CLLU1 as an emerging biomarker in chronic lymphoid leukemia

CLLU1, a disease-specific gene associated with chronic lymphoid leukemia (CLL), is located on chromosome 12q22. Previous studies considered CLLU1 to be a non-coding RNA; however, recent research has discovered that its coding sequence region possesses the potential to encode a short peptide similar to interleukin-4. Remarkably, abnormally elevated expression of CLLU1 has only been detected in chronic lymphoid leukemia among all hematological cancers. High CLLU1 expression often indicates more malignant pathological features and an unfavorable prognosis for patients. Importantly, the expression level of CLLU1 remains unaffected by the passage of time or therapeutic interventions, thus rendering it a novel prognostic marker. This article provides a comprehensive summary of relevant research findings on CLLU1 in the context of CLL prognosis and clinical applications, aiming to guide subsequent theoretical and clinical investigations in this field.

Genes selection using deep learning and explainable artificial intelligence for chronic lymphocytic leukemia predicting the need and time to therapy

Analyzing gene expression profiles (GEP) through artificial intelligence provides meaningful insight into cancer disease. This study introduces DeepSHAP Autoencoder Filter for Genes Selection (DSAF-GS), a novel deep learning and explainable artificial intelligence-based approach for feature selection in genomics-scale data. DSAF-GS exploits the autoencoder's reconstruction capabilities without changing the original feature space, enhancing the interpretation of the results. Explainable artificial intelligence is then used to select the informative genes for chronic lymphocytic leukemia prognosis of 217 cases from a GEP database comprising roughly 20,000 genes. The model for prognosis prediction achieved an accuracy of 86.4%, a sensitivity of 85.0%, and a specificity of 87.5%. According to the proposed approach, predictions were strongly influenced by CEACAM19 and PIGP, moderately influenced by MKL1 and GNE, and poorly influenced by other genes. The 10 most influential genes were selected for further analysis. Among them, FADD, FIBP, FIBP, GNE, IGF1R, MKL1, PIGP, and SLC39A6 were identified in the Reactome pathway database as involved in signal transduction, transcription, protein metabolism, immune system, cell cycle, and apoptosis. Moreover, according to the network model of the 3D protein-protein interaction (PPI) explored using the NetworkAnalyst tool, FADD, FIBP, IGF1R, QTRT1, GNE, SLC39A6, and MKL1 appear coupled into a complex network. Finally, all 10 selected genes showed a predictive power on time to first treatment (TTFT) in univariate analyses on a basic prognostic model including IGHV mutational status, del(11q) and del(17p), NOTCH1 mutations, β2-microglobulin, Rai stage, and B-lymphocytosis known to predict TTFT in CLL. However, only IGF1R [hazard ratio (HR) 1.41, 95% CI 1.08-1.84, P=0.013), COL28A1 (HR 0.32, 95% CI 0.10-0.97, P=0.045), and QTRT1 (HR 7.73, 95% CI 2.48-24.04, P<0.001) genes were significantly associated with TTFT in multivariable analyses when combined with the prognostic factors of the basic model, ultimately increasing the Harrell's c-index and the explained variation to 78.6% (versus 76.5% of the basic prognostic model) and 52.6% (versus 42.2% of the basic prognostic model), respectively. Also, the goodness of model fit was enhanced (χ2 = 20.1, P=0.002), indicating its improved performance above the basic prognostic model. In conclusion, DSAF-GS identified a group of significant genes for CLL prognosis, suggesting future directions for bio-molecular research.

Deep RNA profiling identified CLOCK and molecular clock genes as pathophysiological signatures in collagen VI myopathy

Collagen VI myopathies are genetic disorders caused by mutations in collagen 6 A1, A2 and A3 genes, ranging from the severe Ullrich congenital muscular dystrophy to the milder Bethlem myopathy, which is recapitulated by collagen-VI-null (Col6a1(-/-)) mice. Abnormalities in mitochondria and autophagic pathway have been proposed as pathogenic causes of collagen VI myopathies, but the link between collagen VI defects and these metabolic circuits remains unknown. To unravel the expression profiling perturbation in muscles with collagen VI myopathies, we performed a deep RNA profiling in both Col6a1(-/-)mice and patients with collagen VI pathology. The interactome map identified common pathways suggesting a previously undetected connection between circadian genes and collagen VI pathology. Intriguingly, Bmal1(-/-)(also known as Arntl) mice, a well-characterized model displaying arrhythmic circadian rhythms, showed profound deregulation of the collagen VI pathway and of autophagy-related genes. The involvement of circadian rhythms in collagen VI myopathies is new and links autophagy and mitochondrial abnormalities. It also opens new avenues for therapies of hereditary myopathies to modulate the molecular clock or potential gene-environment interactions that might modify muscle damage pathogenesis.

Identification of a 20-gene expression-based risk score as a predictor of clinical outcome in chronic lymphocytic leukemia patients

Despite the improvement in treatment options, chronic lymphocytic leukemia (CLL) remains an incurable disease and patients show a heterogeneous clinical course requiring therapy for many of them. In the current work, we have built a 20-gene expression (GE)-based risk score predictive for patients overall survival and improving risk classification using microarray gene expression data. GE-based risk score allowed identifying a high-risk group associated with a significant shorter overall survival (OS) and time to treatment (TTT) (P ≤ .01), comprising 19.6% and 13.6% of the patients in two independent cohorts. GE-based risk score, and NRIP1 and TCF7 gene expression remained independent prognostic factors using multivariate Cox analyses and combination of GE-based risk score together with NRIP1 and TCF7 gene expression enabled the identification of three clinically distinct groups of CLL patients. Therefore, this GE-based risk score represents a powerful tool for risk stratification and outcome prediction of CLL patients and could thus be used to guide clinical and therapeutic decisions prospectively.

Short telomere length is associated with NOTCH1/SF3B1/TP53 aberrations and poor outcome in newly diagnosed chronic lymphocytic leukemia patients

Most previous studies on telomere length (TL) in chronic lymphocytic leukemia (CLL) are based on referral cohorts including a high proportion of aggressive cases. Here, the impact of TL was analyzed in a population-based cohort of newly diagnosed CLL (n = 265) and in relation to other prognostic markers. Short telomeres were particularly associated with high-risk genetic markers, such as NOTCH1, SF3B1, or TP53 aberrations, and predicted a short time to treatment (TTT) and overall survival (OS) (both P < 0.0001). TL was an independent prognostic factor and subdivided patients with otherwise good-prognostic features (e.g., mutated IGHV genes, favorable cytogenetics) into subgroups with different outcome. Furthermore, in follow-up samples (n = 119) taken 5-8 years after diagnosis, TL correlated well with TL at diagnosis and remained unaffected by treatment. Altogether, these novel data indicate that short TL already at diagnosis is associated with poor outcome in CLL and that TL can be measured at later stages of the disease.

Chronic lymphocytic leukemia: molecular heterogeneity revealed by high-throughput genomics

Chronic lymphocytic leukemia (CLL) has been consistently at the forefront of genetic research owing to its prevalence and the accessibility of sample material. Recently, genome-wide technologies have been intensively applied to CLL genetics, with remarkable progress. Single nucleotide polymorphism arrays have identified recurring chromosomal aberrations, thereby focusing functional studies on discrete genomic lesions and leading to the first implication of somatic microRNA disruption in cancer. Next-generation sequencing (NGS) has further transformed our understanding of CLL by identifying novel recurrently mutated putative drivers, including the unexpected discovery of somatic mutations affecting spliceosome function. NGS has further enabled in-depth examination of the transcriptional and epigenetic changes in CLL that accompany genetic lesions, and has shed light on how different driver events appear at different stages of disease progression and clonally evolve with relapsed disease. In addition to providing important insights into disease biology, these discoveries have significant translational potential. They enhance prognosis by highlighting specific lesions associated with poor clinical outcomes (for example, driver events such as mutations in the splicing factor subunit gene SF3B1) or with increased clonal heterogeneity (for example, the presence of subclonal driver mutations). Here, we review new genomic discoveries in CLL and discuss their possible implications in the era of precision medicine.

Prognostic markers and their clinical applicability in chronic lymphocytic leukemia: where do we stand?

Chronic lymphocytic leukemia (CLL) is a clinically and biologically heterogeneous disease where the majority of patients have an indolent disease course, while others may experience a far more aggressive disease, treatment failure and poor overall survival. During the last two decades, there has been an intense search to find novel biomarkers that can predict prognosis as well as guide treatment decisions. Two of the most reliable molecular prognostic markers, both of which are offered in routine diagnostics, are the immunoglobulin heavy chain variable (IGHV) gene mutational status and fluorescence in situ hybridization (FISH) detection of prognostically relevant genomic aberrations (e.g. 11q-, 13q-, +12 and 17p-). In addition to these markers, a myriad of additional biomarkers have been postulated as potential prognosticators in CLL, on the protein (e.g. CD38, ZAP70, TCL1), the RNA (e.g. LPL, CLLU1, micro-RNAs) and the genomic (e.g. TP53, NOTCH1, SF3B1 and BIRC3 mutations) level. Efforts are now being made to test these novel markers in larger patient cohorts as well as in prospective trials, with the ultimate goal to combine the "best" markers in a "CLL prognostic index" applicable for the individual patient. Although it is clear that these studies have significantly improved our knowledge regarding both prognostication and the biology of the disease, there is still an immediate need for recognizing biomarkers that can predict therapy response, and efforts should now focus on addressing this pertinent issue. In the present article, we review the extensive literature in the field of prognostic markers in CLL, focus on the most clinically relevant markers and discuss future directions regarding biomarkers in CLL.

CLLU1 expression has prognostic value in chronic lymphocytic leukemia after first-line therapy in younger patients and in those with mutated IGHV genes

CLLU1, located at chromosome 12q22, encodes a transcript specific to chronic lymphocytic leukemia and has potential prognostic value. We assessed the value of CLLU1 expression in the LRF CLL4 randomized trial. Samples from 515 patients with chronic lymphocytic leukemia were collected immediately before the start of treatment. After RNA extraction and cDNA synthesis, CLLU1 expression was assessed by quantitative polymerase chain reaction. In total, 247 and 268 samples were identified as having low and high CLLU1 expression, respectively. The median follow-up was 88 months. High CLLU1 expression was significantly correlated with unmutated IGHV genes, ZAP-70 and CD38 positivity, and absence of 13q deletion (all r>0.2, P<0.0001). At 6 years, patients with high CLLU1 expression had significantly worse progression-free survival (9% versus 17%; P=0.03) and overall survival (42% versus 57%; P=0.0003) than patients with low CLLU1 expression. Among patients with mutated IGHV genes, overall survival at 6 years was 50% in those with high CLLU1 expression and 76% in those with low CLLU1 expression (P=0.005). However, CLLU1 expression was not an independent predictor of overall survival in a multivariate model including TP53 aberrations, beta-2 microglobulin level, age and IGHV mutation status. Nor did it predict response to treatment. CLLU1 expression analysis helps to refine the prognosis of patients with chronic lymphocytic leukemia who have mutated IGHV genes.