Prognostic and Predictive Value of p21-activated Kinase 6 Associated Support Vector Machine Classifier in Gastric Cancer Treated by 5-fluorouracil/Oxaliplatin Chemotherapy

Abstract

To determine whether p21-activated Kinase (PAK) 6 is a prognostic and predictive marker in gastric cancer (GC) and to construct a classifier that can identify a subset of patients who are highly sensitive to 5-fluorouracil/oxaliplatin chemotherapy. We retrospectively analyzed the expression levels of PAK6, cyclooxygenase 2, p21^WAF1, Ki-67, excision repair cross-complementing gene 1, and thymidylate synthase in 242 paraffin-embedded GC specimens of the training cohort by immunohistochemistry. Then, we used support vector machine (SVM)-based methods to develop a predictive classifier for chemotherapy (chemotherapy score - CS-SVM classifier). Further validation was performed in an independent cohort of 279 patients. High PAK6 expression was associated with poor prognosis and increased chemoresistance to 5-FU/oxaliplatin chemotherapy. The CS-SVM classifier distinguished patients with stage II and III GC into low- and high-CS-SVM groups, with significant differences in the 5-year disease-free survival (DFS) and overall survival (OS) in chemotherapy patients. Moreover, chemotherapy significantly prolonged the DFS and OS of the high CS-SVM patients in the training and validation cohorts. In conclusion, PAK6 was an independent prognostic factor and increased chemoresistance. The CS-SVM classifier distinguished a subgroup of stage II and III patients who would highly benefit from chemotherapy, thus facilitating patient counseling and individualizing the management.

Keywords: 5-FU/oxaliplatin chemotherapy; Gastric cancer; Nomogram; SVM classifier; p21-activated kinase 6.

Fig. 1

PAK6 expression in GC tissues. Representative IHC photographs reveal high PAK6 density in tumor tissue, low density in nontumor tissue (A), and density from TNM stage I–IV (B). (C) Scatter plots for IHC staining score in unpaired nontumor tissue (n = 242) and tumor tissue (n = 242) from the training cohort. (D) Percentage of patients with high intratumoral PAK6 expression increased moderately accompanied by disease progression from TNM stage I–IV (data from the trainging and validation cohort). Scale bar, 100 μm.

Fig. 2

Kaplan–Meier analysis of DFS and OS according to intratumoral PAK6 expression in GC patients. Left panel: CT patients; right panel: no CT patients. Training cohort: n = 241, validation cohort: n = 342.

Fig. 3

Effect of adjuvant chemotherapy on overall survival (OS) in different subgroups of stage II and III GC patients. A, training cohort; B, validation cohort.

Fig. 4

Kaplan–Meier analysis of DFS and OS according to CS-SVM signature in stage II and III GC patients. Left panel: CT patients; right panel: no CT patients. Training cohort (n = 203), validation cohort (n = 279).

Fig. 5

Kaplan–Meier analysis of DFS and OS according to chemotherapy in stage II and III GC patients. Training cohort (n = 203), validation cohort (n = 279). Left panel: CS-SVM high patients; right panel: CS-SVM low patients.

Fig. 6

Nomogram to predict 1-, 3- and 5-year survival probability in gastric cancer. (A) Nomogram for predicting proportion of GC patients with OS after surgery. (B) Plots depict the calibration of each model in terms of agreement between predicted and observed outcomes. Model performance is shown by the plot, relative to the 45-degree line, which represents perfect prediction. All predictions lie within a 7.5% margin of error (within the dashed line). (C) Time-dependent ROC curves by nomogram for 1-, 3- and 5-year OS probability in the training cohort and validation cohort. (D) (E) Decision curve analysis for the nomogram. The y-axis measures the net benefit. The red and blue solid line represents the nomogram for 3, 5-year OS. The dotted line represents the assumption that all patients have 3, 5-year OS. Thin black line represents the assumption that no patients have 3, 5-year OS. The net benefit was calculated by subtracting the proportion of all patients who are false positive from the proportion who are true positive, weighting by the relative harm of forgoing treatment compared with the negative consequences of an unnecessary treatment.(Vickers et al., 2008) Here, the relative harm was calculated by (p_t/[1 - p_t]). “p_t” (threshold probability) is where the expected benefit of treatment is equal to the expected benefit of avoiding treatment; at which time a patient will opt for treatment informs us of how a patient weighs the relative harms of false-positive results and false-negative results ([a–c]/[b–d] = [1 - pt]/pt); a - c is the harm from a false-negative result; b–d is the harm from a false-positive result. a, b, c and d give, respectively, the value of true positive, false positive, false negative, and true negative.(Vickers et al., 2008).