Abstract
BACKGROUND: Next-generation sequencing is used to increase targeted treatment opportunities, particularly for patients who have exhausted standard options. Where randomized controlled trial evidence for a targeted therapy is available for molecular alterations in one tumor type, the dilemma for the clinician is whether 'matching' targeted agents should be recommended off-label for the same molecular alterations detected in other tumor types, for which no trial data are available to guide practice. To judge the likely benefits, it may be possible to extrapolate evidence from cancers where treatment benefits have been established. METHODS: We present a framework for assessing the appropriateness of extrapolation using trastuzumab, an anti-HER2 antibody, for HER2-amplified tumors where trastuzumab use would be off-label as an illustrative example. RESULTS: The following should be considered for the tumor type where trastuzumab would be off-label: (a) reliability of the NGS assay for detecting HER2 amplification; (b) criteria for defining HER2 positivity; (c) strength of evidence supporting the actionability of HER2 amplification and trastuzumab; (d) whether better clinical outcomes with trastuzumab are due to a more favorable natural history rather than trastuzumab effect; (e) signals of trastuzumab activity and whether it translates to clinically meaningful benefit; (f) whether the safety profile of trastuzumab differs from established indications; and (g) discussion points for shared decision making (SDM) to facilitate informed consent. CONCLUSION: We present a systematic approach for appraising evidence to support extrapolating trastuzumab benefits from established indications to off-label applications. Extrapolation criteria and areas of uncertainty to inform SDM are outlined. This framework is potentially generalizable to other tumor-agnostic biomarker-targeted therapy scenarios. It is a practical approach for clinicians to apply in routine practice and should be considered by molecular tumor boards who make off-label recommendations.