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

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

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.

Individualized dynamic risk assessment for multiple myeloma

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.

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

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.

Chromosome Territories in Hematological Malignancies

Chromosomes are organized in distinct nuclear areas designated as chromosome territories (CT). The structural formation of CT is a consequence of chromatin packaging and organization that ultimately affects cell function. Chromosome positioning can identify structural signatures of genomic organization, especially for diseases where changes in gene expression contribute to a given phenotype. The study of CT in hematological diseases revealed chromosome position as an important factor for specific chromosome translocations. In this review, we highlight the history of CT theory, current knowledge on possible clinical applications of CT analysis, and the impact of CT in the development of hematological neoplasia such as multiple myeloma, leukemia, and lymphomas. Accumulating data on nuclear architecture in cancer allow one to propose the three-dimensional nuclear genomic landscape as a novel cancer biomarker for the future.