Senex Biotechnology (United States)

This study is significant as it discovered deep proteomic signatures for the highly aggressive and malignant metaplastic breast cancer (MCB) which is now considered a fifth subtype based upon its remarkable intra-tumoral heterogeneity that illustrates its unique cell plasticity. To efficiently analyze formalin-fixed paraffin-embedded (FFPE) breast tissues from patients with different breast cancer subtypes and disease-free individuals, we optimized a novel workflow in which we combined paraffinization and Folch extraction. We identified and confidently quantified ∼6,000 protein groups. We were able to find robust changes in extracellular matrix (ECM) with cancer, even though no ECM enrichments were performed. Interestingly, despite the relatively small human cohort size (42 patients), distinct protein signatures emerged throughout all cancer subtypes - common and unique - with remarkable statistical significance for many cancer-relevant proteins and pathways. This Pilot study indicates the hypothesis that an altered stroma can dictate epithelial tumor cell fate. We also observed that MBC was characterized by an especially immunosuppressed tumor environment. We do note the limitation of the relatively small cohort size of our study, and in the future additional patient cohorts will be needed to further validate our findings.

Abstract Androgen deprivation therapy (ADT) is the mainstay of prostate cancer (PCa) treatment. However, most patients with aggressive PCa become resistant to ADT and develop castration-resistant prostate cancer (CRPC). Newer androgen receptor (AR)-targeting CRPC treatments extend survival by only a few months, and metastatic CRPC (mCRPC) remains an incurable disease. Multiple mechanisms of ADT resistance have been identified, among which non-genetic (transcriptional) mechanisms are prominent, due to the high transcriptomic plasticity of PCa and the role of tumor microenvironment (TME) as a determinant of ADT resistance. PCa progression involves changes in the expression of CDK8 and CDK19, enzymatic components of the cyclin-dependent kinase module (CKM), associated with the transcriptional Mediator complex and implicated in transcriptional reprogramming and immune surveillance. We have investigated the roles of CDK8/19 Mediator kinases in CRPC, through bioinformatic analysis of primary PCa and mCRPC and by analyzing the effects of genetic and pharmacological inhibition of CDK8 and CDK19 on tumor growth and gene expression. CDK8 and CDK19 were found to be differentially regulated by androgen, which downregulates CDK8 and upregulates CDK19. Accordingly, CDK8 is decreased and CDK19 increased in primary PCa, where CDK19 is expressed higher than in any other tissues or tumors. However, both CDK19 and CDK8, as well as the other components of the CKM, are upregulated in mCRPC, concordantly with the changes in AR-mediated transcriptional signaling. Selective Mediator kinase inhibitors did not suppress PCa models driven by canonical AR signaling but inhibited in vivo growth of cell line-based and patient-derived xenograft (PDX) models of CRPC. Genetic inactivation of CDK8 and CDK19 suppressed 22Rv1 CRPC tumors in surgically or chemically castrated (but not intact) male NSG mice, whereas the expression of CDK19 or CDK8 reversed this phenotype. Transcriptomic analysis revealed that Mediator kinase inhibition exerted the strongest effects on gene expression in tumors growing in castrated mice, increasing the transcriptomic effects of castration on most of the genes regulated by CDK8/19, while decreasing its effects on some of the genes implicated in PCa progression. A gene signature reflecting Mediator kinase activity in CRPC xenografts correlated with shorter overall survival among mCRPC patients. Systemic treatment with a CDK8/19 inhibitor also affected stromal gene expression; many of these effects were reproduced by Mediator kinase mutagenesis in tumor cells alone, suggesting that Mediator kinases of tumor cells are involved in shaping the TME. Prolonged (up to 300 days) CDK8/19 inhibition induced not only growth inhibition but also shrinkage of CRPC tumors in nude mice (which contain NK cells that are stimulated by CDK8/19 inhibition) and even produced cures in 18-27% of animals. These results warrant the exploration of CDK8/19 Mediator kinase inhibitors for the treatment of the presently incurable CRPC. Citation Format: Jing Li, Thomas Hilimire, Yueying Liu, Lili Wang, Jiaxin Liang, Balazs Gyorffy, Vitali Sikitzhytski, Hao Ji, Li Zhang, Chen Cheng, Xiaokai Ding, Kendall Kerr, Charles Dowling, Gary Schools, Chang-Uk Lim, Eugenia Broude, George Wilding, Michael Lilly, Igor Roninson, Mengqian Chen. CDK8/19-regulated transcriptional reprogramming: a druggable driver of castration-resistant prostate cancer [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr A043.

The pathogenesis of chronic chest pain after cardiac surgery has not been determinate. If left untreated, postoperative sternal pain reduces the quality of life and patient satisfaction with cardiac surgery. The purpose of the study was to examine the effect of chest inflammation on postoperative pain, risk factors for chronic pain after cardiac surgery and to explore how chest reconstruction was associated with the intensity of pain. The authors performed a study of acute and chronic thoracic pain after cardiac surgery in patients with and without sternal infection and compared different techniques for chest reconstruction. 42 high-risk patients for the development of mediastinitis were included. Patients with mediastinitis received chest reconstruction (group 1). Their demographics and risk factors were matched with no-infection patients with chest reconstruction (group 2) and subjects who underwent conventional sternal closure (group 3). Chronic pain was assessed by the numeric rating scale after surgery. The assessment of the incidence and intensity of chest pain at 3 months post-surgery demonstrated that 14 out of 42 patients across all groups still experienced chronic pain. Specifically, in group 1 with sternal infection five patients had mild pain, while one patient experienced mild pain in group 2, and eight patients in group 3. Also, follow-up results indicated that the highest pain score was in group 3. While baseline levels of cytokines were increased among patients with sternal infection, at discharge only the level of interleukin 6 remained high compared to no infection groups. Compared to conventional closure, after chest reconstruction, we found better healing scores at 3-month follow-up and a higher percentage of patients with the complete sternal union. Overall, 14 out of 42 patients have chronic pain after cardiac surgery. The intensity of the pain in mediastinitis patients significantly decreased at 3 months follow-up after chest reconstruction. Thus, post-surgery mediastinitis is not a determining factor for development the chronic chest pain. There is no correlation between cytokines levels and pain score except interleukin 6 which remains elevated for a long time after treatment. Correlation between sternal healing score and chronic chest pain was demonstrated.

Abstract Permeability is an important parameter that describes the flow characteristics of rocks (hydrocarbons in the oil and gas reservoirs or groundwater in aquifers). Currently, laboratory experiments using cored samples and well testing are the main methods to determine rock permeability. However, these methods are time-consuming and/or resource-intensive. This paper proposes a novel machine learning approach to predict permeability scores. Field drilling and wireline data are acquired from 80 wells in the Surat Basin, Australia. The permeability scores are based on petrophysical interpretations from wireline logs. Before applying machine learning modeling, some preprocessing steps are conducted, including the elimination of non-numerical values and outliers. In this study, artificial neural network (ANN) and extreme gradient boost (XGBoost) are used to predict the permeability scores. There are 7 permeability scores in the original data and additional groupings (3 or 4 permeability groups) are conducted. As the data is highly imbalanced (i.e., almost 90% of samples are labeled with permeability score 0), the undersampling technique and oversampling technique (SMOTE) are used to enhance the model performance. Finally, the confusion matrix is used as an evaluation metric to quantify the model performance of this multiclass classification task. Results show that machine learning models have skewed performance (predicted permeability scores are lower than actual permeability scores) due to imbalanced data and have the best performance on the 0 permeability score samples (>99% accuracy). The implementation of undersampling and oversampling techniques can balance the model performance and improve the performance on high permeability score samples. Among all simulations, XGBoost with undersampling technique can achieve over 80% accuracy for the 3 permeability classes. The proposed machine learning approach provides a fast and cost-effective way to estimate rock permeability. It can be used to identify high and low permeability zones (e.g., identification of sweet spots).

Abstract Objectives / Scope Time savings and bit longevity is one of the major challenges in Coal Seam Gas unconventional fields Onshore Queensland. Maximize rate of penetration based on best drilling parameter was the key target to tackle these issues. The dataset was accumulated from more than 80 bits run. A supervised machine-learning algorithm was used to classify drilling parameters that increase ROP and bit endurance. To achieve this goal, our focus was on a) optimizing PDC bit design, drilling hydraulics and b) developing a Drillers Roadmap to ensure optimal parameters were run in the field. Methods, Procedures, Process Operator and Service Company worked closely to select two PDC frames and benchmark performance between them. A novel approach was then taken to capture the rig sensors data. These steps were as follows: Capture big data using sensors integrated on the rig to produce drilling parameters data sets. An algorithm to clean large data sets with the use of Python Pandas libraries was created. Using supervised machine learning and clustering the data to identify which drilling parameters gave the best results. Process the data set using visualization tools to develop heat maps vs depth for each of the drilling parameters (WOB, RPM, flow rates and Torque). Create a Drillers Roadmap identify drilling parameter trends by depth and provide a "sweet spot" based on machine learning for fine tuning operational window. Results, Observations, Conclusions A formal process for optimizing performance was developed. After 6 wells, optimal operating ranges were identified. This process leads the driller to respond to changes in formation and deliver more consistent drilling performance with excellent results. ROP increased from 50m/hr to 150m/hr, driven by improved ROP in the lower section of the well. Time saving of more than 150hrs for the drilling campiagn. Refine PDC bit designs based on optimized parameters, therefore improving bit life through cutter and hydraulic enhancements. Novel / Additive information The innovation of this methodology is to gather drilling information, cleaning the data with Python using Pandas library thus making this process more efficient. After the data files were clean and QAQC, a supervised machine Learning algorithm was used to optimize ROP's by taking drilling datasets and fine-tuning the parameters (WOB, RPM, Flow rate) using drilling dynamics (vibration) and MSE models. Finally, innovative visualizations (heat maps) were produced in order to give a clear insight of the drilling information gathered by the rig sensors to the company men and drillers.

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Abstract The Mediator complex-associated cyclin-dependent kinase CDK8 is an oncogenic transcription-regulating serine/threonine kinase that mediates multiple cancer-associated transcriptional pathways. Despite recent high-profile attention to CDK8 as a novel cancer drug target, very little is known about the function of CDK8’s closely related paralog CDK19. Using CRISPR/Cas9n system we generated CDK8/CDK19 single-knockout (CDK8-KO and CDK19-KO) and double-knockout (CDK8/19-dKO) derivatives of HEK293 cells. RNA-Seq was used to characterize the effects of a highly selective small-molecule CDK8/19 kinase inhibitor Senexin B on gene expression in the parental, single-knockout and double-knockout cell lines. This analysis was conducted in the absence or in the presence of TNF-alpha, an inducer of transcription factor NF-kappa-B that we have previously shown to be potentiated by CDK8. The following results were obtained. (1) CDK8 and CDK19 have complementary functions in stabilization of their partner Cyclin C (independent of their kinase activity) and phosphorylation of transcription factor STAT1 at S727 (dependent on the kinase activity). (2) Senexin B treatment affected gene expression in wild-type 293 cells but had almost no effect in CDK8/19-dKO cells, indicating a very high degree of target selectivity. (3) In contrast to the results with CDK8/19-dKO, most of the genes affected by Senexin B in the wild-type cells were also affected in CDK8-KO and CDK19-KO cells, indicating complementary functions of CDK8 and CDK19. (4) Re-expression of either CDK8 or CDK19, but not of a CDK8 kinase-dead (D173A) mutant, in CDK8/19-dKO cells restored CDK8/19 kinase-dependent gene expression as well as the regulatory effects of Senexin B. (5) Many more genes were inhibited rather than induced by Senexin B, indicating that CDK8/19 act primarily as positive regulators of transcription. (6) The total number of genes affected by Senexin B was greatly increased in cells treated with TNF-alpha, suggesting that cooperation with other transcription factors (such as NF-kappa-B) is the primary role of CDK8/19. Our results indicate that complete suppression of cancer-relevant activities of CDK8 requires simultaneous inhibition of both CDK8 and CDK19. Citation Format: Mengqian Chen, Bing Hu, Hao Ji, Serena Altilia, Jiaxin Liang, Martina McDermott, Chang-uk Lim, Donald C. Porter, Eugenia Broude, Igor Roninson. Functional characterization of novel transcription-regulating cancer drug targets, CDK8 and CDK19, using CRISPR/Cas9 knockout and a highly selective CDK8/19 kinase inhibitor [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1512. doi:10.1158/1538-7445.AM2017-1512

Agile and lean management practices (which we define broadly to include Scrum, XP, Lean Startup and other related approaches) roughly triple the success rate of software projects over traditional management approaches [1]. Despite these results, the software industry continues to start new projects using traditional approaches. Because software projects contribute so broadly to the human condition, research on agile methods may produce significant productivity gains. The impact extends beyond software, agile manufacturing [2] and agile organizational strategy [3] share many fundamentals with agile software.

In this mini-track, research papers and experience reports examine how agile development and lean product management interact with organizations, their structures, cultures and products.