Co-targeting CDK2 and CDK4/6 overcomes resistance to aromatase and CDK4/6 inhibitors in ER+ breast cancer.

Resistance to aromatase inhibitor (AI) treatment and combined CDK4/6 inhibitor (CDK4/6i) and endocrine therapy (ET) are crucial clinical challenges in treating estrogen receptor-positive (ER+) breast cancer. Understanding the resistance mechanisms and identifying reliable predictive biomarkers and novel treatment combinations to overcome resistance are urgently needed. Herein, we show that upregulation of CDK6, p-CDK2, and/or cyclin E1 is associated with adaptation and resistance to AI-monotherapy and combined CDK4/6i and ET in ER+ advanced breast cancer. Importantly, co-targeting CDK2 and CDK4/6 with ET synergistically impairs cellular growth, induces cell cycle arrest and apoptosis, and delays progression in AI-resistant and combined CDK4/6i and fulvestrant-resistant cell models and in an AI-resistant autocrine breast tumor in a postmenopausal xenograft model. Analysis of CDK6, p-CDK2, and/or cyclin E1 expression as a combined biomarker in metastatic lesions of ER+ advanced breast cancer patients treated with AI-monotherapy or combined CDK4/6i and ET revealed a correlation between high biomarker expression and shorter progression-free survival (PFS), and the biomarker combination was an independent prognostic factor in both patients cohorts. Our study supports the clinical development of therapeutic strategies co-targeting ER, CDK4/6 and CDK2 following progression on AI-monotherapy or combined CDK4/6i and ET to improve survival of patients exhibiting high tumor levels of CDK6, p-CDK2, and/or cyclin E1.

Co-targeting CDK4/6 and AKT with endocrine therapy prevents progression in CDK4/6 inhibitor and endocrine therapy-resistant breast cancer.

CDK4/6 inhibitors (CDK4/6i) combined with endocrine therapy have shown impressive efficacy in estrogen receptor-positive advanced breast cancer. However, most patients will eventually experience disease progression on this combination, underscoring the need for effective subsequent treatments or better initial therapies. Here, we show that triple inhibition with fulvestrant, CDK4/6i and AKT inhibitor (AKTi) durably impairs growth of breast cancer cells, prevents progression and reduces metastasis of tumor xenografts resistant to CDK4/6i-fulvestrant combination or fulvestrant alone. Importantly, switching from combined fulvestrant and CDK4/6i upon resistance to dual combination with AKTi and fulvestrant does not prevent tumor progression. Furthermore, triple combination with AKTi significantly inhibits growth of patient-derived xenografts resistant to combined CDK4/6i and fulvestrant. Finally, high phospho-AKT levels in metastasis of breast cancer patients treated with a combination of CDK4/6i and endocrine therapy correlates with shorter progression-free survival. Our findings support the clinical development of ER, CDK4/6 and AKT co-targeting strategies following progression on CDK4/6i and endocrine therapy combination, and in tumors exhibiting high phospho-AKT levels, which are associated with worse clinical outcome.

MCM3 upregulation confers endocrine resistance in breast cancer and is a predictive marker of diminished tamoxifen benefit.

Resistance to endocrine therapy in estrogen receptor-positive (ER+) breast cancer is a major clinical problem with poorly understood mechanisms. There is an unmet need for prognostic and predictive biomarkers to allow appropriate therapeutic targeting. We evaluated the mechanism by which minichromosome maintenance protein 3 (MCM3) influences endocrine resistance and its predictive/prognostic potential in ER+ breast cancer. We discovered that ER+ breast cancer cells survive tamoxifen and letrozole treatments through upregulation of minichromosome maintenance proteins (MCMs), including MCM3, which are key molecules in the cell cycle and DNA replication. Lowering MCM3 expression in endocrine-resistant cells restored drug sensitivity and altered phosphorylation of cell cycle regulators, including p53(Ser315,33), CHK1(Ser317), and cdc25b(Ser323), suggesting that the interaction of MCM3 with cell cycle proteins is an important mechanism of overcoming replicative stress and anti-proliferative effects of endocrine treatments. Interestingly, the MCM3 levels did not affect the efficacy of growth inhibitory by CDK4/6 inhibitors. Evaluation of MCM3 levels in primary tumors from four independent cohorts of breast cancer patients receiving adjuvant tamoxifen mono-therapy or no adjuvant treatment, including the Stockholm tamoxifen (STO-3) trial, showed MCM3 to be an independent prognostic marker adding information beyond Ki67. In addition, MCM3 was shown to be a predictive marker of response to endocrine treatment. Our study reveals a coordinated signaling network centered around MCM3 that limits response to endocrine therapy in ER+ breast cancer and identifies MCM3 as a clinically useful prognostic and predictive biomarker that allows personalized treatment of ER+ breast cancer patients.

Discriminating isogenic cancer cells and identifying altered unsaturated fatty acid content as associated with metastasis status, using k-means clustering and partial least squares-discriminant analysis of Raman maps.

Raman spectroscopy is a label-free, real-time diagnostic tool that shows great promise in identifying cell differences. We have evaluated the discriminatory power of Raman spectroscopy using a unique model system consisting of two isogenic cancer cell lines derived from the MDA-MB-435 cell line. The two cell lines are equally tumorigenic in mice, but while M-4A4 gives rise to metastasis, NM-2C5 only disseminates single cells that remain dormant in distant organs. Previous comparative proteomic and transcriptomic analyses of the two cell lines have shown that they differ only in the expression level of a few proteins and genes. Raman maps were recorded of single cells after fixation and drying using 785 nm laser excitation. K-means clustering reduced the amount of data from each cell and improved the signal-to-noise ratio of cluster-averaged spectra. Spectra representing the nucleus were discarded as they showed much smaller differences between the two cell lines compared to cytoplasm spectra. Partial least squares-discriminant analysis (PLS-DA) was applied to distinguish the two cell lines. A cross-validated PLS-DA resulted in 92% correctly classified samples. Spectral differences were assigned to a higher unsaturated fatty acid content in the metastatic vs nonmetastatic cell line. Our study demonstrates the unique ability of Raman spectroscopy to distinguish minute differences at the subcellular level and yield new biological information. Our study is the first to demonstrate the association between polyunsaturated fatty acid content and metastatic ability in this unique cell model system and is in agreement with previous studies on this topic.

Identification of genes with altered expression in medullary breast cancer vs. ductal breast cancer and normal breast epithelia.

Medullary breast cancer (MCB) is a morphologically and biologically distinct subtype that, despite cytologically highly malignant characteristics, has a favorable prognosis compared to the more common infiltrating ductal breast carcinoma. MCB metastasizes less frequently, which has been attributed to both immunological and endogenous cellular factors, although little is known about the distinct biology of MCB that may contribute to the improved outcome of MCB patients. To identify candidate genes, we performed gene array expression analysis of cell lines of MCB, ductal breast cancer and normal breast epithelia, and the differential expression of a panel of candidate genes was further validated by quantitative PCR and immunohistochemical analysis of cell lines and tumor biopsies. A limited number of genes, including several members of the GAGE and insulin growth factor binding protein (IGFBP) gene families, Vav1, monoglyceride lipase and NADP+-dependent malic enzyme, exhibited altered expression in MCB vs. ductal breast cancer, and the differences for some of these genes were confirmed on an extended panel of cell lines by quantitative PCR. Immunohistochemical analysis further established that the expression of monoglyceride lipase was restricted to ductal breast cancer and present in 77% of these tumors, while Vav1 was restricted to MCB and present in 60% of tumors. In this study, we have identified genes that are differentially expressed in MCB vs. ductal breast cancer and further analysis of the gene products should illuminate the biological differences between MCB and ductal breast cancer.

Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry.

The recent abundance of genome sequence data has brought an urgent need for systematic proteomics to decipher the encoded protein networks that dictate cellular function. To date, generation of large-scale protein-protein interaction maps has relied on the yeast two-hybrid system, which detects binary interactions through activation of reporter gene expression. With the advent of ultrasensitive mass spectrometric protein identification methods, it is feasible to identify directly protein complexes on a proteome-wide scale. Here we report, using the budding yeast Saccharomyces cerevisiae as a test case, an example of this approach, which we term high-throughput mass spectrometric protein complex identification (HMS-PCI). Beginning with 10% of predicted yeast proteins as baits, we detected 3,617 associated proteins covering 25% of the yeast proteome. Numerous protein complexes were identified, including many new interactions in various signalling pathways and in the DNA damage response. Comparison of the HMS-PCI data set with interactions reported in the literature revealed an average threefold higher success rate in detection of known complexes compared with large-scale two-hybrid studies. Given the high degree of connectivity observed in this study, even partial HMS-PCI coverage of complex proteomes, including that of humans, should allow comprehensive identification of cellular networks.