We co-assessed PD-L1 expression and CD8+ tumor-infiltrating lymphocytes in gastric cancer (GC), and categorized into 4 microenvironment immune types. Immunohistochemistry (PD-L1, CD8, Foxp3, E-cadherin, and p53), PD-L1 mRNA in situ hybridization (ISH), microsatellite instability (MSI), and EBV ISH were performed in 392 stage II/III GCs treated with curative surgery and fluoropyrimidine-based adjuvant chemotherapy, and two public genome databases were analyzed for validation. PD-L1+ was found in 98/392 GCs (25.0%). The proportions of immune types are as follows: PD-L1+/CD8High, 22.7%; PD-L1−/CD8Low, 22.7%; PD-L1+/CD8Low, 2.3%; PD-L1−/CD8High, 52.3%. PD-L1+/CD8High type accounted for majority of EBV+ and MSI-high (MSI-H) GCs (92.0% and 66.7%, respectively), and genome analysis from public datasets demonstrated similar pattern. PD-L1−/CD8High showed the best overall survival (OS) and PD-L1−/CD8Low the worst (P < 0.001). PD-L1 expression alone was not associated with OS, however, PD-L1−/CD8High type compared to PD-L1+/CD8High was independent favorable prognostic factor of OS by multivariate analysis (P = 0.042). Adaptation of recent molecular classification based on EBV, MSI, E-cadherin, and p53 showed no significant survival differences. These findings support the close relationship between PD-L1/CD8 status based immune types and EBV+, MSI-H GCs, and their prognostic significance in stage II/III GCs.
BackgroundAnti-EGFR antibody–based treatment is an important therapeutic strategy for advanced colorectal cancer (CRC); despite this, several mutations—including KRAS, BRAF, and PIK3CA mutations, and HER2 amplification—are associated with the mechanisms underlying the development of resistance to anti-EGFR therapy. The aim of our study was to investigate the frequencies and clinical implications of these genetic alterations in advanced CRC.MethodsKRAS, BRAF, and PIK3CA mutations were determined by Cobas real-time polymerase chain reaction (PCR) in 191 advanced CRC patients with distant metastasis. Microsatellite instability (MSI) status was determined by a fragmentation assay and HER2 amplification was assessed by silver in situ hybridization. In addition, KRAS mutations were investigated by the Sanger sequencing method in 97 of 191 CRC cases.ResultsMutations in KRAS, BRAF, and PIK3CA were found in 104 (54.5%), 6 (3.1%), and 25 (13.1%) cases of advanced CRC, respectively. MSI-high status and HER2 amplification were observed in 3 (1.6%) and 16 (8.4%) cases, respectively. PIK3CA mutations were more frequently found in KRAS mutant type (18.3%) than KRAS wild type (6.9%) (P = 0.020). In contrast, HER2 amplifications and BRAF mutations were associated with KRAS wild type with borderline significance (P = 0.052 and 0.094, respectively). In combined analyses with KRAS, BRAF and HER2 status, BRAF mutations or HER2 amplifications were associated with the worst prognosis in the wild type KRAS group (P = 0.004). When comparing the efficacy of detection methods, the results of real time PCR analysis revealed 56 of 97 (57.7%) CRC cases with KRAS mutations, whereas Sanger sequencing revealed 49 cases (50.5%).ConclusionsKRAS mutations were found in 54.5% of advanced CRC patients. Our results support that subgrouping using PIK3CA and BRAF mutation or HER2 amplification status, in addition to KRAS mutation status, is helpful for managing advanced CRC patients.
Background. Gastric cancer (GC) is a heterogeneous disease, and substantial efforts have been made to develop a molecular biology-based classification system for GC. Analysis of the genomic signature is not always feasible, and thus, we aimed to (i) develop and validate a practical immunohistochemistry (IHC)-and polymerase chain reaction (PCR)-based molecular classification of GC and (ii) to assess HER2 status according to this classification. Materials and Methods. A total of 894 consecutive patients with GC from two individual cohorts (training, n = 507; validation, n = 387) were classified using Epstein-Barr virus (EBV) in situ hybridization, microsatellite instability (MSI) testing, and IHC for E-cadherin and p53. Results. We were able to classify patients into five groups in the training cohort: group 1 (MSI + ), group 2 (EBV − , MSI − , non-epithelial-mesenchymal transition [non-EMT]-like, p53 − ), group 3 (EBV + ), group 4 (EBV − , MSI − , non-EMT-like, p53 + ), and group 5 (EBV − , MSI − , EMT-like). In the training cohort, each group showed different overall survival (OS) after gastrectomy (p < .001); group 1 had the best prognosis, and group 5 showed the worst survival outcome. The significant impact of the classification system on OS was also verified in the validation cohort (p = .004). HER2 positivity was observed in 6.5% of total population, and most of HER2-positive cases (93.1%) were included in groups 2 and 4. Conclusion. We developed and validated a modified IHCand PCR-based molecular classification system in GC, which showed significant impact on survival, irrespective of stage or other clinical variables. We also found close association between HER2 status and non-EMT phenotype in our classification system. The Oncologist 2019;24:e1321-e1330 Implications for Practice: Molecular classification of gastric cancer suggested by previous studies mostly relies on extensive genomic data analysis, which is not always available in daily practice. The authors developed a simplified immunohistochemistry-and polymerase chain reaction-based molecular classification of gastric cancer and proved the prognostic significance of this classification, as well as the close association between HER2 status and certain groups of the classification, in the largest consecutive cohort of gastric cancer. Results of this study suggest that this scheme is a cost-effective, easy-to-implement, and feasible way of classifying gastric cancer in daily clinical practice, also serving as a practical tool for aiding therapeutic decisions and predicting prognosis.
Background: Recently, molecular classifications of gastric cancer (GC) have been proposed that include TP53 mutations and their functional activity. We aimed to demonstrate the correlation between p53 immunohistochemistry (IHC) and TP53 mutations as well as their clinicopathological significance in GC. Methods: Deep targeted sequencing was performed using surgical or biopsy specimens from 120 patients with GC. IHC for p53 was performed and interpreted as strong, weak, or negative expression. In 18 cases (15.0%) with discrepant TP53 mutation and p53 IHC results, p53 IHC was repeated. Results: Strong expression of p53 was associated with TP53 missense mutations, negative expression with other types of mutations, and weak expression with wild-type TP53 (p < .001). The sensitivity for each category was 90.9%, 79.0%, and 80.9%, and the specificity was 95.4%, 88.1%, and 92.3%, respectively. The TNM stage at initial diagnosis exhibited a significant correlation with both TP53 mutation type (p = .004) and p53 expression status (p = .029). The Kaplan-Meier survival analysis for 109 stage II and III GC cases showed that patients with TP53 missense mutations had worse overall survival than those in the wild-type and other mutation groups (p = .028). Strong expression of p53 was also associated with worse overall survival in comparison to negative and weak expression (p = .035). Conclusions: Results of IHC of the p53 protein may be used as a simple surrogate marker of TP53 mutations. However, negative expression of p53 and other types of mutations of TP53 should be carefully interpreted because of its lower sensitivity and different prognostic implications.
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