Comprehensive transcriptomic profiling and mutational landscape of primary gastric linitis plastica.

BACKGROUND: Primary gastric linitis plastica (GLP) is a distinct phenotype of gastric cancer with poor survival. Comprehensive molecular profiles and putative therapeutic targets of GLP remain undetermined. METHODS: We subjected 10 tumor-normal tissue pairs to whole exome sequencing (WES) and whole transcriptome sequencing (WTS). 10 tumor samples were all GLP which involves 100% of the gastric wall macroscopically. TCGA data were compared to generate the top mutated genes and the overexpressed genes in GLP. RESULTS: Our results reveal that GLP has distinctive genomic and transcriptomic features, dysfunction in the Hippo pathway is likely to be a key step during GLP development. 6 genes were identified as significantly highly mutated genes in GLP, including AOX1, ANKRD36C, CPXM1, PTPN14, RPAP1, and DCDC1). MUC6, as a previously identified gastric cancer driver gene, has a high mutation rate (20%) in GLP. 20% of patients in our GLP cohort had CDH1 mutations, while none had RHOA mutations. GLP exhibits high immunodeficiency and low AMPK pathway activity. Our WTS results showed that 3 PI3K-AKT pathway-related genes (PIK3R2, AKT3, and IGF1) were significantly up-regulated in GLP. Two genes were identified using immunohistochemistry (IHC), IGF2BP3 and MUC16, which specifically expressed in diffuse-type-related gastric cancer cell lines, and its knockdown inhibits PI3K-AKT pathway activity. CONCLUSIONS: We provide the first integrative genomic and transcriptomic profiles of GLP, which may facilitate its diagnosis, prognosis, and treatment.

Proposal of an automated tumor-stromal ratio assessment algorithm and a nomogram for prognosis in early-stage invasive breast cancer.

BACKGROUND: The tumor-stromal ratio (TSR) has been verified to be a prognostic factor in many solid tumors. In most studies, it was manually assessed on routinely stained H&E slides. This study aimed to assess the TSR using image analysis algorithms developed by the Qupath software, and integrate the TSR into a nomogram for prediction of the survival in invasive breast cancer (BC) patients. METHODS: A modified TSR assessment algorithm based on the recognition of tumor and stroma tissues was developed using the Qupath software. The TSR of 234 invasive BC specimens in H&E-stained tissue microarrays (TMAs) were assessed with the algorithm and categorized as stroma-low or stroma-high. The consistency of TSR estimation between Qupath prediction and pathologist annotation was analyzed. Univariable and multivariable analyses were applied to select potential risk factors and a nomogram for predicting survival in invasive BC patients was constructed and validated. An extra TMA containing 110 specimens was obtained to validate the conclusion as an independent cohort. RESULTS: In the discovery cohort, stroma-low and stroma-high were identified in 43.6% and 56.4% cases, respectively. Good concordance was observed between the pathologist annotated and Qupath predicted TSR. The Kaplan-Meier curve showed that stroma-high patients were associated with worse 5-DFS compared to stroma-low patients (p = 0.007). Multivariable analysis identified age, T stage, N status, histological grade, ER status, HER-2 gene, and TSR as potential risk predictors, which were included in the nomogram. The nomogram was well calibrated and showed a favorable predictive value for the recurrence of BC. Kaplan-Meier curves showed that the nomogram had a better risk stratification capability than the TNM staging system. In the external validation of the nomogram, the results were further validated. CONCLUSIONS: Based on H&E-stained TMAs, this study successfully developed image analysis algorithms for TSR assessment and constructed a nomogram for predicting survival in invasive BC.