Alice Cleynen

Role of additional chromosomal changes in the prognostic value of t(4;14) and del(17p) in multiple myeloma: the IFM experience

In multiple myeloma, cytogenetic changes are important predictors of patient outcome. In this setting, the most important changes are deletion 17p, del(17p), and translocation of chromosomes 4 and 14, t(4;14), conferring a poor outcome. However, a certain degree of heterogeneity is observed in the survival of these high-risk patients. We hypothesized that other chromosomal changes may impact the outcome. We retrospectively analyzed a large series of 242 patients displaying either t(4;14) (157 patients) or del(17p) (110 patients), 25 patients presenting both abnormalities, using single nucleotide polymorphism array. In patients with t(4;14), del(1p32), del22q, and >30 chromosomal structural changes negatively impacted progression-free survival (PFS). For overall survival (OS), del(13q14), del(1p32), and the number of chromosomal structural changes worsened the prognosis of patients. For patients with del(17p), del6q worsened the prognosis of patients, whereas trisomy 15 and monosomy 14 were found to have a protective effect on PFS. For OS, del(1p32) worsened the prognosis of patients, whereas having >8 numerical changes was found to have a protective effect on survival. This study, which is the largest series of high-risk patients analyzed with the most modern genomic technique, identified 1 main factor negatively impacting survival: del(1p32).

Genomics of Multiple Myeloma

Multiple myeloma (MM) is characterized by wide variability in the chromosomal/genetic changes present in tumor plasma cells. Genetically, MM can be divided into two groups according to ploidy and hyperdiploidy versus nonhyperdiploidy. Several studies in gene expression profiling attempted to identify subentities in MM without convincing results. These studies mostly confirmed the cytogenetic data and subclassified patients according to 14q32 translocations and ploidy. More-recent data that are based on whole-exome sequencing have confirmed this heterogeneity and show many gene mutations but without a unifying mutation. These newer studies have shown the frequent alteration of the mitogen-activated protein kinase pathway. The most interesting data have demonstrated subclonality in all patients with MM, including subclonal mutations of supposed driver genes KRAS, NRAS, and BRAF.

Expressed fusion gene landscape and its impact in multiple myeloma

Multiple myeloma is a plasma cell malignancy characterized by recurrent IgH translocations and well described genomic heterogeneity. Although transcriptome profiles in multiple myeloma has been described, landscape of expressed fusion genes and their clinical impact remains unknown. To provide a comprehensive and detailed fusion gene cartography and suggest new mechanisms of tumorigenesis in multiple myeloma, we performed RNA sequencing in a cohort of 255 newly diagnosed and homogeneously treated multiple myeloma patients with long follow-up. Here, we report that patients have on average 5.5 expressed fusion genes. Kappa and lambda light chains and IgH genes are main partners in a third of all fusion genes. We also identify recurrent fusion genes that significantly impact both progression-free and overall survival and may act as drivers of the disease. Lastly, we find a correlation between the number of fusions, the age of patients and the clinical outcome, strongly suggesting that genomic instability drives prognosis of the disease.Multiple myeloma is a malignancy of plasma cells in the blood. Here, the authors establish the landscape of fusion genes within this disease, identifying novel recurrent fusion genes that impact survival and may drive disease progression.

Long intergenic non-coding RNAs have an independent impact on survival in multiple myeloma

Although long intergenic non-coding RNAs (lincRNA) role in various cancers is described, their significance in Multiple Myeloma (MM) remains poorly defined. Here we have studied the lincRNA profile and their clinical impact in MM. We performed RNA-seq on MM cells from 308 newly diagnosed and uniformly treated patients, 16 normal plasma cells and utilized RNA-seq data from 532 newly diagnosed patients from CoMMpass study to analyze for lincRNAs. We observed 869 differentially expressed lincRNAs in MM compared to normal plasma cells. We identified 14 lincRNAs associated with PFS and calculated a risk score to stratify patients. The median PFS between high vs low-risk groups was 17 months vs not-reached (NR); and OS 30 months vs NR, respectively (p < 0.0001 for both). In the independent validation dataset between high and low-risk groups, PFS was 27 vs 42 months (HR 2.06 [1.44−2.96]; p < 0.0005); and 4-year OS 62% vs 86% (HR 2.76 [1.51–5.05]; p < 0.0005) confirming significant clinical relevance of lincRNA in MM. Importantly, lincRNA signature was able to further identify patients with significant differential outcomes within each low and high-risk categories identified using standard risk categorization including cytogenetic/FISH, ISS, and MRD negative or positive. Our results suggest that lincRNAs have an independent effect on MM outcome and provide a rationale to evaluate its molecular and biological impact.

Nucleotide excision repair is a potential therapeutic target in multiple myeloma

Despite the development of novel drugs, alkylating agents remain an important component of therapy in multiple myeloma (MM). DNA repair processes contribute towards sensitivity to alkylating agents and therefore we here evaluate the role of nucleotide excision repair (NER), which is involved in the removal of bulky adducts and DNA crosslinks in MM. We first evaluated NER activity using a novel functional assay and observed a heterogeneous NER efficiency in MM cell lines and patient samples. Using next-generation sequencing data, we identified that expression of the canonical NER gene, excision repair cross-complementation group 3 (ERCC3), significantly impacted the outcome in newly diagnosed MM patients treated with alkylating agents. Next, using small RNA interference, stable knockdown and overexpression, and small-molecule inhibitors targeting xeroderma pigmentosum complementation group B (XPB), the DNA helicase encoded by ERCC3, we demonstrate that NER inhibition significantly increases sensitivity and overcomes resistance to alkylating agents in MM. Moreover, inhibiting XPB leads to the dual inhibition of NER and transcription and is particularly efficient in myeloma cells. Altogether, we show that NER impacts alkylating agents sensitivity in myeloma cells and identify ERCC3 as a potential therapeutic target in MM.

Multiple myeloma clonal evolution in homogeneously treated patients

Clonal evolution drives tumor progression, chemoresistance and relapse in cancer. Little is known about clonal selection induced by therapeutic pressure in multiple myeloma. To address this issue, we performed large targeted sequencing of bone marrow plasma cells in 43 multiple myeloma patients at diagnosis and at relapse from exactly the same intensive treatment. The most frequently mutated genes at diagnosis were KRAS (35%), NRAS (28%), DIS3 (16%), BRAF, and LRP1B (12% each). At relapse, the mutational burden was unchanged. Many of the mutations were present at the subclonal level at both time points, including driver ones. According to patients and mutations, we observed different scenarios: selection of a very rare subclone present at diagnosis, appearance, or disappearance of mutations, but also stability. Our data highlight that chemoresistance and relapse could be induced by newly acquired mutations in myeloma drivers but also by (sub)clonal mutations preexisting to the treatment. Importantly, no specific mutation or rearrangement was observed at relapse, demonstrating that intensive treatment has a nonspecific effect on clonal selection in multiple myeloma. Finally, we identified 22 cases of biallelic event, including a double event deletion 17p/TP53mut.

Change-point detection for piecewise deterministic Markov processes

We consider a change-point detection problem for a simple class of Piecewise Deterministic Markov Processes (PDMPs). A continuous-time PDMP is observed in discrete time and through noise, and the aim is to propose a numerical method to accurately detect both the date of the change of dynamics and the new regime after the change. To do so, we state the problem as an optimal stopping problem for a partially observed discrete-time Markov decision process taking values in a continuous state space and provide a discretization of the state space based on quantization to approximate the value function and build a tractable stopping policy. We provide error bounds for the approximation of the value function and numerical simulations to assess the performance of our candidate policy.