A commonly cited challenge in use of systematic review in toxicology is the evaluation and integration of mechanistic data. Recently, the Ten Key Characteristics of Carcinogens (TKCC) have been proposed as an organizational approach for the evaluation of mechanistic data related to carcinogenicity. However, considerations of data quality as well as relevance to carcinogenic outcomes are needed to advance the application of such an approach for use in risk assessment. In this study, a framework for carrying out assessment of these elements was developed. The proposed framework has three components (reliability, strength, and activity) which are quantitatively evaluated using a mathematical algorithm to provide an overall score for each KCC. Scores are then interpreted and categorized as weak/moderate/strong. Reliability scores provide a measure of study quality (internal validity); scoring is based on the Klimish scoring approach with modifications for mechanistic data (e.g., cytotoxicity, solubility). Strength scores provide a measure of relevance for each model (external validity) used to characterize applicability to evaluation of carcinogenicity in humans; this component considers both the number of models and the number of assays. Activity scores account for active/inactive results on a per-endpoint basis. The algorithm allows for flexibility in component weighting, and the scoring approach allows for the incorporation of many study types, including HTS data Subsequently, data are considered relative to animal and human evidence streams and to tumor responses (including plausibility and adverse outcome pathways). Application to mechanistic datasets demonstrates that the framework can be applied to individual studies more rapidly than existing approaches, and that simple categorization of data by TKCC are not alone sufficient. Results also demonstrate that a priori definitions of data included within each KCC are required. The framework provides a quantitative approach that accommodates data quality and relevance, thus increasing the utility of TKCC, as well as providing a transparent and reproducible process (i.e., reduced bias) for assessment of mechanistic data in systematic reviews.