Abstract PD6-06: Somatic mutation patterns differentially affect survival in breast cancer molecular subtypes

Background: The prognostic effects of somatic gene mutations and correlated gene expression in breast cancer is argument of debate. In this study we analyzed the impact of specific mutations on gene expression and their relevance in the prognosis of breast cancer subtypes.

Materials and methods: Exome sequencing and RNA-seq data obtained from TCGA were analyzed. Data was processed using MuTect, MapSplice and RSEM. All together data from 757 patients (ER-/HER2- [n=143], HER2+ including ER positive and negative patients, [n=136], and ER+/HER2- [n=478]) were included. Univariate Receiver Operating Characteristic (ROC) analysis was performed for the top mutated genes (mutated in at least 5% of patients) using the ROC Bioconductor library in R to identify genes whose expression was significantly associated with a mutation. Then, the mean expression of the significant genes was designated as a metagene for each genotype. We assessed the correlation with survival for each metagene by Cox proportional hazards regression and by plotting Kaplan-Meier survival plots. A significance threshold of p<1E-04 was set for each gene to be considered in the survival analysis, and only the top 100 genes were used when there were more than 100 genes significant.

Results: In the overall population only few mutated genes including TP53 (HR=1.66), CDH1 (HR=0.61), AKT1 (HR=0.54), ATM (HR=1.76), NF1 (HR=0.58), KMT2D (HR=2.32), and UBR5 (HR=1.94) were significantly associated with survival. In ER-/HER2- mutant samples the PIK3CA (HR=2.79) and MAP3K1 (HR=2.98), and in HER2+ mutant samples the ARID1A (HR=0.26) and PIK3CA (HR=0.27) metagenes were associated with survival, respectively. Overall, using the combined metagene the majority of the significant mutated genes retained their prognostic power. Mutations of specific genes impacted their own expression and prognosis. The expression of TP53 (AUC=0.609, p=2.60E-06), and MAP3K1 (AUC=0.617, p=6.07E-03) was higher in samples with a mutation while the expression of CDH1 (AUC=0.684, p=2.72E-07), PTEN (AUC=0.687, p=1.47E-04), and BRCA1 (AUC=0.608, p=2.24E-02) was lower.

Conclusions: Our finding support that specific mutated genes may differentially impact prognosis in breast cancer subtypes. Further efforts are required to understand the biological and prognostic role of specific activating and inactivating mutations across molecular breast cancer subtypes.

Citation Format: Gyorffy B, Pongor L, Szabo A, Bottai G, Pusztai L, Santarpia L. Somatic mutation patterns differentially affect survival in breast cancer molecular subtypes. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr PD6-06.

Abstract P5-04-10: Phosphodiesterase type 5 promotes the invasive potential of breast cancer cells through Rho GTPase activation

The impairment of cyclic guanosine monophosphate (cGMP) signaling by overexpression of PDE5 isoform has been recently described in multiple human carcinomas. In addition, accumulating evidences indicate that PDE5 inhibitors could have direct anti-cancer activities as well as they may enhance the sensitivity of certain types of cancer to standard chemotherapeutic drugs. However, despite these studies, neither the expression of PDE5 in breast cancer subtypes nor the underlying regulatory molecular mechanisms by which PDE5 expression may contribute to breast cancer progression have been deeply studied.

We demonstrated that PDE5 was expressed in different subtypes of breast cancer cell lines at higher levels than in non tumorogenic human epithelial breast cell lines. Increased levels were detected in more aggressive endocrine non responsive basal-like breast cancer cells. Interestingly, PDE5 was expressed at very low levels in luminal A-type breast cancer cell lines, which display low ki67 expression, weak invasive behavior and endocrine responsiveness (MCF-7 and T47D cells) compared to luminal B-like cells (such as ZR-75 cells). These results well correlated with data obtained in immunohistochemistry analyses of human breast cancer tissues, showing PDE5 expression in 30 of 35 tumor entities analyzed, with the highest intensity staining in high-grade tumors. Concomitantly, no cytoplasmic PDE5 staining was observed in non neoplastic tissues examined (n=5). In addition, retrospective analyses (n=1959, median follow-up time: 25 years) showed that high PDE5 expression in breast cancer patients was correlated with a statistically significant poorer survival compared to low PDE5-expressing patients. A more relevant discrimination is achieved in lymphnode-negative patients, suggesting a role of PDE5 for identifying early patients at high risk of rapid progression.

In order to better ascertain the role of PDE5 in breast tumorogenesis, we selected a breast tumor cell line that express low levels of this enzyme, MCF-7 and engineered stable clones for overexpression studies. Both vector- and PDE5-stable MCF-7 clones demonstrated comparable proliferation rates; whereas, cell motility and invasion were dramatically increased in PDE5-overexpressing cells. RNA sequencing to compare the transcriptomes of vector- and PDE5-overexpressing MCF-7 cells identified differential expression of genes involved in cell migration and invasion. Particularly, based on pathway analysis we found marked changes in the expression of Rho GTPase family members, proteins involved in cell cytoskeleton organization, migration, and metastasis dissemination (Rho A, cdc42 and Rac signaling, activation score= 1.9, 1.342, and 0.302, respectively). Indeed, Rho and cdc42 pull-down assays revealed increased Rho GTPase activity in cells overexpressing PDE5. Moreover, the selective ROCK inhibitor Y-27632 as well as the PDE5 inhibitor sildenafil were able to significantly reduce both migration and invasion of PDE5 clones.

Our data reveal that PDE5 expression enhances motility and invasiveness of breast cancer cells through the activation of the Rho family of GTPases, and highlight, for the first time, a novel role for PDE5 as a marker of poor outcome in breast cancer patients.

Citation Format: Barone I, Campana A, Giordano C, Tarallo R, Rinaldi A, Bruno G, Gyorffy B, Lanzino M, Bonofiglio D, Catalano S, Ando' S. Phosphodiesterase type 5 promotes the invasive potential of breast cancer cells through Rho GTPase activation. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P5-04-10.

Abstract P4-03-03: Determination of lymph node status using the primary tumor’s gene expression signature

Introduction:Lymph node status is one of the most important clinical parameters of breast cancer. Axillary lymph node dissection and sentinel lymph node biopsy have considerable morbidity associated with them and a method to accurately predict lymph node positivity would be clinically useful. To date, no gene expression signature has been established that is capable ofestimatin lymph node positivity. Our aim was to develop a new predictor using a large set of patient samples and compare its performance to known clinical variables.

Methods: An integrated database was constructed using publicly available Affymetrix microarrays with known lymph node status. The patients were divided into the four molecular subtypes of basal, luminal A, luminal B and HER2 positive using the St. Gallen criteria and the gene chip based gene expression data of estrogen receptor, HER2 receptor and MKI67. ROC analysis was performed for each gene within each subtype. Then, the top 10 genes of each list were combined, and using the expression data of these 40 genes across all patients, Manhattan or Euclidean distanceswere computed to calculate the distance between each sample. All the patients were ranked, and lymph node status was defined by using the proportional lymph node positivity of “x” nearest ranked patients. Multiple regression was performed to compare the classification using the gene expression data to known clinical variables. Statistical significance was set at p<0.01.

Results: The database includes 2756 patients, of whom 983 are lymph node positive. To optimize the classification, different number of genes (all/top100/top40) and different number of closest patients (x = 1/2/5/10/25/25/100/250/500) and two different distance metrics (Euclidean and Manhattan distance) were assessed. The best performance was achieved using the top40 genes with Manhattan distance to the 100 nearest patients. This setting reached a sensitivity of 0.70, specificity of 0.73 and accuracy of 0.72 across all patients. When compared to ESR1 status, HER2 status, MKI67 expression, grade, size, and agein the multiple regression, only the gene-expression based classification (1.56E-31) and size (1.7E-24) were significant.

Discussion:We have established a pipeline capable of determining lymph node positivity using the gene expression data of 40 genes.

Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P4-03-03.

A comprehensive overview of targeted therapy in metastatic renal cell carcinoma

Chemotherapy and immunotherapy failed to deliver decisive results in the systemic treatment of metastatic renal cell carcinoma. Agents representing the current standards operate on members of the RAS signal transduction pathway. Sunitinib (targeting vascular endothelial growth factor), temsirolimus (an inhibitor of the mammalian target of rapamycin - mTOR) and pazopanib (a multi-targeted receptor tyrosine kinase inhibitor) are used in the first line of recurrent disease. A combination of bevacizumab (inhibition of angiogenesis) plus interferon α is also first-line therapy. Second line options include everolimus (another mTOR inhibitor) as well as tyrosine kinase inhibitors for patients who previously received cytokine. We review the results of clinical investigations focusing on survival benefit for these agents. Additionally, trials focusing on new agents, including the kinase inhibitors axitinib, tivozanib, dovitinib and cediranib and monoclonal antibodies including velociximab are also discussed. In addition to published outcomes we also include follow-up and interim results of ongoing clinical trials. In summary, we give a comprehensive overview of current advances in the systemic treatment of metastatic renal cell carcinoma.

P1-07-18: Expanding an Online Tool for Genome-Wide Validation of Survival-Associated Biomarkers in Breast and Ovarian Cancer Using Microarray Data of 3,862 Patients

The pre-clinical validation of prognostic gene candidates in large independent patient cohorts is a pre-requisite for the development of robust biomarkers. We earlier implemented an online tool to assess the prognostic or predictive value of the expression levels of all microarray quantified genes in breast cancer patients. In present study, we further expanded our database, added additional analytical options and implemented the tool for ovarian cancer patients.

The database was set up using gene expression data and survival information of breast and ovarian cancer patients downloaded from GEO and TCGA (Affymetrix HGU133A, HGU133A 2.0 and HGU133+2 microarrays). After quality control and normalization only probes present on all three Affymetrix platforms were retained (n=22,277). Patients can be stratified into the various robust subtypes either by histology or by various gene expression profiling based methods. To analyze the prognostic value of the selected gene in the various cohorts the patients are divided into two groups according to the median expression of the gene. A Kaplan-Meier survival plot is generated and significance is computed.

All together 2,472 breast cancer patients and 1,390 ovarian cancer patients were entered into the database. These groups can be compared using relapse free survival (n=2,414 in breast cancer and 1,090 in ovarian cancer) or overall survival (n=463 and n=1,290). Follow-up threshold has been implemented to exclude long-term effects. The combination of several probe sets can be employed to assess the mean of their expression as a multigene predictor of survival and therapy efficiency.

In summary, we expanded our global online biomarker validation platform to mine all available microarray data to assess the prognostic power of 22,277 genes in 2,472 breast and 1,390 ovarian cancer patients. The tool can be accessed online at: www.kmplot.com/breast and www.kmplot.com/dev/ovar.

Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P1-07-18.

Modeling the fission yeast cell cycle: quantized cycle times in wee1- cdc25Delta mutant cells

A detailed mathematical model for the fission yeast mitotic cycle is developed based on positive and negative feedback loops by which Cdc13/Cdc2 kinase activates and inactivates itself. Positive feedbacks are created by Cdc13/Cdc2-dependent phosphorylation of specific substrates: inactivating its negative regulators (Rum1, Ste9 and Wee1/Mik1) and activating its positive regulator (Cdc25). A slow negative feedback loop is turned on during mitosis by activation of Slp1/anaphase-promoting complex (APC), which indirectly re-activates the negative regulators, leading to a drop in Cdc13/Cdc2 activity and exit from mitosis. The model explains how fission yeast cells can exit mitosis in the absence of Ste9 (Cdc13 degradation) and Rum1 (an inhibitor of Cdc13/Cdc2). We also show that, if the positive feedback loops accelerating the G(2)/M transition (through Wee1 and Cdc25) are weak, then cells can reset back to G(2) from early stages of mitosis by premature activation of the negative feedback loop. This resetting can happen more than once, resulting in a quantized distribution of cycle times, as observed experimentally in wee1(-) cdc25Delta mutant cells. Our quantitative description of these quantized cycles demonstrates the utility of mathematical modeling, because these cycles cannot be understood by intuitive arguments alone.

Kinetic analysis of a molecular model of the budding yeast cell cycle

The molecular machinery of cell cycle control is known in more detail for budding yeast, Saccharomyces cerevisiae, than for any other eukaryotic organism. In recent years, many elegant experiments on budding yeast have dissected the roles of cyclin molecules (Cln1-3 and Clb1-6) in coordinating the events of DNA synthesis, bud emergence, spindle formation, nuclear division, and cell separation. These experimental clues suggest a mechanism for the principal molecular interactions controlling cyclin synthesis and degradation. Using standard techniques of biochemical kinetics, we convert the mechanism into a set of differential equations, which describe the time courses of three major classes of cyclin-dependent kinase activities. Model in hand, we examine the molecular events controlling "Start" (the commitment step to a new round of chromosome replication, bud formation, and mitosis) and "Finish" (the transition from metaphase to anaphase, when sister chromatids are pulled apart and the bud separates from the mother cell) in wild-type cells and 50 mutants. The model accounts for many details of the physiology, biochemistry, and genetics of cell cycle control in budding yeast.

Model scenarios for evolution of the eukaryotic cell cycle

Progress through the division cycle of present day eukaryotic cells is controlled by a complex network consisting of (i) cyclin-dependent kinases (CDKs) and their associated cyclins, (ii) kinases and phosphatases that regulate CDK activity, and (iii) stoichiometric inhibitors that sequester cyclin-CDK dimers. Presumably regulation of cell division in the earliest ancestors of eukaryotes was a considerably simpler affair. Nasmyth (1995) recently proposed a mechanism for control of a putative, primordial, eukaryotic cell cycle, based on antagonistic interactions between a cyclin-CDK and the anaphase promoting complex (APC) that labels the cyclin subunit for proteolysis. We recast this idea in mathematical form and show that the model exhibits hysteretic behaviour between alternative steady states: a Gl-like state (APC on, CDK activity low, DNA unreplicated and replication complexes assembled) and an S/M-like state (APC off, CDK activity high, DNA replicated and replication complexes disassembled). In our model, the transition from G1 to S/M ('Start') is driven by cell growth, and the reverse transition ('Finish') is driven by completion of DNA synthesis and proper alignment of chromosomes on the metaphase plate. This simple and effective mechanism for coupling growth and division and for accurately copying and partitioning a genome consisting of numerous chromosomes, each with multiple origins of replication, could represent the core of the eukaryotic cell cycle. Furthermore, we show how other controls could be added to this core and speculate on the reasons why stoichiometric inhibitors and CDK inhibitory phosphorylation might have been appended to the primitive alternation between cyclin accumulation and degradation.

Mathematical model of the fission yeast cell cycle with checkpoint controls at the G1/S, G2/M and metaphase/anaphase transitions

All events of the fission yeast cell cycle can be orchestrated by fluctuations of a single cyclin-dependent protein kinase, the Cdc13/Cdc2 heterodimer. The G1/S transition is controlled by interactions of Cdc13/Cdc2 and its stoichiometric inhibitor, Rum1. The G2/M transition is regulated by a kinase-phosphatase pair, Wee1 and Cdc25, which determine the phosphorylation state of the Tyr-15 residue of Cdc2. The meta/anaphase transition is controlled by interactions between Cdc13/Cdc2 and the anaphase promoting complex, which labels Cdc13 subunits for proteolysis. We construct a mathematical model of fission yeast growth and division that encompasses all three crucial checkpoint controls. By numerical simulations we show that the model is consistent with a broad selection of cell cycle mutants, and we predict the phenotypes of several multiple-mutant strains that have not yet been constructed.