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A two-step error correction process for high throughput–sequenced T- and B-cell receptors allows the elimination of most errors while not diminishing the natural complexity of the repertoires. Deep profiling of antibody and T cell–receptor repertoires by means of high-throughput sequencing has become an attractive approach for adaptive immunity studies, but its power is substantially compromised by the accumulation of PCR and sequencing errors. Here we report MIGEC (molecular identifier groups–based error correction), a strategy for high-throughput sequencing data analysis. MIGEC allows for nearly absolute error correction while fully preserving the natural diversity of complex immune repertoires.

Chronic lymphocytic leukemia (CLL) is a common adult leukemia characterized by the accumulation of neoplastic mature B cells in blood, bone marrow, lymph nodes, and spleen. The disease biology remains unresolved in many aspects, including the processes underlying the disease progression and relapses. However, studying CLL poses a considerable challenge due to its complexity and dependency on the microenvironment. Several approaches are utilized to overcome this issue, such as co-culture of CLL cells with other cell types, supplementing culture media with growth factors, or setting up a three-dimensional (3D) culture. Previous studies have shown that 3D cultures, compared to conventional ones, can lead to enhanced cell survival and altered gene expression. 3D cultures can also give valuable information while testing treatment response since they mimic the cell spatial organization more accurately than conventional culture. In our study, we investigated the behavior of CLL cells in two types of material: (i) solid porous collagen scaffolds and (ii) gel composed of carboxymethyl cellulose and polyethylene glycol (CMC-PEG). We studied CLL cells' distribution, morphology, and viability in these materials by a transmitted-light and confocal microscopy. We also measured the metabolic activity of cultured cells. Additionally, the expression levels of , and genes in CLL cells were studied by qPCR to observe whether our novel culture approaches lead to increased adhesion, lower apoptotic rates, or activation of cell signaling in relation to the enhanced contact with co-cultured cells. Both materials were biocompatible, translucent, and permeable, as assessed by metabolic assays, cell staining, and microscopy. While collagen scaffolds featured easy manipulation, washability, transferability, and biodegradability, CMC-PEG was advantageous for its easy preparation process and low variability in the number of accommodated cells. Both materials promoted cell-to-cell and cell-to-matrix interactions due to the scaffold structure and generation of cell aggregates. The metabolic activity of CLL cells cultured in CMC-PEG gel was similar to or higher than in conventional culture. Compared to the conventional culture, there was (i) a lower expression of in both materials, (ii) a higher expression of in collagen scaffolds, and (iii) a lower expression of and (transcript variant 2) in collagen scaffolds, while it was higher in a CMC-PEG gel. Hence, culture in the material can suppress the expression of a pro-apoptotic gene ( in collagen scaffolds) or replicate certain gene expression patterns attributed to CLL cells in lymphoid organs (low , high in collagen scaffolds) or blood (high in CMC-PEG).

Early identification of resistant cancer cells is currently a major challenge, as their expansion leads to refractoriness. To capture the dynamics of these cells, we made a comprehensive analysis of disease progression and treatment response in a chronic lymphocytic leukemia (CLL) patient using a combination of single‐cell and bulk genomic methods. At diagnosis, the patient presented with unfavorable genetic markers, including notch receptor 1 (NOTCH1) mutation and loss(11q). The initial and subsequent treatment lines did not lead to a durable response and the patient developed refractory disease. Refractory CLL cells featured substantial dysregulation in B‐cell phenotypic markers such as human leukocyte antigen (HLA) genes, immunoglobulin (IG) genes, CD19 molecule (CD19), membrane spanning 4‐domains A1 (MS4A1; previously known as CD20), CD79a molecule (CD79A) and paired box 5 (PAX5), indicating B‐cell de‐differentiation and disease transformation. We described the clonal evolution and characterized in detail two cell populations that emerged during the refractory disease phase, differing in the presence of high genomic complexity. In addition, we successfully tracked the cells with high genomic complexity back to the time before treatment, where they formed a rare subpopulation. We have confirmed that single‐cell RNA sequencing enables the characterization of refractory cells and the monitoring of their development over time. We studied a patient with chronic lymphocytic leukemia by using a combination of single‐cell and bulk genomic methods. We analyzed B‐cells and DNA from time of diagnosis (DG), progression (PR), relapse (RL) and refractory disease phase (RF), and DNA from second relapse (RL2). We reconstructed the clonal evolution and tracked the cells with high genomic complexity (HGC) back to the pre‐treatment time.

Background A highly complex karyotype (high-CK) is a key biomarker of poor prognosis in chronic lymphocytic leukemia (CLL). While conventional methods lack the resolution to fully characterize complex structural variants (SVs), emerging technologies such as short-read WGS (sr-WGS), nanopore sequencing (ONT), optical genome mapping (OGM), and chromatin conformation capture (Micro-C) offer powerful tools for high-resolution SVs analysis, illuminating the genomic architecture underlying CLL. Methods We selected nine CLL cases bearing diverse genomic alterations. Each underwent routine diagnostic evaluation via chromosome banding analysis (CBA), multicolor fluorescence in situ hybridization (mFISH), and chromosomal microarray (CMA) and was further analyzed using sr-WGS, ONT, OGM, and Micro-C. We performed a comprehensive comparison of copy number variants (CNVs) and SVs across all methods. Results Across five methods (CMA, ONT, OGM, sr-WGS, Micro-C), 56.3% (179/318) of CNVs were consistently detected. A high-confidence CNV set, defined as those identified by ≥ 3 methods, comprised 70.4% (224/318). SV detection varied by genome complexity: 2910 unique breakends (BNDs) were identified, with only 10.9% (320/2910) supported by all methods. A high-confidence SV set, supported by ≥ 3 methods, included 20.4% (595/2910) of BNDs. Dicentric chromosomes (DICs) and complex derivative chromosomes (CDERs), particularly those involving BNDs near centromeric or telomeric regions, were the most difficult to resolve. Micro-C fully confirmed 71.4% (5/7) of CDERs and all ten DICs. Overall, Micro-C aligned best with classical cytogenetics, confirming 85.5% (47/55) of aberrations, followed by OGM (65.5%) and both ONT and sr-WGS (56.4%). Conclusion Each technology offers unique insights into the leukemia genome. Combining classical cytogenetics with high-throughput methods improves the detection of structural complexity and clinically relevant alterations.

TP53 gene abnormalities represent the most important biomarker in chronic lymphocytic leukemia (CLL). Altered protein modifications could also influence p53 function, even in the wild‐type protein. We assessed the impact of p53 protein phosphorylations on p53 functions as an alternative inactivation mechanism. We studied p53 phospho‐profiles induced by DNA‐damaging agents (fludarabine, doxorubicin) in 71 TP53‐intact primary CLL samples. Doxorubicin induced two distinct phospho‐profiles: profile I (heavily phosphorylated) and profile II (hypophosphorylated). Profile II samples were less capable of activating p53 target genes upon doxorubicin exposure, resembling TP53‐mutant samples at the transcriptomic level, whereas standard p53 signaling was triggered in profile I. ATM locus defects were more common in profile II. The samples also differed in the basal activity of the hypoxia pathway: the highest level was detected in TP53‐mutant samples, followed by profile II and profile I. Our study suggests that wild‐type TP53 CLL cells with less phosphorylated p53 show TP53‐mutant‐like behavior after DNA damage. p53 hypophosphorylation and the related lower ability to respond to DNA damage are linked to ATM locus defects and the higher basal activity of the hypoxia pathway. In chronic lymphocytic leukemia cells, increased activity of the hypoxic pathway and ATM locus defects can render wild‐type p53 hypophosphorylated. Such a phospho‐profile, termed profile II, cannot fully respond to DNA damage; accumulated p53 remains inactive and does not trigger transcription of its target genes upon stimulus, as compared to the standard phospho‐profile I. Thus, hypophosphorylated p53 resembles cases with TP53 gene mutation.

Background Telomeres are protective structures at chromosome ends which shorten gradually with increasing age. In chronic lymphocytic leukemia (CLL), short telomeres have been associated with unfavorable disease outcome, but the link between clonal evolution and telomere shortening remains unresolved. Methods We investigated relative telomere length (RTL) in a well-characterized cohort of 198 CLL patients by qPCR and focused in detail on a subgroup 26 patients who underwent clonal evolution of TP53 mutations (evol TP53 ). In the evol TP53 subgroup we explored factors influencing clonal evolution and corresponding changes in telomere length through measurements of telomerase expression, lymphocyte doubling time, and BCR signaling activity. Results At baseline, RTL of the evol TP53 patients was scattered across the entire RTL spectrum observed in our CLL cohort. RTL changed in the follow-up samples of 16/26 (62%) evol TP53 cases, inclining to reach intermediate RTL values, i.e., longer telomeres shortened compared to baseline while shorter ones prolonged. For the first time we show that TP53 clonal shifts are linked to RTL change, including unexpected RTL prolongation. We further investigated parameters associated with RTL changes. Unstable telomeres were significantly more frequent among younger patients ( P  = 0.032). Shorter telomeres were associated with decreased activity of the B-cell receptor signaling components p-ERK1/2, p-ZAP-70/SYK, and p-NFκB ( P  = 0.04, P  = 0.01, and P  = 0.02, respectively). Conclusions Our study revealed that changes of telomere length reflect evolution in leukemic subclone proportion, and are associated with specific clinico-biological features of the explored cohort.

Here we report a C-terminal RUNX1 mutation in a family with platelet disorder and predisposition to myeloid malignancies. We identified the mutation c.866delG:p.Gly289Aspfs*22 (NM_001754) (RUNX1 b-isoform NM_001001890; c.785delG:p.Gly262Aspfs*22) using exome sequencing of samples obtained from eight members of a single family. The mutation found in our pedigree is within exon eight and the transactivation domain of RUNX1. One of the affected individuals developed myelodysplastic syndrome (MDS), which progressed to acute myelogenous leukemia (AML). A search for the second hit which led to the development of MDS and later AML in this individual revealed the PHF6 gene variant (exon9:c.872G > A:p.G291E; NM_001015877), BCORL1 (exon3:c.1111A > C:p.T371P; NM_001184772) and BCOR gene variant (exon4:c.2076dupT:p.P693fs; NM_001123383), which appear to be very likely second hits participating in the progression to myeloid malignancy.

Extensive genome rearrangements, known as chromothripsis, have been recently identified in several cancer types. Chromothripsis leads to complex structural variants (cSVs) causing aberrant gene expression and the formation of fusion genes, which can trigger cancer development, or worsen its clinical course. The functional impact of cSVs can be studied at the RNA level using whole transcriptome sequencing (total RNA-Seq). It represents a powerful tool for discovering, profiling, and quantifying changes of gene expression in the overall genomic context. However, bioinformatic analysis of transcriptomic data, especially in cases with cSVs, is a complex and challenging task, and the development of proper bioinformatic tools for transcriptome studies is necessary. We designed a bioinformatic workflow for the analysis of total RNA-Seq data consisting of two separate parts (pipelines): The first pipeline incorporates a statistical solution for differential gene expression analysis in a biologically heterogeneous sample set. We utilized results from transcriptomic arrays which were carried out in parallel to increase the precision of the analysis. The second pipeline is used for the identification of fusion genes. Special attention was given to the filtering of false positives (FPs), which was achieved through consensus fusion calling with several fusion gene callers. We applied the workflow to the data obtained from ten patients with chronic lymphocytic leukemia (CLL) to describe the consequences of their cSVs in detail. The fusion genes identified by our pipeline were correlated with genomic break-points detected by genomic arrays. We set up a novel solution for differential gene expression analysis of individual samples and fusion gene detection from total RNA-Seq data. The results of the differential gene expression analysis were concordant with results obtained by transcriptomic arrays, which demonstrates the analytical capabilities of our method. We also showed that the consensus fusion gene detection approach was able to identify true positives (TPs) efficiently. Detected coordinates of fusion gene junctions were in concordance with genomic breakpoints assessed using genomic arrays. Byapplying our methods to real clinical samples, we proved that our approach for total RNA-Seq data analysis generates results consistent with other genomic analytical techniques. The data obtained by our analyses provided clues for the study of the biological consequences of cSVs with far-reaching implications for clinical outcome and management of cancer patients. The bioinformatic workflow is also widely applicable for addressing other research questions in different contexts, for which transcriptomic data are generated.

Retroelements in the human genome are silenced via multiple mechanisms, including DNA methylation, to prevent their potential mutagenic effect. Retroelement activity, demonstrated by their expression and somatic retrotransposition events, was shown to be deregulated in multiple tumors but not yet in leukemia. We hypothesized that treatment with hypomethylating agents, commonly used in myelodysplastic syndromes and acute myeloid leukemia, could lead to increased retroelement activity and somatic retrotranspositions, thus contributing to disease progression. To address this hypothesis, we induced the expression of ORF1p protein with hypomethylating agents in DAMI and HL‐60 myeloid cell lines. To study whether long‐term hypomethylating agent therapy induces somatic retrotranspositions, we analyzed (i) both cell lines treated for 4 weeks, and (ii) sequential samples from 17 patients with myelodysplastic syndrome treated with hypomethylating agents. Using a sensitive next‐generation sequencing (NGS)‐based method, no retroelement events were identified. To conclude, we show that although hypomethylating agents induce the expression of LINE‐1‐encoded proteins in myeloid cell lines, de novo somatic retrotransposition events do not arise during the long‐term exposure to these agents. We investigated whether hypomethylating agents (HMAs) used in myeloid malignancies induce somatic retrotransposition. Our findings indicate that HMA treatment increases L1‐encoded protein expression but does not lead to detectable de novo retrotransposition events in either patient samples or cell lines. This suggests HMAs do not promote new insertional mutagenesis.