Protein kinase C isozymes; predictors of progression free survival in NSCLC patients

doi:10.21203/rs.2.10276/v1

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Protein kinase C isozymes; predictors of progression free survival in NSCLC patients

https://doi.org/10.21203/rs.2.10276/v1

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Background

Protein expression is deregulated in cancer, and the proteomic changes observed in lung cancer may be a consequence of mutations in essential genes. The purpose of this study was to identify protein expression associated with prognosis in lung cancers stratified by smoking status, molecular subtypes, and EGFR-, TP53- and KRAS-mutations.

Methods

We performed profiling of 295 cancer-relevant phosphorylated and non-phosphorylated proteins, using reverse phase protein arrays. Biopsies from 80 patients with operable lung adenocarcinomas were analyzed for protein expression and association with progression free survival (PFS) were studied.

Results

Spearman rank correlation analysis identified 56 proteins with significant association to PFS (p<0.05). High expression of protein kinase C (PKC)-α and the phosporylated state of PKC-α, PKC-β and PKC-δ, showed the strongest positive correlation to PFS, especially in the wild type samples. This was confirmed in gene expression data from 186 samples. Based on protein expression, unsupervised hierarchical clustering separated the samples into four subclusters enriched with the molecular subtypes TRU, PI or PP (p=0.0001). Subcluster 2 contained a smaller cluster (2a) enriched with samples of the subtype PP, low expression of the PKC isozymes, and associated with poor PFS (p=0.003) compared to the other samples. Subcluster 2a revealed increased expression of neuroendocrine markers, supporting the aggressive behavior. Low expression of the PKC isozymes in the subtype PP and a reduced relapse free survival was confirmed with the TCGA LUAD samples.

Conclusion

This study identified different proteins associated with PFS depending on molecular subtype, smoking- and mutational-status, with PKC-α, PKC-β and PKC-δ showing the strongest correlation. Cluster analysis detected a subgroup of samples enriched for samples of the PP subtype and poor PFS, which may benefit from a more aggressive treatment regimen.

Oncology

expression subtypes

PKC

NSCLC

adenocarcinoma

protein profiling

neuroendocrine markers

progression free survival

Proteins are the functional players driving both normal and disease processes. Some of the most important types of mutations in lung cancer occur in EGFR, TP53 and KRAS. Mutations in these genes may lead to changes in many interacting pathways, leading to significantly altered protein expression. They are known to influence treatment response and regarded as essential for progression of lung cancer [

                 1
              </a>]. Some of the changes in protein expression observed in lung cancer are a consequence
           of mutations in essential driver genes, and targeted therapy is usually efficient
           for these subgroups of patients [<a href="#_ENREF_2">
              
                 2
              </a>].</p>

Patients with somatic genomic alterations in the EGFR gene are routinely treated with EGFR inhibitors, which have improved the outcome for this patient group [

                 3
              </a>]. Mutations in <em>KRAS</em> leads to constitutively and persistent stimulus-independent activation of downstream
           pathways affecting tumor growth, proliferation and survival. Developing treatment
           targeting KRas has proved to be complicated, but ongoing studies investigate inhibition
           of effector-molecules downstream of KRas, including ERK and MEK [<a href="#_ENREF_4">
              
                 4
              </a>]. Nevertheless, MEK inhibitors are associated with early development of resistance
           due to crosstalk with other signaling pathways which make this approach challenging
           [<a href="#_ENREF_5">
              
                 5
              </a>]. So far, no efficient therapy to re-establish the function of p53is in clinical use, but studies with reactivation of p53 have been performed [<a href="#_ENREF_6">
              
                 6
              </a>]. In order to improve outcome for lung cancer patients, stratification based on alterations
           in essential genes and the affected pathways may lead to better treatment strategies
           and increased response rate. </p>

To sub-classify non-small cell lung cancer (NSCLC), intrinsic molecular subtypes have been explored based on gene expression profiling. Three subtypes have been identified for the adenocarcinomas, namely the Bronchioid, Magnoid, and Squamoid subtype, later re-named to terminal respiratory unit (TRU), proximal proliferative (PP), and proximal inflammatory (PI), respectively [

                 7-9
              </a>]. The TRU subtype is most common among females and never-smokers, and often harbours
           <em>EGFR</em> mutations and a less invasive phenotype. Early stages of the TRU subtype have better
           prognosis. Gene expression profiles related to biological processes involved in excretion,
           asthma, and surfactants are associated with the TRU subtype. The PP subtype is reported
           with a high frequency of <em>KRAS</em> and <em>TP53</em> mutations, over-expression of DNA repair genes and is often found in heavy smokers.
           The PI subtype is recognized with over-expression of defense response genes such as
           CXCL10 and is most common in high grade tumors [<a href="#_ENREF_7">
              
                 7
              </a>]. At late stages, the PI subtype is associated with better survival compared to the
           other subtypes [<a href="#_ENREF_8">
              
                 8
              </a>]. Chromosomal instability (CIN), copy number alterations, and genomewide DNA methylations
           are also reported to differ among the three subtypes, with the PP subtype having the
           highest CIN [<a href="#_ENREF_8">
              
                 8
              </a>]. So far, microRNA expression and protein expression are reported to only partially
           correlate with the molecular subtypes of adenocarcinoma [<a href="#_ENREF_9">
              
                 9
              </a>]. In order to treat lung cancer patients more efficiently, groups of patients who
           share common biological features such as mutations or pathway alterations should be
           identified and treated with drugs optimized for their subgroup. It has been known
           for a long time that never-smoking NSCLCs are recognized with a different underlying
           biology compared to ever-smokers. However, except from a handful of known genetic
           aberrations such as <em>EGFR</em> mutations and <em>ALK</em> rearrangement, no subgroups of NSCLC are stratified for optimized treatment based
           on the molecular profile of the tumor.</p>

The purpose of this study of lung adenocarcinoma, was to identify differences in protein expression levels associated with prognosis, stratified on EGFR, TP53 and KRAS mutations status, and smoking status.

Patients diagnosed with operable NSCLC adenocarcinoma from 2006 to 2011, were included and analyzed for protein expression (n=80) in the Oslo cohort. The patients underwent curatively intended surgical resection at Rikshospitalet, Oslo University Hospital, Norway. Tumor samples were snap frozen in liquid nitrogen and stored at -80°C until protein extraction. Pathological stage, mutation status and smoking status are outlined in Table 1. All the samples were classified as adenocarcinomas. Never-smokers are defined as those who have smoked less than 100 cigarettes.

The study was approved by the Regional Ethics Committee (S-05307), and written informed consent was obtained from all patients.

Table 1. Patient characteristics

Table 1. Mutation status, smoking history and stage for the patients analyzed for protein expression (n=80). Samples not mutated, wild type (WT), are shown in brackets.

Reverse phase protein arrays

We have performed profiling of 295 cancer relevant proteins of which 60 were in a phosphorylated state (S1 Table and supplementary material and methods) on the Oslo cohort, using the reverse phase protein array (RPPA) core facility at MD Anderson Cancer Center (Houston, TX). RPPA data (n=131 proteins) and phenotypes from the LUAD cohort were extracted from the cancer genome atlas (TCGA) data generated by the TCGA Research Network:

                    <a href="http://cancergenome.nih.gov/">http://cancergenome.nih.gov/</a>
                 </a>. Samples with no expression subtype assigned and without registered relapse free
           survival were filtered out. The remaining 181 LUAD samples were utilized as validation
           set. Time to relapse was extracted for survival analyses. Of note, the median follow-up
           time for alive patients was 23.9 months for the LUAD cohort, compared to 60 months
           for the Oslo cohort. </p>

EGFR, TP53 and KRAS analyses

Mutation analyses of EGFR exons 18–21 were performed using the TheraScreen EGFR mutation kit (DxS, Manchester, UK,) designed to detect 28 specific mutations in the EGFR gene. Assays were carried out according to the manufacturer’s protocol and with the use of the Roche LightCycler 480 real-time PCR system. Some of the results were previously published by Helland et al [

              10
           </a>].</p>

The TP53 gene was analyzed by the Sanger Sequencing method in all the tumor samples. The procedure was performed on an Applied Biosystems 3730 DNA analyser according to the supplier’s handbook, Applied Biosystem 3730/3730X/DNA Analysers Part 4331467 Rev.B, as previously described [

              11
           </a>]. More details are provided in supplementary material and methods.</p>

We used the wobble-enhanced ARMS (WE-ARMS) methodfor detecting KRAS mutations in the lung adenocarcinoma samples. This mutation assay detects the seven most commonly reported mutations in the KRAS gene—KRAS g.34G>C (p.G12R), g.34G>A (p.G12S), g.34G>T (p.G12C), g.35G>A (p.G12D), g.35G>C (p.G12A), g.35G>T (p.G12V) and g.38G>A (p.G13D)—by real-time PCR [

              12
           </a>].</p>

Gene expression and subtyping of adenocarcinoma samples

Gene expression was performed on 186 adenocarcinomas (including 79 of those with protein expression) from the same cohort, using hybridization arrays (SurePrint G3 Human, 8x60K, Agilent Technologies). More details are provided in supplementary material and methods. The adenocarcinoma samples were assigned a gene expression subtype using the previously described 506 gene centroid classifier and Pearson correlation [

              7
           </a>, <a href="#_ENREF_8">
           
              8
           </a>]. Four of the samples had a negative correlation with all three subtypes and were
        not assigned to any subtype. The raw data and normalized data are deposited in ArrayExpress
        database: <a href="https://www.ebi.ac.uk/arrayexpress/">
                 <a href="http://www.ebi.ac.uk/arrayexpress">www.ebi.ac.uk/arrayexpress</a></a> under accession number: E-MTAB-7708.&nbsp; </p>

Immunohistochemistry (IHC)

IHC staining for the neuroendocrine markers CD56, synaptophysin, chromogranin A and NSE were performed using the antibodies as outlined in S2 Table and supplementary material and methods.

Statistics

All the statistical analysis were performed in R (v 3.3.2) [

              13
           </a>] using RStudio (v 1.1.447). Hierarchical clustering was performed to visualize the
        total protein expression for all the samples with mutation status, stage, subtype,
        smoking history and progression free survival (PFS) data included. Hierarchical clustering
        was performed using the publicly available R-package Clustermap (Lingjærde and Steen,
        submitted). In brief, median-centered and log2-transformed RPPA-data were clustered
        using Pearson distance and average linkage, and with N=200 iterations in the PART
        method used to estimate the number of clusters, as described previously [<a href="#_ENREF_14">
           
              14
           </a>]. For heatmap visualization of the data, values are normalized to the range [-1.2,
        1.2] by application of a nonlinear sigmoid transformation f(x) = 1.2*tanh(x/1.2).
        This limits the visual dominance of outlier values while maintaining the order of
        the values, since f is strictly increasing. PFS was calculated from the date of surgery
        until the date of event, defined as relapse, metastases or death from lung cancer.
        Correlation between protein expression and PFS was calculated using two-sided Spearman
        test. Student t-test was performed to identify differentially expressed proteins between
        the groups of interests. Survival analysis was performed using cox regression analysis
        and Kaplan Meyer survival plot. The significance threshold was set to p&lt;0.05.  </p>

In this study we examined the expression of 235 proteins and 60 phospho-proteins in tumors from 80 patients diagnosed with lung adenocarcinomas. We identified 56 proteins and phospho-proteins likely to impact the patient outcome. When stratifying the samples according to mutations in the genes TP53, EGFR or KRAS, more proteins were associated with PFS in samples without mutations. This was also seen when analyzing the corresponding mRNA data. When we compared proteins associated with progression in never-smokers and former/current smokers, no overlapping proteins were found, confirming the differences between these groups of lung cancer. Cluster analysis identified a small subcluster containing 11 patients enriched with tumors of the PP subtype, recognized with early relapse, low expression of PKC isozymes, and increased expression of neuroendocrine markers.

Cluster analysis

Based on the expression of 295 proteins, unsupervised hierarchical clustering separated the samples into four subclusters. However, pathological stage, smoking status, or mutations in the genes TP53, KRAS or EGFR did not seem to impact the clustering. Interestingly, the four subclusters were significantly correlated with the molecular subtypes TRU, PP and PI. Previous work using protein expression identified six subgroups of adenocarcinoma, where the subgroups partially overlapped with the three mRNA-derived subtypes. The PP subtype was further divided into two groups [

              9
           </a>], which also was seen in our data set. Subcluster 2a, enriched with PP subtype samples,
        was recognized with lower expression of members of the mTOR pathway and the MAPK pathway.
        This is in line with previous finding where TRU samples are associated with higher
        expression of these proteins when compared to PP samples [<a href="#_ENREF_15">
           
              15
           </a>]. Interestingly, cluster analysis performed on mRNAs corresponding to the proteins,
        grouped the samples with PP subtype from the protein-derived subcluster 2a together,
        but not the non-PP subtypes. However, the distinct pattern with very low expressed
        proteins within subcluster 2a, was not reflected using mRNA data. This can be explained
        with a lower correlation between phosphorylated proteins and the corresponding genes.
        Nine of the 11 samples within subcluster 2a showed a positive staining for synaptophysin
        which are considered as a neuroendocrine marker and can be used to confirm the diagnosis
        [<a href="#_ENREF_16">
           
              16
           </a>]. In total, all the 11 samples showed positive staining for at least one of the neuroendocrine
        marker or NSE. In a recent study on NSCLCs, a molecular subgroup enriched with PP
        subtype and shorter survival was identified. These samples had a mixed histology predominantly
        with adenocarcinomas with molecular expression pattern associated with neuroendocrine
        tumors [<a href="#_ENREF_15">
           
              15
           </a>]. LCNEC (large cell neuroendocrine carcinoma) can share some of the same pathological
        features as adenocarcinomas, and LCNEC with areas of adenomatous differentiations
        (mixed LCNEC) is described. Both pure LCNEC and mixed LCNEC tumors exhibit an aggressive
        behavior and are associated with poor survival [<a href="#_ENREF_17">
           
              17
           </a>] as seen within subcluster 2a in our analyses. </p>

The hierarchical clustering performed on 186 mRNA samples resulted in a significantly different distribution of the molecular subtypes between the clusters, although the PI samples did not cluster together. Of note, the overlap between the genes used for the original sub-typing [

              8
           </a>] and our clustering based on protein expression was sparse (n=18), indicating that
        the proteins included in our analysis seem to be important for the subtypes.  Both
        the TRU- and PP-subtype clustered based on our mRNA data, and we suggest that the
        TRU- and PP- subtypes are more distinct subtypes compared to PI. In a large meta-study,
        the TRU subtype was identified as the most prognostically important subtype compared
        to the non-TRU subtype, arguing for the need to identify additional classifiers [<a href="#_ENREF_18">
           
              18
           </a>]. </p>

Protein kinase C levels associated with survival

The protein kinase C is a group of enzymes known to be involved in diverse cellular functions, including cell proliferation, apoptosis and cell migration, and has been regarded as an onco-protein. The members of the PKC-family are encoded from nine different genes which have several known splice variants. Recent work has demonstrated that these proteins may have a more complex role than first assumed, which is supported by the many failed clinical trials for cancer using PKC-inhibitors. In addition, mutational studies have revealed that most cancers have loss of function (LOF) mutations in genes belonging to the PKC-family, suggesting a tumor suppressor role for the proteins [

              19
           </a>, <a href="#_ENREF_20">
           
              20
           </a>]. A meta-study on the use of PKC-inhibitors combined with chemotherapy in lung cancer
        patients reported decreased response rate and disease control, compared to chemotherapy
        alone [<a href="#_ENREF_21">
           
              21
           </a>]. In our study, low expression of PKC-α, and phophorylated PKC-α, PKC-βII and PKC-δ
        were associated with poor PFS. In addition, the levels of the PKC isozymes were strikingly
        lower in subcluster 2a which also contained the samples with the overall poorest PFS.
        An association with low expression of PKC- δ and decreased relapse free survival was
        confirmed in the LUAD TCGA samples. Low levels of PKC-α, and phophorylated PKC-α and
        PKC-δ in subtype PP were also found in the TCGA samples. This support the findings
        in the Oslo cohort, with poor PFS and low expression of the isozymes of PKC in subgroup
        2a containing mainly PP samples. Unfortunately, sparse information on mutational status,
        reduced number of proteins analyzed, short follow-up time, limited further validation
        on the TCGA samples. </p>

Thus, with regard to the classical role of the PKC-family, our results suggest a general tendency towards a tumor suppressor role for PKC-α, PKC-βII and PKC-δ in NSCLC.

In the EGFR mutated samples, PKC-δ levels positively correlated to patient survival. It has been reported that an activation of PKC-δ can be promoted by an activated EGFR [

              22
           </a>]. Further, activation of PKC-δ may induce apoptosis and growth arrest resulting in
        reduced tumorigenesis [<a href="#_ENREF_23">
           
              23
           </a>]. As a response to DNA damage, it has been shown that over-expression of p53 increases
        the transcription of PKC-δ resulting in apoptosis. This may explain the significant
        correlation we discovered between expression of PKC-δ and PFS in <em>TP53</em> wild type samples, but not in the <em>TP53</em> mutated samples. These results were also reflected by gene expression data. It’s
        been demonstrated that PKC can phosphorylate many oncoproteins to suppress their activity,
        including KRas, PI3K and several tyrosine kinase receptors [<a href="#_ENREF_24">
           
              24
           </a>]. The oncoprotein KRas, recognized with activating mutations in cancer, can be suppressed
        by activated PKC, which is proposed as a novel approach to target KRas [<a href="#_ENREF_25">
           
              25
           </a>]. A negative correlation between KRas and PKC-α was recently described in colorectal
        cancer. Further, low expression of PKC-α was also associated with poor prognosis [<a href="#_ENREF_26">
           
              26
           </a>]. This is in concordance with our results, where high levels of PKC-α was associated
        with better PFS in <em>KRAS</em> mutated samples, whereas PKC-δ showed a higher correlation to PFS in those with <em>KRAS</em> wild type. Interestingly, no isozymes of PKC did influence on survival in never-smoking
        patients, further supporting this group as a distinct lung cancer disease driven by
        other mechanisms. </p>

This leads to the hypothesis that PKC may also have an essential role keeping oncoproteins in check [

              24
           </a>]. Based on our results, we suggest that the association to PFS for the different
        PKC- isozymes is connected to mutational and smoking status. Results from gene expression
        analysis performed on 186 NSCLC samples strengthen these observations.</p>

Proteins associated with PFS in subgroups of NSCLC

Interestingly, high expression of B7-H3, a molecule involved in immune checkpoint signaling, was correlated to poor outcome in our study, especially in smokers, those without any detected mutations in KRAS or TP53, and in those harboring an EGFR mutation. B7-H3 is a molecule known to inhibit T-cell activation in an immune suppressive manner. This protein has been shown to be linked to poor survival in cancer, and have been suggested as a new immune checkpoint target [

              27
           </a>]. In a recent study of lung cancer patients, expression of B7-H3 was associated with
        overall survival only in smokers [<a href="#_ENREF_28">
           
              28
           </a>]. This indicates that future anti-B7-H3 therapy may have higher success rate among
        ever smoking lung cancer patients with <em>KRAS</em> or <em>TP53</em> wild type tumors, or an <em>EGFR</em> mutation.</p>

Within subcluster 2a, eight proteins were significantly associated with PFS, where low expression of myosin II showed the highest correlation with better PFS. This is also supported by a protein study on early stage lung cancer where myosin IIa was reported to be upregulated in stage Ia/Ib lung cancer patients with early relapse [

              29
           </a>]. Interestingly, low expression of YAP, phosphorylated YAP(s127) and phosphorylated
        HSP27(s82) were associated with increased PFS in subcluster 2a. It has been shown
        that high expression of HSP27 leads to less phosphorylated YAP(s127). Further, phosphorylation
        of YAP on S127 decreased the activity of YAP since this prevent its translocation
        to the nucleus [<a href="#_ENREF_30">
           
              30
           </a>]. This also means that un-phosporylated YAP promotes tumor aggressiveness and is
        related to poor prognosis which is in line with our study. These finding highlight
        the central role HSP27 has in several pathways, including the Hippo pathway. </p>

Correlation analysis between mRNA and protein expression

Spearman Rank correlation revealed a high correlation (R >0.3) between expression levels of almost half of the proteins and mRNAs. Previous studies have reported that much of the variation in mean-level protein expression can be explained by variation in mRNA expression [

              31
           </a>, <a href="#_ENREF_32">
           
              32
           </a>]. However, the variance in the proteomes across different tissue types can poorly
        be explained by the mRNA levels, highlighting a tissue-specific posttranscriptional
        regulation of gene expression. In a study of lung cancer, proteins involved in metabolic
        and translational pathways were highly correlated with mRNA expression, whereas proteins
        involved in extracellular matrix and adhesion, were not correlated or anti-correlated
        [<a href="#_ENREF_32">
           
              32
           </a>]. In a study of breast cancer, 35% of the proteins correlated significantly (R &gt;
        0.3) with mRNA expression. The proteins, Cyclin B1, cyclin E1, 4E-BP1, PKC-α and RAB 25 were highly correlated with mRNA expression in breast cancer
           [<a href="#_ENREF_33">
           
              33
           </a>]. This is in line with our study, where these proteins showed a high correlation
        value (Rho &gt; 0.6). Interestingly, HER2 was highly correlated in the breast cancer
        study across all subtypes, but this protein was poorly correlated with mRNA expression
        in our lung study (Rho =0.17). On the other hand, EGFR revealed Rho=0.72 in our study,
        while in breast cancer a correlation between R=0.15 - 0.3 was found. This indicates
        that genes known to be deregulated in a specific cancer type may be regulated by other
        mechanisms. Proteins such as p53, CDKN1B, and MAPK14 showed very low correlation with the mRNA expression both in our
           study on lung cancer and in the breast cancer study [<a href="#_ENREF_33">
              
                 33
              </a>]. Lack of synergy between the level of proteins and mRNAs measured in the cells can
           have several explanations including copy number aberrations, miRNA expression and
           methylation.</p>

Conclusion

These results demonstrate that essential mutations in lung carcinomas affect several proteins associated with outcome. Based on our results, expression of PKCα and phosphorylated PKCα, PKCβ, and PKCδ seem to be positively associated with PFS, with different isozymes linked to smoking and mutational status of EGFR, KRAS and TP53. These results illustrate the need to better understand the biological context in order to further improve targeted therapy in cancer. This study supports that a therapy restoring the level of specific isozymes of PKC activity may be beneficial for subgroups of lung cancer patients based on the genetic background. We identified a subgroup of samples enriched with the molecular subtype PP, recognized with early relapse, increased expression of neuroendocrine markers, and a distinct protein expression pattern, including low levels of PKC isozymes. These patients may benefit from a more aggressive treatment regimen. Proteins associated with PFS among never smokers were strikingly different compared to the other investigated subgroups. This is not surprising, but underscores the need for a more stratified therapy in order to improve clinical outcome.

Table 1. Patient characteristics

Condition		Number
EGFR	Mutated (WT)	9 (71)

KRAS	Mutated (WT)(na)	31 (48)(1)

TP53	Mutated (WT)	31 (49)

Smoking history	Never (current or former)	9 (71)
Stage	Ia/Ib	44
	IIa/IIB	17
	IIIa	17
	na	2

Table 1. Mutation status, smoking history and stage for the patients analyzed for protein expression (n=80). Samples not mutated, wild type (WT), are shown in brackets.

Table 2. Proteins associated with PFS

Protein	Estimate	P-value
c-Abl	-0.386	0.0004
PKC-βII_pS660	0.361	0.0010
MIF	-0.359	0.0011
PAI-1	-0.348	0.0016
PKC-α _pS657	0.337	0.0023
Jak2	0.318	0.0041
JNK_pT183_Y185	-0.311	0.0051
PKC-α	0.310	0.0052
PKC-δ _pS664	0.310	0.0052
Vimentin	0.307	0.0056
eEF2K	0.300	0.0069
RPA32	0.296	0.0077
Caveolin1	0.296	0.0077
LC3A-B	-0.293	0.0083
PI3K-p85	0.289	0.0093
B-Raf	0.285	0.0105
Pdcd-1L1	0.283	0.0110
Fibronectin	-0.280	0.0117
Rictor	0.279	0.0123
B7-H3	-0.273	0.0145

Table 2. Top twenty proteins associated with PFS (p<0.05) in the total group of samples (n=80).

Table 3. The pair-wise spearman Rank correlation between mRNA data and protein data

Pair-wise correlation mRNA/protein			mRNA/protein significantly correlated to PFS
Gene name	Protein name	Spearmans Rho	Est mRNA	P-Val mRNA	Est protein	P-Val protein
AR	AR	0.475	0.162	0.027	0.224	0.045
CCNE1	Cyclin-E1	0.682	-0.162	0.027	-0.245	0.029
LCK	Lck	0.615	0.160	0.029	0.271	0.015
MIF	MIF	0.549	-0.236	0.001	-0.359	0.001
PIK3R1	PI3K-p85	0.380	0.194	0.008	0.289	0.009
PRKCA	PKC-α	0.648	0.205	0.005	0.310	0.005
PRKCD	PKC-δ _pS664	0.180	0.208	0.004	0.310	0.005
RPS6KA1	RSK	0.543	0.222	0.002	0.239	0.033
SHC1	Shc_pY317	0.309	-0.152	0.038	0.235	0.036
STAT5A	Stat5a	0.462	0.210	0.004	0.254	0.023

Table 3. The pair-wise spearman Rank correlation was performed on 79 samples, while the PFS analysis performed on mRNA data contained 186 samples. The overlap between PFS associated mRNA data and protein data are based on a threshold of p-value < 0.05.

S1 Fig. Hierarchical clustering of subcluster 2

S2 Fig. Hierarchical clustering of genes corresponding to the proteins included in RPPA

S3 Fig. Proteins associated with PFS in groups stratified on mutational status and smoking status

S4 Fig. Waterfall plot shows the correlation between protein expression (included proteins in phosporylated state) and mRNA expression. Y-axis displays the Spearman’s rho coefficient. The proteins/genes are distributed on x-axis ordered after degree of correlation. The 10 overlapping PFS associated proteins/genes are displayed with black spikes.

S1 Table. List of proteins and the types of antibodies used in the RPPA analysis

S2 Table. List of antibodies used for IHC

S3 Table. Proteins significantly differentially expressed (FDR<0.05) in samples from subcluster 2a compared to the other subclusters

S4 Table. Proteins significantly associated with PFS in the total group, in subgroup 2a, and after stratification for smoking- and mutational status

S5 Table. IHC staining of neuroendocrine markers in samples from subcluster 2a

S6 Table. Spearman Rank correlation between gene expression and expression of proteins/phosphoproteins

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Protein kinase C isozymes; predictors of progression free survival in NSCLC patients

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Abstract

Figures

Introduction

Material and methods

Reverse phase protein arrays

EGFR, TP53 and KRAS analyses

Gene expression and subtyping of adenocarcinoma samples

Immunohistochemistry (IHC)

Statistics

Discussion

Cluster analysis

Protein kinase C levels associated with survival

Proteins associated with PFS in subgroups of NSCLC

Correlation analysis between mRNA and protein expression

Conclusion

Tables

Supplemental File Legend

Supplementary Files

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