Resistance to targeted cancer therapies such as trastuzumab is a frequent clinical problem not solely because of insufficient expression of HER2 receptor but also because of the overriding activation states of cell signaling pathways. Systems biology approaches lend themselves to rapid in silico testing of factors, which may confer resistance to targeted therapies. In this study, we aimed to develop a new kinetic model that could be interrogated to predict resistance to receptor tyrosine kinase (RTK) inhibitor therapies and directly test predictions in vitro and in clinical samples. The new mathematical model included RTK inhibitor antibody binding, HER2/HER3 dimerization and inhibition, AKT/mitogenactivated protein kinase cross-talk, and the regulatory properties of PTEN. The model was parameterized using quantitative phosphoprotein expression data from cancer cell lines using reverse-phase protein microarrays. Quantitative PTEN protein expression was found to be the key determinant of resistance to anti-HER2 therapy in silico, which was predictive of unseen experiments in vitro using the PTEN inhibitor bp(V). When measured in cancer cell lines, PTEN expression predicts sensitivity to anti-HER2 therapy; furthermore, this quantitative measurement is more predictive of response (relative risk, 3.0; 95% confidence interval, 1.6-5.5; P < 0.0001) than other pathway components taken in isolation and when tested by multivariate analysis in a cohort of 122 breast cancers treated with trastuzumab. For the first time, a systems biology approach has successfully been used to stratify patients for personalized therapy in cancer and is further compelling evidence that PTEN, appropriately measured in the clinical setting, refines clinical decision making in patients treated with anti-HER2 therapies. [Cancer Res 2009;69(16):6713-20]

A B S T R A C T PurposeThe Tamoxifen and Exemestane Adjuvant Multinational (TEAM) trial included a prospectively planned pathology substudy testing the predictive value of progesterone receptor (PgR) expression for outcome of estrogen receptor-positive (ER-positive) early breast cancer treated with exemestane versus tamoxifen. Patients and MethodsPathology blocks from 4,781 TEAM patients randomly assigned to exemestane versus tamoxifen followed by exemestane for 5 years of total therapy were collected centrally, and tissue microarrays were constructed from samples from 4,598 patients. Quantitative analysis of hormone receptors (ER and PgR) was performed by using image analysis and immunohistochemistry, and the results were linked to outcome data from the main TEAM trial and analyzed relative to disease-free survival and treatment. ResultsOf 4,325 eligible ER-positive patients, 23% were PgR-poor (Allred Ͻ 4) and 77% were PgRrich (Allred Ն 5). No treatment-by-marker effect for PgR was observed for exemestane versus tamoxifen (PgR-rich hazard ratio [HR], 0.83; 95% CI, 0.65 to 1.05; PgR-poor HR, 0.85; 95% CI, 0.61 to 1.19; P ϭ .88 for interaction). Both PgR and ER expression were associated with patient prognosis in univariate (PgR HR, 0.53; 95% CI, 0.43 to 0.65; P Ͻ .001; ER HR, 0.66; 95% CI, 0.51 to 0.86; P ϭ .002), and multivariate analyses (P Ͻ .001 and P ϭ .001, respectively). A trend toward a treatment-by-marker effect for ER-rich patients was observed. ConclusionPreferential exemestane versus tamoxifen treatment benefit was not predicted by PgR expression; conversely, patients with ER-rich tumors may derive additional benefit from exemestane. Quantitative analysis of ER and PgR expression provides highly significant information on risk of early relapse (within 1 to 3 years) during treatment. Oncol 29:1531Oncol 29: -1538 J Clin

A four-gene predictive model of clinical response to AIs by 2 weeks has been generated and validated. Deregulated immune and apoptotic responses before treatment and cell proliferation that is not reduced 2 weeks after initiation of treatment are functional characteristics of breast tumors that do not respond to AIs.