The quality control and filtration of cancer somatic mutations (CAMs), including the elimination of false positives due to technical bias and the selection of key mutation candidates, are crucial steps for downstream analysis in cancer genomics. However, due to diverse needs and the lack of standardized filtering criteria, the filtering strategies applied vary from study to study, often resulting in reduced efficiency, accuracy, and reproducibility. Here, we present CaMutQC, a heuristic quality control and soft-filtering R/Bioconductor package designed specifically for CAMs. CaMutQC enables users to remove false positive mutations, select potential mutation candidates, and estimate Tumor Mutation Burden (TMB) with a single line of code, using either default or customized parameters. A filter report and a code log can also be generated after the filtration process to facilitate reproducibility and comparison. The application of CaMutQC to a Whole-exome Sequencing (WES) benchmark dataset demonstrated its strong capability by eliminating 85.55 % of false positive Single nucleotide variants (SNVs) while retaining 90.72 % of true positive SNVs. Additionally, an additional 11.56 % of true positive SNVs were rescued through CaMutQC's built-in union strategy. Similar results were observed for Insertions and Deletions (INDELs). CaMutQC is freely available through Bioconductor at https://bioconductor.org/packages/CaMutQC/ under the GPL v3 license.