Citations

Abstract IntroductionLiver disease such as cirrhosis is known to cause changes in the composition of volatile organic compounds (VOC) present in patient breath samples. Previous studies have demonstrated the diagnosis of liver cirrhosis from these breath samples, but studies are limited to a handful of discrete, well-characterized compounds.MethodsWe utilized VOC profiles from breath samples from 46 individuals, 35 with cirrhosis and 11 healthy controls. A deep-neural network was optimized to discriminate between healthy controls and individuals with cirrhosis.ResultsA 1D convolutional neural network (CNN) was accurate in predicting which patients had cirrhosis with an AUC of 0.90 (95% CI: 0.75, 0.99). Shapley Additive Explanations characterized the presence of discrete, observable peaks which were implicated in prediction, and the top peaks (based on the average SHAP profiles on the test dataset) were noted.ConclusionCNNs demonstrate the ability to predict the presence of cirrhosis based on a full volatolomics profile of patient breath samples. SHAP values indicate the presence of discrete, detectable peaks in the VOC signal.

Extracellular vesicles (EVs) are a heterogeneous group of biological nano‐sized vesicles that are released from cells and contribute to intercellular communication. Emerging knowledge about their biogenesis, composition, release, and uptake has resulted in broad interest in elucidating their potential roles in disease pathophysiology. The distinct biological properties of these biological nanoparticles emphasize several appealing advantages for potential therapeutic applications compared with the use of synthetic nanoparticles. When administered systemically, EVs are taken up and sequestered within the liver, further emphasizing opportunities for therapeutic use. Consequently, there is growing interest in their use for liver diseases. EVs can be used directly as therapeutics, and several studies have highlighted the intrinsic therapeutic properties of mesenchymal stem cell–derived EVs for chronic and acute liver diseases. Alternatively, EVs can be modified to facilitate their use for the delivery of therapeutic cargo. In this review, we discuss the cellular sources of EV, provide a concise overview of their potential use in diverse processes, and outline several promising applications for the use of EV‐based therapeutics for liver diseases. The use of EV‐based therapeutics provides a viable approach to target hepatic pathophysiology.

Abstract Disease-related effects on hepatic metabolism can alter the composition of chemicals in the circulation and subsequently in breath. The presence of disease related alterations in exhaled volatile organic compounds (VOC) could provide a basis for non-invasive biomarkers of hepatic disease. This study examined the feasibility of combining global VOC (volatolomic) profiles from breath analysis and machine learning to develop signature pattern-based biomarkers for cirrhosis. Breath samples were analyzed using thermal desorption-gas chromatography-field asymmetric ion mobility spectroscopy to generate volatolomic profiles. Samples were collected from 35 persons with cirrhosis, 4 with non-cirrhotic portal hypertension, and 11 healthy participants. Molecular features of interest were identified to determine their ability to classify cirrhosis or portal hypertension. A molecular feature score was derived that increased with the stage of cirrhosis and had an AUC of 0.78 for detection. Chromatographic breath profiles were utilized to generate machine learning-based classifiers. Algorithmic models could discriminate presence or stage of cirrhosis with a sensitivity of 88-92% and specificity of 75%. These results demonstrate the feasibility of volatolomic profiling to classify clinical phenotypes without identifying specific compounds. These studies will pave the way in developing non-invasive biomarkers of liver disease based on volatolomic signatures found in breath.

Abstract Purpose: Protein kinases are known to play a prominent role in oncogenic progression across multiple cancer subtypes, yet their role in prostate cancer progression remains underexplored. The purpose of this study was to identify kinases that drive prostate cancer progression. Experimental Design: To discover kinases that drive prostate cancer progression, we investigated the association between gene expression of all known kinases and long-term clinical outcomes in tumor samples from 545 patients with high-risk disease. We evaluated the impact of genetic and pharmacologic inhibition of the most significant kinase associated with metastatic progression in vitro and in vivo. Results: DNA-dependent protein kinase (DNAPK) was identified as the most significant kinase associated with metastatic progression in high-risk prostate cancer. Inhibition of DNAPK suppressed the growth of both AR-dependent and AR-independent prostate cancer cells. Gene set enrichment analysis nominated Wnt as the top pathway associated with DNAPK. We found that DNAPK interacts with the Wnt transcription factor LEF1 and is critical for LEF1-mediated transcription. Conclusions: Our data show that DNAPK drives prostate cancer progression through transcriptional regulation of Wnt signaling and is an attractive therapeutic target in aggressive prostate cancer.

Abstract Background/purpose: Despite recent advances in the development of androgen deprivation therapies (ADT), prostate cancer (PCa) remains the second leading cause of cancer death in American men. Thus, there is a critical need to improve treatment outcomes for men with aggressive PCa. The purpose of this study was to identify and functionally characterize kinases that can serve as both prognostic biomarkers and therapeutic targets in PCa. Methods: To identify kinases associated with metastatic progression of PCa, we utilized high-density oligonucleotide arrays to interrogate the expression of all kinases in 545 prostatectomy samples, obtained from high-risk patients with long-term clinical follow-up (>10 years). We ranked all kinases by their fold change in expression between PCas that subsequently metastasized versus those that did not. We then characterized the top ranked kinase in preclinical PCa models with in vitro mechanistic experiments and in vivo therapeutic studies. Results: We nominated DNA-dependent protein kinase (DNAPK) as the top kinase associated with metastatic PCa progression. Interrogation of pathways associated with DNAPK knockdown in PC3, DU145, VCaP and C4-2B, identified canonical Wnt signaling as the top DNAPK-activated pathway. In PCa cells (PC3, LNCaP C.S. {LNCaP grown under charcoal-stripped serum condition}, C4-2B and LNCaP-AR) DNAPK inhibition significantly abrogates Wnt signaling and abolishes cell proliferation, migration and invasion. Interestingly, we found that DNAPK directly modulates the Wnt signaling independent of the androgen receptor (AR), highlighting the therapeutic potential of DNAPK inhibition in cancers resistant to ADT. Indeed, in VCaP xenograft models, pharmacologic inhibition of DNAPK with NU7441 demonstrated 3.4 fold (p<0.01) reduction of tumor growth at non-toxic doses. Using an independent prostate cancer tissue cohort, we validate DNAPK expression as a biomarker for metastatic disease progression (p = 6.4 e−06, hazard ratio = 2). Conclusion: In summary, we nominate DNAPK as a potential biomarker of disease progression and as a novel therapeutic target in aggressive prostate cancer. Our data suggests that DNAPK mediates PCa progression by upregulating Wnt signaling, and demonstrates that DNAPK inhibition results in significant tumor responses in PCa xenografts. Citation Format: Vishal Kothari, Jonathan F. Goodwin, Shuang Zhao, Elai Davicioni, Jeffrey R. Karnes, Robert B. Den, Rohit Mehra, Karen E. Knudsen, Felix Y. Feng. Investigating DNAPK as a biomarker and a novel therapeutic target in aggressive prostate cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2853. doi:10.1158/1538-7445.AM2015-2853

Abstract Emerging evidence demonstrates that the DNA repair kinase DNA-PK exerts divergent roles in transcriptional regulation of unsolved consequence. Here, in vitro and in vivo interrogation demonstrate that DNA-PK functions as a selective modulator of transcriptional networks that induce cell migration, invasion, and metastasis. Accordingly, suppression of DNA-PK inhibits tumor metastases. Clinical assessment revealed that DNA-PK is significantly elevated in advanced disease, and independently predicts for metastases, recurrence, and reduced overall survival. Further investigation demonstrated that DNA-PK in advanced tumors is highly activated, independently of DNA damage indicators. Combined, these findings put forth new paradigms for DNA-PK function, identify DNA-PK as a potent driver of tumor progression and metastases, and nominate DNA-PK as a therapeutic target for advanced malignancies. Citation Format: Jonathan F. Goodwin, Vishal Kothari, Justin M. Drake, Shuang Zhao, Emanuela Dylgjeri, Jeffry L. Dean, Matthew J. Schiewer, Christopher McNair, Michael S. Magee, Robert B. Den, Ziqi Zhu, Nicholas A. Graham, Ajay A. Vashisht, James A. Wohlschlegel, Thomas G. Graeber, R Jeffrey Karnes, Mandeep Takhar, Elai Davicioni, Scott A. Tomlins, Nima Sharifi, Owen N. Witte, Felix Y. Feng, Karen E. Knudsen. DNA-PK-mediated transcriptional regulation drives tumor progression and metastasis. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1860. doi:10.1158/1538-7445.AM2015-1860