Identifying the optimal treatment strategy in patients with resectable non-cardia gastric cancer.

BACKGROUND: Multimodal treatment strategy including perioperative chemotherapy (PEC), postoperative chemoradiation therapy (POCR), and postoperative chemotherapy (POC) has been accepted as the standard of care in gastric cancer (GC). The ideal sequence and type of therapy remain undetermined. METHOD: The National Cancer Database was examined from 2006 to 2016 to identify patients with resectable non-cardia gastric cancer. Patient outcomes were compared based on the receipt of PEC, POCR, and POC. This comparison was repeated in a sub-group of patients who received optimal treatment. Optimal treatment was defined as initial chemotherapy within 45 days of diagnosis, resection within 45 days of diagnosis, negative margins, adjuvant chemotherapy within 90 days of resection and standard radiation dose (45 Gy). Kaplan-Meier test, log-rank test, and multivariable analysis (MVA) were performed. RESULTS: We identified 9589 patients. Median survival was greater in the PEC group followed by POCR and POC (60.6, 42.3, and 31.2 months, respectively). On MVA, factors associated with worse overall survival included age above median (≥ 63 years), Charlson-Deyo score of ≥ 1, non-academic/research program, poorly differentiated/undifferentiated grade, positive margins, and positive lymph nodes. Both PEC and POCR were associated with improved survival when compared to POC (HR 0.78 and 0.79; p < 0.001). When compared with PEC, no significant difference was noted with POCR (HR 1.01; p = 0.987). These results were maintained in optimally treated cohort (n = 3418). CONCLUSION: In patients with resectable non-cardia gastric cancer, both perioperative chemotherapy and postoperative chemoradiation therapy were associated with improved survival when compared to postoperative chemotherapy. No difference was noted between perioperative chemotherapy and postoperative chemoradiation therapy. These results were maintained in the optimally treated cohort.

Impact of nodal status in determining multimodal treatment strategies in non-cardia gastric cancer.

BACKGROUND: Patients with resectable noncardia gastric cancer may be subjected to perioperative chemotherapy (PEC), postoperative chemoradiation (POCR), or postoperative chemotherapy (POC). We analyzed these treatment strategies to determine optimal therapy based on nodal status. METHOD: The National Cancer Database was used to identify patients with resected noncardia gastric cancer (2004-2016). Patients were stratified based on clinical nodal status-negative (cLN-), positive (cLN+) and pathological nodal status (pLN-, pLN+). In cLN- patients who underwent upfront resection and were upstaged to pLN+, POC, and POCR were compared. Overall survival (OS) with PEC, POCR, and POC were compared in cLN- and cLN+. RESULTS: We identified 6142 patients (cLN-: 3831; cLN+: 2311). In cLN- patients who underwent upfront resection (N = 3423), 69% were upstaged to pLN+ disease (N = 2499; POCR = 1796, POC = 703). On MVA, POCR was associated with significantly improved OS when compared to POC (hazard ratio [HR]: 0.75; p < 0.001). In patients with cLN- disease (PEC = 408; POCR = 2439; POC = 984), PEC(HR: 0.77; p = 0.01) and POCR(HR: 0.81; p < 0.001) were associated with improved OS compared with POC. In cLN+ group (PEC = 452; POCR = 1284; POC = 575), POCR was associated with improved OS compared with POC (HR: 0.81; p < 0.01), and trend towards improved OS was noted when PEC(HR: 0.83; p = 0.055) was compared with POC. CONCLUSION: Postoperative chemoradiation may be the preferred treatment strategy over postoperative chemotherapy in non-cardia gastric cancer patients who receive upfront resection and are upstaged from clinically node negative to pathologically node positive disease.

Does non-metastatic gastric cancer of the cardia warrant a different treatment strategy?

BACKGROUND: Multimodal treatment strategies with surgery as its centerpiece have been accepted as the standard of care in nonmetastatic cardia gastric cancer (CGC). There remains a lack of consensus regarding the optimal multimodal treatment strategy. METHOD: We queried National Cancer Database from 2004 to 2016 to identify patients with resected nonmetastatic CGC who received perioperative chemotherapy (PEC), postoperative chemoradiation therapy (POCR), or postoperative chemotherapy (POC). A subgroup analysis was performed in optimally treated patients defined as initial chemotherapy within 45 days of diagnosis, resection within 45 days of diagnosis, negative margins, adjuvant chemotherapy within 90 days of resection, and standard radiation dose (45 Gy). Kaplan-Meier, Univariate analysis (UVA), and Multivariable analysis (MVA) were performed. RESULTS: We identified 2387 patients. Median survival was 38.8 months in the PEC group, 36 months in the POCR group, and 32.3 months in the POC group (p = 0.1025). On UVA, patients treated with PEC had an association with improved survival (HR, 0.83; p = 0.037) when compared with POC. On MVA, no significant difference was noted in overall survival (OS) between PEC, POCR, and POC, similar to subgroup analysis of optimally treated cohort. CONCLUSION: OS rate in nonmetastatic CGC is not significantly different between patients receiving PEC, POCR, or POC.

The Prognostic Value of Lymphovascular Invasion in Truncal and Extremity Soft Tissue Sarcomas: An Analysis from the National Cancer Database.

OBJECTIVE: The aim of this study was to determine the association between lymphovascular invasion (LVI) and overall survival (OS) in truncal/extremity soft tissue sarcomas (STS). METHODS: The National Cancer Database (NCDB) was queried for all patients, ages 18-85 years, who underwent resection of primary, truncal/extremity STS between 2010 and 2012, and had LVI data. The primary endpoint was OS. RESULTS: Among 6169 patients identified, the most common histology groups were (1) liposarcoma (LPS, 24%), (2) undifferentiated pleiomorphic sarcoma (UPS, 19%), and (3) leiomyosarcoma (LMS, 15%); 449 patients (7%) were LVI-positive. There were no differences in demographics or comorbidities between the LVI groups. Compared with LVI-negative patients, LVI-positive patients were more likely to have larger (> 5 cm: 80% vs. 66%), deep (80% vs. 68%), and high-grade tumors (82% vs. 57%). They were also more likely to have positive margins (27% vs. 17%), nodal (16% vs. 2%) and metastatic disease (21% vs. 4%), and receive chemotherapy (37% vs. 18%; all p < 0.001). LVI was associated with worse median OS (39 months vs. MNR; p < 0.001), which persisted on stratum-specific analyses for all tumor grades, size categories, and stages I-III, but not stage IV. On multivariable Cox regression, LVI was associated with worse OS (hazard ratio [HR] 1.84, 95% confidence interval [CI] 1.39-2.44), while accounting for other significant prognostic factors. Among non-metastatic, curative-intent resections (n = 5696), LVI was still associated with worse OS (HR 1.79, 95% CI 1.28-2.49). CONCLUSIONS: LVI appears to be an important adverse pathologic factor in truncal and extremity STS. Even when taking into account other established prognostic factors, LVI was predictive of worse OS. Knowledge of LVI status may help to better risk-stratify patients and guide management strategies, and should be considered in future prognostic classification schemes and nomograms.

Gene integrated set profile analysis: a context-based approach for inferring biological endpoints.

The identification of genes with specific patterns of change (e.g. down-regulated and methylated) as phenotype drivers or samples with similar profiles for a given gene set as drivers of clinical outcome, requires the integration of several genomic data types for which an 'integrate by intersection' (IBI) approach is often applied. In this approach, results from separate analyses of each data type are intersected, which has the limitation of a smaller intersection with more data types. We introduce a new method, GISPA (Gene Integrated Set Profile Analysis) for integrated genomic analysis and its variation, SISPA (Sample Integrated Set Profile Analysis) for defining respective genes and samples with the context of similar, a priori specified molecular profiles. With GISPA, the user defines a molecular profile that is compared among several classes and obtains ranked gene sets that satisfy the profile as drivers of each class. With SISPA, the user defines a gene set that satisfies a profile and obtains sample groups of profile activity. Our results from applying GISPA to human multiple myeloma (MM) cell lines contained genes of known profiles and importance, along with several novel targets, and their further SISPA application to MM coMMpass trial data showed clinical relevance.