The prognosis for the most common type of pancreatic cancer, pancreatic ductal adenocarcinoma, also known as PDAC, remains poor. Only around 4% of PDAC patients are likely to live 5 years after being diagnosed. The immune system plays a part in the progression of PDAC, as increased inflammation contributes to the growth of the tumour and its ability to resist treatment.

The NF-κB proteins of the immune system are transcription factors that control when and how much certain other proteins are produced. The protein RELA is an important member of the NF-κB family. It is known to be overactivated in PDAC tumours and may be responsible for the high levels of inflammation found in this type of pancreatic cancer. A better understanding of how RELA is activated in PDAC cells will help develop medicines targeting this process.

Butera et al. combined experimental and computational approaches to model a network of interactions between key molecules in lines of human PDAC cells grown in the laboratory to investigate how RELA is controlled. Usually, when the RELA protein is activated, it is located in the cell’s nucleus, this means that tracking the location of RELA within the cell can reveal its activated form. To follow RELA, it was labelled with a fluorescent marker and visualised using live, high-throughput fluorescence microscopy.

When RELA was activated with a pro-inflammatory molecule TNFα, it changed how it moved in and out of the cell’s nucleus. Using computational modelling approaches, Butera et al. could build a statistical model that revealed that the location and activity of RELA is affected by actin, a key component of the cell’s molecular scaffolding called the cytoskeleton.

When actin was disrupted, RELA's activity was altered. Moreover, profiling targets of RELA using RNA sequencing revealed two genes that encode proteins related to the regulation of actin. This indicates that RELA activity – which is itself affected by actin organisation – can feed back to regulate actin.

Butera et al. have identified genes regulated by RELA in PDAC cells that could be used as targets for anti-cancer drug development. Further research into the feedback between RELA and actin will help untangle the complex network that causes inflammation in pancreatic cancer.

The NF-κB transcription factor RELA is an essential mediator of the inflammatory and immune responses in all mammals (Hayden et al., 2006) and is central to the canonical NF-κB signalling pathway (Ghosh et al., 1998). As a transcription factor, RELA activation is controlled in large part through its localisation. Inactive RELA is sequestered in the cytoplasm by IκB proteins and IκB degradation by upstream cues, such as the potent inflammatory cytokine tumour necrosis factor α (TNFα), enables RELA translocation to the nucleus where RELA regulates gene expression (DiDonato et al., 1997; Zandi et al., 1997).

Live imaging experiments of fluorescently labelled RELA and electrophoretic mobility shift assays have shown that RELA oscillates between the nucleus and cytoplasm in response to TNFα (Hoffmann et al., 2002; Nelson et al., 2004; Sung et al., 2009; Tay et al., 2010; Sero et al., 2015; Zambrano et al., 2016). Oscillations are driven by a negative feedback loop between RELA and particular IκB isoforms since the genes encoding IκB proteins are RELA transcriptional targets (Brown et al., 1993; Scott et al., 1993; Sun et al., 1993; Hoffmann et al., 2002). The pattern of RELA translocation has been shown to dictate the specificity and timing of RELA target gene expression, including the genes encoding IκBα, IκBε, and the chemokine RANTES (Ashall et al., 2009; Zambrano et al., 2016; Lane et al., 2017).

Previously, we showed that cell shape is a regulator of RELA dynamics in breast cancer cell lines and that breast cancer cells with mesenchymal cell shape (protrusive with low cell–cell contacts) have higher RELA nuclear translocation (Sero et al., 2015). We also identified that RELA activity, coupled to cell shape, is predictive of breast cancer progression (Sailem and Bakal, 2017). Although the mechanistic basis for how cell shape regulates RELA remains poorly understood, studies have shown that chemically inhibiting actin or tubulin dynamics can increase RELA binding to DNA and RELA-dependent gene expression (Rosette and Karin, 1995; Bourgarel-Rey et al., 2001; Németh et al., 2004).

Despite frequent upregulation of both TNFα and RELA in PDAC tumours (Weichert et al., 2007; Zhao et al., 2016), the dynamics and regulation of single-cell RELA translocation, as well as RELA transcriptional output, are poorly understood for PDAC cells. Here, we used CRISPR-CAS9 to tag RELA with GFP in the human PDAC cell lines MIA PaCa2 and PANC1 and identified rapid, non-oscillatory, and heterogeneous RELA dynamics by live imaging. To explore potential cytoskeletal or cell shape regulation of RELA in PDAC, we constructed Bayesian models using single-cell datasets from TNFα-stimulated PDAC cells with variation in RELA, actin, and tubulin measurements for hypothesis generation: one dataset with five PDAC cell lines and another with diverse small molecules targeting cytoskeletal components. Notably, nuclear RELA was statistically predicted as dependent on actin features across Bayesian models. Finally, we used RNA-seq to identify genes with distinct patterns of expression based on dependence on TNFα dose and RELA activation. Using live imaging with knockdown of selected targets, our results uncover novel mechanisms regulating inflammation-associated RELA dynamics in PDAC, including negative feedback loops between RELA and the actin modulators NUAK2 and ARHGAP31, and between RELA and the non-canonical NF-κB protein RELB.

To characterise previously unknown single-cell RELA dynamics in PDAC cells, we used CRISPR-CAS9 genome editing to tag RELA endogenously with GFP in the human PDAC cell lines MIA PaCa2 and PANC1. Using live imaging and automated image analysis, we characterised RELA responses to the cytokine TNFα, which is upregulated with PDAC progression (Zhao et al., 2016). Strikingly, PDAC cells show atypical RELA dynamics compared to reports in cells from other tissues (Ashall et al., 2009; Tay et al., 2010; Sero et al., 2015), as we observed that PDAC cells maintain prolonged nuclear RELA localisation and RELA responses are cell cycle independent. However, we identified that a key difference between MIA PaCa2 and PANC1 cells is that MIA PaCa2 cells respond with higher and sustained RELA nuclear localisation compared to PANC1 cells, while PANC1 cells have a damped RELA response over time. Similar to other cell lines (Ashall et al., 2009; Tay et al., 2010; Sero et al., 2015), we found that RELA nuclear translocation in PDAC cells occurs immediately following TNFα addition and peak nuclear RELA localisation occurs around 1 hr post-TNFα.

The distinct RELA dynamics between PDAC cells with reports from other tissues may be attributed to the use of knock-in RELA-GFP in this study since many studies used exogenous RELA fusion constructs (Ashall et al., 2009; Tay et al., 2010; Sero et al., 2015). As RELA upregulates several genes involved in positive and negative feedback (Collart et al., 1990; Libermann and Baltimore, 1990; Brown et al., 1993; Scott et al., 1993; Sun et al., 1993), which we observed in the present study using RNA-seq, RELA overexpression could interfere with its intrinsic dynamics. Nonetheless, other studies that have tagged RELA endogenously did detect oscillations, including MEFs (Sung et al., 2009; Zambrano et al., 2016) and MCF7 breast cancer cells (Stewart-Ornstein and Lahav, 2016).

Here, we identified that MIA PaCa2 and PANC1 cells in all cell cycle stages are responsive to 0.1 and 10 ng/ml TNFα, with minimal dissimilarities in RELA translocation responses between cell cycle stages in terms of mean nuclear RELA or the amplitude and timing of peak RELA. In contrast, RELA translocation responses to TNFα were identified in HeLa cells by Ankers et al., 2016 as dependent on the cell cycle phase at the time of TNFα addition in a study using double thymidine block to synchronise cells, or Fluorescent Ubiquitination-based Cell Cycle Indicator (FUCCI) labelling to infer cell cycle phase. HeLa cells treated with 10 ng/ml TNFα in late G1 displayed a stronger response than the population average, while S-phase cells showed a suppressed or delayed response. Furthermore, RELA was found to interact with E2F1, a transcription factor regulating the G1 to S transition, in late G1 when E2F1 levels are highest during the cell cycle (Ankers et al., 2016). Conflicting reports of whether TNFα-induced RELA translocation is cell cycle regulated may be due to cancer cell type-specific deregulation of cell cycle proteins (Cordon-Cardo, 1995; Otto and Sicinski, 2017) or RELA may not physically interact with E2F1 in PDAC cells in general or specifically in unsynchronised cells.

The high sensitivity of PDAC cells to TNFα and lack of oscillations suggest that PDAC cells may suppress negative feedback imposed by IκBα, as Hoffmann et al., 2002 demonstrated that oscillations in NF-κB DNA binding are due to negative feedback with IκBα, which is encoded by a gene (NFKBIA) upregulated by NF-κB factors. Hoffman et al. also identified that cells with high IκBβ and IκBε expression, or absence of IκBα, lose oscillations stimulated by TNFα. These findings motivated our inspection into RELA interaction with IκB proteins and upregulation of IκB genes by TNFα in PDAC cells. Using co-immunoprecipitation of RELA with MIA PaCa2 cells, we found that RELA binds to IκBα, IκBβ, and IκBε and binding to each is suppressed in high TNFα concentrations. However, contrary to our expectations based on the findings by Hoffmann et al., we did not observe RELA oscillations with NFKBIB or NFKBIE depletion in MIA PaCa2 or PANC1 cells, suggesting that the lack of RELA oscillations in these cell lines may be due to enhanced positive feedback and/or insufficient NFKBIA expression.

Co-immunoprecipitation also revealed that the NF-κB protein REL had reduced interaction with RELA with TNFα. This is in line with a recent study (Rahman et al., 2022) that endogenously fluorescently labelled RelA and c-Rel in double knockin mice and used fluorescence cross-correlation spectroscopy to probe for binding/dimerisation in primary ear fibroblasts to quantify the abundance of RelA:c-Rel heterodimers pre- and post-TNFα. Rahman et al., 2022 identified that the relative abundance of the RelA:c-Rel dimer was higher in the nucleus of resting cells then higher in the cytoplasm of TNFα-stimulated cells, despite the individual concentrations of RelA and c-Rel increasing in the nucleus with TNFα. Our combined results suggest that binding of RELA and REL is reduced with TNFα in multiple cell types and in both human and mouse models.

We used Bayesian modelling as an unbiased and high dimensional approach to determine whether descriptors of cell shape and the cytoskeleton correlate with the observed heterogeneity in RELA localisation, having previously used Bayesian modelling to show that RELA localisation in breast cells is strongly dependent on neighbour contact, cell area, and protrusiveness in the presence and absence of TNFα (Sero et al., 2015). In the present study, we extended the analysis to include measurements of actin and tubulin organisation. Since Bayesian modelling relies on heterogeneity in measurements to make predictions, we used a dataset with five PDAC cell lines with high intra- and inter-line variability in RELA, as well as distinct cell shape and cytoskeleton features between the cell lines. We independently ran Bayesian modelling with a dataset of cells with perturbation of the cytoskeleton using small molecules, based on the seminal study by Sachs et al., 2005 that used Bayesian inference to derive cell signalling networks. Consistently among our models, differences in cytoplasmic actin intensity, as well as measures of actin localisation (cortical versus cytoplasmic actin), were predictive of differences in nuclear RELA between single PDAC cells.

Interestingly, the Bayesian-inferred arrows in the RELA network generated from TNFα-treated cells (Figure 2C) sometimes point in opposite directions compared to the network from cells treated with biochemical inhibitors (Figure 2F). This arises from the inherent properties and flexibility of Bayesian networks (Scutari et al., 2018). These networks use a directed acyclic graph (DAG) to represent global probability distributions broken down into smaller local distributions, ensuring no loops or cycles. Multiple valid DAG configurations can exist, so the dependence between two variables A and B can be represented as either A→B or B→A if both are probabilistically equivalent (Heckerman et al., 1995; Scutari, 2010; Scutari et al., 2019). Thus, reversing an arc can leave the overall network structure unchanged as long as local distributions remain consistent with the data.

One route through which cytoskeletal structures may influence RELA dynamics is by inducing changes in TNF receptor turnover or conformation. In the literature, evidence exists for the modulation of nuclear RELA by actin and tubulin inhibitors (Bourgarel-Rey et al., 2001; Németh et al., 2004), in addition to TNFα/RELA regulation of the cytoskeleton (Georgouli et al., 2019; Huber et al., 2004). However, there is limited evidence for cytoskeletal regulation of TNF receptors. One example is that the actin-binding protein Filamin interacts with TNF receptor-associated factor 2 (TRAF2), which is not itself a TNF receptor but is involved in TNF receptor intracellular signal transduction (Leonardi et al., 2000). On a related topic, a recently published study (Alraies et al., 2024) subjected dendritic cells to space confinement and found upregulation of the chemokine receptor Ccr7, in a manner dependent on expression and function of IKKβ and the lipid metabolism enzyme cPLA₂. In turn, the prostaglandin E₂ receptor (PGE₂) receptor, which is in the cPLA₂ pathway, induces NF-κB nuclear translocation. This study also identifies the role of ARP2/3 activity in regulating cPLA₂ activation via nuclear envelope tensioning. Interestingly, transcriptomics of confined cPLA₂^WT and cPLA₂ ^KO dendritic cells revealed correlated expression between Ccr7, the gene encoding IKKβ (Ikbkb), and the major subunits of ARP2/3 (Actr2 and Actr3). Overall, the impact of cytoskeletal dynamics on TNF receptors remains a source for further study.

To identify potential regulatory loops involving RELA in PDAC, we used RNA-seq analysis with MIA PaCa2 and PANC1 cells treated with varying TNFα doses +/-IκB-SR. We identified 254 genes significantly regulated by TNFα, which may be viewed as candidates for targeting NF-κB signalling or output in PDAC. Identifying novel targets for PDAC is important as the 5-year survival rate of non-resected PDAC patients has remained unchanged from 1975 to 2011 (Bengtsson et al., 2020). Moreover, RELA is hyperactive in 50–70% of PDAC tumours (Wang et al., 1999; Weichert et al., 2007) and contributes to both cancer progression (Fujioka et al., 2003; Melisi et al., 2009) and resistance to chemotherapy (Bold et al., 2001; Kunnumakkara et al., 2007).

Of note, we present the first evidence that ARHGAP31 is a transcriptional target of TNFα or RELA. Moreover, ARHGAP31 is not previously associated with pancreatic cancer, but has well-studied roles in actin modulation, since ARHGAP31 is the human orthologue for the mouse protein CdGAP (mCdc42 GTPase-activating protein) and ARHGAP31 inactivates the GTPases CDC42 and RAC1 (Lamarche-Vane and Hall, 1998; Tcherkezian et al., 2006). CDC42 is involved in the formation of premigratory filopodia in PDAC cells and promotes invasiveness (Razidlo et al., 2018), while RAC1 expression is required for the development of PDAC tumours, in addition to the formation of the ADM and PanIN precursors in KRAS^G12D mouse models (Heid et al., 2011), indicating that RAC1 may play a role in actin remodelling during PDAC initiation.

Our results also demonstrate that TNFα upregulates RNA expression of the kinase-encoding gene NUAK2 (SNARK), while NUAK2 expression is abrogated in the presence of IκB-SR. Our identification of a feedback loop between RELA nuclear translocation and NUAK2 expression is analogous to prior findings that NUAK2 increases the activity of the mechanosensitive transcriptional coactivator YAP through stimulation of actin polymerisation and myosin activity (Yuan et al., 2018). A potential source for further study could consider myosin phosphatase target subunit 1 (MYPT1), a substrate for NUAK2 (Yamamoto et al., 2008) that is reported to mediate NUAK2 regulation of actin stress fibres in growing cells (Vallenius et al., 2011). From a therapeutic perspective, NUAK2 may be a promising target to follow-up for potential use in PDAC therapy as kinases contain an ATP-binding cleft that is a druggable pocket and can contain additional druggable sites distal to the ATP or substrate binding pockets conferring inhibitor specificity (Lamba and Ghosh, 2012). However, NUAK2 is a prognostic marker for PDAC and is associated with a favourable prognosis according to The Human Protein Atlas, which proposes a potential tumour suppressor role for NUAK2 in PDAC, suggesting that enhancing NUAK2 may be a favourable strategy for PDAC. Therefore, our data provide an incentive for the exploration of a RELA-NUAK2 signalling axis in PDAC progression and response to therapy.

Further information and requests for resources should be directed to and will be fulfilled by Chris Bakal (chris.bakal@icr.ac.uk).

All data generated for this study have been included as source data files. RNAseq data (FASTQ files and processed counts) are available from GEO under accession GSE268743.

1. 1. Alraies Z
   2. Rivera CA
   3. Delgado M-G
   4. Sanséau D
   5. Maurin M
   6. Amadio R
   7. Maria Piperno G
   8. Dunsmore G
   9. Yatim A
   10. Lacerda Mariano L
   11. Kniazeva A
   12. Calmettes V
   13. Sáez PJ
   14. Williart A
   15. Popard H
   16. Gratia M
   17. Lamiable O
   18. Moreau A
   19. Fusilier Z
   20. Crestey L
   21. Albaud B
   22. Legoix P
   23. Dejean AS
   24. Le Dorze A-L
   25. Nakano H
   26. Cook DN
   27. Lawrence T
   28. Manel N
   29. Benvenuti F
   30. Ginhoux F
   31. Moreau HD
   32. P F Nader G
   33. Piel M
   34. Lennon-Duménil A-M

   (2024) Cell shape sensing licenses dendritic cells for homeostatic migration to lymph nodes

   Nature Immunology 25:1193–1206.

   https://doi.org/10.1038/s41590-024-01856-3

   • PubMed
   • Google Scholar
2. 1. Ankers JM
   2. Awais R
   3. Jones NA
   4. Boyd J
   5. Ryan S
   6. Adamson AD
   7. Harper CV
   8. Bridge L
   9. Spiller DG
   10. Jackson DA
   11. Paszek P
   12. Sée V
   13. White MR

   (2016) Dynamic NF-κB and E2F interactions control the priority and timing of inflammatory signalling and cell proliferation

   eLife 5:e10473.

   https://doi.org/10.7554/eLife.10473

   • PubMed
   • Google Scholar
3. 1. Ashall L
   2. Horton CA
   3. Nelson DE
   4. Paszek P
   5. Harper CV
   6. Sillitoe K
   7. Ryan S
   8. Spiller DG
   9. Unitt JF
   10. Broomhead DS
   11. Kell DB
   12. Rand DA
   13. Sée V
   14. White MRH

   (2009) Pulsatile stimulation determines timing and specificity of NF-kappaB-dependent transcription

   Science 324:242–246.

   https://doi.org/10.1126/science.1164860

   • PubMed
   • Google Scholar
4. 1. Baeuerle PA
   2. Baltimore D

   (1988) Activation of DNA-binding activity in an apparently cytoplasmic precursor of the NF-kappa B transcription factor

   Cell 53:211–217.

   https://doi.org/10.1016/0092-8674(88)90382-0

   • PubMed
   • Google Scholar
5. 1. Barr AR
   2. Cooper S
   3. Heldt FS
   4. Butera F
   5. Stoy H
   6. Mansfeld J
   7. Novák B
   8. Bakal C

   (2017) DNA damage during S-phase mediates the proliferation-quiescence decision in the subsequent G1 via p21 expression

   Nature Communications 8:14728.

   https://doi.org/10.1038/ncomms14728

   • PubMed
   • Google Scholar
6. 1. Bengtsson A
   2. Andersson R
   3. Ansari D

   (2020) The actual 5-year survivors of pancreatic ductal adenocarcinoma based on real-world data

   Scientific Reports 10:16425.

   https://doi.org/10.1038/s41598-020-73525-y

   • PubMed
   • Google Scholar
7. 1. Bindels DS
   2. Haarbosch L
   3. van Weeren L
   4. Postma M
   5. Wiese KE
   6. Mastop M
   7. Aumonier S
   8. Gotthard G
   9. Royant A
   10. Hink MA
   11. Gadella TJ

   (2017) mScarlet: a bright monomeric red fluorescent protein for cellular imaging

   Nature Methods 14:53–56.

   https://doi.org/10.1038/nmeth.4074

   • PubMed
   • Google Scholar
8. 1. Bold RJ
   2. Virudachalam S
   3. McConkey DJ

   (2001) Chemosensitization of pancreatic cancer by inhibition of the 26S proteasome

   The Journal of Surgical Research 100:11–17.

   https://doi.org/10.1006/jsre.2001.6194

   • PubMed
   • Google Scholar
9. 1. Bourgarel-Rey V
   2. Vallee S
   3. Rimet O
   4. Champion S
   5. Braguer D
   6. Desobry A
   7. Briand C
   8. Barra Y

   (2001) Involvement of nuclear factor kappaB in c-Myc induction by tubulin polymerization inhibitors

   Molecular Pharmacology 59:1165–1170.

   https://doi.org/10.1124/mol.59.5.1165

   • PubMed
   • Google Scholar
10. 1. Bren GD
    2. Solan NJ
    3. Miyoshi H
    4. Pennington KN
    5. Pobst LJ
    6. Paya CV

    (2001) Transcription of the RelB gene is regulated by NF-kappaB

    Oncogene 20:7722–7733.

    https://doi.org/10.1038/sj.onc.1204868

    • PubMed
    • Google Scholar
11. 1. Brown K
    2. Park S
    3. Kanno T
    4. Franzoso G
    5. Siebenlist U

    (1993) Mutual regulation of the transcriptional activator NF-kappa B and its inhibitor, I kappa B-alpha

    PNAS 90:2532–2536.

    https://doi.org/10.1073/pnas.90.6.2532

    • PubMed
    • Google Scholar
12. 1. Chen Z
    2. Hagler J
    3. Palombella VJ
    4. Melandri F
    5. Scherer D
    6. Ballard D
    7. Maniatis T

    (1995) Signal-induced site-specific phosphorylation targets I kappa B alpha to the ubiquitin-proteasome pathway

    Genes & Development 9:1586–1597.

    https://doi.org/10.1101/gad.9.13.1586

    • PubMed
    • Google Scholar
13. 1. Collart MA
    2. Baeuerle P
    3. Vassalli P

    (1990) Regulation of tumor necrosis factor alpha transcription in macrophages: involvement of four kappa B-like motifs and of constitutive and inducible forms of NF-kappa B

    Molecular and Cellular Biology 10:1498–1506.

    https://doi.org/10.1128/mcb.10.4.1498-1506.1990

    • PubMed
    • Google Scholar
14. 1. Cong L
    2. Ran FA
    3. Cox D
    4. Lin S
    5. Barretto R
    6. Habib N
    7. Hsu PD
    8. Wu X
    9. Jiang W
    10. Marraffini LA
    11. Zhang F

    (2013) Multiplex genome engineering using CRISPR/Cas systems

    Science 339:819–823.

    https://doi.org/10.1126/science.1231143

    • PubMed
    • Google Scholar
15. 1. Cordon-Cardo C

    (1995)

    Mutations of cell cycle regulators. Biological and clinical implications for human neoplasia

    The American Journal of Pathology 147:545–560.

    • PubMed
    • Google Scholar
16. 1. Deer EL
    2. González-Hernández J
    3. Coursen JD
    4. Shea JE
    5. Ngatia J
    6. Scaife CL
    7. Firpo MA
    8. Mulvihill SJ

    (2010) Phenotype and genotype of pancreatic cancer cell lines

    Pancreas 39:425–435.

    https://doi.org/10.1097/MPA.0b013e3181c15963

    • PubMed
    • Google Scholar
17. 1. Desai A
    2. Mitchison TJ

    (1997) Microtubule polymerization dynamics

    Annual Review of Cell and Developmental Biology 13:83–117.

    https://doi.org/10.1146/annurev.cellbio.13.1.83

    • PubMed
    • Google Scholar
18. 1. DiDonato JA
    2. Hayakawa M
    3. Rothwarf DM
    4. Zandi E
    5. Karin M

    (1997) A cytokine-responsive IkappaB kinase that activates the transcription factor NF-kappaB

    Nature 388:548–554.

    https://doi.org/10.1038/41493

    • PubMed
    • Google Scholar
19. 1. Frasor J
    2. Weaver A
    3. Pradhan M
    4. Dai Y
    5. Miller LD
    6. Lin CY
    7. Stanculescu A

    (2009) Positive cross-talk between estrogen receptor and NF-kappaB in breast cancer

    Cancer Research 69:8918–8925.

    https://doi.org/10.1158/0008-5472.CAN-09-2608

    • PubMed
    • Google Scholar
20. 1. Fujioka S
    2. Sclabas GM
    3. Schmidt C
    4. Frederick WA
    5. Dong QG
    6. Abbruzzese JL
    7. Evans DB
    8. Baker C
    9. Chiao PJ

    (2003)

    Function of nuclear factor kappaB in pancreatic cancer metastasis

    Clinical Cancer Research 9:346–354.

    • PubMed
    • Google Scholar
21. 1. Georgouli M
    2. Herraiz C
    3. Crosas-Molist E
    4. Fanshawe B
    5. Maiques O
    6. Perdrix A
    7. Pandya P
    8. Rodriguez-Hernandez I
    9. Ilieva KM
    10. Cantelli G
    11. Karagiannis P
    12. Mele S
    13. Lam H
    14. Josephs DH
    15. Matias-Guiu X
    16. Marti RM
    17. Nestle FO
    18. Orgaz JL
    19. Malanchi I
    20. Fruhwirth GO
    21. Karagiannis SN
    22. Sanz-Moreno V

    (2019) Regional activation of myosin II in cancer cells drives tumor progression via a secretory cross-talk with the immune microenvironment

    Cell 176:757–774.

    https://doi.org/10.1016/j.cell.2018.12.038

    • PubMed
    • Google Scholar
22. 1. Ghosh S
    2. May MJ
    3. Kopp EB

    (1998) NF-kappa B and Rel proteins: evolutionarily conserved mediators of immune responses

    Annual Review of Immunology 16:225–260.

    https://doi.org/10.1146/annurev.immunol.16.1.225

    • PubMed
    • Google Scholar
23. 1. Gigant B
    2. Wang C
    3. Ravelli RBG
    4. Roussi F
    5. Steinmetz MO
    6. Curmi PA
    7. Sobel A
    8. Knossow M

    (2005) Structural basis for the regulation of tubulin by vinblastine

    Nature 435:519–522.

    https://doi.org/10.1038/nature03566

    • PubMed
    • Google Scholar
24. 1. Hayden MS
    2. West AP
    3. Ghosh S

    (2006) NF-kappaB and the immune response

    Oncogene 25:6758–6780.

    https://doi.org/10.1038/sj.onc.1209943

    • PubMed
    • Google Scholar
25. 1. Heckerman D
    2. Geiger D
    3. Chickering DM

    (1995) Learning Bayesian networks: The combination of knowledge and statistical data

    Machine Learning 20:197–243.

    https://doi.org/10.1007/BF00994016

    • Google Scholar
26. 1. Heid I
    2. Lubeseder-Martellato C
    3. Sipos B
    4. Mazur PK
    5. Lesina M
    6. Schmid RM
    7. Siveke JT

    (2011) Early requirement of Rac1 in a mouse model of pancreatic cancer

    Gastroenterology 141:719–730.

    https://doi.org/10.1053/j.gastro.2011.04.043

    • PubMed
    • Google Scholar
27. 1. Hoffmann A
    2. Levchenko A
    3. Scott ML
    4. Baltimore D

    (2002) The IkappaB-NF-kappaB signaling module: temporal control and selective gene activation

    Science 298:1241–1245.

    https://doi.org/10.1126/science.1071914

    • PubMed
    • Google Scholar
28. 1. Huber MA
    2. Azoitei N
    3. Baumann B
    4. Grünert S
    5. Sommer A
    6. Pehamberger H
    7. Kraut N
    8. Beug H
    9. Wirth T

    (2004) NF-kappaB is essential for epithelial-mesenchymal transition and metastasis in a model of breast cancer progression

    The Journal of Clinical Investigation 114:569–581.

    https://doi.org/10.1172/JCI21358

    • PubMed
    • Google Scholar
29. 1. Isogai T
    2. van der Kammen R
    3. Innocenti M

    (2015) SMIFH2 has effects on Formins and p53 that perturb the cell cytoskeleton

    Scientific Reports 5:9802.

    https://doi.org/10.1038/srep09802

    • PubMed
    • Google Scholar
30. 1. Kieran M
    2. Blank V
    3. Logeat F
    4. Vandekerckhove J
    5. Lottspeich F
    6. Le Bail O
    7. Urban MB
    8. Kourilsky P
    9. Baeuerle PA
    10. Israël A

    (1990) The DNA binding subunit of NF-kappa B is identical to factor KBF1 and homologous to the rel oncogene product

    Cell 62:1007–1018.

    https://doi.org/10.1016/0092-8674(90)90275-j

    • PubMed
    • Google Scholar
31. 1. Kovács M
    2. Tóth J
    3. Hetényi C
    4. Málnási-Csizmadia A
    5. Sellers JR

    (2004) Mechanism of blebbistatin inhibition of myosin II

    The Journal of Biological Chemistry 279:35557–35563.

    https://doi.org/10.1074/jbc.M405319200

    • PubMed
    • Google Scholar
32. 1. Kunnumakkara AB
    2. Guha S
    3. Krishnan S
    4. Diagaradjane P
    5. Gelovani J
    6. Aggarwal BB

    (2007) Curcumin potentiates antitumor activity of gemcitabine in an orthotopic model of pancreatic cancer through suppression of proliferation, angiogenesis, and inhibition of nuclear factor-kappaB-regulated gene products

    Cancer Research 67:3853–3861.

    https://doi.org/10.1158/0008-5472.CAN-06-4257

    • PubMed
    • Google Scholar
33. 1. Kurki P
    2. Vanderlaan M
    3. Dolbeare F
    4. Gray J
    5. Tan EM

    (1986) Expression of proliferating cell nuclear antigen (PCNA)/cyclin during the cell cycle

    Experimental Cell Research 166:209–219.

    https://doi.org/10.1016/0014-4827(86)90520-3

    • PubMed
    • Google Scholar
34. 1. Lamarche-Vane N
    2. Hall A

    (1998) CdGAP, a novel proline-rich GTPase-activating protein for Cdc42 and Rac

    The Journal of Biological Chemistry 273:29172–29177.

    https://doi.org/10.1074/jbc.273.44.29172

    • PubMed
    • Google Scholar
35. 1. Lamba V
    2. Ghosh I

    (2012) New directions in targeting protein kinases: focusing upon true allosteric and bivalent inhibitors

    Current Pharmaceutical Design 18:2936–2945.

    https://doi.org/10.2174/138161212800672813

    • PubMed
    • Google Scholar
36. 1. Lane K
    2. Van Valen D
    3. DeFelice MM
    4. Macklin DN
    5. Kudo T
    6. Jaimovich A
    7. Carr A
    8. Meyer T
    9. Pe’er D
    10. Boutet SC
    11. Covert MW

    (2017) Measuring signaling and RNA-seq in the same cell links gene expression to dynamic patterns of NF-κB activation

    Cell Systems 4:458–469.

    https://doi.org/10.1016/j.cels.2017.03.010

    • PubMed
    • Google Scholar
37. 1. Leonardi A
    2. Ellinger-Ziegelbauer H
    3. Franzoso G
    4. Brown K
    5. Siebenlist U

    (2000) Physical and functional interaction of filamin (actin-binding protein-280) and tumor necrosis factor receptor-associated factor 2

    The Journal of Biological Chemistry 275:271–278.

    https://doi.org/10.1074/jbc.275.1.271

    • PubMed
    • Google Scholar
38. 1. Libermann TA
    2. Baltimore D

    (1990) Activation of interleukin-6 gene expression through the NF-kappa B transcription factor

    Molecular and Cellular Biology 10:2327–2334.

    https://doi.org/10.1128/mcb.10.5.2327-2334.1990

    • PubMed
    • Google Scholar
39. 1. Lombardi L
    2. Ciana P
    3. Cappellini C
    4. Trecca D
    5. Guerrini L
    6. Migliazza A
    7. Maiolo AT
    8. Neri A

    (1995) Structural and functional characterization of the promoter regions of the NFKB2 gene

    Nucleic Acids Research 23:2328–2336.

    https://doi.org/10.1093/nar/23.12.2328

    • PubMed
    • Google Scholar
40. 1. Love MI
    2. Huber W
    3. Anders S

    (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

    Genome Biology 15:550.

    https://doi.org/10.1186/s13059-014-0550-8

    • PubMed
    • Google Scholar
41. 1. Machesky LM
    2. Hall A

    (1997) Role of actin polymerization and adhesion to extracellular matrix in Rac- and Rho-induced cytoskeletal reorganization

    The Journal of Cell Biology 138:913–926.

    https://doi.org/10.1083/jcb.138.4.913

    • PubMed
    • Google Scholar
42. 1. Melisi D
    2. Niu J
    3. Chang Z
    4. Xia Q
    5. Peng B
    6. Ishiyama S
    7. Evans DB
    8. Chiao PJ

    (2009) Secreted interleukin-1alpha induces a metastatic phenotype in pancreatic cancer by sustaining a constitutive activation of nuclear factor-kappaB

    Molecular Cancer Research 7:624–633.

    https://doi.org/10.1158/1541-7786.MCR-08-0201

    • PubMed
    • Google Scholar
43. 1. Mullins RD
    2. Heuser JA
    3. Pollard TD

    (1998) The interaction of Arp2/3 complex with actin: nucleation, high affinity pointed end capping, and formation of branching networks of filaments

    PNAS 95:6181–6186.

    https://doi.org/10.1073/pnas.95.11.6181

    • PubMed
    • Google Scholar
44. 1. Nelson DE
    2. Ihekwaba AEC
    3. Elliott M
    4. Johnson JR
    5. Gibney CA
    6. Foreman BE
    7. Nelson G
    8. See V
    9. Horton CA
    10. Spiller DG
    11. Edwards SW
    12. McDowell HP
    13. Unitt JF
    14. Sullivan E
    15. Grimley R
    16. Benson N
    17. Broomhead D
    18. Kell DB
    19. White MRH

    (2004) Oscillations in NF-kappaB signaling control the dynamics of gene expression

    Science 306:704–708.

    https://doi.org/10.1126/science.1099962

    • PubMed
    • Google Scholar
45. 1. Németh ZH
    2. Deitch EA
    3. Davidson MT
    4. Szabó C
    5. Vizi ES
    6. Haskó G

    (2004) Disruption of the actin cytoskeleton results in nuclear factor-kappaB activation and inflammatory mediator production in cultured human intestinal epithelial cells

    Journal of Cellular Physiology 200:71–81.

    https://doi.org/10.1002/jcp.10477

    • PubMed
    • Google Scholar
46. 1. Nolan GP
    2. Ghosh S
    3. Liou HC
    4. Tempst P
    5. Baltimore D

    (1991) DNA binding and I kappa B inhibition of the cloned p65 subunit of NF-kappa B, a rel-related polypeptide

    Cell 64:961–969.

    https://doi.org/10.1016/0092-8674(91)90320-x

    • PubMed
    • Google Scholar
47. 1. Otto T
    2. Sicinski P

    (2017) Cell cycle proteins as promising targets in cancer therapy

    Nature Reviews. Cancer 17:93–115.

    https://doi.org/10.1038/nrc.2016.138

    • PubMed
    • Google Scholar
48. 1. Pe’er D
    2. Regev A
    3. Elidan G
    4. Friedman N

    (2001) Inferring subnetworks from perturbed expression profiles

    Bioinformatics 17:S215–S224.

    https://doi.org/10.1093/bioinformatics/17.suppl_1.S215

    • Google Scholar
49. 1. Pruyne D
    2. Evangelista M
    3. Yang C
    4. Bi E
    5. Zigmond S
    6. Bretscher A
    7. Boone C

    (2002) Role of formins in actin assembly: nucleation and barbed-end association

    Science 297:612–615.

    https://doi.org/10.1126/science.1072309

    • PubMed
    • Google Scholar
50. 1. Rahman SMT
    2. Aqdas M
    3. Martin EW
    4. Tomassoni Ardori F
    5. Songkiatisak P
    6. Oh KS
    7. Uderhardt S
    8. Yun S
    9. Claybourne QC
    10. McDevitt RA
    11. Greco V
    12. Germain RN
    13. Tessarollo L
    14. Sung MH

    (2022) Double knockin mice show NF-κB trajectories in immune signaling and aging

    Cell Reports 41:111682.

    https://doi.org/10.1016/j.celrep.2022.111682

    • PubMed
    • Google Scholar
51. 1. Razidlo GL
    2. Burton KM
    3. McNiven MA

    (2018) Interleukin-6 promotes pancreatic cancer cell migration by rapidly activating the small GTPase CDC42

    The Journal of Biological Chemistry 293:11143–11153.

    https://doi.org/10.1074/jbc.RA118.003276

    • PubMed
    • Google Scholar
52. 1. Rizvi SA
    2. Neidt EM
    3. Cui J
    4. Feiger Z
    5. Skau CT
    6. Gardel ML
    7. Kozmin SA
    8. Kovar DR

    (2009) Identification and characterization of a small molecule inhibitor of formin-mediated actin assembly

    Chemistry & Biology 16:1158–1168.

    https://doi.org/10.1016/j.chembiol.2009.10.006

    • PubMed
    • Google Scholar
53. 1. Rosette C
    2. Karin M

    (1995) Cytoskeletal control of gene expression: depolymerization of microtubules activates NF-kappa B

    The Journal of Cell Biology 128:1111–1119.

    https://doi.org/10.1083/jcb.128.6.1111

    • PubMed
    • Google Scholar
54. 1. Sachs K
    2. Perez O
    3. Pe’er D
    4. Lauffenburger DA
    5. Nolan GP

    (2005) Causal protein-signaling networks derived from multiparameter single-cell data

    Science 308:523–529.

    https://doi.org/10.1126/science.1105809

    • PubMed
    • Google Scholar
55. 1. Sailem HZ
    2. Bakal C

    (2017) Identification of clinically predictive metagenes that encode components of a network coupling cell shape to transcription by image-omics

    Genome Research 27:196–207.

    https://doi.org/10.1101/gr.202028.115

    • PubMed
    • Google Scholar
56. 1. Sasaki Y
    2. Suzuki M
    3. Hidaka H

    (2002) The novel and specific Rho-kinase inhibitor (S)-(+)-2-methyl-1-[(4-methyl-5-isoquinoline)sulfonyl]-homopiperazine as a probing molecule for Rho-kinase-involved pathway

    Pharmacology & Therapeutics 93:225–232.

    https://doi.org/10.1016/S0163-7258(02)00191-2

    • Google Scholar
57. 1. Schliwa M

    (1982) Action of cytochalasin D on cytoskeletal networks

    The Journal of Cell Biology 92:79–91.

    https://doi.org/10.1083/jcb.92.1.79

    • PubMed
    • Google Scholar
58. 1. Scott ML
    2. Fujita T
    3. Liou HC
    4. Nolan GP
    5. Baltimore D

    (1993) The p65 subunit of NF-kappa B regulates I kappa B by two distinct mechanisms

    Genes & Development 7:1266–1276.

    https://doi.org/10.1101/gad.7.7a.1266

    • Google Scholar
59. 1. Scutari M

    (2010) Learning bayesian networks with the bnlearn R Package

    Journal of Statistical Software 35:1–22.

    https://doi.org/10.18637/jss.v035.i03

    • Google Scholar
60. Conference

    1. Scutari M
    2. Graafland CE
    3. Gutierrez JM

    (2018)

    Who learns better bayesian network structures: constraint-based, score-based or hybrid algorithms?

    Proceedings of Machine Learning Research. pp. 416–427.

    • Google Scholar
61. 1. Scutari M
    2. Vitolo C
    3. Tucker A

    (2019) Learning Bayesian networks from big data with greedy search: computational complexity and efficient implementation

    Statistics and Computing 29:1095–1108.

    https://doi.org/10.1007/s11222-019-09857-1

    • Google Scholar
62. 1. Sero JE
    2. Sailem HZ
    3. Ardy RC
    4. Almuttaqi H
    5. Zhang T
    6. Bakal C

    (2015) Cell shape and the microenvironment regulate nuclear translocation of NF-κB in breast epithelial and tumor cells

    Molecular Systems Biology 11:790.

    https://doi.org/10.15252/msb.20145644

    • PubMed
    • Google Scholar
63. 1. Spencer CM
    2. Faulds D

    (1994) Paclitaxel

    Drugs 48:794–847.

    https://doi.org/10.2165/00003495-199448050-00009

    • Google Scholar
64. 1. Stewart-Ornstein J
    2. Lahav G

    (2016) Dynamics of CDKN1A in single cells defined by an endogenous fluorescent tagging toolkit

    Cell Reports 14:1800–1811.

    https://doi.org/10.1016/j.celrep.2016.01.045

    • PubMed
    • Google Scholar
65. 1. Sun SC
    2. Ganchi PA
    3. Ballard DW
    4. Greene WC

    (1993) NF-kappa B controls expression of inhibitor I kappa B alpha: evidence for an inducible autoregulatory pathway

    Science 259:1912–1915.

    https://doi.org/10.1126/science.8096091

    • PubMed
    • Google Scholar
66. 1. Sung M-H
    2. Salvatore L
    3. De Lorenzi R
    4. Indrawan A
    5. Pasparakis M
    6. Hager GL
    7. Bianchi ME
    8. Agresti A

    (2009) Sustained oscillations of NF-kappaB produce distinct genome scanning and gene expression profiles

    PLOS ONE 4:e7163.

    https://doi.org/10.1371/journal.pone.0007163

    • PubMed
    • Google Scholar
67. 1. Tangutur AD
    2. Kumar D
    3. Krishna KV
    4. Kantevari S

    (2017) Microtubule targeting agents as cancer chemotherapeutics: an overview of molecular hybrids as stabilizing and destabilizing agents

    Current Topics in Medicinal Chemistry 17:2523–2537.

    https://doi.org/10.2174/1568026617666170104145640

    • PubMed
    • Google Scholar
68. 1. Tay S
    2. Hughey JJ
    3. Lee TK
    4. Lipniacki T
    5. Quake SR
    6. Covert MW

    (2010) Single-cell NF-kappaB dynamics reveal digital activation and analogue information processing

    Nature 466:267–271.

    https://doi.org/10.1038/nature09145

    • PubMed
    • Google Scholar
69. 1. Tcherkezian J
    2. Triki I
    3. Stenne R
    4. Danek EI
    5. Lamarche-Vane N

    (2006) The human orthologue of CdGAP is a phosphoprotein and a GTPase-activating protein for Cdc42 and Rac1 but not RhoA

    Biology of the Cell 98:445–456.

    https://doi.org/10.1042/BC20050101

    • PubMed
    • Google Scholar
70. 1. Tian B
    2. Nowak DE
    3. Brasier AR

    (2005) A TNF-induced gene expression program under oscillatory NF-kappaB control

    BMC Genomics 6:137.

    https://doi.org/10.1186/1471-2164-6-137

    • PubMed
    • Google Scholar
71. 1. Vallenius T
    2. Vaahtomeri K
    3. Kovac B
    4. Osiceanu AM
    5. Viljanen M
    6. Mäkelä TP

    (2011) An association between NUAK2 and MRIP reveals a novel mechanism for regulation of actin stress fibers

    Journal of Cell Science 124:384–393.

    https://doi.org/10.1242/jcs.072660

    • PubMed
    • Google Scholar
72. 1. Vasquez RJ
    2. Howell B
    3. Yvon AM
    4. Wadsworth P
    5. Cassimeris L

    (1997) Nanomolar concentrations of nocodazole alter microtubule dynamic instability in vivo and in vitro

    Molecular Biology of the Cell 8:973–985.

    https://doi.org/10.1091/mbc.8.6.973

    • PubMed
    • Google Scholar
73. 1. Wang W
    2. Abbruzzese JL
    3. Evans DB
    4. Larry L
    5. Cleary KR
    6. Chiao PJ

    (1999)

    The nuclear factor-kappa B RelA transcription factor is constitutively activated in human pancreatic adenocarcinoma cells

    Clinical Cancer Research 5:119–127.

    • PubMed
    • Google Scholar
74. 1. Weichert W
    2. Boehm M
    3. Gekeler V
    4. Bahra M
    5. Langrehr J
    6. Neuhaus P
    7. Denkert C
    8. Imre G
    9. Weller C
    10. Hofmann HP
    11. Niesporek S
    12. Jacob J
    13. Dietel M
    14. Scheidereit C
    15. Kristiansen G

    (2007) High expression of RelA/p65 is associated with activation of nuclear factor-kappaB-dependent signaling in pancreatic cancer and marks a patient population with poor prognosis

    British Journal of Cancer 97:523–530.

    https://doi.org/10.1038/sj.bjc.6603878

    • PubMed
    • Google Scholar
75. Book

    1. Wickham H

    (2016) Ggplot2: Elegant Graphics for Data Analysis

    Springer.

    https://doi.org/10.1007/978-3-319-24277-4_5

    • Google Scholar
76. 1. Yamamoto H
    2. Takashima S
    3. Shintani Y
    4. Yamazaki S
    5. Seguchi O
    6. Nakano A
    7. Higo S
    8. Kato H
    9. Liao Y
    10. Asano Y
    11. Minamino T
    12. Matsumura Y
    13. Takeda H
    14. Kitakaze M

    (2008) Identification of a novel substrate for TNFα-induced kinase NUAK2

    Biochemical and Biophysical Research Communications 365:541–547.

    https://doi.org/10.1016/j.bbrc.2007.11.013

    • Google Scholar
77. 1. Yuan WC
    2. Pepe-Mooney B
    3. Galli GG
    4. Dill MT
    5. Huang HT
    6. Hao M
    7. Wang Y
    8. Liang H
    9. Calogero RA
    10. Camargo FD

    (2018) NUAK2 is a critical YAP target in liver cancer

    Nature Communications 9:4834.

    https://doi.org/10.1038/s41467-018-07394-5

    • PubMed
    • Google Scholar
78. 1. Zambrano S
    2. De Toma I
    3. Piffer A
    4. Bianchi ME
    5. Agresti A

    (2016) NF-κB oscillations translate into functionally related patterns of gene expression

    eLife 5:e09100.

    https://doi.org/10.7554/eLife.09100

    • PubMed
    • Google Scholar
79. 1. Zandi E
    2. Rothwarf DM
    3. Delhase M
    4. Hayakawa M
    5. Karin M

    (1997) The IkappaB kinase complex (IKK) contains two kinase subunits, IKKalpha and IKKbeta, necessary for IkappaB phosphorylation and NF-kappaB activation

    Cell 91:243–252.

    https://doi.org/10.1016/s0092-8674(00)80406-7

    • PubMed
    • Google Scholar
80. 1. Zhang G
    2. Gurtu V
    3. Kain SR

    (1996) An enhanced green fluorescent protein allows sensitive detection of gene transfer in mammalian cells

    Biochemical and Biophysical Research Communications 227:707–711.

    https://doi.org/10.1006/bbrc.1996.1573

    • PubMed
    • Google Scholar
81. 1. Zhao X
    2. Fan W
    3. Xu Z
    4. Chen H
    5. He Y
    6. Yang G
    7. Yang G
    8. Hu H
    9. Tang S
    10. Wang P
    11. Zhang Z
    12. Xu P
    13. Yu M

    (2016) Inhibiting tumor necrosis factor-alpha diminishes desmoplasia and inflammation to overcome chemoresistance in pancreatic ductal adenocarcinoma

    Oncotarget 7:81110–81122.

    https://doi.org/10.18632/oncotarget.13212

    • Google Scholar

Author details

1. Francesca Butera

   Chester Beatty Laboratories, Division of Cancer Biology, Institute of Cancer Research, London, United Kingdom

   Present address

   Murdoch Children’s Research Institute, The Royal Children’s Hospital, Melbourne, Australia

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   frankie.butera@mcri.edu.au

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6606-4678

2. Julia E Sero

   Department of Life Sciences, University of Bath, Bath, United Kingdom

   Contribution

   Conceptualization, Supervision

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0299-9212

3. Lucas G Dent

   Chester Beatty Laboratories, Division of Cancer Biology, Institute of Cancer Research, London, United Kingdom

   Contribution

   Supervision, Visualization, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8573-4617

4. Chris Bakal

   Chester Beatty Laboratories, Division of Cancer Biology, Institute of Cancer Research, London, United Kingdom

   Contribution

   Conceptualization, Supervision, Funding acquisition, Writing – original draft, Writing – review and editing

   For correspondence

   cbakal@icr.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0413-6744

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