The vast majority of all somatic mutations in cancer are passenger mutations, with a small minority being driver mutations. Yes, a passenger mutation can disrupt the function of a protein, but this does not imply that it has a phenotypic effect that promotes tumor growth. For example, a nonsense mutation in an olfactory receptor may completely eliminate the protein, but, because olfactory receptors do not result in a phenotype relevant to cancer, the mutation is a passenger. Because of the large amount of cancer sequencing, a somatic mutation will have been observed at least once for all genes in the genome. So generally one needs to be more quantitative about what "a lot" of mutations means. There are various statistical methods to formally test whether the number of mutations in a gene is above that expected based on a background rate of mutations.
By your use of term "pathogenic" or "likely pathogenic", this makes me think you are using a database like clinvar, which is not necessarily very comprehensive for somatic mutations. So a first issue is you could try to annotate variants based on cancer-specific databases like OncoKB (https://www.oncokb.org/). However, an issue with all curated databases is that they will likely miss the majority of driver mutations because most driver mutations outside a few hotspots have not been studied in the literature. Thus, computational predictions of whether a mutation is a driver becomes the only practical option. I've previously developed a method CHASMplus to predict which missense mutations may be cancer drivers (https://chasmplus.readthedocs.io/en/latest/ ). You could annotate your variants with CHASMplus using OpenCRAVAT (https://opencravat.org/ ) by either submitting your vcf to the webserver or running a local copy. OpenCRAVAT also has other annotators that might help determine if a mutation is a driver, for example annotations on mutation "hotspots" etc.
Regarding whether a pathway is "disrupted" by mutations in cancer, you need to demonstrate that there is appreciable statistical evidence that many mutations in your pathway are indeed likely to be cancer drivers. Even better would be to then show that those driver mutations are associated with altered pathway activation as inferred by RNA-seq, etc. Depending on whether you are claiming a mechanistic argument, you might then need experimental evidence.
I would recommend to be very careful about claiming a passenger mutation disrupts a pathway, unless you are trying to make an argument about synthetic lethal interactions.
You can always show all somatic mutations in a plot. However, I might recommend that you color differently those that are known/predicted oncogenic versus those with highly uncertain significance.