Background: An effective and accurate blood test to detect localised breast cancer may increase the screening detection rate and improve patient outcomes. We have previously reported a series of lipidomic studies and derived a lipid signature from plasma enriched extracellular vesicles (EVs) that effectively distinguished people with localised breast cancer from cancer-free controls. here we report on a significant refinement to the test methodology allowing the assessment of the lipid signature directly from plasma samples and its performance, with the aim of advancing the commercial viability of the test as we move towards clinical application. Methods: Lipids were extracted from enriched EVs from plasma samples donated by fasted people with localised breast cancer and control samples (n=793) and analysed by high resolution accurate mass liquid chromatography-mass spectrometry (LC-MS). Over 400 manually curated lipids were quantified. Following variable selection, a lipid signature capable of distinguishing breast cancer samples from controls was derived. The lipid signature was modelled on each of the cohorts using leave-one-out internal cross-validation. Next, we analysed the lipids in cancer and control (n=256) plasma samples corresponding to patients from Cohorts 3 and 4 previously used for EV preparations, and applied the signature derived using EVs on plasma lipidomic data. Results: EV samples of people with breast cancer were distinguished from controls with an area under the curve (AUC) of 0.77-0.89 across 4 cohorts. When the lipid signature was assessed directly from plasma the test achieved a comparable AUC of 0.84. Assessing the markers directly from plasma samples would make the test more scalable, higher throughput and easier to perform. Conclusions: Our study demonstrated the high performance of a lipid biomarker signature derived from plasma enriched EVs for early detection of localised breast cancer. Our results suggest that that the lipidomic signature could potentially be assessed directly from plasma samples instead of EVs reducing the test complexity. Ongoing studies will optimise the plasma lipidomic signature and prospectively compare the test against mammographic and pathological diagnoses.