Genomic events stratifying prognosis of early gastric cancer

Abstract

Background: The purpose of the study was to conduct a comprehensive genomic characterization of gene alterations, microsatellite instability (MSI), and tumor mutational burden (TMB) in submucosal-penetrating (Pen) early gastric cancers (EGCs) with varying prognoses.

Methods: Samples from EGC patients undergoing surgery and with 10-year follow-up data available were collected. Tissue genomic alterations were characterized using Trusight Oncology panel (TSO500). Pathway instability (PI) scores for a selection of 218 GC-related pathways were calculated both for the present case series and EGCs from the TCGA cohort.

Results: Higher age and tumor location in the upper-middle tract are significantly associated with an increased hazard of relapse or death from any cause (p = 0.006 and p = 0.032). Even if not reaching a statistical significance, Pen A tumors more frequently present higher TMB values, higher frequency of MSI-subtypes and an overall increase in PI scores, along with an enrichment in immune pathways. ARID1A gene was observed to be significantly more frequently mutated in Pen A tumors (p = 0.006), as well as in patients with high TMB (p = 0.027). Tumors harboring LRP1B alterations seem to have a higher hazard of relapse or death from any cause (p = 0.089), being mutated mainly in relapsed patients (p = 0.093).

Conclusions: We found that the most aggressive subtype Pen A is characterized by a higher frequency of ARID1A mutations and a higher genetic instability, while LRP1B alterations seem to be related to a lower disease-free survival. Further investigations are needed to provide a rationale for the use of these markers to stratify prognosis in EGC patients.

Keywords: ARID1A; LRP1B; EGC; Pen; Prognosis.

Fig. 1

Forest plot. Univariate Cox regression analysis for disease-free survival. The following reference categories were used: “female” for gender; “Pen B” for Kodama classification; “antrum” for tumor location; “intestinal” for Lauren classification; “G1+G2” for grade; “N0” for lymph node status; “Absent” for lymphovascular invasion. Age, Tumor size, and lymph node ratio are reported as continuous variables. HR hazard ratio, CI confidence interval, p p-value

Fig. 2

Mutational characterization of EGCs based on DNA sequencing. a For each patient (x-axis, ordered by TMB) relative frequency of variant types is shown for mutations in exonic regions of genes. b Relative frequency of SNV types. c MSI and TMB status  of EGC patients. Patients are grouped by Kodama classification and relapse status in all subfigures

Fig. 3

The OncoPrint chart.  The heatmap represents genomic alterations including pathogenic variants and VUS, found in at least four patients. Patients are ordered by non-relapsed or relapsed status. The chart was built by ComplexHeatmap R package

Fig. 4

ARID1A status in EGC patients. The histogram represents the percentage of ARID1A alterations in the two different subtypes of Pen tumors. wt wild-type, mut mutant

Fig. 5

Pathway instability analysis. a Heatmap representation of the 218 GC pathways identified with the Reactome enrichment analysis. Pathways are sorted in columns and grouped according to their corresponding Top Level pathways, with hierarchical clustering applied within the groupings. TMB and MSI class, as well as relapse status and Kodama classification are annotated on the left side for all patients. b, c PI score distribution across  b Kodama classification subtypes and c relapse status, shown for pathways grouped according to selected top level pathways. Each data point indicates a PI score value for an individual GC-related pathway, for each patient separately. Violin plots with horizontal black lines marking the median values convey the overall distribution of PI scores between different patient groupings. Note that since no information on Kodama classification was available for EGC-TCGA patients, these patients are excluded from comparisons between Pen A and Pen B groups. d, e Two-dimensional t-SNE representation of PI scores obtained from 218 pathways of interest, for the combined case series of EGC and TCGA EGC. Clustering overlaid with patients’ d Kodama classification and e TMB classification