Comprehensive translational profiling and STE AI uncover rapid control of protein biosynthesis during cell stress.

Translational control is important in all life, but it remains a challenge to accurately quantify. When ribosomes translate messenger (m)RNA into proteins, they attach to the mRNA in series, forming poly(ribo)somes, and can co-localize. Here, we computationally model new types of co-localized ribosomal complexes on mRNA and identify them using enhanced translation complex profile sequencing (eTCP-seq) based on rapid in vivo crosslinking. We detect long disome footprints outside regions of non-random elongation stalls and show these are linked to translation initiation and protein biosynthesis rates. We subject footprints of disomes and other translation complexes to artificial intelligence (AI) analysis and construct a new, accurate and self-normalized measure of translation, termed stochastic translation efficiency (STE). We then apply STE to investigate rapid changes to mRNA translation in yeast undergoing glucose depletion. Importantly, we show that, well beyond tagging elongation stalls, footprints of co-localized ribosomes provide rich insight into translational mechanisms, polysome dynamics and topology. STE AI ranks cellular mRNAs by absolute translation rates under given conditions, can assist in identifying its control elements and will facilitate the development of next-generation synthetic biology designs and mRNA-based therapeutics.

In Multiple Myeloma, High-Risk Secondary Genetic Events Observed at Relapse Are Present From Diagnosis in Tiny, Undetectable Subclonal Populations.

PURPOSE: Multiple myeloma (MM) is characterized by copy number abnormalities (CNAs), some of which influence patient outcomes and are sometimes observed only at relapse(s), suggesting their acquisition during tumor evolution. However, the presence of micro-subclones may be missed in bulk analyses. Here, we use single-cell genomics to determine how often these high-risk events are missed at diagnosis and selected at relapse. MATERIALS AND METHODS: We analyzed 81 patients with plasma cell dyscrasias using single-cell CNA sequencing. Sixty-six patients were selected at diagnosis, nine at first relapse, and six in presymptomatic stages. A total of 956 newly diagnosed patients with MM and patients with first relapse MM have been identified retrospectively with required cytogenetic data to evaluate enrichment of CNA risk events and survival impact. RESULTS: A total of 52,176 MM cells were analyzed. Seventy-four patients (91%) had 2-16 subclones. Among these patients, 28.7% had a subclone with high-risk features (del(17p), del(1p32), and 1q gain) at diagnosis. In a patient with a subclonal 1q gain at diagnosis, we analyzed the diagnosis, postinduction, and first relapse samples, which showed a rise of the high-risk 1q gain subclone (16%, 70%, and 92%, respectively). In our clinical database, we found that the 1q gain frequency increased from 30.2% at diagnosis to 43.6% at relapse (odds ratio, 1.78; 95% CI, 1.58 to 2.00). We subsequently performed survival analyses, which showed that the progression-free and overall survival curves were superimposable between patients who had the 1q gain from diagnosis and those who seemingly acquired it at relapse. This strongly suggests that many patients had 1q gains at diagnosis in microclones that were missed by bulk analyses. CONCLUSION: These data suggest that identifying these scarce aggressive cells may necessitate more aggressive treatment as early as diagnosis to prevent them from becoming the dominant clone.

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.

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.

Dysregulated IL-18 Is a Key Driver of Immunosuppression and a Possible Therapeutic Target in the Multiple Myeloma Microenvironment.

Tumor-promoting inflammation and avoiding immune destruction are hallmarks of cancer. Here, we demonstrate that the pro-inflammatory cytokine interleukin (IL)-18 is critically involved in these hallmarks in multiple myeloma (MM). Mice deficient for IL-18 were remarkably protected from Vk∗MYC MM progression in a CD8+ T cell-dependent manner. The MM-niche-derived IL-18 drove generation of myeloid-derived suppressor cells (MDSCs), leading to accelerated disease progression. A global transcriptome analysis of the immune microenvironment in 73 MM patients strongly supported the negative impact of IL-18-driven MDSCs on T cell responses. Strikingly, high levels of bone marrow plasma IL-18 were associated with poor overall survival in MM patients. Furthermore, our preclinical studies suggested that IL-18 could be a potential therapeutic target in MM.

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.

Understanding the role of hyperdiploidy in myeloma prognosis: which trisomies really matter?

The prognosis of multiple myeloma is mainly dependent upon chromosomal changes. The 2 major abnormalities driving poor outcome are del(17p) and t(4;14). However, the outcome of these high-risk patients is not absolutely uniform, with some patients presenting long survival. We hypothesized that these better outcomes might be related to concomitant "good-risk" chromosomal changes exploring hyperdiploidy. We analyzed a large series of 965 myeloma patients, including 168 patients with t(4;14) and 126 patients with del(17p), using high-throughput single-nucleotide polymorphism arrays after plasma cell sorting. As expected, trisomic chromosomes were highly associated. Using the LASSO model, we found that only chromosome 3, when trisomic, was associated with a longer progression-free survival and that 3 trisomies modulated overall survival (OS) in myeloma patients: trisomies 3 and 5 significantly improved OS, whereas trisomy 21 worsened OS. In patients with t(4;14), trisomies 3 and/or 5 seemed to overcome the poor prognosis. For the first time, using a specific modeling approach, we show that not all trisomies display the same prognostic impact. This finding could be important for routine assessment of prognosis in myeloma, and some high-risk patients with a traditional evaluation could in fact be standard-risk patients.

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).

Segmentor3IsBack: an R package for the fast and exact segmentation of Seq-data.

BACKGROUND: Change point problems arise in many genomic analyses such as the detection of copy number variations or the detection of transcribed regions. The expanding Next Generation Sequencing technologies now allow to locate change points at the nucleotide resolution. RESULTS: Because of its complexity which is almost linear in the sequence length when the maximal number of segments is constant, and as its performance had been acknowledged for microarrays, we propose to use the Pruned Dynamic Programming algorithm for Seq-experiment outputs. This requires the adaptation of the algorithm to the negative binomial distribution with which we model the data. We show that if the dispersion in the signal is known, the PDP algorithm can be used, and we provide an estimator for this dispersion. We describe a compression framework which reduces the time complexity without modifying the accuracy of the segmentation. We propose to estimate the number of segments via a penalized likelihood criterion. We illustrate the performance of the proposed methodology on RNA-Seq data. CONCLUSIONS: We illustrate the results of our approach on a real dataset and show its good performance. Our algorithm is available as an R package on the CRAN repository.