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The steady advances in machine learning and accumulation of biomedical data have contributed to the development of numerous computational models that assess the impact of missense variants. Different methods, however, operationalize impact differently. Two common tasks in this context are the prediction of the pathogenicity of variants and the prediction of their effects on a protein's function. These are related but distinct problems and it is unclear whether methods developed for one are optimized for the other. The Critical Assessment of Genome Interpretation (CAGI) experiment provides a means to address this question empirically. To this end, we participated in various protein-specific challenges in CAGI with two objectives in mind. First, to compare the performance of methods in the MutPred family with the state-of-the-art. Second and more importantly, to investigate the applicability of general-purpose pathogenicity predictors to the classification of specific function-altering variants without additional training or calibration. We find that our pathogenicity predictors performed competitively with other methods, outputting score distributions in agreement with experimental outcomes. Overall, we conclude that binary classifiers learned from disease-causing mutations are capable of modeling important aspects of the underlying biology and the alteration of protein function resulting from mutations.
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Traditional and biologic disease-modifying antirheumatic drugs (DMARDs) are effective medications for the management of rheumatoid arthritis (RA). However, the effects of these medications on immune function raises concern that they may increase long-term cancer risk. The baseline risk for some cancers appears to differ in patients with RA compared to the general population, with the former having an increased risk of lymphoma, lung cancer and renal cancer, but a decreased risk of colorectal and breast cancer. Some DMARDs appear to increase the rate of specific cancer types (such as bladder cancer with cyclophosphamide), but few appear to increase the overall cancer risk. Studying the link between lymphoma and disease severity in RA is complicated because patients with persistently active disease are at increased risk for lymphoma, and disease severity correlates with more intense use of immunosuppressive medications. Overall, cancer risk in patients with RA is slightly above that of the general population, with the increased risk likely secondary to an increased risk of lymphomas in those with high disease activity. Risk mitigation includes management of RA disease activity as well as age- and sex-appropriate cancer screening.