Browsing by Subject "Cancer biology"

Now showing 1 - 4 of 4
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    GCN2 eIF2 kinase promotes prostate cancer by maintaining amino acid homeostasis
    (eLife Sciences, 2022-09-15) Cordova, Ricardo A.; Misra, Jagannath; Amin, Parth H.; Klunk, Anglea J.; Damayanti, Nur P.; Carlson, Kenneth R.; Elmendorf, Andrew J.; Kim, Hyeong-Geug; Mirek, Emily T.; Elzey, Bennet D.; Miller, Marcus J.; Dong, X. Charlie; Cheng, Liang; Anthony, Tracy G.; Pili, Roberto; Wek, Ronald C.; Staschke, Kirk A.; Biochemistry and Molecular Biology, School of Medicine
    A stress adaptation pathway termed the integrated stress response has been suggested to be active in many cancers including prostate cancer (PCa). Here, we demonstrate that the eIF2 kinase GCN2 is required for sustained growth in androgen-sensitive and castration-resistant models of PCa both in vitro and in vivo, and is active in PCa patient samples. Using RNA-seq transcriptome analysis and a CRISPR-based phenotypic screen, GCN2 was shown to regulate expression of over 60 solute-carrier (SLC) genes, including those involved in amino acid transport and loss of GCN2 function reduces amino acid import and levels. Addition of essential amino acids or expression of 4F2 (SLC3A2) partially restored growth following loss of GCN2, suggesting that GCN2 targeting of SLC transporters is required for amino acid homeostasis needed to sustain tumor growth. A small molecule inhibitor of GCN2 showed robust in vivo efficacy in androgen-sensitive and castration-resistant mouse models of PCa, supporting its therapeutic potential for the treatment of PCa.
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    Loss of TIP30 Accelerates Pancreatic Cancer Progression and Metastasis
    (2019-07) Imasuen Williams, Imade E.; Hurley, Thomas; Harrington, Maureen; Herbert, Brittney-Shea; Nakshatri, Harikrishna
    Pancreatic ductal adenocarcinoma (PDAC) is currently the fourth leading cause of cancer-related death in the United States, and is characterized by key driver mutations (e.g. KRAS, TP53, CDKN2A, and SMAD4), elevated expression of growth factors such as TGF-βs and the EGF receptor (EGFR), a markedly desmoplastic stroma, and a propensity to develop multi-organ metastases and chemoresistance. Consistent with its aggressive nature, the 5-year survival rate for PDAC is 8-9%, which demonstrates an urgent need to develop novel therapies. High expression levels of microRNA-10b (miR-10b) in PDAC tissues are associated with decreased patient survival and earlier appearance of metastatic disease following neoadjuvant chemoradiotherapy. miR-10b downregulates the expression of transcription coactivator Tat-Interacting Protein 30 (TIP30) by targeting its 3’UTR. TIP30 has multiple reported functions. TIP30 suppresses tumor formation and metastasis, forms a complex that regulates EGFR trafficking and degradation, and transcriptionally upregulates pro-apoptotic genes. Alterations in TIP30 have been reported in multiple human cancers, including pancreatic cancer. We hypothesized that Tip30-deficiency accelerates PDAC progression and metastasis in a murine model of PDAC. To test this hypothesis, we crossed mice with oncogenic Kras (KC) localized to the pancreas epithelium, with Tip30-deficient mice (K30C). We compared PDAC histopathology between Tip30-heterozygous (K30+/-C) and Tip30-null (K30-/-C) mice. Tip30-heterozygosity accelerated PDAC-lesion-associated pancreatic cancer cell (PCC) pulmonary seeding. By contrast, total loss of Tip30 enhanced PCC micrometastatic seeding to the liver and hepatic metastasis. K30+/-C mice also presented with an early, increased penetrance of lung lesions and lung adenocarcinoma; and PCCs isolated from K30+/-C pancreata exhibited increased EGFR protein levels. These findings suggest that TIP30 deficiency can have a dose-dependent effect on organotropic metastasis and EGFR levels in PCCs. Future studies will delineate the molecular consequences of TIP30 loss in PDAC and contribute to a broader understanding of pancreatic cancer metastasis.
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    Network Models for Capturing Molecular Feature and Predicting Drug Target for Various Cancers
    (2020-12) Liu, Enze; Liu, Xiaowen; Wu, Huanmei; Zhang, Chi; Wan, Jun; Cao, Sha; Liu, Lang
    Network-based modeling and analysis have been widely used for capturing molecular trajectories of cellular processes. For complex diseases like cancers, if we can utilize network models to capture adequate features, we can gain a better insight of the mechanism of cancers, which will further facilitate the identification of molecular vulnerabilities and the development targeted therapy. Based on this rationale, we conducted the following four studies: A novel algorithm ‘FFBN’ is developed for reconstructing directional regulatory networks (DEGs) from tissue expression data to identify molecular features. ‘FFBN’ shows unique capability of fast and accurately reconstructing genome-wide DEGs compared to existing methods. FFBN is further used to capture molecular features among liver metastasis, primary liver cancers and primary colon cancers. Comparisons among these features lead to new understandings of how liver metastasis is similar to its primary and distant cancers. ‘SCN’ is a novel algorithm that incorporates multiple types of omics data to reconstruct functional networks for not only revealing molecular vulnerabilities but also predicting drug targets on top of that. The molecular vulnerabilities are discovered via tissue-specific networks and drug targets are predicted via cell-line specific networks. SCN is tested on primary pancreatic cancers and the predictions coincide with current treatment plans. ‘SCN website’ is a web application of ‘SCN’ algorithm. It allows users to easily submit their own data and get predictions online. Meanwhile the predictions are displayed along with network graphs and survival curves. ‘DSCN’ is a novel algorithm derived from ‘SCN’. Instead of predicting single targets like ‘SCN’, ‘DSCN’ applies a novel approach for predicting target combinations using multiple omics data and network models. In conclusion, our studies revealed how genes regulate each other in the form of networks and how these networks can be used for unveiling cancer-related biological processes. Our algorithms and website facilitate capturing molecular features for cancers and predicting novel drug targets.
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    Osteosarcoma-enriched transcripts paradoxically generate osteosarcoma-suppressing extracellular proteins
    (eLife Sciences, 2023-03-21) Li, Kexin; Huo, Qingji; Dimmitt, Nathan H.; Qu, Guofan; Bao, Junjie; Pandya, Pankita H.; Saadatzadeh, M. Reza; Bijangi-Vishehsaraei, Khadijeh; Kacena, Melissa A.; Pollok, Karen E.; Lin, Chien-Chi; Li, Bai-Yan; Yokota, Hiroki; Biomedical Engineering, School of Engineering and Technology
    Osteosarcoma (OS) is the common primary bone cancer that affects mostly children and young adults. To augment the standard-of-care chemotherapy, we examined the possibility of protein-based therapy using mesenchymal stem cells (MSCs)-derived proteomes and OS-elevated proteins. While a conditioned medium (CM), collected from MSCs, did not present tumor-suppressing ability, the activation of PKA converted MSCs into induced tumor-suppressing cells (iTSCs). In a mouse model, the direct and hydrogel-assisted administration of CM inhibited tumor-induced bone destruction, and its effect was additive with cisplatin. CM was enriched with proteins such as calreticulin, which acted as an extracellular tumor suppressor by interacting with CD47. Notably, the level of CALR transcripts was elevated in OS tissues, together with other tumor-suppressing proteins, including histone H4, and PCOLCE. PCOLCE acted as an extracellular tumor-suppressing protein by interacting with amyloid precursor protein, a prognostic OS marker with poor survival. The results supported the possibility of employing a paradoxical strategy of utilizing OS transcriptomes for the treatment of OS.