PROTEOMIC SIGNATURE PREDICTING COMPLETE RESPONSE TO PAD IN REFRACTORY MULTIPLE MYELOMA PATIENTS QUALIFIED FOR AUTOPBSCT AND VD CONSOLIDATION – MULTICENTER, POLISH MYELOMA CONSORTIUM 001 STUDY
(Abstract release date: 05/19/16)
EHA Library. Dytfeld D. 06/09/16; 132801; E1252
Dr. Dominik Dytfeld
Contributions
Contributions
Abstract
Abstract: E1252
Type: Eposter Presentation
Background
Despite the new drugs introduced to multiple myeloma (MM) treatment the disease remains incurable due to increasing resistance to therapy. Understanding biology of disease and identification of biomarkers of resistance is a key research challenge and may enable the individualization of treatment.
Aims
We aimed to identify biomarkers and signaling pathways differing plasma cells (PCs) of patients (pts) with refractory/relapsed MM (RRMM) who achieved complete response (CR) after PAD (bortezomib, adriamycin, dexamethasone) from those with lower response. All the patients were qualified to undergo a high dose melphalan and a VD (bortezomib, dexamethasone) consolidation afterwards.
Methods
Comparative proteome analysis was performed on purified by positive selection (EasySep), pretreatment PCs from pts, who were refractory to the first line (CTD) and qualified for PAD chemotherapy followed by high dose melphalan and 2 cycles of VD consolidation according to standard recommendations of Polish Myeloma Group. Treatment response was assessed using IMWG criteria. PCs acquired from pre-treatment bone marrow after obtaining informed consent from pts (IRB# 434/14 UMP) were lysed and protein was isolated. Proteins from each patient were analyzed using two different, independent proteomic methods: Isobaric Tag for Relative and Absolute Quantitation iTRAQ (4-plex) and Label Free-based (LF) proteomic approach. Peptides were analyzed using Q-Exactive hybrid quadrupole-Orbitrap mass spectrometer coupled to the chromatograph Dionex 3000 Ultimate nanoLC (Thermo Scientific). Proteins which accumulation in the analyzed subgroups differed by at least 50% in both methods (iTRAQ and LF) were considered to differentiate. The dysregulated proteins were subjected to REACTOME and PANTHER bioinformatic tools for identifying enriched signaling pathways and networks.
Results
As of January 31th 2016 30/45 (66%) pts (median age: 60 (38-74)) received 6 planned PAD. Treatment was interrupted due to toxicity for 8 pts (G3 neuropathy, G2 infection) and disease progression (PD) (7 pts - 15%). After PAD 8 pts achieved CR (17%), 14 VGPR (30%), 14 PR (30%) and 9 - PD (20%). 4 pts died due to PD during PAD. Transplantation was so far performed in 12 pts and consolidation planned 3 months after autoPBSCT in - 7. Proteomic analysis was performed on PCs from 28 pts. Out of 499 proteins identified (FDR <1%), 56 showed significant differences in accumulation between patients, who, after treatment with PAD, achieved CR and patients with worse response. Accumulation of 13 proteins was up-regulated in resistant patients, among them thioredoxin domain-containing protein 5 (TXNDC5), proteasome activator complex subunit 2 (PSMA2) and peroxiredoxin-5 whereas relative abundance of 42 proteins were down-regulated in resistant patients. Among the latter were vimentin, annexin A1. Based on the log of p-value from Fisher’s test and number of identified differential proteins, the most affected pathways constitute: proteasome pathway, apoptosis and programmed cell death, signalling by Rho GTPases, detoxification of ROS and activation of DNA fragmentation factor and apoptosis induced DNA fragmentation.
Conclusion
We indicated pathways involved in resistance to investigated regimen in RRMM and confirmed our previous finding in newly diagnosed MM and RRMM (Dytfeld BJH 2015, ASH 2015). We pointed significant biomarkers of resistance and potential therapeutic targets as basis for individualized MM therapy.
Session topic: E-poster
Keyword(s): Drug resistance, Myeloma, Proteomics
Type: Eposter Presentation
Background
Despite the new drugs introduced to multiple myeloma (MM) treatment the disease remains incurable due to increasing resistance to therapy. Understanding biology of disease and identification of biomarkers of resistance is a key research challenge and may enable the individualization of treatment.
Aims
We aimed to identify biomarkers and signaling pathways differing plasma cells (PCs) of patients (pts) with refractory/relapsed MM (RRMM) who achieved complete response (CR) after PAD (bortezomib, adriamycin, dexamethasone) from those with lower response. All the patients were qualified to undergo a high dose melphalan and a VD (bortezomib, dexamethasone) consolidation afterwards.
Methods
Comparative proteome analysis was performed on purified by positive selection (EasySep), pretreatment PCs from pts, who were refractory to the first line (CTD) and qualified for PAD chemotherapy followed by high dose melphalan and 2 cycles of VD consolidation according to standard recommendations of Polish Myeloma Group. Treatment response was assessed using IMWG criteria. PCs acquired from pre-treatment bone marrow after obtaining informed consent from pts (IRB# 434/14 UMP) were lysed and protein was isolated. Proteins from each patient were analyzed using two different, independent proteomic methods: Isobaric Tag for Relative and Absolute Quantitation iTRAQ (4-plex) and Label Free-based (LF) proteomic approach. Peptides were analyzed using Q-Exactive hybrid quadrupole-Orbitrap mass spectrometer coupled to the chromatograph Dionex 3000 Ultimate nanoLC (Thermo Scientific). Proteins which accumulation in the analyzed subgroups differed by at least 50% in both methods (iTRAQ and LF) were considered to differentiate. The dysregulated proteins were subjected to REACTOME and PANTHER bioinformatic tools for identifying enriched signaling pathways and networks.
Results
As of January 31th 2016 30/45 (66%) pts (median age: 60 (38-74)) received 6 planned PAD. Treatment was interrupted due to toxicity for 8 pts (G3 neuropathy, G2 infection) and disease progression (PD) (7 pts - 15%). After PAD 8 pts achieved CR (17%), 14 VGPR (30%), 14 PR (30%) and 9 - PD (20%). 4 pts died due to PD during PAD. Transplantation was so far performed in 12 pts and consolidation planned 3 months after autoPBSCT in - 7. Proteomic analysis was performed on PCs from 28 pts. Out of 499 proteins identified (FDR <1%), 56 showed significant differences in accumulation between patients, who, after treatment with PAD, achieved CR and patients with worse response. Accumulation of 13 proteins was up-regulated in resistant patients, among them thioredoxin domain-containing protein 5 (TXNDC5), proteasome activator complex subunit 2 (PSMA2) and peroxiredoxin-5 whereas relative abundance of 42 proteins were down-regulated in resistant patients. Among the latter were vimentin, annexin A1. Based on the log of p-value from Fisher’s test and number of identified differential proteins, the most affected pathways constitute: proteasome pathway, apoptosis and programmed cell death, signalling by Rho GTPases, detoxification of ROS and activation of DNA fragmentation factor and apoptosis induced DNA fragmentation.
Conclusion
We indicated pathways involved in resistance to investigated regimen in RRMM and confirmed our previous finding in newly diagnosed MM and RRMM (Dytfeld BJH 2015, ASH 2015). We pointed significant biomarkers of resistance and potential therapeutic targets as basis for individualized MM therapy.
Session topic: E-poster
Keyword(s): Drug resistance, Myeloma, Proteomics
Abstract: E1252
Type: Eposter Presentation
Background
Despite the new drugs introduced to multiple myeloma (MM) treatment the disease remains incurable due to increasing resistance to therapy. Understanding biology of disease and identification of biomarkers of resistance is a key research challenge and may enable the individualization of treatment.
Aims
We aimed to identify biomarkers and signaling pathways differing plasma cells (PCs) of patients (pts) with refractory/relapsed MM (RRMM) who achieved complete response (CR) after PAD (bortezomib, adriamycin, dexamethasone) from those with lower response. All the patients were qualified to undergo a high dose melphalan and a VD (bortezomib, dexamethasone) consolidation afterwards.
Methods
Comparative proteome analysis was performed on purified by positive selection (EasySep), pretreatment PCs from pts, who were refractory to the first line (CTD) and qualified for PAD chemotherapy followed by high dose melphalan and 2 cycles of VD consolidation according to standard recommendations of Polish Myeloma Group. Treatment response was assessed using IMWG criteria. PCs acquired from pre-treatment bone marrow after obtaining informed consent from pts (IRB# 434/14 UMP) were lysed and protein was isolated. Proteins from each patient were analyzed using two different, independent proteomic methods: Isobaric Tag for Relative and Absolute Quantitation iTRAQ (4-plex) and Label Free-based (LF) proteomic approach. Peptides were analyzed using Q-Exactive hybrid quadrupole-Orbitrap mass spectrometer coupled to the chromatograph Dionex 3000 Ultimate nanoLC (Thermo Scientific). Proteins which accumulation in the analyzed subgroups differed by at least 50% in both methods (iTRAQ and LF) were considered to differentiate. The dysregulated proteins were subjected to REACTOME and PANTHER bioinformatic tools for identifying enriched signaling pathways and networks.
Results
As of January 31th 2016 30/45 (66%) pts (median age: 60 (38-74)) received 6 planned PAD. Treatment was interrupted due to toxicity for 8 pts (G3 neuropathy, G2 infection) and disease progression (PD) (7 pts - 15%). After PAD 8 pts achieved CR (17%), 14 VGPR (30%), 14 PR (30%) and 9 - PD (20%). 4 pts died due to PD during PAD. Transplantation was so far performed in 12 pts and consolidation planned 3 months after autoPBSCT in - 7. Proteomic analysis was performed on PCs from 28 pts. Out of 499 proteins identified (FDR <1%), 56 showed significant differences in accumulation between patients, who, after treatment with PAD, achieved CR and patients with worse response. Accumulation of 13 proteins was up-regulated in resistant patients, among them thioredoxin domain-containing protein 5 (TXNDC5), proteasome activator complex subunit 2 (PSMA2) and peroxiredoxin-5 whereas relative abundance of 42 proteins were down-regulated in resistant patients. Among the latter were vimentin, annexin A1. Based on the log of p-value from Fisher’s test and number of identified differential proteins, the most affected pathways constitute: proteasome pathway, apoptosis and programmed cell death, signalling by Rho GTPases, detoxification of ROS and activation of DNA fragmentation factor and apoptosis induced DNA fragmentation.
Conclusion
We indicated pathways involved in resistance to investigated regimen in RRMM and confirmed our previous finding in newly diagnosed MM and RRMM (Dytfeld BJH 2015, ASH 2015). We pointed significant biomarkers of resistance and potential therapeutic targets as basis for individualized MM therapy.
Session topic: E-poster
Keyword(s): Drug resistance, Myeloma, Proteomics
Type: Eposter Presentation
Background
Despite the new drugs introduced to multiple myeloma (MM) treatment the disease remains incurable due to increasing resistance to therapy. Understanding biology of disease and identification of biomarkers of resistance is a key research challenge and may enable the individualization of treatment.
Aims
We aimed to identify biomarkers and signaling pathways differing plasma cells (PCs) of patients (pts) with refractory/relapsed MM (RRMM) who achieved complete response (CR) after PAD (bortezomib, adriamycin, dexamethasone) from those with lower response. All the patients were qualified to undergo a high dose melphalan and a VD (bortezomib, dexamethasone) consolidation afterwards.
Methods
Comparative proteome analysis was performed on purified by positive selection (EasySep), pretreatment PCs from pts, who were refractory to the first line (CTD) and qualified for PAD chemotherapy followed by high dose melphalan and 2 cycles of VD consolidation according to standard recommendations of Polish Myeloma Group. Treatment response was assessed using IMWG criteria. PCs acquired from pre-treatment bone marrow after obtaining informed consent from pts (IRB# 434/14 UMP) were lysed and protein was isolated. Proteins from each patient were analyzed using two different, independent proteomic methods: Isobaric Tag for Relative and Absolute Quantitation iTRAQ (4-plex) and Label Free-based (LF) proteomic approach. Peptides were analyzed using Q-Exactive hybrid quadrupole-Orbitrap mass spectrometer coupled to the chromatograph Dionex 3000 Ultimate nanoLC (Thermo Scientific). Proteins which accumulation in the analyzed subgroups differed by at least 50% in both methods (iTRAQ and LF) were considered to differentiate. The dysregulated proteins were subjected to REACTOME and PANTHER bioinformatic tools for identifying enriched signaling pathways and networks.
Results
As of January 31th 2016 30/45 (66%) pts (median age: 60 (38-74)) received 6 planned PAD. Treatment was interrupted due to toxicity for 8 pts (G3 neuropathy, G2 infection) and disease progression (PD) (7 pts - 15%). After PAD 8 pts achieved CR (17%), 14 VGPR (30%), 14 PR (30%) and 9 - PD (20%). 4 pts died due to PD during PAD. Transplantation was so far performed in 12 pts and consolidation planned 3 months after autoPBSCT in - 7. Proteomic analysis was performed on PCs from 28 pts. Out of 499 proteins identified (FDR <1%), 56 showed significant differences in accumulation between patients, who, after treatment with PAD, achieved CR and patients with worse response. Accumulation of 13 proteins was up-regulated in resistant patients, among them thioredoxin domain-containing protein 5 (TXNDC5), proteasome activator complex subunit 2 (PSMA2) and peroxiredoxin-5 whereas relative abundance of 42 proteins were down-regulated in resistant patients. Among the latter were vimentin, annexin A1. Based on the log of p-value from Fisher’s test and number of identified differential proteins, the most affected pathways constitute: proteasome pathway, apoptosis and programmed cell death, signalling by Rho GTPases, detoxification of ROS and activation of DNA fragmentation factor and apoptosis induced DNA fragmentation.
Conclusion
We indicated pathways involved in resistance to investigated regimen in RRMM and confirmed our previous finding in newly diagnosed MM and RRMM (Dytfeld BJH 2015, ASH 2015). We pointed significant biomarkers of resistance and potential therapeutic targets as basis for individualized MM therapy.
Session topic: E-poster
Keyword(s): Drug resistance, Myeloma, Proteomics
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