Mst1 is an interacting protein that mediates PHLPPs' induced apoptosis.

PHLPP1 and PHLPP2 phosphatases exert their tumor-suppressing functions by dephosphorylation and inactivation of Akt in several breast cancer and glioblastoma cells. However, Akt, or other known targets of PHLPPs that include PKC and ERK, may not fully elucidate the physiological role of the multifunctional phosphatases, especially their powerful apoptosis induction function. Here, we show that PHLPPs induce apoptosis in cancer cells independent of the known targets of PHLPPs. We identified Mst1 as a binding partner that interacts with PHLPPs both in vivo and in vitro. PHLPPs dephosphorylate Mst1 on the T387 inhibitory site, which activate Mst1 and its downstream effectors p38 and JNK to induce apoptosis. The same T387 site can be phosphorylated by Akt. Thus, PHLPP, Akt, and Mst1 constitute an autoinhibitory triangle that controls the fine balance of apoptosis and proliferation that is cell type and context dependent.

Breast surgery in stage IV breast cancer: impact of staging and patient selection on overall survival.

PURPOSE: Retrospective analyses suggest patients with stage IV breast cancer who undergo breast surgery have improved survival. We sought to determine whether surgery and other clinical and staging factors affected overall survival. METHODS: We performed a review of our prospectively maintained database of patients who presented with stage IV breast cancer between 1998 and 2005. We compared survival between women who received therapeutic surgery to the breast (S) versus those who did not (NS). RESULTS: Of the 147 women who presented with stage IV breast carcinoma, 61 (41%) underwent mastectomy or lumpectomy. Median overall survival unadjusted was 3.52 years for S versus 2.36 years for NS (P = 0.093). ER and Her2neu status were positive predictors of survival (HR: 0.191 and 0.285 P < 0.0001); CNS and liver metastases were adverse predictors (HR: 2.05 and 1.59 P = 0.015 P = 0.059). On multivariate survival was significantly superior in the surgery group (HR: 0.47 P = 0.003 mean 4.13 years versus 2.36 years). In those undergoing surgery, 36 women were diagnosed with metastatic disease postoperatively and 25 preoperatively. These groups had median survival of 4.0 years and 2.4 years, respectively, comparable to those in the NS group (2.36 years, (P = 0.18). CONCLUSIONS: Breast surgery is associated with improved survival in stage IV breast cancer. However, in our experience, this benefit is only realized among patients operated on before diagnosis of metastatic disease and is likely a consequence of stage migration bias. While some women may warrant palliative surgery to the breast, it is unclear that such surgery otherwise improves clinical outcomes.

Transformation of different human breast epithelial cell types leads to distinct tumor phenotypes.

We investigated the influence of normal cell phenotype on the neoplastic phenotype by comparing tumors derived from two different normal human mammary epithelial cell populations, one of which was isolated using a new culture medium. Transformation of these two cell populations with the same set of genetic elements yielded cells that formed tumor xenografts exhibiting major differences in histopathology, tumorigenicity, and metastatic behavior. While one cell type (HMECs) yielded squamous cell carcinomas, the other cell type (BPECs) yielded tumors closely resembling human breast adenocarcinomas. Transformed BPECs gave rise to lung metastases and were up to 10(4)-fold more tumorigenic than transformed HMECs, which are nonmetastatic. Hence, the pre-existing differences between BPECs and HMECs strongly influence the phenotypes of their transformed derivatives.

Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data.

BACKGROUND: Like microarray-based investigations, high-throughput proteomics techniques require machine learning algorithms to identify biomarkers that are informative for biological classification problems. Feature selection and classification algorithms need to be robust to noise and outliers in the data. RESULTS: We developed a recursive support vector machine (R-SVM) algorithm to select important genes/biomarkers for the classification of noisy data. We compared its performance to a similar, state-of-the-art method (SVM recursive feature elimination or SVM-RFE), paying special attention to the ability of recovering the true informative genes/biomarkers and the robustness to outliers in the data. Simulation experiments show that a 5%- approximately 20% improvement over SVM-RFE can be achieved regard to these properties. The SVM-based methods are also compared with a conventional univariate method and their respective strengths and weaknesses are discussed. R-SVM was applied to two sets of SELDI-TOF-MS proteomics data, one from a human breast cancer study and the other from a study on rat liver cirrhosis. Important biomarkers found by the algorithm were validated by follow-up biological experiments. CONCLUSION: The proposed R-SVM method is suitable for analyzing noisy high-throughput proteomics and microarray data and it outperforms SVM-RFE in the robustness to noise and in the ability to recover informative features. The multivariate SVM-based method outperforms the univariate method in the classification performance, but univariate methods can reveal more of the differentially expressed features especially when there are correlations between the features.

Loss of heterozygosity and its correlation with expression profiles in subclasses of invasive breast cancers.

Gene expression array profiles identify subclasses of breast cancers with different clinical outcomes and different molecular features. The present study attempted to correlate genomic alterations (loss of heterozygosity; LOH) with subclasses of breast cancers having distinct gene expression signatures. Hierarchical clustering of expression array data from 89 invasive breast cancers identified four major expression subclasses. Thirty-four of these cases representative of the four subclasses were microdissected and allelotyped using genome-wide single nucleotide polymorphism detection arrays (Affymetrix, Inc.). LOH was determined by comparing tumor and normal single nucleotide polymorphism allelotypes. A newly developed statistical tool was used to determine the chromosomal regions of frequent LOH. We found that breast cancers were highly heterogeneous, with the proportion of LOH ranging widely from 0.3% to >60% of heterozygous markers. The most common sites of LOH were on 17p, 17q, 16q, 11q, and 14q, sites reported in previous LOH studies. Signature LOH events were discovered in certain expression subclasses. Unique regions of LOH on 5q and 4p marked a subclass of breast cancers with "basal-like" expression profiles, distinct from other subclasses. LOH on 1p and 16q occurred preferentially in a subclass of estrogen receptor-positive breast cancers. Finding unique LOH patterns in different groups of breast cancer, in part defined by expression signatures, adds confidence to newer schemes of molecular classification. Furthermore, exclusive association between biological subclasses and restricted LOH events provides rationale to search for targeted genes.