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Purpose: Predictive biomarkers offer the potential to improve the benefit:risk ratio of a therapeutic agent. Ixabepilone achieves comparable pathologic complete response (pCR) rates to other active drugs in the neoadjuvant setting. This phase II trial was designed to investigate potential biomarkers that differentiate response to this agent.Experimental Design: Women with untreated, histologically confirmed primary invasive breast adenocarcinoma received neoadjuvant doxorubicin/cyclophosphamide, followed by 1:1 randomization to ixabepilone (n ¼ 148) or paclitaxel (n ¼ 147). Rates of pCR were compared between treatment arms based on predefined biomarker sets: TUBB3, TACC3, and CAPG gene expression, a 20-and 26-gene expression model, MDR1 protein expression, and other potential markers of sensitivity. bIII-tubulin protein expression is reported separately but is referred to here for completeness. All patients underwent a core needle biopsy of the primary cancer for molecular marker analysis before chemotherapy. Gene expression profiling data were used for molecular subtyping.Results: There was no significant difference in the rate of pCR in both treatment arms in bIII-tubulinpositive patients. Higher pCR rates were observed among bIII-tubulin-positive patients than in bIIItubulin-negative patients. Furthermore, no correlation was evident between TUBB3, TACC3, and CAPG gene expression, MDR1 protein expression, multi-gene expression models, and the efficacy of ixabepilone or paclitaxel, even within the estrogen receptor-negative subset.Conclusion: These results indicate that bIII-tubulin protein and mRNA expression, MDR1 protein expression, TACC3 and CAPG gene expression, and multigene expression models (20-and 26-gene) are not predictive markers for differentiating treatment benefit between ixabepilone and paclitaxel in early-stage breast cancer.

Background: Clinically validated prognostic models for overall survival (OS) do not exist for patients with relapsed/refractory chronic lymphocytic leukaemia (CLL) on targeted therapies. A prognostic model is needed to identify high risk individuals not adequately served by available targeted therapies. Methods: We evaluated 28 candidate factors to derive and validate a risk model for OS in 2,475 previously treated patients with CLL from six randomized trials of ibrutinib, idelalisib, and venetoclax, and the Mayo Clinic CLL Database (MCCD). We applied univariate and multivariate analyses to derive the risk model in an ibrutinib/chemoimmunotherapy (CIT) training dataset (n=727). The primary endpoint was OS. We validated the model in an ibrutinib/CIT internalvalidation (n=242) and three external-validations (idelalisib/CIT dataset, n=897; venetoclax/CIT dataset, n=389; MCCD, n=220), applying C-statistics (CS) as a measure of discrimination. Findings: The derived model consists of four factors (one point each; serum ß 2-microglobulin ≥5mg/dL, lactate dehydrogenase >upper limit of normal, hemoglobin <110g/L for women or Soumerai et al.