Prognostic marker profile to assess risk in stage I-III hormone receptor-positive breast cancer patients.

Single markers are insufficient to accurately assess risk of relapse for adjuvant therapy guidance in operable breast cancer patients. In addition, the accuracy and interpretability of current multi-marker tests is generally limited by their simply additive algorithms and their overlap with clinicopathologic risks. Here, we report the development and validation of a nonlinear algorithm that combines protein (ER, PGR, ERBB2, BCL2 and TP53) and genomic (MYC/8q24) markers with standard clinicopathologic features (tumor size, tumor grade and nodal status) into a global risk assessment profile. The algorithm was trained using statistical pattern recognition in 200 stage I-III hormone receptor-positive patients treated with hormone therapy. Continuous risk scores (0-10+) were then generated for 232 independent patients. In hormone therapy-treated patients, the profile achieved a hazard ratio of 6.2 (95% confidence interval [CI], 1.8-20) in high- vs. low-risk groups for time to distant metastasis with the low-risk group having a 10-year metastasis rate of just 4% (95% CI, 0-8%). Similar results were achieved in untreated patients and for disease-specific survival. In multivariate analyses with standard prognostic factors and clinical practice guidelines, the profile was the only significant variable. Furthermore, the profile reclassified as low risk over half of node-negative patients at elevated risk according to the guidelines, which could have spared such patients from unnecessary cytotoxic chemotherapy. It also accurately identified a group of high-risk patients within a guideline low-risk group. In summary, the profile intelligently combines biologically relevant marker pathways and established clinicopathologic risks to help guide breast cancer patients to the most appropriate level of adjuvant therapy.

A multimarker model to predict outcome in tamoxifen-treated breast cancer patients.

PURPOSE: This study was designed to produce a model to predict outcome in tamoxifen-treated breast cancer patients based on clinicopathologic features and multiple molecular markers. EXPERIMENTAL DESIGN: This was a retrospective study of 324 stage I to III female breast cancer patients treated with tamoxifen for whom standard clinicopathologic data and tumor tissue microarrays were available. Nine molecular markers were studied by semiquantitative immunohistochemistry and/or fluorescence in situ hybridization. Cox proportional hazards analysis was used to determine the contributions of each variable to disease-specific and overall survival, and machine learning was used to produce a model to predict patient outcome. RESULTS: On a univariate basis, the following features were significantly associated with worse survival: high pathologic tumor or nodal class, histologic grade, epidermal growth factor receptor, ERBB2, MYC, or TP53; absent estrogen receptor (ER) or progesterone receptor; and low BCL2. CCND1 and CDKN1B did not reach statistical significance. On a multivariate basis, nodal class, ER, and MYC were statistically significant as independent factors for survival. However, the benefit of ER-positive status was moderated by BCL2, ERBB2, and progesterone receptor. BCL2 and TP53 also interacted as an independent risk factor. A kernel partial least squares polynomial model was developed with an area under the receiver operating characteristic curve of 0.90. CONCLUSIONS: Our data show the predictive value of BCL2, ERBB2, MYC, and TP53 in addition to the standard hormone receptors and clinicopathologic features, and they show the importance of conditional interpretation of certain molecular markers. Our multimarker predictive model performed significantly better than standard guidelines.

Study of the biologic effects of lapatinib, a reversible inhibitor of ErbB1 and ErbB2 tyrosine kinases, on tumor growth and survival pathways in patients with advanced malignancies.

PURPOSE: This was a pilot study to assess the biologic effects of lapatinib on various tumor growth/survival pathways in patients with advanced ErbB1 and/or ErbB2-overexpressing solid malignancies. PATIENTS AND METHODS: Heavily pretreated patients with metastatic cancers overexpressing ErbB2 and/or expressing ErbB1 were randomly assigned to one of five dose cohorts of lapatinib (GW572016) administered orally once daily continuously. The biologic effects of lapatinib on tumor growth and survival pathways were assessed in tumor biopsies obtained before and after 21 days of therapy. Clinical response was determined at 8 weeks. RESULTS: Sequential tumor biopsies from 33 patients were examined. Partial responses occurred in four patients with breast cancer, and disease stabilization occurred in 11 others with various malignancies. Responders exhibited variable levels of inhibition of p-ErbB1, p-ErbB2, p-Erk1/2, p-Akt, cyclin D1, and transforming growth factor alpha. Even some nonresponders demonstrated varying degrees of biomarker inhibition. Increased tumor cell apoptosis (TUNEL) occurred in patients with evidence of tumor regression but not in nonresponders (progressive disease). Clinical response was associated with a pretreatment TUNEL score > 0 and increased pretreatment expression of ErbB2, p-ErbB2, Erk1/2, p-Erk1/2, insulin-like growth factor receptor-1, p70 S6 kinase, and transforming growth factor alpha compared with nonresponders. CONCLUSION: Lapatinib exhibited preliminary evidence of biologic and clinical activity in ErbB1 and/or ErbB2-overexpressing tumors. However, the limited sample size of this study and the variability of the biologic endpoints suggest that further work is needed to prioritize biomarkers for disease-directed studies, and underscores the need for improved trial design strategies in early clinical studies of targeted agents.

Frequency characterization of blood glucose dynamics.

Examples of the frequency range of blood glucose dynamics of normal subjects and subjects with diabetes are reported here, based on data from the literature. The frequency band edge was determined from suitable, frequently sampled blood glucose recordings using two methods: frequency domain estimation and signal reconstruction. The respective maximum acceptable sampling intervals, or Nyquist sampling periods (NSP), required to accurately represent blood glucose dynamics were calculated. Preliminary results based on the limited data available in the literature indicate that although blood glucose NSP values are higher in most diabetic subjects, values in some diabetic subjects are indistinguishable from those of normal subjects. High fidelity monitoring sufficient to follow the intrinsic blood glucose dynamics of all diabetic subjects requires a NSP of approximately 10 min, corresponding to a continuous frequency band edge of approximately 1 x 10(-3) Hz. This analysis provides key information for the design of clinical studies that include blood glucose dynamics and for the design of new glucose monitoring systems.