Abstract:Purpose: We designed a comprehensive multiple myeloma (MM) targeted sequencing panel to identify common genomic abnormalities in a single assay and validated it against known standards. Experimental Design: The panel comprised 228 genes/exons for mutations, 6 regions for translocations, and 56 regions for copy number abnormalities (CNAs). Toward panel validation, targeted sequencing was conducted on 233 patient samples and further validated using clinical fluorescence in situ hybridization (FISH) (translocatio… Show more
“…These panels were thoughtfully crafted based on MM-related literature and underwent validation using diverse methods such as FISH and analysis of whole-genome sequencing (WGS) data, etc. Upon scrutinizing the validated variant profiles, we noted that, alongside our proposed panel, Sudha et al [17] also validated their panel on SNVs, CNVs, and SVs, encompassing translocations linked to IGH and MYC. However, Sudha et al’s panel validation was carried out on WGS cohorts of MM samples and MM cell lines and did not account for potentially distinguishing genomic biomarkers between MGUS and MM.…”
Section: Resultsmentioning
confidence: 89%
“…Upon scrutinizing the validated variant profiles, we noted that, alongside our proposed panel, Sudha et al [17] Survival Analysis using feature generated by combining 4 features (SNV profile, CNV profile, SV profile, and LOF profile) using Factor Analysis for Mixed Data (FAMD) [30]. 346-gene set validated via Geo2R analysis in at least one MM-related dataset.…”
Section: Comparison Of Proposed 282-genes Panel With Previously Publi...mentioning
Multiple myeloma (MM) is a hematological cancer that evolves from the benign precursor stage termed monoclonal gammopathy of undetermined significance (MGUS). Understanding the pivotal biomarkers, genomic events, and gene interactions distinguishing MM from MGUS can significantly contribute to early detection and an improved understanding of MM’s pathogenesis. In this study, we present a curated comprehensive targeted sequencing panel focusing on 282 MM-relevant genes and employing clinically oriented NGS targeted sequencing approaches. To identify these 282 MM-relevant genes, we designed an innovative AI-basedbio-inspired graph network learning based gene gene interactions (Bio-DGI)model for detecting biomarkers and gene interactions that can potentially differentiate MM from MGUS. The Bio-DGI model leverages gene interactions from nine protein-protein interaction (PPI) networks and analyzes the genomic features from 1154 MM and 61 MGUS samples, respectively. The proposed model out-performed baseline machine learning (ML) and deep learning (DL) models, demonstrating quantitative and qualitative superiority by identifying the highest number of MM-relevant genes in the post-hoc analysis. The pathway analysis underscored the importance of top-ranked genes by highlighting the MM-relevant pathways as the top-significantly altered pathways. The 282-gene panel encompasses 9272 coding regions and has a length of 2.577 Mb. Additionally, the 282-gene panel showcased superior performance compared to previously published panels, excelling in detecting both genomic and transformative events. Notably, the proposed gene panel also highlighted highly influential genes and their interactions within gene communities in MM. The clinical relevance is confirmed through a two-fold univariate survival analysis. The study’s findings shed light on essential gene biomarkers and their interactions, providing valuable insights into disease progression.
“…These panels were thoughtfully crafted based on MM-related literature and underwent validation using diverse methods such as FISH and analysis of whole-genome sequencing (WGS) data, etc. Upon scrutinizing the validated variant profiles, we noted that, alongside our proposed panel, Sudha et al [17] also validated their panel on SNVs, CNVs, and SVs, encompassing translocations linked to IGH and MYC. However, Sudha et al’s panel validation was carried out on WGS cohorts of MM samples and MM cell lines and did not account for potentially distinguishing genomic biomarkers between MGUS and MM.…”
Section: Resultsmentioning
confidence: 89%
“…Upon scrutinizing the validated variant profiles, we noted that, alongside our proposed panel, Sudha et al [17] Survival Analysis using feature generated by combining 4 features (SNV profile, CNV profile, SV profile, and LOF profile) using Factor Analysis for Mixed Data (FAMD) [30]. 346-gene set validated via Geo2R analysis in at least one MM-related dataset.…”
Section: Comparison Of Proposed 282-genes Panel With Previously Publi...mentioning
Multiple myeloma (MM) is a hematological cancer that evolves from the benign precursor stage termed monoclonal gammopathy of undetermined significance (MGUS). Understanding the pivotal biomarkers, genomic events, and gene interactions distinguishing MM from MGUS can significantly contribute to early detection and an improved understanding of MM’s pathogenesis. In this study, we present a curated comprehensive targeted sequencing panel focusing on 282 MM-relevant genes and employing clinically oriented NGS targeted sequencing approaches. To identify these 282 MM-relevant genes, we designed an innovative AI-basedbio-inspired graph network learning based gene gene interactions (Bio-DGI)model for detecting biomarkers and gene interactions that can potentially differentiate MM from MGUS. The Bio-DGI model leverages gene interactions from nine protein-protein interaction (PPI) networks and analyzes the genomic features from 1154 MM and 61 MGUS samples, respectively. The proposed model out-performed baseline machine learning (ML) and deep learning (DL) models, demonstrating quantitative and qualitative superiority by identifying the highest number of MM-relevant genes in the post-hoc analysis. The pathway analysis underscored the importance of top-ranked genes by highlighting the MM-relevant pathways as the top-significantly altered pathways. The 282-gene panel encompasses 9272 coding regions and has a length of 2.577 Mb. Additionally, the 282-gene panel showcased superior performance compared to previously published panels, excelling in detecting both genomic and transformative events. Notably, the proposed gene panel also highlighted highly influential genes and their interactions within gene communities in MM. The clinical relevance is confirmed through a two-fold univariate survival analysis. The study’s findings shed light on essential gene biomarkers and their interactions, providing valuable insights into disease progression.
“…The main limitation was the small number of true positive cases for some specific alterations, leading to a smaller sensitivity than recent studies [ 36 ]. Increasing the density of probes in certain regions for CNV and breakpoint detection is the next step.…”
Next-generation sequencing (NGS) has greatly improved our ability to detect the genomic aberrations occurring in multiple myeloma (MM); however, its transfer to routine clinical labs and its validation in clinical trials remains to be established. We designed a capture-based NGS targeted panel to identify, in a single assay, known genetic alterations for the prognostic stratification of MM. The NGS panel was designed for the simultaneous study of single nucleotide and copy number variations, insertions and deletions, chromosomal translocations and V(D)J rearrangements. The panel was validated using a cohort of 149 MM patients enrolled in the GEM2012MENOS65 clinical trial. The results showed great global accuracy, with positive and negative predictive values close to 90% when compared with available data from fluorescence in situ hybridization and whole-exome sequencing. While the treatments used in the clinical trial showed high efficacy, patients defined as high-risk by the panel had shorter progression-free survival (p = 0.0015). As expected, the mutational status of TP53 was significant in predicting patient outcomes (p = 0.021). The NGS panel also efficiently detected clonal IGH rearrangements in 81% of patients. In conclusion, molecular karyotyping using a targeted NGS panel can identify relevant prognostic chromosomal abnormalities and translocations for the clinical management of MM patients.
“…Diagnostically, FISH probes traditionally used in the identification of 4;14 translocations are not suitable for precisely locating the breakpoints and thus identifying high-risk late-disruption patients. As a practical solution, we suggest incorporating a next generation sequencing–based approach, such as the recently described MGP Myeloma Panel, 21 for identifying these patients. Figure 4.…”
Although translocation events between chromosome 4 (NSD2 gene) and chromosome 14 (IgH locus) (t(4;14)) is considered high-risk in newly diagnosed multiple myeloma (NDMM), only ~30-40% of t(4;14) patients are clinically high-risk. We generated and compared a large whole genome sequencing (WGS) and transcriptome (RNA-sequencing) from 258 t(4;14) (N=153 discovery, N=105 replication) and 183 non-t(4;14) NDMM patients with associated clinical data. A landmark survival analysis indicated only ~25% of t(4;14) patients had an overall survival (OS) <24 months, and a comparative analysis of the patient subgroups identified biomarkers associated with this poor outcome, including translocation breakpoints located in NSD2 gene and expression of IGH-NSD2 fusion transcripts. Three breakpoints were identified and are designated as: "no-disruption" (upstream of NSD2), "early-disruption" (in the 5' UTR) and "late-disruption" (within the NSD2 gene). Our results show a significant difference in OS based on the location of DNA breakpoints (mOS 28.6 "late-disruption" vs 59.2 "early disruption" vs 75.1 mo "no disruption"). These findings have been replicated in an independent replication dataset. Also, univariate and multivariate analysis suggest high-risk markers such as del17p, 1p independently contribute to poor outcome t(4;14) MM patients.
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