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Risk stratification is increasingly important in the management of multiple myeloma (MM)
.
Although the introduction of proteasome inhibitor (PI) and immunomodulatory (IMiD) drugs has led to advances in MM treatment over the past few decades, these drugs combined with autologous hematopoietic stem cell transplantation (ASCT) have resulted in median overall survival in this group of patients The survival period (OS) was extended from 3 to 8 years, but still about 20% of patients survived only about 3 years
.
How to improve the prognosis of these high-risk patients is currently one of the most important therapeutic challenges in MM
.
Current risk stratification methods identify t(4;14) or t(14;16) or del(17p13) as high-risk genetic abnormalities by FISH and clinical information in the R-ISS staging system
.
More recently, a type of double-hit MM, named del(17p13) and TP53 mutation or 1q amplification (≥4 copies) and ISS stage III, also showed poor OS, moderate The median progression-free survival (PFS) was 15.
4 months, and the median OS was 20.
7 months
.
Many studies have also evaluated MM gene expression signatures, such as EMC92, GEP70, to identify high-risk patients, although this is not routinely used in clinical practice; some clinical features, such as extramedullary plasmacytoma, presence of circulating tumor cells, and renal failure Also associated with high-risk disease, but they are not consistently included in clinical trial criteria
.
Recently, multiple studies have shown that MM patients with suboptimal response to induction therapy or with early relapse have been shown to be ineffective
.
The Australian and New Zealand Myeloma and Related Disease Registry (MRDR) real-world outcomes study of 1320 newly diagnosed patients showed that 40% of patients who did not respond well to induction therapy died within 3 years of diagnosis and 12 months after starting induction therapy Median OS was only 20.
2 months for patients with early disease progression within the 20.
2 months; these patients were classified as functional high-risk (FHR) MM patients
.
Based on this, the criteria for evaluating patients with FHR were more precisely defined to include only patients who did not respond well to treatment or who progressed within 12 months of starting induction therapy without any clinically applicable high-risk genetic features
.
The genomic features such as DNA mutations, mutational markers (MS), transcriptional markers, and copy number abnormalities were further compared with other MM patients to analyze the driving reasons for such poor outcomes
.
Methods We evaluated genomic sequencing and high-throughput molecular testing data from newly diagnosed MM (NDMM) patients in the CoMMpass dataset, a publicly available dataset from the Multiple Myeloma Research Foundation (MMRF)
.
Patients were further divided into three groups: t(4;14) or t(14;16) or complete loss of TP53 function [TP53 biallelic deletion or 17p13 monoallelic deletion, i.
e.
del(17p13) and TP53 mutations] Genomic high-risk (GHR) group of patients with or 1q21 amplification and ISS stage III; FHR group for patients with no GHR markers but refractory to induction therapy or early relapse within 12 months; standard risk (SR) group for non-compliance Patients with any criteria for GHR or FHR
.
To classify patients into their respective risk groups, we used the following CoMMpass data: IGH locus translocations and their partners (canonical Ig translocations using RNA-seq data), copy number aberrations (CNAs) (using CNA segmentation) data), response to first-line therapy (using per-patient response data), ISS stage information using per-patient comprehensive clinical information, and disease progression information using per-patient survival data
.
In this study, a random forest algorithm was used to predict FHR cases
.
All 6 individual models were evaluated on multiple metrics such as Specificity, Sensitivity, False Negative Rate (FNR), False Positive Rate (FPR), Accuracy, F1 Score and Matthews Correlation Coefficient (MCC) for prediction on the test dataset the performance of the results
.
Results FHR patients had the worst OS among 512 evaluable patients, 345 in the SR group, 106 in the GHR group, and 61 in the FHR group
.
The available baseline clinical characteristics of these patients are detailed in Table 1
.
As expected, there were more patients with ISS stage III and R-ISS stage III in the GHR group
.
FHR patients do not have unique clinical features, especially R-ISS stage III patients are very rare
.
Most patients were treated with a combination of PI and IMiD drugs as first-line therapy, while a minority received PI or IMiD-based therapy
.
The three groups of patients received similar treatment
.
Compared with the SR group, FHR and GHR had significantly worse outcomes, among which the FHR group was the worst
.
The median OS in the FHR group was 27.
6 months, compared with 44.
7 months in the GHR group, and the median OS in the SR group was not reached (Fig.
1A)
.
Similar prognostic differences were observed among the three groups of patients when patients received combined PI-based and PI/IMiD induction therapy (Fig.
1B–F)
.
Table 1: Baseline characteristics of enrolled patients Figure 1: Survival curves of FHR, GHR, and SR MM patients in the CoMMpass dataset FHR patients generally do not have known high-risk characteristics Most FHR patients do not have established high-risk disease gene expression characteristics, including proliferation (PI), chromosomal instability (CIN70, CINSARC, CINGEC), centrosomes (CI), cell death (HZDCD) and others (EMC92, HMCL7, IFM15, UAMS70 and UAMS80) (Fig.
2A)
.
When compared between the three groups (SR, GHR and FHR), between SR and FHR (HMCL7, UAMS80, UAMS70, EMC92, IFM15 and CINGEC) or between GHR and FHR (PI, HZDCD, CINSARC, CI, CIN70 and PR ) were not significantly different between these features, although indices associated with chromosomal instability (CINSARC, CI, and CIN70) or tumor aggressiveness (PI and PR) appeared to be generally higher in FHR patients (Fig.
2B)
.
Figure 2: Association of FHR MM patients with known high-risk markers Genomic characterization of FHR MM patients We analyzed NS mutations in the CoMMpass data and found that the GHR group had a higher mutational burden compared to the other groups (P =0.
003)
.
Genes such as FGFR3, PRKD2, and TP53 were the predominant mutations in the GHR group; KIAA1549L, LUZP2, and BMPR1B were the predominant mutations in the FHR group (Figure 3)
.
In the FHR group, the IL6-JAKSTAT3 pathway was found to be significantly enriched, while the estrogen response, KRAS and WNTβ-catenin signaling pathways were enriched in the GHR group
.
Among the 471 evaluable patients, 224 (47.
6%) were non-hyperdiploid and 247 (52.
4%) were hyperdiploid
.
The FHR group was predominantly hyperdiploid (57.
9% vs 42.
1% non-hyperdiploid), whereas the GHR group was predominantly non-hyperdiploid (90.
8% vs 9.
2% hyperdiploid)
.
Compared with FHR group and SR group, 13q deletion and 1q21 amplification were more common in GHR group
.
The SR group had more hyperdiploid (64.
9% vs 35.
1% non-hyperdiploid) patients, which was not statistically significant compared to the FHR group (Fig.
3)
.
Therefore, the copy number profile of FHR patients is similar to that of SR patients
.
Further studies found that FHR patients were enriched in genes related to mitotic cell cycle and DNA replication, C2H2 zinc fingers and DNA repair
.
Compared with the SR group, the FHR group showed enrichment in multiple gene sets known to be involved in MM including E2F Targets, G2M Checkpoint, MTORC1 signaling, Glycolysis, Unfolded protein response, Myc Target, DNA repair, while the SR group was not found to be significantly enriched.
gene set enrichment
.
For the GHR group, 5 gene sets were found to be enriched (androgen response, estrogen response, glycolysis, UV response and IL2STAT5 signaling) compared to the SR group
.
The investigators also analyzed MS using SigProfiler and the Cancer Somatic Mutation Catalog (COSMIC) reference catalog
.
SBS1 and SBS5 are highly specific for SR
.
SBS3 is highly specific for the GHR and FHR groups
.
SBS6 is very specific for GHR (Figure 4)
.
Therefore, there is no MS for FHR only
.
Figure 3: Composite heatmap combining gene expression, copy number aberrations, mutations, and gene expression signatures in MM patients Figure 4: Mutation signatures in SR, GHR, and FHR groups Conclusions of the study This study showed that FHR MM patients, even without any high-risk genetic factors, Outcomes were also very poor; the researchers developed a machine-learning-based classifier that could identify the majority of FHR patients in MM at the time of diagnosis
.
References Cinnie Yentia Soekojo, Tae-Hoon Chung, Muhammad Shaheryar Furqan, et al.
Genomic characterization of functional high-risk multiple myeloma patients.
Blood Cancer J.
2022 Jan 31;12(1):24.
Revision: Quinta Typesetting: Quinta pokes "read the original text", we make progress together
.
Although the introduction of proteasome inhibitor (PI) and immunomodulatory (IMiD) drugs has led to advances in MM treatment over the past few decades, these drugs combined with autologous hematopoietic stem cell transplantation (ASCT) have resulted in median overall survival in this group of patients The survival period (OS) was extended from 3 to 8 years, but still about 20% of patients survived only about 3 years
.
How to improve the prognosis of these high-risk patients is currently one of the most important therapeutic challenges in MM
.
Current risk stratification methods identify t(4;14) or t(14;16) or del(17p13) as high-risk genetic abnormalities by FISH and clinical information in the R-ISS staging system
.
More recently, a type of double-hit MM, named del(17p13) and TP53 mutation or 1q amplification (≥4 copies) and ISS stage III, also showed poor OS, moderate The median progression-free survival (PFS) was 15.
4 months, and the median OS was 20.
7 months
.
Many studies have also evaluated MM gene expression signatures, such as EMC92, GEP70, to identify high-risk patients, although this is not routinely used in clinical practice; some clinical features, such as extramedullary plasmacytoma, presence of circulating tumor cells, and renal failure Also associated with high-risk disease, but they are not consistently included in clinical trial criteria
.
Recently, multiple studies have shown that MM patients with suboptimal response to induction therapy or with early relapse have been shown to be ineffective
.
The Australian and New Zealand Myeloma and Related Disease Registry (MRDR) real-world outcomes study of 1320 newly diagnosed patients showed that 40% of patients who did not respond well to induction therapy died within 3 years of diagnosis and 12 months after starting induction therapy Median OS was only 20.
2 months for patients with early disease progression within the 20.
2 months; these patients were classified as functional high-risk (FHR) MM patients
.
Based on this, the criteria for evaluating patients with FHR were more precisely defined to include only patients who did not respond well to treatment or who progressed within 12 months of starting induction therapy without any clinically applicable high-risk genetic features
.
The genomic features such as DNA mutations, mutational markers (MS), transcriptional markers, and copy number abnormalities were further compared with other MM patients to analyze the driving reasons for such poor outcomes
.
Methods We evaluated genomic sequencing and high-throughput molecular testing data from newly diagnosed MM (NDMM) patients in the CoMMpass dataset, a publicly available dataset from the Multiple Myeloma Research Foundation (MMRF)
.
Patients were further divided into three groups: t(4;14) or t(14;16) or complete loss of TP53 function [TP53 biallelic deletion or 17p13 monoallelic deletion, i.
e.
del(17p13) and TP53 mutations] Genomic high-risk (GHR) group of patients with or 1q21 amplification and ISS stage III; FHR group for patients with no GHR markers but refractory to induction therapy or early relapse within 12 months; standard risk (SR) group for non-compliance Patients with any criteria for GHR or FHR
.
To classify patients into their respective risk groups, we used the following CoMMpass data: IGH locus translocations and their partners (canonical Ig translocations using RNA-seq data), copy number aberrations (CNAs) (using CNA segmentation) data), response to first-line therapy (using per-patient response data), ISS stage information using per-patient comprehensive clinical information, and disease progression information using per-patient survival data
.
In this study, a random forest algorithm was used to predict FHR cases
.
All 6 individual models were evaluated on multiple metrics such as Specificity, Sensitivity, False Negative Rate (FNR), False Positive Rate (FPR), Accuracy, F1 Score and Matthews Correlation Coefficient (MCC) for prediction on the test dataset the performance of the results
.
Results FHR patients had the worst OS among 512 evaluable patients, 345 in the SR group, 106 in the GHR group, and 61 in the FHR group
.
The available baseline clinical characteristics of these patients are detailed in Table 1
.
As expected, there were more patients with ISS stage III and R-ISS stage III in the GHR group
.
FHR patients do not have unique clinical features, especially R-ISS stage III patients are very rare
.
Most patients were treated with a combination of PI and IMiD drugs as first-line therapy, while a minority received PI or IMiD-based therapy
.
The three groups of patients received similar treatment
.
Compared with the SR group, FHR and GHR had significantly worse outcomes, among which the FHR group was the worst
.
The median OS in the FHR group was 27.
6 months, compared with 44.
7 months in the GHR group, and the median OS in the SR group was not reached (Fig.
1A)
.
Similar prognostic differences were observed among the three groups of patients when patients received combined PI-based and PI/IMiD induction therapy (Fig.
1B–F)
.
Table 1: Baseline characteristics of enrolled patients Figure 1: Survival curves of FHR, GHR, and SR MM patients in the CoMMpass dataset FHR patients generally do not have known high-risk characteristics Most FHR patients do not have established high-risk disease gene expression characteristics, including proliferation (PI), chromosomal instability (CIN70, CINSARC, CINGEC), centrosomes (CI), cell death (HZDCD) and others (EMC92, HMCL7, IFM15, UAMS70 and UAMS80) (Fig.
2A)
.
When compared between the three groups (SR, GHR and FHR), between SR and FHR (HMCL7, UAMS80, UAMS70, EMC92, IFM15 and CINGEC) or between GHR and FHR (PI, HZDCD, CINSARC, CI, CIN70 and PR ) were not significantly different between these features, although indices associated with chromosomal instability (CINSARC, CI, and CIN70) or tumor aggressiveness (PI and PR) appeared to be generally higher in FHR patients (Fig.
2B)
.
Figure 2: Association of FHR MM patients with known high-risk markers Genomic characterization of FHR MM patients We analyzed NS mutations in the CoMMpass data and found that the GHR group had a higher mutational burden compared to the other groups (P =0.
003)
.
Genes such as FGFR3, PRKD2, and TP53 were the predominant mutations in the GHR group; KIAA1549L, LUZP2, and BMPR1B were the predominant mutations in the FHR group (Figure 3)
.
In the FHR group, the IL6-JAKSTAT3 pathway was found to be significantly enriched, while the estrogen response, KRAS and WNTβ-catenin signaling pathways were enriched in the GHR group
.
Among the 471 evaluable patients, 224 (47.
6%) were non-hyperdiploid and 247 (52.
4%) were hyperdiploid
.
The FHR group was predominantly hyperdiploid (57.
9% vs 42.
1% non-hyperdiploid), whereas the GHR group was predominantly non-hyperdiploid (90.
8% vs 9.
2% hyperdiploid)
.
Compared with FHR group and SR group, 13q deletion and 1q21 amplification were more common in GHR group
.
The SR group had more hyperdiploid (64.
9% vs 35.
1% non-hyperdiploid) patients, which was not statistically significant compared to the FHR group (Fig.
3)
.
Therefore, the copy number profile of FHR patients is similar to that of SR patients
.
Further studies found that FHR patients were enriched in genes related to mitotic cell cycle and DNA replication, C2H2 zinc fingers and DNA repair
.
Compared with the SR group, the FHR group showed enrichment in multiple gene sets known to be involved in MM including E2F Targets, G2M Checkpoint, MTORC1 signaling, Glycolysis, Unfolded protein response, Myc Target, DNA repair, while the SR group was not found to be significantly enriched.
gene set enrichment
.
For the GHR group, 5 gene sets were found to be enriched (androgen response, estrogen response, glycolysis, UV response and IL2STAT5 signaling) compared to the SR group
.
The investigators also analyzed MS using SigProfiler and the Cancer Somatic Mutation Catalog (COSMIC) reference catalog
.
SBS1 and SBS5 are highly specific for SR
.
SBS3 is highly specific for the GHR and FHR groups
.
SBS6 is very specific for GHR (Figure 4)
.
Therefore, there is no MS for FHR only
.
Figure 3: Composite heatmap combining gene expression, copy number aberrations, mutations, and gene expression signatures in MM patients Figure 4: Mutation signatures in SR, GHR, and FHR groups Conclusions of the study This study showed that FHR MM patients, even without any high-risk genetic factors, Outcomes were also very poor; the researchers developed a machine-learning-based classifier that could identify the majority of FHR patients in MM at the time of diagnosis
.
References Cinnie Yentia Soekojo, Tae-Hoon Chung, Muhammad Shaheryar Furqan, et al.
Genomic characterization of functional high-risk multiple myeloma patients.
Blood Cancer J.
2022 Jan 31;12(1):24.
Revision: Quinta Typesetting: Quinta pokes "read the original text", we make progress together
