Genomic and Immunologic Correlates in Prostate Cancer with High Expression of KLK2

The identification of surfaceome proteins is a main goal in cancer research to design antibody-based therapeutic strategies. T cell engagers based on KLK2, a kallikrein specifically expressed in prostate cancer (PRAD), are currently in early clinical development. Using genomic information from different sources, we evaluated the immune microenvironment and genomic profile of prostate tumors with high expression of KLK2. KLK2 was specifically expressed in PRAD but it was not significant associated with Gleason score. Additionally, KLK2 expression did not associate with the presence of any immune cell population and T cell activating markers. A mild correlation between the high expression of KLK2 and the deletion of TMPRSS2 was identified. KLK2 expression associated with high levels of surface proteins linked with a detrimental response to immune checkpoint inhibitors (ICIs) including CHRNA2, FAM174B, OR51E2, TSPAN1, PTPRN2, and the non-surface protein TRPM4. However, no association of these genes with an outcome in PRAD was observed. Finally, the expression of these genes in PRAD did not associate with an outcome in PRAD and any immune populations. We describe the immunologic microenvironment on PRAD tumors with a high expression of KLK2, including a gene signature linked with an inert immune microenvironment, that predicts the response to ICIs in other tumor types. Strategies targeting KLK2 with T cell engagers or antibody-drug conjugates will define whether T cell mobilization or antigen release and stimulation of immune cell death are sufficient effects to induce clinical activity.

Abstract P2-07-12: Triple negative breast cancer subtypes and early dynamics of the 27-gene IO score predict pCR in the NeoTRIPaPDL1 trial

Background A post-hoc of NeoTRIP trial showed that 27-gene IO score assessed on baseline samples is predictive of increased pathological complete response (pCR) with the addition of atezolizumab to carboplatin/nab-paclitaxel, whereas the LAR subtype has the lowest rate of pCR with and without atezolizumab (Bianchini G ESMO 2021). We evaluated 27-gene IO score and TNBC subtypes on biopsies collected during treatment, assessed biomarker dynamics, and studied the association with pCR. Methods In NeoTRIP, patients randomly received 8 cycles of nab-paclitaxel/carbo alone (CT) or with atezolizumab (CT/A). 258 patients were evaluable for pCR (Per-Protocol Population). We assessed IO score as binary and continuous variable, and the five 101-gene TNBC types (BL1, BL2, LAR, M, and MSL; Ring et al 2016) by RNA-seq on biopsies at baseline and day 1 of second treatment cycle (d1c2) (n: baseline 242/258, 94%; d2c2 161/258, 62%; paired 152/258, 59%). Forty-four paired samples were excluded due to lack of tumor cells at d1c2. PD-L1 (Ventana SP142) and sTILs data were available. We evaluated the association with pCR of biomarkers assessed at d1c2 and their dynamics from baseline. Results Frequency of TNBC types at d1c2 showed minor differences between arms (p = 0.055). TNBC type frequencies were 22.9% BL1, 11.4% BL2, 22.9% LAR, 21.4% M, and 21.4% MSL in the CT/A arm and 43.8% BL1, 6.2% BL2, 11.2% LAR, 21.2% M, and 17.5% MSL in the CT arm. Individual TNBC type changes from baseline to d1c2 were observed, but overall, it was not significant. Frequency of IO positive score at d1c2 was similar in CT and CT/A arm (p = 0.75). Only in CT/A, an increase from baseline to d1c2 was observed (30.9% to 49.3%, p = 0.04).Overall, TNBC types at d1c2 were predictive of pCR (p = 0.00002). Compared to BL1, LAR and M were associated with lower pCR rate in CT (OR = 0.09, 95% CI = 0.01-0.83, p = 0.034 for LAR; OR = 0.16, 95% CI = 0.04-0.66, p = 0.011 for M) and CT/A arm (OR = 0.05, 95% CI = 0.01-0.49, p = 0.010 for LAR; OR = 0.28, 95% CI = 0.06-1.28, p = 0.102). pCR rate in LAR was 11.1% and 6.2% in CT and CT/A arm, respectively. TNBC types were predictive of pCR independently of PD-L1 and sTILs.Continuous IO score at d1c2 was predictive of pCR in both CT/A (p = 0.004) and CT arms (p = 0.009). The binary IO score was significantly associated to higher pCR rate in CT/A arm only (OR = 5.42, 95% CI = 1.95-15.07, p = 0.001). A strong predictive value of the highest quartile of IO score compared to the lowest was observed in CT/A (OR = 14.73, 95% CI = 2.97-73.21, p = 0.001) and CT (OR = 4.38, 95% CI = 1.21-15.81, p = 0.024) arms. pCR rates for the highest and lowest quartiles were 72.2% vs 15.0% in CT/A and 65.2% vs 30.0% in CT arm. In CT/A binary IO score at d1c2 retained significance after adjustment for baseline PD-L1 and sTILs (p = 0.036).Combining baseline and d1c2 IO score, only d1c2 assessment was informative in CT arm. In CT/A arm, both biomarkers were informative, with assessment at d1c2 being more informative than baseline IO score when continuous scores were considered. Baseline binary IO score (OR = 25.0, 95% CI = 3.31-188.9, p = 0.002) and ΔIO score (d1c2-baseline) (OR = 11.3, 95% CI = 1.07-120.1, p = 0.044) retained significance. The combination of baseline and d1c2 binary IO score defined four groups with different likelihood of pCR: 73.7% vs 15.2% in positive/positive and negative/negative groups, respectively (OR = 15.68, 95% CI = 3.88-63.32, p = 0.0001). Conclusions Dynamic of IO score early on treatment was linked to likelihood of pCR independently of baseline biomarkers and may be an early surrogate of treatment benefit especially in atezolizumab arm. LAR and M are associated with lower pCR rate, suggesting that different therapeutic strategies may be beneficial. Combining baseline and on-treatment biomarkers can be more informative than baseline only of the complex tumor/immune co-evolution dynamic and of clinical outcome.

Citation Format: Matteo Dugo, Chiun-Sheng Huang, Daniel Egle, Begoña Bermejo, Claudio Zamagni, Robert S. Seitz, Tyler J. Nielsen, Marc Thill, Antonio Anton, Stefania Russo, Eva Maria Ciruelos, Brock L. Schweitzer, Douglas T. Ross, Barbara Galbardi, Richard Greil, Vladimir Semiglazov, Balázs Gyorffy, Marco Colleoni, Catherine Kelly, Gabriella Mariani, Lucia Del Mastro, Pinuccia Valagussa, Giuseppe Viale, Maurizio Callari, Luca Gianni, Giampaolo Bianchini. Triple negative breast cancer subtypes and early dynamics of the 27-gene IO score predict pCR in the NeoTRIPaPDL1 trial [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P2-07-12.

Abstract P1-04-02: Immune milieu associated with PD-L1 status in TNBC is dependent on time of biomarker assessment and treatment received: A secondary analysis of the NeoTRIPaPDL1 trial

Background - In the NeoTRIPaPDL1 phase III trial, triple-negative breast cancer (TNBC) patients were randomized to receive nab-paclitaxel/carboplatin for 8 cycles (CT arm) with or without atezolizumab (CT/A arm). We previously reported that the majority of patients (65%) with PD-L1- at baseline converted to PD-L1+ (the majority with IC2/IC3) after the first cycle of treatment in the atezolizumab arm. Here we studied treatment associated changes in the molecular tumour features and immune microenvironment by PD-L1 expression. Methods – A total of 158 (56.4%) of patients enrolled in the NeoTRIPaPDL1 trial (76 and 82 from CT/A and CT arm, respectively) were included in this analysis by satisfying the following criteria: i) availability of a paired core biopsy at baseline and at day 1 of the second treatment cycle (D1C2); ii) evaluation of stromal TILs (sTILs) and staining for PD-L1 (Ventana SP142); iii) successful RNA-seq gene expression profiling. PD-L1 groups were defined as IC0/IC1 (<5% PD-L1low) vs IC2/IC3 (>=5% PD-L1high). Presence of different immune cell populations was quantified by gene expression profile deconvolution using ConsensusTME R package. Cancer hallmark gene set collection and custom signature activation status were estimated in each sample using singscore R package. Differences in score distribution were evaluated by 2-sided t-test. Results At baseline, both sTILs and immune cell signatures were upregulated in PD-L1high compared to PD-L1low tumours (p<0.001). No significant differences were found between the two treatment arms in the PD-L1high subpopulation. In the PD-L1low cases, both sTILs and immune related signatures were slightly downregulated in the CT/A arm (p<0.05), suggesting a modest unbalance among treatment arms with CT arm being slightly more inflamed than CT/A arm.At D1C2, PD-L1high tumours in the CT arm systematically had high sTILs (median=70%, range=30-90%), while PD-L1low tumours receiving CT/A had low sTILs in a significant proportion of cases (median=30%, range=0-90%; p<0.001). Similarly, at D1C2 several gene expression-estimated immune cell populations and immune-related signatures were upregulated in the CT arm compared to CT/A arm in PD-L1high tumours, with the weakest association observed for M2 macrophages (p=0.058). No tumour-related signatures were differentially expressed among the two treatment arms within groups with PD-L1high, suggesting that different treatment. modulate PD-L1 by engaging a different immune mileau instead of modulating tumor related features.Considering PD-L1low groups at D1C2, in the CT arm 21 tumors had >30% sTILs (n=21/69, 30.4%), while in the CT/A arm only 2 had sTILs >30% (2/27, 7.4%) (p<0.001). Analysis of gene expression data identified IFN-related signatures as the most upregulated in the CT compared to CT/A arm in PD-L1low cases at D1C2 (p<0.05). Conclusions Integrated dynamic analysis of PD-L1 expression and gene expression data highlighted significant treatment-specific changes of the immune landscape according to PD-L1 expression, when this biomarker is assessed during treatment. This indicates that the immune milieu associated with PD-L1 status is strongly dependent on the time of assessment in relationship to treatment received. Such observation may explain why in the NeoTRIP trial, baseline PD-L1 but not on-treatment PD-L1 was predictive of pCR in CT/A arm. In addition, our findings could have implications related to the use of PD-L1 as a predictive biomarker in pre-treated patients, especially when assessed early on during treatment.

Citation Format: Maurizio Callari, Chiun-Sheng Huang, Daniel Egle, Begoña Bermejo, Claudio Zamagni, Matteo Dugo, Marc Thill, Antonio Anton, Marco Barreca, Stefania Russo, Eva Maria Ciruelos, Richard Greil, Stefania Zambelli, Balázs Gyorffy, Chanel Smart, Olivia Biasi, Pinuccia Valagussa, Giuseppe Viale, Luca Gianni, Giampaolo Bianchini. Immune milieu associated with PD-L1 status in TNBC is dependent on time of biomarker assessment and treatment received: A secondary analysis of the NeoTRIPaPDL1 trial [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-04-02.

Abstract PD10-06: Predictive value of RT-qPCR 27-gene IO score and comparison with RNA-Seq IO score in the NeoTRIPaPDL1 trial

Background The identification of biomarkers for optimization of immune checkpoint inhibitors (ICI) treatment is an unmet clinical need. In the Phase III randomized trial, NeoTRIPaPDL1, a post-hoc analysis of whole transcriptome RNA-Seq data, previously showed that the 27-gene IO score is a potential predictive biomarker of increased pathological complete response with the addition of atezolizumab to carboplatin/nab-paclitaxel (Bianchini G ESMO 2021). However, the laboratory implementation of gene-expression signatures measured using RNA-seq is challenging. Therefore, we further assessed the predictive value of the IO score using a twenty-seven gene RT-qPCR assay on NeoTRIP samples, and compared to the previously reported RNA-Seq version of the assay. Methods The NeoTRIP study randomized patients to eight cycles of carboplatin/nab-paclitaxel (CT) with or without atezolizumab (CT/A). 258 patients were evaluable for pCR (breast and nodes) as Per-Protocol Population. We assessed the IO score as binary and continuous variables using the CAP/CLIA validated DetermaIO qPCR test (Saltman et al 2021) on pre-treatment core biopsies (n=220/258; 85.3%), all of which have RNA-Seq data available. We evaluated the association between IO score defined by RT-qPCR and RNA-Seq, and the association of the IO score defined by RT-qPCR test with PD-L1 IHC (Ventana SP142), stromal TILs (sTILs), and pCR. Results Comparison of continuous IO scores between the RT-qPCR assay and the RNA-Seq algorithm had a Pearson’s correlation of 0.94 (p < 0.0001). High agreement between categorical IO scores was also observed (Cohens’ kappa = 0.84; 95% confidence interval [CI] = 0.77-0.91; p < 0.0001). RT-qPCR IO score was balanced in the two arms (p = 0.65) with 44% and 40% positive patients in the CT and CT/A arms, respectively. The RT-qPCR IO score was correlated with both PD-L1 (Pearson’s r = 0.64; p < 0.0001) and sTILs (Pearson’s r = 0.67; p < 0.0001). Continuous IO score was significantly predictive of pCR in CT/A (Odds ratio [OR] = 3.12; 95% CI = 1.20-8.10; p<0.019), but not CT arm (OR = 1.28; 95% CI = 0.54-3.01; p = 0.578). Considering the binary IO score, OR were 2.87 [1.27-6.47] (p = 0.011) and 0.91 [0.43-1.93] (p = 0.812), in CT/A and CT, respectively (interaction test p = 0.043). The pCR rate for CT/A vs CT was 69.8% vs 46.9% in IO score positive [+22.9%, p = 0.046, Chi-squared test] and 44.6% vs 49.2% [-4.6%, p = 0.73] in IO score negative. A significant interaction was found between continuous PD-L1 and continuous IO-score (p = 0.006). Among PD-L1-neg, 9 patients were IO score positive (10.1%). The pCR rate in this group was 3/4 (75%) in the CT/A arm and 1/5 (20%) in CT arm. The predictive value of IO score by RT-qPCR was similar to RNA-Seq. Conclusions We observed a high level of agreement and concordance between IO scores assessed by RT-qPCR and RNA-Seq, indicating that the 27-gene IO assay and algorithm is robust and the choice of platform has limited impact. This finding also demonstrates the high quality of NeoTRIP RNA-Seq data. In this post-hoc analysis, IO score assessment by this CLIA validated RT-qPCR test was confirmed to be predictive of atezolizumab benefit over CT alone in a randomized trial.

Citation Format: Matteo Dugo, Chiun-Sheng Huang, Daniel Egle, Begoña Bermejo, Claudio Zamagni, Robert S. Seitz, Tyler J. Nielsen, Marc Thill, Antonio Anton, Stefania Russo, Eva Maria Ciruelos, Brock L. Schweitzer, Douglas T. Ross, Barbara Galbardi, Richard Greil, Vladimir Semiglazov, Balázs Gyorffy, Marco Colleoni, Catherine Kelly, Gabriella Mariani, Lucia Del Mastro, Pinuccia Valagussa, Giuseppe Viale, Maurizio Callari, Luca Gianni, Giampaolo Bianchini. Predictive value of RT-qPCR 27-gene IO score and comparison with RNA-Seq IO score in the NeoTRIPaPDL1 trial [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr PD10-06.

Genomic Signatures of Immune Activation Predict Outcome in Advanced Stages of Ovarian Cancer and Basal-Like Breast Tumors

There is an unmet need for new therapies in metastatic ovarian cancer and basal-like breast cancer since no curative therapies are currently available. Immunotherapy has shown to be active in several solid tumors, but particularly more in those where a pre-activated immune state does exist. In this work, we aim to identify biomarkers that could distinguish immune-activated tumors and predict response to therapies in ovarian and basal-like breast cancer, as well as their association with the level of tumor immune infiltration. We found that the combined expression of IFNG, CD30, CXCL13, and PRF1 correlated with better overall survival (OS) in advanced stage ovarian cancer. This was confirmed using an independent dataset from TCGA. Interestingly, we observed that this gene combination also predicted for better prognosis in ovarian tumors with low mutational load, which typically respond less to immunotherapy. Expression of IFNG, CD30, CXCL13, and PRF1 was associated with increased level of immune infiltrates (CD8⁺ T cells, dendritic cells, and neutrophils) within the tumor. Moreover, we found that these gene signature also correlated with an increased OS and with a higher level of tumor immune infiltrates (B cells, CD8⁺ T cells, CD4⁺ T cells, neutrophils, and dendritic cells) in basal-like breast cancer. In conclusion, our analysis identifies genes signatures with potential to recognize immune activated ovarian and basal-like breast cancers with favorable prognosis and with a remarkable predictive capacity in tumors with low mutational burden. The presented results led to a hypothesis being formulated, but prospective clinical studies are needed to support a potential clinical application.

A Transcriptomic Immunologic Signature Predicts Favorable Outcome in Neoadjuvant Chemotherapy Treated Triple Negative Breast Tumors

Limited therapeutic options exist for the treatment of patients with triple negative breast cancer (TNBC). Neoadjuvant chemotherapy is currently the standard of care treatment in the early stages of the disease, although reliable biomarkers of response have been scarcely described. In our study we explored whether immunologic signatures associated with inflamed tumors or hot tumors could predict the outcome to neoadjuvant chemotherapy. Publicly available transcriptomic data of more than 2,000 patients were evaluated. ROC plots were generated to assess the response to therapy. Cox proportional hazards regression was computed. Kaplan-Meier plots were drawn to visualize the survival differences. Higher expression of IDO1, CXCL9, CXCL10, HLA-DRA, HLA-E, STAT1, and GZMB were associated with a higher proportion without relapse in the first 5 y after chemotherapy in TNBC. The expression of these genes was associated with a high presence of CD8 T cells in responder patients using the EPIC bioinformatic tool. The strongest effect was observed for STAT1 (p = 1.8e-05 and AUC 0.69, p = 2.7e-06). The best gene-set signature to predict favorable RFS was the combination of IDO1, LAG3, STAT1, and GZMB (HR = 0.28, CI = 0.17–0.46, p = 9.8 E-08, FDR = 1%). However, no influence on pathological complete response (pCR) was observed. Similarly, no benefit was identified in any other tumor subtype: HER2 or estrogen receptor positive. In conclusion, we describe a set of immunologic genes that predict the outcome to neoadjuvant chemotherapy in TNBC, but not pCR, suggesting that this non-time to event endpoint is not a good surrogate marker to detect the long term outcome for immune activated tumors.

Challenging the heterogeneity of disease presentation in malignant melanoma-impact on patient treatment

There is an increasing global interest to support research areas that can assist in understanding disease and improving patient care. The National Cancer Institute (NIH) has identified precision medicine-based approaches as key research strategies to expedite advances in cancer research. The Cancer Moonshot program ( https://www.cancer.gov/research/key-initiatives/moonshot-cancer-initiative ) is the largest cancer program of all time, and has been launched to accelerate cancer research that aims to increase the availability of therapies to more patients and, ultimately, to eradicate cancer. Mass spectrometry-based proteomics has been extensively used to study the molecular mechanisms of cancer, to define molecular subtypes of tumors, to map cancer-associated protein interaction networks and post-translational modifications, and to aid in the development of new therapeutics and new diagnostic and prognostic tests. To establish the basis for our melanoma studies, we have established the Southern Sweden Malignant Melanoma Biobank. Tissues collected over many years have been accurately characterized with respect to the tumor and patient information. The extreme variability displayed in the protein profiles and the detection of missense mutations has confirmed the complexity and heterogeneity of the disease. It is envisaged that the combined analysis of clinical, histological, and proteomic data will provide patients with a more personalized medical treatment. With respect to disease presentation, targeted treatment and medical mass spectrometry analysis and imaging, this overview report will outline and summarize the current achievements and status within malignant melanoma. We present data generated by our cancer research center in Lund, Sweden, where we have built extensive capabilities in biobanking, proteogenomics, and patient treatments over an extensive time period.

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.

Implementing an online tool for genome-wide validation of survival-associated biomarkers in ovarian-cancer using microarray data from 1287 patients

The validation of prognostic biomarkers in large independent patient cohorts is a major bottleneck in ovarian cancer research. We implemented an online tool to assess the prognostic value of the expression levels of all microarray-quantified genes in ovarian cancer patients. First, a database was set up using gene expression data and survival information of 1287 ovarian cancer patients downloaded from Gene Expression Omnibus and The Cancer Genome Atlas (Affymetrix HG-U133A, HG-U133A 2.0, and HG-U133 Plus 2.0 microarrays). After quality control and normalization, only probes present on all three Affymetrix platforms were retained (n=22,277). To analyze the prognostic value of the selected gene, we divided the patients into two groups according to various quantile expressions of the gene. These groups were then compared using progression-free survival (n=1090) or overall survival (n=1287). A Kaplan-Meier survival plot was generated and significance was computed. The tool can be accessed online at www.kmplot.com/ovar. We used this integrative data analysis tool to validate the prognostic power of 37 biomarkers identified in the literature. Of these, CA125 (MUC16; P=3.7×10(-5), hazard ratio (HR)=1.4), CDKN1B (P=5.4×10(-5), HR=1.4), KLK6 (P=0.002, HR=0.79), IFNG (P=0.004, HR=0.81), P16 (P=0.02, HR=0.66), and BIRC5 (P=0.00017, HR=0.75) were associated with survival. The combination of several probe sets can further increase prediction efficiency. In summary, we developed a global online biomarker validation platform that mines all available microarray data to assess the prognostic power of 22,277 genes in 1287 ovarian cancer patients. We specifically used this tool to evaluate the effect of 37 previously published biomarkers on ovarian cancer prognosis.

Parallel evolution under chemotherapy pressure in 29 breast cancer cell lines results in dissimilar mechanisms of resistance

Background

Developing chemotherapy resistant cell lines can help to identify markers of resistance. Instead of using a panel of highly heterogeneous cell lines, we assumed that truly robust and convergent pattern of resistance can be identified in multiple parallel engineered derivatives of only a few parental cell lines.

Methods

Parallel cell populations were initiated for two breast cancer cell lines (MDA-MB-231 and MCF-7) and these were treated independently for 18 months with doxorubicin or paclitaxel. IC50 values against 4 chemotherapy agents were determined to measure cross-resistance. Chromosomal instability and karyotypic changes were determined by cytogenetics. TaqMan RT-PCR measurements were performed for resistance-candidate genes. Pgp activity was measured by FACS.

Results

All together 16 doxorubicin- and 13 paclitaxel-treated cell lines were developed showing 2–46 fold and 3–28 fold increase in resistance, respectively. The RT-PCR and FACS analyses confirmed changes in tubulin isofom composition, TOP2A and MVP expression and activity of transport pumps (ABCB1, ABCG2). Cytogenetics showed less chromosomes but more structural aberrations in the resistant cells.

Conclusion

We surpassed previous studies by parallel developing a massive number of cell lines to investigate chemoresistance. While the heterogeneity caused evolution of multiple resistant clones with different resistance characteristics, the activation of only a few mechanisms were sufficient in one cell line to achieve resistance.

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.

[Next generation sequencing technologies (NGST) -- development and applications]

In the past ten years the development of next generation sequencing technologies brought a new era in the field of quick and efficient DNA sequencing. In our study we give an overview of the methodological achievements from Sanger's chain-termination sequencing in 1975 to those allowing real-time DNA sequencing today. Sequencing methods that utilize clonal amplicons for parallel multistrand sequencing comprise the basics of currently available next generation sequencing techniques. Nowadays next generation sequencing is mainly used for basic research in functional genomics, providing quintessential information in the meta-analyses of data from signal transduction pathways, onthologies, proteomics and metabolomics. Although next generation sequencing is yet sparsely used in clinical practice, cardiology, oncology and epidemiology already show an immense need for the additional knowledge obtained by this new technology. The main barrier of its spread is the lack of standardization of analysis evaluation methods, which obscure objective assessment of the results.

Predictive biomarker discovery through the parallel integration of clinical trial and functional genomics datasets

The European Union multi-disciplinary Personalised RNA interference to Enhance the Delivery of Individualised Cytotoxic and Targeted therapeutics (PREDICT) consortium has recently initiated a framework to accelerate the development of predictive biomarkers of individual patient response to anti-cancer agents. The consortium focuses on the identification of reliable predictive biomarkers to approved agents with anti-angiogenic activity for which no reliable predictive biomarkers exist: sunitinib, a multi-targeted tyrosine kinase inhibitor and everolimus, a mammalian target of rapamycin (mTOR) pathway inhibitor. Through the analysis of tumor tissue derived from pre-operative renal cell carcinoma (RCC) clinical trials, the PREDICT consortium will use established and novel methods to integrate comprehensive tumor-derived genomic data with personalized tumor-derived small hairpin RNA and high-throughput small interfering RNA screens to identify and validate functionally important genomic or transcriptomic predictive biomarkers of individual drug response in patients. PREDICT's approach to predictive biomarker discovery differs from conventional associative learning approaches, which can be susceptible to the detection of chance associations that lead to overestimation of true clinical accuracy. These methods will identify molecular pathways important for survival and growth of RCC cells and particular targets suitable for therapeutic development. Importantly, our results may enable individualized treatment of RCC, reducing ineffective therapy in drug-resistant disease, leading to improved quality of life and higher cost efficiency, which in turn should broaden patient access to beneficial therapeutics, thereby enhancing clinical outcome and cancer survival. The consortium will also establish and consolidate a European network providing the technological and clinical platform for large-scale functional genomic biomarker discovery. Here we review our current understanding of molecular mechanisms driving resistance to anti-angiogenesis agents, the current limitations of laboratory and clinical trial strategies and how the PREDICT consortium will endeavor to identify a new generation of predictive biomarkers.

[Gene expression based multigene prognostic and predictive tests in breast cancer]

Patient tailored therapy will serve the fundamentals of future cancer treatment. For this it will be imperative to characterize the tumor and to acquire precise predictive and prognostic information. We can achieve this by using not only monogenic (like ER, PR, HER-2, Ki-67) but also multigene assays, which can provide answers to several diagnostic questions simultaneously. We present a summary of currently available RT-PCR and microarray based multigene tests including MammaPrint, Oncotype DX, BLN Assay, Theros Breast Cancer Index SM, MapQuant DX, ARUP Breast Bioclassifier, Celera Metastatic Score, eXagen BCtm, Invasive Gene Signature, Wound Response Indicator and Mammostrat. Two of these (Oncotype DX and MammaPrint) are already incorporated in several diagnostic protocols. However, multiple unsolved issues deteriorate the value of these tests: generally the validation is poor, the gene sets do not confirm each other, the associated costs are high and the necessary bioinformatics is highly complex.

Promoter analysis suggests the implication of NFkappaB/C-Rel transcription factors in biliary atresia

BACKGROUND/AIMS

Microarray studies used to describe altered gene expression patterns in tissues are a promising new way to provide insight into the pathomechanism of biliary atresia (BA). The hypothesis in this study was that altered gene expression in BA may be linked to regulatory transcription factors (TF). The overrepresentation of transcription factor binding sites (TFBS) in the promoter regions of genes with altered expression would support this hypothesis.

METHODOLOGY

Using previously published data, the prevalence of TFBSs in the promoter regions of genes with altered expression in BA was analyzed and compared with the overall prevalence in known promoter sequences.

RESULTS

In the pooled BA gene list 195 different TFBS were identified. The prevalence of TFBSs of the members of NFkappaB/c-Rel family was higher compared with the background model.

CONCLUSIONS

NFkappaB/c-Rel play a role in the development and function of the immune system. Thus, the results of this study support current theories and experiments which link the immune system to perinatal BA. The information obtained in this analysis will help to understand the pathogenesis of BA, but further experimental and human data are required to validate the significance of these TFs in disease pathogenesis.

[RNA interference and its clinical applications]

RNA interference is a type of posttranscriptional gene silencing, when short RNA molecules suppress the function of RNAs and block gene expression. Double-stranded RNAs or short interfering RNAs injected into cells activate the RNA-induced silencing complex which degrades the target messenger RNA. The short RNAs produced inside the cell are called micro RNAs. These form a hairpin and then have the same function as double-stranded RNAs. RNA interference is an evolutionary important mechanism having a role in the protection against transposon and viral infection and regulate gene expression. While a number of studies demonstrate the in vivo applicability of RNAi, the first potential clinical trials are arising. So far it has been used to treat viral infections, inhibit macula degeneration, decrease the level of cholesterol in blood, treat cancer and neurodegenerative diseases. However, its application is hampered by ineffective bioinformatics algorithms unable to design effective short interfering RNAs, by low delivery efficiency and by the limited use to temporary antagonist gene silencing. The most important advantage of its application is the exceptional specificity resulting minimal side-effects. For this reason therapies based on RNA interference can be expected to spread in the near future.

[Identification of colorectal cancer, adenoma, and inflammatory bowel disease specific gene expression patterns using whole genomic oligonucleotide microarray system]

BACKGROUND

Discrimination and classification of colorectal diseases (adenoma, colorectal cancer, inflammatory bowel disease) using biopsy samples and expression microarrays, has not been solved yet, nevertheless, it can contribute to the understanding of the colonic diseases.

METHODS

Total ribonucleic acid was extracted, amplified and biotinylated from frozen colonic biopsies of 15 patients with colorectal cancer, 15 with adenoma, 14 with inflammatory bowel disease and 8 normal controls. Genome-wide gene expression profile was evaluated by Human Genome U133 Plus 2.0 microarrays. Two independent methods were used for data normalization and "Prediction Analysis of Microarrays" was performed for feature selection. Leave one-out stepwise discriminant analysis was performed. The expression results were verified by real-time polymerase chain reaction.

RESULTS

Top validated genes included CD44 antigen, met proto-oncogene, chemokine ligand-12, ADAM-like decysin-1 and ATP-binding casette-A8 genes in adenoma; collagen IValpha1, lipocalin-2, calumenin, aquaporin-8 genes in colorectal cancer; and lipocalin-2, ubiquitin D and interferon induced transmembrane protein 2 genes in inflammatory bowel disease. The discriminant analysis was able to classify the samples in overall 96.2% using 7 discriminatory genes. The expression of 94% of the 52 genes measured by Taqman real-time polymerase chain reaction correlated with the results obtained using Affymetrix microarrays at a significance of p < 0.05.

CONCLUSIONS

We successfully performed whole genomic microarray analysis to identify discriminative signatures using routine biopsy samples. The results set up data warehouse which can be further mined.

[The Danubian Biobank Initiative: synchronizing the biobanking activities of the Danube universities]

Aging disorders pose an increasing challenge for the public health care systems in Europe. An important approach to cope with this task is the identification of relevant novel disease genes and the control of risk factors using new technological capabilities. A key element in this process is the availability of well classified, large enough patient cohorts and the establishment of quality-controlled central banks for DNA, serum, plasma, and cells/tissues/RNA/proteins together with the development of an IT based infrastructure to provide samples and data required for biomedical studies. The Danubian Biobank initiative connects universities, associated teaching hospitals and endpoint-related rehabilitation clinics along the Danube river and in neighbouring regions. The scientific network focuses on diabetes-related endpoints, vascular disease (e.g. myocardial infarction, stroke, arterial thrombosis, kidney failure), metabolic disease (e.g. obesity, diabetes, metabolic syndrome), and neurodegenerative disorders (e.g. dementia syndromes, Parkinsonism). Task forces are set up for the relevant topics of the biobank project including patient recruitment, sample and data management, public health, epidemiology and genetics, enabling technologies, and research strategies. The project aims to select the most relevant and promising scientific targets utilizing the core competences developed in the individual partner institutions. For this purpose a series of dedicated workshops and conferences are organized as well as joint research grant proposals are submitted.

Internet-based biobank-registers in Hungary

A genomikai vizsgálatok – elsősorban a genetikai polimorfizmus- és a génexpressziós vizsgálatok, valamint a betegségek kockázata közötti kapcsolat elemzése – egyre nagyobb szerepet kap az orvosi kutatásokban. Ezek természetszerűleg elválaszthatatlanok a betegek mintáitól. A gyűjtött, tárolt és megfelelen regisztrált biológiai minták, valamint hozzájuk tartozó klinikai adatok összességükben a biobankot alkotják. A biobankok együttműködésével, az itt tárolt minták és adatok kollaboratív megosztásával számottevően nő a kutatás hatékonysága. A szerzők közleményükben magyarországi kezdeményezésekről számolnak be, melyek célja biobankregiszterek kialakítása, az egyes biobankok működésének a feltérképezése és összehangolása. Bemutatják az Országos Biobank-honlap, a Semmelweis Biobank, a ritka betegségekre szakosodott Orphanet, a neurológiai és pszichiátriai betegségben szenvedők mintáira kifejlesztett NEPSYBANK szerkezetét, célkitűzéseit. Az utóbbi években egyre több biobankot hoznak létre genomikai vizsgálatok céljára. A közeljövőben a törvényi háttér szabályozásával ez a folyamat várhatóan felgyorsul, és jobban koordinálhatóvá válik. Várható, hogy a bemutatott biobankhálózatok ebben segíteni fognak.

[Microelectrodes and their application in diagnostic medicine]

Microelectrodes are in common use in medical detection systems. The binding of two complementary nucleic acid sequences is called hybridization. Today the major obstacle of large-scale hybridization approaches is the large time-dependency of a single reaction, which is up to 16 hours. As the DNA molecules can be electronically charged, the binding could be facilitated and confirmed using an electronic control system. The authors' team aimed to develop a microelectrode system capable for the detection and control of hybridization. A microelectrode head is immersed in small liquid drop. Here, the platinum counterelectrode is surrounded by a non-conducting quartz capillary. The reference electrode is chloridized silver immersed in saturated Ag/Cl dilution. The Ag/AgCl/1 M KCl +AgCl microelectrode in stabilized against the calomel electrode in the first hours, and remains stable between 7th and 30th hours. This can be verified by the minimal drop in the potential difference. Thus the AgCl saturated KCl electrode is usable for several days for actual measurements. The detector is controlled by an attached computer. The system can be used to detect hybridization in a micro-cell located on a gold-plate. The electrode can be dismounted and reused after repeated chloridization of the Ag wire. The microelectrode is simple, cheap; thus is best suited for application in future automated diagnostic detection systems.

[Hybridization and their application in the DNA array technology]

Hybridization and their application in the DNA array technology. DNA hybridization arrays measure simultaneously the expression of several genes. First, a known DNA sequence (probe) is fixed on a firm basis. Then the complementer sequence (target sequence) is linked to it during the hybridization process. The target sequence extracted from biological samples is fluorescently, enzimatically or radioactively labeled before detection. Higher expression results in higher signal in the detection system. Unlabeled DNA strands can also be detected, as the electronic and optical characteristics of the DNA is altered after complementer hybridization. In this review we summarize the basics of hybridisation and its newest application area in the DNA array systems.

[The problem of multiple testing and solutions for genome-wide studies]

The problem of multiple testing and its solutions for genome-wide studies. Even if there is no real change, the traditional p = 0.05 can cause 5% of the investigated tests being reported significant. Multiple testing corrections have been developed to solve this problem. Here the authors describe the one-step (Bonferroni), multi-step (step-down and step-up) and graphical methods. However, sometimes a correction for multiple testing creates more problems, than it solves: the universal null hypothesis is of little interest, the exact number of investigations to be adjusted for can not determined and the probability of type II error increases. For these reasons the authors suggest not to perform multiple testing corrections routinely. The calculation of the false discovery rate is a new method for genome-wide studies. Here the p value is substituted by the q value, which also shows the level of significance. The q value belonging to a measurement is the proportion of false positive measurements when we accept it as significant. The authors propose using the q value instead of the p value in genome-wide studies.

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.