Patient centered outcome measures and prediction tools in lymphangioleiomyomatosis

ABSTRACT: Lymphangioleiomyomatosis (LAM) is a progressive, female-predominant, cystic lung disease caused by mutations in the tuberous sclerosis complex genes. In a recent phase 3 trial, treatment with sirolimus was shown to stabilize lung function (FEV1) decline and improve quality of life in patients with LAM. However,

treatment with sirolimus is suppressive rather than remission inducing and doesn’t work for everyone, highlighting the need to develop new therapies in LAM. Pivotal trials of such therapies must overcome the ceiling effect of FEV1, as sirolimus-containing combination regimens are compared with sirolimus alone. For these trials

to be successful, two critical needs must be met: 1) the development of a cohort enrichment strategy to identify patients at risk for progression and suboptimal treatment response; and 2) the identification of a sensitive clinical outcome assessment (COA) for use as an endpoint. There is wide inter-individual variability in disease progression and response to sirolimus in patients with

LAM. Developing a modeling-based prediction system that make accurate, dynamic predictions of future FEV1 can aid individualized prognostication, foster timely therapeutic decision-making, and identify patients at greatest risk for progression or suboptimal treatment response to enrich future trial cohorts. Given the relative stability of

lung function on sirolimus, FEV1 decline is unlikely to be a practical primary endpoint in future LAM trials. We submit that a reliable, valid, responsive, LAM-specific patient-reported outcome measure (PROM) that assesses symptoms and health-related quality of life (HRQOL) is likely to be the most meaningful COA to meet this need.

The Multicenter International Durability and Safety of Sirolimus in LAM (MIDAS) Registry contains longitudinal physiological and patient-reported HRQOL data on ~400 LAM patients with an average length of follow up of ~4-years. We postulate that: 1) by employing novel stochastic modeling we can accurately identify

impending FEV1 decline in untreated patients with LAM and predict the likelihood of response to treatment in patients on sirolimus, and 2) successful validation of a LAM-specific PROM that captures clinically meaningful outcomes will result in the development of a high-tier novel endpoint for future therapeutic trials in LAM.

To test our hypotheses, we will use the MIDAS data to pursue the following specific aims: 1) Characterize heterogeneous treatment effects and develop a data-driven approach to optimize treatment initiation in patients with LAM, and 2) Validate the use of a LAM-specific PROM to identify clinically meaningful outcomes. Successful

completion of Aim 1 will facilitate optimal treatment decisions and provide a mechanism for cohort enrichment in LAM trials. Successful completion of Aim 2 will yield systematic analysis of a LAM-specific PROM’s measurement properties and support its reliability, validity, and responsiveness for capturing patient-centered outcomes. This

project is highly significant as it will usher in a new era of personalized medicine by providing precise prognostic predictions and facilitate the design of rapid, efficient, and adequately powered, patient-centered trials in LAM.