Breast cancer is a heterogeneous disease and accounts for 25% of all cancer cases. To date there is no FDA-approved targeted adjuvant drug treatment for triple negative breast cancer (TNBC), which has a poor prognosis. This study was focused on the effect of styrene-maleic acid-encapsulated second-generation curcumin analogue, RL71 (SMA-RL71), on TNBC tumor developed form MDA-MB-231 cell using SCID mice. Previously we had discovered that SMA-RL71 (10 mg/kg, intravenously, twice a week for 2 weeks) suppressed tumor growth by 60% through anti- angiogenesis (26% decreased of CD105-microvesel density) as well as increased apoptotic cells (24%), with a corresponding 51% increase in cleaved caspase-3. SMA-RL71 nanomicelles also modulated individual signaling proteins including EGFR, PI3K/Akt/mTOR, and Wnt/b-catenin pathways.The observed action of SMA-RL71 on tumour-related signaling proteins may, however, involve a network of interactions between them which cannot be detected by analysing one protein at a time. A combination of multivariate statistical and data mining (MVSDM) methods including multiple linear regression (MLR), linear discriminant function (LDF) random forest classification (RFC) and cluster analysis (CA) were performed to identify biological networks modulated by drug treatment. The MLR predicted the suppression in tumor growth with an adjusted R2 value of 0.896 and was significant according to an ANOVA (F(13,8) = 14.98, P ≤ 0.0001), supported by lack of autocorrelation by the Durban-Watson statistic value of 2.12.pEGFR, pAkt, Ki67, pAKT/Akt, PP2Aa, PP2Ab, EGFR, PKC-a, pEGFR/EGFR and CaD-1 were identified as biological networks that could predict the treatment outcome. The treatment groups were 77% and 95% correctly predicted by LDF and RFC, respectively. CA naturally grouped molecular network with Ki67(anti-proliferation), CD105 (anti-angiogenesis) and Apoptag (apoptosis) and showed that EGFR and pEGFR, Akt and pAKT, PP2Aa and PP2Ab were closely associated. Thus the use of MVSDM methods is a powerful tool to identifying clinical and biological relevant targets modulated by SMA-RL71.