Prognostic gene expression analysis in a retrospective, multinational cohort of 155 multiple myeloma patients treated outside clinical trials

Chen, Yanting; Valent, Erik T.; Beers, Erik H. van; Kuiper, Rowan; Oliva, Stefania; Haferlach, Torsten; Chng, Wee‐Joo; Vliet, Martin H. van; Sonneveld, Pieter; Larocca, Alessandra

doi:10.1111/ijlh.13691

Cited by 6 publications

(4 citation statements)

References 36 publications

(69 reference statements)

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“…For many years, gene expression profiling via microarray has represented a high-resolution and valid technique that has enabled the identification of gene expression fluctuations under different experimental conditions [ 20 , 21 , 22 ]. Moreover, the extrapolation of a restricted list of genes, named “signature”, has allowed patients’ stratification in prognostic meaningful subgroups [ 23 , 24 , 25 ]. However, one of the pitfalls of this approach has been represented by the need to be validated by other techniques, which possibly should be more manageable and ready to use in daily clinical practice.…”

Section: Discussionmentioning

confidence: 99%

Identification of a Maturation Plasma Cell Index through a Highly Sensitive Droplet Digital PCR Assay Gene Expression Signature Validation in Newly Diagnosed Multiple Myeloma Patients

Martello

Solli

Termini

et al. 2022

IJMS

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DNA microarrays and RNA-based sequencing approaches are considered important discovery tools in clinical medicine. However, cross-platform reproducibility studies undertaken so far have highlighted that microarrays are not able to accurately measure gene expression, particularly when they are expressed at low levels. Here, we consider the employment of a digital PCR assay (ddPCR) to validate a gene signature previously identified by gene expression profile. This signature included ten Hedgehog (HH) pathways’ genes able to stratify multiple myeloma (MM) patients according to their self-renewal status. Results show that the designed assay is able to validate gene expression data, both in a retrospective as well as in a prospective cohort. In addition, the plasma cells’ differentiation status determined by ddPCR was further confirmed by other techniques, such as flow cytometry, allowing the identification of patients with immature plasma cells’ phenotype (i.e., expressing CD19+/CD81+ markers) upregulating HH genes, as compared to others, whose plasma cells lose the expression of these markers and were more differentiated. To our knowledge, this is the first technical report of gene expression data validation by ddPCR instead of classical qPCR. This approach permitted the identification of a Maturation Index through the integration of molecular and phenotypic data, able to possibly define upfront the differentiation status of MM patients that would be clinically relevant in the future.

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Section: Discussionmentioning

confidence: 99%

Identification of a Maturation Plasma Cell Index through a Highly Sensitive Droplet Digital PCR Assay Gene Expression Signature Validation in Newly Diagnosed Multiple Myeloma Patients

Martello

Solli

Termini

et al. 2022

IJMS

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“…98,99 Although estimates vary, in recent real-world data SKY92 high-risk patients experienced median OS under 3-years. 100 The PROMMIS trial evaluated SKY92s influence on risk stratification and treatment decision making, herein SKY92 results led to risk reclassification in 42% and altered treatment decisions in 37% of patients. 99 This effect may be because SKY92 captures a previously hidden HRMM group who lack HRCAs, in analysis of the Myeloma XI trial SKY92 predicted PFS and OS regardless of induction regimen and 12% of patient without any HRCAs were reclassified as high-risk by SKY92.…”

Section: Gene Expression Profilingmentioning

confidence: 99%

“…SKY92 was developed from the HOVON/GMMG‐HD4 trial and assesses the expression of 92 genes, it has the most extensive validation across both trial and real‐world cohorts and categorizes around 20% of NDMM patients as high‐risk 98,99 . Although estimates vary, in recent real‐world data SKY92 high‐risk patients experienced median OS under 3‐years 100 …”

Section: Gene Expression Profilingmentioning

confidence: 99%

High‐risk multiple myeloma: Redefining genetic, clinical, and functional high‐risk disease in the era of molecular medicine and immunotherapy

Rees,

Kumar

2024

American J Hematol

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Multiple myeloma (MM) exhibits significant heterogeneity in its presentation, genetics, and treatment response. Despite therapeutic advances, some patients continue to relapse early (ER, <18‐months) and rapidly cycle through therapies. Myriad prognostic factors have been identified and incorporated into risk stratification models; however, these produce discordant, often three‐tiered outputs that fail to identify many patients destined for ER. Treatment strategies are increasingly focused on disease biology and trials enriched for high‐risk (HR)MM, but consensus on the minimum required testing and a succinct, specific, and clinically meaningful definition for HRMM remains elusive. We review the risk‐factors, definitions, and future directions for HRMM.

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“…(15,16) While gene expression panels are not routinely used in clinical practice they continue to play a role in advancing research efforts. (17)(18)(19) Recognizing their role in research, it is evident that there exists an unmet need to develop more accurate tools for risk assessment. In particular, a clinical test to longitudinally assess MM prognosis at various stages could prove transformational in improving outcomes by enabling dynamic calibration of personalized treatment plans based on the unique disease trajectory of each patient.…”

Section: Introductionmentioning

confidence: 99%

Individualized dynamic risk assessment for multiple myeloma

Murie,

Turkarslan,

Patel

et al. 2024

Preprint

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Background. Individualized treatment decisions for patients with multiple myeloma (MM) requires accurate risk stratification that takes into account patient-specific consequences of genetic abnormalities and tumor microenvironment on disease outcome and therapy responsiveness. Methods. Previously, SYstems Genetic Network AnaLysis (SYGNAL) of multi-omics tumor profiles from 881 MM patients generated the mmSYGNAL network, which uncovered different causal and mechanistic drivers of genetic programs associated with disease progression across MM subtypes. Here, we have trained a machine learning (ML) algorithm on activities of mmSYGNAL programs within individual patient tumor samples to develop a risk classification scheme for MM that significantly outperformed cytogenetics, International Staging System, and multi-gene biomarker panels in predicting risk of PFS across four independent patient cohorts. Results. We demonstrate that, unlike other tests, mmSYGNAL can accurately predict disease progression risk at primary diagnosis, pre- and post-transplant and even after multiple relapses, making it useful for individualized dynamic risk assessment throughout the disease trajectory. Conclusion. mmSYGNAL provides improved individualized risk stratification that accounts for a patient's distinct set of genetic abnormalities and can monitor risk longitudinally as each patient's disease characteristics change.

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Prognostic gene expression analysis in a retrospective, multinational cohort of 155 multiple myeloma patients treated outside clinical trials

Cited by 6 publications

References 36 publications

Identification of a Maturation Plasma Cell Index through a Highly Sensitive Droplet Digital PCR Assay Gene Expression Signature Validation in Newly Diagnosed Multiple Myeloma Patients

Identification of a Maturation Plasma Cell Index through a Highly Sensitive Droplet Digital PCR Assay Gene Expression Signature Validation in Newly Diagnosed Multiple Myeloma Patients

High‐risk multiple myeloma: Redefining genetic, clinical, and functional high‐risk disease in the era of molecular medicine and immunotherapy

Individualized dynamic risk assessment for multiple myeloma

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