Natural killer cell and stroma abundance are independently prognostic and predict gastric cancer chemotherapy benefit

Abstract

BACKGROUNDSpecific features of the tumor microenvironment (TME) may provide useful prognostic information. We conducted a systematic investigation of the cellular composition and prognostic landscape of the TME in gastric cancer.METHODSWe evaluated the prognostic significance of major stromal and immune cells within the TME. We proposed a composite TME-based risk score and tested it in 6 independent cohorts of 1678 patients with gene expression or IHC measurements. Further, we devised a patient classification system based on TME characteristics.RESULTSWe identified NK cells, fibroblasts, and endothelial cells as the most robust prognostic markers. The TME risk score combining these cell types was an independent prognostic factor when adjusted for clinicopathologic variables (gene expression, HR [95% CI], 1.42 [1.22-1.66]; IHC, 1.34 [1.24-1.45], P < 0.0001). Higher TME risk scores consistently associated with worse survival within every pathologic stage (HR range, 2.18-3.11, P < 0.02) and among patients who received surgery only. The TME risk score provided additional prognostic value beyond stage, and combination of the two improved prognostication accuracy (likelihood-ratio test χ2 = 235.4 vs. 187.6, P < 0.0001; net reclassification index, 23%). The TME risk score can predict the survival benefit of adjuvant chemotherapy in nonmetastatic patients (stage I-III) (interaction test, P < 0.02). Patients were divided into 4 TME subtypes that demonstrated distinct genetic and molecular patterns and complemented established genomic and molecular subtypes.CONCLUSIONWe developed and validated a TME-based risk score as an independent prognostic and predictive factor, which has the potential to guide personalized management of gastric cancer.FUNDINGThis project is partially supported by NIH grant 1R01 CA222512.

Keywords: Gastric cancer; Oncology.

Figure 1. Prognostic significance of the TME risk score in the GEP and IHC cohorts.

(A) The formula to define the TME risk score. The abundance level of each cell type is calculated by taking the average expression of preselected marker genes listed in Supplemental Table 2. (B) Increased TME risk score was significantly correlated with inferior overall survival in all 3 GEP cohorts (ACRG, n = 300; GSE15459, n = 192; and GSE84437, n = 433). A fixed-effect model indicated a strong overall prognostic effect of the TME risk score. Cox regression was used to measure the prognostic effects of the TME risk score. (C–E) The high TME risk group was associated with worse overall survival in these cohorts (ACRG, n = 300; GSE15459, n = 192; and GSE84437, n = 433). The GEP cutoff value for TME risk score was defined by optimizing the Cox regression P value in the ACRG cohort. (F–I) Same as in C–E with for 3 IHC cohorts (SMU1, n = 247; SMU2, n = 234; and SYSU, n = 272). The IHC cutoff value was defined by optimizing the Cox regression P value in the SMU1 cohort. HRs and CIs were estimated by Cox regression. P values were generated by log-rank test.

Figure 2. The prognostic effects of the TME risk score in patients within each pathological stage in the combined IHC cohorts.

A high TME risk score was consistently associated with worse overall survival in patients with stage I (n = 113, A), stage II (n = 141, B), stage III (n = 401, C), and stage IV (n = 98, D) disease. The cutoff value for the TME risk score was the same as in Figure 1. HRs and CIs were estimated by Cox regression. P values were generated by log-rank test.

Figure 3. Predictive relevance of the TME risk score for the benefit of chemotherapy in stage I–III gastric cancer.

(A) Patients with a high TME risk score (n = 419) derived a significant survival benefit from adjuvant chemotherapy at 5 years. However, patients with a low TME risk score (n = 175) did not benefit from adjuvant chemotherapy (B). The patients treated with or without chemotherapy were matched according to 5 clinicopathologic factors. HRs and CIs were estimated by Cox regression. P values were generated by log-rank test. The P value for the interaction between the TME risk group and adjuvant chemotherapy was 0.0148.

Figure 4. The definition of the TME subtypes and their prognostic significance.

(A) Patients were divided into 4 TME subtypes, based on the distribution of NK cell abundance and stroma scores in the merged GEP cohorts (n = 1340). In the merged GEP cohorts (except TCGA) (n = 925) (B) and IHC cohorts (n = 753) (C), the NK-high and stroma-low and NK-low and stroma-high groups correspond to the best and the worst prognosis, respectively. The NK-high and stroma-high and NK-low and stroma-low subtypes were associated with an intermediate prognosis. (D) Representative examples of IHC images for 4 TME subtypes. CD57, CD34, and αSMA are stains for NK cells, endothelial cells, and fibroblasts. Original magnification, ×200. P values were generated by log-rank test.

Figure 5. Complementary prognostic value of the TME subtypes to the ACRG subtypes.

(A) The correspondence between patients classified according to the TME subtypes and ACRG subtypes in the merged GEP cohorts (n = 1340). (B and C) The TME subtypes can further stratify patients within the ACRG MSI (n = 222) and MSS/TP53^+/– (n = 562) subtypes into groups with distinct prognoses. The survival difference within the ACRG MSS/EMT (n = 141) subgroup showed a trend due to a smaller number of patients (D). P values were generated by log-rank test.

Figure 6. The correspondence between patients classified according to TCGA subtypes and the TME subtypes in TCGA cohort.

Genomic features that were significantly enriched in certain TME subtypes in TCGA STAD cohort (n = 415) are presented.