ESMO adaptation of Lines of Systemic Therapy (EnLiST): a consensus framework for standardising the designation of lines of therapy in solid tumours.

Enumeration of lines of therapy (LoT) is critical across oncology for ensuring optimal patient care, establishing uniform eligibility for clinical trial enrolment, standardising use of real-world data and pooling data for research purposes. Building on an earlier proposal for assigning LoT in oncology, the European Society for Medical Oncology (ESMO) developed the ESMO adaptation of Lines of Systemic Therapy (EnLiST) framework applicable to solid tumours across the full range of common clinical settings. A Delphi process was adopted to reach a consensus between expert representatives of multiple stakeholder perspectives including medical oncologists, clinical trialists, regulators, academics, patient advocates, experts from the pharmaceutical industry and clinical research organisations, artificial intelligence professionals, funding body specialists and ethicists. EnLiST provides a set of standard definitions, the format of reporting LoT, the minimum required data to be recorded and guidelines for assigning LoT. In EnLiST, a LoT is separately assigned to systemic anticancer therapies in the early (eLoT), advanced (aLoT) and investigational (iLoT) settings; each expressed as two numerals separated by a decimal point (e.g. 1.0). A change in systemic anticancer therapy results in either a 'New' LoT, a 'Modified' LoT, or the 'Same' LoT. A New LoT results from clinical progression of disease or lack of adequate tumour response, and is recorded as the next sequential value to the left of the decimal point (e.g. following LoT 1.0, the New LoT will be LoT 2.0). If the change in treatment is for other reasons (e.g. intolerability), a Modified LoT is recorded and assigned the next sequential value to the right of the decimal point (e.g. following LoT 1.0, the Modified LoT will be LoT 1.1). EnLiST has been designed to serve the needs of a wide range of stakeholders while enhancing the quality of clinical care, clinical research and real-world data.

Duke Scholars

Author Susan Halabi Biostatistics & Bioinformatics, Division of Biostatistics