1. Cancer Biology

Oncogenic PKA signaling increases c-MYC protein expression through multiple targetable mechanisms

  1. Gary KL Chan
  2. Samantha Maisel
  3. Yeonjoo C Hwang
  4. Bryan C Pascual
  5. Rebecca RB Wolber
  6. Phuong Vu
  7. Krushna C Patra
  8. Mehdi Bouhaddou
  9. Heidi L Kenerson
  10. Huat C Lim
  11. Donald Long
  12. Raymond S Yeung
  13. Praveen Sethupathy
  14. Danielle L Swaney
  15. Nevan J Krogan
  16. Rigney E Turnham
  17. Kimberly J Riehle
  18. John D Scott
  19. Nabeel Bardeesy
  20. John D Gordan  Is a corresponding author
  1. Division of Hematology/Oncology, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, United States
  2. Quantitative Biosciences Institute (QBI), University of California San Francisco, United States
  3. Department of Medicine, Harvard Medical School, United States
  4. Massachusetts General Hospital Cancer Center, United States
  5. Department of Cellular and Molecular Pharmacology, University of California San Francisco, United States
  6. J. David Gladstone Institute, United States
  7. Department of Surgery and Northwest Liver Research Program, University of Washington, United States
  8. Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, United States
  9. Department of Pharmacology, University of Washington Medical Center, United States
https://doi.org/10.7554/eLife.69521
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Cite this article
  1. Gary KL Chan
  2. Samantha Maisel
  3. Yeonjoo C Hwang
  4. Bryan C Pascual
  5. Rebecca RB Wolber
  6. Phuong Vu
  7. Krushna C Patra
  8. Mehdi Bouhaddou
  9. Heidi L Kenerson
  10. Huat C Lim
  11. Donald Long
  12. Raymond S Yeung
  13. Praveen Sethupathy
  14. Danielle L Swaney
  15. Nevan J Krogan
  16. Rigney E Turnham
  17. Kimberly J Riehle
  18. John D Scott
  19. Nabeel Bardeesy
  20. John D Gordan
(2023)
Oncogenic PKA signaling increases c-MYC protein expression through multiple targetable mechanisms
eLife 12:e69521.
https://doi.org/10.7554/eLife.69521
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Abstract

Genetic alterations that activate protein kinase A (PKA) are found in many tumor types. Yet, their downstream oncogenic signaling mechanisms are poorly understood. We used global phosphoproteomics and kinase activity profiling to map conserved signaling outputs driven by a range of genetic changes that activate PKA in human cancer. Two signaling networks were identified downstream of PKA: RAS/MAPK components and an Aurora Kinase A (AURKA)/glycogen synthase kinase (GSK3) sub-network with activity toward MYC oncoproteins. Findings were validated in two PKA-dependent cancer models: a novel, patient-derived fibrolamellar carcinoma (FLC) line that expresses a DNAJ-PKAc fusion and a PKA-addicted melanoma model with a mutant type I PKA regulatory subunit. We identify PKA signals that can influence both de novo translation and stability of the proto-oncogene c-MYC. However, the primary mechanism of PKA effects on MYC in our cell models was translation and could be blocked with the eIF4A inhibitor zotatifin. This compound dramatically reduced c-MYC expression and inhibited FLC cell line growth in vitro. Thus, targeting PKA effects on translation is a potential treatment strategy for FLC and other PKA-driven cancers.

Editor's evaluation

The authors employed global kinome profiling to identify key effectors of protein kinase A (PKA) oncogenic signalling in fibrolamellar carcinoma and melanoma cell line models. Based on subsequent cell line-based validation using standard molecular and cellular biology assays, authors propose a model whereby the oncogenic effects of PKA are at least in part mediated by c-MYC. In addition to stabilizing c-MYC protein, the authors provide some evidence that PKA may stimulate c-MYC protein synthesis in an eukaryotic translation initiation factor 4F (eIF4F)-dependent manner. Notwithstanding that the underlying mechanisms remain obscure, it was thought that this study is of broad interest inasmuch as it provides hitherto unacknowledged insights into the molecular underpinnings of oncogenic PKA signalling and accordingly, it was thought that this manuscript may be of interest to researchers in the fields of cancer research, therapeutics, signal transduction and molecular and cell biology.

https://doi.org/10.7554/eLife.69521.sa0

Introduction

Protein kinase A (PKA) is an evolutionarily conserved signaling enzyme with established roles in diverse physiological processes, including the regulation of growth, differentiation, and metabolism (Turnham and Scott, 2016). PKA is controlled by cyclic AMP (cAMP) generated by the activation of G protein-coupled receptor (GPCR) signaling. Genomic alterations in the components of the GPCR-PKA signaling pathway lead to constitutive activation of this kinase in many human diseases including cancer (Taylor et al., 2013), such as amplified ligands of upstream GPCRs (Coles et al., 2020; McCudden et al., 2005), point mutations in the G-protein subunit GNAS (Patra et al., 2018), inactivation of PKA regulatory protein PKA-RIα (Yin et al., 2011), and mutations that directly alter the activity of the PKA catalytic subunit (PKAc; Berthon et al., 2015). Elevated PKA activity as a consequence of GNAS or PRKACA mutations has been reported in a variety of endocrine tumors (Salpea and Stratakis, 2014). A prototypical example is the PRKACA L205R mutation, which generates an unregulated PKAc variant found in adrenocortical and ACTH(Adrenocorticotropic Hormone)-producing pituitary tumors in patients with Cushing’s syndrome (Cao et al., 2014). Patients with germline inactivating mutations in PRKAR1A are predisposed to develop myxomas, thyroid, and gonadal tumors, referred to as Carney Complex (Yin et al., 2011). Recently, a DNAJB1-PRKACA gene fusion has emerged as the dominant oncogenic event in a rare liver cancer, fibrolamellar carcinoma (FLC; Honeyman et al., 2014). This genetic lesion is found in 79–100% of FLC (Honeyman et al., 2014; Cornella et al., 2015), with rare cases instead bearing PRKAR1A deletion (Graham et al., 2018). DNAJB1-PRKACA fusions have also been described in very small subsets of hepatocellular carcinoma (Cancer Genome Atlas Research Network, 2017), cholangiocarcinoma (Nakamura et al., 2015), and oncocytic biliary tumors (Singhi et al., 2020). Thus, oncogenic activation of PKA signaling is found in a substantial number of cancers.

The PKA holoenzyme is composed of two catalytic (C) and two regulatory (R) subunits (Taylor et al., 2013). In the inactive state, R subunits form a homodimer that binds and inhibits the C subunits. cAMP is generated by GPCR/Gαs-mediated stimulation of adenylyl cyclase. This diffusible second messenger binds R subunits, causing a conformational change that allows greater mobility and activity of the C subunits, while maintaining localization of active kinase complexes (Smith et al., 2017). The spatiotemporal specificity in cAMP signaling is provided by A-kinase anchoring proteins (AKAPs). This family of 60 human proteins sequester PKA at subcellular locations, creating nanodomains for relay and modulation of local cAMP signals (Langeberg and Scott, 2015; Omar and Scott, 2020).

PKA signaling modulates cancer-relevant processes including growth factor signaling, cell migration, cell cycle regulation, and control of cell metabolism. However, it remains unclear which oncogenic pathways downstream of PKA are essential and in which tumor types and contexts they have the greatest impact (Burton and McKnight, 2007; London et al., 2020). For example, DNAJ-PKAc stimulates ERK activation in an FLC model system (Turnham et al., 2019), operating via its interaction with AKAP-Lbc (Smith et al., 2010). In GNAS-mutant pancreatic tumor cells, PKA-mediated suppression of the salt-inducible kinases (SIK1-3) supports tumor growth (Patra et al., 2018). PKA has also been connected to control of the G2/M transition (Grieco et al., 1996; Kotani et al., 1998) and cell survival under glucose starvation (Palorini et al., 2016). Interestingly, PKA also has context-specific tumor suppressive functions including modulation of the Hedgehog and Hippo signaling pathways and is mutationally inactivated in a subset of cancers (Iglesias-Bartolome et al., 2015; Tokita et al., 2019).

Despite its oncogenic action in multiple tumor types, PKA is challenging to target directly with small molecules. The ubiquitous role of PKAc in normal physiology makes global inhibition of the kinase a challenge and selective inhibitors of this kinase have intolerable side effects (Wang et al., 2022; Toyota et al., 2022). This challenge is particularly unfortunate in the context of FLC, a disease of young adults with only limited reported impact of chemotherapy, immunotherapy, or targeted therapy to date (Dinh et al., 2022). Thus, a better understanding of the essential downstream PKA targets in individual tumor types is a more tractable path for therapeutic development. To gain insight into oncogenic PKA signaling networks and identify potential drug targets, we have investigated effects downstream of PKA activation. Accordingly, we generated cell models with regulatable PKA activity and derived proteomic profiles of PKA signaling. We show that common downstream effects of PKA include increased c-MYC protein expression. In this report, we demonstrate that Aurora Kinase A (AURKA), glycogen synthase kinase (GSK)–3B and the eukaryotic Initiation Factor (eIF)–4B all link PKA and c-MYC. Of these, control of translation appears to exert the most important effect in FLC and is targetable with the clinical eukaryotic Initiation Factor 4A (eIF4A) inhibitor zotatifin, leading to reduced c-MYC protein expression and tumor cell viability.

Results

PRKACA alterations are common among tumor types

We first analyzed the frequency of PKA-activating somatic alterations in the TCGA Pan Cancer Atlas (Weinstein et al., 2013), including both PRKACA gain-of-function and PRKAR1A loss-of-function mutations in addition to copy number alterations across multiple cancers (Figure 1A). We found a frequency of PRKACA amplification of 0.3–11.3% and a rate of activating mutations of 0.2–2.7%. The greatest frequency of activation occurred in malignant peripheral nerve sheath tumors and ovarian cancers. PRKAR1A loss of function mutations were rarer, including both inactivating mutations (0.2–5.3%) and deep deletions (0.4–4%), that were predominantly detected in adrenocortical carcinoma (Figure 1B).

Figure 1
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Recurrent PKA activating somatic alterations in human cancer.

(A) Pathway illustrations of different PKA activating genomic alterations in order from top: PRKACA amplification, DNAJB1-PRKACA fusion, PRKACA activating mutation, and PRKAR1A inactivation or deletion. (B) TCGA PanCancer Project analysis showing the frequency of PRKACA gain-of-function (red and yellow) and PRKAR1A loss-of-function (green and blue) alterations in various cancer types. The reported frequency of DNAJB1-PRKACA fusion in fibrolamellar carcinoma (FLC) clinical samples is also included. (C) Cell lines used in this study, their PKA-related mutation, PRKACA dependency, and inclusion in proteomic analyses. (D) Immunoblots showing the change of PKA activity, as indicated by phospho-PKA substrate, in different cell lines with dox-inducible 3xFLAG-PRKACA or PRKAR1AG325D with 1 µg/ml doxycycline (dox) for 48 hr. Left: engineered cell lines with inducible 3xFLAG-PRKACA. Right: engineered cell lines with inducible 3xFLAG- PRKAR1AG325D.

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Kinome profile of oncogenic PKA signaling

Cell lines with PKA-activating mutations were engineered for inducible PKAc activation or inhibition to study PKA signaling effects (Figure 1C). These models include the bladder cancer line 639V (PRKACA copy number gain Barretina et al., 2012) and Colo741 skin and ML1 thyroid (PRKAR1A frameshift mutations Ghandi et al., 2019) lines; of note, Colo741 was derived from a patient with colon cancer but is thought to be a melanoma (Vincent and Postovit, 2017). 639V and Colo741 have been profiled for PKA dependency in the Cancer Dependency Map program, with Colo741 highly dependent on PRKACA. Although not available when the proteomic analysis was performed, we also used FLX1, a novel cell line from a patient-derived xenograft FLC model (Oikawa et al., 2015). FLX1 contains a fusion of PKAc with biochemical gain-of-function and may have distinct signaling effects from other PKA-activating mutations (Turnham et al., 2019). To create stably inducible cell models for proteomic analysis, we introduced doxycycline (dox)-controlled 3xFLAG-PRKACA or PRKAR1AG325D, a dominant inhibitor of PKAc with impaired cAMP binding (Viste et al., 2005; Willis et al., 2011), into 639V, Colo741, and ML1 cells via lentiviral infection. Inducible expression of PKAc and PKA RIa was confirmed by immunoblot analysis using a phospho-PKA substrate antibody (Figure 1D).

The engineered cells described above were cultured with or without dox for 48 hr and analyzed with global phosphoproteomics and multiplex inhibitor bead (MIB) kinome profiing (Coles et al., 2020; Donnella et al., 2018; Sos et al., 2014; Budzik et al., 2020). Bioinformatic analysis was performed on the global phosphoproteomic data set with the Phosfate analysis tool to infer changes in kinase activity (Ochoa et al., 2016). These strategies allow us to measure known kinase/substrate relationships (Phosfate) and assay the activity of kinases whose substrates are not well known (MIBs). We initially confirmed the expected impact of PRKACA and PRKAR1G325D constructs. Using the engineered 639V cell lines, we showed that phosphorylation levels of the PKA target VASP pS239 increased with PRKACA induction and decreased with PRKAR1AG325D induction in our global phosphoproteomics analysis (Figure 2A). Similarly, we detected upregulation of PKAc with both Phosfate and MIBs platforms following dox treatment of PRKACA-inducible cells (Figure 2B).

Figure 2
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Kinome profiling to identify signaling nodes downstream of PRKACA.

(A) Global phosphorylation changes in 639V with induction of 3xFLAG-PRKACA or 3xFLAG-PRKAR1AG325D; VASP is shown as a positive control for PKA activation, technical replicates shown. (B) Change in kinase activity from Phosfate analysis or multiplex inhibitor beads (MIBs) pipeline using 639V with induction of 3xFLAG-PRKACA compared to control; PKA catalytic (PKAc) shown as a positive control. Technical replicates shown. (C) Summary of overlapping activity in Phosfate data sets: effect size for all inferred kinases identified in at least two samples were averaged, shown with SD. Top panel shows results from 3xFLAG-PRKACA induction, bottom panel from 3xFLAG-PRKAR1AG325D induction. (D) Summary of overlapping activity in MIBs data sets: abundance of all bead-enriched kinases identified in at least two samples were averaged, shown with SD. Top panel shows results from 3xFLAG-PRKACA induction, bottom panel from 3xFLAG- PRKAR1AG325D induction. (E) Network integration of MIBs and Phosfate kinome profiles from 639V, Colo741, and ML1 with doxycycline (dox)-inducible 3xFLAG-PRKACA and 639V and ML1 with dox-inducible 3xFLAG-PRKAR1AG325D. Kinases marked in red show increased activity in PRKACA data sets and/or decreased activity in PRKAR1AG325D data sets, while those marked in blue show the converse. (F) Confirmation of PKA-induced signaling changes: Colo741 and FLX1 were treated with 50 μM forskolin (FSK)/3-isobutyl-1-methylxanthine (IBMX) for 30 or 120 min and then analyzed by immunoblot.

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We integrated the proteomics data in four categories: (1) Phosfate for cells with inducible PRKACA (Figure 2C, top), (2) Phosfate for cells with inducible PRKAR1G325D (Figure 2C, bottom), (3) MIBs for cells with inducible PRKACA (Figure 2D, top), and (4) MIBs for cells with inducible PRKAR1AG325D (Figure 2D, bottom). This analysis showed that YES, LYN, EPHB4, LIMK1, LIMK2, CDK5, and CDK7 kinase activities were reduced following PKAc overexpression and increased following PKA inhibition by PRKAR1AG325D induction. We also saw that ROCK1 was upregulated by PRKACA and downregulated by PRKAR1AG325D induction. These results provide proof of concept for our genetic model system. We focused on credentialed drug targets among the list of candidates: we observed upregulation of the pro-proliferative kinases AURKA, BRAF, and AKT2 by PKA (Figure 2C, yellow). Interestingly, the tumor suppressor STK11 was downregulated by PKA. Fewer signaling changes influencing proliferation were observed upon PRKAR1AG325D induction (Figure 2C bottom, 2D bottom).

Differences in isoform expression and shared kinase functions can obscure relationships between proteomic datasets. Thus, we used network propagation to integrate data across all of our cell models (Cowen et al., 2017), applying established pathway relationships from the ReactomeFI network to define connect activated kinases in the PKA-regulated kinome (Gillespie et al., 2022). Cytoscape was used to visualize PKA and its kinase network neighbors that are significantly altered by PRKACA or PRKAR1AG325D induction (Figure 2E), with kinases that are upregulated by PKA function marked as positive (red) and downregulated negative (blue). Non-kinase network nodes and non-PKAc-adjacent kinases were also found (Supplementary file 3). This analysis defined two PKA-dependent clusters with both networks including potential drug targets. One cluster is characterized by growth factor signaling effectors such as BRAF, multiple MAPKs, AKT, PKCs, and ERBB2. A second network emerged, with cell cycle kinases involved in the regulation of G2/M including AURKA, PLK1, GSK3A/B, and several casein kinase family members. Importantly, both AURKA (Walter et al., 2000) and GSK3 (Gregory et al., 2003) have been previously described as PKA targets and can regulate MYC family proteins (Dauch et al., 2016; Gustafson et al., 2014; Gregory et al., 2003).

We confirmed key proteomic results by western blot in Colo741 and FLX1 cells treated with forskolin (FSK) and 3-isobutyl-1-methylxanthine (IBMX), to pharmacologically activate PKA. We observed strong activation of MAPK1/3 by FSK/IBMX in Colo741 and mild reduction in FLX1. Marked inhibition of GSK3B marked by phosphorylation of its inhibitory site serine 9 in FLX1, with a smaller, transient effect in Colo741 (Figure 2F).

PKA signaling induces c-MYC and n-MYC expression in cell lines and tumor specimens

Our finding that PKA regulates AURKA and GSK3A/B suggested that MYC-family proteins might be responsive to PKA signaling. To extend these findings, we focused on our two key PKA-driven models, the FLX1 FLC line and Colo741, to determine whether PKA induces c-MYC or n-MYC expression. Cells were treated with FSK/IBMX for 0.5, 2, or 4 hr, leading to rapid phosphorylation of PKA substrates that correlated with progressive increase in c-MYC protein levels. Relatively low levels of n-MYC were detected in FLX1 but did increase as well (Figure 3A). Interestingly, sustained PKAc activation also resulted in mildly elevated MYC mRNA levels in FLX1 but not in Colo741 cells (Figure 3B).

Figure 3 with 1 supplement see all
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PKA activity correlates with c-MYC and n-MYC protein levels.

(A) Immunoblots showing the change of PKA activity, as indicated by phospho-PKA substrate, and c-MYC and n-MYC expression in Colo741 and FLX1 cells after treatment with 50 μM forskolin (FSK) and 3-isobutyl-1-methylxanthine (IBMX) for 30 min, 2 hr, or 4 hr. n-MYC was not detected in Colo741 so is not shown. (B) Impact of 0–4 hr treatment with FSK/IBMX on MYC mRNA levels in Colo741 and FLX1; ± SD. Technical replicates from a representative experiment shown. (C) Immunoblots showing the change of PKA activity and c-MYC and n-MYC levels in engineered FLX1 cells with doxycycline (dox)-inducible 3xFLAG- PRKAR1AG325D with or without dox for 72 hr. (D) Immunoblots showing the basal level of PKA activity with phosphorylated PKA substrate and c-MYC expression in AML12 wild type (WT; left) and AML12DNAJ-PKAc cells (right). (E) Effect of 4 hr treatment with PKA-inhibiting tool compound H89 over a dose range from 1.25 to 20 μM on PKA substrate phosphorylation and c-MYC level. (F) Immunoblot showing the presence of DNAJ-PKAc and different level of c-MYC and n-MYC in fibrolamellar carcinoma (FLC) tumor samples (FLC) vs adjacent liver (N) from four FLC patients. (G) Summary gene set enrichment analysis (GSEA) of PRKACA amplified/mutant and PRKAR1A inactivated adrenocortical carcinoma or ovarian serous carcinoma vs. WT from TCGA. All significant ‘Hallmark’ gene sets are shown.

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Additionally, we generated an FLX1 cell line with dox-inducible 3xFLAG-PRKAR1AG325D, which produced the expected reductions in PKA substrate phosphorylation and c-MYC and n-MYC levels (Figure 3C). In control experiments, siRNA directed against PRKACA and dox induction of 3xFLAG-PRKAR1AG325D greatly reduce the cell proliferation rate of FLX1 (Figure 3—figure supplement 1A–B). To confirm the relationship between PKA and MYC, we used the isogenic FLC model (Turnham et al., 2019), where an allele of the Dnajb1-Prkaca fusion was CRISPR engineered into AML12 murine hepatocytes. Immunoblot analysis confirmed that the engineered FLC clone had increased basal PKA activation, as well as higher c-MYC expression (Figure 3D). In an additional control, treating FLX1 with the PKA inhibiting tool compound H89 also reduced c-MYC levels (Figure 3E).

Finally, we assessed MYC protein levels in resected human FLC specimens. Immunoblot detection of the slower migrating DNAJ-PKAc fusion protein was used as a marker for FLC (Figure 3E, mid lower lane). Importantly, expression of this oncogenic PKAc form correlated with increased protein levels of both c-MYC and n-MYC (Figure 3F). To determine whether the relationship between PKA and MYC exists in additional cancers, we applied gene set enrichment analysis (GSEA) to RNASeq data from the TCGA adrenocortical carcinoma and serous ovarian carcinoma data sets. We compared all tumors with a genetic alteration conferring PKA activation to the tumors in the same dataset without genetic PKA activation. The higher rate of PKA-activating alleles in adrenal cancers allowed a more robust comparison, identifying multiple upregulated Hallmark Gene Sets, including MYC Targets V1 and V2. In the ovarian cancer dataset, MYC Targets V2 was in fact the only significantly upregulated gene set (Figure 3G). These data support a recurrent pattern of MYC activation by PKA.

c-MYC effects on transcription and cell proliferation in PKA-driven cancers

To connect PKA- and MYC-driven gene expression effects on cellular behavior, we first performed RNASeq. This analysis compared a non-targeting control (NTC) siRNA to four pooled anti-PRKACA siRNA in FLX1 (Figure 4A, key targets highlighted). These caused a dramatic alteration in the FLX1 transcriptome, resulting in downregulation of Hallmark MYC Targets gene sets and upregulation of inflammatory and tumor suppressive gene sets (Figure 4B). Using individual siRNA, we knocked down PRKACA and MYC, confirming that both genes support the expression of the canonical c-MYC transcriptional target ornithine decarboxylase (ODC; Figure 4C). Because of its low level of expression in FLX1, MYCN knockdown is not shown. Control experiments did show a minor, inconsistent decrease in MYC mRNA levels following PRKACA knockdown (Figure 4—figure supplement 1A), which did not match effects on ODC and cyclin D1 (CCND1). Similarly, treatment with FSK/IBMX caused a time-dependent increase in ODC and CCND1 mRNA in FLX1 (Figure 4D).

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c-MYC alters transcription and proliferation in PKA-dependent cell models.

(A) RNASEQ data from FLX1 cells after 48 hr treatment with four pooled siRNA against PRKACA. PRKACA, ornithine decarboxylase (ODC) and Cyclin D1 (CCND1) are highlighted. (B) Gene set enrichment analysis of Hallmark Gene Sets altered by PRKACA siRNA treatment. MYC Targets V1 and MYC Targets V2 are among the most significantly reduced. (C) Confirmation of overlapping effects of PKA and c-MYC on gene expression: FLX1 cells were transfected with individual siRNA directed against PRKACA and MYC. ODC expression was measured 48 hr later by quantitative RT-PCR (MYC and PRKACA knockdown shown in Figure 1C). Log(2)fold change vs. cells transfected with a non-targeting control (NTC) siRNA is shown ± SD; technical replicates shown from a representative experiment. p Value determined using two-tailed Student’s t-test (D) Impact of 0–4 hr treatment with forskolin (FSK)/3-isobutyl-1-methylxanthine (IBMX) on ODC and CCND1 mRNA levels in FLX1; ± SD. (E) Relative confluence of FLX1 cells in 96 well plates after MYC knockdown with four pooled siRNA. FLX1 cells were incubated 36 hr before recording to ensure attachment and then monitored with real-time microscopy for 120 hr. Experiments were done in duplicate, and representative results were shown with mean of technical replicates ± SD, n=10 for each condition; confirmation of knockdown shown by western blot. (F) Summary data of relative cell confluence shown as average AUC measurement from technical replicates of FLX1 treated with NTC or individual PRKACA and MYC-targeting siRNA. Representative results of technical replicates shown. p Value determined using two-tailed Student’s t-test, *p<0.05 and ** p<0.001. (G) Relative confluence of engineered FLX1 cells with doxycycline (dox)-controlled 3xFLAG-MYC in 96 well plates after treatment with or without 1 μg/ml dox. Experiment was duplicate, the representative results shown with mean of technical replicates ± SD, n=6 for each condition; induction confirmed by Immunoblot at 48 hr dox treatment. 3xFLAG-tagged c-MYC (FL-c-MYC) is shown.

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We next tested the role of c-MYC in PKA-driven proliferation in Colo741 and FLX1 cells. Knockdown of MYC with four pooled siRNAs suppressed proliferation in FLX1 cells (Figure 4E). MYC siRNA knockdown also reduced proliferation in Colo741 cells, although to a lesser extent than in FLX1 (Figure 4—figure supplement 1B). Individual siRNAs were used to confirm the impact of MYC knockdown on FLX1 proliferation. AUC analysis of growth curves is shown, demonstrating that silencing MYC leads to a significant decrease in proliferation vs. NTC; PRKACA knockdown is shown for comparison (Figure 4F). Conversely, ectopic expression of MYC using a dox-inducible system increased proliferation of FLX1 cells (Figure 4G). Thus, c-MYC can play a significant role in the regulation of proliferation in PKA-dependent cancers.

AURKA, PIM and GSK3B can influence c-MYC expression in PKA-driven cells

Our next objective was to dissect the signaling mechanisms that might control c-MYC protein expression downstream of PKA. To generate a dataset of broad utility, we first undertook a screen of 352 advanced kinase inhibitors to identify compounds that impact proliferation in FLX1 cells. We found several Aurora kinase inhibitors in addition to the PKA-inhibiting tool compound H89 were particularly potent (Figure 5A). To illuminate potential PKA-regulated growth effects, we repeated this analysis in FLX1 cells upon induction of PRKAR1AG325D. These experiments revealed that blocking PKA activity increased the potency of RTK, RAS/MAPK, and Aurora Kinase inhibitors, while PI 3-kinase/mTOR pathway inhibitor effects were diminished (Figure 5B). In addition, we identified three GSK3A/B inhibitors with differential activity following induction of PRKAR1AG325D. Two compounds showed a minor increase in activity when PKAc was inhibited. The third, tideglusib, may have additional off-targets given its simple structure (Mathuram et al., 2018).

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AURKA and GSK3B regulate c-MYC in PKA-dependent cell models.

(A) Summary data from the FLX1 cell line treated with 352 kinase inhibitors from an advanced clinical compound library at 2 μM for 120 hr. The targets of selected compounds with a z-score ≥2 are highlighted, with the PKA inhibiting tool compound H89 shown. Average of three biological replicates is shown. (B) Impact of doxycycline (dox) induction of 3xFLAG-PRKAR1AG325D on drug sensitivity in FLX1: Cells were incubated with 1 μg/ml dox overnight and compound added on the following day; log2FC vs. median was derived ± dox, and then subtracted to identify those compounds whose activity was altered by 3xFLAG-PRKAR1AG325D. Data are averaged from three biological replicates. Inhibitors with p<0.05 were marked. Selected inhibitors were color coded based on their targets. (C) Kinase pooled siRNA library screen with FLX1 in 384 well plates shows the effect of each target kinase on cell proliferation (average of four biological replicates). Selected non-metabolic kinases that decrease cell proliferation with z-score –1 were marked. (D) FLX1 cells treated with dose curves of multiple AURKA inhibitors for 120 hr. Relative cell viability was measured by CTG assay vs. untreated control samples. Results are the mean ± SEM of triple biological replicates, three technical replicates per biological replicate. Inhibitors are color coded based on their binding mode. (E) FLX1 cells treated with dose curves of multiple PIM inhibitors as in B. (F) Effect of 24 hr treatment with 5 μM of different PIM inhibitors ±4 hr treatment with 50 μM forskolin (FSK)/3-isobutyl-1-methylxanthine (IBMX). (G) Immunoblot showing the change of PKA activity, as indicated by phospho-PKA substrate, and c-MYC and n-MYC levels in Colo741 and FLX1 cells after treatment with DMSO, 1 μM CD532, MLN8237, CX6258, or combination of 1 μM MLN8237 and 1 μM CX6258 for 24 hr.

Figure 5—source data 1

Tables for Figure 5A.

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Tables for Figure 5B.

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Tables for Figure 5C.

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Tables for Figure 5D.

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Tables for Figure 5E.

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Images for Figure 5F.

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To expand this analysis beyond established drug targets, we next screened a kinome-wide siRNA library for modifiers of cellular proliferation in FLX1 cells. In a key control, the common essential genes WEE1 and PLK1 both showed a z-score of <–1. We identified a total of 30 kinases whose genetic depletion reduced cell proliferation with a z-score <–1 and 20 kinases that increased proliferation (Supplementary file 7). Sensitivity to PIM2, EGFR, RPS6KB1, and PRKACA knockdown were also noted, while AURKA knockdown did not significantly alter cell confluence. PIM2 is a serine/threonine kinase with similar substrates and function to AKT (Fox et al., 2003).

AURKA (Dauch et al., 2016; Gustafson et al., 2014), GSK3 (Gregory et al., 2003), and PIM2 (Zhang et al., 2008) are established regulators of MYC protein stability. To connect these screening results to c-MYC regulation, we first tested a panel of AURKA inhibitors against the PKA-dependent Colo741 and FLX1 cell lines. We noted that the conformation-disrupting AURKA inhibitor (CD-AURKAi) CD532 had the strongest effect on cell viability. This agent inhibits AURKA catalytic activity and also alters its conformation, resulting in destabilization of c-MYC and n-MYC (Dauch et al., 2016; Gustafson et al., 2014). Increasing our interest in this class of AURKA inhibitors, the partial CD-AURKAi MLN-8237 (alisertib) showed some effect in Colo-741, albeit not in FLX1 (Figure 5D, Figure 5—figure supplement 1A). Importantly, drug sensitivities (EC50=217.3 nM for Colo741; 692.8 nM for FLX1) matched the reported dose range for AURKA kinase inhibition (Gustafson et al., 2014). As a control, we confirmed that FSK/IBMX can increase c-MYC expression levels in an FLX1 cells treated with nocodazole. AURKA pT288 was increased by nocodazole and nocodazole +FSK/IBMX, but we did not detect total AURKA. AURKA pT288 did not correlate with increased c-MYC levels, and nocodazole did not block PKA effects on c-MYC (Figure 5—figure supplement 1B). We next tested a collection of PIM1/2 inhibitors on FLX1 viability (Figure 5E). We found that CX6258 and SGI1776 can each reduce c-MYC protein levels in FLX1, although this effect is overwhelmed by chronic stimulation of cAMP production (Figure 5F).

As inhibitors of both kinases only exerted partial effects on c-MYC levels, we tested combinations of PIM and AURKA inhibition. We first confirmed that both CD532 and MLN8237 alone can reduce c-MYC expression in FLX1 and Colo741. We note an off-target effect of CD532 on PKA activity, which may explain its potent effect on cell viability. Thus, we focused on MLN-8237 for combinations. Treatment with only MLN8237 was able to reduce c-MYC levels in Colo741, but not FLX1, and the PIM inhibitor CX6258 had only a mild cooperative effect with MLN8237 in reducing MYC levels in FLX1 cells (Figure 5G). Combination treatment with CX6258 and MLN8237 did not synergize to reduce viability in FLX1 (not shown).

Finally, as our data above (Figure 3F) show that GSK3B is phosphorylated on an inhibitory site by PKA and can regulate c-MYC degradation, we tested its impact on c-MYC levels. Pharmacologically blocking GSK activity with CHIR99021 significantly augmented the impact of PKAc activation with FSK/IBMX on c-MYC expression (Figure 5—figure supplement 1C).

Our finding show that numerous kinases converge on c-MYC protein stability but that single or combination inhibition fails to overwhelm PKA stimulation. Thus, we directly assessed the contribution of altered protein stability in PKA effects on c-MYC. Treating FLX1 cells with the proteasome inhibitor MG132 augmented the impact of FSK/IBMX on c-MYC levels (Figure 5—figure supplement 2A). Similarly, when PKA was inhibited with PRKAR1AG325D induction, MG132 did not rescue c-MYC levels (Figure 5—figure supplement 2B). These results suggested that reduced degradation is not a major mechanism of PKA effects on c-MYC. Similarly, our proteomics did not reveal significant changes in c-MYC phosphorylation on T58, T62, or the putative PKA site S281 (Padmanabhan et al., 2013; Supplementary file 2). Thus, we tested c-MYC levels over time following treatment with cycloheximide (CHX) with or without FSK/IBMX treatment, finding no significant effect on c-MYC half-life following FSK/IBMX treatment (Figure 5—figure supplement 2C).

PKA increases in c-MYC expression depend on eIF4A activity

These results raise the possibility that PKA could instead increase c-MYC translation. We performed GSEA on the altered phosphoproteins from our prior study of PKA signaling (Coles et al., 2020) and the phosphoproteomic data sets reported here. We observed statistically significant enrichment of proteins involved in translation initiation in all cases (Figure 6A). Our previous study showed that the eIF4F complex member eIF4B can be directly phosphorylated by PKA (Coles et al., 2020). Consistent with this, we observed increased eIF4B phospho-S422 by western blot following FSK/IBMX treatment (Figure 6B). Conversely, eIF4B phosphorylation is reduced following induction of PKAR1AG325D (Figure 6C) or pooled siRNA knockdown of PRKACA in either Colo741 or FLX1 cells (Figure 6D).

Figure 6
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PKA signaling supports translation initiation.

(A) Gene set enrichment analysis (GSEA) of significantly altered phosphoproteins following doxycycline (dox) induction of PKA in proteomics data from this study or chemical PKA stimulation in our prior publications. Results are shown for Hallmark gene sets, with statistically significant enrichment of altered phosphoproteins annotated to be involved in translation initiation. (B) Time course of forskolin (FSK)/3-isobutyl-1-methylxanthine (IBMX) in Colo741 and FLX1 showing increased phosphorylation of eIF4B Ser422. (C) Impact of 24 hr dox treatment on phosphorylation of eIF4B at Ser422 in engineered FLX1 cells with dox-inducible 3xFLAG-PRKAR1AG325D. (D) Immunoblots showing the change of PKA activity and PKAc and c-MYC expression and phosphorylation of eIF4B Ser422 in Colo741 and FLX1 cells after pooled PRKACA siRNA knockdown for 24, 48, and 72 hr vs. 72 hr with non-targeting control (NTC). Long and short exposures are used in FLX1 to show knockdown effect in wild type (WT) PKAc and the DNAJ-PKAc fusion.

Figure 6—source data 1

Tables for Figure 6A.

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Images for Figure 6B.

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eIF4B phosphorylation at S422 increases the activity of the RNA helicase eIF4A (Harms et al., 2014), which unwinds the complex 5’ untranslated regions (UTR) of multiple pro-growth genes including MYC (Wolfe et al., 2014). We found that the related eIF4A inhibitors rocaglamide and zotatifin markedly attenuate the induction of c-MYC by FSK/IBMX to near baseline levels (Figure 7A). Similarly, rocaglamide reduces the level of c-MYC to one similar to that achieved by induction of PKAR1AG325D, with limited additive effect. Interestingly, napabucasin, which blocks eIF4E (Zuo et al., 2018) and has been described to reduce MYC levels in FLC (Lalazar et al., 2021), had relatively little effect in our system (Figure 7B). Furthermore, we found that protein levels of exogenously introduced c-MYC lacking a 5’UTR are not reduced by PRKACA knockdown with pooled siRNA (Figure 7—figure supplement 1A) or zotatifin treatment (Figure 7C; Figure 7—figure supplement 1B).

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PKA effects on c-MYC are blocked by eIF4A inhibition.

(A) Immunoblots showing the c-MYC protein levels in FLX1 cells after treatment with 100 nM rocaglamide or zotatifin for 24 hr and/or 50 μM forskolin (FSK)/3-isobutyl-1-methylxanthine (IBMX) for 4 hr. (B) Immunoblots showing the change of c-MYC level in engineered FLX1 cells with doxycycline (dox)-inducible 3xFLAG-PRKAR1AG325D after dox induction for 48 hr and treatment with 1 μM napabucasin or 100 nM rocaglamide for 24 hr. (C) Parental FLX1 cells or FLX1 with 48 hr dox-induced 3xFLAG-MYC lacking a 5’UTR treated with escalating doses of zotatifin for 24 hr and blotted for c-MYC. (D) Impact of 72 hr of a dose curve of zotatifin alone or in combination with dox-induced 3xFLAG- PRKAR1AG325D on FLX1 viability by Cell Titer-Glo. Results from technical replicates of a representative experiment ± SD relative to DMSO for each curve. (E) Parental Colo741 treated as in D. (F) Zotatifin sensitivity tested in FLX1 as in D, following siRNA knockdown of PRKACA or MYC. Results combined from three siRNA. Quantitative RT-PCR (qRT-PCR) confirmation of knockdown is shown in Figure 1C. (G) Impact of 24 hr 100 nM zotatifin on c-MYC targets by qRT-PCR; eIF-4A2 induction is a known effect of zotatifin and is shown as a control. Mean of technical replicates from one representative experiment is shown ± SD, p value determined with two-tailed Student’s t-test. (H) Schematic of DNAJ-PKAc mediating cell proliferation in fibrolamellar carcinoma (FLC) by increasing c-MYC expression by increased translation, with additional effects via GSK3B, AURKA, and PIM2.

Figure 7—source data 1

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Figure 7—source data 2

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Tables for Figure 7E.

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Tables for Figure 7F.

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Tables for Figure 7G.

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Images for Figure 7H.

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Finally, we assessed whether eIF4A inhibitor sensitivity was connected to a signaling effect of PKAc. Zotatifin potently reduced FLX1 and Colo741 viability with an EC50 of 7 nM for FLX1 and 22 nM for Colo741. These concentrations are similar to those that predict in vivo potency for zotatifin in other cell lines (Gerson-Gurwitz et al., 2021). We further found that the impact of zotatifin is significantly blunted by PRKAR1AG325D induction in FLX1 (Figure 7D–E), with siRNA knockdown of PRKACA and MYC also largely abrogating the effect of zotatifin on FLX1 proliferation (Figure 7F; Figure 7—figure supplement 1C). Zotatifin treatment also resulted in reduced expression of CCND1 and ODC, mirroring the impact of PRKACA or MYC knockdown; eIF4A2 is known to be induced by zotatifin (Ho et al., 2021) and is shown as a control (Figure 7G). Thus, PKA effects on c-MYC translation are amenable to therapeutic inhibition.

Discussion

Over the last decade, tumor sequencing and mouse modeling studies have demonstrated the importance of GNAS/PKA signaling in cancer, including frequent oncogenic mutations in GNAS (O’Hayre et al., 2013) across multiple tumor types. Related studies have delineated the essential role of PKA as its effector (Coles et al., 2020; Patra et al., 2018). Here, we define the tissue distribution of genetic alterations in PRKACA and PRKAR1A that result in PKA activation in cancer and map the multiple conserved pathways downstream of oncogenic PKA signaling, many of which impinge on the expression of c-MYC (Figure 7H).

Our proteomic analysis has uncovered both expected and novel effects of PKA in cancer cell lines. We note that these findings may represent both direct and indirect effects of PKA, with PKA effects on the cell cycle and cellular metabolism potentially influencing kinase signaling due to changes in cell state. The analysis of kinase signaling recapitulated findings from previous studies, most notably activation of the AKT and RAS/MAPK pathways (Coles et al., 2020; Turnham et al., 2019; Isobe et al., 2017; Dinh et al., 2020) and inhibitory effects of PKA on various kinases, including STK11 (Collins et al., 2000) and its effectors. We also noted substantial effects on kinases involved in cell migration (e.g. YES, EPHB4, LIMK1, LIMK2, and ROCK1), with the majority being inhibited by PKA. These interesting observations merit further investigation for their mechanistic impact in PKA-associated malignancies. When data were integrated using network propagation, we found two key clusters in PKA-driven signaling, one driven primarily by the RAS/MAPK pathway and the other made up of multiple kinases involved in the G2/M transition, also influencing the stability of MYC-family proteins. These findings are supported by other studies demonstrating PKA effects on GSK3A/B (Fang et al., 2000) and upregulation of AURKA in GNAS (Coles et al., 2020) and DNAJ-PKAc-driven malignancies (Simon et al., 2015). Analysis of global phosphoproteomic data further revealed an activity of PKA toward mRNA translation, also seen in the upregulation of mTORC1 targets in our transcriptional analysis (Figures 3G and 4B). We found few reports connecting PKA to translation in mammalian cells other than our own previous work showing direct phosphorylation of eIF4B by PKAc (Coles et al., 2020), but multiple studies in yeast have demonstrated PKA effects on translation (Leipheimer et al., 2019), with reports from both yeast and plants that PKA impacts eIF4A (Bush et al., 2016).

A major objective of this study was to identify targetable signaling mechanisms downstream of PKA in FLC. This is particularly critical given that directly targeting PKA appears unlikely to be clinically possible due to the critical physiological functions of PKA. We found that upregulation of c-MYC, and to a lesser extent n-MYC, is an effect of PKA signaling. siRNA-mediated MYC knockdown decreased proliferation in FLC, and to a small extent in the melanoma line Colo741. Of note, while FLCs rarely harbors additional oncogenic mutations (Cornella et al., 2015), Colo741 has an activating BRAF mutation (Ghandi et al., 2019) which may maintain its proliferation even when c-MYC expression is blocked. The overlapping results from these genetically distinct cell lines suggest that PKA specifically exerts an influence on c-MYC in carcinogenesis. That premise is supported by our finding of upregulated MYC target gene expression in PKA-activated adrenal and ovarian cancers in the TCGA. When transcriptional activation of c-MYC drives oncogenesis, it is often considered to be ‘undruggable’ (Dang et al., 2017). We hypothesized, however, when that c-MYC is induced by an oncogenic kinase that disrupting the upstream signaling to c-MYC could in turn block its effects.

Our small molecule and siRNA kinome screens identified AURKA, GSK3A/B, and PIM1/2 as potential regulators of c-MYC levels, given prior publications connecting them to MYC stability. We present data that PKA stimulation can result in inhibition of GSK3B, and others have shown PKA phosphorylation of AURKA (Walter et al., 2000), although effects in FLX1 are not fully clear. We did not identify an effect of PKA on PIM kinases. While inhibition of AURKA and PIM kinases somewhat reduced c-MYC in our cell models, they had minor effects on cell proliferation and were variable between the two lines tested. Similarly, no significant phosphorylation changes were seen in sites that regulate MYC degradation by proteomics, and proteasome inhibition did not abrogate the effects of PKA on c-MYC in FLC. Finally, PKA could increase c-MYC levels without altering its stability.

These observations, coupled with findings that PKA stimulation increases eIF4B phosphorylation, suggested that PKA effects on translation initiation might be responsible for its induction of c-MYC expression. Consistent with this, inhibition of eIF4A with the natural product rocaglamide, or its clinically used derivative zotatifin, significantly reduced c-MYC protein levels and potently inhibited proliferation of our cell models. These effects were confirmed to be at least partially dependent on PKA and c-MYC expression.

Our study has several key limitations. While a valuable feature of our genetic approach to modulate PKA signaling is the lack of off-target effects seen with commonly used PKA-modulating tool compounds, PKA signaling is well known to be precisely spatiotemporally regulated (Bauman et al., 2006; Coghlan et al., 1995) and our overexpression systems do not allow compartmentalized control of PKA signaling. We also note that while our FLX1 cells provide unique insight into the biology of FLC, they have significant limitations. Their long doubling time posed significant challenges in generating stably engineered cell lines, particularly with knockdown of growth mechanisms. Similarly, the FLX1 has proven more resistant to siRNA than our other exemplar line, Colo741 (Figure 6D), particularly when treated with individual rather than pooled siRNAs. Furthermore, our FLC clinical samples and the FLX1 cell line expressed both c-MYC and n-MYC. However, expression of n-MYC was quite low in FLX1, and it was not possible to clearly assess its regulation and contribution to FLC growth. Thus, the development of more precisely engineered PKA-driven cancer cell models is essential to provide genetic validation for the mechanisms that we have outlined using signaling and small molecule inhibitors, with more FLC cell models specifically also needed to confirm dependency on MYC proteins for proliferation. Such models would also enable detailed characterization of the biochemical methods by which PKA can influence mRNA translation.

This manuscript reports a network map of signaling downstream of oncogenic PKA. We use functional studies to prioritize signaling mediators for their effect on cell growth in PKA-driven cancers, with a focus on FLC models. While our focus in this study has been on FLC, the systems-level mapping of PKA effects in cancer may have distinct implications for other tumor types. This may include a more significant role for PKA effects on c-MYC stability, including via AURKA and PIM2. Whereas FLC has few secondary mutations, the common co-occurrence of PKA-activating mutations with those impacting RAS/MAPK signaling suggests that PKA effects on other targets such as the SIK kinases (Patra et al., 2018) may also have a more important role in other cancers. Similarly, PKA activation in APC-mutant colorectal cancer could exert important effects on CTNNB1 via inhibition of GSK3. Finally, given PKA’s role in metabolism, its analysis in patient-derived tissues may yield additional nuance. In FLC, our results identify zotatifin as a potential mechanism-driven therapy for FLC and other PKA-driven cancers but require in vivo validation in multiple models to confirm their relevance. With more study, it may be possible to provide proof of concept that targeting MYC by inhibiting its translation is a potential treatment for patients with FLC or other PKA-driven cancers, for whom few options currently exist.

Materials and methods

Cell culture reagents and treatment

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Human bladder 639V cells (DSMZ #ACC 413), human skin Colo741 cells (ECACC 93052621), and human thyroid ML1 (DSMZ #ACC 464) cells were maintained in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum (FBS), penicillin (100 U/ml), and streptomycin (100 U/ml). The murine hepatocyte AML12 wild type (WT) and AML12DNAJ-PKAc cell lines were developed as described previously by the Scott lab (Turnham et al., 2019). These cells were maintained in 50:50 DMEM/Nutrient Mixture F-12 (F-12) supplemented with 10% FBS, 0.1× ITS liquid media supplement, dexamethasone (0.1 μM), and gentamicin (50 μg/ml). FLX1 cells were derived in the Bardeesy lab from a human FLC tumor and xenografted to mice through dispersal and direct plating onto cell culture and maintained in RPMI with 50 ng/ml HGF(hepatocyte growth factor), 10% FBS, penicillin (100 U/ml), and streptomycin (100 U/ml). All cells were cultured in a 37°C incubator with 5% CO2. Cells were tested for mycoplasma contamination routinely. Recombinant HGF was obtained from PeproTech; dexamethasone, FSK, gentamicin, IBMX, and 100× ITS liquid media supplement from Millipore Sigma; CD532, DMEM, DMEM/F-12, RPMI, FBS, Lipofectamine RNAiMAX Reagent, Opti-MEM, and penicillin-streptomycin from Thermo Fisher Scientific; Zotatifin from MedChemExpress; Rocaglamide, MLN8237, CX-6258, and kinase inhibitor library (L1200) from Selleckchem. The human protein kinase siGENOME siRNA library was obtained from GE Dharmacon. FuGENE 6 transfection reagent and CellTiter-Glo assay system were obtained from Promega. siGENOME single and SMARTpool siRNA targeting NTC, MYC, and PRKACA were purchased from Dharmacon.

For individual experiments, cells were seeded at 200,000 cells in 6 cm dishes overnight before treatment, except FLX1 cells, which grew for two nights. For drug treatment, a final concentration of 50 μM IBMX, 50 μM FSK, and 1 μM of the indicated drug were added to the cells in this order for the desired time periods and harvested, with the exception of zotatifin and rocaglamide, which were used at several doses. For siRNA treatment, 12 μl of 20 μM siRNA was added to the cells with Lipofectamine RNAiMAX reagent in Opti-MEM, incubated for 72 hr, and harvested. For CHX or MG132, a final concentration of 10 μg/μl and 20 μM, respectively, was added for the indicated time.

DNA transfections and lentivirus production

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Plasmids containing PRKACA, PRKAR1A, and MYC were obtained from the Human ORFeome v8.1 Collection (courtesy of Sourav Bandyopadhyay, UCSF or DNASU) and cloned into a gateway compatible version of pLVX-Tet-One (puro) with 3xFLAG tag at the N terminal (for PRKAR1A and MYC) or C terminal (for PRKACA). The PRKAR1AG325D single point mutation was introduced using standard PCR site-directed mutagenesis. The final plasmids were packaged in HEK 293T cells for 72 hr to produce lentivirus, which were used to establish cell lines with each respective transgene.

SDS-PAGE and immunoblotting

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Cells were harvested by scraping in chilled PBS and lysed in RIPA buffer with protease and protein phosphatase inhibitors. Protein concentration of cleared lysate was determined by BCA protein assay (Pierce). Lysates were separated in 4–12% NuPAGE gradient gels (Thermo Fisher), transferred to nitrocellulose membrane and blocked with 5% milk in TBST using standard technique. Blocked membranes were immunoblotted with antibodies against the following targets separately: Phospho-PKA substrate (CST#9624), c-MYC (CST#18583), n-MYC (CST#84406), AURKA pThr288 (CST#3079), eIF4B pSer422 (CST#3591), eIF4B (CST#13088), GSK3B pSer9 (CST#9336), GSK3B (CST#12456), MAPK1/3 pThr202/Thr204 (CST#4370), MAPK1/3 (CST#4695), PKAC-α (CST#4782), PKAR1a (CST#5675), FLAG (Sigma#F1804), Actin (CST#3700), Vinculin (CST#13901), or COXIV (CST#5247). Afterward, blotted membranes were washed in TBST, incubated with appropriate HRP(Horseradish peroxidase)-labeled secondary antibodies (CST#7074, 7076), probed with ECL reagents (Thermo Fisher), and developed by x-ray. Blots were washed and stripped with Restore Plus stripping buffer (Thermo Fisher) if multiple probes were required. At least two distinct biological replicates were performed for any western blot analysis.

Li-Cor western blot analysis

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Cells were seeded, treated, harvested, separated by SDS-PAGE, and immunoblotted as described above, except Li-Cor specific secondary antibodies (CAT#926–32210, 926–32211) were used. The image was taken and quantified with the Li-Cor odyssey imaging system, and the half-life values were calculated using Prism.

RNA sequencing

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Total RNA was isolated using the Total Purification kit (Norgen Biotek, Thorold, ON, Canada). High capacity RNA to cDNA kit (Life Technologies, Grand Island, NY, USA) was used for reverse transcription of RNA. Libraries were generated by the Cornell Transcriptional Regulation and Expression (TREx) facility using the NEBNext Ultra II Directional Library Prep Kit (New England Biolabs, Ipswich, MA, USA) and subjected to paired-end sequencing on the NextSeq500 platform (Illumina) at the Genomics Facility in the Cornell University Biotechnology Resource Center. At least 80 M reads per sample were acquired. Reads were aligned to the human genome (build hg38) using STAR (Dobin et al., 2013) (v2.5.3a) for identification and quality control. Salmon (Patro et al., 2017) (v0.06.0) was used for transcript quantification with annotations from GENCODE release version 25. Normalization and differential gene expression analysis were carried out using DESeq2 (Love et al., 2014). Each of the samples had at least 25 million uniquely mapped reads and greater than 90% unique-mapping rate.

Gene set enrichment analysis

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GSEA for TCGA data was performed using the TCGA adrenocortical carcinoma (TCGA-ACC) and serous ovarian carcinoma (TCGA-OV) datasets, for which we obtained tumor somatic mutation and RNASEQ gene level read counts (normalized using the FPKM-UQ method) from the Genomic Data Commons Data Portal. There were 79 TCGA-ACC cases and 378 TCGA-OV cases for which RNASEQ data were available. For these cases, we ran GSEA (Subramanian et al., 2005) using default parameters and compared cases harboring PKAc amplifications/activating mutations to those without these alterations. For RNASeq data and proteomics data, we used the Enrichr web analysis tool (Kuleshov et al., 2016) to assess Hallmark Gene Sets (analysis performed 7/2022).

Quantitative RT-PCR

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Colo741 and FLX1 cells were seeded and treated in the same manner as described for immunoblotting in preparation for siRNA treatments. RNA was extracted with Trizol reagent (Thermo Fisher) according to the manufacturer’s instructions and quantified with a NanoDrop instrument. Normalized RNA was reverse transcribed with SuperScript II Reverse Transcriptase (Invitrogen). cDNAs were added to PerfeCTa SYBR Green FastMix Reaction Mixes (QuantaBio) and respective primers and analyzed using the BioRad CFX Connect Real-Time PCR Detection System. Primers were designed with Primer3 and obtained from Elim Biopharmaceuticals (Supplementary file 5). Quality control was performed for each primer using amplification and melting curves. All experiments were done in at least biological duplicate with three technical replicates per condition. If only one technical replicate did not show an appropriate amplification or melting curve, it was excluded from analyses.

Cell viability assays

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Cells were seeded into 96 well white opaque plates (Greiner) at 2000 cells per well and incubated at 37°C and 5% CO2 overnight. Cells were treated with selected drugs at different final concentrations and incubated for another 72 hr except for the initial studies of Aurora Kinase inhibitors in FLX1, where incubation was 120 hr. After incubation, plates and CellTiter-Glo (CTG, Promega) reagent were allowed to equilibrate at room temperature on the bench for 30 min. The CTG assay was performed following the manufacturer’s instructions and measured with a SpectraMax i3 Multi-Mode Platform (Molecular Devices). All experiments were done in at least biological duplicate with three technical replicates per condition. When multiple individual siRNA were used, the results are shown averaged in a standard dose response curve. In addition, AUC is calculated for each siRNA using GraphPad Prizm and shown as a separate point.

Cell proliferation assays

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For experiments with engineered FLX1 cells, 5333 cells of each line were seeded into black clear bottom 96 well plates (Corning) in 100 μl of media with or without dox (1 μg/ml). After seeding, plates were immediately incubated at 37°C and 5% CO2 inside the Incucyte Zoom system (Essen BioScience) for live cell image and confluence analysis. For experiments with parental cells and siRNA, Colo741 and FLX1 cells were plated and treated with siRNA as described above. Cells were trypsinized after 24 hr of siRNA treatment and transferred to a black clear bottom 96 well plate at 500 cells per well. All experiments were done in at least biological duplicate with a minimum of three technical replicates per condition. The plates were allowed to incubate at 37°C and 5% CO2 for 24 or 36 hr and moved to the Incucyte for further incubation. Once the plates were mounted inside the Incucyte system, pictures of each well were taken every 2 hr for confluence analysis.

Kinase inhibitor library screening

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FLX1 cells were seeded at 600 cells per well in 40 μl in 384 well plates. 5 μl of 10 μg/μl dox were added 24 hr after plating, and kinases inhibitors were added 48 hr after plating for a final concentration of 2 μM or 5 μM in total volume of 50 μl as listed. 5 d post inhibitor addition, cell viability of the cells were measured using Cell-Titer Glo as described above. The precise inhibitors screened are listed in Supplementary file 5 and Supplementary file 6; they were purchased in library format from SelleckChem in 2017. For the data where FLX1 was exposed to the entire library without any genetic modification, statistical analysis was performed by developing a z-score within each screened plate and then averaging the z-scores across three biological replicates, allowing internal normalization. No samples or data points were excluded. For the data where FLX1 with dox-inducible 3xFLAG-PRKAR1AG325D was screened with the drug library ± dox treatment, a log(2)fold change vs. median was derived for each plate. This avoids amplifications in small differences in cell viability that may occur with a z-score. The normalized values ± dox were subtracted to generate an average log(2)fold change, and Student’s t-test performed to determine statistical significance; given the relatively small number of compounds screened, no false discovery correction was used.

siRNA kinase library screening

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384 well plates containing the human protein kinase siGENOME siRNA library (Dharmacon Cat#G-003505) were thawed at room temperature and centrifuged at 1000 rpm for 5 min prior to foil removal. 50 µl of nuclease-free dH2O was added to each well to reconstitute the siRNA at a final concentration 5 µM. Using a Labcyte Echo 525 liquid handling machine, 200 nl of reconstituted siRNA from each well from the master plates was transferred to the same position of the corresponding black transparent bottom 384 well daughter plates (Thermo Fisher). Unused aliquoted plates were sealed with foil, covered with plastic lid, and stored at –80°C. For subsequent experiments, daughter plates with deposited siRNA were thawed at room temperature and centrifuged. 5 µl of nuclease-free dH2O was added to each well and agitated at room temperature for 30 min. 10 µl of a mixture of RNAiMAX and Opti-MEM was then added to each well and incubated at RT for 20 min. Finally, 500 FLX1 cells in 30 μl media were added to each well. The plates were transferred to the Incucyte for cell proliferation monitoring. Statistical analysis was performed by developing a z-score within each screened plate and then averaging the z-scores across four biological replicates, allowing internal normalization. No samples or data points were excluded.

TCGA analysis

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TCGA PanCancer Project data between 3/13/18 and 4/23/18 were accessed through cBioPortal (at https://www.cbioportal.org) and queried by gene (e.g. PRKACA and PRKAR1A). Data were sorted through Cancer Types Summary function and exported to Microsoft Excel and Prism for reorganization and analysis.

Phosphoproteomics

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Engineered cell lines with dox-controlled 3xFLAG-PRKACA or PRKAR1AG325D constructs were treated with PBS or dox for 48 hr. Cells were then harvested in PBS, lysed in lysis buffer (8 μM urea, 50 mM Tris pH 8, 75 mM NaCl, and 1× protease and phosphatase inhibitors) and sonicated at 20% for 15 s. BCA protein assay was performed to quantify protein lysates. Samples were reduced with 5 mM dithiothreitol (DTT), cooled to room temperature, alkylated with 15 mM iodoacetamide, quenched with 15 mM DTT, diluted with 50 mM Tris pH 8 to <2 M urea, and subjected to trypsin digestion at 37°C overnight. Samples were acidified with 10% trifluoroacetic acid (TFA).

50 mg Seppak cartridges were set up on vacuum, and columns were washed with series of MS-grade acetonitrile (ACN), 70% ACN/0.25% acetic acid (AA), and 0.1% TFA buffers. After letting samples drip through columns, columns were washed with 0.1% TFA and 0.5% AA. Samples were eluted and lyophilized in a speed vacuum concentrator, and phosphopeptide enrichment was performed with immobilized metal affinity chromatography following established protocols (Budzik et al., 2020). Phosphopeptides were eluted in 50% ACN/0.1% formic acid (FA) and dried on a speed vacuum concentrator. Enriched samples were analyzed on a Q Exactive Orbitrap Plus mass spectrometry system (Thermo Fisher Scientific) with an Easy nLC 1200 ultra-high pressure liquid chromatography system (Thermo Fisher Scientific) interfaced via a Nanospray Flex nanoelectrospray source. Samples were injected on a C18 reverse phase column (25 cm × 75 μM packed with ReprosilPur C18 AQ 1.9 μM particles). Mobile phase A consisted of 0.1% FA and mobile phase B consisted of 80% ACN/0.1% FA. Peptides were separated by an organic gradient from 2 to 18% mobile phase B over 94 min followed by an increase to 34% B over 40 min, then held at 90% B for 6 min at a flow rate of 300 nl/min. MS1 data was acquired with a 3e6 AGC target, maximum injection time of 100 ms, and 70 K resolution. MS2 data was acquired for the 15 most abundant precursors using automatic dynamic exclusion, a normalized collision energy of 27, 1e5 AGC, a maximum injection time of 120 ms, and a 17.5 K resolution. All mass spectrometry was performed at the Thermo Fisher Scientific Proteomics Facility for Disease Target Discovery at UCSF and the J. David Gladstone Institutes.

Mass spectrometry data were assigned to human sequences, and peptide identification and label-free quantification were performed with MaxQuant (version 1.5.5.1) (Tyanova et al., 2016). Data were searched against the UniProt human protein database (downloaded 2017). Trypsin/P was selected allowing up to two missed cleavages. Standard quality control with variable modification was allowed for methionine oxidation, N-terminal protein acetylation, and phosphorylation of serine, threonine, and tyrosine, in addition to a fixed modification for carbamidomethyl cysteine. The other MaxQuant settings were left as default. Statistical analysis was performed using R (version 3.6.3), RStudio, and the MSstats Bioconductor package (Choi et al., 2014). These are broadly accepted statistical methods for mass spectrometry. Contaminants and decoy hits were removed, and samples were normalized across fractions by equalizing the median log2-transformed MS1 intensity distributions. Log2(fold change) for protein phosphorylation sites were calculated, along with p values. Phosphoproteomic data was uploaded to the PhosFate profiler tool (Ochoa et al., 2016; http://phosfate.com/) to infer kinase activity. Mass spectrometry RAW mass spectrum files are deposited into ProteomeXchange via PRIDE with the dataset identifier PXD025508.

Multiplex inhibitor beads

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MIBs were performed as described previously (Donnella et al., 2018; Sos et al., 2014). Kinase inhibitor compounds were purchased or synthesized and coupled to sepharose beads using 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide chemistry. Engineered cell lines with dox-controlled 3xFLAG-PRKACA or PRKAR1AG325D constructs were treated with PBS or dox for 48 hr then collected in PBS. Samples were lysed in 150 mM NaCl buffer with protease and phosphatase inhibitors. Lysates were diluted with 5 M NaCl and high-salt binding buffer (50 mM Hepes pH 7.5, 1 M NaCl, 0.5% Triton X-100, 1 mM EDTA, and 1 mM EGTA). Pre-washed columns containing ECH sepharose 4B and EAH sepharose 4B beads were layered with kinase inhibitor-coupled beads as follows: 200 µl JG-4, 100 µl VI-16832, 75 µl staurosporin, 100 µl PP-hydroxyl, 100 µl purvalanol B, 50 µl AKTi-46, 100 µl dasatinib, 50 µl sorafenib, 50 µl crizotinib, 50 µl lapatinib, 50 µl SB202190, and 50 µl bisindolylmaleimide X. Columns were washed with high-salt buffer without disturbing bead layers, and affinity purification was performed with gravity chromatography. Bound kinases were washed with high-salt buffer, low-salt buffer (50 mM Hepes pH 7.5, 150 mM NaCl, 0.5% Triton X-100, 1 mM EDTA, and 1 mM EGTA), and 0.1% (w/v) SDS in high-salt buffer. Samples were eluted twice by capping the column, applying 300 µl of elution buffer (0.5% SDS/1% BME/0.1 M Tris-HCL pH 6.8) to the column, vortexing, heating to 98°C, removing caps, and allowing elution to flow through by gravity. Samples were frozen at –80°C overnight, reduced with 500 mM DTT, cooled to room temperature, and treated with 500 mM iodoacetamide. Methanol/chloroform precipitation, trypsin digestion at 37°C overnight, and desalting were performed on all samples. Enriched samples were analyzed on a Q Exactive Orbitrap Plus mass spectrometry system (Thermo Fisher Scientific) with an Easy nLC 1200 ultra-high pressure liquid chromatography system (Thermo Fisher Scientific) interfaced via a Nanospray Flex nanoelectrospray source as described above for global phosphoproteomics. All mass spectrometry was performed at the Thermo Fisher Scientific Proteomics Facility for Disease Target Discovery at UCSF and the J. David Gladstone Institutes.

Peptides were identified with MaxQuant (version 1.5.5.1). Label-free quantification was performed with Skyline (Schilling et al., 2012), with Trypsin (KR|P) selected. Standard quality control was used, allowing up to two missed cleavages. Full scan MS1 filtering was performed with 70,000 resolving power at 400 m/z using the Orbitrap. Statistical analysis was performed with R, RStudio, and MSstats (Choi et al., 2014) to calculate log2(fold change) and p values of detected kinases. These are broadly accepted statistical methods for mass spectrometry. As above, mass spectrometry RAW mass spectrum files are deposited into ProteomeXchange via PRIDE with the dataset identifier PXD025508.

Proteomics data integration and network propagation

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Initial integration of the proteomics data was performed by identifying all kinases that were present in Phosfate or MIBs data from at least two cell lines. The abundance (MIBs) or imputed activity (Phosfate) was averaged between all cells with inducible PRKACA or PRKAR1AG325D and shown ± SD. For network propagation, the log(2)fold change values of MIBs data and effect size of Phosfate data for each engineered cell line treated with or without dox were separately normalized out of one. The union of these two datasets was generated, and any duplicate genes were averaged. Z-scores were then calculated, and the absolute values of the z-scores for each cell line were separately propagated using a random walk with restart (alpha = 0.2) across the ReactomeFI network using a MATLAB script available on github (Huang et al., 2018). Propagated heat scores for each gene were multiplied across cell lines containing the same construct (either dox-inducible 3xFLAG-PRKACA or PRKAR1AG325D), and significance was calculated based on the probability that propagated heat scores match a permuted value by chance. Significant genes (p value<0.05) brought out by the network were then extracted and imported into Cytoscape (Shannon et al., 2003). To integrate engineered cell lines with Tet-on 3xFLAG-PRKACA or PRKAR1AG325D, overlapping direct kinase neighbors of PRKACA, and their interconnections were extracted. The signs of the averaged z-scores of the Tet-on 3xFLAG-PRKAR1AG325D lines were flipped and averaged with the averaged z-scores of the Tet-on 3xFLAG-PRKACA lines, resulting in a final subnetwork for PKAc. Nodes representing the genes were filled to represent the original z-scores which were averaged across cell lines. Networks were searched on Cytoscape for PKAc and its direct neighbors and any interconnections.

Human FLC samples

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Human FLCs and paired normal livers were obtained from the University of Washington Medical Center and Seattle Children’s Hospital after institutional review board approval (Seattle Children’s Hospital IRB #15277). For prospective fresh tissue collections, informed consent was obtained from the subject and/or parent prior to resection.

Fresh/frozen human FLC and paired non-tumor livers were homogenized in RIPA buffer with protease inhibitors using a hand-held Pro200 homegenizer (ProScientific). Protein concentration of cleared lysate was determined by BCA protein assay (Pierce). Lysate were separated by 10% TGX gels (Biorad), transferred to nitrocellulose membrane, and blocked with 5% milk in TBST using standard technique. Blocked membranes were immunoblotted with antibodies against following targets separately: PKAC-α (CST#4782), c-MYC (CST#18583), n-MYC (CST#84406), or Actin (Sigma#A5441). Afterward, blotted membranes were washed in TBST, incubated with appropriate HRP-labeled secondary antibodies (GE Healthcare Life Sciences), washed as before, and developed using ECL (Thermo Fisher) on an iBright FL1000.

Materials availability

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We will share all renewable reagents including plasmids, cell lines as well as assay methods, and protocols with the scientific community at large upon direct request to our laboratory. These include the FLX1 cell line (available under MTA) and all plasmids reported here.

Data and code availability

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All data generated or analyzed during this study are included in the manuscript and supporting files. Mass spectrometry RAW mass spectrum files have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD025508. Code used for network propagation is available on github as cited in the manuscript where it was initially described (Huang et al., 2018).

Data availability

All data generated or analyzed during this study are included in the manuscript and supporting files. Mass spectrometry RAW mass spectrum files have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD025508. The TCGA Adrenocortical Carcinoma and TCGA Ovarian Serous Cystadenocarcinoma datasets (https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000178.v11.p8) were used.

The following data sets were generated
    1. Gordan
    (2023) PRIDE
    ID PXD025508. Oncogenic PKA signaling stabilizes MYC oncoproteins via an aurora kinase A-dependent mechanism.
    https://www.ebi.ac.uk/pride/archive/projects/PXD025508
The following previously published data sets were used

References

Decision letter

  1. Ivan Topisirovic
    Reviewing Editor; Jewish General Hospital, Canada
  2. Erica A Golemis
    Senior Editor; Fox Chase Cancer Center, United States
  3. Ivan Topisirovic
    Reviewer; Jewish General Hospital, Canada

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Oncogenic PKA signaling stabilizes MYC oncoproteins via an aurora kinase A-dependent mechanism" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, including Ivan Topisirovic as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Erica Golemis as the Senior Editor.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1. Relative lack of data regarding the signaling mechanisms to corroborate the proposed model was found to be a major drawback of this study. In particular, it was thought that additional mechanistic evidence linking oncogenic PKA signaling to PIM activity is warranted. In addition, it should be established whether the activation of AURKA by PKA occurs directly or indirectly (e.g. via the effects of PKA on cell proliferation). Addressing these issues is required to support key conclusions of the article.

2. Evidence for the role of MYC family members as key effectors of PKA signaling in neoplasia was deemed to be insufficient. Additional experiments are required to firmly establish that the levels of MYC family members are indeed regulated via the PKA-AURKA/PIM axis and not secondary to the effects of modulation of PKA signaling on the proliferative state of the cell. In addition, alternative mechanisms that may underpin the effects of PKA on c-MYC and n-MYC protein levels (e.g. alterations in translation of corresponding mRNAs) should be considered. Finally, the mechanism(s) whereby AURKA and PIM regulate MYC family member levels remain largely elusive. Overall, it was thought that additional mechanistic evidence related to MYC regulation in the context of constitutive PKA activation is merited.

3. Some methodological problems were observed. Specifically, several key experiments rely on a single siRNA and/or pharmacological inhibitor. Orthogonal approaches, employing additional siRNAs, appropriate rescues, and/or MYC mutants are advised.

4. The cancer relevance of results obtained in the studies that relied on the ectopic expression of proteins is questionable. These concerns were based on apparent discrepancies between AURKA and PIM2 levels in FLC tumor lesions and the lack of their correlation with c-MYC and n-MYC expression. In addition, observed variability between AURKA and PIM2 expression between tumor and adjacent liver suggest potential additional PKA-dependent mechanisms of MYC regulation. This, in conjunction with the absence of in vivo studies, suggests that the authors should consider toning down claims regarding clinical translatability of their findings.

Reviewer #1 (Recommendations for the authors):

– As authors pointed out, in figure 6B there was a high variability between AURKA and PIM2 expression between tumor and adjacent liver, thus suggesting that additional mechanisms of PKA-dependent MYC stabilization may be in play in vivo. Furthermore, the most of experiments were done in cell lines and thus do not represent what may be transpiring under physiological conditions. To this end, it appears to be warranted that the authors test their model in vivo (e.g. by studying the effects of AURKA inhibitors in PKA-driven cancer xenograft models).

– Mechanistically, it remains largely unclear how is PKA signaling linked to PIM activity. Although it is reasonable that precise mechanistic dissection may be outside of the scope of the present manuscript, some additional mechanistic experiments are warranted to support the correlative data provided by the authors. Moreover, it is not clear why there is a discrepancy in the effects of PIM1 vs. PIM2 in Colo741 vs. FLX cells, and what was the motivation to pursue PKA effects on PIM2 in FLX1 cells, wherein PIM1, but not PIM2 depletion resulted in reduction of c-MYC levels.

– Validation of the phosphoproteome and kinome activity data relies heavily on pharmacological approaches. It was thought that orthogonal genetic approaches (and possibly some longer lasting than using siRNA) are merited to further corroborate the authors model.

– There is appreciable difference between the effects of AURKA inhibitors on MYC between Colo741 and FLX1 cells, whereby in the latter case the effects especially with the MLN compound are not very convincing. The authors should comment on this.

– Experiments employing non-degradable MYC mutants to rescue the effects of DN PRKACA overexpression on e.g. proliferation and clonogenic growth seem to be appropriate to firmly establish the extent to which the effects of PRKACA are mediated via MYC.

– In figure 1D siRNA approach was used for a clonogenic assay. The latter assay takes relatively long time compared to relatively transient effects of siRNA. What was the expression of PKA at the end point of the clonogenic assay? Also, the control for the efficiency of PRKACA siRNA should be included. Similar controls also appear to be missing in figure 1E.

– The authors should consider improving the description of figure 1G, as in the text it is not indicated that expression of DN PRKACA was compared to the overexpression of WT PRKACA in the left panel.

– As far as I know, the origin of Colo741 cell lines is still disputed. The authors should note this in the text.

– Methodology was not described in sufficient detail. More detailed information how phosphoproteome and proteome data were analyzed and integrated would be appreciated. In particular in some parts of the paper it is not clear what statistical analyses were employed. This should be clearly indicated in figure legends and methods section.

– This is a comment that is outside the scope of the article (comes purely from the enthusiasm of the reviewer for this study), but considering initial findings of the authors implicating LKB1 it may be pertinent to test the effects of biguanides and mTOR inhibitors in experiments presented in figure 3A. Perhaps simultaneous use of these compounds with AURKA inhibitors should also be considered.

Reviewer #2 (Recommendations for the authors):

1. Cause or Consequence Issue: One of the main issues is that the regulation of MYC and/or N-MYC may be caused by PKA signaling, as proposed, but may also be an indirect consequence of the growth state of the cell. MYC is highly regulated with a ~30 min half-life that is highly responsive to signaling pathways controlling cell proliferation. To distinguish this 'cause or consequence' issue the authors must measure the growth state of the cell in response to PKA activation/inhibition, by conducting cell cycle profiling for example, and show that the regulation of MYC occurs prior to the effect on cell growth. This will involve kinetic analysis of MYC expression at short intervals following PKA activation/inhibition.

2. PKA regulation of protein translation vs MYC stability per se: Regulation of MYC stability is often stated throughout the manuscript (e.g. line 207; Figure 4), yet MYC half-life experiments were not performed. Is the net result of PKA inactivation a 'change' in MYC half-life or is MYC translation blocked and the decay of existing MYC pools then dissipating with the usual ~30 min half-life? Perhaps the substrates of PKA signaling that contribute to growth occurs at the level of protein translation. What happens to other short half-life proteins in the cell in response to PKA regulation (e.g. MCL-1)? Is this PKA-mediated effect on protein stability MYC specific?

3. MYC phosphorylation and degradation: The authors draw MYC interaction with AURKA and PIM kinases (Figure 5G) suggesting these kinases directly phosphorylate MYC. Further evidence that these kinases regulate MYC directly would add weight to the model proposed. Phosphosite plus lists many sites phosphorylated on MYC and N-MYC, yet I don't think any of these have been ascribed to AURKA and/or PIM. One well-studied phosphosite is Serine 62, which is phosphorylated by mitogen stimulated kinases to promote MYC activity and cell growth. It is also conserved between MYC and N-MYC. Is this the site of phosphorylation by AURKA and/or PIM? Or is this the role of the RAS/MAPK pathway downstream of PKA? Serine 62 is a priming site for GSK-3 to phosphorylate Threonine 58, which then recruits SCF(Fbxw7) to degrade MYC so the Serine 62 site is a high-probability candidate residue for PKA regulation by one or both of these downstream pathways. Interestingly, GSK3 is in the same node as AURKA in the authors analyses (Figure 2E), although α is increased and β decreased. Further interrogation on the site of phosphorylation would add weight to the authors model. Determining the role of the GSK-3-SCF(Fbxw7) in PKA regulation of MYC is important and can be achieved by knocking out Fbxw7 and then determining whether MYC expression continues to be regulated by PKA.

4. Only one siRNA used throughout: it is the standard in the field to evaluate more than on siRNA per gene of interest to ensure effect is specific and not due to off-target effects. For example, see Figure (3D, F, 5B, 5D, etc.)

5. Lines 186-191: Are cells dependent on PKA also dependent on MYC?

What is the dependency score as reported in the Cancer Dependency Map for MYC in the Colo741 cells as they were 'highly dependent on PKA (line 104)' ? Provide relative dependency scores of PKA and MYC in the cell lines used in this study and across all solid cancers.

6. Kinome-wide siRNA library screen: The results of this screen need to be provided as a supplementary table so hit analysis can be reviewed by the reader. Did the expected positive controls come up as hits? For example, the FLX1 cells are grown in HGF, so a positive control hit would be HGFR (Met). Was AURKA a hit?

7. Data interpretation (Figure 5C): In Colo741 cells MLN8237 appears to decrease MYC expression more than the MLN8237 and CS6258 together. It is more like an additive effect, leading to intermediate levels of MYC. C- and N-myc levels are so low in the FLX1 cells it is difficult to really see any significant decrease. Densitometry would help score the response to drug.

8. Data weak (Figure 5D): Decrease of PIM2 is not evident in Colo741 as described in text for Figure 5D. Try stimulating PKA with FSK/IMBX (as in Figure 4) in the presence and absence of siRNA to PRKACA.

9. Use of AML12: This transgenic knockin of DNAJB1-PRKACA in murine hepatocytes may be the 'cleanest' cell system to interrogate the proposed PKA-AURKA/PIM-MYC axis as the only genetic alteration is the transgene (Figure 6A).

10. Figure 6B: Many mechanisms regulate MYC expression. It remains unclear whether the PKA-AURKA/PIM-MYC axis is functional in FLC as described as Figure 6B not convincing.

Reviewer #3 (Recommendations for the authors):

The study by Chan, Gordan et al., utilizes state of the art global kinome profiling to map the shared signaling networks driven by diverse genetic changes resulting in PKA activation in human cancer. Among the many kinases whose activity is modulated by active PKA, they authors centered their study on Aurora Kinase A (AURKA), and its ability to regulate c-MYC and n-MYC protein levels. They propose an AURKA-MYC regulatory network, and a possible positive feedback loop mediated by the kinase PIM2, which can be disrupted by AURKA inhibition. The study has many elements of novelty, which could be of translational importance. The strength of the study is the use of global kinome profiling and the identification of multiple candidate signaling nodes downstream from PKA. The weakness is the limited mechanistic information provided on possible direct regulatory processes intervening in kinase activation, and the need to enhance the rigor of the studies and to provide quantitative analysis of the data to increase the confidence regarding the proposed novel mechanisms at this stage.

What is the mechanism by which PKA activates AURKA? Is AURKA phosphorylated by PKA? AURKA expression levels and enzymatic activity changes occur with the cell cycle, so it is unclear what the direct link between PKA and AURKA is, or if this (and other) effects are due to the impact of PKA on general cell growth or other indirect processes.

Similarly, the proposed link between PKA-AURKA, MYC, and the positive feedback through PIM2 is not mechanistically defined.

Figure 1:

The frequency (not the incidence?) of PRKACA amplification described in the text (0.3-3.2%) does not match with the information in the corresponding figure (1B).

With all the emphasis on the validity of FLX1 cells as a model for PKA-driven FLC, it is not clear why these cell were not used for proteomics analysis (Panel C).

Unless mistaken, Colo741 skin cancer cells have a frameshift mutation in PRKAR1, but the expression levels of this protein did not change (1G, third and fourth lane).

There seems to be little expression of PKA C subunit in ML1 cells after dox induction.

Figure 2:

The kinases whose activity is regulated by PKA C expression may be direct or indirect, and hence caution may need to be taken regarding the direct regulatory role of PKA on these kinases. For example, the authors mention that many of these kinases are involved in G2M, and hence it is possible that PKA C regulates these kinases indirectly through promoting cell cycle progression, among other indirect mechanisms.

In Sup 1, ML1 cells that are not sensitive to PKA inhibition were used, without a clear explanation. That said, cell proliferation inhibition by MEK1/2 and ERK1/2 inhibitors were less sensitive in these cells, and mentioned as probably of no interest, whereas there was a very limited increase in the sensitivity to one AURKAi, CD532, and changes in the responses to another MLN8237, which itself was not very effective, but the latter deemed of importance. This reviewer is unclear about the rationale and biological interpretation of the data. The comment on "older generation" AURKAi (ENMD-2076) affecting PKA C induction appears to be odd (line 172).

Figure 3:

Seems that only CD532 is effective in reducing proliferation of PKA C dependent cells among all AURKAi tested, raising the possibility of an off target effect.

In that venue, changes in gene expression reported, most of which are involved in cell cycle progression, may be due to decrease cell proliferation and not acting directly downstream from PKA C, including c-MYC.

Data on C-MYC siRNA effects (and in fact all siRNA studies, including MYCN) require more than one siRNA and quantitative analysis of the impact on protein expression and changes.

This also applies to cell proliferation. For example, in Colo741 cells there appears to be less than 20% decrease in cell proliferation after MYC siRNA use, which is of questionable biological relevance.

Information of cell groups (grey/red/yellow) in FLX1 experiments is missing and cannot be reviewed.

The text on c-MYC and n-MYC knock down in Colo741 cells seems to be incorrect (line 187) as these cells do not express n-MYC. The impact of n-MYC siRNA on the protein expression of n-MYC (Figure 3D) is not clear, and needs quantification as for most other similar knockdown studies.

Figure 4:

Panel B. As for other similar studies, need to use of more than 1 siRNA for PRKACA to decrease the possibility of off-target effects.

Figure 5.

Panel B. Use more than one siRNA for PIM1 and PIM2. This is emphasized by the fact that PIM2 silencing abrogates the expression of both PIM proteins.

The use of MLN8237 and CX6258 does not seem to provide "cooperating" or even additive effects with respect to c-MYC and n-MYC expression.

The positive feedback loop mediated by PIM2 downstream AURKA and MYC appears to be speculative, and not mechanistically explained, at the protein interaction, activity changes, and/or expression level.

Figure 6:

AURKA and PIM2 levels are not regulated in FLC tumor lesions harboring endogenous PKA C activating protein as proposed by the studies using ectopically expressed proteins, nor do they appear to correlate with c-MYC and n-MYC expression levels. This raises concerns about the overall cancer relevant significance of the proposed regulatory model in this study.

https://doi.org/10.7554/eLife.69521.sa1

Author response

Essential revisions:

1. Relative lack of data regarding the signaling mechanisms to corroborate the proposed model was found to be a major drawback of this study. In particular, it was thought that additional mechanistic evidence linking oncogenic PKA signaling to PIM activity is warranted. In addition, it should be established whether the activation of AURKA by PKA occurs directly or indirectly (e.g. via the effects of PKA on cell proliferation). Addressing these issues is required to support key conclusions of the article.

As described in our summary, we identified altered translation as the likely primary mechanism of PKA-induced increases in c-MYC during the revision process. As a result, we have shifted the focus of this manuscript away from the AURKA/PIM signaling axis towards effects on protein translation. However, we also include new data showing that PKA-induced increases in c-MYC protein level occur in cells that are synchronized into G2/M with nocodazole (Figure 5—figure supplement 1B), mitigating the concern that alterations in cell cycle distribution underly PKA-induced c-MYC changes.

We agree that the mechanistic connection between PKA and PIM is not well substantiated. Unfortunately, there are only limited reagents available to study PIM kinases directly, and their substrates are poorly defined and/or shared with many other kinases. While our revision includes our data showing PIM2 inhibitor effects on c-MYC protein levels, we have removed the data connecting PIM2 to PKA activity and do not make any claims that it is activated by PKA.

2. Evidence for the role of MYC family members as key effectors of PKA signaling in neoplasia was deemed to be insufficient. Additional experiments are required to firmly establish that the levels of MYC family members are indeed regulated via the PKA-AURKA/PIM axis and not secondary to the effects of modulation of PKA signaling on the proliferative state of the cell. In addition, alternative mechanisms that may underpin the effects of PKA on c-MYC and n-MYC protein levels (e.g. alterations in translation of corresponding mRNAs) should be considered. Finally, the mechanism(s) whereby AURKA and PIM regulate MYC family member levels remain largely elusive. Overall, it was thought that additional mechanistic evidence related to MYC regulation in the context of constitutive PKA activation is merited.

We appreciate this insight. In addressing these concerns, we find that while AURKA and PIM2 do regulate MYC levels in PKA-driven cancers, the key influence is in fact at the level of translation as proposed by the reviewers. Substantial new data have been added to support this observation, with additional investigation of cell cycle dependence on PKA effects on MYC (Figure 4—figure supplement 1B and Author response image 1) and PKA effects on translation and MYC, and the use of relevant mutant constructs (Figure 6-7; Figure 5—figure supplement 1; Figure 7—figure supplement 1). We further provide data from TCGA that MYC transcriptional targets are upregulated in the presence of PKA-activating mutations, unlikely to be solely due to cell cycle effects. Specific results are detailed in the response to reviewers.

Author response image 1
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PKA effects on cell cycle distribution in FLX1.

(A) FLX1 parental cells were treated with DMSO or 50 μM FSK/IBMX for 4 hours, with BrdU added for the last 20 minutes. Cells were then stained for nuclear content with 7-AAD and active DNA synthesis with anti-BrdU antibody, then acquired by FACS. Average of 3 samples is shown. Statistically significant increase in S-phase% with FSK/IBMX was noted by one-tailed Student’s t-Test (p = 0.002). (B) FLX1 cells with dox-inducible 3xFLAG-PKAR1AG325D were treated with ±dox for 48 hours and then analyzed as above. Statistically significant decrease in S-phase% with dox was noted by one-tailed Student’s t-Test (p = 0.03). We note the markedly low S-phase percentage shown here. FLX1 cells have a >72 hour doubling time, and the same labeling/staining conditions show significantly higher S-phase% in many other tumor cell lines in our hands.

3. Some methodological problems were observed. Specifically, several key experiments rely on a single siRNA and/or pharmacological inhibitor. Orthogonal approaches, employing additional siRNAs, appropriate rescues, and/or MYC mutants are advised.

As recommended, we have added individual siRNA analysis, additional compounds and the use of MYC mutants throughout the manuscript.

4. The cancer relevance of results obtained in the studies that relied on the ectopic expression of proteins is questionable. These concerns were based on apparent discrepancies between AURKA and PIM2 levels in FLC tumor lesions and the lack of their correlation with c-MYC and n-MYC expression. In addition, observed variability between AURKA and PIM2 expression between tumor and adjacent liver suggest potential additional PKA-dependent mechanisms of MYC regulation. This, in conjunction with the absence of in vivo studies, suggests that the authors should consider toning down claims regarding clinical translatability of their findings.

This point is well taken. The objective of this manuscript is to map the multiple signaling mechanisms downstream of oncogenic PKA, and we acknowledge that further clinical validation is required. We have altered the text of the discussion to further emphasize this point. In addition, the manuscript has been reorganized and data regarding AURKA and PIM2 in tumor specimens was removed. We have also strengthened the data in support of the relevance of PKA activation to MYC transcriptional activity in patient genomic datasets (Figure 3F).

Reviewer #1 (Recommendations for the authors):

– As authors pointed out, in figure 6B there was a high variability between AURKA and PIM2 expression between tumor and adjacent liver, thus suggesting that additional mechanisms of PKA-dependent MYC stabilization may be in play in vivo. Furthermore, the most of experiments were done in cell lines and thus do not represent what may be transpiring under physiological conditions. To this end, it appears to be warranted that the authors test their model in vivo (e.g. by studying the effects of AURKA inhibitors in PKA-driven cancer xenograft models).

We fully acknowledge this point. We have restructured the manuscript so that the primary in vivo data relate to the expression and transcriptional activity of MYC proteins in PKA-driven cancers (Figure 3F-G). The ensuing in vitro analysis delineates several mechanisms by which PKA can regulate c-MYC and posits that translational regulation is the most important. With the scope of this additional work, and relative difficulties in obtaining clinically relevant model systems and compounds, we feel that additional therapeutic studies to are outside of the scope of this manuscript. We state these as key next steps and plan to undertake them in future work.

– Mechanistically, it remains largely unclear how is PKA signaling linked to PIM activity. Although it is reasonable that precise mechanistic dissection may be outside of the scope of the present manuscript, some additional mechanistic experiments are warranted to support the correlative data provided by the authors. Moreover, it is not clear why there is a discrepancy in the effects of PIM1 vs. PIM2 in Colo741 vs. FLX cells, and what was the motivation to pursue PKA effects on PIM2 in FLX1 cells, wherein PIM1, but not PIM2 depletion resulted in reduction of c-MYC levels.

We acknowledge the reviewer’s point. Unfortunately, the limited reagents and knowledge of PIM-specific functions has proven a challenge in our analysis of the potential connection between PKA, MYC and PIM. Accordingly, we have de-emphasized these data in the manuscript in favor of more expansive work focusing on the eIF4F complex. We have also removed several of these figures as we do not believe that they contribute significantly to the story and may distract from key points.

– Validation of the phosphoproteome and kinome activity data relies heavily on pharmacological approaches. It was thought that orthogonal genetic approaches (and possibly some longer lasting than using siRNA) are merited to further corroborate the authors model.

Additional direct validation of our proteomics results have been added, both with signaling analysis (Figure 2F) and through an integrated pharmacological/genetic analysis in our FLC cell model (Figure 4B). We note some variability in signaling between the cell lines studied in 2F, but this is anticipated given their distinct genetic backgrounds. The PKA inhibiting tool compound H89 was also used. We agree that longer lasting genetic strategies (e.g. CRISPRi) would strengthen the manuscript. This was vigorously attempted, but unsuccessful. Unfortunately, the FLX1 cell model was not tolerant of chronic knockdown of the targets under study. We note this as a specific limitation of the study in our discussion

– There is appreciable difference between the effects of AURKA inhibitors on MYC between Colo741 and FLX1 cells, whereby in the latter case the effects especially with the MLN compound are not very convincing. The authors should comment on this.

Additional comments have been added to the results and discussion reflecting this point.

– Experiments employing non-degradable MYC mutants to rescue the effects of DN PRKACA overexpression on e.g. proliferation and clonogenic growth seem to be appropriate to firmly establish the extent to which the effects of PRKACA are mediated via MYC.

The use of genetic models of MYC stabilization, either with constructs lacking a 5’UTR or with the T58A mutation have been added. Constructs are used in Figure 7 and Figure 7—figure supplement 1A-B to confirm that the effects of PKAc knockdown and eIF4A inhibition are abrogated in when MYC lacks a 5’ untranslated region (UTR). Given our new findings, non-degradable MYC alleles such as T58A are no longer as relevant to the focus of the story but is included in an additional figure for reviewers’ interest (Author response image 2). An experiment combining PKA inhibition and an insensitive variant of c-MYC (lacking a 5’UTR) is included (Figure 7—figure supplement 1A) using pooled siRNA in place of DN-PKA.

Author response image 2
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FLX1 and Colo741 with dox-induced 3xFLAG-c-MYC lacking a 5’UTR and including the stabilizing T58A mutation.

Dox treatment was 1 μG/mL for 40 hours prior to harvest with zotatifin or DMSO for 24 hours. Of note, the MYCT58A allele was toxic in both cells but particularly in FLX1, resulting in reduced expression of vinculin and other housekeeping proteins.

– In figure 1D siRNA approach was used for a clonogenic assay. The latter assay takes relatively long time compared to relatively transient effects of siRNA. What was the expression of PKA at the end point of the clonogenic assay? Also, the control for the efficiency of PRKACA siRNA should be included. Similar controls also appear to be missing in figure 1E.

This point is well taken. We have removed the clonogenic assay and added multiple assays of PKA-dependent proliferation in FLX1 with appropriate controls (Figure 3—figure supplement 1A-B).

– The authors should consider improving the description of figure 1G, as in the text it is not indicated that expression of DN PRKACA was compared to the overexpression of WT PRKACA in the left panel.

The description of this figure has been clarified.

– As far as I know, the origin of Colo741 cell lines is still disputed. The authors should note this in the text.

This point is now included in the text (line 108-109).

– Methodology was not described in sufficient detail. More detailed information how phosphoproteome and proteome data were analyzed and integrated would be appreciated. In particular in some parts of the paper it is not clear what statistical analyses were employed. This should be clearly indicated in figure legends and methods section.

An additional section has been added to the methods section to explain our data integration in Figure 2C-D (lines 643-646), and comments added to the figure legends clarifying what statistical tests were used in each case.

– This is a comment that is outside the scope of the article (comes purely from the enthusiasm of the reviewer for this study), but considering initial findings of the authors implicating LKB1 it may be pertinent to test the effects of biguanides and mTOR inhibitors in experiments presented in figure 3A. Perhaps simultaneous use of these compounds with AURKA inhibitors should also be considered.

We appreciate and share the reviewer’s interest in the LKB1 result. This is of particular interest given that the PKA-inhibited SIK kinases are direct LKB1 substrates. Our new data in Figure 5B speak to this question, with reduced sensitivity to PI3K/mTOR inhibition when PKA is inhibited, potentially due to reduced mTOR pathway signaling as a result of LKB1 activation. We look forward to investigating this relationship in future work.

Reviewer #2 (Recommendations for the authors):

1. Cause or Consequence Issue: One of the main issues is that the regulation of MYC and/or N-MYC may be caused by PKA signaling, as proposed, but may also be an indirect consequence of the growth state of the cell. MYC is highly regulated with a ~30 min half-life that is highly responsive to signaling pathways controlling cell proliferation. To distinguish this 'cause or consequence' issue the authors must measure the growth state of the cell in response to PKA activation/inhibition, by conducting cell cycle profiling for example, and show that the regulation of MYC occurs prior to the effect on cell growth. This will involve kinetic analysis of MYC expression at short intervals following PKA activation/inhibition.

We are grateful for this insightful recommendation. The kinetic analysis of c-MYC expression is included (Figure 5—figure supplement 2C), and demonstrates no change in c-MYC stability following PKA stimulation. c-MYC levels were largely abolished by PKA inhibition, so its stability could not be tested in that context. In addition to the proliferation data that have been added to this manuscript, we have tested the impact of FSK/IBMX and PRKAR1AG325D induction on cell cycle progression in FLX1. These cells are markedly slow growing, with approximately 1% of cells labeling with BrdU in conditions that label ~20% of cells acquired for another experiment in parallel. While statistically significant changes in % of cells labeling with BrdU were noted, we do not feel that they are adequate to explain the increase in c-MYC protein levels (Author response image 1). This is supported by our testing of c-MYC mRNA (Figure 3B), which shows a doubling in mRNA in FLX1 but not Colo-741, despite both cell lines showing increased protein levels. Similarly, synchronization with nocodazole did not block PKA’s induction of c-MYC expression. We acknowledge that altered growth rates/cell cycle status may contribute to c-MYC increases, but these many lines of evidence support a model where PKA increases c-MYC levels in part via direct effects.

2. PKA regulation of protein translation vs MYC stability per se: Regulation of MYC stability is often stated throughout the manuscript (e.g. line 207; Figure 4), yet MYC half-life experiments were not performed. Is the net result of PKA inactivation a 'change' in MYC half-life or is MYC translation blocked and the decay of existing MYC pools then dissipating with the usual ~30 min half-life? Perhaps the substrates of PKA signaling that contribute to growth occurs at the level of protein translation. What happens to other short half-life proteins in the cell in response to PKA regulation (e.g. MCL-1)? Is this PKA-mediated effect on protein stability MYC specific?

As alluded to above, this valuable recommendation has been acted on. We completed the proposed experiment (Figure 5—figure supplement 2C), showing that MYC stability is in fact not decreased by PKA. This analysis motivated our study of protein translation and the inclusion of multiple new figures connecting PKA and protein translation (Figure 6, 7; Figure 7—figure supplement 1). The text has been modified as well accordingly. Given our finding that c-MYC stability is not altered, we did not assess the stability of other short half-life proteins. However, we do include for the reviewer’s interest evidence that protein levels of another translationally regulated protein (ERBB2, see Gerson-Gewirtz et al., 2021; manuscript reference 57) are also increased by PKAc stimulation and blocked by the eIF4A inhibitor zotatifin (Author response image 3).

Author response image 3
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FSK/IBMX and zotatifin effects on other translationally regulated transcripts.

(A) Time course of FSK/IBMX in Colo741 and FLX1 showing increased expression of ERBB2; kinetics are somewhat different between the two cell lines. (B) Effect of 24 hour treatment with 100 nM zotatifin on ERBB2 protein levels performed on same samples as shown in Figure 7C.

3. MYC phosphorylation and degradation: The authors draw MYC interaction with AURKA and PIM kinases (Figure 5G) suggesting these kinases directly phosphorylate MYC. Further evidence that these kinases regulate MYC directly would add weight to the model proposed. Phosphosite plus lists many sites phosphorylated on MYC and N-MYC, yet I don't think any of these have been ascribed to AURKA and/or PIM. One well-studied phosphosite is Serine 62, which is phosphorylated by mitogen stimulated kinases to promote MYC activity and cell growth. It is also conserved between MYC and N-MYC. Is this the site of phosphorylation by AURKA and/or PIM? Or is this the role of the RAS/MAPK pathway downstream of PKA? Serine 62 is a priming site for GSK-3 to phosphorylate Threonine 58, which then recruits SCF(Fbxw7) to degrade MYC so the Serine 62 site is a high-probability candidate residue for PKA regulation by one or both of these downstream pathways. Interestingly, GSK3 is in the same node as AURKA in the authors analyses (Figure 2E), although α is increased and β decreased. Further interrogation on the site of phosphorylation would add weight to the authors model. Determining the role of the GSK-3-SCF(Fbxw7) in PKA regulation of MYC is important and can be achieved by knocking out Fbxw7 and then determining whether MYC expression continues to be regulated by PKA.

We appreciate this insightful point as well. As our pursuit of this reviewers’ recommendation 2 moved the manuscript away from a focus on MYC degradation, we did not pursue detailed mechanistic analysis to link activated kinases to specific c-MYC phosphosites. We do note that there were no significant alterations in phosphorylation of any MYC phosphosites in our proteomic data sets. In addition, further experiments studying PKA effects on AURKA and GSK3, as well as GSK3 effects on MYC expression (Figure 5—figure supplement 1C) and the impact of proteosome inhibition were included (Figure 5—figure supplement 2). Mutant forms of MYC lacking a 5’UTR are studied in Figure 7 and Figure 7—figure supplement 1. The T58A allele is not studied in the main text but is included for the reviewer’s interest (Author response image 2).

4. Only one siRNA used throughout: it is the standard in the field to evaluate more than on siRNA per gene of interest to ensure effect is specific and not due to off-target effects. For example, see Figure (3D, F, 5B, 5D, etc.)

We apologize for the lack of clarity in our prior submission – pooled siRNA were used in all cases. In response to the reviewers’ concerns, single siRNAs have been substituted for mRNA expression analysis and proliferation analysis as advised. Pooled siRNAs are still used for some protein experiments as the knockdown achieved with single siRNA was often limited in larger scale cellular assays. We have complemented the siRNA experiments with additional compounds to overcome this limitation.

5. Lines 186-191: Are cells dependent on PKA also dependent on MYC?

What is the dependency score as reported in the Cancer Dependency Map for MYC in the Colo741 cells as they were 'highly dependent on PKA (line 104)' ? Provide relative dependency scores of PKA and MYC in the cell lines used in this study and across all solid cancers.

The DepMap reports the following:

Author response table 1
CRISPR
PRKACAMYC
Average-0.24-1.46
639V-0.14-0.67
Colo741-0.35-1.38
siRNA
PRKACAMYC
Average-0.15-0.48
Colo741-1.38-0.35

Unfortunately, ML-1 was not included in these data sets. We note that MYC is a common essential gene and many cell lines demonstrate dependency on MYC when tested by CRISPR. Colo741 is one of the most PKA dependent cells in both the CRISPR and siRNA datasets. We have amended the text to make this point more completely.

6. Kinome-wide siRNA library screen: The results of this screen need to be provided as a supplementary table so hit analysis can be reviewed by the reader. Did the expected positive controls come up as hits? For example, the FLX1 cells are grown in HGF, so a positive control hit would be HGFR (Met). Was AURKA a hit?

The results of this screen are included as a data supplement. AURKA in fact was not a hit, a point that we have highlighted in the text. PRKACA serves as a positive control and is labeled in the figure. We note that common essential genes WEE1 and PLK1 each have a z-score of -1, slightly greater that PRKACA but less than other genes highlighted in the figure. MET has a z-score of -0.62.

7. Data interpretation (Figure 5C): In Colo741 cells MLN8237 appears to decrease MYC expression more than the MLN8237 and CS6258 together. It is more like an additive effect, leading to intermediate levels of MYC. C- and N-myc levels are so low in the FLX1 cells it is difficult to really see any significant decrease. Densitometry would help score the response to drug.

We agree with this point and have altered the language around how these drugs may cooperate to reflect it (lines 255-258 and 351-353). We did not include densitometry as PIM/AURKA co-targeting is not a major conclusion of the revised study. Further, the poor detection of endogenous c-MYC with the available antibodies limit their measurement with the low-sensitivity systems used for quantification. We also acknowledge the low levels of n-MYC expression and have focused the manuscript on c-MYC given its higher level of expression and clearer role.

8. Data weak (Figure 5D): Decrease of PIM2 is not evident in Colo741 as described in text for Figure 5D. Try stimulating PKA with FSK/IMBX (as in Figure 4) in the presence and absence of siRNA to PRKACA.

This figure was intended to show a relative lack of effect in Colo741, where PIM2 appears less important, but in retrospect may have been confusing. We have removed this figure.

9. Use of AML12: This transgenic knockin of DNAJB1-PRKACA in murine hepatocytes may be the 'cleanest' cell system to interrogate the proposed PKA-AURKA/PIM-MYC axis as the only genetic alteration is the transgene (Figure 6A).

We agree that the AML12 system has many advantages. In response to the reviewer’s suggestion, we examined it further, but found that the long-term presence of the DNAJ-PKAc fusion drove parallel changes in signaling and kinase gene expression (i.e. protein levels were upregulated in parallel to phosphorylation). For this reason, genetic and pharmacological manipulation of human cells proved more illustrative and are shown throughout the manuscript.

10. Figure 6B: Many mechanisms regulate MYC expression. It remains unclear whether the PKA-AURKA/PIM-MYC axis is functional in FLC as described as Figure 6B not convincing.

This figure has been revised to more accurately reflect the balance of effects on c-MYC downstream of PKA.

Reviewer #3 (Recommendations for the authors):

The study by Chan, Gordan et al., utilizes state of the art global kinome profiling to map the shared signaling networks driven by diverse genetic changes resulting in PKA activation in human cancer. Among the many kinases whose activity is modulated by active PKA, they authors centered their study on Aurora Kinase A (AURKA), and its ability to regulate c-MYC and n-MYC protein levels. They propose an AURKA-MYC regulatory network, and a possible positive feedback loop mediated by the kinase PIM2, which can be disrupted by AURKA inhibition. The study has many elements of novelty, which could be of translational importance. The strength of the study is the use of global kinome profiling and the identification of multiple candidate signaling nodes downstream from PKA. The weakness is the limited mechanistic information provided on possible direct regulatory processes intervening in kinase activation, and the need to enhance the rigor of the studies and to provide quantitative analysis of the data to increase the confidence regarding the proposed novel mechanisms at this stage.

What is the mechanism by which PKA activates AURKA? Is AURKA phosphorylated by PKA? AURKA expression levels and enzymatic activity changes occur with the cell cycle, so it is unclear what the direct link between PKA and AURKA is, or if this (and other) effects are due to the impact of PKA on general cell growth or other indirect processes.

Similarly, the proposed link between PKA-AURKA, MYC, and the positive feedback through PIM2 is not mechanistically defined.

We appreciate the reviewer’s positive assessment of the novelty and potential impact of our study. We have added additional data showing phosphorylation changes in proposed PKA targets including GSK3B and AURKA, noting that AURKA T288 has been previously described to be a PKA target (Walter et al., Oncogene 2000). We note that we were unable to consistently detect AURKA in our cells, and was more sensitive to nocodazole synchronization, supporting our investigation of other mechanisms. We further add phosphoproteomic data demonstrating increased phosphorylation of sets of proteins involved in translation initiation and western blot data showing increased phosphorylation of eIF4B at Ser422. These and other data fill in the missing mechanistic links from the initial submission.

Figure 1:

The frequency (not the incidence?) of PRKACA amplification described in the text (0.3-3.2%) does not match with the information in the corresponding figure (1B).

This section was not clearly written and has been corrected. We apologize for any confusion.

With all the emphasis on the validity of FLX1 cells as a model for PKA-driven FLC, it is not clear why these cell were not used for proteomics analysis (Panel C).

Previous work has shown biochemical gain of function in FLX1 cells due to the DNAJ-PKAc fusion, making them different from lines with PRKACA amplification or PRKAR1A inactivation (see Turnham et al., 2019). Furthermore, these cells were not available when our initial data set was generated and have not proven as amenable to engineering. The text has been clarified to explain this.

Unless mistaken, Colo741 skin cancer cells have a frameshift mutation in PRKAR1, but the expression levels of this protein did not change (1G, third and fourth lane).

We are grateful for this sharp observation. There appears to have been an error in the initial western blot, potentially due incomplete stripping of a previously blotted protein. We re-confirmed the identity of the engineered cells using STR analysis and repeated the western blot with the cell stocks frozen at the time of our proteomic analysis. New data are shown.

There seems to be little expression of PKA C subunit in ML1 cells after dox induction.

This may also have been due to an error when the figure was prepared. The repeat blot shown in Figure 1D has a higher level and clearly demonstrates increase phosphorylation of PKA substrates.

Figure 2:

The kinases whose activity is regulated by PKA C expression may be direct or indirect, and hence caution may need to be taken regarding the direct regulatory role of PKA on these kinases. For example, the authors mention that many of these kinases are involved in G2M, and hence it is possible that PKA C regulates these kinases indirectly through promoting cell cycle progression, among other indirect mechanisms.

We appreciate this point and have modified the text to note that both direct and indirect mechanisms are anticipated to be shown in these findings. We also include additional cell cycle analysis in supplementary data and Author response images 2 and 3, supporting a signaling effect of PKA on these targets.

In Sup 1, ML1 cells that are not sensitive to PKA inhibition were used, without a clear explanation. That said, cell proliferation inhibition by MEK1/2 and ERK1/2 inhibitors were less sensitive in these cells, and mentioned as probably of no interest, whereas there was a very limited increase in the sensitivity to one AURKAi, CD532, and changes in the responses to another MLN8237, which itself was not very effective, but the latter deemed of importance. This reviewer is unclear about the rationale and biological interpretation of the data. The comment on "older generation" AURKAi (ENMD-2076) affecting PKA C induction appears to be odd.

We appreciate these observations. The data from ML1 have been removed, and more comprehensive data from FLX1 substituted in Figures 5A-B. To expand on our rationale in the experiments with Aurora Kinase inhibitors, our focus has been to identify agents that are potent or whose activity is significantly modified by PKA signaling activation. As many pharmacological studies pointed us towards AURKA as a target, we investigated a range of compounds targeting Aurora kinases and noted CD532 to be particularly active. CD532 alters AURKA conformation, causing c-MYC and n-MYC degradation. However, our western blots showed that CD532 also inhibits PKA directly. Thus, we selected MLN8237 as it had been demonstrated to also alter AURKA conformation in a way that influences c-MYC expression. We have expanded on our explanation of the rationale for selecting these agents in the text.

Figure 3:

Seems that only CD532 is effective in reducing proliferation of PKA C dependent cells among all AURKAi tested, raising the possibility of an off target effect.

We agree. This point has been made clearer in the text.

In that venue, changes in gene expression reported, most of which are involved in cell cycle progression, may be due to decrease cell proliferation and not acting directly downstream from PKA C, including c-MYC.

These data have been removed with more relevant data substituted (Figure 4A-C). We do note that many of the expression targets identified are still involved in cell cycle progression, but the controls reported in Author response image 1 and Figure 5, Figure 5—figure supplement 1B support a separable PKA signaling effect on c-MYC.

Data on C-MYC siRNA effects (and in fact all siRNA studies, including MYCN) require more than one siRNA and quantitative analysis of the impact on protein expression and changes.

4 individual siRNA were pooled for these experiments; we regret that this was not clearer in the initial manuscript. We have also added in significant data using individual siRNA (Figure 4C; Figure 3—figure supplement 1; Figure 7F; Figure 7—figure supplement 1C). The design of these experiments did not enable quantitative western blot analysis, but quantitative PCR is included to demonstrate the degree of knockdown. We also acknowledge that the low level expression of n-MYC made interpretation of its knockdown studies difficult. The revised manuscript has been focused specifically on c-MYC to avoid any uncertainties posed by weak detection of n-MYC by western blot and quantitative PCR.

This also applies to cell proliferation. For example, in Colo741 cells there appears to be less than 20% decrease in cell proliferation after MYC siRNA use, which is of questionable biological relevance.

We agree, this point has been made clearer in the text.

Information of cell groups (grey/red/yellow) in FLX1 experiments is missing and cannot be reviewed.

We apologize for this error; the missing label has been added.

The text on c-MYC and n-MYC knock down in Colo741 cells seems to be incorrect as these cells do not express n-MYC. The impact of n-MYC siRNA on the protein expression of n-MYC (Figure 3D) is not clear, and needs quantification as for most other similar knockdown studies.

We apologize for any lack of clarity in our description of the results. We also acknowledge that the very low levels of n-MYC make the impact of knockdown difficult to assess. Similarly, the very low levels of MYCN mRNA made the follow up experiments with individual siRNA difficult to assess. Accordingly, we have removed the experiments with MYCN knockdown and only show those with MYC knockdown.

Figure 4:

Panel B. As for other similar studies, need to use of more than 1 siRNA for PRKACA to decrease the possibility of off-target effects.

4 individual siRNA were pooled for these experiments. We undertook experiments with individual siRNAs as well but found that a less than 50% reduction in PKA protein expression rendering it difficult to see reductions in PKA signaling. In place of this experiment, we have substituted treatment with the PKA inhibiting tool compound H89 (Figure 3E). We believe that our pooled siRNA experiments showing signaling differences are still relevant to the manuscript and do replicate the findings of both PRKAR1AG325D induction and PKA inhibition with H89. This, we believe they merit inclusion as additional supportive data, but we have added further acknowledgement of their limitations.

Figure 5.

Panel B. Use more than one siRNA for PIM1 and PIM2. This is emphasized by the fact that PIM2 silencing abrogates the expression of both PIM proteins.

We acknowledge that these data were lacking; they have been removed.

The use of MLN8237 and CX6258 does not seem to provide "cooperating" or even additive effects with respect to c-MYC and n-MYC expression.

We have re-worded the description of these results.

The positive feedback loop mediated by PIM2 downstream AURKA and MYC appears to be speculative, and not mechanistically explained, at the protein interaction, activity changes, and/or expression level.

We acknowledge that our prior data implicated but did not confirm this mechanism. We have removed it from the manuscript.

Figure 6:

AURKA and PIM2 levels are not regulated in FLC tumor lesions harboring endogenous PKA C activating protein as proposed by the studies using ectopically expressed proteins, nor do they appear to correlate with c-MYC and n-MYC expression levels. This raises concerns about the overall cancer relevant significance of the proposed regulatory model in this study.

We acknowledge this concern and have addressed it in our restructuring of the manuscript.

https://doi.org/10.7554/eLife.69521.sa2

Article and author information

Author details

  1. Gary KL Chan

    1. Division of Hematology/Oncology, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, United States
    2. Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8450-7831

  2. Samantha Maisel

    1. Division of Hematology/Oncology, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, United States
    2. Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Visualization, Writing – original draft
    Competing interests
    No competing interests declared

  3. Yeonjoo C Hwang

    1. Division of Hematology/Oncology, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, United States
    2. Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, United States
    Contribution
    Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared

  4. Bryan C Pascual

    1. Division of Hematology/Oncology, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, United States
    2. Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, United States
    Contribution
    Investigation, Writing – review and editing
    Competing interests
    No competing interests declared

  5. Rebecca RB Wolber

    1. Division of Hematology/Oncology, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, United States
    2. Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, United States
    Contribution
    Investigation, Writing – review and editing
    Competing interests
    No competing interests declared

  6. Phuong Vu

    1. Department of Medicine, Harvard Medical School, Boston, United States
    2. Massachusetts General Hospital Cancer Center, Boston, United States
    Contribution
    Investigation, Methodology
    Competing interests
    No competing interests declared

  7. Krushna C Patra

    1. Department of Medicine, Harvard Medical School, Boston, United States
    2. Massachusetts General Hospital Cancer Center, Boston, United States
    Contribution
    Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared

  8. Mehdi Bouhaddou

    1. Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, United States
    2. J. David Gladstone Institute, San Francisco, United States
    Contribution
    Software, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared

  9. Heidi L Kenerson

    Department of Surgery and Northwest Liver Research Program, University of Washington, Seattle, United States
    Contribution
    Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared

  10. Huat C Lim

    1. Division of Hematology/Oncology, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, United States
    2. Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, United States
    Contribution
    Investigation, Writing – review and editing
    Competing interests
    No competing interests declared

  11. Donald Long

    Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, New York, United States
    Contribution
    Investigation, Writing – review and editing
    Competing interests
    No competing interests declared

  12. Raymond S Yeung

    Department of Surgery and Northwest Liver Research Program, University of Washington, Seattle, United States
    Contribution
    Conceptualization, Resources, Writing – review and editing
    Competing interests
    No competing interests declared

  13. Praveen Sethupathy

    Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, New York, United States
    Contribution
    Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared

  14. Danielle L Swaney

    1. Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, United States
    2. J. David Gladstone Institute, San Francisco, United States
    Contribution
    Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6119-6084

  15. Nevan J Krogan

    Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, United States
    Contribution
    Resources, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared

  16. Rigney E Turnham

    1. Division of Hematology/Oncology, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, United States
    2. Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, United States
    Contribution
    Conceptualization, Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared

  17. Kimberly J Riehle

    Department of Surgery and Northwest Liver Research Program, University of Washington, Seattle, United States
    Contribution
    Conceptualization, Resources, Methodology, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared

  18. John D Scott

    Department of Pharmacology, University of Washington Medical Center, Seattle, United States
    Contribution
    Conceptualization, Resources, Methodology, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0367-8146

  19. Nabeel Bardeesy

    1. Department of Medicine, Harvard Medical School, Boston, United States
    2. Massachusetts General Hospital Cancer Center, Boston, United States
    Contribution
    Conceptualization, Resources, Funding acquisition, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared

  20. John D Gordan

    1. Division of Hematology/Oncology, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, United States
    2. Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing
    For correspondence
    John.Gordan@ucsf.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8997-5725

Funding

Fibrolamellar Cancer Foundation

  • John D Scott
  • John D Gordan
  • Nabeel Bardeesy

Burroughs Wellcome Fund Career Award

  • John D Gordan

National Institutes of Health (DK119192)

  • John D Scott

DOD Peer Reviewed Cancer Research Program (12715138)

  • Raymond S Yeung

National Institutes of Health (F32CA239333)

  • Mehdi Bouhaddou

National Institutes of Health (U54 CA209891)

  • Nevan J Krogan

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This work was supported by the Fibrolamellar Cancer Foundation. Dr. Gordan is the recipient of a Burroughs Wellcome Career Award for Medical Scientists. Dr. Scott is supported by the National Institutes of Health (NIH) Grant DK119192, Dr. Yeung by the DOD CDMRP Grant# 12715138, Dr. Bouhaddou by NIH Grant F32CA239333 and Dr. Krogan by NIH Grant U54 CA209891. We are grateful to all the patients and caregivers in the Fibrolamellar Liver Cancer community.

Ethics

Human subjects: Human FLCs and paired normal livers were obtained from the University of Washington Medical Center and Seattle Children's Hospital after institutional review board approval (SCH IRB #15277). For prospective fresh tissue collections, informed consent was obtained from the subject and/or parent prior to resection.

Senior Editor

  1. Erica A Golemis, Fox Chase Cancer Center, United States

Reviewing Editor

  1. Ivan Topisirovic, Jewish General Hospital, Canada

Reviewer

  1. Ivan Topisirovic, Jewish General Hospital, Canada

Publication history

  1. Preprint posted: April 16, 2021 (view preprint)
  2. Received: April 17, 2021
  3. Accepted: January 22, 2023
  4. Accepted Manuscript published: January 24, 2023 (version 1)
  5. Version of Record published: February 13, 2023 (version 2)

Copyright

© 2023, Chan et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Gary KL Chan
  2. Samantha Maisel
  3. Yeonjoo C Hwang
  4. Bryan C Pascual
  5. Rebecca RB Wolber
  6. Phuong Vu
  7. Krushna C Patra
  8. Mehdi Bouhaddou
  9. Heidi L Kenerson
  10. Huat C Lim
  11. Donald Long
  12. Raymond S Yeung
  13. Praveen Sethupathy
  14. Danielle L Swaney
  15. Nevan J Krogan
  16. Rigney E Turnham
  17. Kimberly J Riehle
  18. John D Scott
  19. Nabeel Bardeesy
  20. John D Gordan
(2023)
Oncogenic PKA signaling increases c-MYC protein expression through multiple targetable mechanisms
eLife 12:e69521.
https://doi.org/10.7554/eLife.69521

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