Plasma Cell Neoplasms Tables: Recurrent Cytogenomic Alterations

From Compendium of Cancer Genome Aberrations
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Chromosome Region (whole chromosome or segmental, including cytobands) Abnormality Type (gain, loss, LOH) Relevant genes (if known) Significance (Recurrent, Diagnostic, Prognostic, Targeted treatment) Strength of Evidence (Level 1, 2, 3, see legend below table for criteria) References PMID (year)
1 1p32 Loss FAF1, CDKN2C Poor prognostic marker 1, 2 [1] 24987674 (2014)[2], 25145975 (2015)[3]
1p22.2-p22.1 Loss BARHL2, TGFBR3, and others; HSP90B3P, TGFER3, BRDT, EPHAX4, BTBD8 Prognostic 1, 3 24987674 (2014)[2]  25145975 (2015)[3] 26912802 (2016)[4]
1p21.3 Loss SNX7 Recurrent 2 24429703-(2014)[5]
1p13.2 Loss MAG13(kinase), BCL2 like and others Recurrent 3 24757046 (2014)[6]
1p12 Loss MAN1A2, FAM46C, GDAP2 Recurrent 2 [1] 24987674 (2014)[2]
1p cnLOH Recurrent 2 25145975 (2015)[3]
1p Loss Recurrent 1 23010713 (2012)[7], 22833442 (2012)[8], 24987674 (2014)[2],  27588520 (2016)[9], 24757046 (2014)[6]
1q21.2-q23 Gain CKS1B and ANP32E Recurrent 1 23010713 (2012)[7], 22833442 (2012)[8], 24987674 (2014)[2], 27588520 (2016)[9], 24757046 (2014)[6]
1q Gain Poor prognostic marker 1 23010713 (2012)[7], 22833442 (2012)[8], 24987674 (2014)[2], 24757046 (2014)[6]
2 2 Gain Recurrent 2 22833442 (2012)[8]
2q Loss Recurrent 3 23010713 (2012)[7]
3 3 Gain Recurrent 1 23010713 (2012)[7], 22833442 (2012)[8], 24987674 (2014)[2], 24757046 (2014)[6]
3q21-23 Gain Recurrent 2 25145975 (2015)[3]
4 4p16.3 Loss FGFR3 and WHSC1 Recurrent 3 24987674 (2014)[2]
4p15.2 Loss LGI2, SEPSECS, PI4K2B and others Recurrent 3 24987674 (2014)[2]
4q35.1 Loss DCTD, ING2, and others Recurrent 2 26912802 (2016)[4]
5 5 Gain Recurrent 1 22833442 (2012)[8], 24987674 (2014)[2], 23010713 (2012)[7], 27588520 (2016)[9], 24757046 (2014)[6]
5p Gain Recurrent 3 24987674 (2014)[2]
5p Loss Recurrent 2 22833442 (2012)[8]
5p14.3 Gain CDH12,10 Recurrent 3 24757046 (2014)[6]
5q Gain Recurrent 2 25145975 (2015)[3] 24987674 (2014)[2]
5q13.2 Loss OCLN, NAIP, and others Recurrent 2 26912802 (2016)[4]
6 6p Gain Recurrent 2 25636340 (2015)[1], 22833442 (2012)[8], 24987674 (2014)[2]
6pter-p22.3 Gain Recurrent 3 24987674 (2014)[2]
6q Loss Poor prognostic marker 2 25636340 (2015)[1], 22833442 (2012)[8], 24987674 (2014)[2], 23010713 (2012)[7], 24757046 (2014)[6]  25145975 (2015)[3]
6q11.1-q13 Gain MTRNR2L9 Recurrent 3 26912802 (2016)[4]
6q16.3 Loss COQR, GRIK2 Recurrent 3 24987674 (2014)[2]
6q25.3 Loss IGFR3 Recurrent 3 24757046 (2014)[6]
6q16.3 Loss COQR, GRIK2 Recurrent 3 24987674 (2014)[2]
7 7 Gain Recurrent 1 22833442 (2012)[8], 24987674 (2014)[2], 23010713 (2012)[7], 24757046 (2014)[6]
7p Gain Recurrent 3 24987674 (2014)[2]
7p15.2 Gain CBX3, etc Recurrent 3 26912802 (2016)[4]
7q Gain Recurrent 2 25145975 (2015)[3], 24987674 (2014)[2]
8 8p Loss Recurrent 2 25636340 (2015)[1], 23010713 (2012)[7], 22833442 (2012)[8], 24987674 (2014)[2]  25145975 (2015)[3] 24757046 (2014)[6]
8p23.1 Loss DEFB4 and others Recurrent 2 26912802 (2016)[4]
8p21.3/p21.2 Loss TNFRSF10B, DOCK5 and others Recurrent 3 24757046 (2014)[6]  
8q Gain Recurrent 3 24987674 (2014)[2]
8q24.2 Gain/amplification and Loss MYC Recurrent 2 24757046 (2014)[6], 27588520 (2016)[9], 26338801 (2016)[10],  24987674 (2014)[2], 27811368-(2016)[11]
8q24.3 Gain MAPK15, TOP1MT, CYP11B11 (P450), ZNF41, 616, 707 and ZNF517 Recurrent 3 24757046 (2014)[6], 27811368-(2016)[11]
9 9 Gain Recurrent 1 22833442 (2012)[8], 24987674 (2014)[2], 23010713 (2012)[7], 24757046 (2014)[6]
9p Gain Recurrent 2 25145975 (2015)[3]   24987674 (2014)[2]
9q Gain Recurrent 25145975 (2015)[3]    24987674 (2014)[2]
9q34.3 Gain ZNF79, CDK9, SET Recurrent 3 24757046 (2014)[6]
10 10p Loss Recurrent 3 24987674 (2014)[2]
10q Loss Recurrent 3 24987674 (2014)[2]
10q23.31 Loss PTEN Recurrent 2 16112193 (2006)[12]
11 11 Gain Recurrent 1 22833442 (2012)[8], 24987674 (2014)[2], 23010713 (2012)[7], 24757046 (2014)[6]
11p Gain Recurrent 3 24987674 (2014)[2]
11q Gain Recurrent 2 25145975 (2015)[3], 23010713 (2012)[7]
11q13.1/q13.4 Gain SCYL1, MAP3K11, CCND1, FGF4, FGF3, NUMA, and RELT Recurrent 3 24757046 (2014)[6]
11q22 Loss Recurrent 3 24987674 (2014)[2]
11q22.1-q22.3 Homozygous Loss BIRC3, BIRC2, MMP cluster Recurrent 3 24987674 (2014)[2], 22529291 (2012)[13]
12 12p Loss Recurrent 2 25636340 (2015)[1], 22833442 (2012)[8], 24987674 (2014)[2], 24757046 (2014)[6]
12p LOH Recurrent 2 25145975 (2015)[3], 22833442 (2012)[8]
12p13.1 Loss CDKN1B, APOLD1 Recurrent 3 24987674 (2014)[2]
13 13q/13 Loss Poor prognostic marker 1 25145975 (2015)[3] 24987674 (2014)[2], 22833442 (2012)[8], 23010713 (2012)[7], 24757046 (2014)[6], 27588520 (2016)[9],
13q14.11/q14.2 Loss TNFSF11, RB1, P2RY5, RCBTB2 Poor prognostic marker 1 24757046 (2014)[6], 24987674 (2014)[2], 25636340 (2015)[1]
13q32.2 Loss TGDS Recurrent 2 24429703-(2014)[5]
14 14q/14 Loss Better prognostic marker 2 25636340 (2015)[1], 22833442 (2012)[8], 24987674 (2014)[2], 23010713 (2012)[7], 24757046 (2014)[6], 25145975 (2015)[3]
14q/14 Gain Recurrent 3 23010713 (2012)[7]
14q cnLOH Recurrent 2 25145975 (2015)[3], 22833442 (2012)[8]
14q24.1-q24.3 Loss MLH3 Recurrent 2 26912802 (2016)[4]
14q32.32 Homozygous Loss RCOR1, TRAF3, AMN, CDC42BPB Recurrent 3 24987674 (2014)[2]
15 15 Gain Recurrent 1 22833442 (2012)[8], 24987674 (2014)[2], 23010713 (2012)[7], 24757046 (2014)[6]  25145975 (2015)[3]
15q24.1 Gain CYP11A1, ARID3B, CSK, etc. Recurrent 3 26912802 (2016)[4]
16 16p11.2 Loss TP53TG3 Recurrent 3 26912802 (2016)[4]
16q Loss Recurrent 1 25636340 (2015)[1], 23010713 (2012)[7], 22833442 (2012)[8], 24987674 (2014)[2], 24757046 (2014)[6]
16q12.1-q12.2 Homozygous Loss CYLD, SALL1 Recurrent 3 24987674 (2014)[2]
16q24.3 Loss CBFA2T3 and others Recurrent 3 24757046 (2014)[6]
16 cnLOH Recurrent 2 22833442 (2012)[8]
17 17p/17 Gain Recurrent 3, 3 23010713 (2012)[7], 24987674 (2014)[2]
17p Loss Predictive & prognostic 1 22833442 (2012)[8], 23010713 (2012)[7], 24757046 (2014)[6], 24429703-(2014)[5]
17p13 Loss ATP1B2, TP53, WRAP5, EFNB3 Predictive & prognostic 1 24987674 (2014)[2], 27588520 (2016)[9]
17 cnLOH Recurrent 2 22833442 (2012)[8]
17q21.33 and 17qter Gain Recurrent 3 24987674 (2014)[2]
17q25 Gain Recurrent 2 22833442 (2012)[8]
18 18 Gain Recurrent 2, 3 22833442 (2012)[8], 24987674 (2014)[2]
19 19 Gain Recurrent 2 22833442 (2012)[8], 24987674 (2014)[2], 23010713 (2012)[7], 24757046 (2014)[6],
19p/ 19p13 Gain ICAM4,ICAM4, IBCL2L12, TYK2, IL2, and DNMT1 Recurrent 3 24987674 (2014)[2], 26912802 (2016)[4], 24757046 (2014)[6]
19q Gain Recurrent 2 24987674 (2014)[2], 25145975 (2015)[3]
20 20p Loss Recurrent 2 22833442 (2012)[8], 24987674 (2014)[2]
20/20q Gain Recurrent 2 22833442 (2012)[8], 24987674 (2014)[2]
20/20q Loss Recurrent 3 24987674 (2014)[2], 23010713 (2012)[7]
21 21 Gain Recurrent 1 22833442 (2012)[8], 24987674 (2014)[2], 23010713 (2012)[7], 24757046 (2014)[6]
22 22 Loss Recurrent 2 25636340 (2015)[1], 23010713 (2012)[7], 22833442 (2012)[8], 24987674 (2014)[2], 24757046 (2014)[6]
22q21 mostly Gain PRAME Recurrent, Associated with relapse 2 27811368-(2016)[11]
X X Gain/ Loss Recurrent 2 22833442 (2012)[8], 25145975 (2015)[3]
X LOH Recurrent 2 25145975 (2015)[3]
Xp Loss Recurrent 3 24987674 (2014)[2]
Xp22.33 Loss SHOX, CRLF2, IL3RA Recurrent 3 26912802 (2016)[4]
Xq Gain (in males) Poor prognostic marker 2 25636340 (2015)[1], 24987674 (2014)[2]
Xq Loss Recurrent 3 24987674 (2014)[2]
Xq21.31-q21.32 Loss PABPC5, PCDHX Recurrent 3 26912802 (2016)[4]
Xq27.3-q28 Gain AFF2, MTMR1, etc Recurrent 3 26912802 (2016)[4]
Y Y Loss 2 25636340 (2015)[1]
Genome wide load of CNA > 100Mb gain/loss associated with significant change in GEP at relapse 2 27811368-(2016)[11]
  1. ↑ 1.00 1.01 1.02 1.03 1.04 1.05 1.06 1.07 1.08 1.09 1.10 1.11 B, Hebraud; et al. (2015). "Role of additional chromosomal changes in the prognostic value of t(4;14) and del(17p) in multiple myeloma: the IFM experience". doi:10.1182/blood-2014-07-587964. PMC 4375107. PMID 25636340.CS1 maint: PMC format (link)
  2. ↑ 2.00 2.01 2.02 2.03 2.04 2.05 2.06 2.07 2.08 2.09 2.10 2.11 2.12 2.13 2.14 2.15 2.16 2.17 2.18 2.19 2.20 2.21 2.22 2.23 2.24 2.25 2.26 2.27 2.28 2.29 2.30 2.31 2.32 2.33 2.34 2.35 2.36 2.37 2.38 2.39 2.40 2.41 2.42 2.43 2.44 2.45 2.46 2.47 2.48 2.49 2.50 2.51 2.52 2.53 2.54 2.55 J, Smetana; et al. (2014). "Genome-wide screening of cytogenetic abnormalities in multiple myeloma patients using array-CGH technique: a Czech multicenter experience". doi:10.1155/2014/209670. PMC 4060785. PMID 24987674.CS1 maint: PMC format (link)
  3. ↑ 3.00 3.01 3.02 3.03 3.04 3.05 3.06 3.07 3.08 3.09 3.10 3.11 3.12 3.13 3.14 3.15 3.16 3.17 3.18 M, Kim; et al. (2015). "Copy number variations could predict the outcome of bortezomib plus melphalan and prednisone for initial treatment of multiple myeloma". PMID 25145975.
  4. ↑ 4.00 4.01 4.02 4.03 4.04 4.05 4.06 4.07 4.08 4.09 4.10 4.11 4.12 E, Kjeldsen (2016). "Identification of Prognostically Relevant Chromosomal Abnormalities in Routine Diagnostics of Multiple Myeloma Using Genomic Profiling". PMID 26912802.
  5. ↑ 5.0 5.1 5.2 N, Bolli; et al. (2014). "Heterogeneity of genomic evolution and mutational profiles in multiple myeloma". doi:10.1038/ncomms3997. PMC 3905727. PMID 24429703.CS1 maint: PMC format (link)
  6. ↑ 6.00 6.01 6.02 6.03 6.04 6.05 6.06 6.07 6.08 6.09 6.10 6.11 6.12 6.13 6.14 6.15 6.16 6.17 6.18 6.19 6.20 6.21 6.22 6.23 6.24 6.25 6.26 6.27 6.28 6.29 T, Boneva; et al. (2014). "Can genome array screening replace FISH as a front-line test in multiple myeloma?". PMID 24757046.
  7. ↑ 7.00 7.01 7.02 7.03 7.04 7.05 7.06 7.07 7.08 7.09 7.10 7.11 7.12 7.13 7.14 7.15 7.16 7.17 7.18 7.19 7.20 7.21 7.22 Bk, Zehentner; et al. (2012). "Array-based karyotyping in plasma cell neoplasia after plasma cell enrichment increases detection of genomic aberrations". PMID 23010713.
  8. ↑ 8.00 8.01 8.02 8.03 8.04 8.05 8.06 8.07 8.08 8.09 8.10 8.11 8.12 8.13 8.14 8.15 8.16 8.17 8.18 8.19 8.20 8.21 8.22 8.23 8.24 8.25 8.26 8.27 8.28 8.29 8.30 M, Stevens-Kroef; et al. (2012). "High detection rate of clinically relevant genomic abnormalities in plasma cells enriched from patients with multiple myeloma". PMID 22833442.
  9. ↑ 9.0 9.1 9.2 9.3 9.4 9.5 N, Bolli; et al. (2016). "A DNA target-enrichment approach to detect mutations, copy number changes and immunoglobulin translocations in multiple myeloma". doi:10.1038/bcj.2016.72. PMC 5056967. PMID 27588520.CS1 maint: PMC format (link)
  10. ↑ K, Rack; et al. (2016). "Genomic profiling of myeloma: the best approach, a comparison of cytogenetics, FISH and array-CGH of 112 myeloma cases". PMID 26338801.
  11. ↑ 11.0 11.1 11.2 11.3 P, Krzeminski; et al. (2016). "Integrative analysis of DNA copy number, DNA methylation and gene expression in multiple myeloma reveals alterations related to relapse". doi:10.18632/oncotarget.13025. PMC 5348347. PMID 27811368.CS1 maint: PMC format (link)
  12. ↑ H, Chang; et al. (2006). "Analysis of PTEN deletions and mutations in multiple myeloma". PMID 16112193.
  13. ↑ Jb, Egan; et al. (2012). "Whole-genome sequencing of multiple myeloma from diagnosis to plasma cell leukemia reveals genomic initiating events, evolution, and clonal tides". doi:10.1182/blood-2012-01-405977. PMC 3412329. PMID 22529291.CS1 maint: PMC format (link)
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