Endocrine-exocrine signaling drives obesity-associated pancreatic ductal adenocarcinoma

Chung, Katherine; Singh, Jaffarguriqbal; Lawres, Lauren; Dorans, Kimberly Judith; Robbins, Rebecca; Babić, Ana; Väyrynen, Sara A.; Costa, Andressa Dias; Nowak, Jonathan A.; Chang, Daniel T.; Dunne, Richard F.; Hezel, Aram F.; Koong, Albert C.; Wilhelm, Joshua J.; Bellin, Melena D.; Nylander, Vibe; Gloyn, Anna L.; McCarthy, Mark; Wolpin, Brian M.; Jacks, Tyler; Fuchs, Charles S.; Muzumdar, Mandar Deepak

doi:10.1101/663583

Cited by 5 publications

(9 citation statements)

References 78 publications

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“…Im-portantly, these models often support the biological specificities of the epidemiological link between obesity and cancer. For example, consistent with human data, obesity leads to increased tumor burden and faster disease progression in oncogenic Kras-driven pancreatic ductal adenocarcinoma (PDAC), but not in lung cancer [7]. Table 1 presents an overview that details how both genetic and DIO models have been applied to cancer research.…”

Section: Animal Models Of the Obesity-cancer Connec-tionmentioning

confidence: 71%

“…A key question in the field is to what degree reversing the obese phenotype affects tumor outcomes. In the ob/ob model, reestablishing leptin expression through adenoviral expression in muscle reversed the obese phenotype and related biochemical parameters, leading to the suppression of pancreatic cancer progression [7]. In the same study, the enhanced tumor progression in the ob/ob model was also reversed by dietary caloric restriction.…”

Section: Reversibility Of Obesity-induced Effects On Tumor Outcomesmentioning

confidence: 78%

“…In sum, several studies support a role of leptin in tumorigenesis which favors cell growth and survival by increasing proliferation and decreasing apoptosis, regulating inflammatory processes, modulating cancer stem cell properties as well as metabolic activity. However, augmented tumor formation is observed for several cancers, including cancers of the liver, kidney and pancreas in ob/ob and db/db compared with wildtype mice [7,11,41,42] suggesting leptin-independent mechanisms. A number of the early mammary cancer studies were performed with the mouse mammary tumor virus (MMTV)-transforming growth factor-alpha (TGF-α) transgenic mouse model -a cancer model in which TGF-α overexpression is under the control of the MMTV promoter.…”

Section: Leptinmentioning

confidence: 99%

“…Inhibition and genetic knockout of COX-2 prevented pancreatic inflammation and initiation of PDAC, suggesting important roles for COX-2-dependent inflammatory processes [98]. In contrast, treatment with aspirin, a nonsteroidal anti-inflammatory agent (NSAID) that blocks the cyclooxygenase enzymes, did not impact tumor progression, immune cell infiltration or fibrosis in an obese genetic model of PDAC [7]. Consistently, the growth advantage of orthotopically implanted oncogenic Kras-driven PDAC cell lines in the obese environment was sustained in obese C57Bl6 mice lacking T-, B-and NK-cells (Rag2 -/-::CD47 -/-::Il2rg -/-) [105].…”

Section: Blocking Obesity-induced Inflammationmentioning

confidence: 99%

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Cellular mechanisms linking cancers to obesity

Liu¹,

Pedersen²,

Halberg³

2021

CST

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Obesity is epidemiologically linked to 13 forms of cancer. The local and systemic obese environment is complex and likely affect tumors through multiple avenues. This includes modulation of cancer cell phenotypes and the composition of the tumor microenvironment. A molecular understanding of how obesity links to cancer holds promise for identifying candidate genes for targeted therapy for obese cancer patient. Herein, we review both the cell-autonomous and non-cell-autonomous mechanisms linking obesity and cancer as well as provide an overview of the mouse model systems applied to study this.

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Section: Animal Models Of the Obesity-cancer Connec-tionmentioning

confidence: 71%

Section: Reversibility Of Obesity-induced Effects On Tumor Outcomesmentioning

confidence: 78%

Section: Leptinmentioning

confidence: 99%

Section: Blocking Obesity-induced Inflammationmentioning

confidence: 99%

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Cellular mechanisms linking cancers to obesity

Liu¹,

Pedersen²,

Halberg³

2021

CST

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“…This framework could potentially be extended to patient level measurements where patients phenotypes as measured with clinical variables and laboratory values can be associated with enriched states in disease or treatment conditions. Indeed MELD has already seen use in several contexts[50][51][52][53][54]. To facilitate the application of these tools for future scRNA-seq analysis, we provide open-source Python implementations that inherit the Scikit-learn API in the MELD package on GitHub https://github.com/KrishnaswamyLab/MELD.…”

mentioning

confidence: 99%

Quantifying the effect of experimental perturbations at single-cell resolution

Burkhardt¹,

Stanley

Tong

et al. 2019

Preprint

Self Cite

103

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4Single-cell RNA-sequencing (scRNA-seq) is a powerful tool to quantify transcriptional states in 5 thousands to millions of cells. It is increasingly common for scRNA-seq data to be collected in 6 multiple experimental conditions, yet quantifying differences between scRNA-seq datasets re-7 mains an analytical challenge. Previous efforts at quantifying such differences focus on discrete 8 regions of the transcriptional state space such as clusters of cells. Here, we describe a contin-9 uous measure of the effect of an experiment across the transcriptomic space. First, we use the 10 manifold assumption to model the cellular state space as a graph (or network) with cells as nodes 11 and edges connecting cells with similar transcriptomic profiles. Next, we create an Enhanced 12 Experimental Signal (EES) that estimates the likelihood of observing cells from each condition 13 at every point in the manifold. We show that the EES has useful properties and information that 14 can be extracted. The EES can be used to identify how gene expression is affected by a given 15 perturbation, including identifying non-monotonic changes from only two conditions. We also 16 show that we can use both the magnitude and frequency of the EES, using an algorithm we 17 call vertex frequency clustering, to derive subsets of cells at appropriate levels of granularity 18 (tailored to areas that change) that are enriched in the experimental or control conditions or that 19 are unaffected between conditions. We demonstrate both algorithms using a combination of 20 biological and synthetic datasets. Implementations are provided in the MELD Python package, 21 which is available at https://github.com/KrishnaswamyLab/MELD. 22As single-cell RNA-sequencing (scRNA-seq) has become more accessible, the design of single-cell exper-24 iments has become increasingly complex. Researchers regularly use scRNA-seq to quantify the effect of 25 a drug, gene knockout, or other experimental perturbation on a biological system. However, quantifying 26 the compositional differences between single-cell datasets collected from multiple experimental conditions 27 1 remains an analytical challenge [1] because of the heterogeneity and noise in both the data and the effects 28 of a given perturbation. 29 Previous work has shown the utility of modelling the transcriptomic state space as a continuous low-30 dimensional manifold, or set of manifolds, to characterize cellular heterogeneity and dynamic biological 31 processes [2][3][4][5][6][7][8]. In the manifold model, the biologically valid combinations of gene expression are rep-32 resented as a smooth, low-dimensional surface in a high dimensional space, such as a two-dimensional 33 sheet embedded in three dimensions. The main challenge in developing tools to quantify compositional 34 differences between single-cell datasets is that each dataset comprises several intrinsic structures of hetero-35 geneous cells, and the effect of the experimental condition could be diffuse or isolated to particular areas 36 of...

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Quantifying the effect of experimental perturbations at single-cell resolution

et al. 2021

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Single-cell RNA-sequencing (scRNA-seq) is a powerful tool to quantify transcriptional states in thousands to millions of cells. It is increasingly common for scRNA-seq data to be collected in multiple conditions to measure the effect of an experimental perturbation. However, quantifying differences between scRNA-seq datasets remains an analytical challenge. Previous efforts at quantifying such differences focus on discrete regions of the transcriptional state space such as clusters of cells. Here, we describe a continuous measure of the effect of an experiment across the transcriptomic space with single cell resolution. First, we use the manifold assumption to model the cellular state space as a graph with cells as nodes and edges connecting cells with similar transcriptomic profiles. Next, we calculate an Enhanced Experimental Signal (EES) that estimates the likelihood of observing cells from each condition at every point in the manifold. We show that the EES has useful properties for analysis of single cell perturbation studies. We show that we can use the magnitude and frequency of the EES, using an algorithm we call vertex frequency clustering, to identify specific populations of cells that are or are not affected by an experimental treatment at the appropriate level of granularity. Using these selected populations we can derive gene signatures of affected populations of cells. We demonstrate both algorithms using a combination of biological and synthetic datasets. Implementations are provided in the MELD Python package, which is available at https://github.com/KrishnaswamyLab/MELD. IntroductionAs single-cell RNA-sequencing (scRNA-seq) has become more accessible, the design of single-cell experiments has become increasingly complex. Researchers regularly use scRNA-seq to quantify the effect of a drug, gene knockout, or other experimental perturbation on a biological system. However, quantifying the 1 .

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Endocrine-exocrine signaling drives obesity-associated pancreatic ductal adenocarcinoma

Cited by 5 publications

References 78 publications

Cellular mechanisms linking cancers to obesity

Cellular mechanisms linking cancers to obesity

Quantifying the effect of experimental perturbations at single-cell resolution

Quantifying the effect of experimental perturbations at single-cell resolution

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