High levels of CRBN isoform lacking IMiDs binding domain predicts for a worse response to IMiDs-based upfront therapy in newly diagnosed myeloma patients

In recent years, the immunoderivative (IMiD) agents have been extensively used for the treatment of multiple myeloma (MM). IMiDs and their newer derivatives CRBN E3 ligase modulator bind the E3 ligase substrate recognition adapter protein cereblon (CRBN), which has been recognized as one of the IMiDs’ direct target proteins, and it is essential for the therapeutic effect of these agents. High expression of CRBN was associated with improved clinical response in patients with MM treated with IMiDs, further confirming that the expression of IMiDs’ direct target protein CRBN is required for the anti-MM activity. CRBN’s central role as a target of IMiDs suggests potential utility as a predictive biomarker of response or resistance to IMiDs therapy. Additionally, the presence of alternatively spliced variants of CRBN in MM cells, especially those lacking the drug-binding domain for IMiDs, raise questions concerning their potential biological function, making difficult the transcript measurement, which leads to inaccurate overestimation of full-length CRBN transcripts. In sight of this, in the present study, we evaluated the CRBN expression, both full-length and spliced isoforms, by using real-time assay data from 87 patients and RNA sequencing data from 50 patients (n = 137 newly diagnosed MM patients), aiming at defining CRBN’s role as a predictive biomarker for response to IMiDs-based induction therapy. We found that the expression level of the spliced isoform tends to be higher in not-responding patients, confirming that the presence of a more CRBN spliced transcript predicts for lack of IMiDs response

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

Multi-dimensional scaling techniques unveiled gain1q&loss13q co-occurrence in Multiple Myeloma patients with specific genomic, transcriptional and adverse clinical features

The complexity of Multiple Myeloma (MM) is driven by several genomic aberrations, interacting with disease-related and/or -unrelated factors and conditioning patients’ clinical outcome. Patient’s prognosis is hardly predictable, as commonly employed MM risk models do not precisely partition high- from low-risk patients, preventing the reliable recognition of early relapsing/refractory patients. By a dimensionality reduction approach, here we dissect the genomic landscape of a large cohort of newly diagnosed MM patients, modelling all the possible interactions between any MM chromosomal alterations. We highlight the presence of a distinguished cluster of patients in the low-dimensionality space, with unfavorable clinical behavior, whose biology was driven by the co-occurrence of chromosomes 1q CN gain and 13 CN loss. Presence or absence of these alterations define MM patients overexpressing either CCND2 or CCND1, fostering the implementation of biology-based patients’ classification models to describe the different MM clinical behaviors