Deregulated kinase action in prostate cancer: molecular basis and therapeutic implications

in Endocrine-Related Cancer
Authors:
Nidhi Singh Department of Cancer Biology, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA

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Hannelore V Heemers Department of Cancer Biology, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA

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https://orcid.org/0000-0001-9137-5083

Correspondence should be addressed to H Heemers: heemerh@ccf.org
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Prostate cancer (CaP) remains the second leading cause of cancer-related mortality in American men. Systemic treatments for metastatic CaP, which causes the majority of deaths, include androgen deprivation therapy and chemotherapy. These treatments induce remissions but do not cure CaP. Novel and functionally diverse therapeutic targets that control the cell biology that drives aggressive CaP progression are needed to overcome treatment resistance. Because signal transduction that mediates CaP cell behavior is tightly regulated by phosphorylation, kinases have attracted interest as alternative targets for CaP treatments. Here, we examine emerging evidence from recent NextGen sequencing and (phospho) proteomics analyses on clinical CaP specimens that were obtained during lethal disease progression to determine the role of deregulated kinase action in CaP growth, treatment resistance, and recurrence. We provide an overview of kinases that are impacted by gene amplification, gene deletion or somatic mutations during the progression from localized treatment-naïve CaP to metastatic castration-resistant CaP or neuroendocrine CaP, and the potential impact of such alterations on aggressive CaP behavior and treatment efficacy. Furthermore, we review knowledge on alterations in the phosphoproteome that occur during the progression to treatment-resistant CaP, the molecular mechanisms in the control of these changes, and the signal transduction associated with them. Finally, we discuss kinase inhibitors under evaluation in CaP clinical trials and the potential, challenges, and limitations to moving knowledge on the CaP kinome forward to new therapeutic strategies.

Abstract

Prostate cancer (CaP) remains the second leading cause of cancer-related mortality in American men. Systemic treatments for metastatic CaP, which causes the majority of deaths, include androgen deprivation therapy and chemotherapy. These treatments induce remissions but do not cure CaP. Novel and functionally diverse therapeutic targets that control the cell biology that drives aggressive CaP progression are needed to overcome treatment resistance. Because signal transduction that mediates CaP cell behavior is tightly regulated by phosphorylation, kinases have attracted interest as alternative targets for CaP treatments. Here, we examine emerging evidence from recent NextGen sequencing and (phospho) proteomics analyses on clinical CaP specimens that were obtained during lethal disease progression to determine the role of deregulated kinase action in CaP growth, treatment resistance, and recurrence. We provide an overview of kinases that are impacted by gene amplification, gene deletion or somatic mutations during the progression from localized treatment-naïve CaP to metastatic castration-resistant CaP or neuroendocrine CaP, and the potential impact of such alterations on aggressive CaP behavior and treatment efficacy. Furthermore, we review knowledge on alterations in the phosphoproteome that occur during the progression to treatment-resistant CaP, the molecular mechanisms in the control of these changes, and the signal transduction associated with them. Finally, we discuss kinase inhibitors under evaluation in CaP clinical trials and the potential, challenges, and limitations to moving knowledge on the CaP kinome forward to new therapeutic strategies.

Introduction – kinases as drivers of CaP progression

Prostate cancer (CaP) remains the second leading cause of cancer deaths in American men (Siegel et al. 2023). These deaths occur because of acquired resistance to therapies used to treat metastatic CaP, which is mostly based on CaP’s well-recognized dependence on androgen receptor (AR) (Dai et al. 2017). AR is a ligand-activated transcription factor that is a major driver of CaP progression. Androgen deprivation therapy (ADT) prevents the interaction between AR and androgens, the male hormones that bind to and thereby activate AR. The majority of so-called castration-recurrent CaP (CRPC) that invariably continues to grow after an initial remission following each of several rounds of ADT still depends on AR (Dai et al. 2017, Davies & Zoubeidi 2021, Mitsiades & Kaochar 2021), while a subset of 20–30% cases eventually undergo lineage plasticity. Among these new lineages, neuroendocrine CaP (NEPC) has been best characterized (Beltran & Demichelis 2021), although others such as stem cell or gastrointestinal lineages are increasingly recognized. The growth of the latter lineages is mediated by drivers other than AR, for instance, Brn2 (NEPC (Bishop et al. 2017)), AP-1 (stem cell (Tang et al. 2022)), or HNF4G (gastrointestinal (Shukla et al. 2017)).

That alternative drivers can control CaP progression has been appreciated also from genomics and transcriptomics analyses on patient CaP specimens obtained before and after ADT. For instance, loss of wildtype p53 function has been recognized as a mediator of resistance to ADT (Abida et al. 2019). Transcriptomics-based subtyping of treatment-naive localized CaP has uncovered subclasses with low AR and AR activity (Spratt et al. 2019, Ben-Salem et al. 2020). PAM50 classification of CRPC isolated a basal subtype that was largely resistant to ADT and docetaxel, a chemotherapeutic used to treat CRPC, and showed elevated IL6-STAT3 signaling (Coleman et al. 2022). Increased availability of treatment-resistant CaP specimens and technologies to molecularly characterize these tissues has continued to provide new insights into the drivers of CaP progression. Here, we review emerging knowledge that kinases are relevant for CaP progression and may have the potential for therapeutic interventions.

Human kinases are a group of 536 enzymes that are collectively termed the human kinome, which constitute ~1.7% of the total human genome (Manning et al. 2002). Kinases are categorized based on their ability to phosphorylate specific amino acids or on their catalytic domain sequence (Table 1). Since their discovery in the 1950s, kinases have been reported to play crucial roles in multiple cell signaling pathways and events such as cell proliferation and migration, cell cycle progression, apoptosis, DNA repair, and angiogenesis, all of which are relevant to cancer progression. Hardly any aspect of cell function and behavior is not regulated by kinase-mediated phosphorylation (Johnson 2009). Genetic mutations and chromosomal relocation of kinases are often associated with carcinogenesis, progression, and recurrence of multiple human malignancies (Bardelli et al. 2003, Maurer et al. 2011, Kittler & Tschandl 2018, Saraon et al. 2021). Uncontrolled kinase activity, either because of mutations or suppression of their inhibitory molecules, has frequently been observed in cancer cells and multiple kinases are designated as oncogenes in human cancers. Because of their enzymatic moiety, kinases represent attractive druggable targets, with kinase inhibitors currently one of the most important areas for drug development for cancer therapy. Since the approval of the first kinase inhibitor, imatinib (Gleevec) for cancer therapy in 2001 (Dagher et al. 2002), and with maturation of knowledge on the human kinome, ~76 kinase inhibitors have been approved for clinical use, including treatment of multiple cancers, with many more kinase inhibitors under investigation (Cohen et al. 2021).

Table 1

Classification of kinases. Kinases are classified based on their ability to phosphorylate specific amino acid residues (top) or based on sequence and domain similarities (bottom).

Based on the ability to phosphorylate specific amino acid residues
Kinase class Description
 Serine/threonine kinase  Phosphorylates serine or threonine residue
 Tyrosine kinase  Phosphorylates tyrosine residue
 Serine/threonine/tyrosine kinase  Phosphorylates serine/threonine and tyrosine residue
 Histidine kinase  Phosphorylates histidine residue
 Aspartic acid kinase  Phosphorylates aspartic acid residue
Based on sequence and domain similarity
Kinase class Description
AGC  PKA, PKG, and PKCC protein kinases
CAMK  Ca2+/CAM-dependent protein kinases
CK1  casein kinase 1
CMGC  CDK, MAPK, GSK3, CLK kinases
RGC  Receptor guanylate cyclase
STE  Homologues of yeast STE7, STE11, STE20 kinases
TK  Tyrosine kinases
TKL  Tyrosine kinases-like protein kinases
Atypical  No structural similarity to eukaryotic protein kinases
Others  Eukaryotic protein kinases that do not fit into any group

While the use of kinase inhibitors has improved clinical care of many cancers, their potential has not yet been fully explored in CaP. However, individual kinases such as BMX are upregulated under ADT and contribute to CRPC growth, whereas RET and AURKA kinases mediate NEPC development and progression (Dai et al. 2010, Beltran et al. 2011, Kivinummi et al. 2017, VanDeusen et al. 2020). Moreover, phospho-proteomics analyses on patient specimens before and after the development of treatment-resistance have begun to uncover a spectrum of kinase substrates that execute critical functions during CaP progression (Drake et al. 2012, 2013, Lee et al. 2014, Drake et al. 2016, Faltermeier et al. 2016, Xu et al. 2021).

In this review, we provide an overview of the kinases whose expression and/or action is deregulated during the progression to treatment-resistant CaP and the manner in which these kinases have been proposed to control aggressive CaP behavior and impact treatment efficacy. We explore knowledge regarding their phosphorylation targets or substrates, the molecular mechanisms in the control of their deregulated activity and the extent to which inhibitors targeting these kinases are currently in clinical trials and/or effective in CaP.

Kinases that are deregulated during the progression to treatment-resistant CaP

While a role for some kinases in CaP progression and treatment resistance has been recognized, the extent to which the kinome is altered during progression from localized CaP to CRPC and NEPC is not fully appreciated. In this section, we review publically available genomics, transcriptomics, and proteomics data sets and literature for evidence of deregulated expression and function of the kinome during the progression to lethal CaP that has recurred during standard-of-care treatments. Figure 1 summarizes the workflow used to identify kinases that are mutated, amplified, deleted, or phosphorylated during CaP progression.

Figure 1
Figure 1

Workflow used to identify kinases that are mutated, amplified, deleted, or phosphorylated during CaP progression. Left: The cBIO database was examined to isolate kinases that are subject to somatic mutations, gene amplification or gene deletions in localized prostate cancer (LOC), castration-resistant prostate cancer (CRPC), and neuroendocrine prostate cancer (NEPC). Kinases showing >5% frequency in mutation, homo-deletion, or amplification were considered for more detailed review. Right: Phosphoproteomics studies were reviewed for kinases showing phosphorylation during CaP progression and kinases reported in at least two studies were considered for detailed review.

Citation: Endocrine-Related Cancer 30, 9; 10.1530/ERC-23-0011

Kinases whose expression or function are altered at the genomic level

For a comprehensive and unbiased evaluation of genomic alterations impacting the kinome during clinical CaP progression, we retrieved cBIO portal (Cerami et al. 2012) data for somatic mutation, deletions, and gene amplification that affect the 536 human kinases listed in the Kinhub database (Eid et al. 2017). We included data from six studies on treatment-naïve localized CaP (LOC), four studies on CRPC, and two studies on NEPC. We selected data from these cBIO studies because they represent different stages of CaP progression and treatment resistance, provide genome-wide information on gene alterations that allows for unbiased review, examine the same type of patient specimens (radical prostatectomy or biopsy), and evaluate gene sequence and copy number alterations (CNAs) and, in some cases, gene expression. cBIO analyses were performed in August of 2022. Information on somatic mutations, gene amplifications, and deletions from NextGen sequencing on LOC, CRPC, and NEPC cases was downloaded from the cBIO dataportal and results pertaining to the 536 kinases listed in Kinhub were considered. Data were not otherwise processed or filtered and we did not exclude patient samples from analysis. Results from different studies were compiled, and we performed statistical analyses to compare data between LOC, CRPC, and NEPC studies.

Somatic mutations

We first reviewed cBIO data on somatic mutations and identified 509 kinases that were mutated in at least 1 dataset (Supplementary Table 1, see section on supplementary materials given at the end of this article). These mutations impacting the genes encoding for kinases were present in either one (LOC, CRPC, or NEPC), two, or all three disease stages (Fig. 2A and B). Most mutations were found at low incidence, in less than 3% of cases, and were thus present in the long tail of CaP genomic alterations (Armenia et al. 2018). Overall, CRPC and NEPC showed more kinase alterations compared to LOC, consistent with literature reports that the overall frequency of somatic alterations increases during CaP progression (Taylor et al. 2010, Grasso et al. 2012, Cancer Genome Atlas Research 2015, Robinson et al. 2015). A few kinases were mutated only in LOC, suggesting that these could be involved more in CaP development than progression (Supplementary Table 1, Fig. 2A and B). Statistical analyses (Mann–Whitney tests, GraphPad version 9.4.0) revealed 80 kinases for which a significant difference (P < 0.05) in the percentage of CaP cases with somatic mutations was noted between LOC and CRCP, with an average higher mutation frequency in CRPC. For 36 kinases, similar significant changes were observed between LOC and NEPC. The latter lower number and the lack of statistically significant differences between CRPC and NEPC may be due to the inclusion of fewer NEPC studies for analysis (Supplementary Table 1). Restricting the analyses to those alterations that were present in at least 5% of LOC, CRPC, or NEPC cases, an incidence range that has been previously used to genomically subtype CaP (Cancer Genome Atlas Research 2015), we noted 27 kinase-encoding genes were subject to somatic mutations in 1 or more CaP datasets (Fig. 2C). The specific somatic mutations in these 27 kinases were retrieved and are listed in Supplementary Table 2. The majority of these mutations had been reported before and were also present in the cosmic database (Tate et al. 2019). Notably, mutations were present in the kinase domain-encoding regions for the majority (n = 24) of these genes (Supplementary Table 2), suggesting they may impact the kinase function. While some of these kinases have been recognized for their roles in CaP progression and treatment response (e.g. ATM), others (e.g. TTN) have been less extensively studied and may warrant further attention. In the following section, we discuss five kinases for which some of the highest mutation frequencies were noted in both CRPC and NEPC, as compared to LOC (Supplementary Table 1, Fig. 2C). Whenever possible, we discuss available knowledge about the relevance of alterations affecting these kinases to treatment response and progression of CaP.

Figure 2
Figure 2

Kinases subject to somatic mutations at different stages of CaP progression. (A) The figure summarizes information derived from cBIO database in August of 2022 that is included in Supplementary Table 1. Information on somatic mutations from genome-wide NextGen sequencing on LOC, CRPC, and NEPC cases was downloaded from cBIO dataportal. All somatic mutations were considered. Kinases listed in the kinhub database were matched in the cBIO datasets. The kinases that showed mutation events exclusively in the LOC datasets, without any instance in any of the CRPC or NEPC datasets were considered as kinases mutated only in LOC and are represented in the top blue area that is separated from other regions of the figure by a solid arrow (below) and a dashed line (right). The top green region that is separated from other areas of the figure by a solid arrow (below) and dashed lines (left and right) lists kinases that are subject to somatic mutations in CRPC but not LOC or NEPC. The top pink region is divided from other regions by a solid arrow (below) and the dashed line (left) shows kinases that are subject to somatic mutations in NEPC but not in LOC or CRPC. The middle portion of the figure, between two solid horizontal arrows, shows the kinases subject to increased frequency of somatic mutations from LOC to CRPC. The kinases listed in the bottom part of the figure, below the second solid arrow, show increased frequency for somatic mutations from LOC to CRPC to NEPC with the highest frequency of mutations in NEPC (frequency in LOC < CRPC < NEPC). LOC datasets reviewed were LOC 1: Broad/Cornell, Cell 2013; LOC 2: Broad/Cornell, Nature Genetics 2012; LOC 3: TCGA, Cell 2015; LOC 4: CPC-Gene, Nature 2017; LOC5: SMMU, Eur Urol 2017; and LOC6: TCGA, Firehose legacy 2016. CRPC datasets were CRPC1: MCTP, Nature 2012; CRPC2: SU2C/PCF Dream Team, Cell 2015, CRPC 3: MSKCC/DCFI, Nature Genetics 2018, and CRPC4: Fred Hutchinson CRC, Nature Medicine, 2016. NEPC cBIO datasets were NEPC1: MultiInstitute, Nature Medicine 2016 and NEPC2: SU2C/PCF dream Team, PNAS 2019. (B) Venn diagram summarizing a number of unique or overlapping kinase-encoding genes subject to somatic mutations in LOC, CRPC, and/or NEPC. (C) Heatmap representing somatic mutations that occurred in at least of 5% cases in at least one of these cBIO studies. Colors represent the level of mutation frequency. Scale, numbers reflect the percent of cases in which alterations affecting the kinase-encoding gene occur.

Citation: Endocrine-Related Cancer 30, 9; 10.1530/ERC-23-0011

Ataxia–telangiectasia mutated

ATM (ataxia–telangiectasia mutated) is an atypical serine-threonine cell cycle checkpoint kinase with functions in DNA damage response and maintenance of genome stability. In cBIO data, ATM exhibited mostly missense mutations and to a lesser extent truncating mutations. Approximately 72% of cancer mutations in ATM are missense mutations in its kinase domain (Yamamoto et al. 2016, Putti et al. 2021). cBIO identified 11 mutations in its kinase domain, and these may impact CaP progression. For instance, the N2875K mutation in the kinase domain has been reported to result in loss of kinase activity (Canman et al. 1998). Somatic loss of ATM activity is an early event in CaP (Antonarakis et al. 2019). Patients carrying pathogenic germline ATM variants are at higher risk of developing CaP and are more likely to die at an early age (Na et al. 2017, Karlsson et al. 2021). A study by Neeb et al. (2021) showed 11% of CaPs cases to harbor somatically inactive ATM, and this loss was associated with genomic instability. They also reported increased anti-tumor effect for combined PARP (Poly (ADP-ribose) polymerase) and ATR inhibition in in vitro models with ATM loss (Neeb et al. 2021) compared to monotherapies, suggesting ATM mutational status may serve as a biomarker for targeted therapies. This is consistent with PARP inhibition having therapeutic benefits in CRPC harboring BRCA1/2 and ATM mutations (Nizialek & Antonarakis 2020). Mechanistically, ATM-dependent phosphorylation of SAM68 protein at Thr61 promotes its DNA damage response function by modulating its ability to bind PARP1 and be recruited to DNA lesions (Stagni et al. 2022).

LRRK2

A lesser-studied kinase, LRRK2 (leucine repeat kinase-2) from the TKL family of kinases, was recently shown to sensitize ovarian cancer cells to PARP inhibition (Chen et al. 2021). The study showed that inhibition of LRRK2 suppresses homologous recombination by disrupting the interaction of RAD51 and BRCA2, and thus the recruitment of RAD51 to DNA damage foci. This inhibition also increased the susceptibility of ovarian cancer cells to treatment with the PARP inhibitor olaparib (Chen et al. 2021). Additionally, in response to DNA damage, LRRK2 interacts directly with ATM and regulates the MDM2-P53 signaling axis, leading to cell proliferation by accelerating the cell’s entry into S-phase (Chen et al. 2017). In the cBIO database, LRRK2 showed higher average somatic mutation frequency in CRPC and NEPC compared to LOC (Fig. 2C). Mostly missense mutations were noted, along with some truncation and splice site mutations. Eight mutations were noted in its kinase domain, of which two were splice mutations. Mutations impacting LRRK2 activity have been associated with increased risk for non-skin cancers, including hormone-related cancers such as CaP (Agalliu et al. 2015). A more recent study correlated LRRK2 expression levels with survival ratio in 32 different cancers, including CaP, and showed its involvement in immune responses. Specifically, LRRK2 is positively associated with the recruitment of macrophages in tumor microenvironment and LRRK2 deficiency disabled macrophage function, which impacted cancer-type specific cancer progression (Yan et al. 2022) – suggesting that LRRK2 mutation status may also modulate CaP’s response to immunotherapies.

TTN

TTN or titin, a serine-threonine kinase, is a giant muscle filament that is essential for sarcomere function. It acts as a spring to maintain the elasticity of the striated muscles (skeletal and cardiac). cBIO CaP data showed mostly missense mutations that could impact regular TTN protein function with five additional mutations in its kinase domain. The functional impact of these mutations on TTN activity has not been fully explored yet. Previously, defects in this protein have been associated with hypertrophic cardiomyopathy (Gerull et al. 2002) and scleroderma (Ohyama et al. 2015), but more recently, TTN mutations have been associated with high tumor mutation burden (TMB) and better response to immune checkpoint blockade therapy in solid cancers (Jia et al. 2019). In colorectal cancer, the high mutation rate in both obscurin (OBSCN, see next section) and TTN and OBSCN together was associated with a more favorable prognosis, TMB, immune checkpoint expression, immune cell infiltration, immune-hot cancers, and potential better response to immunotherapies than cancer with either OBSCN or TTN alteration alone (Liu et al. 2021). While we know that the mutation rate of TTN is higher in solid tumors including CaP (Jia et al. 2019), so far there have not been any studies on its role in CaP pathogenesis, progression or treatment responses.

OBSCN

OBSCN is a serine-threonine kinase that belongs to the family of sarcomeric signaling proteins, which along with TTN and nebulin organizes myofibrils and sarcomeric structures (Kontrogianni‐Konstantopoulos et al. 2006). cBIO data showed the highest mutation frequency for OBSCN in CRPC cases compared to LOC and NEPC. The majority of the mutations were missense mutations, with three additional mutations reported in its kinase domain. A diverse spectrum of aberrations seems to afflict OBSCN, with multiple nonsense, missense, and intronic mutations reported in CaP biopsy samples, along with two complete gene mutations and one GATAD2B-OBSCN fusion product (Barbieri et al. 2012, Grasso et al. 2012, Baca et al. 2013, Nalla et al. 2016). While the clinical relevance of these mutations needs further investigation, the gene deletion and GATAD2B-OBSCN fusion product reduced OBSCN mRNA levels in some CaPs, suggesting a tumor-suppressive role. OBSCN depletion has been reported as a driver of CaP and its altered expression has been suggested as a potential diagnostic marker. A lentiviral-mediated insertional mutagenesis screen in an orthotopic human xenograft LNCaP model found two pro-viral integration sites in OBSCN that reduced OBSCN mRNA levels in the CRPCs that emerged after castration (i.e. ADT). These results indicate that OBSC loss mediates aggressive CaP progression and is involved in the development of castration resistance (Nalla et al. 2016). The authors also reported that low mRNA levels of OBSCN along with decreased expression of genes such as CLDN7 and ARFGAP3 are predictors of CaP recurrence after prostatectomy (Nalla et al. 2016).

CDK12

While the majority of cyclin-dependent kinases have functions controlling the cell cycle, CDK12 is a serine-threonine kinase that regulates the transcription of genes involved in heat shock, DNA damage and stress responses (Blazek et al. 2011). In cBIO data, CDK12 showed higher average mutation frequency in CRPC. Most of the mutations seen were considered ‘drivers’ in cBIO data and 31 mutations were seen in the kinase domain of CDK12. CDK12 is well-known to be mutated, amplified, or deleted in CaP (Lui et al. 2018). In line with this finding, Wu et al. (2018) showed that loss of function mutations of CDK12 are highly enriched in CRPC and that patients harboring CDK12 mutations have a high neo-antigen burden with extensive immune filtration, forming a distinct immunocompromised class of CRPC (Wu et al. 2018). Specifically, CDK12-mutant tumors overexpressed chemokines and chemokine receptors which encourage the higher entry of immune cells into the tumors. CDK12 mutations are linked with increased sensitivity toward immune checkpoint inhibitors (ICIs). Clinical efficacy for ICI are often low in unselected CRPC (Beer et al. 2017, Antonarakis et al. 2020b ). CRPC patients harboring CDK12 mutations have higher levels of neo-antigens, which may explain improved ICI efficacy. Recent clinical trials have also shown a higher response rate to nivolumab and pembrolizumab in CRPC patients with CDK12 mutations compared to those patients lacking these mutations (Antonarakis et al. 2020a ). Moreover, olaparib proved beneficial in these CRPC patients by prolonging progression-free survival, compared to patients without CDK12 mutations.

Copy number alterations

In addition to somatic mutations, we noted 513 and 521 kinase-encoding genes that showed gene deletions and gene amplifications, respectively, at any incidence (Supplementary Table 3 and 4, Fig. 3 and 4) in cBIO studies. These alterations once again occurred either in LOC, CPRC, or NEPC or in two or three of these CaP stages with treatment-resistant CaPs showing higher incidences (up to 34.9% amplification), consistent with the overall increases in CNAs during the progression from LOC to CRPC and/or NEPC. With regard to homodeletions, nine kinase-encoding genes were significantly impacted during the progression of CaP, with increased frequency in CRPC compared to LOC in six of these kinases. In contrast, 70 gene amplifications were significantly altered and upregulated in CRPC compared to LOC (Supplementary Table 4). Applying the cutoff of 5% of cases in any study or stage of CaP progression, 53 deletions and 143 amplifications were noted that were again mostly enriched in CRPC and NEPC over LOC (Fig. 3 and 5). Some CNAs (e.g. MAP3K7 loss, TRIB1 gain) have been recognized before (Cher et al. 1994, Taylor et al. 2010, Armenia et al. 2018, Liu et al. 2020), while the relevance of others in CaP (e.g. NEK5 depletion and WNK3 gain) is less appreciated. We discuss a few representative kinases whose genes are subject to the highest deletion and amplification frequencies as well as currently underappreciated CNA events and their contribution to CaP in the next sections.

Figure 3
Figure 3

Kinases subject to copy number alterations at different stages of CaP progression. (A) Left: The figure summarizes information derived from cBIO database in August of 2022 that is included in Supplementary Table 3. Information on gene homodeletion from genome-wide NextGen sequencing on LOC, CRPC, and NEPC cases was downloaded from cBIO dataportal. Kinases listed in the kinhub database were matched in the cBIO datasets. The kinases that showed homodeletion events exclusively in the LOC datasets, without any instance in any of the CRPC or NEPC datasets, were considered as kinases deleted only in LOC and are represented in the top blue area that is separated from other regions of the figure by a solid arrow (below) and a dashed line (right). A similar approach was used to identify kinases that were exclusively deleted in CRPC (not in LOC or NEPC) and NEPC (not in LOC and CRPC). The top green region that is separated from other areas of the figure by a solid arrow (below) and dashed lines (left and right) lists kinases that are subject to homodeletions in CRPC but not LOC or NEPC. The top pink region divided from other regions by a solid arrow (below) and dashed line (left) shows kinases that are subject to homodeletions in NEPC but not in LOC or CRPC. The middle portion of the figure, between two solid horizontal arrows, shows the kinases subject to increased frequency of homodeletions from LOC to CRPC. The kinases listed in the bottom part of the figure, below the second solid arrow, show increased frequency for homodeletions from LOC to CRPC to NEPC with the highest frequency of mutations in NEPC (frequency in LOC < CRPC < NEPC). Right: Venn diagram summarizing a number of unique or overlapping kinase-encoding genes subject to homodeletions in LOC, CRPC, and/or NEPC. LOC datasets reviewed were LOC 1: Broad/Cornell, Cell 2013; LOC 2: Broad/Cornell, Nature Genetics 2012; LOC 3: TCGA, Cell 2015; LOC5: SMMU, Eur Urol 2017; and LOC6: TCGA, Firehose legacy 2016. CRPC datasets were CRPC1: MCTP, Nature 2012; CRPC2: SU2C/PCF Dream Team, Cell 2015, CRPC 3: MSKCC/DCFI, Nature Genetics 2018, and CRPC4: Fred Hutchinson CRC, Nature Medicine, 2016. NEPC cBIO datasets were NEPC2: SU2C/PCF dream Team, PNAS 2019. (B) Information for kinases affected by gene amplifications derived and organized as in panel (A). The figure summarizes information that is included in Supplementary Table 4.

Citation: Endocrine-Related Cancer 30, 9; 10.1530/ERC-23-0011

Figure 4
Figure 4

Heatmap representing kinase genes impacted by gene homo deletions in at least 5% CaPs in at least one cBIO study. The figure summarizes information derived from cBIO in August of 2022 that is included in Supplementary Table 3. LOC datasets reviewed were LOC 1: Broad/Cornell, Cell 2013; LOC 2: Broad/Cornell, Nature Genetics 2012; LOC 3: TCGA, Cell 2015; LOC5: SMMU, Eur Urol 2017; and LOC6: TCGA, Firehose legacy 2016. CRPC datasets were CRPC1: MCTP, Nature 2012; CRPC2: SU2C/PCF Dream Team, Cell 2015, CRPC 3: MSKCC/DCFI, Nature Genetics 2018, and CRPC4: Fred Hutchinson CRC, Nature Medicine, 2016. NEPC cBIO datasets were NEPC2: SU2C/PCF dream Team, PNAS 2019. Kinases showing a homodeletion frequency of ≥5% in at least one of the datasets in Supplementary Table 3 were withheld and are represented in the heatmap, with colors representing the level of mutation frequency. Scale, numbers reflect the percent of cases in which alterations affecting the kinase-encoding gene occur.

Citation: Endocrine-Related Cancer 30, 9; 10.1530/ERC-23-0011

Figure 5
Figure 5

Heatmap representing kinase genes impacted by gene amplifications in at least 5% CaPs in at least one cBIO study. The figure summarizes information derived from cBIO in August of 2022 that is included in Supplementary Table 4. LOC datasets reviewed were LOC 1: Broad/Cornell, Cell 2013; LOC 2: Broad/Cornell, Nature Genetics 2012; LOC 3: TCGA, Cell 2015; LOC5: SMMU, Eur Urol 2017; LOC6: TCGA, Firehose legacy 2016. CRPC datasets were CRPC1: MCTP, Nature 2012; CRPC2: SU2C/PCF Dream Team, Cell 2015, CRPC 3: MSKCC/DCFI, Nature Genetics 2018, CRPC4: Fred Hutchinson CRC, Nature Medicine, 2016. NEPC cBIO datasets were NEPC2: SU2C/PCF dream Team, PNAS 2019. Studies in which alterations were found in at least 5% of CaPs are included in the heatmap. Kinases showing a gene amplification frequency of ≥5% in at least one of the datasets in Supplementary Table 4 were withheld and are represented in the heatmap, with colors representing the level of mutation frequency. Scale, numbers reflect the percent of cases in which alterations affecting the kinase-encoding gene occur.

Citation: Endocrine-Related Cancer 30, 9; 10.1530/ERC-23-0011

NEK5

NEK5 (NIMA-related kinase 5) is a serine-threonine kinase that regulates the cell cycle (Prosser et al. 2015) and mitochondrial-mediated apoptosis (Melo Hanchuk et al. 2015). This kinase showed an overall higher frequency of homodeletion events in all CaP stages. At present, NEK5 expression is poorly characterized in CaP, although a few studies have attempted to elucidate its role with inconclusive results. Nikitina et al. (2017) have shown significantly higher mRNA expression of NEK5 in CaP compared to benign hyperplasia tissues in Russian patients (Nikitina et al. 2017). On the other hand, Melo-Hanchuk et al. (2020) studied the expression pattern of several NEK family kinases (NEK1, NEK2, NEK3, and NEK5) in ten cancer tissue microarrays (Melo-Hanchuk et al. 2020) and observed lower expression of NEK3 and NEK5, higher expression of NEK1, and moderately higher expression of NEK2 in CaP samples compared to matched normal tissues. They also found lower expression of NEK family kinases, including NEK3 and NEK5 in lung tumor samples compared to control, but higher expression in thyroid cancer. NEK5 expression was specifically higher in thyroid cancer samples that showed metastasis and invasion and had tumor sizes of more than 4 cm (Melo-Hanchuk et al. 2020). These contradicting reports suggest that NEK5 function may vary depending on the type of cancer and that its specific role in CaP is in need of further evaluation.

NEK3

Noteworthy, the second most frequent homodeletion event was observed for another NEK family kinase, NEK3. NEK3 function is not well characterized yet, although some studies linked it to cell migration, proliferation, and neuronal development (Chang et al. 2009, Moniz et al. 2011, Harrington & Clevenger 2016). Homozygous deletion of NEK3 has been previously reported in BRCA2-deficient CaP cases, compared to BRCA2-intact cases in GENIE and TCGA databases (Kensler et al. 2022). Men with BRCA2 deficiency are at higher risk of early onset of CaP and of having more aggressive cancer (Castro et al. 2013, 2019). NEK3 is located on chromosome 13q14.3 and loss of heterozygosity (LOH) of this chromosome is frequently reported in CaP (Melamed et al. 1997, Hyytinen et al. 1999, Yin et al. 1999). LOH of this chromosome has also been found to be associated with insertion/deletion polymorphisms and alternative splice variants of NEK3 in CaP. An increase in NEK3 homozygous genotype (A8/A8) and a decrease in genotype (A7/A8 and A7/A7) has been reported in CaP cases, compared to controls (Hernández & Almeida 2006). The authors suggested that the resulting insertion/deletion event may have a direct implication in the development of cancer (Hernández & Almeida 2006).

MAP3K7

MAP3K7 (Mitogen-activated protein kinase kinase kinase 7) ranks third in terms of kinase gene homodeletion frequency in CaP. MAP3K7 is a serine-threonine kinase that is activated by TNFα, transforming growth factor β, and lipopolysaccharides to activate IKK (IkB kinase) and subsequently NFkB (Karin et al. 2002). MAP3K7 is often deleted in CaP (Armenia et al. 2018, Liu et al. 2020). Huang et al. (2021) have shown that MAP3K7–IKKβ signaling leads to AR degradation in CaP while loss of MAP3K7 increases AR activity and CRPC cell proliferation both in vitro and in vivo (Huang et al. 2021). Other studies have characterized an aggressive CaP subtype with double deletion of MAP3K7 and CHD1. CHD1 and MAP3K7 deletions were found to be co-dependent, with rarely only one lost in CaP. Co-suppression of these two genes led to increased expression of ADT-resistant AR variant AR-v7 and an increase in resistance to enzalutamide treatment (Rodrigues et al. 2015, Ormond et al. 2019, Jillson et al. 2021). Low expression of MAP3K7 is also strongly associated with biochemical recurrence (Jillson et al. 2021) and led to the defect in DNA repair pathways. CDK1/CDK2 inhibitors (Dinaciclib) in combination with PARP inhibitors or DNA damaging agents were effective in decreasing cell viability in MAP3K7 and CHD1 double-negative CaPs (Washino et al. 2019).

TRIB1

TRIB1 (Tribbles homolog 1) is a CAMK serine-threonine kinase that showed the highest amplification frequency in CRPC compared to LOC. TRIB1 copy number and chromosomal location (8q24) are previously reported to be amplified in CaP (Cher et al. 1994, Taylor et al. 2010), consistent with TRIB1 mRNA and protein levels being elevated in clinical CaP (Mashima et al. 2014). Its knockdown inhibited the growth of 3D CaP spheroids cell culture and CaP xenografts (Mashima et al. 2014). TRIB1 is essential for the expression of the endoplasmic reticulum chaperone, GRP78, which is specifically expressed in a population of CaP cells capable of tumor propagation, and thus inhibiting TRIB1 specifically affected this cell population (Mashima et al. 2014). GRP78 activates AKT by regulating its phosphorylation (Fu et al. 2008, Liu et al. 2013a ), and reduction in AKT phosphorylation and activity was noted upon either GRP78 or TRIB1 knockdown, suggesting that TRIB1 can also regulate cell growth and survival via GRP78-AKT axis (Mashima et al. 2014). TRIB1 is also reported to regulate AR via ERK and AKT phosphorylation (Munkley et al. 2015) and NFkB via IkB expression (Bornigen et al. 2016). In addition, TRIB1 promotes CD163+ macrophage infiltration in CaP (Liu et al. 2019). TRIB1 also inhibits IKB-zeta, which promotes CXCL and IL8 secretion leading CaP monocytes to convert into M2 macrophages, which can suppress anti-tumor immunity and increases angiogenesis (Liu et al. 2019). Increases in TRIB1 expression were noted also in cisplatin-treated cells which promotes cell proliferation, invasion, migration, accumulation of cancer stem cells, and drug resistance (Wang et al. 2017). These observations suggest that inhibiting TRIB1 could be a potential new strategy to treat cancer (Miyajima et al. 2015).

WNK3

Of the 143 kinases for which gene amplification frequencies above 5% were found, 31 showed statistically significant changes in frequency from LOC to CRPC (Supplementary Table 4). Many of these kinases have never been explored in CaP. One of these isWNK3 (WNK lysine deficient protein kinase 3), a serine-threonine protein kinase. No reports exist on the role of WNK3 in CaP, although its oncogenic functions have been reported in other cancers. For example, in non-small cell lung cancer cell lines, Yoon et al. reported that WNK3 knockdown increases cancer cell death (Yoon et al. 2022). These authors found that WNK3 kinase activity promotes PD-L1 expression, and reported upregulated cytokine expression and activated CD4+ and CD8+ T cells against cancer cells, suggesting that WNK3 alterations may modulate the efficacy of immunotherapies. In another study, suppressing WNK3 expression by RNA interference was reported to activate caspase-3-mediated apoptosis in HeLa cells (Verissimo et al. 2006).

To further validate CNA findings impacting kinase-encoding genes, we also considered gene expression data, which was available for a subset of the analyzed cBIO studies (four LOC, three CRPC, and two NEPC datasets). For the vast majority of these kinase genes, expression was altered in more than 5% of cases. In addition, no significant differences were found in the number of cases impacted between different stages of CaP. These results may be attributed to the presence of fewer studies with available gene expression data or the inability to easily distinguish between mRNA overexpression or underexpression (not shown).

Kinases whose expression or function is altered at the proteomics level

Discrepancies between the CaP genome and the ensuing transcriptome and proteome expression patterns are well-recognized. In addition, post-translational modifications (PTMs) such as kinase-induced phosphorylation patterns impact protein activity as well as kinase action (Cohen 2000, Johnson & Lewis 2001, Burke et al. 2010, Watanabe & Osada 2012). To better understand kinome activity before and after ADT failure, we also explored the current knowledge on the evolution of phosphorylation events and their molecular determinants during CaP progression.

Protein phosphorylation is one of the most common PTMs in nature, which is present in about 75% of mammalian proteins (Hubbard & Cohen 1993, Sharma et al. 2014). Serine and threonine phosphorylations account for nearly 98% of these events (~86 and ~12%, respectively), with tyrosine phosphorylation contributing approximately 2% (Olsen et al. 2006). Phosphorylation of some kinase substrates, such as nuclear receptors (e.g. AR) and their coactivators, can change their transcriptional activities and downstream signaling. Phosphorylation of a protein can also directly alter its interactions with other proteins or DNA or may have indirect effects via changes in other PTM events such as sumoylation and acetylation. These modifications can impact the degradation and cellular localization of proteins (Koryakina et al. 2014). Mass spectrometry (MS)-based analyses are often employed to decipher phosphorylation events that occur in cancer (Garcia et al. 2005). Because of the reversible or transient nature of phosphorylation, phosphomarks can be difficult to detect, and therefore, various enrichment steps for isolating phosphopeptides from complex mixtures have been implemented. These include immobilized metal affinity chromatography, titanium oxide enrichment, and antibody-based enrichment (Ong et al. 2002, Garcia et al. 2005, Rush et al. 2005). In the field of CaP research, these techniques have been widely used on cell lines, mouse xenograft tissues, as well as patient samples, which has significantly improved the identification of phosphorylation events in CaP cells and tissues.

Here, we collate and discuss some key findings from MS-based analyses of the CaP phosphoproteome that were performed with the common intent of identifying critical phosphorylation events responsible for metastasis and treatment resistance in CaP, with each approach slightly different from the other. With one exception (Myung & Sadar 2012), all of these studies focused on CRCP. Global phosphorylation levels as well as phosphomarks on both kinase (which can reflect kinase activity or protein-protein interactions) and non-kinase substrates were evaluated with efforts directed also at deriving the downstream kinase-driven signaling cascades. We reviewed reports returned by a PubMed search using combinations of the terms ‘phosphoproteomics’, ‘prostate cancer’, ‘castration-resistant prostate cancer’, and ‘metastatic prostate cancer’ and summarized results from these publications here. No data was omitted. As outlined in more detail in the next sections, more intense phosphorylation levels were noted following ADT failure. These shifts in CaP phosphoproteome patterns were associated with the activation of signaling cascades, which were influenced by the presence of oncogenes such as AKT and K-Ras. Potential mediators of altered phosphoproteome in CRPC included SRC and substrates emerging as targets for treatment included YAP1.

Myung and Sadar (2012) analyzed the steady-state phosphoproteome of LNCaP cells, which are ADT-responsive, and found 116 proteins undergoing phosphorylation at 746 sites. Most of these proteins were highly phosphorylated in serine residues (88%), followed by threonine (12%), and a very small percentage (<1%) of tyrosine residues. Proteins involved in Wnt signaling pathway were found to be more frequently phosphorylated, followed by members of the cadherin signaling pathway. The authors also identified three proteins, HDAC2 (histone deacetylase 2), USP10 (ubiquitin carboxyl-terminal hydrolase 10), and CTNNB1 (β-catenin) which are well-known AR interactors when phosphorylated. Most of the phosphorylation motifs were recognition sites for casein kinase 1 and 2 and GSK3 (Myung & Sadar 2012).

Jiang et al. (2015) aimed to identify mechanisms sustaining the growth of CRPC by performing unbiased stable isotope labeling with amino acids in cell culture (SILAC) MS-based phosphoproteome analysis of orthotopic LNCaP xenograft tissues from intact and castrated mice (which model ADT). They found 44 phosphopeptides from 33 proteins that had increased levels and 54 phosphopeptides from 39 proteins with decreased levels in CaPs grown in castrated mice compared to CaPs from intact mice. Among other marks, the authors observed high phosphorylation of the serine-threonine kinase PAK2, a regulator of cell mobility, and YAP1, a coactivator for Hippo signaling. The findings from MS assays were validated by parallel transcriptomics and bioinformatics analyses and verified using tissue microarrays from LOCs and CRPCs. Silencing of both YAP1 and PAK2 decreased CRPC cell colony formation and cell invasion activity while pharmacologic inhibition of PAK2 and YAP1 inhibited CRPC cell growth, showing the potential of MS to isolate novel targets for CRPC treatment (Jiang et al. 2015).

In the first of three studies, Drake et al. (2012) performed IHC for global phosphotyrosine marks, which were elevated in CRPC compared to LOC and benign human and mouse prostate tissues. Because these patterns occurred in the absence of mutated tyrosine kinases, the authors examined oncogene-driven phosphorylation in mouse models. They used models driven by activated AKT in combination with either AR, ERG, or activated K-Ras (over)expression, which are well-recognized oncogenes in CaP. CaP (precursor)s from these models yielded differential, unique, and overlapping patterns for pTyr marks with an increased abundance of tyrosine phosphorylation events observed in the more advanced carcinomas that developed in CaPs with activated AKT/AR and AKT/K-Ras. Computational analyses revealed activation of different and specific pathways in each model: activation of the JAK/STAT pathway in AKT/AR activated tumors and an increase in phosphorylation of PTK2B/PYK2/FAK2 in AKT/K-RASG12V activated tumors, which was confirmed via western blotting with phospho-specific antibodies. Inference of kinase activity in AKT/K-RASG12V tumors further revealed enrichment of ERK1/2 and MEK1/2 substrates, consistent with direct activation of MAPK signaling by the K-RASG12V oncogene. AKT/AR phosphopeptides, on the other hand, revealed the enrichment of motifs associated with ABL1 and SRC kinases (Drake et al. 2012).

In a second study, these authors performed phosphotyrosine peptide enrichment and quantitative MS on metastatic CRPCs obtained at rapid autopsy to identify druggable targets. Phosphopeptide patterns in CRPC were distinct from those obtained from LOC and CaP cell line-derived xenografts. Analyses for tyrosine phosphorylation and upstream kinase targets in CRPC revealed activities for SRC, epidermal growth factor receptor (EGFR), rearranged during transfection (RET), anaplastic lymphoma kinase, and MAPK1/3, among others. Based on phospho-enriched MS data, the authors’ stratification of metastatic CRPC patients’ kinase activation patterns suggested that simultaneous targeting of SRC and MEK kinases could be of therapeutic value. Based on their observations, the authors suggested that phosphoproteomics analyses of individual CRPC patient specimens could guide the molecular stratification of patients to direct treatment with a combination of kinase inhibitors in a personalized medicine approach (Drake et al. 2013).

In a third study, the same group extended their MS assays and analytical approaches to isolate and include ~8000 pSer and pThr marks derived from lethal metastatic CRPC tissues obtained at rapid autopsy. After incorporating the ~300 pTyr signals from their previous studies, the authors combined phosphoproteomics data with pathway analyses and found enrichment for mRNA splicing and processing, DNA replication, and AR transcription factor pathways as well as loss of integrin signaling, focal adhesion, and axon guidance pathways in metastatic CRPC. Combining phosphoproteomics data with genomic and transcriptomic data on the same tissues and applying sophisticated bioinformatics analyses, they developed a tool, ‘phosphorylation-based cancer hallmarks using integrated personalized signatures’ (pCHIPs). They proposed the latter as a method to capture multiple perspectives of cellular biology from phosphoproteomic and transcriptomic data integration and summarize such data at the individual level. Using this tool, they implicated several signaling proteins such as PRKDC, PRKAA2, PTK2, RPS6KA4, and CDK family members within these pathways as possible new therapeutic targets and/or biomarkers in CaP. For most CaP cases that the authors studied, an individualized therapeutic strategy was suggested by the phosphoproteomic data (Drake et al. 2016).

Treatment opportunities based on phosphorylation patterns were suggested also by other studies. For instance, Lee et al. (2014) identified increased phosphorylation of focal adhesion kinase (FAK) at Y397 and Y576 in docetaxel-resistant metastatic CaP cells, compared with control cells, using MS-based phosphoproteomics analysis. They also showed an increase in the efficacy of docetaxel treatment when combined with a FAK tyrosine kinase inhibitor (TKI) PF-00562271 in overcoming chemoresistance (Lee et al. 2014).

While most of the studies are focused on the phosphorylation of serine, threonine, and tyrosine residues, a recent report utilized a high-throughput screening method to identify histidine and aspartic acid phosphorylation levels in CaP progression. The authors identified 3 histidine-phosphorylated and 14 aspartic acid-phosphorylated proteins in metastatic CaP. The three histidine phosphorylated proteins were unique to metastatic CaP, that is, present but not phosphorylated in prostate epithelial cells or non-metastatic CaPs. However, a reduction in both histidine and aspartic acid phosphorylation occurred as CaP progressed. In addition, the highest expression of NM23-H1, nucleoside diphosphate kinase-A, was seen in normal prostate which decreased in CaP and was completely lost in metastatic CaP (Lapek et al. 2015). NM23-H1 has a primary role in metastasis-suppressing mechanisms and its expression is also proposed as one of the best indicators of survivability for patients with most cancers (Marshall et al. 2009).

Furthermore, Ino et al. (2016) compared the phosphoproteomic profile of AR-dependent and treatment-resistant prostate CaP cells and found differential expression of 69 phosphopeptides in 66 proteins (Ino et al. 2016) using MS-based phosphoproteomic analysis. Katsogianno et al. (2019) utilized the SILAC-MS approach to identify the phosphoproteome of four prostate (cancer) cell lines – PNT1A (benign prostate), LNCaP (AR-sensitive), DU145, and PC3 (treatment-resistant). They were able to identify proteins specifically expressed and phosphorylated in each of the four cell lines (Katsogiannou et al. 2019). A recent study by Xu et al. (2021) performed phosphoproteome analysis to identify significant biomarkers involved in toxin regulation in highly metastatic CaP cell lines. Hainan Toxin-III and JZTX-I (Jingzhao Toxin-I) were used because they regulate sensitive voltage-gated sodium channels (Liu et al. 2013b ) that are involved in cancer metastasis and invasiveness. Quantitative phosphoproteomics integrated with multiple bioinformatics analysis showed five kinases (PKN2, NRBP1, CDK2, CDK3, CDK1/CDC2) to be phosphorylated in castration resistance and three candidate substrate proteins, EEF2, U2AF2, and FLNC, involved in tumor metastasis and invasion (Xu et al. 2021).

Combined, these studies identified 166 kinases with altered phosphorylation in metastatic or CRPC. Of these, 28 kinases were identified in more than one study (Table 2). Kinases such as TRIM28, PKN1, CDK13, PAK2, BRAF, EGFR, EPHA2, LYN, RAF1, SRC, PRPF4B, and MET were upregulated or showed increased phosphorylation in CRPC, while others such as CDK2, CDK3, and RIPK2 were downregulated or showed decreased phosphorylation. Kinases such as MAPK1/ERK2 showed both upregulation and downregulation in separate studies. Also, a key aspect to consider is that phosphorylation at a specific site of a kinase could alter its regulation. This is exemplified by LMTK2, whose expression was increasing when it is phosphorylated at S886, but decreased when phosphorylated at S1450 in CRPC. Another serine-threonine kinase, STK39, showed a high fold change in its expression when phosphorylated at S385 and S315, which reduced when phosphorylated at T354.

Table 2

Kinase phosphorylation reported in at least two CRPC phosphoproteomics studies. A literature search was conducted to identify phosphoproteomics studies on CaP cells and tissues. The phosphoproteomics data from each study were retrieved and searched for kinases listed in the Kinhub database. The kinases that were retrieved, and their phosphorylation sites, in two or more studies are listed.

Kinase Kinase group Phosphosite (sequence)
AAK1 Other pS618, pS624, pT620 (VGpSLpTPPSpSPK)
AKT1 AGC pT308 (KDGATMKpTFCGTPE), pS473 (PHFPQFpSYSASGT)
BRAF TKL pS446 (RDpSSDDWEIPDGQITVGQR), pS729 (SApSEPSLNR); pS364 (SpSSAPNVHINTIEPVNIDDLIR)
CDK1 CMGC pT161(VYTHEVVTLWYR); pT14, pY15 (IGEGpTpYGVVYK)
CDK13 CMGC pS315, pS317(pSLpSPLGGR), pS383 (GGDVpSPSPYSSSSWR), pT1058 (TNpTPQGVLPSSQLK), pS397, pS400 (pSPYpSPVLR)
EPHA2 TK pS897, pS901 (LPpSTSGpSEGVPFR), pY594 (TYVDPHTpYEDPNQAVLK), pY772 (VLEDDPEATpYTTSGGK)
ERK1 CMGC pT202, pY204 (HTGFLpTEpYVATRW)
ERK2 CMGC pT185, pY187 (HDHTGFLpTEpYVATR)
LMTK2 TK pS886 (SQDpSPGESEETLR), pS1450 (YFpSPPPPAR)
LYN TK pS13 (KGKDSLpSDDGVDL), pY508 (AEERPTFDYLQSVLDDFYTATEGQpYQQQ)
PAK1 STE pS223, pS230 (DVATSPIpSPTENNTpTPPDALTR)
PAK2 STE pS141 (VKQKYLpSFTPPEK), pS2 (pSDNGELEDKPPAPPVR)
PKN2 AGC pS583 (ASpSLGEIDESSELR), pT958 (GREDVSNFDDEFTSEAPILpTPPREPR)
PRPF4B CMGC pS20 (EQPEMEDANpSEK), pS8 (AAAETQpSLR), pS257 (KIGKARpSPTDDKVKI),
RAF1 TKL pS259 (STpSTPNVHMVSTTLPVDSR), pS621 (SASEPSLHR)
RIPK2 TKL pS363 (pSLPAPQDNDFLSR), pS531 (SPpSLNLLQNK)
SLK STE pS518 (EANIQAVDpSEVGLTK), pS779 (DSGpSISLQETR)
STK39 STE pS385 (TEDGDWEWpSDDEMDEK), pS315 (LLpSLCLQK), pT354 (pTPDIAQR)
TRIM28 Atypical pS19, pS26 (AASAAAASAAAASAASGpSPGPGEGpSAGGEKR), pS473 (SRSGEGEVSGLMR), pS757 (LSPPYSSPQEFAQDVGR)
SRC TK pT420, pY419 (LIEDNEpYpTAR)
DDR1 Other pY796 (RNLYAGDpYYRVQGR)
MET TK pS990 (SVpSPTTEMVSNESVDYR), pY1234 (MYDKEpYYSVHNK)
ERBB2 TK pY1221(SPAFDNLpYYWDQDPP)
PKN1 AGC pS916 (TDVSNFDEEFTGEAPTLpSPPR)
EGFR TK pY1172 (GSHQISLDNPDpYQQDFFPK)
ABL1 TK pY213 (TLEPVKPPTVPNDpYMTSPAR)
CDK2 CMGC pY15, pT14 (QKVEKIGEGpTpYGVVYK)
CDK3 CMGC pY15, pT14 (IGEGpTpYGVVYK)

Eleven of the kinases reported to be altered in CaP cBIO data were also identified in the CaP phosphoproteomics data (Fig. 6, Supplementary Table 5). For instance, SLK was found to be deleted in CRPC (2.7% average) and NEPC (2.3%) in cBIO data, and its phosphorylated version S779 was downregulated in CRPC in the phosphoproteomics studies. Conversely, LYN (10.3% average), CDK1 (5.7% average), BRAF (3.2% average), and PAK2 (3.2% average) showed amplification in CRPC, and their phosphorylated versions showed expression in CRPC in the phosphoproteomics studies (Drake et al. 2016, Faltermeier et al. 2016, Ino et al. 2016).

Figure 6
Figure 6

Overlap in kinases that are subject to genomic alterations in cBIO datasets and returned in phosphoproteomics data. Venn diagram represents the overlap in kinase-encoding genes found to be subject to somatic mutation, deletion, and amplification in at least 5% of cases of one cBIO study analysis and kinases identified as phosphorylated in least two phosphoproteomics studies. Details on common and mutually exclusive entries from cBIO and phopshorptoemotcs studies are provided in Supplementary Table 5.

Citation: Endocrine-Related Cancer 30, 9; 10.1530/ERC-23-0011

Kinases as targets in treatment-resistant CaP – overview of clinical trials

As indicated earlier, a considerable fraction of the kinome shows deregulated expression and/or activity during CaP progression, supporting the relevance of kinases for the development of treatment resistance and for sustained growth after the failure of standards-of-care for metastatic CaP. It is therefore not surprising that several clinical trials are currently testing kinase inhibitors in treatment-resistant CaP (Table 3).

Table 3

Ongoing clinical trials testing kinase inhibitors in CRPC trials are listed in chronological order. The final column clarifies if the kinase that is targeted for treatment was noted in this review. The mutations or phosphorylation sites mentioned in the last column are not necessarily those studied in the clinical trials.

Drug(s) Target Cancer Study/ID Phase Start Recruitment status Kinase alteration observed in this review
1 Abivertinib, Abiraterone EGFR, BTK1 CRPC NCT05361915 II 2022 Recruiting EGFR phosphorylation – pY1172
2 Abemaciclib Abiraterone CDK4, CDK6 Metastatic-hormone-sensitive prostate cancer NCT05288166 III 2022 Recruiting CDK4/6 – amplified
3 Hydroxychloroquine, Metformin, Sirolimus, Nelfinavir, Dasatinib BCR-ABL, SRC Relapsed advanced CaP NCT05036226 I/II 2022 Recruiting SRC phosphorylation – pT420, pY419

ABL1 amplified and phosphorylation at pY213
4 Zenocutuzumab EGFR2, EGFR3 Metastatic CRPC NCT05588609 II 2022 Recruiting EGFR – amplified
5 Abemaciclib, 177Lu-PSMA-617 CDK4, CDK6 Metastatic CRPC NCT05113537 I/II 2021 Recruiting CDK4/6 – amplified
6 Atezolizumab, Tivozanib PD-1 and PD-L1, VEGF Multiple (including mCRPC) NCT05000294 I/II 2021 Recruiting
7 Dasatinib, Darolutamide BCR-ABL, SRC Metastatic CaP NCT04925648 II 2021 Recruiting SRC phosphorylation – pT420, pY419

ABL1 amplified and phosphorylation at pY213
8 Apatinib VEGFR-2 Metastatic CRPC NCT04869488 II 2021 Active, not recruiting
9 Pembrolizumab, Lenvatinib PD-1, VEGF Metastatic NEPC NCT04848337 II 2021 Recruiting
10 Palbociclib CDK4/6 Metastatic CRPC NCT04606446 I 2020 Recruiting CDK4/6 – amplified
11 Afuresertib Akt Metastatic CRPC NCT04060394 I/II 2019 Active, not recruiting AKT1 phosphorylation – pT308, pS473
12 Peposertib DNA-PK Metastatic CRPC NCT04071236 I/II 2019 Recruiting
13 Ceralasertib ATR Solid tumors (including Metastatic CRPC) NCT03682289 II 2018 Recruiting ATR amplified
14 ESK981 PIKfyve, Pan-VEGFR-Tie2 Metastatic CRPC NCT03456804 II 2018 Active, not recruiting
15 Onvansertib Abiraterone PLK-1 Metastatic CRPC NCT03414034 II 2018 Active, not recruiting
16 Pembrolizumab, Lenvatinib PD-1, VEGF Metastatic CRPC NCT02861573 I/II 2016 Recruiting
17 Palbociclib CDK4/6 Metastatic CRPC NCT02905318 II 2016 Active, not recruiting CDK4/6 – amplified
18 Trametinib MEK Metastatic CRPC NCT02881242 II 2016 Active, not recruiting
19 Cediranib VEGFR Advanced CaP, Metastatic CRPC NCT02893917 II 2016 Active, not recruiting VEGFR1 – mutated
20 Sitravatinib MET, AXL, MER, VEGFR, PDGFR, DDR2, TRK, Eph Metastatic CaP NCT02219711 I 2014 Active, not recruiting VEGFR1 – mutated
21 Trametinib, Dasatinib MEK, BCR-ABL, SRC Metastatic CRPC NCT01990196 II 2013 Active, not recruiting
22 Axitinib VEGFR CaP with lymph node metastasis NCT01409200 II 2011 Active, not recruiting VEGFR1 – mutated
23 Dasatinib, Sunitinib BCR-ABL, SRC, VEGFR, PDGFR, FLT3 Metastatic CRPC NCT01254864 II 2010 Active, not recruiting SRC phosphorylation – pT420, pY419

ABL1 amplified and phosphorylation at pY213

In most cases, administration of the kinase inhibitor under investigation has been combined with one or more other agents, such as an ADT drug. One example of a clinical trial combining a kinase inhibitor with ADT is co-administering abivertinib with abiraterone acetate (NCT05361915). Abivertinib is a tyrosine kinase inhibitor (TKI) that selectively targets mutant forms of the EGFR and Bruton’s tyrosine kinase (BTK)1 in non-small cell lung cancer (Zhou et al. 2022). The EGFR/FOXO3A/LXR-α signaling axis promotes CaP metastasis and BTK expression – particularly the BTK-C isoform – is high in CaP (Kokabee et al. 2015, Chen et al. 2020). Abivertinib is evaluated in combination with abiraterone acetate, which inhibits CYP17, a key enzyme mediating androgen biosynthesis that drives CRPC growth. Another example of combinational kinase inhibitor therapy is the combination of dasatinib, a TKI that inhibits also SRC and ABL, which are activated in CRPC (Drake et al. 2012) with the next-generation ADT drug darolutamide which is currently under investigation in metastatic CaP. Previously, combining dasatinib with ADT prolonged overall survival in metastatic hormone-sensitive CaP than ADT alone (Sweeney et al. 2015). Inhibiting phospho-SRC with dasatinib in combination with ADT altered the expression of the AR target gene PSMA (prostate specific membrane antigen). This is consistent with SRC kinase activating AR-mediated transcriptional signaling, and its inhibition suppresses this signaling leading to the inhibition of CaP cell proliferation (Chattopadhyay et al. 2017). SRC also phosphorylates AR directly, which is an important event for its activation (Cai et al. 2011). SRC activation leads to castration resistance and CaP metastasis (Su et al. 2013). Yet another trial is investigating the effect of co-administration of abemaciclib, a CDK4/6 inhibitor with abiraterone in metastatic hormone-sensitive CaP (NCT05288166), a rationale that fits with AR action inducing cell cycle progression via upregulation of cyclin D1 expression and subsequent activation of CDK4/6 (Stice et al. 2017).

Apart from AR signaling inhibitors, clinical investigations are testing the pairing of kinase inhibitors with key target antibodies. For example, a clinical trial is currently in progress to study the effects of pembrolizumab, a humanized monoclonal anti-PD1 antibody with therapeutic efficacy in, for example, lung cancer (Kwok et al. 2016) when combined with lenvatinib, an FDA-approved TKI that exerts antiangiogenic effects by inhibiting VEGF receptors in metastatic NEPC, where ADT induces neuroendocrine differentiation as well as angiogenesis (Zhang et al. 2018). In other cases, the application of kinase inhibitors with radioactive drugs that target cancer cells is being explored. It is theorized that treatment with abemaciclib, a CDK4/6 inhibitor, before administering Lu 177 vipivotide tetraxetan IV will increase the sensitivity to the latter. 177Lu-PSMA-617 carries a radioactive component (Lutetium) and binds specifically to CaP cells via the PSMA receptor. The role of kinase inhibitors in the efficacy of immune checkpoint inhibition is also considered as a treatment strategy. Atezolizumab inhibits the binding of programmed death-1 receptor to its ligand – programmed death-ligand 1, thereby inhibiting T-cell activation (Jiang et al. 2019). A study examining the combined effects of atezolizumab with tivozanib, an inhibitor of VEGFR, is currently underway, and it is hypothesized that by inhibiting VEGF activity, the effects of immune checkpoint blockade could be increased in CaPs, which generally show low response to checkpoint inhibitor therapy alone. In some cases, antibodies directed against specific kinases can also bring about anti-tumorigenesis effects. Zenocutuzumab is a bispecific humanized immunoglobulin G1 with Fab arms that can target both HER-2 and -3, which has been shown to reduce AKT-mediated downstream signaling and tumorigenesis in non-small cell lung cancer (Schram et al. 2022). The relevance is that NRG1 genomic rearrangement is frequently observed in solid tumors including CaP, where it fosters HER2–HER3 heterodimerization resulting in activation of oncogenic pathways. Inhibiting HER2–HER3 signaling in patients with NRG1 fusion is a promising therapeutic approach (Schram et al. 2022). Zenocutuzumab along with ADT drugs abiraterone or enzalutamide is tested in a phase II clinical CRPC trial (NCT05588609).

Yet other trials seek to explore the potential of existing, approved drugs and re-purpose them to treat CaP. One such study aims to identify a possible combinatorial effect of hydroxychloroquine, and sirolimus (both immunosuppressives) with metformin (anti-type 2 diabetes drug that affects AMPK), nelfinavir (anti-HIV protease inhibitor), and the TKI dasatinib. Completed clinical trials to test kinase inhibitors for CaP treatment are listed in Supplementary Table 6. Kinases for which inhibitors are or have been tested in trials are marked also in Fig. 7.

Figure 7
Figure 7

Overview of kinases that are altered and/or considered as targets for therapeutic intervention in CaP. This figure summarizes kinases that were found to be deregulated during CaP progression. This includes kinases whose phosphorylation status was reported in ≥2 phosphoproteomics datasets and kinases showing >5% frequency of mutation and homo-deletion in cBIO and those showing >5% frequency of amplification with significant P-values in LOC vs CRPC in cBIO. Kinases subject to somatic mutations are shown in a darker blue box, kinases subject to homodeletion in a light blue box, kinases subject to amplification in a green box and kinases with reported phosphorylation status in a red box. If a kinase falls in ≥2 categories, the border of the box has been adjusted with the corresponding color(s). Kinases belonging to or regulating a particular pathway or cellular process are grouped together. The pathways regulating each other are connected by dashed lines (not necessarily unidirectional). Kinases whose functions are not clearly established are placed in a gray box. The kinases for which inhibitors are undergoing testing in CaP clinical trials are italicized and underlined.

Citation: Endocrine-Related Cancer 30, 9; 10.1530/ERC-23-0011

Of all the kinases that showed up in phosphoproteomics data reviewed here, ~76 kinases have been the focus of clinical trials in different types of cancers. A few of these kinases (e.g. CDK4, VEGFR, SRC, and BTK) are also currently being investigated in clinical trials for CaP treatment (Table 3). However, these represent only a fraction of the kinases of interest in cancer-related clinical trials, suggesting that a vast set of kinases remains to be investigated, and at least some of these kinases could be potential targets in CaP. We also noted also some lesser-known kinases that have not been pursued actively for therapeutic intervention in clinical trials, such as the NIMA-related kinases. Nonetheless, both NEK1 and NEK2 were associated with CRPC progression, with NEK1 shown to drive CRPC growth, and NEK2 mRNA levels positively correlated with pathologic stage (Zeng et al. 2015, Singh et al. 2019). Table 4 provides a short list of kinases that have interesting profiles in our review of genomics and/or proteomics data sets, or may serve as CaP drivers or as therapeutic targets but are currently understudied. Overall, kinases constitute a key class of enzymes whose therapeutic blockade has many important consequences for managing cancer, and whose untapped therapeutic potential requires more attention, at least in the case of CaP.

Table 4

Representative examples of kinases that appear understudied in CaP.

Kinase Classification
BMPR1A TKL
CASK CAMK
DSTYK Other
HASPIN Other
KALRN CAMK
MINK1 STE
NEK3 Other
NEK5 Other
OBSCN CAMK
SLK STE
SPEG CAMK
STRADA STE
TTN CAMK
Wnk3 Other

Conclusions – considerations when pursuing kinase action for CaP treatment

Consistent with results from NextGen sequencing on clinical CaPs cases, a relatively small percentage of cBIO CaP cases showed somatic mutations or CNAs that impacted kinase-encoding genes, with increasing frequency observed during progression to treatment-resistant CaP. Although these observations suggest that somatic kinase alterations would not allow for CaP stratification, at least one such CRPC subtype has been isolated. This subtype showed bi-allelic loss of CDK12 and had increased neo-antigen burden, over-expressed chemokines and chemokine receptors, and higher immune cell infiltration. The findings implied that CRPCs harboring CDK12 mutations may respond better to immunotherapy (Wu et al. 2018) and spurred a phase II clinical trial to determine the efficacy of checkpoint inhibitor immunotherapy in this patient population (IMPACT, ClinicalTrials.gov identifier NCT03570619).

Review of cBIO data identified kinases and cellular pathways whose involvement in CaP aggressiveness are well-reported (e.g. ATM, DNA damage response), but also kinases that may have been somewhat overlooked, even when they are found to be altered in a relatively large proportion of CaPs cases and control cell functions relevant to the hallmarks of cancer. For instance, somatic mutations in OBSCN and TTN, which mediate sarcomere function (Kontrogianni‐Konstantopoulos et al. 2006) and may thus impact CaP cell adhesion and migration, are seen in up to 12.1 and 27.3% of CRPC cases. Further examination of somatic alterations affecting the CaP kinome may thus lead to novel insights into the mechanisms of CaP progression. However, the low incidence of such alterations suggests that these are not the only or even the main reasons for the dysregulated phosphoproteome observed during CaP’s evolution to a lethal disease. Rather, other genomic alterations or oncogenes may also underlie these changes. This possibility was supported by increased and oncogene-specific tyrosine phosphorylation in aggressive carcinoma that emerged in mouse models when AKT activation was combined with AR or activated K-Ras overexpression (Drake et al. 2012). The latter observations also fit well with AR dependence on kinase and phosphatase in CaP (Venkadakrishnan et al. 2020). The extent to which pSer, pThr, and pTyr signatures in clinical CaP cases differ in genomic or oncogene-based subtypes is an important question that remains to be explored in depth when therapeutic interventions targeting kinases are considered. Genomics and proteomics mechanisms, in all likelihood, do not fully describe the evolution of the phosphoproteome during CaP progression. Another relevant issue is to address to what extent the stage and lineage of CaP depend on the action of specific kinases. Stage- and lineage-dependency has been supported by cBIO data (Supplementary Tables 1, 3 and 4), which, for instance, shows a subset of kinase alterations enriched in NEPC (e.g. ULK1 (Zhang et al. 2017, Lin et al. 2021)). Alternatively, ADT induces AKT activity, which has led to clinical trials exploring the therapeutic potential of combined ADT and AKT inhibition in CRPC.

Along the same lines, could oncogene expression or specific phospho-protein signatures guide the selection of kinase-targeting therapies? While exploring this question in AKT-AR and AKT-activated K-Ras-driven mouse CaP models, increased sensitivity to SRC/ABL inhibition was noted in AKT/AR-activated mouse model (Drake et al. 2012). The extent to which this applies to information derived from more genomically complex human CaP specimens remains to be explored, especially as discrepancies in CaP phosphorylation patterns have been observed between cell lines, xenografts and patient specimens (Drake et al. 2013). The pCHIPS tool has been proposed to facilitate individualized kinase inhibitor treatments (Drake et al. 2016).

With regard to phosphoproteomic data, particular attention has been paid to phosphorylation of kinases as well, as this could indicate kinase activation and their potential as targets for therapy. The studies we reviewed showed ~166 kinases with altered phosphorylation statuses in CRPC (majority of these studies were on CRPC samples). In total, at least 28 kinases were found in two or more phosphoproteomics datasets (Table 2).

Phosphoproteomics data also allowed the identification of kinase substrates – predicted or experimentally validated. The phosphorylation status of kinase substrates may guide treatment considerations or serve as biomarkers of response to kinase inhibitor treatments. Approximately 158 kinases obtained from cBIO and phosphoproteomics data have predicted or experimentally validated substrates in phosphosite.org. These 158 kinases are targeting ~2772 substrate proteins. Some of these are also identified in the phosphoproteomics data, although the phosphorylation sites vary for some. As an example, for LRRK2 (discussed in section 2a), 21 substrates were predicted, and 8 of these were enriched in CRPC phosphoproteomics data. The proteins RAB10, EZR, and MSN were upregulated in CRPC (Katsogiannou et al. 2019), while RPS15 (Katsogiannou et al. 2019) and SNAPIN (Ino et al. 2016) were downregulated. LRRK2 is predicted to phosphorylate RAB10 at T73, EZR at T567, RPS15 at T136 and SNAPIN at T117. LRRK2 also directly phosphorylates AKT1 at the S473 position (Ohta et al. 2011), which is a marker for poor clinical LOC outcome and biochemical recurrence (Ayala et al. 2004, Kreisberg et al. 2004). AKT1 pS473 was also enriched in CRPC phosphoproteomic data (Yan & Huang 2019). This example represents just the tip of the kinome iceberg, and many of the identified kinases and substrates should serve as potential factors of interest in CaP. While some of these kinases/substrates have been studied in other diseases and cancers, they require further study in CaP. Figure 7 provides an overview of the kinases found altered in our review of cBIO and phosphoproteomics data.

In addition to identifying the kinases and their substrates, some other important considerations remain. One of these would be how to deduce the relevance of genomic, phosphoproteomic, or other alterations impacting kinases for CaP biology and progression. Mining shRNA- or CRISPR-based DePMap (Shimada et al. 2021) cancer dependencies can provide some guidance on the importance of loss of expression during CaP. But, this would rely on cell lines and would not completely capture in vivo progression, metastasis and treatment resistance. In-vivo functional screens could also be considered, as evidenced by the xenograft-based viral insertion studies that revealed OBSCN as a CaP tumor suppressor (Nalla et al. 2016). Another in vivo functional screening of kinases using integrated MS-based phosphoproteomics data and gene expression data (from overexpressed kinases of interest in murine prostate cells) identified 20 kinases that promoted in vivo metastasis. In a secondary in vivo screen using human prostate cells, ARAF, BRAF, CRAF, MERTK, and NTRK2 were found to drive bone and visceral metastasis (Faltermeier et al. 2016). An important question that arises during these studies is how to extend the efforts of assessing the relevance of mutations, CNAs, and phosphorylation sites impacting kinase action (alone or in specific combinations) in clinically relevant model systems that accurately reflect CaP progression. Even with the incorporation of information on functional consequences of somatic alterations or site-specific phosphorylations from databases such as cosmic or phosphosite.plus (Bamford et al. 2004, Hornbeck et al. 2015), this remains a Herculean task. It should also be considered that patient-derived phosphoproteomics datasets have been mostly obtained from CRPC, and are yet to be explored in treatment-resistant NEPC and other emerging lineages.

Nonetheless, targeting kinases or the phosphoproteome for CaP treatment appears promising when considering that kinase activities exhibit intra-patient similarity and inter-patient heterogeneity (Drake et al. 2013). The efficacy of kinase inhibition may, therefore, not be hampered by inter- and intra-CaP heterogeneity in an individual patient. In terms of kinase inhibition as a treatment option, CaP is considerably lagging behind other human malignancies. Even though several clinical trials testing kinase inhibitors are active and recruiting, results from previous trials have not yet led to FDA approval of any kinase inhibitors for treatment-resistant CaP. A better understanding of kinases driving the aggressive behavior and treatment resistance of CaP may help select the most promising inhibitors for further evaluation – to the extent these are available and specific to their kinase target. In this respect, it will also be important to determine to what extent somatic alterations and/or phosphorylation status of the CaP kinome impact efficacy of KIs. Given the importance of several kinases for development and normal physiology (Fisher 2011, Malumbres 2011, Thompson & Sahai 2015), any off-target effects will need to be carefully considered. However, the lack of treatment options for CRPC, NEPC, and other emerging CaP lineages that are resistant to standardized care and therapies, underlines the need of considering the therapeutic potential of inhibiting kinases that contribute to the lethal progression of CaP as an alternative treatment strategy.

Supplementary materials

This is linked to the online version of the paper at https://doi.org/10.1530/ERC-23-0011.

Declaration of interest

Nidhi Singh has declared that no conflict of interest exists. Hannelore V. Heemers is an inventor on US patent application 63/170,898 ‘Citron kinase inhibitors”.’

Funding

This work was supported by NIH NCI grants CA166440 (HVH), CA248048 (HVH) and a Falk Medical Research Trust Catalyst Award (HVH).

Author contribution statement

NS wrote the manuscript. HVH edited the manuscript. NS reviewed cBIO data. NS and HVH performed literature searches, selected data to be discussed, and critically reviewed the manuscript. Both authors reviewed and approved the final manuscript.

Acknowledgements

The authors thank Gideon Jebaraj Srinivasan for review of Supplementary table entries.

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