Tumour infiltrating lymphocytes and survival after adjuvant chemotherapy in patients with gastric cancer: post-hoc analysis of the CLASSIC trial.

BACKGROUND: Only a subset of gastric cancer (GC) patients with stage II-III benefits from chemotherapy after surgery. Tumour infiltrating lymphocytes per area (TIL density) has been suggested as a potential predictive biomarker of chemotherapy benefit. METHODS: We quantified TIL density in digital images of haematoxylin-eosin (HE) stained tissue using deep learning in 307 GC patients of the Yonsei Cancer Center (YCC) (193 surgery+adjuvant chemotherapy [S + C], 114 surgery alone [S]) and 629 CLASSIC trial GC patients (325 S + C and 304 S). The relationship between TIL density, disease-free survival (DFS) and clinicopathological variables was analysed. RESULTS: YCC S patients and CLASSIC S patients with high TIL density had longer DFS than S patients with low TIL density (P = 0.007 and P = 0.013, respectively). Furthermore, CLASSIC patients with low TIL density had longer DFS if treated with S + C compared to S (P = 0.003). No significant relationship of TIL density with other clinicopathological variables was found. CONCLUSION: This is the first study to suggest TIL density automatically quantified in routine HE stained tissue sections as a novel, clinically useful biomarker to identify stage II-III GC patients deriving benefit from adjuvant chemotherapy. Validation of our results in a prospective study is warranted.

Systematic selection of competing metabolomics methods in a metabolite-sensory relationship study.

INTRODUCTION: The relationship between the chemical composition of food products and their sensory profile is a complex association confronting many challenges. However, new untargeted methodologies are helping correlate metabolites with sensory characteristics in a simpler manner. Nevertheless, in the pilot phase of a project, where only a small set of products are used to explore the relationships, choices have to be made about the most appropriate untargeted metabolomics methodology. OBJECTIVE: To provide a framework for selecting a metabolite-sensory methodology based on: the quality of measurements, the relevance of the detected metabolites in terms of distinguishing between products or in terms of whether they can be related to the sensory attributes of the products. METHODS: In this paper we introduce a systematic approach to explore all these different aspects driving the choice for the most appropriate metabolomics method. RESULTS: As an example we have used a tomato soup project where the choice between two sampling methods (SPME and SBSE) had to be made. The results are not always consistently pointing to the same method as being the best. SPME was able to detect metabolites with a better precision, SBSE seemed to be able to provide a better distinction between the soups. CONCLUSION: The three levels of comparison provide information on how the methods could perform in a follow up study and will help the researcher to make a final selection for the most appropriate method based on their strengths and weaknesses.

Computational Image Analysis of T-Cell Infiltrates in Resectable Gastric Cancer: Association with Survival and Molecular Subtypes.

BACKGROUND: Gastric and gastro-esophageal junction cancers (GCs) frequently recur after resection, but markers to predict recurrence risk are missing. T-cell infiltrates have been validated as prognostic markers in other cancer types, but not in GC because of methodological limitations of past studies. We aimed to define and validate the prognostic role of major T-cell subtypes in GC by objective computational quantification. METHODS: Surgically resected chemotherapy-naïve GCs were split into discovery (n = 327) and validation (n = 147) cohorts. CD8 (cytotoxic), CD45RO (memory), and FOXP3 (regulatory) T-cell densities were measured through multicolor immunofluorescence and computational image analysis. Cancer-specific survival (CSS) was assessed. All statistical tests were two-sided. RESULTS: CD45RO-cell and FOXP3-cell densities statistically significantly predicted CSS in both cohorts. Stage, CD45RO-cell, and FOXP3-cell densities were independent predictors of CSS in multivariable analysis; mismatch repair (MMR) and Epstein-Barr virus (EBV) status were not statistically significant. Combining CD45RO-cell and FOXP3-cell densities into the Stomach Cancer Immune Score showed highly statistically significant (all P ≤ .002) CSS differences (0.9 years median CSS to not reached). T-cell infiltrates were highest in EBV-positive GCs and similar in MMR-deficient and MMR-proficient GCs. CONCLUSION: The validation of CD45RO-cell and FOXP3-cell densities as prognostic markers in GC may guide personalized follow-up or (neo)adjuvant treatment strategies. Only those 20% of GCs with the highest T-cell infiltrates showed particularly good CSS, suggesting that a small subgroup of GCs is highly immunogenic. The potential for T-cell densities to predict immunotherapy responses should be assessed. The association of high FOXP3-cell densities with longer CSS warrants studies into the biology of regulatory T cells in GC.

Histological intratumoral heterogeneity in pretreatment esophageal cancer biopsies predicts survival benefit from neoadjuvant chemotherapy: results from the UK MRC OE02 trial.

Despite the use of multimodal treatment, survival of esophageal cancer (EC) patients remains poor. One proposed explanation for the relatively poor response to cytotoxic chemotherapy is intratumor heterogeneity. The aim was to establish a statistical model to objectively measure intratumor heterogeneity of the proportion of tumor (IHPoT) and to use this newly developed method to measure IHPoT in the pretreatment biopsies from from EC patients recruited to the OE02 trial. A statistical mixed effect model (MEM) was established for estimating IHPoT based on variation in hematoxylin/eosin (HE) stained pretreatment biopsy pieces from the same individual in 218 OE02 trial patients (103 treated by chemotherapy and surgery (chemo+surgery); 115 patients treated by surgery alone). The relationship between IHPoT, prognosis, chemotherapy survival benefit, and clinicopathological variables was assessed. About 97 (44.5%) and 121 (55.5%) ECs showed high and low IHPoT, respectively. There was no significant difference in IHPoT between surgery (median [range], 0.1637 [0-3.17]) and chemo+surgery (median [range], 0.1692 [0-2.69]) patients (P = 0.43). Chemo+surgery patients with low IHPoT had a significantly longer survival than surgery patients (HR = 1.81, 95% CI: 1.20-2.75, P = 0.005). There was no survival difference between chemo+surgery and surgery patients with high IHPoT (HR = 1.15, 95% CI: 0.72-1.81, P = 0.566). This is the first study suggesting that IHPoT measured in the pretreatment biopsy can predict chemotherapy survival benefit in EC patients. IHPoT may represent a clinically useful biomarker for patient treatment stratification. Future studies should determine if pathologists can reliably estimate IHPoT.