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The authors raise important challenges for health technology assessment (HTA) organizations in evaluating the benefits, risks and cost-effectiveness of drugs that attack a genetic mutation independent of tumor type. In my opinion, asking manufacturers for more robust or comparative data is unlikely to succeed in today's regulatory environment. Instead, HTA bodies should require manufacturers to bring more analysis and extrapolation of existing data to the table. For example, in cases where the distribution of mutation prevalence across tumor types is reasonably well-known, cost-effectiveness modeling should reflect the expected distribution of use rather than the distribution of patients enrolled in the pivotal trials. Alternatively, modeling could be restricted only to those tumor types where there is evidence of a reasonable correlation between the surrogate endpoints used in trials and those outcomes of most importance, such as overall survival and incidence of key harms. Arguably that is something regulators should have addressed, but there is no reason for an HTA body to agree to adopt a new treatment with scant evidence in a few patients that is not even linkable to longer-term outcomes of greatest import.