In this study, we performed a comprehensive and unbiased analysis of non-coding SNVs and indels in 212 GC genomes, the largest studied cohort thus far. In addition to a previously identified indel enrichment at LIPF39, our analysis identified two other gastric lineage-specific genes with broad enrichment of non-coding indels (PGC and MUC6). Our results show that the accumulation of indels occur in multiple lineage specific genes in GC. Yet, indels at these three genes were not associated with change in gene expression. The functional consequences of these indels are therefore still unclear. Strikingly, genome-wide analysis of somatic SNVs revealed 34 significant mutation hotspots (Bonferroni adjusted P-value < 0.01) that were disproportionately enriched in CBSs. Katainen et al. previously reported an increased mutation load at CBSs in colorectal cancer15, and a subsequent study by Kaiser et al. confirmed the general hypermutation at CBSs in 11 cancer types18. Both studies generally discounted CBS mutations as passengers, yet they did not rigorously explore the hypothesis that a subset of these mutated CBSs may be undergoing positive selection within individual cancer types. Indeed, a recent study on motif-breaking mutations identified a recurrent CBS mutation that disrupts CTCF binding40, confirming the motif-breaking potential of CBS mutations. Here, we used a large cohort of GC genomes in combination with rigorous statistics, to show that mutation rates at 11 specific CBSs are unexpectedly high and cannot alone be explained by a genome-wide elevated mutation burden at CBS, indicating positive selection at these sites. Out of the four CBS hotspots we examined, three of them were associated with nominally significant expression changes of neighboring genes (CENPQ, KCNQ5, and SPG20), and these associations were validated in an independent tumor cohort. Furthermore, it is possible that mutations at these CBS hotspots also have long-range or spatio-temporal regulatory effects on gene expression that are not captured by bulk tumor transcriptome profiling. Overall, our analyses nominate these CBS hotspots as potential drivers in GC, and support the hypothesis that driver mutations may arise as a by-product of the increased mutation load at CBSs followed by positive selection at specific CBSs. This is comparable to a model of genomic rearrangement hotspots in breast cancer, where rearrangements initially arise from defective homologous-recombination-repair and those affecting cancer risk loci are subsequently positively selected, forming rearrangement hotspots58.