Cell Cycle Genes Co-Expression in Multiple Myeloma and Plasma Cell Leukemia Fedor Kryukov, Elena Dementyeva, Lenka Kubiczkova, Jiri Jarkovsky, Lucie Brozova, Jakub Petrik, Pavel Nemec, Sabina Sevcikova, Jiri Minarik, Zdena Stefanikova, Petr Kuglik, Roman Hajek PII: DOI: Reference:
S0888-7543(13)00128-6 doi: 10.1016/j.ygeno.2013.06.007 YGENO 8534
To appear in:
Genomics
Received date: Accepted date:
24 February 2013 25 June 2013
Please cite this article as: Fedor Kryukov, Elena Dementyeva, Lenka Kubiczkova, Jiri Jarkovsky, Lucie Brozova, Jakub Petrik, Pavel Nemec, Sabina Sevcikova, Jiri Minarik, Zdena Stefanikova, Petr Kuglik, Roman Hajek, Cell Cycle Genes CoExpression in Multiple Myeloma and Plasma Cell Leukemia, Genomics (2013), doi: 10.1016/j.ygeno.2013.06.007
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Cell Cycle Genes Co-Expression in Multiple Myeloma and Plasma Cell Leukemia
T
Fedor Kryukov1, Elena Dementyeva1, Lenka Kubiczkova1, Jiri Jarkovsky1,2,Lucie Brozova1,2Jakub Petrik1, Pavel Nemec1, Sabina Sevcikova1, Jiri Minarik3, Zdena Stefanikova4 Petr Kuglik1,5, Roman Hajek1,6,7 1
RI P
Babak Myeloma Group, Department of Pathological Physiology, Faculty of Medicine, Masaryk University, Brno, Czech Republic Institute of Biostatistics and Analyses, Faculty of Medicine, Masaryk University, Brno, Czech Republic 3 Department of Internal medicine, Faculty of Medicine and Faculty Hospital, Palacky University, Olomouc, Czech Republic 4 Department of Hematology and Blood Transfusion, University Hospital Bratislava, Slovak Republic 5 Integrated Laboratory of Molecular Cytogenetics, Department of Experimental Biology, Faculty of Science, Masaryk University, Brno, Czech Republic 6 Department of Clinical Hematology, University Hospital Brno, Czech Republic 7 Instituteof Clinical Hematology, University Hospital, Ostrava, Czech Republic
Contact information for correspondence
MA NU
SC
2
Fedor Kryukov, Babak Myeloma Group, Dept. of Pathological Physiology Faculty of Medicine, Masaryk University Kamenice 5 / A3, 62500 Brno, Czech Republic
ED
Phone number: 00420549495681
AC
CE
PT
E-mail:
[email protected]
ACCEPTED MANUSCRIPT
time-to-progression benefit in newly diagnosed
The objective of this study was to describe co-
patients (remission median was 20.8±3.6 months for
expression correlations of cell cycle regulatory genes
‘low’ and 8.4±2.7 months for ‘high’ expressed group,
in multiple myeloma (MM) and plasma cell leukemia
p80% plasma cells were provided for
evaluated in a duplicate reaction using TaqMan Gene
total RNA isolation.
Expression Assays and human glyceraldehydes 3-
and
sorted
using
AutoMACS
phosphate dehydrogenase (GAPDH) as an internal Fluorescence in situ hybridization (FISH) control on 7500 Real Time PCR System. Raw data FISH was performed as a part of routine diagnostic procedure according to protocol previously described
were analyzed with SDS 1.4 software and relative fold
ACCEPTED MANUSCRIPT
change of expression for each gene was calculated
among groups of patients was analyzed using
using Ct approach.
nonparametric Kruskal-Wallis or Mann-Whitney U test. For discrete variables, chi-square test or Fisher’s exact test was used. For the robust analysis of
Proteins from 4 MM patients PCs, 2 PCL patients PCs
continuous parameters relationship the Spearman
and Hela cell line (positive control) were extracted
correlation
using RIPA buffer (2M Tris, 5M NaCl, 0,5M EDTA, 0,5%
correlation analysis was applied for the analysis of
NP-40, 0,2% NaF) containing 10% PhosSTOP and 10%
relationship between set of variables. Survival and
Complete (Roche). Equal amounts of proteins were
progression rates were estimated using the Kaplan-
RI P
was
adopted.
Canonical
SC
coefficient
MA NU
separated by SDS-PAGE, transferred onto PVDF
T
Immunoblotting
Meier method. The overall survival (OS) and time-toprogression (TTP) were determined according to
mouse mAb p-16 (DCS-50), rabbit Ab p-14 ARF (H-132)
International Myeloma Working Group guidelines
(both Santa Cruz) and GAPDH Ab (Cell Signaling
[10]. Differences in survival among subgroups of
ED
membrane (Millipore) and incubated with primary
patients were compared using the log-rank test. Time-
buffer. Subsequently, membrane was washed in TBS-T
dependent receiver operating characteristic (ROC)
and incubated with secondary antibodies conjugated
was used for the identification of cut-off value of
CE
PT
Technology) overnight at 4°C in 5% milk in TBS-T
continuous variables for survival of patients. Cox
rabbit IgG, 1:5000, Sigma-Aldrich) in TBS-T buffer and
proportional hazards models were used to assess the
AC
with horseradish peroxidase (anti-mouse IgG and anti-
visualized by chemiluminescence (Immobilon Western
association of prognostic factors with OS and TTP. P-
Chemiluminiscent HRP Substrate, Millipore). The same
values below 0.05 were considered as statistically
exposition time (5 minutes) was used for all samples.
significant in all analyses.
Statistical analysis
Results Standard descriptive statistics were applied in the analysis; median supplemented by min-max range for
Gene sets coexpression in newly diagnosed and
continuous variables and absolute and relative
relapsed MM
frequencies for categorical variables. Statistical
Chosen genes, connected with G1-to-S cell-cycle
significance of differences in continuous variables
progression, were divided into 2 groups based on
ACCEPTED MANUSCRIPT
Among inhibitors set noteworthy changes were
CCND3, CDK2, CDK4, CDK6, CCNA1, CCNE1, and
revealed for CDKN2A. This inhibitor in relapsed cohort
MDM2) and inhibitors (TP53, CDKN1A, CDKN1B,
lost its correlation with RB1 and CCND1 but
CDKN2A, ATM, ATR and CHEK2). All information about
correlation with CDK6 was restored. Moreover, TP53
genes function was taken from Gene Ontology
and CHEK2 restore association with CCND1 expression
database [11].
in relapsed cohort as well (Tab 2). analysis
defined
RI P
correlation
strong
significant positive correlation (R=0.905, p0.043), CCND1 (8.1-fold change, p=0.005) in PCL samples. Analysis of Spearman correlation between different activator/inhibitor genes in MM and PCL revealed following tendencies. In PCL cohort CCND1 and CDK6
Comparison of gene expression in newly diagnosed expression lost correlation with expression of blocker
AC
and relapsed MM patients revealed significant mild genes. Moreover in PCL cohort CDKN1B expression
up-regulation of CDK4 and MDM2 (1.4-fold change, lost correlation with expression of activator genes
p>0.030), TP53 (1.5-fold change, p=0.008), ATM (1.6(Tab 3). Interesting tendencies were found for fold change, p=0.039), CDKN2A, CHEK2 and CDK4 (1.8CDKN2A and CDKN1A blocker genes. For both genes fold change, p>0.040) in relapsed MM samples. loss of correlation with some activators were Analysis of Spearman correlation between different associated with significant increasing of correlation activator/inhibitor genes in in newly diagnosed and coefficient with other activators (Fig 1). relapsed MM patients revealed following major tendencies. In relapsed cohort CCNE1, CCNA1 and CDK6 genes lost their correlation with inhibitors set in comparison with newly diagnosed MM patients.
Overall survival
ACCEPTED MANUSCRIPT
Univariate Cox proportional hazards survival model
subgroups have similar distribution of Durie-Salmon
with one explanatory variable showed prognostic
staging (p=0.930) as well as ISS staging (p=0.190).
1.688]; p=0.031) in MM cohort for newly diagnosed patients. To further characterize the prognostic significance
of
CDKN2A,
CCND3
and
CCNA1,
CCNA1 have different distribution of Durie-Salmon staging (p=0.020). Thus, CCND3 and CCNA1 expression subgroups were excluded from further analysis. To determine the prognostic impact of defined
MA NU
multivariate Cox proportional hazards survival model
T
1.957]; p=0.001), CCNA1 (HR 1.316 [HR95%CI: 1.026;
different distribution of ISS staging (p=0.033) and
RI P
1.029]; p=0.045), CCND3 (HR 1.515 [HR95%CI: 1.173;
CCND3 expression-based patient subgroups have
SC
impact for CDKN2A (HR 1.014 [HR95%CI: 1.000;
was used. The variables in the multivariate model were the only variables which remained statistically
significant when potential predictors were combined
ED
together as well as genes expression which were forced into the model. The results suggest a CDKN2A
PT
and CCNDA1 expression importance for survival of
CE
patients due its statistical significance even when combined with other predictors (p
