et al. CRISPR-induced exon skipping is dependent on premature termination codon mutations[J]. Genome Biol, 2018, 19(1): 164. doi: 10.1186/s13059-018-1532-z
|
| [134] |
Li P, Estrada J, Zhang F, et al. Isolation, characterization, and nuclear reprogramming of cell lines derived from porcine adult liver and fat[J]. Cell Reprogram, 2010, 12(5): 599–607. doi: 10.1089/cell.2010.0006
|
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Figures(3) / Tables(1)
Supported by: Beijing Renhe Information Technology Co., Ltd. E-mail:
info@rhhz.net 
| (n [%]) | ||||
| Characteristics | Total patients (n=240) | LT+PCT (n=146) | PCT (n=94) | P- value |
| Age (years) a | 0.202 | |||
| <49 | 117 (48.8) | 76 (52.1) | 41 (43.6) | |
| ≥49 | 123 (51.2) | 70 (47.9) | 53 (56.4) | |
| Sex | 0.104 | |||
| Female | 51 (21.2) | 26 (17.8) | 25 (26.6) | |
| Male | 189 (78.8) | 120 (82.2) | 69 (73.4) | |
| BMI | 0.653 | |||
| <23 | 109 (45.4) | 68 (46.6) | 41 (43.6) | |
| ≥23 | 131 (54.6) | 78 (53.4) | 53 (56.4) | |
| KPS | 0.644 | |||
| ≤80 | 104 (43.3) | 65 (44.5) | 39 (41.5) | |
| >80 | 136 (56.7) | 81 (55.5) | 55 (58.5) | |
| T stageb | 0.126 | |||
| T1 | 27 (11.3) | 11 (7.5) | 16 (17.0) | |
| T2 | 43 (17.9) | 27 (18.5) | 16 (17.0) | |
| T3 | 75 (31.2) | 50 (34.2) | 25 (26.6) | |
| T4 | 95 (39.6) | 58 (39.8) | 37 (39.4) | |
| N stageb | 0.940 | |||
| N0 | 3 (1.2) | 2 (1.4) | 1 (1.1) | |
| N1 | 77 (32.1) | 48 (32.9) | 29 (30.9) | |
| N2 | 109 (45.4) | 64 (43.8) | 45 (47.9) | |
| N3 | 51 (21.3) | 32 (21.9) | 19 (20.1) | |
| DFI (months) | 0.682 | |||
| ≤12 | 78 (32.5) | 46 (31.5) | 32 (34.0) | |
| >12 | 162 (67.5) | 100 (68.5) | 62 (66.0) | |
| Lung metastasis | <0.001 | |||
| No | 138 (57.5) | 98 (67.1) | 40 (42.6) | |
| Yes | 102 (42.5) | 48 (32.9) | 54 (57.4) | |
| Bone metastasis | <0.001 | |||
| No | 126 (52.5) | 61 (41.8) | 65 (69.1) | |
| Yes | 114 (47.5) | 85 (58.2) | 29 (30.9) | |
| Liver metastasis | 0.001 | |||
| No | 172 (71.7) | 116 (79.5) | 56 (59.6) | |
| Yes | 68 (28.3) | 30 (20.5) | 38 (40.4) | |
| Distant nodal metastasis | <0.001 | |||
| No | 165 (68.8) | 115 (78.8) | 50 (53.2) | |
| Yes | 75 (31.2) | 31 (21.2) | 44 (46.8) | |
| No. of metastatic lesions | <0.001 | |||
| 1 | 63 (26.2) | 50 (34.3) | 13 (13.8) | |
| 2–5 | 82 (34.2) | 50 (34.2) | 32 (34.0) | |
| >5 | 95 (39.6) | 46 (31.5) | 49 (52.2) | |
| Pretreatment EBV DNA | 0.148 | |||
| Undetectable | 49 (20.4) | 35 (24.0) | 14 (14.9) | |
| Detectable | 69 (28.8) | 37 (25.3) | 32 (34.0) | |
| Not available | 122 (50.8) | 74 (50.7) | 48 (51.1) | |
| Locoregional recurrence | 0.051 | |||
| No | 207 (86.2) | 131 (89.7) | 76 (80.9) | |
| Yes | 33 (13.8) | 15 (10.3) | 18 (19.1) | |
| No. of PCT cycles | 0.002 | |||
| <4 | 120 (50.0) | 85 (58.2) | 35 (37.2) | |
| ≥4 | 120 (50.0) | 61 (41.8) | 59 (62.8) | |
| aAge at diagnosis of distant metastasis. bAccording to the 8th UICC/AJCC staging system. LT: local treatment of metastases; PCT: palliative chemotherapy; BMI: body mass index; KPS: Karnofsky performance score; DFI: disease-free interval; EBV: Epstein-Barr virus; No.: number. | ||||
DownLoad:
CSV
| Variables | Univariable | Multivariable | |||
| HR (95% CI) | P-value | HR (95% CI) | P-value | ||
| Age (years)a | |||||
| <49 | Reference | ||||
| ≥49 | 1.137 (0.839–1.542) | 0.407 | |||
| Sex | |||||
| Female | Reference | Reference | |||
| Male | 1.484 (1.009–2.182) | 0.045 | 1.690 (1.124–2.542) | 0.012 | |
| BMI | |||||
| <23 | Reference | ||||
| ≥23 | 0.991 (0.732–1.342) | 0.952 | |||
| KPS | |||||
| ≤80 | Reference | ||||
| >80 | 0.918 (0.677–1.244) | 0.580 | |||
| T stageb | |||||
| T1 | Reference | ||||
| T2 | 1.057 (0.602–1.857) | 0.847 | |||
| T3 | 0.847 (0.507–1.415) | 0.526 | |||
| T4 | 0.722 (0.436–1.196) | 0.206 | |||
| N stageb | |||||
| N0 | Reference | ||||
| N1 | 3.265 (0.451–23.624) | 0.241 | |||
| N2 | 3.572 (0.496–25.717) | 0.206 | |||
| N3 | 4.840 (0.663–35.327) | 0.120 | |||
| DFI (months) | |||||
| ≤12 | Reference | Reference | |||
| >12 | 0.604 (0.442–0.825) | 0.001 | 0.650 (0.466–0.908) | 0.012 | |
| Lung metastasis | |||||
| No | Reference | Reference | |||
| Yes | 0.754 (0.554–1.025) | 0.072 | 0.848 (0.580–1.239) | 0.394 | |
| Bone metastasis | |||||
| No | Reference | Reference | |||
| Yes | 1.397 (1.033–1.890) | 0.030 | 0.895 (0.612–1.310) | 0.569 | |
| Liver metastasis | |||||
| No | Reference | Reference | |||
| Yes | 1.737 (1.261–2.393) | 0.001 | 1.580 (1.113–2.243) | 0.011 | |
| Distant nodal metastasis | |||||
| No | Reference | Reference | |||
| Yes | 1.659 (1.210–2.276) | 0.002 | 0.866 (0.579–1.295) | 0.483 | |
| No. of metastatic lesions | |||||
| 1 | Reference | Reference | |||
| 2–5 | 1.746 (1.115–2.736) | 0.015 | 2.875 (1.749–4.725) | <0.001 | |
| >5 | 4.713 (3.052–7.279) | <0.001 | 7.955 (4.597–13.766) | <0.001 | |
| Locoregional recurrence | |||||
| No | Reference | Reference | |||
| Yes | 1.473 (0.974–2.229) | 0.067 | 2.079 (1.338–3.230) | 0.001 | |
| No. of PCT cycles | |||||
| <4 | Reference | Reference | |||
| ≥4 | 0.630 (0.465–0.853) | 0.003 | 0.403 (0.281–0.576) | <0.001 | |
| aAge at diagnosis of distant metastasis. bAccording to the 8th UICC/AJCC staging system. BMI: body mass index; KPS: Karnofsky performance score; DFI: disease-free interval; No.: number; PCT: palliative chemotherapy; HR: hazard ratio; CI: confidence interval. | |||||
DownLoad:
CSV
| Variable | Low-risk (n=116) | High-risk (n=124) | P-value |
| Age (years)a | 0.240 | ||
| <49 | 52 (44.8) | 65 (52.4) | |
| ≥49 | 64 (55.2) | 59 (47.6) | |
| Sex | 0.020 | ||
| Female | 32 (27.6) | 19 (15.3) | |
| Male | 84 (72.4) | 105 (84.7) | |
| BMI | 0.390 | ||
| <23 | 56 (48.3) | 53 (42.7) | |
| ≥23 | 60 (51.7) | 71 (57.3) | |
| KPS | 0.330 | ||
| ≤80 | 54 (46.6) | 50 (40.3) | |
| >80 | 62 (53.4) | 74 (59.7) | |
| T stageb | 0.852 | ||
| T1 | 11 (9.5) | 16 (1.9) | |
| T2 | 21 (18.1) | 22 (17.7) | |
| T3 | 38 (32.8) | 37 (29.8) | |
| T4 | 46 (39.6) | 49 (39.6) | |
| N stageb | 0.530 | ||
| N0 | 2 (1.7) | 1 (0.8) | |
| N1 | 41 (35.3) | 36 (29.0) | |
| N2 | 52 (44.8) | 57 (46.0) | |
| N3 | 21 (18.2) | 30 (24.2) | |
| DFI (months) | 0.034 | ||
| ≤12 | 30 (25.9) | 48 (38.7) | |
| >12 | 86 (74.1) | 76 (61.3) | |
| Lung metastasis | 0.219 | ||
| No | 62 (53.4) | 76 (61.3) | |
| Yes | 54 (46.6) | 48 (38.7) | |
| Bone metastasis | 0.009 | ||
| No | 71 (61.2) | 55 (44.4) | |
| Yes | 45 (38.8) | 69 (55.6) | |
| Liver metastasis | 0.005 | ||
| No | 93 (80.2) | 79 (63.7) | |
| Yes | 23 (19.8) | 45 (36.3) | |
| Distant nodal metastasis | <0.001 | ||
| No | 96 (82.8) | 69 (55.6) | |
| Yes | 20 (17.2) | 55 (44.4) | |
| No. of metastatic lesions | <0.001 | ||
| 1 | 60 (51.7) | 3 (2.4) | |
| 2–5 | 53 (45.7) | 29 (23.4) | |
| >5 | 3 (2.6) | 92 (74.2) | |
| Locoregional recurrence | 0.138 | ||
| No | 104 (89.7) | 103 (83.1) | |
| Yes | 12 (10.3) | 21 (16.9) | |
| No. of PCT cycles | <0.001 | ||
| <4 | 44 (37.9) | 76 (61.3) | |
| ≥4 | 72 (62.1) | 48 (38.7) | |
| aAge at diagnosis of distant metastasis. bAccording to the 8th UICC/AJCC staging system. BMI: body mass index; KPS: Karnofsky performance score; DFI: disease-free interval; No.: number; PCT: palliative chemotherapy; LT: local treatment of metastases. | |||
DownLoad:
CSV
| Variable | Low-risk | High-risk | |||
| HR (95% CI) | P-value | HR (95% CI) | P-value | ||
| Treatment (LT+PCT vs. PCT) | 0.570 (0.343–0.947) | 0.030 | 0.994 (0.678–1.458) | 0.975 | |
| Risk score | 1.018 (1.007–1.028) | 0.001 | 1.022 (1.014–1.029) | <0.001 | |
| LT: local treatment of metastases; PCT: palliative chemotherapy; HR: hazard ratio; CI: confidence interval. | |||||
DownLoad:
CSV
Authors and Reviewers
