S66 A binary prognostic score, incorporating Fibulin-3, predicts 1-year survival in pleural mesothelioma

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Introduction The Brims model classifies patients with pleural mesothelioma (PM) into 4 prognostic groups. However, at diagnosis, clinicians and patients commonly face a binary choice between immediate treatment or ‘watch-and-wait’. The latter is high-risk if patients rapidly progress but reasonable if clinical progression is slow, since immediate treatment may result in unacceptable toxicity. We applied plasma Fibulin-3 data, which in isolation has yet to find a useful clinical role, to design and validate a binary prognostic model that could be used to inform clinical decision-making in this context. The study aspired to create a classifier with prognostic performance that was at least equal to the Brims model using data from the multicentre DIAPHRAGM study (Tsim, JTO 2021) and Scottish National MDT. Methods We first performed a retrospective analysis of prospectively recorded DIAPHRAGM data (n=749, 23 UK centres). Using 100 recruits with a final diagnosis of PM and baseline plasma Fibulin-3 data, we defined a 3-group 1-yr survival model based on hazard ratios generated by multivariable Cox regression. This defined clear high/low-risk groups, plus an intermediate group for stratification using Fibulin-3. Next, we externally validated the performance of the 3-group model in an independent set of 203 Scottish National MDT cases. Finally, to create the intended binary model, we stratified intermediate-risk cases in the validated 3-group model by baseline Fibulin-3, generating the Glasgow Mesothelioma Score (GMS). Results Six independent features contributed to the initial 3-group DIAPHRAGM model (figure 1a), which delivered superior performance to Brims (C=0.72 v 0.66). The 3-group model was successfully validated in the Scottish MDT dataset, with significant prognostic performance retained (C=0.60, 1-yr survival 38%, 56%, 75% in high/intermediate/low-risk; figure 1b). In the final, binary GMS model, stratification of intermediate-risk cases by Fibulin-3 (dichotomised using an optimal threshold of 16μg/mL), improved performance (C=0.73, 1-yr survival 70% v 9% in high/low risk; figure 1c-d). Conclusion The GMS combines Fibulin-3 with an externally validated 3-group model to create a powerful binary classifier. This model may be useful in identifying patients unsuitable for ‘watch-and-wait’. Ongoing work includes further external validation of the final binary GMS using international cohorts with available Fibulin-3 data.

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