guest ::
| Home > Publications database > RNA-sequencing based first choice of treatment and determination of risk in multiple myeloma. > print |
| Home > Publications database > RNA-sequencing based first choice of treatment and determination of risk in multiple myeloma. > print |
| 001 | 285713 | ||
| 005 | 20240229155116.0 | ||
| 024 | 7 | _ | |a 10.3389/fimmu.2023.1286700 |2 doi |
| 024 | 7 | _ | |a pmid:38035078 |2 pmid |
| 024 | 7 | _ | |a pmc:PMC10684778 |2 pmc |
| 024 | 7 | _ | |a altmetric:156965122 |2 altmetric |
| 037 | _ | _ | |a DKFZ-2023-02521 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Emde-Rajaratnam, Martina |b 0 |
| 245 | _ | _ | |a RNA-sequencing based first choice of treatment and determination of risk in multiple myeloma. |
| 260 | _ | _ | |a Lausanne |c 2023 |b Frontiers Media |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1701441067_30227 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a Immunotherapeutic targets in multiple myeloma (MM) have variable expression height and are partly expressed in subfractions of patients only. With increasing numbers of available compounds, strategies for appropriate choice of targets (combinations) are warranted. Simultaneously, risk assessment is advisable as patient's life expectancy varies between months and decades.We first assess feasibility of RNA-sequencing in a multicenter trial (GMMG-MM5, n=604 patients). Next, we use a clinical routine cohort of untreated symptomatic myeloma patients undergoing autologous stem cell transplantation (n=535, median follow-up (FU) 64 months) to perform RNA-sequencing, gene expression profiling (GEP), and iFISH by ten-probe panel on CD138-purified malignant plasma cells. We subsequently compare target expression to plasma cell precursors, MGUS (n=59), asymptomatic (n=142) and relapsed (n=69) myeloma patients, myeloma cell lines (n=26), and between longitudinal samples (MM vs. relapsed MM). Data are validated using the independent MMRF CoMMpass-cohort (n=767, FU 31 months).RNA-sequencing is feasible in 90.8% of patients (GMMG-MM5). Actionable immune-oncological targets (n=19) can be divided in those expressed in all normal and >99% of MM-patients (CD38, SLAMF7, BCMA, GPRC5D, FCRH5, TACI, CD74, CD44, CD37, CD79B), those with expression loss in subfractions of MM-patients (BAFF-R [81.3%], CD19 [57.9%], CD20 [82.8%], CD22 [28.4%]), aberrantly expressed in MM (NY-ESO1/2 [12%], MUC1 [12.7%], CD30 [4.9%], mutated BRAF V600E/K [2.1%]), and resistance-conveying target-mutations e.g., against part but not all BCMA-directed treatments. Risk is assessable regarding proliferation, translated GEP- (UAMS70-, SKY92-, RS-score) and de novo (LfM-HRS) defined risk scores. LfM-HRS delineates three groups of 40%, 38%, and 22% of patients with 5-year and 12-year survival rates of 84% (49%), 67% (18%), and 32% (0%). R-ISS and RNA-sequencing identify partially overlapping patient populations, with R-ISS missing, e.g., 30% (22/72) of highly proliferative myeloma.RNA-sequencing based assessment of risk and targets for first choice treatment is possible in clinical routine. |
| 536 | _ | _ | |a 313 - Krebsrisikofaktoren und Prävention (POF4-313) |0 G:(DE-HGF)POF4-313 |c POF4-313 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de |
| 650 | _ | 7 | |a RNA-sequencing |2 Other |
| 650 | _ | 7 | |a immunotherapeutic targets |2 Other |
| 650 | _ | 7 | |a multiple myeloma |2 Other |
| 650 | _ | 7 | |a personalized treatment |2 Other |
| 650 | _ | 7 | |a proliferation |2 Other |
| 650 | _ | 7 | |a risk-adapted treatment |2 Other |
| 650 | _ | 7 | |a survival |2 Other |
| 700 | 1 | _ | |a Beck, Susanne |b 1 |
| 700 | 1 | _ | |a Benes, Vladimir |b 2 |
| 700 | 1 | _ | |a Salwender, Hans |b 3 |
| 700 | 1 | _ | |a Bertsch, Uta |b 4 |
| 700 | 1 | _ | |a Scheid, Christoph |b 5 |
| 700 | 1 | _ | |a Hänel, Mathias |b 6 |
| 700 | 1 | _ | |a Weisel, Katja |b 7 |
| 700 | 1 | _ | |a Hielscher, Thomas |0 P:(DE-He78)743a4a82daab55306a2c88b9f6bf8c2f |b 8 |u dkfz |
| 700 | 1 | _ | |a Raab, Marc S |b 9 |
| 700 | 1 | _ | |a Goldschmidt, Hartmut |b 10 |
| 700 | 1 | _ | |a Jauch, Anna |b 11 |
| 700 | 1 | _ | |a Maes, Ken |b 12 |
| 700 | 1 | _ | |a De Bruyne, Elke |b 13 |
| 700 | 1 | _ | |a Menu, Eline |b 14 |
| 700 | 1 | _ | |a De Veirman, Kim |b 15 |
| 700 | 1 | _ | |a Moreaux, Jérôme |b 16 |
| 700 | 1 | _ | |a Vanderkerken, Karin |b 17 |
| 700 | 1 | _ | |a Seckinger, Anja |b 18 |
| 700 | 1 | _ | |a Hose, Dirk |b 19 |
| 773 | _ | _ | |a 10.3389/fimmu.2023.1286700 |g Vol. 14, p. 1286700 |0 PERI:(DE-600)2606827-8 |p 1286700 |t Frontiers in immunology |v 14 |y 2023 |x 1664-3224 |
| 909 | C | O | |o oai:inrepo02.dkfz.de:285713 |p VDB |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 8 |6 P:(DE-He78)743a4a82daab55306a2c88b9f6bf8c2f |
| 913 | 1 | _ | |a DE-HGF |b Gesundheit |l Krebsforschung |1 G:(DE-HGF)POF4-310 |0 G:(DE-HGF)POF4-313 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-300 |4 G:(DE-HGF)POF |v Krebsrisikofaktoren und Prävention |x 0 |
| 914 | 1 | _ | |y 2023 |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b FRONT IMMUNOL : 2022 |d 2023-10-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2023-10-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2023-10-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0320 |2 StatID |b PubMed Central |d 2023-10-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0501 |2 StatID |b DOAJ Seal |d 2021-05-11T10:28:02Z |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0500 |2 StatID |b DOAJ |d 2021-05-11T10:28:02Z |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b DOAJ : Anonymous peer review |d 2021-05-11T10:28:02Z |
| 915 | _ | _ | |a Creative Commons Attribution CC BY (No Version) |0 LIC:(DE-HGF)CCBYNV |2 V:(DE-HGF) |b DOAJ |d 2021-05-11T10:28:02Z |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2023-10-26 |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0113 |2 StatID |b Science Citation Index Expanded |d 2023-10-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2023-10-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0160 |2 StatID |b Essential Science Indicators |d 2023-10-26 |
| 915 | _ | _ | |a IF >= 5 |0 StatID:(DE-HGF)9905 |2 StatID |b FRONT IMMUNOL : 2022 |d 2023-10-26 |
| 915 | _ | _ | |a Article Processing Charges |0 StatID:(DE-HGF)0561 |2 StatID |d 2023-10-26 |
| 915 | _ | _ | |a Fees |0 StatID:(DE-HGF)0700 |2 StatID |d 2023-10-26 |
| 920 | 1 | _ | |0 I:(DE-He78)C060-20160331 |k C060 |l C060 Biostatistik |x 0 |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-He78)C060-20160331 |
| 980 | _ | _ | |a UNRESTRICTED |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|