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Genomic characterization of functional high-risk multiple myeloma patients.

ABSTRACT: Multiple myeloma (MM) patients with suboptimal response to induction therapy or early relapse, classified as the functional high-risk (FHR) patients, have been shown to have poor outcomes. We evaluated newly-diagnosed MM patients in the CoMMpass dataset and divided them into three groups: genomic high-risk (GHR) group for patients with t(4;14) or t(14;16) or complete loss of functional TP53 (bi-allelic deletion of TP53 or mono-allelic deletion of 17p13 (del17p13) and TP53 mutation) or 1q21 gain and International Staging System (ISS) stage 3; FHR group for patients who had no markers of GHR group but were refractory to induction therapy or had early relapse within 12 months; and standard-risk (SR) group for patients who did not fulfill any of the criteria for GHR or FHR. FHR patients had the worst survival. FHR patients are characterized by increased mutations affecting the IL-6/JAK/STAT3 pathway, and a gene expression profile associated with aberrant mitosis and DNA damage response. This is also corroborated by the association with the mutational signature associated with abnormal DNA damage response. We have also developed a machine learning based classifier that can identify most of these patients at diagnosis.

SUBMITTER: Soekojo CY

PROVIDER: S-EPMC8803925 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Genomic characterization of functional high-risk multiple myeloma patients.

Soekojo Cinnie Yentia CY Chung Tae-Hoon TH Furqan Muhammad Shaheryar MS Chng Wee Joo WJ

Blood cancer journal 20220131 1

Multiple myeloma (MM) patients with suboptimal response to induction therapy or early relapse, classified as the functional high-risk (FHR) patients, have been shown to have poor outcomes. We evaluated newly-diagnosed MM patients in the CoMMpass dataset and divided them into three groups: genomic high-risk (GHR) group for patients with t(4;14) or t(14;16) or complete loss of functional TP53 (bi-allelic deletion of TP53 or mono-allelic deletion of 17p13 (del17p13) and TP53 mutation) or 1q21 gain ...[more]

PMID: 35102139

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Project description:ObjectivePrecise risk stratification is increasingly essential in the management of multiple myeloma (MM) as some standard-risk (SR) patients still exhibit similar poor outcomes as genetically high-risk (GHR) patients in the era of novel agents. It has recently been demonstrated that functional high-risk (FHR) patients, those with suboptimal response to first-line induction therapy or early relapse within 12 months, have identifiable molecular characteristics from the SR group in the CoMMpass dataset. However, these findings lack practical validation in the real world.MethodsMM cells purified by CD138 microbeads from newly diagnosed MM (NDMM) patients received fluorescence in situ hybridization and sequencing with a 92-gene Panel. Cytogenetic abnormalities defined GHR patients with t(4;14) or t(14;16) or complete loss of functional P53 or 1q21 gain and International Staging System (ISS) stage 3. SR group was patients who did not fulfill any criteria for GHR or FHR.ResultsThere were 145 patients with NDMM, 78 in the SR group, 56 in the GHR group, and 11 in the FHR group. In the FHR group, eight patients were suboptimal responses to induction therapy, and three relapsed within 12 months. We found that male patients, patients with extra-medullary plasmacytoma (EMD), circulating clonal plasma cells (CPC) ≥0.05%, and P53 mono-allelic inactivation were significantly higher in the FHR group compared to the SR group. After a median follow-up of 21.0 months, the median progression-free survival (PFS) and overall survival (OS) were 5.0 months, 19.1 months and 36.6 months in the FHR, GHR, and SR groups, respectively. Compared to the SR group, FHR patients had a higher frequency of mutations in MKI67, ERN1, and EML4. GO analysis showed that mutations in FHR were enriched for oxidative stress, chromosomal segregation, and hypoxia tolerance.ConclusionThe FHR found in the SR NDMM patient group has unique clinical features, including being male, with EMD and CPC, and genetic characteristics of mutations affecting oxidative stress, chromosome segregation, and hypoxia tolerance. In contrast to previous reports, our data suggested that patients with P53 mono-allelic inactivation should be classified in the GHR group rather than the FHR group.

Project description:Genomic instability, a well-established hallmark of human cancer, is also a driving force in the natural history of multiple myeloma (MM) - a difficult to treat and in most cases fatal neoplasm of immunoglobulin producing plasma cells that reside in the hematopoietic bone marrow. Long recognized manifestations of genomic instability in myeloma at the cytogenetic level include abnormal chromosome numbers (aneuploidy) caused by trisomy of odd-numbered chromosomes; recurrent oncogene-activating chromosomal translocations that involve immunoglobulin loci; and large-scale amplifications, inversions, and insertions/deletions (indels) of genetic material. Catastrophic genetic rearrangements that either shatter and illegitimately reassemble a single chromosome (chromotripsis) or lead to disordered segmental rearrangements of multiple chromosomes (chromoplexy) also occur. Genomic instability at the nucleotide level results in base substitution mutations and small indels that affect both the coding and non-coding genome. Sometimes this generates a distinctive signature of somatic mutations that can be attributed to defects in DNA repair pathways, the DNA damage response (DDR) or aberrant activity of mutator genes including members of the APOBEC family. In addition to myeloma development and progression, genomic instability promotes acquisition of drug resistance in patients with myeloma. Here we review recent findings on the genetic predisposition to myeloma, including newly identified candidate genes suggesting linkage of germline risk and compromised genomic stability control. The role of ethnic and familial risk factors for myeloma is highlighted. We address current research gaps that concern the lack of studies on the mechanism by which germline risk alleles promote genomic instability in myeloma, including the open question whether genetic modifiers of myeloma development act in tumor cells, the tumor microenvironment (TME), or in both. We conclude with a brief proposition for future research directions, which concentrate on the biological function of myeloma risk and genetic instability alleles, the potential links between the germline genome and somatic changes in myeloma, and the need to elucidate genetic modifiers in the TME.

Project description:PurposeMultiple myeloma is a biologically heterogenous plasma-cell disorder. In this study, we aimed at dissecting the functional impact on transcriptome of gene mutations, copy-number abnormalities (CNA), and chromosomal rearrangements (CR). Moreover, we applied a geno-transcriptomic approach to identify specific biomarkers for personalized treatments.Experimental designWe analyzed 514 newly diagnosed patients from the IA12 release of the CoMMpass study, accounting for mutations in multiple myeloma driver genes, structural variants, copy-number segments, and raw-transcript counts. We performed an in silico drug sensitivity screen (DSS), interrogating the Cancer Dependency Map (DepMap) dataset after anchoring cell lines to primary tumor samples using the Celligner algorithm.ResultsImmunoglobulin translocations, hyperdiploidy and chr(1q)gain/amps were associated with the highest number of deregulated genes. Other CNAs and specific gene mutations had a lower but very distinct impact affecting specific pathways. Many recurrent genes showed a hotspot (HS)-specific effect. The clinical relevance of double-hit multiple myeloma found strong biological bases in our analysis. Biallelic deletions of tumor suppressors and chr(1q)-amplifications showed the greatest impact on gene expression, deregulating pathways related to cell cycle, proliferation, and expression of immunotherapy targets. Moreover, our in silico DSS showed that not only t(11;14) but also chr(1q)gain/amps and CYLD inactivation predicted differential expression of transcripts of the BCL2 axis and response to venetoclax.ConclusionsThe multiple myeloma genomic architecture and transcriptome have a strict connection, led by CNAs and CRs. Gene mutations impacted especially with HS-mutations of oncogenes and biallelic tumor suppressor gene inactivation. Finally, a comprehensive geno-transcriptomic analysis allows the identification of specific deregulated pathways and candidate biomarkers for personalized treatments in multiple myeloma.

Dataset Information

Genomic characterization of functional high-risk multiple myeloma patients.

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Genomic characterization of functional high-risk multiple myeloma patients.

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