Quantitative proteomics identify Tenascin-C as a promoter of lung cancer progression and contributor to a signature prognostic of patient survival

The extracellular microenvironment is an integral component of normal and diseased tissues that is poorly understood owing to its complexity. To investigate the contribution of the microenvironment to lung fibrosis and adenocarcinoma progression, two pathologies characterized by excessive stromal expansion, we used mouse models to characterize the extracellular matrix (ECM) composition of normal lung, fibrotic lung, lung tumors, and metastases. Using quantitative proteomics, we identified and assayed the abundance of 113 ECM proteins, which revealed robust ECM protein signatures unique to fibrosis, primary tumors, or metastases. These analyses indicated significantly increased abundance of several S100 proteins, including Fibronectin and Tenascin-C (Tnc), in primary lung tumors and associated lymph node metastases compared with normal tissue. We further showed that Tnc expression is repressed by the transcription factor Nkx2-1, a well-established suppressor of metastatic progression. We found that increasing the levels of Tnc, via CRISPR-mediated transcriptional activation of the endogenous gene, enhanced the metastatic dissemination of lung adenocarcinoma cells. Interrogation of human cancer gene expression data revealed that high TNC expression correlates with worse prognosis for lung adenocarcinoma, and that a three-gene expression signature comprising TNC, S100A10, and S100A11 is a robust predictor of patient survival independent of age, sex, smoking history, and mutational load. Our findings suggest that the poorly understood ECM composition of the fibrotic and tumor microenvironment is an underexplored source of diagnostic markers and potential therapeutic targets for cancer patients

Dataset to delineate changes in association between Akt1 and its interacting partners as a function of active state of Akt1 protein

Akt1 is a multi-functional protein, implicated in multiple human solid tumors. Pertaining to its key role in cell survival, Akt1 is under focus for development of targeted therapies. Functional diversity of Akt1 is a result of its interactions with other proteins; which changes with changing context. This investigation was designed to capture the dynamics of Akt1 Interactome as a function of its active state. Delineating dynamic changes in association of Akt1 with its interactors could help us comprehend how it changes as a function of inhibition of its active form. Similar information on changes in Akt1 interactome as of now is not well explored. Akt1 expressing HEK293 cells were cultured in light and heavy labeled SILAC media. Normal lysine and arginine were incorporated as light labels while for heavy labeling the isotopes were 8 and 10 Da heavier. Light labeled cells represented the indigenous state of Akt1 interactome while heavy labeled cells represented Akt1 interactome in presence of its allosteric inhibitor, MK-2206. Equal number of cells from both conditions were pooled, lysed and subjected to Affinity Purification coupled to Mass Spectroscopy (AP-MS). Additionally, SILAC labeling aided in quantitative estimation of changing association of a number of proteins which were common to the two experimental conditions, with Akt1. Data are available via ProteomeXchange with identifier PXD005976. Keywords: Akt1, SILAC, Interactome, Affinity purification, Mass spectrometr

Rb interactome data and its modulations during cell cycle progression in HEK 293 cells

The Rb protein is a tumor suppressor protein that regulates the key G1S checkpoint consequently blocking the progression of cell cycle into S-phase. Despite its pertinent role in cell cycle regulation, comprehensive information on its interacting partners across cell cycle progression is lacking. Here, we aim to submit a comprehensive set of Rb interactors as the cell progresses from G0 through G1 and S into G2 phase in HEK 293 cell line. Affinity purification of HA-tagged Rb protein along with its interactors was analyzed by mass spectrometry (AP-MS). SILAC labeling enabled differentiation of Rb interactors in different cell cycle stages as well as their quantification - G0 cells were labeled with light labels of lysine and arginine (K0R0), cells in G1S transition were labeled with heavy labels (K8R10) while the G2 cells were labeled with medium labels (K6R6). LC-MS/MS analysis resulted in 6 wiff files which were submitted to protein pilot software for peptide identification and quantification. Here we submit the dataset which clearly captures the changing interacting partners of the Rb protein as the cell cycle progressed from G0 through G1S checkpoint into G2 phase. Data is publicly available via ProteomeXchange with identifier PXD007708. Keywords: Rb, Cell cycle, Interactome, SILAC, AP-MS, HEK 293 cell

Defining the Akt1 interactome data and delineating alterations in its composition as a function of cell cycle progression

Akt1 is a multi-functional protein implicated in key cellular processes including regulation of proliferation, survival, metabolism and protein synthesis. Its functional diversity results through interactions with other proteins which change with changing context. This study was designed to capture proteins, which interact with Akt1 as the cell cycle progresses from G0 to G1S and then G2 phase. Such an insight might help us understand the role of Akt1 in cell cycle, which as of now is not well explored. Akt1 expressing HEK 293 cells were cultured in light, medium and heavy labeled SILAC media. Normal lysine and arginine were incorporated as light labels; 6 Da (Dalton) heavier isotopes of the same amino acids were used as medium labels; while for heavy labeling the isotopes were 8 and 10 Da heavier. Light labeled cells were arrested in G0 phase while medium and heavy labeled cells were arrested in G2 and G1S phases, respectively. Equal number of cells from each phase was pooled, lysed and subjected to Affinity Purification coupled to Mass Spectroscopy (AP-MS). The obtained Akt1 protein partners were observed to change as the cell cycle progressed from G0 to G1S and then to G2 phase. Additionally, SILAC labeling aided in quantitative estimation of changing association of a number of proteins which were common to two or more phases, with Akt1. Data are available via ProteomeXchange with identifier PXD005557

Delineation of key regulatory elements identifies points of vulnerability in the mitogen-activated signaling network

Drug development efforts against cancer are often hampered by the complex properties of signaling networks. Here we combined the results of an RNAi screen targeting the cellular signaling machinery, with graph theoretical analysis to extract the core modules that process both mitogenic and oncogenic signals to drive cell cycle progression. These modules encapsulated mechanisms for coordinating seamless transition of cells through the individual cell cycle stages and, importantly, were functionally conserved across different cancer cell types. Further analysis also enabled extraction of the core signaling axes that progressively guide commitment of cells to the division cycle. Importantly, pharmacological targeting of the least redundant nodes in these axes yielded a synergistic disruption of the cell cycle in a tissue-type-independent manner. Thus, the core elements that regulate temporally distinct stages of the cell cycle provide attractive targets for the development of multi-module-based chemotherapeutic strategies

Edgotype: a fundamental link between genotype and phenotype.

peer reviewedClassical 'one-gene/one-disease' models cannot fully reconcile with the increasingly appreciated prevalence of complicated genotype-to-phenotype associations in human disease. Genes and gene products function not in isolation but as components of intricate networks of macromolecules (DNA, RNA, or proteins) and metabolites linked through biochemical or physical interactions, represented in 'interactome' network models as 'nodes' and 'edges', respectively. Accordingly, mechanistic understanding of human disease will require understanding of how disease-causing mutations affect systems or interactome properties. The study of 'edgetics' uncovers specific loss or gain of interactions (edges) to interpret genotype-to-phenotype relationships. We review how distinct genetic variants, the genotype, lead to distinct phenotypic outcomes, the phenotype, through edgetic perturbations in interactome networks altogether representing the 'edgotype'

Nanobody-based CAR T cells that target the tumor microenvironment inhibit the growth of solid tumors in immunocompetent mice

Chimeric antigen receptor (CAR) T cell therapy has been successful in clinical trials against hematological cancers, but has experienced challenges in the treatment of solid tumors. One of the main difficulties lies in a paucity of tumor-specific targets that can serve as CAR recognition domains. We therefore focused on developing VHH-based, single-domain antibody (nanobody) CAR T cells that target aspects of the tumor microenvironment conserved across multiple cancer types. Many solid tumors evade immune recognition through expression of checkpoint molecules, such as PD-L1, that down-regulate the immune response. We therefore targeted CAR T cells to the tumor microenvironment via the checkpoint inhibitor PD-L1 and observed a reduction in tumor growth, resulting in improved survival. CAR T cells that target the tumor stroma and vasculature through the EIIIB+ fibronectin splice variant, which is expressed by multiple tumor types and on neovasculature, are likewise effective in delaying tumor growth. VHH-based CAR T cells can thus function as antitumor agents for multiple targets in syngeneic, immunocompetent animal models. Our results demonstrate the flexibility of VHH-based CAR T cells and the potential of CAR T cells to target the tumor microenvironment and treat solid tumors. Keywords: chimeric antigen receptor; tumor microenvironment; immunotherapyLustgarten Foundation (Grant 80939)Howard Hughes Medical Institute (Innovator Award W81XWH-14-1-0240)National Institutes of Health (U.S.) (Grant P30-CA1405)National Cancer Institute (U.S.) (Grant P30-CA14051

Maximizing response to intratumoral immunotherapy in mice by tuning local retention

AbstractDirect injection of therapies into tumors has emerged as an administration route capable of achieving high local drug exposure and strong anti-tumor response. A diverse array of immune agonists ranging in size and target are under development as local immunotherapies. However, due to the relatively recent adoption of intratumoral administration, the pharmacokinetics of locally-injected biologics remains poorly defined, limiting rational design of tumor-localized immunotherapies. Here we define a pharmacokinetic framework for biologics injected intratumorally that can predict tumor exposure and effectiveness. We find empirically and computationally that extending the tumor exposure of locally-injected interleukin-2 by increasing molecular size and/or improving matrix-targeting affinity improves therapeutic efficacy in mice. By tracking the distribution of intratumorally-injected proteins using positron emission tomography, we observe size-dependent enhancement in tumor exposure occurs by slowing the rate of diffusive escape from the tumor and by increasing partitioning to an apparent viscous region of the tumor. In elucidating how molecular weight and matrix binding interplay to determine tumor exposure, our model can aid in the design of intratumoral therapies to exert maximal therapeutic effect.</jats:p