Centrosome associated genes pattern for risk sub-stratification in multiple myeloma
Jazyk angličtina Země Anglie, Velká Británie Médium electronic
Typ dokumentu časopisecké články, práce podpořená grantem
PubMed
27234807
PubMed Central
PMC4884414
DOI
10.1186/s12967-016-0906-9
PII: 10.1186/s12967-016-0906-9
Knihovny.cz E-zdroje
- Klíčová slova
- Gene expression profiling, Multiple myeloma, Risk stratification,
- MeSH
- centrozom metabolismus MeSH
- dospělí MeSH
- hodnocení rizik * MeSH
- Kaplanův-Meierův odhad MeSH
- lidé středního věku MeSH
- lidé MeSH
- mnohočetný myelom genetika MeSH
- nádorový supresorový protein p53 genetika MeSH
- přežití bez známek nemoci MeSH
- regulace genové exprese u nádorů * MeSH
- rizikové faktory MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- stanovení celkové genové exprese * MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Názvy látek
- nádorový supresorový protein p53 MeSH
BACKGROUND: The genome of multiple myeloma (MM) cells is extremely unstable, characterized by a complex combination of structure and numerical abnormalities. It seems that there are several "myeloma subgroups" which differ in expression profile, clinical manifestations, prognoses and treatment response. In our previous work, the list of 35 candidate genes with a known role in carcinogenesis and associated with centrosome structure/function was used as a display of molecular heterogeneity with an impact in myeloma pathogenesis. The current study was devoted to establish a risk stratification model based on the aforementioned candidate genes. METHODS: A total of 151 patients were included in this study. CD138+ cells were separated by magnetic-activated cell sorting (MACS). Gene expression profiling (GEP) and Interphase FISH with cytoplasmic immunoglobulin light chain staining (cIg FISH) were performed on plasma cells (PCs). All statistical analyses were performed using freeware R and its additional packages. Training and validation cohort includes 73 and 78 patients, respectively. RESULTS: We have finally established a model that includes 12 selected genes (centrosome associated gene pattern, CAGP) which appears to be an independent prognostic factor for MM stratification. We have shown that the new CAGP model can sub-stratify prognosis in patients without TP53 loss as well as in IMWG high risk patients' group. CONCLUSIONS: We assume that newly established risk stratification model complements the current prognostic panel used in multiple myeloma and refines the classification of patients in relation to the disease risks. This approach can be used independently as well as in combination with other factors.
Department of Biology Faculty of Medicine Masaryk University Brno Czech Republic
Department of Clinical Hematology University Hospital Brno Jihlavská 20 625 00 Brno Czech Republic
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Fonseca R, Bergsagel PL, Drach J, Shaughnessy J, Gutierrez N, Stewart AK, Morgan G, Van Ness B, Chesi M, Minvielle S, et al. International Myeloma Working Group molecular classification of multiple myeloma: spotlight review. Leukemia. 2009;23:2210–2221. doi: 10.1038/leu.2009.174. PubMed DOI PMC
Greipp PR, San Miguel J, Durie BG, Crowley JJ, Barlogie B, Blade J, Boccadoro M, Child JA, AvetLoiseau H, Kyle RA, et al. International staging system for multiple myeloma. J Clin Oncol. 2005;23:3412–3420. doi: 10.1200/JCO.2005.04.242. PubMed DOI
Durie BG, Salmon SE. A clinical staging system for multiple myeloma. Correlation of measured myeloma cell mass with presenting clinical features, response to treatment, and survival. Cancer. 1975;36:842–854. doi: 10.1002/1097-0142(197509)36:3<842::AID-CNCR2820360303>3.0.CO;2-U. PubMed DOI
Harousseau JL, Shaughnessy J, Richardson P. Multiple myeloma. Hematology Am Soc Hematol Educ Program. 2004;25:237–256. PubMed
Matsui S, Yamanaka T, Barlogie B, Shaughnessy JD, Jr, Crowley J. Clustering of significant genes in prognostic studies with microarrays: application to a clinical study for multiple myeloma. Stat Med. 2008;27:1106–1120. doi: 10.1002/sim.2997. PubMed DOI
Chng WJ, Ahmann GJ, Henderson K, Santana-Davila R, Greipp PR, Gertz MA, Lacy MQ, Dispenzieri A, Kumar S, Rajkumar SV, et al. Clinical implication of centrosome amplification in plasma cell neoplasm. Blood. 2006;107:3669–3675. doi: 10.1182/blood-2005-09-3810. PubMed DOI PMC
Dementyeva E, Nemec P, Kryukov F, Muthu Raja KR, Smetana J, Zaoralova R, Greslikova H, Kupska R, Kuglik P, Hajek R. Centrosome amplification as a possible marker of mitotic disruptions and cellular carcinogenesis in multiple myeloma. Leuk Res. 2010;34:1007–1011. doi: 10.1016/j.leukres.2009.12.018. PubMed DOI
Kryukov F, Dementyeva E, Smetana J, Sevcikova S, Kuglik P, Hajek R. Association of aneuploidy category with centrosome amplification in multiple myeloma. Leuk Lymphoma. 2011;52:2020–2022. doi: 10.3109/10428194.2011.587561. PubMed DOI
Kryukov F, Nemec P, Dementyeva E, Kubiczkova L, Ihnatova I, Budinska E, Jarkovsky J, Sevcikova S, Kuglik P, Hajek R. Molecular heterogeneity and centrosome-associated genes in multiple myeloma. Leuk Lymphoma. 2013;54:1982–1988. doi: 10.3109/10428194.2013.764416. PubMed DOI
Dementyeva E, Kryukov F, Kubiczkova L, Nemec P, Sevcikova S, Ihnatova I, Jarkovsky J, Minarik J, Stefanikova Z, Kuglik P, Hajek R. Clinical implication of centrosome amplification and expression of centrosomal functional genes in multiple myeloma. J Transl Med. 2013;11:77. doi: 10.1186/1479-5876-11-77. PubMed DOI PMC
Sevcikova S, Nemec P, Pour L, Hajek R. Genomics in multiple myeloma research. Klin Onkol. 2011;24(Suppl):S34–S38. PubMed
Nemec P, Zemanova Z, Kuglik P, Michalova K, Tajtlova J, Kaisarova P, Oltova A, Filkova H, Holzerova M, Balcarkova J, et al. Complex karyotype and translocation t(4;14) define patients with high-risk newly diagnosed multiple myeloma: results of CMG2002 trial. Leuk Lymphoma. 2011;53:920. doi: 10.3109/10428194.2011.634042. PubMed DOI
Carvalho BS, Irizarry RA. A framework for oligonucleotide microarray preprocessing. Bioinformatics. 2010;26:2363–2367. doi: 10.1093/bioinformatics/btq431. PubMed DOI PMC
Gautier L, Cope L, Bolstad BM, Irizarry RA. affy–analysis of Affymetrix GeneChip data at the probe level. Bioinformatics. 2004;20:307–315. doi: 10.1093/bioinformatics/btg405. PubMed DOI
Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, Muller M. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform. 2011;12:77. doi: 10.1186/1471-2105-12-77. PubMed DOI PMC
Chng WJ, Dispenzieri A, Chim CS, Fonseca R, Goldschmidt H, Lentzsch S, Munshi N, Palumbo A, Miguel JS, Sonneveld P, et al. IMWG consensus on risk stratification in multiple myeloma. Leukemia. 2014;28:269–277. doi: 10.1038/leu.2013.247. PubMed DOI
Avet-Loiseau H, Durie BG, Cavo M, Attal M, Gutierrez N, Haessler J, Goldschmidt H, Hajek R, Lee JH, Sezer O, et al. Combining fluorescent in situ hybridization data with ISS staging improves risk assessment in myeloma: an International Myeloma Working Group collaborative project. Leukemia. 2013;27:711–717. doi: 10.1038/leu.2012.282. PubMed DOI PMC
Hanamura I, Stewart JP, Huang Y, Zhan F, Santra M, Sawyer JR, Hollmig K, Zangarri M, Pineda-Roman M, van Rhee F, et al. Frequent gain of chromosome band 1q21 in plasma-cell dyscrasias detected by fluorescence in situ hybridization: incidence increases from MGUS to relapsed myeloma and is related to prognosis and disease progression following tandem stem-cell transplantation. Blood. 2006;108:1724–1732. doi: 10.1182/blood-2006-03-009910. PubMed DOI PMC
Avet-Loiseau H, Attal M, Moreau P, Charbonnel C, Garban F, Hulin C, Leyvraz S, Michallet M, Yakoub-Agha I, Garderet L, et al. Genetic abnormalities and survival in multiple myeloma: the experience of the Intergroupe Francophone du Myelome. Blood. 2007;109:3489–3495. doi: 10.1182/blood-2006-08-040410. PubMed DOI
Bergsagel PL, Kuehl WM. Molecular pathogenesis and a consequent classification of multiple myeloma. J Clin Oncol. 2005;23:6333–6338. doi: 10.1200/JCO.2005.05.021. PubMed DOI
Munshi NC, Anderson KC, Bergsagel PL, Shaughnessy J, Palumbo A, Durie B, Fonseca R, Stewart AK, Harousseau JL, Dimopoulos M, et al. Consensus recommendations for risk stratification in multiple myeloma: report of the International Myeloma Workshop Consensus Panel 2. Blood. 2011;117:4696–4700. doi: 10.1182/blood-2010-10-300970. PubMed DOI PMC
Amin SB, Yip WK, Minvielle S, Broyl A, Li Y, Hanlon B, Swanson D, Shah PK, Moreau P, van der Holt B, et al. Gene expression profile alone is inadequate in predicting complete response in multiple myeloma. Leukemia. 2014;28:2229–2234. doi: 10.1038/leu.2014.140. PubMed DOI PMC
Uetake Y, Sluder G. Cell cycle progression after cleavage failure: mammalian somatic cells do not possess a “tetraploidy checkpoint”. J Cell Biol. 2004;165:609–615. doi: 10.1083/jcb.200403014. PubMed DOI PMC
Fukasawa K. Oncogenes and tumour suppressors take on centrosomes. Nat Rev Cancer. 2007;7:911–924. doi: 10.1038/nrc2249. PubMed DOI
Kryukova E, Kryukov F, Hajek R. Centrosome amplification and clonal evolution in multiple myeloma: Short review. Crit Rev Oncol Hematol. 2016;98:116–121. doi: 10.1016/j.critrevonc.2015.10.019. PubMed DOI
Avet-Loiseau H, Attal M, Campion L, Caillot D, Hulin C, Marit G, Stoppa AM, Voillat L, Wetterwald M, Pegourie B, et al. Long-term analysis of the IFM 99 trials for myeloma: cytogenetic abnormalities [t(4;14), del(17p), 1q gains] play a major role in defining long-term survival. J Clin Oncol. 2012;30:1949–1952. doi: 10.1200/JCO.2011.36.5726. PubMed DOI
