Activating Mutations in PIK3CB Confer Resistance to PI3K Inhibition and Define a Novel Oncogenic Role for p110ß.

Activation of the PI3K pathway occurs commonly in a wide variety of cancers. Experience with other successful targeted agents suggests that clinical resistance is likely to arise and may reduce the durability of clinical benefit. Here, we sought to understand mechanisms underlying resistance to PI3K inhibition in PTEN-deficient cancers. We generated cell lines resistant to the pan-PI3K inhibitor GDC-0941 from parental PTEN-null breast cancer cell lines and identified a novel PIK3CB D1067Y mutation in both cell lines that was recurrent in cancer patients. Stable expression of mutant PIK3CB variants conferred resistance to PI3K inhibition that could be overcome by downstream AKT or mTORC1/2 inhibitors. Furthermore, we show that the p110ß D1067Y mutant was highly activated and induced PIP3 levels at the cell membrane, subsequently promoting the localization and activation of AKT and PDK1 at the membrane and driving PI3K signaling to a level that could withstand treatment with proximal inhibitors. Finally, we demonstrate that the PIK3CB D1067Y mutant behaved as an oncogene and transformed normal cells, an activity that was enhanced by PTEN depletion. Collectively, these novel preclinical and clinical findings implicate the acquisition of activating PIK3CB D1067 mutations as an important event underlying the resistance of cancer cells to selective PI3K inhibitors.

Development of a robust RNA-based classifier to accurately determine ER, PR, and HER2 status in breast cancer clinical samples.

Breast cancers are categorized into three subtypes based on protein expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2/ERBB2). Patients enroll onto experimental clinical trials based on ER, PR, and HER2 status and, as receptor status is prognostic and defines treatment regimens, central receptor confirmation is critical for interpreting results from these trials. Patients enrolling onto experimental clinical trials in the metastatic setting often have limited available archival tissue that might better be used for comprehensive molecular profiling rather than slide-intensive reconfirmation of receptor status. We developed a Random Forests-based algorithm using a training set of 158 samples with centrally confirmed IHC status, and subsequently validated this algorithm on multiple test sets with known, locally determined IHC status. We observed a strong correlation between target mRNA expression and IHC assays for HER2 and ER, achieving an overall accuracy of 97 and 96%, respectively. For determining PR status, which had the highest discordance between central and local IHC, incorporation of expression of co-regulated genes in a multivariate approach added predictive value, outperforming the single, target gene approach by a 10% margin in overall accuracy. Our results suggest that multiplexed qRT-PCR profiling of ESR1, PGR, and ERBB2 mRNA, along with several other subtype associated genes, can effectively confirm breast cancer subtype, thereby conserving tumor sections and enabling additional biomarker data to be obtained from patients enrolled onto experimental clinical trials.