The choice of chemotherapy in HER2-negative gastric cancer is based on centre's preferences and adverse effects profile. No schedule is currently accepted as standard, nor are there any factors to predict response, other than HER2 status. We seek to evaluate whether Lauren type influences the efficacy of various chemotherapies and on patient overall survival (OS). We have conducted a multicenter study in 31 hospitals. The eligibility criteria include diagnosis of stomach or gastroesophageal junction adenocarcinoma, HER2 negativity, and chemotherapy containing 2-3 drugs. Cox proportional hazards regression adjusted for confounding factors, with tests of 'treatment-by-histology' interaction, was used to estimate treatment effect. Our registry contains 1303 tumours analysable for OS end points and 730 evaluable for overall response rate (ORR). A decrease in ORR was detected in the presence of a diffuse component: odds ratio 0.719 (95% confidence interval (CI), 0.525-0.987), P=0.039. Anthracycline- or docetaxel-containing schedules increased ORR only in the intestinal type. The diffuse type displayed increased mortality with hazard ratio (HR) of 1.201 (95% CI, 1.054-1.368), P=0.0056. Patients receiving chemotherapy with docetaxel exhibited increased OS limited to the intestinal type: HR 0.65 (95% CI, 0.49-0.87), P=0.024, with no increment in OS for the subset having a diffuse component. With respect to progression-free survival (PFS), a significant interaction was seen in the effect of docetaxel-containing schedules, with better PFS limited to the intestinal type subgroup, in the comparison against any other schedule: HR 0.65 (95% CI, 0.50-0.85), P=0.015, and against anthracycline-based regimens: HR 0.64 (95% CI, 0.46-0.88), P=0.046. As a conclusion, in this registry, Lauren classification tumour subtypes predicted survival and responded differently to chemotherapy. Future clinical trials should stratify effect estimations based on histology.

Research into cancer-associated thrombosis (CAT) entails managing dynamic data that pose an analytical challenge. Thus, methods that assume proportional hazards to investigate prognosis entail a risk of misinterpreting or overlooking key traits or time-varying effects. We examined the AGAMENON registry, which collects data from 2,129 patients with advanced gastric cancer. An accelerated failure time (AFT) multistate model and flexible competing risks regression were used to scrutinize the time-varying effect of CAT, as well as to estimate how covariates dynamically predict cumulative incidence. The AFT model revealed that thrombosis shortened progression-free survival and overall survival with adjusted time ratios of 0.72 and 0.56, respectively. Nevertheless, its prognostic effect was nonproportional and disappeared over time if the subject managed to survive long enough. CAT that occurred later had a more pronounced prognostic effect. In the flexible competing risks model, multiple covariates were seen to have significant time-varying effects on the cumulative incidence of CAT (Khorana score, secondary thromboprophylaxis, high tumor burden, and cisplatin-containing regimen), whereas other predictors exerted a constant effect (signet ring cells and primary thromboprophylaxis). The model that assumes proportional hazards was incapable of capturing the effect of these covariates and predicted the cumulative incidence in a biased way. This study evinces that flexible and multistate models are a useful and innovative method to describe the dynamic effect of variables associated with CAT and should be more widely used.

Second-line treatments boost overall survival in advanced gastric cancer (AGC). However, there is a paucity of information as to patterns of use and the results achieved in actual clinical practice. The study population comprised patients with AGC in the AGAMENON registry who had received second-line. The objective was to describe the pattern of second-line therapies administered, progression-free survival following second-line (PFS-2), and post-progression survival since first-line (PPS). 2311 cases with 2066 progression events since first-line (89.3%) were recorded; 245 (10.6%) patients died during first-line treatment and 1326/2066 (64.1%) received a second-line. Median PFS-2 and PPS were 3.1 (95% CI, 2.9–3.3) and 5.8 months (5.5–6.3), respectively. The most widely used strategies were monoCT (56.9%), polyCT (15.0%), ramucirumab+CT (12.6%), platinum-reintroduction (8.3%), trastuzumab+CT (6.1%), and ramucirumab (1.1%). PFS-2/PPS medians gradually increased in monoCT, 2.6/5.1 months; polyCT 3.4/6.3 months; ramucirumab+CT, 4.1/6.5 months; platinum-reintroduction, 4.2/6.7 months, and for the HER2+ subgroup in particular, trastuzumab+CT, 5.2/11.7 months. Correlation between PFS since first-line and OS was moderate in the series as a whole (Kendall’s τ = 0.613), lower in those subjects who received second-line (Kendall’s τ = 0.539), especially with ramucirumab+CT (Kendall’s τ = 0.413). This analysis reveals the diversity in second-line treatment for AGC, highlighting the effectiveness of paclitaxel-ramucirumab and, for a selected subgroup of patients, platinum reintroduction; both strategies endorsed by recent clinical guidelines.

Pancreatic cancer is one of the deadliest cancers worldwide, mainly due to late diagnosis. Therefore, there is an urgent need for novel diagnostic approaches to identify the disease as early as possible. We have developed a diagnostic assay for pancreatic cancer based on the detection of naturally occurring tumor associated autoantibodies against Mucin‐1 (MUC1) using engineered glycopeptides on nanoparticle probes. We used a structure‐guided approach to develop unnatural glycopeptides as model antigens for tumor‐associated MUC1. We designed a collection of 13 glycopeptides to bind either SM3 or 5E5, two monoclonal antibodies with distinct epitopes known to recognize tumor associated MUC1. Glycopeptide binding to SM3 or 5E5 was confirmed by surface plasmon resonance and rationalized by molecular dynamics simulations. These model antigens were conjugated to gold nanoparticles and used in a dot‐blot assay to detect autoantibodies in serum samples from pancreatic cancer patients and healthy volunteers. Nanoparticle probes with glycopeptides displaying the SM3 epitope did not have diagnostic potential. Instead, nanoparticle probes displaying glycopeptides with high affinity for 5E5 could discriminate between cancer patients and healthy controls. Remarkably, the best‐discriminating probes show significantly better true and false positive rates than the current clinical biomarkers CA19‐9 and carcinoembryonic antigen (CEA).