A total of 87 patients were enrolled in this study between August 2012 and August 2015. The interim analysis of this trial has been previously reported . The current analysis includes all enrolled patients (trial diagram in Figure 1B). There were 67 males and 20 females with a median age of 61 years (range 37-73 years). Their clinical stages were as follows: stage II, 30 cases (34.5%), stage III 44 cases (50.6%) and stage IV 13 cases (14.9%). All patients completed the whole course of CCRT and the majority of them (85.1%) also received 1-2 cycles of adjuvant chemotherapy. The data provided in this report were current as of September 15, 2018 when all patients had a minimum 3-year follow-up after RT. Four patients (4.6%) were excluded from the analysis due to lack of information of disease progression. Of the 83 patients analysed, there were 34 (41%) cases with disease progression.
The distribution of peri-treatment PBC parameters in patients with or without disease progression is summarized in Figure 2. These parameters in both groups exhibited a generally similar trend over time, including periodic oscillation of neutrophils, platelets and monocytes, gradual decline of haemoglobin and lymphocytes, and steady increase of NLR, PLR and MLR. However, significant differences were found in some parameters. Patients with disease progression had elevated levels of neutrophils at the 3rd time point (after 10F of RT) and platelets at the 6th time point (after 25F of RT) compared to patients without. They also showed a trend of increasing level of haemoglobin through the course of RT. These distinct distribution patterns of PBC parameters in patients who failed to respond to CCRT suggest that they have potential predictive value.
The discrimination capacity of 56 peri-treatment PBC parameters in predicting hazard of disease progression was analysed using ROC curves (Figure 3A). Pretreatment (the 1st time point) PBC parameters did not appear to have predictive potential (AUC range 0.52-0.58), whereas modest capacity (AUC up to 0.67) was found in PBC parameters collected after the start of RT (the 2nd - 7th time points). Only neutrophils (3rd) and NLR (3rd) exhibited statistical significance which is consistent with their mean differences in Figure 2. Further analysis reveals that the prediction capability of PBC parameters varies markedly for different progression patterns (Figure 3B-D). Greater discrimination capacity was found for prediction of local (Figure 3B, AUC up to 0.68) or regional progression hazard (Figure 3C, AUC up to 0.76) in a number of PBC parameters, whereas their potential was much less for risk of distant progression (Figure 3D, AUC up to 0.63). Therefore, subsequent analyses concentrated only on local and regional progression. Consistently, higher AUC values were found with PBC parameters during the course of treatment compared to pretreatment PBC parameters for these two failure patterns (Figure 3B,C). Individual PBC parameters also exhibited greater potential than cell ratios (NLR, PLR and MLR) in most cases. Moreover, the panel of parameters with higher AUC (> 0.6) differed between local and regional progression. Parameters with statistical significance include lymphocytes (7th) for local progression, haemoglobin (2nd and 7th) and neutrophils (3rd) for regional progression.
To further augment the predictive power of PBC parameters, integration of multiple parameters was examined. Cell-to-cell ratios (NLR, PLR and MLR) from a single time point only led to inferior predictive potential, removing them from consideration (Figure 3B,C). The distinctive distribution of predictive PBC parameters that emerged from the screen suggested that a novel integration approach based on peri-treatment PBC might stratify patient prognosis. Since the panel of predictive PBC parameters varied between local and regional progression, these two patterns were analysed separately. All 35 PBC parameters were ranked according to their AUC values from high to low (Figure 4A,B). The top 10 parameters were selected for an initial test of integration. These parameters showed a consistent trend of elevated levels in patients with progression versus patients without, suggesting that their summation might integrate their predictive potential (Figure S1,S2). Because PBC parameters have different ranges of normal values, they were first normalized to the mean of each parameter of the whole group. Normalization did not alter their AUC values and rankings (data not shown). Means of the top parameters (1~10) were then calculated and designated as a PBC score (PBCS) to test for hazard prediction of local (PBCS-L) or regional progression (PBCS-R). As shown in Figure 4C, combinations of multiple PBS parameters resulted in greater AUC values. PBCS-L with the highest AUC (0.73) was derived from the top 2 parameters, comprising lymphocyte 7th and monocytes 5th (Figure 4A). PBSC-R with the highest AUC (0.83) was based on the top 4 parameters, including neutrophils (3rd), haemoglobin (2nd and 7th) and platelets (6th) (Figure 4B). They were used in the subsequent analysis of this cohort of patients.
The optimal cut-off of PBCS-L was set at 86 as determined by the Youden index (Figure 5A). It had a sensitivity of 92.3% and a specificity of 57.1% for prediction of risk of local progression. Patients with high PBCS-L had greater 3-year cumulative hazard of local progression compared to patients with low PBCS-L (30% vs 3%, P = 0.002) (Figure 5B). Twelve out of 13 (92.3%) patients confirmed with local progression were PBCS-L high. The cut-off of PBSC-R was set at 107 with a sensitivity of 90.0% and a specificity of 79.5% (Figure 5C). Patients with high PBCS-R had significantly higher 3-year cumulative hazard of regional progression than patients with low PBCS-R (41% vs 2%, P < 0.001) (Figure 5D). Nine out of 10 (90%) patients confirmed with regional progression were PBCS-R high. Multivariate cox regression confirmed that high PBCS-L (HR 16.0, 95%CI 1.9-132.5, P = 0.01) and high PBCS-R (HR 28.6, 95%CI 3.2-254.8, P = 0.003) were independent indicators of local and regional progression, respectively (Table S1,2).
Overall, there were 20 cases (24%) of patients with local or regional disease progression. To evaluate the predictive value of PBCS in hazard of local and regional progression as a whole, patients were divided into 3 subgroups according to their PBCS-L and PBCS-R: PBCS high: both PBCS-L and PBCS-R high; PBCS medium: either PBCS-L or PBCS-R high; PBCS low: both PBCS-L and PBCS-R low. These subgroups of patients displayed significantly different 3-year cumulative hazards of locoregional failure (58 vs 29% vs 7%, P = 0.0017, Figure 6A). In contrast, no significant difference was found in patients with different clinical stages (Figure 6B). Multivariate cox regression analysis confirmed that PBCS was the only independent variable associated with the hazard of locoregional progression (Table 1). Higher risk of locoregional failure was found in patients with PBCS high (HR 12.2, 95%CI 2.0-76.3, P = 0.007) or medium (HR 5.8, 95%CI 1.2-27.7, P = 0.028) versus patients with PBCS low.